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Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation | Hao Tang, Dan Xu, Yan Yan, Philip H.S. Torr, Nicu Sebe | In this paper, we address the task of semantic-guided scene generation. One open challenge widely observed in global image-level generation methods is the difficulty of generating small objects and detailed local texture. To tackle this issue, in this work we consider learning the scene generation in a local context, and correspondingly design a local class-specific generative network with semantic maps as a guidance, which separately constructs and learns sub-generators concentrating on the generation of different classes, and is able to provide more scene details. To learn more discriminative class-specific feature representations for the local generation, a novel classification module is also proposed. To combine the advantage of both global image-level and the local class-specific generation, a joint generation network is designed with an attention fusion module and a dual-discriminator structure embedded. Extensive experiments on two scene image generation tasks show superior generation performance of the proposed model. State-of-the-art results are established by large margins on both tasks and on challenging public benchmarks. The source code and trained models are available at https://github.com/Ha0Tang/LGGAN. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Tang_Local_Class-Specific_and_Global_Image-Level_Generative_Adversarial_Networks_for_Semantic-Guided_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.12215 | https://www.youtube.com/watch?v=RdE4M98Txpo | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Tang_Local_Class-Specific_and_Global_Image-Level_Generative_Adversarial_Networks_for_Semantic-Guided_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Tang_Local_Class-Specific_and_Global_Image-Level_Generative_Adversarial_Networks_for_Semantic-Guided_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Fast Soft Color Segmentation | Naofumi Akimoto, Huachun Zhu, Yanghua Jin, Yoshimitsu Aoki | We address the problem of soft color segmentation, defined as decomposing a given image into several RGBA layers, each containing only homogeneous color regions. The resulting layers from decomposition pave the way for applications that benefit from layer-based editing, such as recoloring and compositing of images and videos. The current state-of-the-art approach for this problem is hindered by slow processing time due to its iterative nature, and consequently does not scale to certain real-world scenarios. To address this issue, we propose a neural network based method for this task that decomposes a given image into multiple layers in a single forward pass. Furthermore, our method separately decomposes the color layers and the alpha channel layers. By leveraging a novel training objective, our method achieves proper assignment of colors amongst layers. As a consequence, our method achieve promising quality without existing issue of inference speed for iterative approaches. Our thorough experimental analysis shows that our method produces qualitative and quantitative results comparable to previous methods while achieving a 300,000x speed improvement. Finally, we utilize our proposed method on several applications, and demonstrate its speed advantage, especially in video editing. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Akimoto_Fast_Soft_Color_Segmentation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.08096 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Akimoto_Fast_Soft_Color_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Akimoto_Fast_Soft_Color_Segmentation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Akimoto_Fast_Soft_Color_CVPR_2020_supplemental.pdf | null | null |
Partial Weight Adaptation for Robust DNN Inference | Xiufeng Xie, Kyu-Han Kim | Mainstream video analytics uses a pre-trained DNN model with an assumption that inference input and training data follow the same probability distribution. However, this assumption does not always hold in the wild: autonomous vehicles may capture video with varying brightness; unstable wireless bandwidth calls for adaptive bitrate streaming of video; and, inference servers may serve inputs from heterogeneous IoT devices/cameras. In such situations, the level of input distortion changes rapidly, thus reshaping the probability distribution of the input. We present GearNN, an adaptive inference architecture that accommodates DNN inputs with varying distortions. GearNN employs an optimization algorithm to identify a tiny set of "distortion-sensitive" DNN parameters, given a memory budget. Based on the distortion level of the input, GearNN then adapts only the distortion-sensitive parameters, while reusing the rest of constant parameters across all input qualities. In our evaluation of DNN inference with dynamic input distortions, GearNN improves the accuracy (mIoU) by an average of 18.12% over a DNN trained with the undistorted dataset and 4.84% over stability training from Google, with only 1.8% extra memory overhead. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Xie_Partial_Weight_Adaptation_for_Robust_DNN_Inference_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.06131 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Xie_Partial_Weight_Adaptation_for_Robust_DNN_Inference_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Xie_Partial_Weight_Adaptation_for_Robust_DNN_Inference_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Deep Facial Non-Rigid Multi-View Stereo | Ziqian Bai, Zhaopeng Cui, Jamal Ahmed Rahim, Xiaoming Liu, Ping Tan | We present a method for 3D face reconstruction from multi-view images with different expressions. We formulate this problem from the perspective of non-rigid multi-view stereo (NRMVS). Unlike previous learning-based methods, which often regress the face shape directly, our method optimizes the 3D face shape by explicitly enforcing multi-view appearance consistency, which is known to be effective in recovering shape details according to conventional multi-view stereo methods. Furthermore, by estimating face shape through optimization based on multi-view consistency, our method can potentially have better generalization to unseen data. However, this optimization is challenging since each input image has a different expression. We facilitate it with a CNN network that learns to regularize the non-rigid 3D face according to the input image and preliminary optimization results. Extensive experiments show that our method achieves the state-of-the-art performance on various datasets and generalizes well to in-the-wild data. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Bai_Deep_Facial_Non-Rigid_Multi-View_Stereo_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Bai_Deep_Facial_Non-Rigid_Multi-View_Stereo_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Bai_Deep_Facial_Non-Rigid_Multi-View_Stereo_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Bai_Deep_Facial_Non-Rigid_CVPR_2020_supplemental.zip | null | null |
Deep Shutter Unrolling Network | Peidong Liu, Zhaopeng Cui, Viktor Larsson, Marc Pollefeys | We present a novel network for rolling shutter effect correction. Our network takes two consecutive rolling shutter images and estimates the corresponding global shutter image of the latest frame. The dense displacement field from a rolling shutter image to its corresponding global shutter image is estimated via a motion estimation network. The learned feature representation of a rolling shutter image is then warped, via the displacement field, to its global shutter representation by a differentiable forward warping block. An image decoder recovers the global shutter image based on the warped feature representation. Our network can be trained end-to-end and only requires the global shutter image for supervision. Since there is no public dataset available, we also propose two large datasets: the Carla-RS dataset and the Fastec-RS dataset. Experimental results demonstrate that our network outperforms the state-of-the-art methods. We make both our code and datasets available at https://github.com/ethliup/DeepUnrollNet. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_Deep_Shutter_Unrolling_Network_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Deep_Shutter_Unrolling_Network_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Deep_Shutter_Unrolling_Network_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Liu_Deep_Shutter_Unrolling_CVPR_2020_supplemental.pdf | null | null |
BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation | Hao Chen, Kunyang Sun, Zhi Tian, Chunhua Shen, Yongming Huang, Youliang Yan | Instance segmentation is one of the fundamental vision tasks. Recently, fully convolutional instance segmentation methods have drawn much attention as they are often simpler and more efficient than two-stage approaches like Mask R-CNN. To date, almost all such approaches fall behind the two-stage Mask R-CNN method in mask precision when models have similar computation complexity, leaving great room for improvement. In this work, we achieve improved mask prediction by effectively combining instance-level information with semantic information with lower-level fine-granularity. Our main contribution is a blender module which draws inspiration from both top-down and bottom-up instance segmentation approaches. The proposed BlendMask can effectively predict dense per-pixel position-sensitive instance features with very few channels, and learn attention maps for each instance with merely one convolution layer, thus being fast in inference. BlendMask can be easily incorporate with the state-of-the-art one-stage detection frameworks and outperforms Mask R-CNN under the same training schedule while being faster. A light-weight version of BlendMask achieves 36.0 mAP at 27 FPS evaluated on a single 1080Ti. Because of its simplicity and efficacy, we hope that our BlendMask could serve as a simple yet strong baseline for a wide range of instance-wise prediction tasks. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_BlendMask_Top-Down_Meets_Bottom-Up_for_Instance_Segmentation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2001.00309 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_BlendMask_Top-Down_Meets_Bottom-Up_for_Instance_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_BlendMask_Top-Down_Meets_Bottom-Up_for_Instance_Segmentation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Chen_BlendMask_Top-Down_Meets_CVPR_2020_supplemental.pdf | null | null |
Towards Learning Structure via Consensus for Face Segmentation and Parsing | Iacopo Masi, Joe Mathai, Wael AbdAlmageed | Face segmentation is the task of densely labeling pixels on the face according to their semantics. While current methods place an emphasis on developing sophisticated architectures, use conditional random fields for smoothness, or rather employ adversarial training, we follow an alternative path towards robust face segmentation and parsing. Occlusions, along with other parts of the face, have a proper structure that needs to be propagated in the model during training. Unlike state-of-the-art methods that treat face segmentation as an independent pixel prediction problem, we argue instead that it should hold highly correlated outputs within the same object pixels. We thereby offer a novel learning mechanism to enforce structure in the prediction via consensus, guided by a robust loss function that forces pixel objects to be consistent with each other. Our face parser is trained by transferring knowledge from another model, yet it encourages spatial consistency while fitting the labels. Different than current practice, our method enjoys pixel-wise predictions, yet paves the way for fewer artifacts, less sparse masks, and spatially coherent outputs. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Masi_Towards_Learning_Structure_via_Consensus_for_Face_Segmentation_and_Parsing_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.00957 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Masi_Towards_Learning_Structure_via_Consensus_for_Face_Segmentation_and_Parsing_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Masi_Towards_Learning_Structure_via_Consensus_for_Face_Segmentation_and_Parsing_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Masi_Towards_Learning_Structure_CVPR_2020_supplemental.pdf | null | null |
Pixel Consensus Voting for Panoptic Segmentation | Haochen Wang, Ruotian Luo, Michael Maire, Greg Shakhnarovich | The core of our approach, Pixel Consensus Voting, is a framework for instance segmentation based on the generalized Hough transform. Pixels cast discretized, probabilistic votes for the likely regions that contain instance centroids. At the detected peaks that emerge in the voting heatmap, backprojection is applied to collect pixels and produce instance masks. Unlike a sliding window detector that densely enumerates object proposals, our method detects instances as a result of the consensus among pixel-wise votes. We implement vote aggregation and backprojection using native operators of a convolutional neural network. The discretization of centroid voting reduces the training of instance segmentation to pixel labeling, analogous and complementary to FCN-style semantic segmentation, leading to an efficient and unified architecture that jointly models things and stuff. We demonstrate the effectiveness of our pipeline on COCO and Cityscapes Panoptic Segmentation and obtain competitive results. Code will be open-sourced. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Pixel_Consensus_Voting_for_Panoptic_Segmentation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.01849 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Pixel_Consensus_Voting_for_Panoptic_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Pixel_Consensus_Voting_for_Panoptic_Segmentation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Towards Unsupervised Learning of Generative Models for 3D Controllable Image Synthesis | Yiyi Liao, Katja Schwarz, Lars Mescheder, Andreas Geiger | In recent years, Generative Adversarial Networks have achieved impressive results in photorealistic image synthesis. This progress nurtures hopes that one day the classical rendering pipeline can be replaced by efficient models that are learned directly from images. However, current image synthesis models operate in the 2D domain where disentangling 3D properties such as camera viewpoint or object pose is challenging. Furthermore, they lack an interpretable and controllable representation. Our key hypothesis is that the image generation process should be modeled in 3D space as the physical world surrounding us is intrinsically three-dimensional. We define the new task of 3D controllable image synthesis and propose an approach for solving it by reasoning both in 3D space and in the 2D image domain. We demonstrate that our model is able to disentangle latent 3D factors of simple multi-object scenes in an unsupervised fashion from raw images. Compared to pure 2D baselines, it allows for synthesizing scenes that are consistent wrt. changes in viewpoint or object pose. We further evaluate various 3D representations in terms of their usefulness for this challenging task. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liao_Towards_Unsupervised_Learning_of_Generative_Models_for_3D_Controllable_Image_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.05237 | https://www.youtube.com/watch?v=s5u4q9IQzZg | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liao_Towards_Unsupervised_Learning_of_Generative_Models_for_3D_Controllable_Image_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liao_Towards_Unsupervised_Learning_of_Generative_Models_for_3D_Controllable_Image_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Exploit Clues From Views: Self-Supervised and Regularized Learning for Multiview Object Recognition | Chih-Hui Ho, Bo Liu, Tz-Ying Wu, Nuno Vasconcelos | Multiview recognition has been well studied in the literature and achieves decent performance in object recognition and retrieval task. However, most previous works rely on supervised learning and some impractical underlying assumptions, such as the availability of all views in training and inference time. In this work, the problem of multiview self-supervised learning (MV-SSL) is investigated, where only image to object association is given. Given this setup, a novel surrogate task for self-supervised learning is proposed by pursuing "object invariant" representation. This is solved by randomly selecting an image feature of an object as object prototype, accompanied with multiview consistency regularization, which results in view invariant stochastic prototype embedding (VISPE). Experiments shows that the recognition and retrieval results using VISPE outperform that of other self-supervised learning methods on seen and unseen data. VISPE can also be applied to semi-supervised scenario and demonstrates robust performance with limited data available. Code is available at https://github.com/chihhuiho/VISPE | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Ho_Exploit_Clues_From_Views_Self-Supervised_and_Regularized_Learning_for_Multiview_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.12735 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Ho_Exploit_Clues_From_Views_Self-Supervised_and_Regularized_Learning_for_Multiview_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Ho_Exploit_Clues_From_Views_Self-Supervised_and_Regularized_Learning_for_Multiview_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Ho_Exploit_Clues_From_CVPR_2020_supplemental.pdf | null | null |
SampleNet: Differentiable Point Cloud Sampling | Itai Lang, Asaf Manor, Shai Avidan | There is a growing number of tasks that work directly on point clouds. As the size of the point cloud grows, so do the computational demands of these tasks. A possible solution is to sample the point cloud first. Classic sampling approaches, such as farthest point sampling (FPS), do not consider the downstream task. A recent work showed that learning a task-specific sampling can improve results significantly. However, the proposed technique did not deal with the non-differentiability of the sampling operation and offered a workaround instead. We introduce a novel differentiable relaxation for point cloud sampling that approximates sampled points as a mixture of points in the primary input cloud. Our approximation scheme leads to consistently good results on classification and geometry reconstruction applications. We also show that the proposed sampling method can be used as a front to a point cloud registration network. This is a challenging task since sampling must be consistent across two different point clouds for a shared downstream task. In all cases, our approach outperforms existing non-learned and learned sampling alternatives. Our code is publicly available. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lang_SampleNet_Differentiable_Point_Cloud_Sampling_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.03663 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lang_SampleNet_Differentiable_Point_Cloud_Sampling_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lang_SampleNet_Differentiable_Point_Cloud_Sampling_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Lang_SampleNet_Differentiable_Point_CVPR_2020_supplemental.pdf | null | null |
Guided Variational Autoencoder for Disentanglement Learning | Zheng Ding, Yifan Xu, Weijian Xu, Gaurav Parmar, Yang Yang, Max Welling, Zhuowen Tu | We propose an algorithm, guided variational autoencoder (Guided-VAE), that is able to learn a controllable generative model by performing latent representation disentanglement learning. The learning objective is achieved by providing signal to the latent encoding/embedding in VAE without changing its main backbone architecture, hence retaining the desirable properties of the VAE. We design an unsupervised and a supervised strategy in Guided-VAE and observe enhanced modeling and controlling capability over the vanilla VAE. In the unsupervised strategy, we guide the VAE learning by introducing a lightweight decoder that learns latent geometric transformation and principal components; in the supervised strategy, we use an adversarial excitation and inhibition mechanism to encourage the disentanglement of the latent variables. Guided-VAE enjoys its transparency and simplicity for the general representation learning task, as well as disentanglement learning. On a number of experiments for representation learning, improved synthesis/sampling, better disentanglement for classification, and reduced classification errors in meta learning have been observed. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Ding_Guided_Variational_Autoencoder_for_Disentanglement_Learning_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.01255 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Ding_Guided_Variational_Autoencoder_for_Disentanglement_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Ding_Guided_Variational_Autoencoder_for_Disentanglement_Learning_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Online Deep Clustering for Unsupervised Representation Learning | Xiaohang Zhan, Jiahao Xie, Ziwei Liu, Yew-Soon Ong, Chen Change Loy | Joint clustering and feature learning methods have shown remarkable performance in unsupervised representation learning. However, the training schedule alternating between feature clustering and network parameters update leads to unstable learning of visual representations. To overcome this challenge, we propose Online Deep Clustering (ODC) that performs clustering and network update simultaneously rather than alternatingly. Our key insight is that the cluster centroids should evolve steadily in keeping the classifier stably updated. Specifically, we design and maintain two dynamic memory modules, i.e., samples memory to store samples' labels and features, and centroids memory for centroids evolution. We break down the abrupt global clustering into steady memory update and batch-wise label re-assignment. The process is integrated into network update iterations. In this way, labels and the network evolve shoulder-to-shoulder rather than alternatingly. Extensive experiments demonstrate that ODC stabilizes the training process and boosts the performance effectively. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhan_Online_Deep_Clustering_for_Unsupervised_Representation_Learning_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.10645 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhan_Online_Deep_Clustering_for_Unsupervised_Representation_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhan_Online_Deep_Clustering_for_Unsupervised_Representation_Learning_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
A Disentangling Invertible Interpretation Network for Explaining Latent Representations | Patrick Esser, Robin Rombach, Bjorn Ommer | Neural networks have greatly boosted performance in computer vision by learning powerful representations of input data. The drawback of end-to-end training for maximal overall performance are black-box models whose hidden representations are lacking interpretability: Since distributed coding is optimal for latent layers to improve their robustness, attributing meaning to parts of a hidden feature vector or to individual neurons is hindered. We formulate interpretation as a translation of hidden representations onto semantic concepts that are comprehensible to the user. The mapping between both domains has to be bijective so that semantic modifications in the target domain correctly alter the original representation. The proposed invertible interpretation network can be transparently applied on top of existing architectures with no need to modify or retrain them. Consequently, we translate an original representation to an equivalent yet interpretable one and backwards without affecting the expressiveness and performance of the original. The invertible interpretation network disentangles the hidden representation into separate, semantically meaningful concepts. Moreover, we present an efficient approach to define semantic concepts by only sketching two images and also an unsupervised strategy. Experimental evaluation demonstrates the wide applicability to interpretation of existing classification and image generation networks as well as to semantically guided image manipulation. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Esser_A_Disentangling_Invertible_Interpretation_Network_for_Explaining_Latent_Representations_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.13166 | https://www.youtube.com/watch?v=_R7wBjVGXqw | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Esser_A_Disentangling_Invertible_Interpretation_Network_for_Explaining_Latent_Representations_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Esser_A_Disentangling_Invertible_Interpretation_Network_for_Explaining_Latent_Representations_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Esser_A_Disentangling_Invertible_CVPR_2020_supplemental.zip | null | null |
SynSin: End-to-End View Synthesis From a Single Image | Olivia Wiles, Georgia Gkioxari, Richard Szeliski, Justin Johnson | View synthesis allows for the generation of new views of a scene given one or more images. This is challenging; it requires comprehensively understanding the 3D scene from images. As a result, current methods typically use multiple images, train on ground-truth depth, or are limited to synthetic data. We propose a novel end-to-end model for this task using a single image at test time; it is trained on real images without any ground-truth 3D information. To this end, we introduce a novel differentiable point cloud renderer that is used to transform a latent 3D point cloud of features into the target view. The projected features are decoded by our refinement network to inpaint missing regions and generate a realistic output image. The 3D component inside of our generative model allows for interpretable manipulation of the latent feature space at test time, e.g. we can animate trajectories from a single image. Additionally, we can generate high resolution images and generalise to other input resolutions. We outperform baselines and prior work on the Matterport, Replica, and RealEstate10K datasets. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wiles_SynSin_End-to-End_View_Synthesis_From_a_Single_Image_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.08804 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wiles_SynSin_End-to-End_View_Synthesis_From_a_Single_Image_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wiles_SynSin_End-to-End_View_Synthesis_From_a_Single_Image_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wiles_SynSin_End-to-End_View_CVPR_2020_supplemental.zip | null | null |
HOPE-Net: A Graph-Based Model for Hand-Object Pose Estimation | Bardia Doosti, Shujon Naha, Majid Mirbagheri, David J. Crandall | Hand-object pose estimation (HOPE) aims to jointly detect the poses of both a hand and of a held object. In this paper, we propose a lightweight model called HOPE-Net which jointly estimates hand and object pose in 2D and 3D in real-time. Our network uses a cascade of two adaptive graph convolutional neural networks, one to estimate 2D coordinates of the hand joints and object corners, followed by another to convert 2D coordinates to 3D. Our experiments show that through end-to-end training of the full network, we achieve better accuracy for both the 2D and 3D coordinate estimation problems. The proposed 2D to 3D graph convolution-based model could be applied to other 3D landmark detection problems, where it is possible to first predict the 2D keypoints and then transform them to 3D. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Doosti_HOPE-Net_A_Graph-Based_Model_for_Hand-Object_Pose_Estimation_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Doosti_HOPE-Net_A_Graph-Based_Model_for_Hand-Object_Pose_Estimation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Doosti_HOPE-Net_A_Graph-Based_Model_for_Hand-Object_Pose_Estimation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Auto-Tuning Structured Light by Optical Stochastic Gradient Descent | Wenzheng Chen, Parsa Mirdehghan, Sanja Fidler, Kiriakos N. Kutulakos | We consider the problem of optimizing the performance of an active imaging system by automatically discovering the illuminations it should use, and the way to decode them. Our approach tackles two seemingly incompatible goals: (1) "tuning" the illuminations and decoding algorithm precisely to the devices at hand---to their optical transfer functions, non-linearities, spectral responses, image processing pipelines---and (2) doing so without modeling or calibrating the system; without modeling the scenes of interest; and without prior training data. The key idea is to formulate a stochastic gradient descent (SGD) optimization procedure that puts the actual system in the loop: projecting patterns, capturing images, and calculating the gradient of expected reconstruction error. We apply this idea to structured-light triangulation to "auto-tune" several devices---from smartphones and laser projectors to advanced computational cameras. Our experiments show that despite being model-free and automatic, optical SGD can boost system 3D accuracy substantially over state-of-the-art coding schemes. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_Auto-Tuning_Structured_Light_by_Optical_Stochastic_Gradient_Descent_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Auto-Tuning_Structured_Light_by_Optical_Stochastic_Gradient_Descent_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Auto-Tuning_Structured_Light_by_Optical_Stochastic_Gradient_Descent_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Chen_Auto-Tuning_Structured_Light_CVPR_2020_supplemental.zip | null | null |
HandVoxNet: Deep Voxel-Based Network for 3D Hand Shape and Pose Estimation From a Single Depth Map | Jameel Malik, Ibrahim Abdelaziz, Ahmed Elhayek, Soshi Shimada, Sk Aziz Ali, Vladislav Golyanik, Christian Theobalt, Didier Stricker | 3D hand shape and pose estimation from a single depth map is a new and challenging computer vision problem with many applications. The state-of-the-art methods directly regress 3D hand meshes from 2D depth images via 2D convolutional neural networks, which leads to artefacts in the estimations due to perspective distortions in the images. In contrast, we propose a novel architecture with 3D convolutions trained in a weakly-supervised manner. The input to our method is a 3D voxelized depth map, and we rely on two hand shape representations. The first one is the 3D voxelized grid of the shape which is accurate but does not preserve the mesh topology and the number of mesh vertices. The second representation is the 3D hand surface which is less accurate but does not suffer from the limitations of the first representation. We combine the advantages of these two representations by registering the hand surface to the voxelized hand shape. In the extensive experiments, the proposed approach improves over the state of the art by47.8% on the SynHand5M dataset. Moreover, our augmentation policy for voxelized depth maps further enhances the accuracy of 3D hand pose estimation on real data. Our method produces visually more reasonable and realistic hand shapes on NYU and BigHand2.2M datasets compared to the existing approaches. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Malik_HandVoxNet_Deep_Voxel-Based_Network_for_3D_Hand_Shape_and_Pose_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.01588 | https://www.youtube.com/watch?v=MFhNBTUkxvY | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Malik_HandVoxNet_Deep_Voxel-Based_Network_for_3D_Hand_Shape_and_Pose_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Malik_HandVoxNet_Deep_Voxel-Based_Network_for_3D_Hand_Shape_and_Pose_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Malik_HandVoxNet_Deep_Voxel-Based_CVPR_2020_supplemental.zip | null | null |
Deep 3D Portrait From a Single Image | Sicheng Xu, Jiaolong Yang, Dong Chen, Fang Wen, Yu Deng, Yunde Jia, Xin Tong | In this paper, we present a learning-based approach for recovering the 3D geometry of human head from a single portrait image. Our method is learned in an unsupervised manner without any ground-truth 3D data. We represent the head geometry with a parametric 3D face model together with a depth map for other head regions including hair and ear. A two-step geometry learning scheme is proposed to learn 3D head reconstruction from in-the-wild face images, where we first learn face shape on single images using self-reconstruction and then learn hair and ear geometry using pairs of images in a stereo-matching fashion. The second step is based on the output of the first to not only improve the accuracy but also ensure the consistency of overall head geometry. We evaluate the accuracy of our method both in 3D and with pose manipulation tasks on 2D images. We alter pose based on the recovered geometry and apply a refinement network trained with adversarial learning to ameliorate the reprojected images and translate them to the real image domain. Extensive evaluations and comparison with previous methods show that our new method can produce high-fidelity 3D head geometry and head pose manipulation results. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Xu_Deep_3D_Portrait_From_a_Single_Image_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.11598 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_Deep_3D_Portrait_From_a_Single_Image_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_Deep_3D_Portrait_From_a_Single_Image_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
AnimalWeb: A Large-Scale Hierarchical Dataset of Annotated Animal Faces | Muhammad Haris Khan, John McDonagh, Salman Khan, Muhammad Shahabuddin, Aditya Arora, Fahad Shahbaz Khan, Ling Shao, Georgios Tzimiropoulos | Several studies show that animal needs are often expressed through their faces. Though remarkable progress has been made towards the automatic understanding of human faces, this has not been the case with animal faces. There exists significant room for algorithmic advances that could realize automatic systems for interpreting animal faces. Besides scientific value, resulting technology will foster better and cheaper animal care. We believe the underlying research progress is mainly obstructed by the lack of an adequately annotated dataset of animal faces, covering a wide spectrum of animal species. To this end, we introduce a large-scale, hierarchical annotated dataset of animal faces, featuring 22.4K faces from 350 diverse species and 21 animal orders across biological taxonomy. These faces are captured `in-the-wild' conditions and are consistently annotated with 9 landmarks on key facial features. The dataset is structured and scalable by design; its development underwent four systematic stages involving rigorous, overall effort of over 6K man-hours. We benchmark it for face alignment using the existing art under two new problem settings. Results showcase its challenging nature, unique attributes and present definite prospects for novel, adaptive, and generalized face-oriented CV algorithms. Further benchmarking the dataset across face detection and fine-grained recognition tasks demonstrates its multi-task applications and room for improvement. The dataset is available at: https://fdmaproject.wordpress.com/. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Khan_AnimalWeb_A_Large-Scale_Hierarchical_Dataset_of_Annotated_Animal_Faces_CVPR_2020_paper.pdf | http://arxiv.org/abs/1909.04951 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Khan_AnimalWeb_A_Large-Scale_Hierarchical_Dataset_of_Annotated_Animal_Faces_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Khan_AnimalWeb_A_Large-Scale_Hierarchical_Dataset_of_Annotated_Animal_Faces_CVPR_2020_paper.html | CVPR 2020 | null | https://cove.thecvf.com/datasets/329 | null |
MANTRA: Memory Augmented Networks for Multiple Trajectory Prediction | Francesco Marchetti, Federico Becattini, Lorenzo Seidenari, Alberto Del Bimbo | Autonomous vehicles are expected to drive in complex scenarios with several independent non cooperating agents. Path planning for safely navigating in such environments can not just rely on perceiving present location and motion of other agents. It requires instead to predict such variables in a far enough future. In this paper we address the problem of multimodal trajectory prediction exploiting a Memory Augmented Neural Network. Our method learns past and future trajectory embeddings using recurrent neural networks and exploits an associative external memory to store and retrieve such embeddings. Trajectory prediction is then performed by decoding in-memory future encodings conditioned with the observed past. We incorporate scene knowledge in the decoding state by learning a CNN on top of semantic scene maps. Memory growth is limited by learning a writing controller based on the predictive capability of existing embeddings. We show that our method is able to natively perform multi-modal trajectory prediction obtaining state-of-the art results on three datasets. Moreover, thanks to the non-parametric nature of the memory module, we show how once trained our system can continuously improve by ingesting novel patterns. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Marchetti_MANTRA_Memory_Augmented_Networks_for_Multiple_Trajectory_Prediction_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.03340 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Marchetti_MANTRA_Memory_Augmented_Networks_for_Multiple_Trajectory_Prediction_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Marchetti_MANTRA_Memory_Augmented_Networks_for_Multiple_Trajectory_Prediction_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Neural Point Cloud Rendering via Multi-Plane Projection | Peng Dai, Yinda Zhang, Zhuwen Li, Shuaicheng Liu, Bing Zeng | We present a new deep point cloud rendering pipeline through multi-plane projections. The input to the network is the raw point cloud of a scene and the output are image or image sequences from a novel view or along a novel camera trajectory. Unlike previous approaches that directly project features from 3D points onto 2D image domain, we propose to project these features into a layered volume of camera frustum. In this way, the visibility of 3D points can be automatically learnt by the network, such that ghosting effects due to false visibility check as well as occlusions caused by noise interferences are both avoided successfully. Next, the 3D feature volume is fed into a 3D CNN to produce multiple planes of images w.r.t. the space division in the depth directions. The multi-plane images are then blended based on learned weights to produce the final rendering results. Experiments show that our network produces more stable renderings compared to previous methods, especially near the object boundaries. Moreover, our pipeline is robust to noisy and relatively sparse point cloud for a variety of challenging scenes. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Dai_Neural_Point_Cloud_Rendering_via_Multi-Plane_Projection_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.04645 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Dai_Neural_Point_Cloud_Rendering_via_Multi-Plane_Projection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Dai_Neural_Point_Cloud_Rendering_via_Multi-Plane_Projection_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Dai_Neural_Point_Cloud_CVPR_2020_supplemental.pdf | null | null |
A2dele: Adaptive and Attentive Depth Distiller for Efficient RGB-D Salient Object Detection | Yongri Piao, Zhengkun Rong, Miao Zhang, Weisong Ren, Huchuan Lu | Existing state-of-the-art RGB-D salient object detection methods explore RGB-D data relying on a two-stream architecture, in which an independent subnetwork is required to process depth data. This inevitably incurs extra computational costs and memory consumption, and using depth data during testing may hinder the practical applications of RGB-D saliency detection. To tackle these two dilemmas, we propose a depth distiller (A2dele) to explore the way of using network prediction and attention as two bridges to transfer the depth knowledge from the depth stream to the RGB stream. First, by adaptively minimizing the differences between predictions generated from the depth stream and RGB stream, we realize the desired control of pixel-wise depth knowledge transferred to the RGB stream. Second, to transfer the localization knowledge to RGB features, we encourage consistencies between the dilated prediction of the depth stream and the attention map from the RGB stream. As a result, we achieve a lightweight architecture without use of depth data at test time by embedding our A2dele. Our extensive experimental evaluation on five benchmarks demonstrate that our RGB stream achieves state-of-the-art performance, which tremendously minimizes the model size by 76% and runs 12 times faster, compared with the best performing method. Furthermore, our A2dele can be applied to existing RGB-D networks to significantly improve their efficiency while maintaining performance (boosts FPS by nearly twice for DMRA and 3 times for CPFP). | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Piao_A2dele_Adaptive_and_Attentive_Depth_Distiller_for_Efficient_RGB-D_Salient_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=KJSwkTiUlvs | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Piao_A2dele_Adaptive_and_Attentive_Depth_Distiller_for_Efficient_RGB-D_Salient_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Piao_A2dele_Adaptive_and_Attentive_Depth_Distiller_for_Efficient_RGB-D_Salient_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Continual Learning With Extended Kronecker-Factored Approximate Curvature | Janghyeon Lee, Hyeong Gwon Hong, Donggyu Joo, Junmo Kim | We propose a quadratic penalty method for continual learning of neural networks that contain batch normalization (BN) layers. The Hessian of a loss function represents the curvature of the quadratic penalty function, and a Kronecker-factored approximate curvature (K-FAC) is used widely to practically compute the Hessian of a neural network. However, the approximation is not valid if there is dependence between examples, typically caused by BN layers in deep network architectures. We extend the K-FAC method so that the inter-example relations are taken into account and the Hessian of deep neural networks can be properly approximated under practical assumptions. We also propose a method of weight merging and reparameterization to properly handle statistical parameters of BN, which plays a critical role for continual learning with BN, and a method that selects hyperparameters without source task data. Our method shows better performance than baselines in the permuted MNIST task with BN layers and in sequential learning from the ImageNet classification task to fine-grained classification tasks with ResNet-50, without any explicit or implicit use of source task data for hyperparameter selection. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lee_Continual_Learning_With_Extended_Kronecker-Factored_Approximate_Curvature_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.07507 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lee_Continual_Learning_With_Extended_Kronecker-Factored_Approximate_Curvature_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lee_Continual_Learning_With_Extended_Kronecker-Factored_Approximate_Curvature_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Lee_Continual_Learning_With_CVPR_2020_supplemental.pdf | null | null |
Domain Balancing: Face Recognition on Long-Tailed Domains | Dong Cao, Xiangyu Zhu, Xingyu Huang, Jianzhu Guo, Zhen Lei | Long-tailed problem has been an important topic in face recognition task. However, existing methods only concentrate on the long-tailed distribution of classes. Differently, we devote to the long-tailed domain distribution problem, which refers to the fact that a small number of domains frequently appear while other domains far less existing. The key challenge of the problem is that domain labels are too complicated (related to race, age, pose, illumination, etc.) and inaccessible in real applications. In this paper, we propose a novel Domain Balancing (DB) mechanism to handle this problem. Specifically, we first propose a Domain Frequency Indicator (DFI) to judge whether a sample is from head domains or tail domains. Secondly, we formulate a light-weighted Residual Balancing Mapping (RBM) block to balance the domain distribution by adjusting the network according to DFI. Finally, we propose a Domain Balancing Margin (DBM) in the loss function to further optimize the feature space of the tail domains to improve generalization. Extensive analysis and experiments on several face recognition benchmarks demonstrate that the proposed method effectively enhances the generalization capacities and achieves superior performance. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Cao_Domain_Balancing_Face_Recognition_on_Long-Tailed_Domains_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13791 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Cao_Domain_Balancing_Face_Recognition_on_Long-Tailed_Domains_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Cao_Domain_Balancing_Face_Recognition_on_Long-Tailed_Domains_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Neural Pose Transfer by Spatially Adaptive Instance Normalization | Jiashun Wang, Chao Wen, Yanwei Fu, Haitao Lin, Tianyun Zou, Xiangyang Xue, Yinda Zhang | Pose transfer has been studied for decades, in which the pose of a source mesh is applied to a target mesh. Particularly in this paper, we are interested in transferring the pose of source human mesh to deform the target human mesh, while the source and target meshes may have different identity information. Traditional studies assume that the paired source and target meshes are existed with the point-wise correspondences of user annotated landmarks/mesh points, which requires heavy labelling efforts. On the other hand, the generalization ability of deep models is limited, when the source and target meshes have different identities. To break this limitation, we proposes the first neural pose transfer model that solves the pose transfer via the latest technique for image style transfer, leveraging the newly proposed component -- spatially adaptive instance normalization. Our model does not require any correspondences between the source and target meshes. Extensive experiments show that the proposed model can effectively transfer deformation from source to target meshes, and has good generalization ability to deal with unseen identities or poses of meshes. Code is available at https://github.com/jiashunwang/Neural-Pose-Transfer. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Neural_Pose_Transfer_by_Spatially_Adaptive_Instance_Normalization_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.07254 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Neural_Pose_Transfer_by_Spatially_Adaptive_Instance_Normalization_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Neural_Pose_Transfer_by_Spatially_Adaptive_Instance_Normalization_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wang_Neural_Pose_Transfer_CVPR_2020_supplemental.zip | null | null |
RoutedFusion: Learning Real-Time Depth Map Fusion | Silvan Weder, Johannes Schonberger, Marc Pollefeys, Martin R. Oswald | The efficient fusion of depth maps is a key part of most state-of-the-art 3D reconstruction methods. Besides requiring high accuracy, these depth fusion methods need to be scalable and real-time capable. To this end, we present a novel real-time capable machine learning-based method for depth map fusion. Similar to the seminal depth map fusion approach by Curless and Levoy, we only update a local group of voxels to ensure real-time capability. Instead of a simple linear fusion of depth information, we propose a neural network that predicts non-linear updates to better account for typical fusion errors. Our network is composed of a 2D depth routing network and a 3D depth fusion network which efficiently handle sensor-specific noise and outliers. This is especially useful for surface edges and thin objects for which the original approach suffers from thickening artifacts. Our method outperforms the traditional fusion approach and related learned approaches on both synthetic and real data. We demonstrate the performance of our method in reconstructing fine geometric details from noise and outlier contaminated data on various scenes. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Weder_RoutedFusion_Learning_Real-Time_Depth_Map_Fusion_CVPR_2020_paper.pdf | http://arxiv.org/abs/2001.04388 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Weder_RoutedFusion_Learning_Real-Time_Depth_Map_Fusion_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Weder_RoutedFusion_Learning_Real-Time_Depth_Map_Fusion_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Weder_RoutedFusion_Learning_Real-Time_CVPR_2020_supplemental.pdf | null | null |
Coherent Reconstruction of Multiple Humans From a Single Image | Wen Jiang, Nikos Kolotouros, Georgios Pavlakos, Xiaowei Zhou, Kostas Daniilidis | In this work, we address the problem of multi-person 3D pose estimation from a single image. A typical regression approach in the top-down setting of this problem would first detect all humans and then reconstruct each one of them independently. However, this type of prediction suffers from incoherent results, e.g., interpenetration and inconsistent depth ordering between the people in the scene. Our goal is to train a single network that learns to avoid these problems and generate a coherent 3D reconstruction of all the humans in the scene. To this end, a key design choice is the incorporation of the SMPL parametric body model in our top-down framework, which enables the use of two novel losses. First, a distance field-based collision loss penalizes interpenetration among the reconstructed people. Second, a depth ordering-aware loss reasons about occlusions and promotes a depth ordering of people that leads to a rendering which is consistent with the annotated instance segmentation. This provides depth supervision signals to the network, even if the image has no explicit 3D annotations. The experiments show that our approach outperforms previous methods on standard 3D pose benchmarks, while our proposed losses enable more coherent reconstruction in natural images. The project website with videos, results, and code can be found at: https://jiangwenpl.github.io/multiperson | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Jiang_Coherent_Reconstruction_of_Multiple_Humans_From_a_Single_Image_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.08586 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Jiang_Coherent_Reconstruction_of_Multiple_Humans_From_a_Single_Image_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Jiang_Coherent_Reconstruction_of_Multiple_Humans_From_a_Single_Image_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Jiang_Coherent_Reconstruction_of_CVPR_2020_supplemental.pdf | null | null |
High-Performance Long-Term Tracking With Meta-Updater | Kenan Dai, Yunhua Zhang, Dong Wang, Jianhua Li, Huchuan Lu, Xiaoyun Yang | Long-term visual tracking has drawn increasing attention because it is much closer to practical applications than short-term tracking. Most top-ranked long-term trackers adopt the offline-trained Siamese architectures, thus,they cannot benefit from great progress of short-term trackers with online update. However, it is quite risky to straightforwardly introduce online-update-based trackers to solve the long-term problem, due to long-term uncertain and noisy observations. In this work, we propose a novel offline-trained Meta-Updater to address an important but unsolved problem: Is the tracker ready for updating in the current frame? The proposed meta-updater can effectively integrate geometric, discriminative, and appearance cues in a sequential manner, and then mine the sequential information with a designed cascaded LSTM module. Our meta-updater learns a binary output to guide the tracker's update and can be easily embedded into different trackers. This work also introduces a long-term tracking framework consisting of an online local tracker, an online verifier, a SiamRPN-based re-detector, and our meta-updater. Numerous experimental results on the VOT2018LT,VOT2019LT, OxUvALT, TLP, and LaSOT benchmarks show that our tracker performs remarkably better than other competing algorithms. Our project is available on the website: https://github.com/Daikenan/LTMU. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Dai_High-Performance_Long-Term_Tracking_With_Meta-Updater_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.00305 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Dai_High-Performance_Long-Term_Tracking_With_Meta-Updater_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Dai_High-Performance_Long-Term_Tracking_With_Meta-Updater_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition From a Domain Adaptation Perspective | Muhammad Abdullah Jamal, Matthew Brown, Ming-Hsuan Yang, Liqiang Wang, Boqing Gong | Object frequency in the real world often follows a power law, leading to a mismatch between datasets with long-tailed class distributions seen by a machine learning model and our expectation of the model to perform well on all classes. We analyze this mismatch from a domain adaptation point of view. First of all, we connect existing class-balanced methods for long-tailed classification to target shift, a well-studied scenario in domain adaptation. The connection reveals that these methods implicitly assume that the training data and test data share the same class-conditioned distribution, which does not hold in general and especially for the tail classes. While a head class could contain abundant and diverse training examples that well represent the expected data at inference time, the tail classes are often short of representative training data. To this end, we propose to augment the classic class-balanced learning by explicitly estimating the differences between the class-conditioned distributions with a meta-learning approach. We validate our approach with six benchmark datasets and three loss functions. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Jamal_Rethinking_Class-Balanced_Methods_for_Long-Tailed_Visual_Recognition_From_a_Domain_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.10780 | https://www.youtube.com/watch?v=F5D0C7nAB7k | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Jamal_Rethinking_Class-Balanced_Methods_for_Long-Tailed_Visual_Recognition_From_a_Domain_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Jamal_Rethinking_Class-Balanced_Methods_for_Long-Tailed_Visual_Recognition_From_a_Domain_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Jamal_Rethinking_Class-Balanced_Methods_CVPR_2020_supplemental.pdf | null | null |
Softmax Splatting for Video Frame Interpolation | Simon Niklaus, Feng Liu | Differentiable image sampling in the form of backward warping has seen broad adoption in tasks like depth estimation and optical flow prediction. In contrast, how to perform forward warping has seen less attention, partly due to additional challenges such as resolving the conflict of mapping multiple pixels to the same target location in a differentiable way. We propose softmax splatting to address this paradigm shift and show its effectiveness on the application of frame interpolation. Specifically, given two input frames, we forward-warp the frames and their feature pyramid representations based on an optical flow estimate using softmax splatting. In doing so, the softmax splatting seamlessly handles cases where multiple source pixels map to the same target location. We then use a synthesis network to predict the interpolation result from the warped representations. Our softmax splatting allows us to not only interpolate frames at an arbitrary time but also to fine tune the feature pyramid and the optical flow. We show that our synthesis approach, empowered by softmax splatting, achieves new state-of-the-art results for video frame interpolation. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Niklaus_Softmax_Splatting_for_Video_Frame_Interpolation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.05534 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Niklaus_Softmax_Splatting_for_Video_Frame_Interpolation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Niklaus_Softmax_Splatting_for_Video_Frame_Interpolation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Niklaus_Softmax_Splatting_for_CVPR_2020_supplemental.zip | null | null |
Cross-Domain Correspondence Learning for Exemplar-Based Image Translation | Pan Zhang, Bo Zhang, Dong Chen, Lu Yuan, Fang Wen | We present a general framework for exemplar-based image translation, which synthesizes a photo-realistic image from the input in a distinct domain (e.g., semantic segmentation mask, or edge map, or pose keypoints), given an exemplar image. The output has the style (e.g., color, texture) in consistency with the semantically corresponding objects in the exemplar. We propose to jointly learn the cross-domain correspondence and the image translation, where both tasks facilitate each other and thus can be learned with weak supervision. The images from distinct domains are first aligned to an intermediate domain where dense correspondence is established. Then, the network synthesizes images based on the appearance of semantically corresponding patches in the exemplar. We demonstrate the effectiveness of our approach in several image translation tasks. Our method is superior to state-of-the-art methods in terms of image quality significantly, with the image style faithful to the exemplar with semantic consistency. Moreover, we show the utility of our method for several applications. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_Cross-Domain_Correspondence_Learning_for_Exemplar-Based_Image_Translation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.05571 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Cross-Domain_Correspondence_Learning_for_Exemplar-Based_Image_Translation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Cross-Domain_Correspondence_Learning_for_Exemplar-Based_Image_Translation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhang_Cross-Domain_Correspondence_Learning_CVPR_2020_supplemental.pdf | null | null |
A Multi-Task Mean Teacher for Semi-Supervised Shadow Detection | Zhihao Chen, Lei Zhu, Liang Wan, Song Wang, Wei Feng, Pheng-Ann Heng | Existing shadow detection methods suffer from an intrinsic limitation in relying on limited labeled datasets, and they may produce poor results in some complicated situations. To boost the shadow detection performance, this paper presents a multi-task mean teacher model for semi-supervised shadow detection by leveraging unlabeled data and exploring the learning of multiple information of shadows simultaneously. To be specific, we first build a multi-task baseline model to simultaneously detect shadow regions, shadow edges, and shadow count by leveraging their complementary information and assign this baseline model to the student and teacher network. After that, we encourage the predictions of the three tasks from the student and teacher networks to be consistent for computing a consistency loss on unlabeled data, which is then added to the supervised loss on the labeled data from the predictions of the multi-task baseline model. Experimental results on three widely-used benchmark datasets show that our method consistently outperforms all the compared state-of- the-art methods, which verifies that the proposed network can effectively leverage additional unlabeled data to boost the shadow detection performance. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_A_Multi-Task_Mean_Teacher_for_Semi-Supervised_Shadow_Detection_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_A_Multi-Task_Mean_Teacher_for_Semi-Supervised_Shadow_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_A_Multi-Task_Mean_Teacher_for_Semi-Supervised_Shadow_Detection_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Closed-Loop Matters: Dual Regression Networks for Single Image Super-Resolution | Yong Guo, Jian Chen, Jingdong Wang, Qi Chen, Jiezhang Cao, Zeshuai Deng, Yanwu Xu, Mingkui Tan | Deep neural networks have exhibited promising performance in image super-resolution (SR) by learning a nonlinear mapping function from low-resolution (LR) images to high-resolution (HR) images. However, there are two underlying limitations to existing SR methods. First, learning the mapping function from LR to HR images is typically an ill-posed problem, because there exist infinite HR images that can be downsampled to the same LR image. As a result, the space of the possible functions can be extremely large, which makes it hard to find a good solution. Second, the paired LR-HR data may be unavailable in real-world applications and the underlying degradation method is often unknown. For such a more general case, existing SR models often incur the adaptation problem and yield poor performance. To address the above issues, we propose a dual regression scheme by introducing an additional constraint on LR data to reduce the space of the possible functions. Specifically, besides the mapping from LR to HR images, we learn an additional dual regression mapping estimates the down-sampling kernel and reconstruct LR images, which forms a closed-loop to provide additional supervision. More critically, since the dual regression process does not depend on HR images, we can directly learn from LR images. In this sense, we can easily adapt SR models to real-world data, e.g., raw video frames from YouTube. Extensive experiments with paired training data and unpaired real-world data demonstrate our superiority over existing methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Guo_Closed-Loop_Matters_Dual_Regression_Networks_for_Single_Image_Super-Resolution_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.07018 | https://www.youtube.com/watch?v=h3Fllxv1VRY | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Guo_Closed-Loop_Matters_Dual_Regression_Networks_for_Single_Image_Super-Resolution_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Guo_Closed-Loop_Matters_Dual_Regression_Networks_for_Single_Image_Super-Resolution_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Guo_Closed-Loop_Matters_Dual_CVPR_2020_supplemental.pdf | null | null |
ROAM: Recurrently Optimizing Tracking Model | Tianyu Yang, Pengfei Xu, Runbo Hu, Hua Chai, Antoni B. Chan | In this paper, we design a tracking model consisting of response generation and bounding box regression, where the first component produces a heat map to indicate the presence of the object at different positions and the second part regresses the relative bounding box shifts to anchors mounted on sliding-window locations. Thanks to the resizable convolutional filters used in both components to adapt to the shape changes of objects, our tracking model does not need to enumerate different sized anchors, thus saving model parameters. To effectively adapt the model to appearance variations, we propose to offline train a recurrent neural optimizer to update tracking model in a meta-learning setting, which can converge the model in a few gradient steps. This improves the convergence speed of updating the tracking model while achieving better performance. We extensively evaluate our trackers, ROAM and ROAM++, on the OTB, VOT, LaSOT, GOT-10K and TrackingNet benchmark and our methods perform favorably against state-of-the-art algorithms. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yang_ROAM_Recurrently_Optimizing_Tracking_Model_CVPR_2020_paper.pdf | http://arxiv.org/abs/1907.12006 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_ROAM_Recurrently_Optimizing_Tracking_Model_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_ROAM_Recurrently_Optimizing_Tracking_Model_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yang_ROAM_Recurrently_Optimizing_CVPR_2020_supplemental.pdf | null | null |
Wavelet Integrated CNNs for Noise-Robust Image Classification | Qiufu Li, Linlin Shen, Sheng Guo, Zhihui Lai | Convolutional Neural Networks (CNNs) are generally prone to noise interruptions, i.e., small image noise can cause drastic changes in the output. To suppress the noise effect to the final predication, we enhance CNNs by replacing max-pooling, strided-convolution, and average-pooling with Discrete Wavelet Transform (DWT). We present general DWT and Inverse DWT (IDWT) layers applicable to various wavelets like Haar, Daubechies, and Cohen, etc., and design wavelet integrated CNNs (WaveCNets) using these layers for image classification. In WaveCNets, feature maps are decomposed into the low-frequency and high-frequency components during the down-sampling. The low-frequency component stores main information including the basic object structures, which is transmitted into the subsequent layers to extract robust high-level features. The high-frequency components, containing most of the data noise, are dropped during inference to improve the noise-robustness of the WaveCNets. Our experimental results on ImageNet and ImageNet-C (the noisy version of ImageNet) show that WaveCNets, the wavelet integrated versions of VGG, ResNets, and DenseNet, achieve higher accuracy and better noise-robustness than their vanilla versions. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Wavelet_Integrated_CNNs_for_Noise-Robust_Image_Classification_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.03337 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Wavelet_Integrated_CNNs_for_Noise-Robust_Image_Classification_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Wavelet_Integrated_CNNs_for_Noise-Robust_Image_Classification_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_Wavelet_Integrated_CNNs_CVPR_2020_supplemental.pdf | null | null |
Towards Causal VQA: Revealing and Reducing Spurious Correlations by Invariant and Covariant Semantic Editing | Vedika Agarwal, Rakshith Shetty, Mario Fritz | Despite significant success in Visual Question Answering (VQA), VQA models have been shown to be notoriously brittle to linguistic variations in the questions. Due to deficiencies in models and datasets, today's models often rely on correlations rather than predictions that are causal w.r.t. data. In this paper, we propose a novel way to analyze and measure the robustness of the state of the art models w.r.t semantic visual variations as well as propose ways to make models more robust against spurious correlations. Our method performs automated semantic image manipulations and tests for consistency in model predictions to quantify the model robustness as well as generate synthetic data to counter these problems. We perform our analysis on three diverse, state of the art VQA models and diverse question types with a particular focus on challenging counting questions. In addition, we show that models can be made significantly more robust against inconsistent predictions using our edited data. Finally, we show that results also translate to real-world error cases of state of the art models, which results in improved overall performance | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Agarwal_Towards_Causal_VQA_Revealing_and_Reducing_Spurious_Correlations_by_Invariant_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.07538 | https://www.youtube.com/watch?v=DGHRJiueSCw | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Agarwal_Towards_Causal_VQA_Revealing_and_Reducing_Spurious_Correlations_by_Invariant_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Agarwal_Towards_Causal_VQA_Revealing_and_Reducing_Spurious_Correlations_by_Invariant_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Agarwal_Towards_Causal_VQA_CVPR_2020_supplemental.pdf | null | null |
FReeNet: Multi-Identity Face Reenactment | Jiangning Zhang, Xianfang Zeng, Mengmeng Wang, Yusu Pan, Liang Liu, Yong Liu, Yu Ding, Changjie Fan | This paper presents a novel multi-identity face reenactment framework, named FReeNet, to transfer facial expressions from an arbitrary source face to a target face with a shared model. The proposed FReeNet consists of two parts: Unified Landmark Converter (ULC) and Geometry-aware Generator (GAG). The ULC adopts an encode-decoder architecture to efficiently convert expression in a latent landmark space, which significantly narrows the gap of the face contour between source and target identities. The GAG leverages the converted landmark to reenact the photorealistic image with a reference image of the target person. Moreover, a new triplet perceptual loss is proposed to force the GAG module to learn appearance and geometry information simultaneously, which also enriches facial details of the reenacted images. Further experiments demonstrate the superiority of our approach for generating photorealistic and expression-alike faces, as well as the flexibility for transferring facial expressions between identities. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_FReeNet_Multi-Identity_Face_Reenactment_CVPR_2020_paper.pdf | http://arxiv.org/abs/1905.11805 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_FReeNet_Multi-Identity_Face_Reenactment_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_FReeNet_Multi-Identity_Face_Reenactment_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Deep Snake for Real-Time Instance Segmentation | Sida Peng, Wen Jiang, Huaijin Pi, Xiuli Li, Hujun Bao, Xiaowei Zhou | This paper introduces a novel contour-based approach named deep snake for real-time instance segmentation. Unlike some recent methods that directly regress the coordinates of the object boundary points from an image, deep snake uses a neural network to iteratively deform an initial contour to match the object boundary, which implements the classic idea of snake algorithms with a learning-based approach. For structured feature learning on the contour, we propose to use circular convolution in deep snake, which better exploits the cycle-graph structure of a contour compared against generic graph convolution. Based on deep snake, we develop a two-stage pipeline for instance segmentation: initial contour proposal and contour deformation, which can handle errors in object localization. Experiments show that the proposed approach achieves competitive performances on the Cityscapes, KINS, SBD and COCO datasets while being efficient for real-time applications with a speed of 32.3 fps for 512 x 512 images on a 1080Ti GPU. The code is available at https://github.com/zju3dv/snake/. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Peng_Deep_Snake_for_Real-Time_Instance_Segmentation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2001.01629 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Peng_Deep_Snake_for_Real-Time_Instance_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Peng_Deep_Snake_for_Real-Time_Instance_Segmentation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Learning Identity-Invariant Motion Representations for Cross-ID Face Reenactment | Po-Hsiang Huang, Fu-En Yang, Yu-Chiang Frank Wang | Human face reenactment aims at transferring motion patterns from one face (from a source-domain video) to an-other (in the target domain with the identity of interest).While recent works report impressive results, they are notable to handle multiple identities in a unified model. In this paper, we propose a unique network of CrossID-GAN to perform multi-ID face reenactment. Given a source-domain video with extracted facial landmarks and a target-domain image, our CrossID-GAN learns the identity-invariant motion patterns via the extracted landmarks and such information to produce the videos whose ID matches that of the target domain. Both supervised and unsupervised settings are proposed to train and guide our model during training.Our qualitative/quantitative results confirm the robustness and effectiveness of our model, with ablation studies confirming our network design. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Huang_Learning_Identity-Invariant_Motion_Representations_for_Cross-ID_Face_Reenactment_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=5NYI_ncWvnI | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Huang_Learning_Identity-Invariant_Motion_Representations_for_Cross-ID_Face_Reenactment_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Huang_Learning_Identity-Invariant_Motion_Representations_for_Cross-ID_Face_Reenactment_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Unsupervised Domain Adaptation via Structurally Regularized Deep Clustering | Hui Tang, Ke Chen, Kui Jia | Unsupervised domain adaptation (UDA) is to make predictions for unlabeled data on a target domain, given labeled data on a source domain whose distribution shifts from the target one. Mainstream UDA methods learn aligned features between the two domains, such that a classifier trained on the source features can be readily applied to the target ones. However, such a transferring strategy has a potential risk of damaging the intrinsic discrimination of target data. To alleviate this risk, we are motivated by the assumption of structural domain similarity, and propose to directly uncover the intrinsic target discrimination via discriminative clustering of target data. We constrain the clustering solutions using structural source regularization that hinges on our assumed structural domain similarity. Technically, we use a flexible framework of deep network based discriminative clustering that minimizes the KL divergence between predictive label distribution of the network and an introduced auxiliary one; replacing the auxiliary distribution with that formed by ground-truth labels of source data implements the structural source regularization via a simple strategy of joint network training. We term our proposed method as Structurally Regularized Deep Clustering (SRDC), where we also enhance target discrimination with clustering of intermediate network features, and enhance structural regularization with soft selection of less divergent source examples. Careful ablation studies show the efficacy of our proposed SRDC. Notably, with no explicit domain alignment, SRDC outperforms all existing methods on three UDA benchmarks. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Tang_Unsupervised_Domain_Adaptation_via_Structurally_Regularized_Deep_Clustering_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.08607 | https://www.youtube.com/watch?v=eEnxOLUI3iM | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Tang_Unsupervised_Domain_Adaptation_via_Structurally_Regularized_Deep_Clustering_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Tang_Unsupervised_Domain_Adaptation_via_Structurally_Regularized_Deep_Clustering_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Tang_Unsupervised_Domain_Adaptation_CVPR_2020_supplemental.pdf | null | null |
Augment Your Batch: Improving Generalization Through Instance Repetition | Elad Hoffer, Tal Ben-Nun, Itay Hubara, Niv Giladi, Torsten Hoefler, Daniel Soudry | Large-batch SGD is important for scaling training of deep neural networks. However, without fine-tuning hyperparameter schedules, the generalization of the model may be hampered. We propose to use batch augmentation: replicating instances of samples within the same batch with different data augmentations. Batch augmentation acts as a regularizer and an accelerator, increasing both generalization and performance scaling for a fixed budget of optimization steps. We analyze the effect of batch augmentation on gradient variance and show that it empirically improves convergence for a wide variety of networks and datasets. Our results show that batch augmentation reduces the number of necessary SGD updates to achieve the same accuracy as the state-of-the-art. Overall, this simple yet effective method enables faster training and better generalization by allowing more computational resources to be used concurrently. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Hoffer_Augment_Your_Batch_Improving_Generalization_Through_Instance_Repetition_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Hoffer_Augment_Your_Batch_Improving_Generalization_Through_Instance_Repetition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Hoffer_Augment_Your_Batch_Improving_Generalization_Through_Instance_Repetition_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Hoffer_Augment_Your_Batch_CVPR_2020_supplemental.pdf | null | null |
AdaCoF: Adaptive Collaboration of Flows for Video Frame Interpolation | Hyeongmin Lee, Taeoh Kim, Tae-young Chung, Daehyun Pak, Yuseok Ban, Sangyoun Lee | Video frame interpolation is one of the most challenging tasks in video processing research. Recently, many studies based on deep learning have been suggested. Most of these methods focus on finding locations with useful information to estimate each output pixel using their own frame warping operations. However, many of them have Degrees of Freedom (DoF) limitations and fail to deal with the complex motions found in real world videos. To solve this problem, we propose a new warping module named Adaptive Collaboration of Flows (AdaCoF). Our method estimates both kernel weights and offset vectors for each target pixel to synthesize the output frame. AdaCoF is one of the most generalized warping modules compared to other approaches, and covers most of them as special cases of it. Therefore, it can deal with a significantly wide domain of complex motions. To further improve our framework and synthesize more realistic outputs, we introduce dual-frame adversarial loss which is applicable only to video frame interpolation tasks. The experimental results show that our method outperforms the state-of-the-art methods for both fixed training set environments and the Middlebury benchmark. Our source code is available at https://github.com/HyeongminLEE/AdaCoF-pytorch | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lee_AdaCoF_Adaptive_Collaboration_of_Flows_for_Video_Frame_Interpolation_CVPR_2020_paper.pdf | http://arxiv.org/abs/1907.10244 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lee_AdaCoF_Adaptive_Collaboration_of_Flows_for_Video_Frame_Interpolation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lee_AdaCoF_Adaptive_Collaboration_of_Flows_for_Video_Frame_Interpolation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Blurry Video Frame Interpolation | Wang Shen, Wenbo Bao, Guangtao Zhai, Li Chen, Xiongkuo Min, Zhiyong Gao | Existing works reduce motion blur and up-convert frame rate through two separate ways, including frame deblurring and frame interpolation. However, few studies have approached the joint video enhancement problem, namely synthesizing high-frame-rate clear results from low-frame-rate blurry inputs. In this paper, we propose a blurry video frame interpolation method to reduce motion blur and up-convert frame rate simultaneously. Specifically, we develop a pyramid module to cyclically synthesize clear intermediate frames. The pyramid module features adjustable spatial receptive field and temporal scope, thus contributing to controllable computational complexity and restoration ability. Besides, we propose an inter-pyramid recurrent module to connect sequential models to exploit the temporal relationship. The pyramid module integrates a recurrent module, thus can iteratively synthesize temporally smooth results without significantly increasing the model size. Extensive experimental results demonstrate that our method performs favorably against state-of-the-art methods. The source code and pre-trained model are available at https://github.com/laomao0/BIN. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Shen_Blurry_Video_Frame_Interpolation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.12259 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Shen_Blurry_Video_Frame_Interpolation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Shen_Blurry_Video_Frame_Interpolation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Self-Learning With Rectification Strategy for Human Parsing | Tao Li, Zhiyuan Liang, Sanyuan Zhao, Jiahao Gong, Jianbing Shen | In this paper, we solve the sample shortage problem in the human parsing task. We begin with the self-learning strategy, which generates pseudo-labels for unlabeled data to retrain the model. However, directly using noisy pseudo-labels will cause error amplification and accumulation. Considering the topology structure of human body, we propose a trainable graph reasoning method that establishes internal structural connections between graph nodes to correct two typical errors in the pseudo-labels, i.e., the global structural error and the local consistency error. For the global error, we first transform category-wise features into a high-level graph model with coarse-grained structural information, and then decouple the high-level graph to reconstruct the category features. The reconstructed features have a stronger ability to represent the topology structure of the human body. Enlarging the receptive field of features can effectively reducing the local error. We first project feature pixels into a local graph model to capture pixel-wise relations in a hierarchical graph manner, then reverse the relation information back to the pixels. With the global structural and local consistency modules, these errors are rectified and confident pseudo-labels are generated for retraining. Extensive experiments on the LIP and the ATR datasets demonstrate the effectiveness of our global and local rectification modules. Our method outperforms other state-of-the-art methods in supervised human parsing tasks. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Self-Learning_With_Rectification_Strategy_for_Human_Parsing_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.08055 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Self-Learning_With_Rectification_Strategy_for_Human_Parsing_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Self-Learning_With_Rectification_Strategy_for_Human_Parsing_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation | Bowen Cheng, Bin Xiao, Jingdong Wang, Honghui Shi, Thomas S. Huang, Lei Zhang | Bottom-up human pose estimation methods have difficulties in predicting the correct pose for small persons due to challenges in scale variation. In this paper, we present HigherHRNet: a novel bottom-up human pose estimation method for learning scale-aware representations using high-resolution feature pyramids. Equipped with multi-resolution supervision for training and multi-resolution aggregation for inference, the proposed approach is able to solve the scale variation challenge in bottom-up multi-person pose estimation and localize keypoints more precisely, especially for small person. The feature pyramid in HigherHRNet consists of feature map outputs from HRNet and upsampled higher-resolution outputs through a transposed convolution. HigherHRNet outperforms the previous best bottom-up method by 2.5% AP for medium person on COCO test-dev, showing its effectiveness in handling scale variation. Furthermore, HigherHRNet achieves new state-of-the-art result on COCO test-dev (70.5% AP) without using refinement or other post-processing techniques, surpassing all existing bottom-up methods. HigherHRNet even surpasses all top-down methods on CrowdPose test (67.6% AP), suggesting its robustness in crowded scene. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Cheng_HigherHRNet_Scale-Aware_Representation_Learning_for_Bottom-Up_Human_Pose_Estimation_CVPR_2020_paper.pdf | http://arxiv.org/abs/1908.10357 | https://www.youtube.com/watch?v=Tm-5H3qSFuM | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Cheng_HigherHRNet_Scale-Aware_Representation_Learning_for_Bottom-Up_Human_Pose_Estimation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Cheng_HigherHRNet_Scale-Aware_Representation_Learning_for_Bottom-Up_Human_Pose_Estimation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
CNN-Generated Images Are Surprisingly Easy to Spot... for Now | Sheng-Yu Wang, Oliver Wang, Richard Zhang, Andrew Owens, Alexei A. Efros | In this work we ask whether it is possible to create a "universal" detector for telling apart real images from these generated by a CNN, regardless of architecture or dataset used. To test this, we collect a dataset consisting of fake images generated by 11 different CNN-based image generator models, chosen to span the space of commonly used architectures today (ProGAN, StyleGAN, BigGAN, CycleGAN, StarGAN, GauGAN, DeepFakes, cascaded refinement networks, implicit maximum likelihood estimation, second-order attention super-resolution, seeing-in-the-dark). We demonstrate that, with careful pre- and post-processing and data augmentation, a standard image classifier trained on only one specific CNN generator (ProGAN) is able to generalize surprisingly well to unseen architectures, datasets, and training methods (including the just released StyleGAN2). Our findings suggest the intriguing possibility that today's CNN-generated images share some common systematic flaws, preventing them from achieving realistic image synthesis. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_CNN-Generated_Images_Are_Surprisingly_Easy_to_Spot..._for_Now_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.11035 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_CNN-Generated_Images_Are_Surprisingly_Easy_to_Spot..._for_Now_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_CNN-Generated_Images_Are_Surprisingly_Easy_to_Spot..._for_Now_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wang_CNN-Generated_Images_Are_CVPR_2020_supplemental.pdf | null | null |
Determinant Regularization for Gradient-Efficient Graph Matching | Tianshu Yu, Junchi Yan, Baoxin Li | Graph matching refers to finding vertex correspondence for a pair of graphs, which plays a fundamental role in many vision and learning related tasks. Directly applying gradient-based continuous optimization on graph matching can be attractive for its simplicity but calls for effective ways of converting the continuous solution to the discrete one under the matching constraint. In this paper, we show a novel regularization technique with the tool of determinant analysis on the matching matrix which is relaxed into continuous domain with gradient based optimization. Meanwhile we present a theoretical study on the property of our relaxation technique. Our paper strikes an attempt to understand the geometric properties of different regularization techniques and the gradient behavior during the optimization. We show that the proposed regularization is more gradient-efficient than traditional ones during early update stages. The analysis will also bring about insights for other problems under bijection constraints. The algorithm procedure is simple and empirical results on public benchmark show its effectiveness on both synthetic and real-world data. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yu_Determinant_Regularization_for_Gradient-Efficient_Graph_Matching_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yu_Determinant_Regularization_for_Gradient-Efficient_Graph_Matching_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yu_Determinant_Regularization_for_Gradient-Efficient_Graph_Matching_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
A Stochastic Conditioning Scheme for Diverse Human Motion Prediction | Sadegh Aliakbarian, Fatemeh Sadat Saleh, Mathieu Salzmann, Lars Petersson, Stephen Gould | Human motion prediction, the task of predicting future 3D human poses given a sequence of observed ones, has been mostly treated as a deterministic problem. However, human motion is a stochastic process: Given an observed sequence of poses, multiple future motions are plausible. Existing approaches to modeling this stochasticity typically combine a random noise vector with information about the previous poses. This combination, however, is done in a deterministic manner, which gives the network the flexibility to learn to ignore the random noise. Alternatively, in this paper, we propose to stochastically combine the root of variations with previous pose information, so as to force the model to take the noise into account. We exploit this idea for motion prediction by incorporating it into a recurrent encoder-decoder network with a conditional variational autoencoder block that learns to exploit the perturbations. Our experiments on two large-scale motion prediction datasets demonstrate that our model yields high-quality pose sequences that are much more diverse than those from state-of-the-art stochastic motion prediction techniques. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Aliakbarian_A_Stochastic_Conditioning_Scheme_for_Diverse_Human_Motion_Prediction_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Aliakbarian_A_Stochastic_Conditioning_Scheme_for_Diverse_Human_Motion_Prediction_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Aliakbarian_A_Stochastic_Conditioning_Scheme_for_Diverse_Human_Motion_Prediction_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Aliakbarian_A_Stochastic_Conditioning_CVPR_2020_supplemental.zip | null | null |
Can Facial Pose and Expression Be Separated With Weak Perspective Camera? | Evangelos Sariyanidi, Casey J. Zampella, Robert T. Schultz, Birkan Tunc | Separating facial pose and expression within images requires a camera model for 3D-to-2D mapping. The weak perspective (WP) camera has been the most popular choice; it is the default, if not the only option, in state-of-the-art facial analysis methods and software. WP camera is justified by the supposition that its errors are negligible when the subjects are relatively far from the camera, yet this claim has never been tested despite nearly 20 years of research. This paper critically examines the suitability of WP camera for separating facial pose and expression. First, we theoretically show that WP causes pose-expression ambiguity, as it leads to estimation of spurious expressions. Next, we experimentally quantify the magnitude of spurious expressions. Finally, we test whether spurious expressions have detrimental effects on a common facial analysis application, namely Action Unit (AU) detection. Contrary to conventional wisdom, we find that severe pose-expression ambiguity exists even when subjects are not close to the camera, leading to large false positive rates in AU detection. We also demonstrate that the magnitude and characteristics of spurious expressions depend on the point distribution model used to model the expressions. Our results suggest that common assumptions about WP need to be revisited in facial expression modeling, and that facial analysis software should encourage and facilitate the use of the true camera model whenever possible. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Sariyanidi_Can_Facial_Pose_and_Expression_Be_Separated_With_Weak_Perspective_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Sariyanidi_Can_Facial_Pose_and_Expression_Be_Separated_With_Weak_Perspective_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Sariyanidi_Can_Facial_Pose_and_Expression_Be_Separated_With_Weak_Perspective_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Sariyanidi_Can_Facial_Pose_CVPR_2020_supplemental.pdf | null | null |
Probability Weighted Compact Feature for Domain Adaptive Retrieval | Fuxiang Huang, Lei Zhang, Yang Yang, Xichuan Zhou | Domain adaptive image retrieval includes single-domain retrieval and cross-domain retrieval. Most of the existing image retrieval methods only focus on single-domain retrieval, which assumes that the distributions of retrieval databases and queries are similar. However, in practical application, the discrepancies between retrieval databases often taken in ideal illumination/pose/background/camera conditions and queries usually obtained in uncontrolled conditions are very large. In this paper, considering the practical application, we focus on challenging cross-domain retrieval. To address the problem, we propose an effective method named Probability Weighted Compact Feature Learning (PWCF), which provides inter-domain correlation guidance to promote cross-domain retrieval accuracy and learns a series of compact binary codes to improve the retrieval speed. First, we derive our loss function through the Maximum A Posteriori Estimation (MAP): Bayesian Perspective (BP) induced focal-triplet loss, BP induced quantization loss and BP induced classification loss. Second, we propose a common manifold structure between domains to explore the potential correlation across domains. Considering the original feature representation is biased due to the inter-domain discrepancy, the manifold structure is difficult to be constructed. Therefore, we propose a new feature named Histogram Feature of Neighbors (HFON) from the sample statistics perspective. Extensive experiments on various benchmark databases validate that our method outperforms many state-of-the-art image retrieval methods for domain adaptive image retrieval. The source code is available at https://github.com/fuxianghuang1/PWCF . | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Huang_Probability_Weighted_Compact_Feature_for_Domain_Adaptive_Retrieval_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.03293 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Huang_Probability_Weighted_Compact_Feature_for_Domain_Adaptive_Retrieval_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Huang_Probability_Weighted_Compact_Feature_for_Domain_Adaptive_Retrieval_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Compositional Convolutional Neural Networks: A Deep Architecture With Innate Robustness to Partial Occlusion | Adam Kortylewski, Ju He, Qing Liu, Alan L. Yuille | Recent work has shown that deep convolutional neural networks (DCNNs) do not generalize well under partial occlusion. Inspired by the success of compositional models at classifying partially occluded objects, we propose to integrate compositional models and DCNNs into a unified deep model with innate robustness to partial occlusion. We term this architecture Compositional Convolutional Neural Network. In particular, we propose to replace the fully connected classification head of a DCNN with a differentiable compositional model. The generative nature of the compositional model enables it to localize occluders and subsequently focus on the non-occluded parts of the object. We conduct classification experiments on artificially occluded images as well as real images of partially occluded objects from the MS-COCO dataset. The results show that DCNNs do not classify occluded objects robustly, even when trained with data that is strongly augmented with partial occlusions. Our proposed model outperforms standard DCNNs by a large margin at classifying partially occluded objects, even when it has not been exposed to occluded objects during training. Additional experiments demonstrate that CompositionalNets can also localize the occluders accurately, despite being trained with class labels only. The code and data used in this work are publicly available. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Kortylewski_Compositional_Convolutional_Neural_Networks_A_Deep_Architecture_With_Innate_Robustness_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.04490 | https://www.youtube.com/watch?v=_k_-3Cdbng4 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Kortylewski_Compositional_Convolutional_Neural_Networks_A_Deep_Architecture_With_Innate_Robustness_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Kortylewski_Compositional_Convolutional_Neural_Networks_A_Deep_Architecture_With_Innate_Robustness_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Kortylewski_Compositional_Convolutional_Neural_CVPR_2020_supplemental.pdf | null | null |
Cascade EF-GAN: Progressive Facial Expression Editing With Local Focuses | Rongliang Wu, Gongjie Zhang, Shijian Lu, Tao Chen | Recent advances in Generative Adversarial Nets (GANs) have shown remarkable improvements for facial expression editing. However, current methods are still prone to generate artifacts and blurs around expression-intensive regions, and often introduce undesired overlapping artifacts while handling large-gap expression transformations such as transformation from furious to laughing. To address these limitations, we propose Cascade Expression Focal GAN (Cascade EF-GAN), a novel network that performs progressive facial expression editing with local expression focuses. The introduction of the local focus enables the Cascade EF-GAN to better preserve identity-related features and details around eyes, noses and mouths, which further helps reduce artifacts and blurs within the generated facial images. In addition, an innovative cascade transformation strategy is designed by dividing a large facial expression transformation into multiple small ones in cascade, which helps suppress overlapping artifacts and produce more realistic editing while dealing with large-gap expression transformations. Extensive experiments over two publicly available facial expression datasets show that our proposed Cascade EF-GAN achieves superior performance for facial expression editing. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wu_Cascade_EF-GAN_Progressive_Facial_Expression_Editing_With_Local_Focuses_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wu_Cascade_EF-GAN_Progressive_Facial_Expression_Editing_With_Local_Focuses_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wu_Cascade_EF-GAN_Progressive_Facial_Expression_Editing_With_Local_Focuses_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wu_Cascade_EF-GAN_Progressive_CVPR_2020_supplemental.pdf | null | null |
TPNet: Trajectory Proposal Network for Motion Prediction | Liangji Fang, Qinhong Jiang, Jianping Shi, Bolei Zhou | Making accurate motion prediction of the surrounding traffic agents such as pedestrians, vehicles, and cyclists is crucial for autonomous driving. Recent data-driven motion prediction methods have attempted to learn to directly regress the exact future position or its distribution from massive amount of trajectory data. However, it remains difficult for these methods to provide multimodal predictions as well as integrate physical constraints such as traffic rules and movable areas. In this work we propose a novel two-stage motion prediction framework, Trajectory Proposal Network (TPNet). TPNet first generates a candidate set of future trajectories as hypothesis proposals, then makes the final predictions by classifying and refining the proposals which meets the physical constraints. By steering the proposal generation process, safe and multimodal predictions are realized. Thus this framework effectively mitigates the complexity of motion prediction problem while ensuring the multimodal output. Experiments on four large-scale trajectory prediction datasets, i.e. the ETH, UCY, Apollo and Argoverse datasets, show that TPNet achieves the state-of-the-art results both quantitatively and qualitatively. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Fang_TPNet_Trajectory_Proposal_Network_for_Motion_Prediction_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.12255 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Fang_TPNet_Trajectory_Proposal_Network_for_Motion_Prediction_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Fang_TPNet_Trajectory_Proposal_Network_for_Motion_Prediction_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Fang_TPNet_Trajectory_Proposal_CVPR_2020_supplemental.zip | null | null |
Part-Aware Context Network for Human Parsing | Xiaomei Zhang, Yingying Chen, Bingke Zhu, Jinqiao Wang, Ming Tang | Recent works have made significant progress in human parsing by exploiting rich contexts. However, human parsing still faces a challenge of how to generate adaptive contextual features for the various sizes and shapes of human parts. In this work, we propose a Part-aware Context Network (PCNet), a novel and effective algorithm to deal with the challenge. PCNet mainly consists of three modules, including a part class module, a relational aggregation module, and a relational dispersion module. The part class module extracts the high-level representations of every human part from a categorical perspective. We design a relational aggregation module to capture the representative global context by mining associated semantics of human parts, which adaptively augments the context for human parts. We propose a relational dispersion module to generate the discriminative and effective local context and neglect disturbing one by making the affinity of human parts dispersed. The relational dispersion module ensures that features in the same class will be close to each other and away from those of different classes. By fusing the outputs of the relational aggregation module, the relational dispersion module and the backbone network, our PCNet generates adaptive contextual features for various sizes of human parts, improving the parsing accuracy. We achieve a new state-of-the-art segmentation performance on three challenging human parsing datasets, i.e., PASCAL-Person-Part, LIP, and CIHP. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_Part-Aware_Context_Network_for_Human_Parsing_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Part-Aware_Context_Network_for_Human_Parsing_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Part-Aware_Context_Network_for_Human_Parsing_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Lighthouse: Predicting Lighting Volumes for Spatially-Coherent Illumination | Pratul P. Srinivasan, Ben Mildenhall, Matthew Tancik, Jonathan T. Barron, Richard Tucker, Noah Snavely | We present a deep learning solution for estimating the incident illumination at any 3D location within a scene from an input narrow-baseline stereo image pair. Previous approaches for predicting global illumination from images either predict just a single illumination for the entire scene, or separately estimate the illumination at each 3D location without enforcing that the predictions are consistent with the same 3D scene. Instead, we propose a deep learning model that estimates a 3D volumetric RGBA model of a scene, including content outside the observed field of view, and then uses standard volume rendering to estimate the incident illumination at any 3D location within that volume. Our model is trained without any ground truth 3D data and only requires a held-out perspective view near the input stereo pair and a spherical panorama taken within each scene as supervision, as opposed to prior methods for spatially-varying lighting estimation, which require ground truth scene geometry for training. We demonstrate that our method can predict consistent spatially-varying lighting that is convincing enough to plausibly relight and insert highly specular virtual objects into real images. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Srinivasan_Lighthouse_Predicting_Lighting_Volumes_for_Spatially-Coherent_Illumination_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.08367 | https://www.youtube.com/watch?v=CH2gmtRW7HU | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Srinivasan_Lighthouse_Predicting_Lighting_Volumes_for_Spatially-Coherent_Illumination_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Srinivasan_Lighthouse_Predicting_Lighting_Volumes_for_Spatially-Coherent_Illumination_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Srinivasan_Lighthouse_Predicting_Lighting_CVPR_2020_supplemental.pdf | null | null |
Joint Texture and Geometry Optimization for RGB-D Reconstruction | Yanping Fu, Qingan Yan, Jie Liao, Chunxia Xiao | Due to inevitable noises and quantization error, the reconstructed 3D models via RGB-D sensors always accompany geometric error and camera drifting, which consequently lead to blurring and unnatural texture mapping results. Most of the 3D reconstruction methods focus on either geometry refinement or texture improvement respectively, which subjectively decouples the inter-relationship between geometry and texture. In this paper, we propose a novel approach that can jointly optimize the camera poses, texture and geometry of the reconstructed model, and color consistency between the key-frames. Instead of computing Shape-From-Shading (SFS) expensively, our method directly optimizes the reconstructed mesh according to color and geometric consistency and high-boost normal cues, which can effectively overcome the texture-copy problem generated by SFS and achieve more detailed shape reconstruction. As the joint optimization involves multiple correlated terms, therefore, we further introduce an iterative framework to interleave the optimal state. The experiments demonstrate that our method can recover not only fine-scale geometry but also high-fidelity texture. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Fu_Joint_Texture_and_Geometry_Optimization_for_RGB-D_Reconstruction_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Fu_Joint_Texture_and_Geometry_Optimization_for_RGB-D_Reconstruction_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Fu_Joint_Texture_and_Geometry_Optimization_for_RGB-D_Reconstruction_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Fu_Joint_Texture_and_CVPR_2020_supplemental.pdf | null | null |
Hyperbolic Visual Embedding Learning for Zero-Shot Recognition | Shaoteng Liu, Jingjing Chen, Liangming Pan, Chong-Wah Ngo, Tat-Seng Chua, Yu-Gang Jiang | This paper proposes a Hyperbolic Visual Embedding Learning Network for zero-shot recognition. The network learns image embeddings in hyperbolic space, which is capable of preserving the hierarchical structure of semantic classes in low dimensions. Comparing with existing zero-shot learning approaches, the network is more robust because the embedding feature in hyperbolic space better represents class hierarchy and thereby avoid misleading resulted from unrelated siblings. Our network outperforms exiting baselines under hierarchical evaluation with an extremely challenging setting, i.e., learning only from 1,000 categories to recognize 20,841 unseen categories. While under flat evaluation, it has competitive performance as state-of-the-art methods but with five times lower embedding dimensions. Our code is publicly available (https://github.com/ShaoTengLiu/Hyperbolic_ZSL). | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_Hyperbolic_Visual_Embedding_Learning_for_Zero-Shot_Recognition_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Hyperbolic_Visual_Embedding_Learning_for_Zero-Shot_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Hyperbolic_Visual_Embedding_Learning_for_Zero-Shot_Recognition_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
LSM: Learning Subspace Minimization for Low-Level Vision | Chengzhou Tang, Lu Yuan, Ping Tan | We study the energy minimization problem in low-level vision tasks from a novel perspective. We replace the heuristic regularization term with a data-driven learnable subspace constraint, and preserve the data term to exploit domain knowledge derived from the first principles of a task. This learning subspace minimization (LSM) framework unifies the network structures and the parameters for many different low-level vision tasks, which allows us to train a single network for multiple tasks simultaneously with shared parameters, and even generalizes the trained network to an unseen task as long as the data term can be formulated. We validate our LSM frame on four low-level tasks including edge detection, interactive segmentation, stereo matching, and optical flow, and validate the network on various datasets. The experiments demonstrate that the proposed LSM generates state-of-the-art results with smaller model size, faster training convergence, and real-time inference. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Tang_LSM_Learning_Subspace_Minimization_for_Low-Level_Vision_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.09197 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Tang_LSM_Learning_Subspace_Minimization_for_Low-Level_Vision_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Tang_LSM_Learning_Subspace_Minimization_for_Low-Level_Vision_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Tang_LSM_Learning_Subspace_CVPR_2020_supplemental.pdf | null | null |
Erasing Integrated Learning: A Simple Yet Effective Approach for Weakly Supervised Object Localization | Jinjie Mai, Meng Yang, Wenfeng Luo | Weakly supervised object localization (WSOL) aims to localize object with only weak supervision like image-level labels. However, a long-standing problem for available techniques based on the classification network is that they often result in highlighting the most discriminative parts rather than the entire extent of object. Nevertheless, trying to explore the integral extent of the object could degrade the performance of image classification on the contrary. To remedy this, we propose a simple yet powerful approach by introducing a novel adversarial erasing technique, erasing integrated learning (EIL). By integrating discriminative region mining and adversarial erasing in a single forward-backward propagation in a vanilla CNN, the proposed EIL explores the high response class-specific area and the less discriminative region simultaneously, thus could maintain high performance in classification and jointly discover the full extent of the object. Furthermore, we apply multiple EIL (MEIL) modules at different levels of the network in a sequential manner, which for the first time integrates semantic features of multiple levels and multiple scales through adversarial erasing learning. In particular, the proposed EIL and advanced MEIL both achieve a new state-of-the-art performance in CUB-200-2011 and ILSVRC 2016 benchmark, making significant improvement in localization while advancing high performance in image classification. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Mai_Erasing_Integrated_Learning_A_Simple_Yet_Effective_Approach_for_Weakly_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=j7UeDzk1DKo | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Mai_Erasing_Integrated_Learning_A_Simple_Yet_Effective_Approach_for_Weakly_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Mai_Erasing_Integrated_Learning_A_Simple_Yet_Effective_Approach_for_Weakly_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Self-Supervised Deep Visual Odometry With Online Adaptation | Shunkai Li, Xin Wang, Yingdian Cao, Fei Xue, Zike Yan, Hongbin Zha | Self-supervised VO methods have shown great success in jointly estimating camera pose and depth from videos. However, like most data-driven methods, existing VO networks suffer from a notable decrease in performance when confronted with scenes different from the training data, which makes them unsuitable for practical applications. In this paper, we propose an online meta-learning algorithm to enable VO networks to continuously adapt to new environments in a self-supervised manner. The proposed method utilizes convolutional long short-term memory (convLSTM) to aggregate rich spatial-temporal information in the past. The network is able to memorize and learn from its past experience for better estimation and fast adaptation to the current frame. When running VO in the open world, in order to deal with the changing environment, we propose an online feature alignment method by aligning feature distributions at different time. Our VO network is able to seamlessly adapt to different environments. Extensive experiments on unseen outdoor scenes, virtual to real world and outdoor to indoor environments demonstrate that our method consistently outperforms state-of-the-art self-supervised VO baselines considerably. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Self-Supervised_Deep_Visual_Odometry_With_Online_Adaptation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.06136 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Self-Supervised_Deep_Visual_Odometry_With_Online_Adaptation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Self-Supervised_Deep_Visual_Odometry_With_Online_Adaptation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_Self-Supervised_Deep_Visual_CVPR_2020_supplemental.pdf | null | null |
Weakly-Supervised Semantic Segmentation via Sub-Category Exploration | Yu-Ting Chang, Qiaosong Wang, Wei-Chih Hung, Robinson Piramuthu, Yi-Hsuan Tsai, Ming-Hsuan Yang | Existing weakly-supervised semantic segmentation methods using image-level annotations typically rely on initial responses to locate object regions. However, such response maps generated by the classification network usually focus on discriminative object parts, due to the fact that the network does not need the entire object for optimizing the objective function. To enforce the network to pay attention to other parts of an object, we propose a simple yet effective approach that introduces a self-supervised task by exploiting the sub-category information. Specifically, we perform clustering on image features to generate pseudo sub-categories labels within each annotated parent class, and construct a sub-category objective to assign the network to a more challenging task. By iteratively clustering image features, the training process does not limit itself to the most discriminative object parts, hence improving the quality of the response maps. We conduct extensive analysis to validate the proposed method and show that our approach performs favorably against the state-of-the-art approaches. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chang_Weakly-Supervised_Semantic_Segmentation_via_Sub-Category_Exploration_CVPR_2020_paper.pdf | http://arxiv.org/abs/2008.01183 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chang_Weakly-Supervised_Semantic_Segmentation_via_Sub-Category_Exploration_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chang_Weakly-Supervised_Semantic_Segmentation_via_Sub-Category_Exploration_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Chang_Weakly-Supervised_Semantic_Segmentation_CVPR_2020_supplemental.pdf | null | null |
Normalizing Flows With Multi-Scale Autoregressive Priors | Apratim Bhattacharyya, Shweta Mahajan, Mario Fritz, Bernt Schiele, Stefan Roth | Flow-based generative models are an important class of exact inference models that admit efficient inference and sampling for image synthesis. Owing to the efficiency constraints on the design of the flow layers, e.g. split coupling flow layers in which approximately half the pixels do not undergo further transformations, they have limited expressiveness for modeling long-range data dependencies compared to autoregressive models that rely on conditional pixel-wise generation. In this work, we improve the representational power of flow-based models by introducing channel-wise dependencies in their latent space through multi-scale autoregressive priors (mAR). Our mAR prior for models with split coupling flow layers (mAR-SCF) can better capture dependencies in complex multimodal data. The resulting model achieves state-of-the-art density estimation results on MNIST, CIFAR-10, and ImageNet. Furthermore, we show that mAR-SCF allows for improved image generation quality, with gains in FID and Inception scores compared to state-of-the-art flow-based models. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Bhattacharyya_Normalizing_Flows_With_Multi-Scale_Autoregressive_Priors_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.03891 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Bhattacharyya_Normalizing_Flows_With_Multi-Scale_Autoregressive_Priors_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Bhattacharyya_Normalizing_Flows_With_Multi-Scale_Autoregressive_Priors_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Bhattacharyya_Normalizing_Flows_With_CVPR_2020_supplemental.pdf | null | null |
Dynamic Neural Relational Inference | Colin Graber, Alexander G. Schwing | Understanding interactions between entities, e.g., joints of the human body, team sports players, etc., is crucial for tasks like forecasting. However, interactions between entities are commonly not observed and often hard to quantify. To address this challenge, recently, `Neural Relational Inference' was introduced. It predicts static relations between entities in a system and provides an interpretable representation of the underlying system dynamics that are used for better trajectory forecasting. However, generally, relations between entities change as time progresses. Hence, static relations improperly model the data. In response to this, we develop Dynamic Neural Relational Inference (dNRI), which incorporates insights from sequential latent variable models to predict separate relation graphs for every time-step. We demonstrate on several real-world datasets that modeling dynamic relations improves forecasting of complex trajectories. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Graber_Dynamic_Neural_Relational_Inference_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Graber_Dynamic_Neural_Relational_Inference_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Graber_Dynamic_Neural_Relational_Inference_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Graber_Dynamic_Neural_Relational_CVPR_2020_supplemental.zip | null | null |
Embedding Expansion: Augmentation in Embedding Space for Deep Metric Learning | Byungsoo Ko, Geonmo Gu | Learning the distance metric between pairs of samples has been studied for image retrieval and clustering. With the remarkable success of pair-based metric learning losses, recent works have proposed the use of generated synthetic points on metric learning losses for augmentation and generalization. However, these methods require additional generative networks along with the main network, which can lead to a larger model size, slower training speed, and harder optimization. Meanwhile, post-processing techniques, such as query expansion and database augmentation, have proposed the combination of feature points to obtain additional semantic information. In this paper, inspired by query expansion and database augmentation, we propose an augmentation method in an embedding space for pair-based metric learning losses, called embedding expansion. The proposed method generates synthetic points containing augmented information by a combination of feature points and performs hard negative pair mining to learn with the most informative feature representations. Because of its simplicity and flexibility, it can be used for existing metric learning losses without affecting model size, training speed, or optimization difficulty. Finally, the combination of embedding expansion and representative metric learning losses outperforms the state-of-the-art losses and previous sample generation methods in both image retrieval and clustering tasks. The implementation is publicly available. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Ko_Embedding_Expansion_Augmentation_in_Embedding_Space_for_Deep_Metric_Learning_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.02546 | https://www.youtube.com/watch?v=EjCcR3O5wp4 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Ko_Embedding_Expansion_Augmentation_in_Embedding_Space_for_Deep_Metric_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Ko_Embedding_Expansion_Augmentation_in_Embedding_Space_for_Deep_Metric_Learning_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Ko_Embedding_Expansion_Augmentation_CVPR_2020_supplemental.zip | null | null |
LT-Net: Label Transfer by Learning Reversible Voxel-Wise Correspondence for One-Shot Medical Image Segmentation | Shuxin Wang, Shilei Cao, Dong Wei, Renzhen Wang, Kai Ma, Liansheng Wang, Deyu Meng, Yefeng Zheng | We introduce a one-shot segmentation method to alleviate the burden of manual annotation for medical images. The main idea is to treat one-shot segmentation as a classical atlas-based segmentation problem, where voxel-wise correspondence from the atlas to the unlabelled data is learned. Subsequently, segmentation label of the atlas can be transferred to the unlabelled data with the learned correspondence. However, since ground truth correspondence between images is usually unavailable, the learning system must be well-supervised to avoid mode collapse and convergence failure. To overcome this difficulty, we resort to the forward-backward consistency, which is widely used in correspondence problems, and additionally learn the backward correspondences from the warped atlases back to the original atlas. This cycle-correspondence learning design enables a variety of extra, cycle-consistency-based supervision signals to make the training process stable, while also boost the performance. We demonstrate the superiority of our method over both deep learning-based one-shot segmentation methods and a classical multi-atlas segmentation method via thorough experiments. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_LT-Net_Label_Transfer_by_Learning_Reversible_Voxel-Wise_Correspondence_for_One-Shot_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=rprqxMGW6ZU | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_LT-Net_Label_Transfer_by_Learning_Reversible_Voxel-Wise_Correspondence_for_One-Shot_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_LT-Net_Label_Transfer_by_Learning_Reversible_Voxel-Wise_Correspondence_for_One-Shot_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wang_LT-Net_Label_Transfer_CVPR_2020_supplemental.pdf | null | null |
Transferring Dense Pose to Proximal Animal Classes | Artsiom Sanakoyeu, Vasil Khalidov, Maureen S. McCarthy, Andrea Vedaldi, Natalia Neverova | Recent contributions have demonstrated that it is possible to recognize the pose of humans densely and accurately given a large dataset of poses annotated in detail. In principle, the same approach could be extended to any animal class, but the effort required for collecting new annotations for each case makes this strategy impractical, despite important applications in natural conservation, science and business. We show that, at least for proximal animal classes such as chimpanzees, it is possible to transfer the knowledge existing in dense pose recognition for humans, as well as in more general object detectors and segmenters, to the problem of dense pose recognition in other classes. We do this by (1) establishing a DensePose model for the new animal which is also geometrically aligned to humans (2) introducing a multi-head R-CNN architecture that facilitates transfer of multiple recognition tasks between classes, (3) finding which combination of known classes can be transferred most effectively to the new animal and (4) using self-calibrated uncertainty heads to generate pseudo-labels graded by quality for training a model for this class. We also introduce two benchmark datasets labelled in the manner of DensePose for the class chimpanzee and use them to evaluate our approach, showing excellent transfer learning performance. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Sanakoyeu_Transferring_Dense_Pose_to_Proximal_Animal_Classes_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.00080 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Sanakoyeu_Transferring_Dense_Pose_to_Proximal_Animal_Classes_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Sanakoyeu_Transferring_Dense_Pose_to_Proximal_Animal_Classes_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Sanakoyeu_Transferring_Dense_Pose_CVPR_2020_supplemental.pdf | null | null |
Suppressing Uncertainties for Large-Scale Facial Expression Recognition | Kai Wang, Xiaojiang Peng, Jianfei Yang, Shijian Lu, Yu Qiao | Annotating a qualitative large-scale facial expression dataset is extremely difficult due to the uncertainties caused by ambiguous facial expressions, low-quality facial images, and the subjectiveness of annotators. These uncertainties suspend the progress of large-scale Facial Expression Recognition (FER) in data-driven deep learning era. To address this problelm, this paper proposes to suppress the uncertainties by a simple yet efficient Self-Cure Network (SCN). Specifically, SCN suppresses the uncertainty from two different aspects: 1) a self-attention mechanism over FER dataset to weight each sample in training with a ranking regularization, and 2) a careful relabeling mechanism to modify the labels of these samples in the lowest-ranked group. Experiments on synthetic FER datasets and our collected WebEmotion dataset validate the effectiveness of our method. Results on public benchmarks demonstrate that our SCN outperforms current state-of-the-art methods with 88.14% on RAF-DB, 60.23% on AffectNet, and 89.35% on FERPlus. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Suppressing_Uncertainties_for_Large-Scale_Facial_Expression_Recognition_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.10392 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Suppressing_Uncertainties_for_Large-Scale_Facial_Expression_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Suppressing_Uncertainties_for_Large-Scale_Facial_Expression_Recognition_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Scale-Space Flow for End-to-End Optimized Video Compression | Eirikur Agustsson, David Minnen, Nick Johnston, Johannes Balle, Sung Jin Hwang, George Toderici | Despite considerable progress on end-to-end optimized deep networks for image compression, video coding remains a challenging task. Recently proposed methods for learned video compression use optical flow and bilinear warping for motion compensation and show competitive rate-distortion performance relative to hand-engineered codecs like H.264 and HEVC. However, these learning-based methods rely on complex architectures and training schemes including the use of pre-trained optical flow networks, sequential training of sub-networks, adaptive rate control, and buffering intermediate reconstructions to disk during training. In this paper, we show that a generalized warping operator that better handles common failure cases, e.g. disocclusions and fast motion, can provide competitive compression results with a greatly simplified model and training procedure. Specifically, we propose scale-space flow, an intuitive generalization of optical flow that adds a scale parameter to allow the network to better model uncertainty. Our experiments show that a low-latency video compression model (no B-frames) using scale-space flow for motion compensation can outperform analogous state-of-the art learned video compression models while being trained using a much simpler procedure and without any pre-trained optical flow networks. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Agustsson_Scale-Space_Flow_for_End-to-End_Optimized_Video_Compression_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Agustsson_Scale-Space_Flow_for_End-to-End_Optimized_Video_Compression_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Agustsson_Scale-Space_Flow_for_End-to-End_Optimized_Video_Compression_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Agustsson_Scale-Space_Flow_for_CVPR_2020_supplemental.pdf | null | null |
StyleRig: Rigging StyleGAN for 3D Control Over Portrait Images | Ayush Tewari, Mohamed Elgharib, Gaurav Bharaj, Florian Bernard, Hans-Peter Seidel, Patrick Perez, Michael Zollhofer, Christian Theobalt | StyleGAN generates photorealistic portrait images of faces with eyes, teeth, hair and context (neck, shoulders, background), but lacks a rig-like control over semantic face parameters that are interpretable in 3D, such as face pose, expressions, and scene illumination. Three-dimensional morphable face models (3DMMs) on the other hand offer control over the semantic parameters, but lack photorealism when rendered and only model the face interior, not other parts of a portrait image (hair, mouth interior, background). We present the first method to provide a face rig-like control over a pretrained and fixed StyleGAN via a 3DMM. A new rigging network, RigNet is trained between the 3DMM's semantic parameters and StyleGAN's input. The network is trained in a self-supervised manner, without the need for manual annotations. At test time, our method generates portrait images with the photorealism of StyleGAN and provides explicit control over the 3D semantic parameters of the face. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Tewari_StyleRig_Rigging_StyleGAN_for_3D_Control_Over_Portrait_Images_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.00121 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Tewari_StyleRig_Rigging_StyleGAN_for_3D_Control_Over_Portrait_Images_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Tewari_StyleRig_Rigging_StyleGAN_for_3D_Control_Over_Portrait_Images_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Tewari_StyleRig_Rigging_StyleGAN_CVPR_2020_supplemental.pdf | null | null |
Semantic Pyramid for Image Generation | Assaf Shocher, Yossi Gandelsman, Inbar Mosseri, Michal Yarom, Michal Irani, William T. Freeman, Tali Dekel | We present a novel GAN-based model that utilizes the space of deep features learned by a pre-trained classification model. Inspired by classical image pyramid representations, we construct our model as a Semantic Generation Pyramid -- a hierarchical framework which leverages the continuum of semantic information encapsulated in such deep features; this ranges from low level information contained in fine features to high level, semantic information contained in deeper features. More specifically, given a set of features extracted from a reference image, our model generates diverse image samples, each with matching features at each semantic level of the classification model. We demonstrate that our model results in a versatile and flexible framework that can be used in various classic and novel image generation tasks. These include: generating images with a controllable extent of semantic similarity to a reference image, and different manipulation tasks such as semantically-controlled inpainting and compositing; all achieved with the same model, with no further training. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Shocher_Semantic_Pyramid_for_Image_Generation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.06221 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Shocher_Semantic_Pyramid_for_Image_Generation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Shocher_Semantic_Pyramid_for_Image_Generation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Towards Backward-Compatible Representation Learning | Yantao Shen, Yuanjun Xiong, Wei Xia, Stefano Soatto | We propose a way to learn visual features that are compatible with previously computed ones even when they have different dimensions and are learned via different neural network architectures and loss functions. Compatible means that, if such features are used to compare images, then "new" features can be compared directly to "old" features, so they can be used interchangeably. This enables visual search systems to bypass computing new features for all previously seen images when updating the embedding models, a process known as backfilling. Backward compatibility is critical to quickly deploy new embedding models that leverage ever-growing large-scale training datasets and improvements in deep learning architectures and training methods. We propose a framework to train embedding models, called backward-compatible training (BCT), as a first step towards backward compatible representation learning. In experiments on learning embeddings for face recognition, models trained with BCT successfully achieve backward compatibility without sacrificing accuracy, thus enabling backfill-free model updates of visual embeddings. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Shen_Towards_Backward-Compatible_Representation_Learning_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.11942 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Shen_Towards_Backward-Compatible_Representation_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Shen_Towards_Backward-Compatible_Representation_Learning_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Shen_Towards_Backward-Compatible_Representation_CVPR_2020_supplemental.pdf | null | null |
Global-Local GCN: Large-Scale Label Noise Cleansing for Face Recognition | Yaobin Zhang, Weihong Deng, Mei Wang, Jiani Hu, Xian Li, Dongyue Zhao, Dongchao Wen | In the field of face recognition, large-scale web-collected datasets are essential for learning discriminative representations, but they suffer from noisy identity labels, such as outliers and label flips. It is beneficial to automatically cleanse their label noise for improving recognition accuracy. Unfortunately, existing cleansing methods cannot accurately identify noise in the wild. To solve this problem, we propose an effective automatic label noise cleansing framework for face recognition datasets, FaceGraph. Using two cascaded graph convolutional networks, FaceGraph performs global-to-local discrimination to select useful data in a noisy environment. Extensive experiments show that cleansing widely used datasets, such as CASIA-WebFace, VGGFace2, MegaFace2, and MS-Celeb-1M, using the proposed method can improve the recognition performance of state-of-the-art representation learning methods like Arcface. Further, we cleanse massive self-collected celebrity data, namely MillionCelebs, to provide 18.8M images of 636K identities. Training with the new data, Arcface surpasses state-of-the-art performance by a notable margin to reach 95.62% TPR at 1e-5 FPR on the IJB-C benchmark. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_Global-Local_GCN_Large-Scale_Label_Noise_Cleansing_for_Face_Recognition_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Global-Local_GCN_Large-Scale_Label_Noise_Cleansing_for_Face_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Global-Local_GCN_Large-Scale_Label_Noise_Cleansing_for_Face_Recognition_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhang_Global-Local_GCN_Large-Scale_CVPR_2020_supplemental.pdf | null | null |
Adaptive Graph Convolutional Network With Attention Graph Clustering for Co-Saliency Detection | Kaihua Zhang, Tengpeng Li, Shiwen Shen, Bo Liu, Jin Chen, Qingshan Liu | Co-saliency detection aims to discover the common and salient foregrounds from a group of relevant images. For this task, we present a novel adaptive graph convolutional network with attention graph clustering (GCAGC). Three major contributions have been made, and are experimentally shown to have substantial practical merits. First, we propose a graph convolutional network design to extract information cues to characterize the intra- and inter-image correspondence. Second, we develop an attention graph clustering algorithm to discriminate the common objects from all the salient foreground objects in an unsupervised fashion. Third, we present a unified framework with encoder-decoder structure to jointly train and optimize the graph convolutional network, attention graph cluster, and co-saliency detection decoder in an end-to-end manner. We evaluate our proposed GCAGC method on three co-saliency detection benchmark datasets (iCoseg, Cosal2015 and COCO-SEG). Our GCAGC method obtains significant improvements over the state-of-the-arts on most of them. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_Adaptive_Graph_Convolutional_Network_With_Attention_Graph_Clustering_for_Co-Saliency_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.06167 | https://www.youtube.com/watch?v=ca3oWuMSNgE | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Adaptive_Graph_Convolutional_Network_With_Attention_Graph_Clustering_for_Co-Saliency_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Adaptive_Graph_Convolutional_Network_With_Attention_Graph_Clustering_for_Co-Saliency_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
UniPose: Unified Human Pose Estimation in Single Images and Videos | Bruno Artacho, Andreas Savakis | We propose UniPose, a unified framework for human pose estimation, based on our "Waterfall" Atrous Spatial Pooling architecture, that achieves state-of-art-results on several pose estimation metrics. UniPose incorporates contextual segmentation and joint localization to estimate the human pose in a single stage, with high accuracy, without relying on statistical postprocessing methods. The Waterfall module in UniPose leverages the efficiency of progressive filtering in the cascade architecture, while maintaining multi-scale fields-of-view comparable to spatial pyramid configurations. Additionally, our method is extended to UniPose-LSTM for multi-frame processing and achieves state-of-the-art results for temporal pose estimation in Video. Our results on multiple datasets demonstrate that UniPose, with a ResNet backbone and Waterfall module, is a robust and efficient architecture for pose estimation obtaining state-of-the-art results in single person pose detection for both single images and videos. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Artacho_UniPose_Unified_Human_Pose_Estimation_in_Single_Images_and_Videos_CVPR_2020_paper.pdf | http://arxiv.org/abs/2001.08095 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Artacho_UniPose_Unified_Human_Pose_Estimation_in_Single_Images_and_Videos_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Artacho_UniPose_Unified_Human_Pose_Estimation_in_Single_Images_and_Videos_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Novel View Synthesis of Dynamic Scenes With Globally Coherent Depths From a Monocular Camera | Jae Shin Yoon, Kihwan Kim, Orazio Gallo, Hyun Soo Park, Jan Kautz | This paper presents a new method to synthesize an image from arbitrary views and times given a collection of images of a dynamic scene. A key challenge for the novel view synthesis arises from dynamic scene reconstruction where epipolar geometry does not apply to the local motion of dynamic contents. To address this challenge, we propose to combine the depth from single view (DSV) and the depth from multi-view stereo (DMV), where DSV is complete, i.e., a depth is assigned to every pixel, yet view-variant in its scale, while DMV is view-invariant yet incomplete. Our insight is that although its scale and quality are inconsistent with other views, the depth estimation from a single view can be used to reason about the globally coherent geometry of dynamic contents. We cast this problem as learning to correct the scale of DSV, and to refine each depth with locally consistent motions between views to form a coherent depth estimation. We integrate these tasks into a depth fusion network in a self-supervised fashion. Given the fused depth maps, we synthesize a photorealistic virtual view in a specific location and time with our deep blending network that completes the scene and renders the virtual view. We evaluate our method of depth estimation and view synthesis on a diverse real-world dynamic scenes and show the outstanding performance over existing methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yoon_Novel_View_Synthesis_of_Dynamic_Scenes_With_Globally_Coherent_Depths_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.01294 | https://www.youtube.com/watch?v=mfo-yeqnQgM | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yoon_Novel_View_Synthesis_of_Dynamic_Scenes_With_Globally_Coherent_Depths_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yoon_Novel_View_Synthesis_of_Dynamic_Scenes_With_Globally_Coherent_Depths_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yoon_Novel_View_Synthesis_CVPR_2020_supplemental.pdf | https://cove.thecvf.com/datasets/337 | null |
Cogradient Descent for Bilinear Optimization | Li'an Zhuo, Baochang Zhang, Linlin Yang, Hanlin Chen, Qixiang Ye, David Doermann, Rongrong Ji, Guodong Guo | Conventional learning methods simplify the bilinear model by regarding two intrinsically coupled factors independently, which degrades the optimization procedure. One reason lies in the insufficient training due to the asynchronous gradient descent, which results in vanishing gradients for the coupled variables. In this paper, we introduce a Cogradient Descent algorithm (CoGD) to address the bilinear problem, based on a theoretical framework to coordinate the gradient of hidden variables via a projection function. We solve one variable by considering its coupling relationship with the other, leading to a synchronous gradient descent to facilitate the optimization procedure. Our algorithm is applied to solve problems with one variable under the sparsity constraint, which is widely used in the learning paradigm. We validate our CoGD considering an extensive set of applications including image reconstruction, inpainting, and network pruning. Experiments show that it improves the state-of-the-art by a significant margin. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhuo_Cogradient_Descent_for_Bilinear_Optimization_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.09142 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhuo_Cogradient_Descent_for_Bilinear_Optimization_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhuo_Cogradient_Descent_for_Bilinear_Optimization_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
AdversarialNAS: Adversarial Neural Architecture Search for GANs | Chen Gao, Yunpeng Chen, Si Liu, Zhenxiong Tan, Shuicheng Yan | Neural Architecture Search (NAS) that aims to automate the procedure of architecture design has achieved promising results in many computer vision fields. In this paper, we propose an AdversarialNAS method specially tailored for Generative Adversarial Networks (GANs) to search for a superior generative model on the task of unconditional image generation. The AdversarialNAS is the first method that can search the architectures of generator and discriminator simultaneously in a differentiable manner. During searching, the designed adversarial search algorithm does not need to comput any extra metric to evaluate the performance of the searched architecture, and the search paradigm considers the relevance between the two network architectures and improves their mutual balance. Therefore, AdversarialNAS is very efficient and only takes 1 GPU day to search for a superior generative model in the proposed large search space. Experiments demonstrate the effectiveness and superiority of our method. The discovered generative model sets a new state-of-the-art FID score of 10.87 and highly competitive Inception Score of 8.74 on CIFAR-10. Its transferability is also proven by setting new state-of-the-art FID score of 26.98 and Inception score of 9.63 on STL-10. Code is at: https://github.com/chengaopro/AdversarialNAS. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Gao_AdversarialNAS_Adversarial_Neural_Architecture_Search_for_GANs_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.02037 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Gao_AdversarialNAS_Adversarial_Neural_Architecture_Search_for_GANs_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Gao_AdversarialNAS_Adversarial_Neural_Architecture_Search_for_GANs_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Belief Propagation Reloaded: Learning BP-Layers for Labeling Problems | Patrick Knobelreiter, Christian Sormann, Alexander Shekhovtsov, Friedrich Fraundorfer, Thomas Pock | It has been proposed by many researchers that combining deep neural networks with graphical models can create more efficient and better regularized composite models. The main difficulties in implementing this in practice are associated with a discrepancy in suitable learning objectives as well as with the necessity of approximations for the inference. In this work we take one of the simplest inference methods, a truncated max-product Belief Propagation, and add what is necessary to make it a proper component of a deep learning model: connect it to learning formulations with losses on marginals and compute the backprop operation. This BP-Layer can be used as the final or an intermediate block in convolutional neural networks (CNNs), allowing us to design a hierarchical model composing BP inference and CNNs at different scale levels. The model is applicable to a range of dense prediction problems, is well-trainable and provides parameter-efficient and robust solutions in stereo, flow and semantic segmentation. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Knobelreiter_Belief_Propagation_Reloaded_Learning_BP-Layers_for_Labeling_Problems_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.06258 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Knobelreiter_Belief_Propagation_Reloaded_Learning_BP-Layers_for_Labeling_Problems_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Knobelreiter_Belief_Propagation_Reloaded_Learning_BP-Layers_for_Labeling_Problems_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Knobelreiter_Belief_Propagation_Reloaded_CVPR_2020_supplemental.pdf | null | null |
DoveNet: Deep Image Harmonization via Domain Verification | Wenyan Cong, Jianfu Zhang, Li Niu, Liu Liu, Zhixin Ling, Weiyuan Li, Liqing Zhang | Image composition is an important operation in image processing, but the inconsistency between foreground and background significantly degrades the quality of composite image. Image harmonization, aiming to make the foreground compatible with the background, is a promising yet challenging task. However, the lack of high-quality publicly available dataset for image harmonization greatly hinders the development of image harmonization techniques. In this work, we contribute an image harmonization dataset iHarmony4 by generating synthesized composite images based on COCO (resp., Adobe5k, Flickr, day2night) dataset, leading to our HCOCO (resp., HAdobe5k, HFlickr, Hday2night) sub-dataset. Moreover, we propose a new deep image harmonization method DoveNet using a novel domain verification discriminator, with the insight that the foreground needs to be translated to the same domain as background. Extensive experiments on our constructed dataset demonstrate the effectiveness of our proposed method. Our dataset and code are available at https://github.com/bcmi/Image_Harmonization_Datasets. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Cong_DoveNet_Deep_Image_Harmonization_via_Domain_Verification_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.13239 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Cong_DoveNet_Deep_Image_Harmonization_via_Domain_Verification_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Cong_DoveNet_Deep_Image_Harmonization_via_Domain_Verification_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Cong_DoveNet_Deep_Image_CVPR_2020_supplemental.pdf | null | null |
Self-Supervised 3D Human Pose Estimation via Part Guided Novel Image Synthesis | Jogendra Nath Kundu, Siddharth Seth, Varun Jampani, Mugalodi Rakesh, R. Venkatesh Babu, Anirban Chakraborty | Camera captured human pose is an outcome of several sources of variation. Performance of supervised 3D pose estimation approaches comes at the cost of dispensing with variations, such as shape and appearance, that may be useful for solving other related tasks. As a result, the learned model not only inculcates task-bias but also dataset-bias because of its strong reliance on the annotated samples, which also holds true for weakly-supervised models. Acknowledging this, we propose a self-supervised learning framework to disentangle such variations from unlabeled video frames. We leverage the prior knowledge on human skeleton and poses in the form of a single part-based 2D puppet model, human pose articulation constraints, and a set of unpaired 3D poses. Our differentiable formalization, bridging the representation gap between the 3D pose and spatial part maps, not only facilitates discovery of interpretable pose disentanglement, but also allows us to operate on videos with diverse camera movements. Qualitative results on unseen in-the-wild datasets establish our superior generalization across multiple tasks beyond the primary tasks of 3D pose estimation and part segmentation. Furthermore, we demonstrate state-of-the-art weakly-supervised 3D pose estimation performance on both Human3.6M and MPI-INF-3DHP datasets. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Kundu_Self-Supervised_3D_Human_Pose_Estimation_via_Part_Guided_Novel_Image_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.04400 | https://www.youtube.com/watch?v=trl3hW7XtV4 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Kundu_Self-Supervised_3D_Human_Pose_Estimation_via_Part_Guided_Novel_Image_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Kundu_Self-Supervised_3D_Human_Pose_Estimation_via_Part_Guided_Novel_Image_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Kundu_Self-Supervised_3D_Human_CVPR_2020_supplemental.pdf | null | null |
Self-Supervised Learning of Interpretable Keypoints From Unlabelled Videos | Tomas Jakab, Ankush Gupta, Hakan Bilen, Andrea Vedaldi | We propose a new method for recognizing the pose of objects from a single image that for learning uses only unlabelled videos and a weak empirical prior on the object poses. Video frames differ primarily in the pose of the objects they contain, so our method distils the pose information by analyzing the differences between frames. The distillation uses a new dual representation of the geometry of objects as a set of 2D keypoints, and as a pictorial representation, i.e. a skeleton image. This has three benefits: (1) it provides a tight 'geometric bottleneck' which disentangles pose from appearance, (2) it can leverage powerful image-to-image translation networks to map between photometry and geometry, and (3) it allows to incorporate empirical pose priors in the learning process. The pose priors are obtained from unpaired data, such as from a different dataset or modality such as mocap, such that no annotated image is ever used in learning the pose recognition network. In standard benchmarks for pose recognition for humans and faces, our method achieves state-of-the-art performance among methods that do not require any labelled images for training. Project page: http://www.robots.ox.ac.uk/ vgg/research/unsupervised_pose/ | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Jakab_Self-Supervised_Learning_of_Interpretable_Keypoints_From_Unlabelled_Videos_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Jakab_Self-Supervised_Learning_of_Interpretable_Keypoints_From_Unlabelled_Videos_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Jakab_Self-Supervised_Learning_of_Interpretable_Keypoints_From_Unlabelled_Videos_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Jakab_Self-Supervised_Learning_of_CVPR_2020_supplemental.pdf | null | null |
Distribution-Aware Coordinate Representation for Human Pose Estimation | Feng Zhang, Xiatian Zhu, Hanbin Dai, Mao Ye, Ce Zhu | While being the de facto standard coordinate representation for human pose estimation, heatmap has not been investigated in-depth. This work fills this gap. For the first time, we find that the process of decoding the predicted heatmaps into the final joint coordinates in the original image space is surprisingly significant for the performance. We further probe the design limitations of the standard coordinate decoding method, and propose a more principled distributionaware decoding method. Also, we improve the standard coordinate encoding process (i.e. transforming ground-truth coordinates to heatmaps) by generating unbiased/accurate heatmaps. Taking the two together, we formulate a novel Distribution-Aware coordinate Representation of Keypoints (DARK) method. Serving as a model-agnostic plug-in, DARK brings about significant performance boost to existing human pose estimation models. Extensive experiments show that DARK yields the best results on two common benchmarks, MPII and COCO. Besides, DARK achieves the 2nd place entry in the ICCV 2019 COCO Keypoints Challenge. The code is available online. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_Distribution-Aware_Coordinate_Representation_for_Human_Pose_Estimation_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Distribution-Aware_Coordinate_Representation_for_Human_Pose_Estimation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Distribution-Aware_Coordinate_Representation_for_Human_Pose_Estimation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Attention Mechanism Exploits Temporal Contexts: Real-Time 3D Human Pose Reconstruction | Ruixu Liu, Ju Shen, He Wang, Chen Chen, Sen-ching Cheung, Vijayan Asari | We propose a novel attention-based framework for 3D human pose estimation from a monocular video. Despite the general success of end-to-end deep learning paradigms, our approach is based on two key observations: (1) temporal incoherence and jitter are often yielded from a single frame prediction; (2) error rate can be remarkably reduced by increasing the receptive field in a video. Therefore, we design an attentional mechanism to adaptively identify significant frames and tensor outputs from each deep neural net layer, leading to a more optimal estimation. To achieve large temporal receptive fields, multi-scale dilated convolutions are employed to model long-range dependencies among frames. The architecture is straightforward to implement and can be flexibly adopted for real-time applications. Any off-the-shelf 2D pose estimation system, e.g. Mocap libraries, can be easily integrated in an ad-hoc fashion. We both quantitatively and qualitatively evaluate our method on various standard benchmark datasets (e.g. Human3.6M, HumanEva). Our method considerably outperforms all the state-of-the-art algorithms up to 8% error reduction (average mean per joint position error: 34.7) as compared to the best-reported results. Code is available at: (https://github.com/lrxjason/Attention3DHumanPose) | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_Attention_Mechanism_Exploits_Temporal_Contexts_Real-Time_3D_Human_Pose_Reconstruction_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=6rkO5-bS57Y | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Attention_Mechanism_Exploits_Temporal_Contexts_Real-Time_3D_Human_Pose_Reconstruction_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Attention_Mechanism_Exploits_Temporal_Contexts_Real-Time_3D_Human_Pose_Reconstruction_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
MaskFlownet: Asymmetric Feature Matching With Learnable Occlusion Mask | Shengyu Zhao, Yilun Sheng, Yue Dong, Eric I-Chao Chang, Yan Xu | Feature warping is a core technique in optical flow estimation; however, the ambiguity caused by occluded areas during warping is a major problem that remains unsolved. In this paper, we propose an asymmetric occlusion-aware feature matching module, which can learn a rough occlusion mask that filters useless (occluded) areas immediately after feature warping without any explicit supervision. The proposed module can be easily integrated into end-to-end network architectures and enjoys performance gains while introducing negligible computational cost. The learned occlusion mask can be further fed into a subsequent network cascade with dual feature pyramids with which we achieve state-of-the-art performance. At the time of submission, our method, called MaskFlownet, surpasses all published optical flow methods on the MPI Sintel, KITTI 2012 and 2015 benchmarks. Code is available at https://github.com/microsoft/MaskFlownet. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhao_MaskFlownet_Asymmetric_Feature_Matching_With_Learnable_Occlusion_Mask_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.10955 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhao_MaskFlownet_Asymmetric_Feature_Matching_With_Learnable_Occlusion_Mask_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhao_MaskFlownet_Asymmetric_Feature_Matching_With_Learnable_Occlusion_Mask_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhao_MaskFlownet_Asymmetric_Feature_CVPR_2020_supplemental.pdf | null | null |
3FabRec: Fast Few-Shot Face Alignment by Reconstruction | Bjorn Browatzki, Christian Wallraven | Current supervised methods for facial landmark detection require a large amount of training data and may suffer from overfitting to specific datasets due to the massive number of parameters. We introduce a semi-supervised method in which the crucial idea is to first generate implicit face knowledge from the large amounts of unlabeled images of faces available today. In a first, completely unsupervised stage, we train an adversarial autoencoder to reconstruct faces via a low-dimensional face embedding. In a second, supervised stage, we interleave the decoder with transfer layers to retask the generation of color images to the prediction of landmark heatmaps. Our framework (3FabRec) achieves state-of-the-art performance on several common benchmarks and, most importantly, is able to maintain impressive accuracy on extremely small training sets down to as few as 10 images. As the interleaved layers only add a low amount of parameters to the decoder, inference runs at several hundred FPS on a GPU. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Browatzki_3FabRec_Fast_Few-Shot_Face_Alignment_by_Reconstruction_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.10448 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Browatzki_3FabRec_Fast_Few-Shot_Face_Alignment_by_Reconstruction_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Browatzki_3FabRec_Fast_Few-Shot_Face_Alignment_by_Reconstruction_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Browatzki_3FabRec_Fast_Few-Shot_CVPR_2020_supplemental.pdf | null | null |
MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction | Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng | The ambiguity in image matching is one of main factors decreasing the quality of the 3D model reconstructed by PatchMatch based multiple view stereo. In this paper, we present a novel method, matching ambiguity reduced multiple view stereo (MARMVS) to address this issue. The MARMVS handles the ambiguity in image matching process with three newly proposed strategies: 1) The matching ambiguity is measured by the differential geometry property of image surface with epipolar constraint, which is used as a critical criterion for optimal scale selection of every single pixel with corresponding neighbouring images. 2) The depth of every pixel is initialized to be more close to the true depth by utilizing the depths of its surrounding sparse feature points, which yields faster convergency speed in the following PatchMatch stereo and alleviates the ambiguity introduced by self similar structures of the image. 3) In the last propagation of the PatchMatch stereo, higher priorities are given to those planes with the related 2D image patch possesses less ambiguity, this strategy further propagates a correctly reconstructed surface to raw texture regions. In addition, the proposed method is very efficient even running on consumer grade CPUs, due to proper parameterization and discretization in the depth map computation step. The MARMVS is validated on public benchmarks, and experimental results demonstrate competing performance against the state of the art. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Xu_MARMVS_Matching_Ambiguity_Reduced_Multiple_View_Stereo_for_Efficient_Large_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=r_QZwNbi4m8 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_MARMVS_Matching_Ambiguity_Reduced_Multiple_View_Stereo_for_Efficient_Large_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_MARMVS_Matching_Ambiguity_Reduced_Multiple_View_Stereo_for_Efficient_Large_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Bodies at Rest: 3D Human Pose and Shape Estimation From a Pressure Image Using Synthetic Data | Henry M. Clever, Zackory Erickson, Ariel Kapusta, Greg Turk, Karen Liu, Charles C. Kemp | People spend a substantial part of their lives at rest in bed. 3D human pose and shape estimation for this activity would have numerous beneficial applications, yet line-of-sight perception is complicated by occlusion from bedding. Pressure sensing mats are a promising alternative, but training data is challenging to collect at scale. We describe a physics-based method that simulates human bodies at rest in a bed with a pressure sensing mat, and present PressurePose, a synthetic dataset with 206K pressure images with 3D human poses and shapes. We also present PressureNet, a deep learning model that estimates human pose and shape given a pressure image and gender. PressureNet incorporates a pressure map reconstruction (PMR) network that models pressure image generation to promote consistency between estimated 3D body models and pressure image input. In our evaluations, PressureNet performed well with real data from participants in diverse poses, even though it had only been trained with synthetic data. When we ablated the PMR network, performance dropped substantially. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Clever_Bodies_at_Rest_3D_Human_Pose_and_Shape_Estimation_From_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.01166 | https://www.youtube.com/watch?v=IE597-sRkto | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Clever_Bodies_at_Rest_3D_Human_Pose_and_Shape_Estimation_From_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Clever_Bodies_at_Rest_3D_Human_Pose_and_Shape_Estimation_From_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Clever_Bodies_at_Rest_CVPR_2020_supplemental.zip | null | null |
Cars Can't Fly Up in the Sky: Improving Urban-Scene Segmentation via Height-Driven Attention Networks | Sungha Choi, Joanne T. Kim, Jaegul Choo | This paper exploits the intrinsic features of urban-scene images and proposes a general add-on module, called height-driven attention networks (HANet), for improving semantic segmentation for urban-scene images. It emphasizes informative features or classes selectively according to the vertical position of a pixel. The pixel-wise class distributions are significantly different from each other among horizontally segmented sections in the urban-scene images. Likewise, urban-scene images have their own distinct characteristics, but most semantic segmentation networks do not reflect such unique attributes in the architecture. The proposed network architecture incorporates the capability exploiting the attributes to handle the urban scene dataset effectively. We validate the consistent performance (mIoU) increase of various semantic segmentation models on two datasets when HANet is adopted. This extensive quantitative analysis demonstrates that adding our module to existing models is easy and cost-effective. Our method achieves a new state-of-the-art performance on the Cityscapes benchmark with a large margin among ResNet101 based segmentation models. Also, we show that the proposed model is coherent with the facts observed in the urban scene by visualizing and interpreting the attention map. Our code and trained models are publicly available. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Choi_Cars_Cant_Fly_Up_in_the_Sky_Improving_Urban-Scene_Segmentation_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=NSh71ptT1z4 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Choi_Cars_Cant_Fly_Up_in_the_Sky_Improving_Urban-Scene_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Choi_Cars_Cant_Fly_Up_in_the_Sky_Improving_Urban-Scene_Segmentation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Choi_Cars_Cant_Fly_CVPR_2020_supplemental.pdf | null | null |
Compressed Volumetric Heatmaps for Multi-Person 3D Pose Estimation | Matteo Fabbri, Fabio Lanzi, Simone Calderara, Stefano Alletto, Rita Cucchiara | In this paper we present a novel approach for bottom-up multi-person 3D human pose estimation from monocular RGB images. We propose to use high resolution volumetric heatmaps to model joint locations, devising a simple and effective compression method to drastically reduce the size of this representation. At the core of the proposed method lies our Volumetric Heatmap Autoencoder, a fully-convolutional network tasked with the compression of ground-truth heatmaps into a dense intermediate representation. A second model, the Code Predictor, is then trained to predict these codes, which can be decompressed at test time to re-obtain the original representation. Our experimental evaluation shows that our method performs favorably when compared to state of the art on both multi-person and single-person 3D human pose estimation datasets and, thanks to our novel compression strategy, can process full-HD images at the constant runtime of 8 fps regardless of the number of subjects in the scene. Code and models are publicly available. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Fabbri_Compressed_Volumetric_Heatmaps_for_Multi-Person_3D_Pose_Estimation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.00329 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Fabbri_Compressed_Volumetric_Heatmaps_for_Multi-Person_3D_Pose_Estimation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Fabbri_Compressed_Volumetric_Heatmaps_for_Multi-Person_3D_Pose_Estimation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Fabbri_Compressed_Volumetric_Heatmaps_CVPR_2020_supplemental.zip | null | null |
3D-MPA: Multi-Proposal Aggregation for 3D Semantic Instance Segmentation | Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Niessner | We present 3D-MPA, a method for instance segmentation on 3D point clouds. Given an input point cloud, we propose an object-centric approach where each point votes for its object center. We sample object proposals from the predicted object centers. Then, we learn proposal features from grouped point features that voted for the same object center. A graph convolutional network introduces inter-proposal relations, providing higher-level feature learning in addition to the lower-level point features. Each proposal comprises a semantic label, a set of associated points over which we define a foreground-background mask, an objectness score and aggregation features. Previous works usually perform non-maximum-suppression (NMS) over proposals to obtain the final object detections or semantic instances. However, NMS can discard potentially correct predictions. Instead, our approach keeps all proposals and groups them together based on the learned aggregation features. We show that grouping proposals improves over NMS and outperforms previous state-of-the-art methods on the tasks of 3D object detection and semantic instance segmentation on the ScanNetV2 benchmark and the S3DIS dataset. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Engelmann_3D-MPA_Multi-Proposal_Aggregation_for_3D_Semantic_Instance_Segmentation_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Engelmann_3D-MPA_Multi-Proposal_Aggregation_for_3D_Semantic_Instance_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Engelmann_3D-MPA_Multi-Proposal_Aggregation_for_3D_Semantic_Instance_Segmentation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Engelmann_3D-MPA_Multi-Proposal_Aggregation_CVPR_2020_supplemental.zip | null | null |
Domain Adaptive Image-to-Image Translation | Ying-Cong Chen, Xiaogang Xu, Jiaya Jia | Unpaired image-to-image translation (I2I) has achieved great success in various applications. However, its generalization capacity is still an open question. In this paper, we show that existing I2I models do not generalize well for samples outside the training domain. The cause is twofold. First, an I2I model may not work well when testing samples are beyond its valid input domain. Second, results could be unreliable if the expected output is far from what the model is trained. To deal with these issues, we propose the Domain Adaptive Image-To-Image translation (DAI2I) framework that adapts an I2I model for out-of-domain samples. Our framework introduces two sub-modules -- one maps testing samples to the valid input domain of the I2I model, and the other transforms the output of I2I model to expected results. Extensive experiments manifest that our framework improves the capacity of existing I2I models, allowing them to handle samples that are distinctively different from their primary targets. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_Domain_Adaptive_Image-to-Image_Translation_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Domain_Adaptive_Image-to-Image_Translation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Domain_Adaptive_Image-to-Image_Translation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Chen_Domain_Adaptive_Image-to-Image_CVPR_2020_supplemental.pdf | null | null |
Video Playback Rate Perception for Self-Supervised Spatio-Temporal Representation Learning | Yuan Yao, Chang Liu, Dezhao Luo, Yu Zhou, Qixiang Ye | In self-supervised spatio-temporal representation learning, the temporal resolution and long-short term characteristics are not yet fully explored, which limits representation capabilities of learned models. In this paper, we propose a novel self-supervised method, referred to as video Playback Rate Perception (PRP), to learn spatio-temporal representation in a simple-yet-effective way. PRP roots in a dilated sampling strategy, which produces self-supervision signals about video playback rates for representation model learning. PRP is implemented with a feature encoder, a classification module, and a reconstructing decoder, to achieve spatio-temporal semantic retention in a collaborative discrimination-generation manner. The discriminative perception model follows a feature encoder to prefer perceiving low temporal resolution and long-term representation by classifying fast-forward rates. The generative perception model acts as a feature decoder to focus on comprehending high temporal resolution and short-term representation by introducing a motion-attention mechanism. PRP is applied on typical video target tasks including action recognition and video retrieval. Experiments show that PRP outperforms state-of-the-art self-supervised models with significant margins. Code is available at github.com/yuanyao366/PRP. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yao_Video_Playback_Rate_Perception_for_Self-Supervised_Spatio-Temporal_Representation_Learning_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=D1wSyZqaAn0 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yao_Video_Playback_Rate_Perception_for_Self-Supervised_Spatio-Temporal_Representation_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yao_Video_Playback_Rate_Perception_for_Self-Supervised_Spatio-Temporal_Representation_Learning_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Warping Residual Based Image Stitching for Large Parallax | Kyu-Yul Lee, Jae-Young Sim | Image stitching techniques align two images captured at different viewing positions onto a single wider image. When the captured 3D scene is not planar and the camera baseline is large, two images exhibit parallax where the relative positions of scene structures are quite different from each view. The existing image stitching methods often fail to work on the images with large parallax. In this paper, we propose an image stitching algorithm robust to large parallax based on the novel concept of warping residuals. We first estimate multiple homographies and find their inlier feature matches between two images. Then we evaluate warping residual for each feature match with respect to the multiple homographies. To alleviate the parallax artifacts, we partition input images into superpixels and warp each superpixel adaptively according to an optimal homography which is computed by minimizing the error of feature matches weighted by the warping residuals. Experimental results demonstrate that the proposed algorithm provides accurate stitching results for images with large parallax, and outperforms the existing methods qualitatively and quantitatively. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lee_Warping_Residual_Based_Image_Stitching_for_Large_Parallax_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lee_Warping_Residual_Based_Image_Stitching_for_Large_Parallax_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lee_Warping_Residual_Based_Image_Stitching_for_Large_Parallax_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
GLU-Net: Global-Local Universal Network for Dense Flow and Correspondences | Prune Truong, Martin Danelljan, Radu Timofte | Establishing dense correspondences between a pair of images is an important and general problem, covering geometric matching, optical flow and semantic correspondences. While these applications share fundamental challenges, such as large displacements, pixel-accuracy, and appearance changes, they are currently addressed with specialized network architectures, designed for only one particular task. This severely limits the generalization capabilities of such networks to new scenarios, where e.g. robustness to larger displacements or higher accuracy is required. In this work, we propose a universal network architecture that is directly applicable to all the aforementioned dense correspondence problems. We achieve both high accuracy and robustness to large displacements by investigating the combined use of global and local correlation layers. We further propose an adaptive resolution strategy, allowing our network to operate on virtually any input image resolution. The proposed GLU-Net achieves state-of-the-art performance for geometric and semantic matching as well as optical flow, when using the same network and weights. Code and trained models are available at https://github.com/PruneTruong/GLU-Net. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Truong_GLU-Net_Global-Local_Universal_Network_for_Dense_Flow_and_Correspondences_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Truong_GLU-Net_Global-Local_Universal_Network_for_Dense_Flow_and_Correspondences_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Truong_GLU-Net_Global-Local_Universal_Network_for_Dense_Flow_and_Correspondences_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Truong_GLU-Net_Global-Local_Universal_CVPR_2020_supplemental.pdf | null | null |
SAINT: Spatially Aware Interpolation NeTwork for Medical Slice Synthesis | Cheng Peng, Wei-An Lin, Haofu Liao, Rama Chellappa, S. Kevin Zhou | Deep learning-based single image super-resolution (SISR) methods face various challenges when applied to 3D medical volumetric data (i.e., CT and MR images) due to the high memory cost and anisotropic resolution, which adversely affect their performance. Furthermore, mainstream SISR methods are designed to work over specific upsampling factors, which makes them ineffective in clinical practice. In this paper, we introduce a Spatially Aware Interpolation NeTwork (SAINT) for medical slice synthesis to alleviate the memory constraint that volumetric data poses. Compared to other super-resolution methods, SAINT utilizes voxel spacing information to provide desirable levels of details, and allows for the upsampling factor to be determined on the fly. Our evaluations based on 853 CT scans from four datasets that contain liver, colon, hepatic vessels, and kidneys show that SAINT consistently outperforms other SISR methods in terms of medical slice synthesis quality, while using only a single model to deal with different upsampling factors | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Peng_SAINT_Spatially_Aware_Interpolation_NeTwork_for_Medical_Slice_Synthesis_CVPR_2020_paper.pdf | http://arxiv.org/abs/2001.00704 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Peng_SAINT_Spatially_Aware_Interpolation_NeTwork_for_Medical_Slice_Synthesis_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Peng_SAINT_Spatially_Aware_Interpolation_NeTwork_for_Medical_Slice_Synthesis_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Peng_SAINT_Spatially_Aware_CVPR_2020_supplemental.pdf | null | null |
StarGAN v2: Diverse Image Synthesis for Multiple Domains | Yunjey Choi, Youngjung Uh, Jaejun Yoo, Jung-Woo Ha | A good image-to-image translation model should learn a mapping between different visual domains while satisfying the following properties: 1) diversity of generated images and 2) scalability over multiple domains. Existing methods address either of the issues, having limited diversity or multiple models for all domains. We propose StarGAN v2, a single framework that tackles both and shows significantly improved results over the baselines. Experiments on CelebA-HQ and a new animal faces dataset (AFHQ) validate our superiority in terms of visual quality, diversity, and scalability. To better assess image-to-image translation models, we release AFHQ, high-quality animal faces with large inter- and intra-domain differences. The code, pretrained models, and dataset are available at https://github.com/clovaai/stargan-v2. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Choi_StarGAN_v2_Diverse_Image_Synthesis_for_Multiple_Domains_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.01865 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Choi_StarGAN_v2_Diverse_Image_Synthesis_for_Multiple_Domains_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Choi_StarGAN_v2_Diverse_Image_Synthesis_for_Multiple_Domains_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Choi_StarGAN_v2_Diverse_CVPR_2020_supplemental.pdf | null | null |
Local Deep Implicit Functions for 3D Shape | Kyle Genova, Forrester Cole, Avneesh Sud, Aaron Sarna, Thomas Funkhouser | The goal of this project is to learn a 3D shape representation that enables accurate surface reconstruction, compact storage, efficient computation, consistency for similar shapes, generalization across diverse shape categories, and inference from depth camera observations. Towards this end, we introduce Local Deep Implicit Functions (LDIF), a 3D shape representation that decomposes space into a structured set of learned implicit functions. We provide networks that infer the space decomposition and local deep implicit functions from a 3D mesh or posed depth image. During experiments, we find that it provides 10.3 points higher surface reconstruction accuracy (F-Score) than the state-of-the-art (OccNet), while requiring fewer than 1% of the network parameters. Experiments on posed depth image completion and generalization to unseen classes show 15.8 and 17.8 point improvements over the state-of-the-art, while producing a structured 3D representation for each input with consistency across diverse shape collections. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Genova_Local_Deep_Implicit_Functions_for_3D_Shape_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.06126 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Genova_Local_Deep_Implicit_Functions_for_3D_Shape_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Genova_Local_Deep_Implicit_Functions_for_3D_Shape_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Weakly-Supervised Domain Adaptation via GAN and Mesh Model for Estimating 3D Hand Poses Interacting Objects | Seungryul Baek, Kwang In Kim, Tae-Kyun Kim | Despite recent successes in hand pose estimation, there yet remain challenges on RGB-based 3D hand pose estimation (HPE) under hand-object interaction (HOI) scenarios where severe occlusions and cluttered backgrounds exhibit. Recent RGB HOI benchmarks have been collected either in real or synthetic domain, however, the size of datasets is far from enough to deal with diverse objects combined with hand poses, and 3D pose annotations of real samples are lacking, especially for occluded cases. In this work, we propose a novel end-to-end trainable pipeline that adapts the hand-object domain to the single hand-only domain, while learning for HPE. The domain adaption occurs in image space via 2D pixel-level guidance by Generative Adversarial Network (GAN) and 3D mesh guidance by mesh renderer (MR). Via the domain adaption in image space, not only 3D HPE accuracy is improved, but also HOI input images are translated to segmented and de-occluded hand-only images. The proposed method takes advantages of both the guidances: GAN accurately aligns hands, while MR effectively fills in occluded pixels. The experiments using Dexter-Object, Ego-Dexter and HO3D datasets show that our method significantly outperforms state-of-the-arts trained by hand-only data and is comparable to those supervised by HOI data. Note our method is trained primarily by hand-only images with pose labels, and HOI images without pose labels. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Baek_Weakly-Supervised_Domain_Adaptation_via_GAN_and_Mesh_Model_for_Estimating_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=6FFBJVV4dAA | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Baek_Weakly-Supervised_Domain_Adaptation_via_GAN_and_Mesh_Model_for_Estimating_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Baek_Weakly-Supervised_Domain_Adaptation_via_GAN_and_Mesh_Model_for_Estimating_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Baek_Weakly-Supervised_Domain_Adaptation_CVPR_2020_supplemental.pdf | null | null |
Global Texture Enhancement for Fake Face Detection in the Wild | Zhengzhe Liu, Xiaojuan Qi, Philip H.S. Torr | Generative Adversarial Networks (GANs) can generate realistic fake face images that can easily fool human beings. On the contrary, a common Convolutional Neural Network(CNN) discriminator can achieve more than99.9%accuracyin discerning fake/real images. In this paper, we conduct an empirical study on fake/real faces, and have two important observations: firstly, the texture of fake faces is substantially different from real ones; secondly, global texture statistics are more robust to image editing and transferable to fake faces from different GANs and datasets. Motivated by the above observations, we propose a new architecture coined as Gram-Net, which leverages global image texture representations for robust fake image detection. Experimental results on several datasets demonstrate that our Gram-Netoutperforms existing approaches. Especially, our Gram-Netis more robust to image editings, e.g. down-sampling, JPEGcompression, blur, and noise. More importantly, our Gram-Net generalizes significantly better in detecting fake faces from GAN models not seen in the training phase and can perform decently in detecting fake natural images | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_Global_Texture_Enhancement_for_Fake_Face_Detection_in_the_Wild_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.00133 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Global_Texture_Enhancement_for_Fake_Face_Detection_in_the_Wild_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Global_Texture_Enhancement_for_Fake_Face_Detection_in_the_Wild_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Liu_Global_Texture_Enhancement_CVPR_2020_supplemental.pdf | null | null |
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