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Learning Student Networks in the Wild | Hanting Chen, Tianyu Guo, Chang Xu, Wenshuo Li, Chunjing Xu, Chao Xu, Yunhe Wang | Data-free learning for student networks is a new paradigm for solving users' anxiety caused by the privacy problem of using original training data. Since the architectures of modern convolutional neural networks (CNNs) are compact and sophisticated, the alternative images or meta-data generated from the teacher network are often broken. Thus, the student network cannot achieve the comparable performance to that of the pre-trained teacher network especially on the large-scale image dataset. Different to previous works, we present to maximally utilize the massive available unlabeled data in the wild. Specifically, we first thoroughly analyze the output differences between teacher and student network on the original data and develop a data collection method. Then, a noisy knowledge distillation algorithm is proposed for achieving the performance of the student network. In practice, an adaptation matrix is learned with the student network for correcting the label noise produced by the teacher network on the collected unlabeled images. The effectiveness of our DFND (Data-Free Noisy Distillation) method is then verified on several benchmarks to demonstrate its superiority over state-of-the-art data-free distillation methods. Experiments on various datasets demonstrate that the student networks learned by the proposed method can achieve comparable performance with those using the original dataset. Code is available at https://github.com/huawei-noah/Data-Efficient-Model-Compression | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Learning_Student_Networks_in_the_Wild_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Learning_Student_Networks_in_the_Wild_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Learning_Student_Networks_in_the_Wild_CVPR_2021_paper.html | CVPR 2021 | null | null |
Distilling Knowledge via Knowledge Review | Pengguang Chen, Shu Liu, Hengshuang Zhao, Jiaya Jia | Knowledge distillation transfers knowledge from the teacher network to the student one, with the goal of greatly improving the performance of the student network. Previous methods mostly focus on proposing feature transformation and loss functions between the same level's features to improve the effectiveness. We differently study the factor of connection path cross levels between teacher and student networks, and reveal its great importance. For the first time in knowledge distillation, cross-stage connection paths are proposed. A new review mechanism becomes vastly effective and structurally simple. Our finally designed nested and compact framework requires negligible computation overhead, and outperforms other methods on a variety of tasks. We apply our method to classification, object detection, and instance segmentation tasks. All of them witness significant student network performance improvement. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Distilling_Knowledge_via_Knowledge_Review_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.09044 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Distilling_Knowledge_via_Knowledge_Review_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Distilling_Knowledge_via_Knowledge_Review_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Distilling_Knowledge_via_CVPR_2021_supplemental.pdf | null |
DoDNet: Learning To Segment Multi-Organ and Tumors From Multiple Partially Labeled Datasets | Jianpeng Zhang, Yutong Xie, Yong Xia, Chunhua Shen | Due to the intensive cost of labor and expertise in annotating 3D medical images at a voxel level, most benchmark datasets are equipped with the annotations of only one type of organs and/or tumors, resulting in the so-called partially labeling issue. To address this issue, we propose a dynamic on-demand network (DoDNet) that learns to segment multiple organs and tumors on partially labeled datasets. DoDNet consists of a shared encoder-decoder architecture, a task encoding module, a controller for dynamic filter generation, and a single but dynamic segmentation head. The information of current segmentation task is encoded as a task-aware prior to tell the model what the task is expected to achieve. Different from existing approaches which fix kernels after training, the kernels in dynamic head are generated adaptively by the controller, conditioned on both input image and assigned task. Thus, DoDNet is able to segment multiple organs and tumors, as done by multiple networks or a multi-head network, in a much efficient and flexible manner. We created a large-scale partially labeled dataset called MOTS and demonstrated the superior performance of our DoDNet over other competitors on seven organ and tumor segmentation tasks. We also transferred the weights pre-trained on MOTS to a downstream multi-organ segmentation task and achieved state-of-the-art performance. This study provides a general 3D medical image segmentation model that has been pre-trained on a large-scale partially labeled dataset and can be extended (after fine-tuning) to downstream volumetric medical data segmentation tasks. Code and models are available at https://git.io/DoDNet. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_DoDNet_Learning_To_Segment_Multi-Organ_and_Tumors_From_Multiple_Partially_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.10217 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_DoDNet_Learning_To_Segment_Multi-Organ_and_Tumors_From_Multiple_Partially_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_DoDNet_Learning_To_Segment_Multi-Organ_and_Tumors_From_Multiple_Partially_CVPR_2021_paper.html | CVPR 2021 | null | null |
Lips Don't Lie: A Generalisable and Robust Approach To Face Forgery Detection | Alexandros Haliassos, Konstantinos Vougioukas, Stavros Petridis, Maja Pantic | Although current deep learning-based face forgery detectors achieve impressive performance in constrained scenarios, they are vulnerable to samples created by unseen manipulation methods. Some recent works show improvements in generalisation but rely on cues that are easily corrupted by common post-processing operations such as compression. In this paper, we propose LipForensics, a detection approach capable of both generalising to novel manipulations and withstanding various distortions. LipForensics targets high-level semantic irregularities in mouth movements, which are common in many generated videos. It consists in first pretraining a spatio-temporal network to perform visual speech recognition (lipreading), thus learning rich internal representations related to natural mouth motion. A temporal network is subsequently finetuned on fixed mouth embeddings of real and forged data in order to detect fake videos based on mouth movements without overfitting to low-level, manipulation-specific artefacts. Extensive experiments show that this simple approach significantly surpasses the state-of-the-art in terms of generalisation to unseen manipulations and robustness to perturbations, as well as shed light on the factors responsible for its performance. | https://openaccess.thecvf.com/content/CVPR2021/papers/Haliassos_Lips_Dont_Lie_A_Generalisable_and_Robust_Approach_To_Face_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Haliassos_Lips_Dont_Lie_A_Generalisable_and_Robust_Approach_To_Face_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Haliassos_Lips_Dont_Lie_A_Generalisable_and_Robust_Approach_To_Face_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Haliassos_Lips_Dont_Lie_CVPR_2021_supplemental.pdf | null |
Exploring Simple Siamese Representation Learning | Xinlei Chen, Kaiming He | Siamese networks have become a common structure in various recent models for unsupervised visual representation learning. These models maximize the similarity between two augmentations of one image, subject to certain conditions for avoiding collapsing solutions. In this paper, we report surprising empirical results that simple Siamese networks can learn meaningful representations even using none of the following: (i) negative sample pairs, (ii) large batches, (iii) momentum encoders. Our experiments show that collapsing solutions do exist for the loss and structure, but a stop-gradient operation plays an essential role in preventing collapsing. We provide a hypothesis on the implication of stop-gradient, and further show proof-of-concept experiments verifying it. Our "SimSiam" method achieves competitive results on ImageNet and downstream tasks. We hope this simple baseline will motivate people to rethink the roles of Siamese architectures for unsupervised representation learning. Code is made available. (https://github.com/facebookresearch/simsiam) | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Exploring_Simple_Siamese_Representation_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.10566 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Exploring_Simple_Siamese_Representation_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Exploring_Simple_Siamese_Representation_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Exploring_Simple_Siamese_CVPR_2021_supplemental.pdf | null |
CAMERAS: Enhanced Resolution and Sanity Preserving Class Activation Mapping for Image Saliency | Mohammad A. A. K. Jalwana, Naveed Akhtar, Mohammed Bennamoun, Ajmal Mian | Backpropagation image saliency aims at explaining model predictions by estimating model-centric importance of individual pixels in the input. However, class-insensitivity of the earlier layers in a network only allows saliency computation with low resolution activation maps of the deeper layers, resulting in compromised image saliency. Remedifying this can lead to sanity failures. We propose CAMERAS, a technique to compute high-fidelity backpropagation saliency maps without requiring any external priors and preserving the map sanity. Our method systematically performs multi-scale accumulation and fusion of the activation maps and backpropagated gradients to compute precise saliency maps. From accurate image saliency to articulation of relative importance of input features for different models, and precise discrimination between model perception of visually similar objects, our high-resolution mapping offers multiple novel insights into the black-box deep visual models, which are presented in the paper. We also demonstrate the utility of our saliency maps in adversarial setup by drastically reducing the norm of attack signals by focusing them on the precise regions identified by our maps. Our method also inspires new evaluation metrics and a sanity check for this developing research direction. | https://openaccess.thecvf.com/content/CVPR2021/papers/Jalwana_CAMERAS_Enhanced_Resolution_and_Sanity_Preserving_Class_Activation_Mapping_for_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.10649 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Jalwana_CAMERAS_Enhanced_Resolution_and_Sanity_Preserving_Class_Activation_Mapping_for_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Jalwana_CAMERAS_Enhanced_Resolution_and_Sanity_Preserving_Class_Activation_Mapping_for_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Jalwana_CAMERAS_Enhanced_Resolution_CVPR_2021_supplemental.pdf | null |
3D AffordanceNet: A Benchmark for Visual Object Affordance Understanding | Shengheng Deng, Xun Xu, Chaozheng Wu, Ke Chen, Kui Jia | The ability to understand the ways to interact with objects from visual cues, a.k.a. visual affordance, is essential to vision-guided robotic research. This involves categorizing, segmenting and reasoning of visual affordance. Relevant studies in 2D and 2.5D image domains have been made previously, however, a truly functional understanding of object affordance requires learning and prediction in the 3D physical domain, which is still absent in the community. In this work, we present a 3D AffordanceNet dataset, a benchmark of 23k shapes from 23 semantic object categories, annotated with 18 visual affordance categories. Based on this dataset, we provide three benchmarking tasks for evaluating visual affordance understanding, including full-shape, partial-view and rotation-invariant affordance estimations. Three state-of-the-art point cloud deep learning networks are evaluated on all tasks. In addition we also investigate a semi-supervised learning setup to explore the possibility to benefit from unlabeled data. Comprehensive results on our contributed dataset show the promise of visual affordance understanding as a valuable yet challenging benchmark. | https://openaccess.thecvf.com/content/CVPR2021/papers/Deng_3D_AffordanceNet_A_Benchmark_for_Visual_Object_Affordance_Understanding_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16397 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Deng_3D_AffordanceNet_A_Benchmark_for_Visual_Object_Affordance_Understanding_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Deng_3D_AffordanceNet_A_Benchmark_for_Visual_Object_Affordance_Understanding_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Deng_3D_AffordanceNet_A_CVPR_2021_supplemental.pdf | null |
Learning To Segment Actions From Visual and Language Instructions via Differentiable Weak Sequence Alignment | Yuhan Shen, Lu Wang, Ehsan Elhamifar | We address the problem of unsupervised localization of key-steps and feature learning in instructional videos using both visual and language instructions. Our key observation is that the sequences of visual and linguistic key-steps are weakly aligned: there is an ordered one-to-one correspondence between most visual and language key-steps, while some key-steps in one modality are absent in the other. To recover the two sequences, we develop an ordered prototype learning module, which extracts visual and linguistic prototypes representing key-steps. On the other hand, to find weak alignment and perform feature learning, we develop a differentiable weak sequence alignment (DWSA) method that finds ordered one-to-one matching between sequences while allowing some items in a sequence to stay unmatched. We develop an efficient forward and backward algorithm for computing the alignment and the loss derivative with respect to parameters of visual and language feature learning modules. By experiments on two instructional video datasets, we show that our method significantly improves the state of the art. | https://openaccess.thecvf.com/content/CVPR2021/papers/Shen_Learning_To_Segment_Actions_From_Visual_and_Language_Instructions_via_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Shen_Learning_To_Segment_Actions_From_Visual_and_Language_Instructions_via_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Shen_Learning_To_Segment_Actions_From_Visual_and_Language_Instructions_via_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Shen_Learning_To_Segment_CVPR_2021_supplemental.pdf | null |
Deep Implicit Templates for 3D Shape Representation | Zerong Zheng, Tao Yu, Qionghai Dai, Yebin Liu | Deep implicit functions (DIFs), as a kind of 3D shape representation, are becoming more and more popular in the 3D vision community due to their compactness and strong representation power. However, unlike polygon mesh-based templates, it remains a challenge to reason dense correspondences or other semantic relationships across shapes represented by DIFs, which limits its applications in texture transfer, shape analysis and so on. To overcome this limitation and also make DIFs more interpretable, we propose Deep Implicit Templates, a new 3D shape representation that supports explicit correspondence reasoning in deep implicit representations. Our key idea is to formulate DIFs as conditional deformations of a template implicit function. To this end, we propose Spatial Warping LSTM, which decomposes the conditional spatial transformation into multiple point-wise transformations and guarantees generalization capability. Moreover, the training loss is carefully designed in order to achieve high reconstruction accuracy while learning a plausible template with accurate correspondences in an unsupervised manner. Experiments show that our method can not only learn a common implicit template for a collection of shapes, but also establish dense correspondences across all the shapes simultaneously without any supervision. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zheng_Deep_Implicit_Templates_for_3D_Shape_Representation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.14565 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_Deep_Implicit_Templates_for_3D_Shape_Representation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_Deep_Implicit_Templates_for_3D_Shape_Representation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zheng_Deep_Implicit_Templates_CVPR_2021_supplemental.zip | null |
Semantic Image Matting | Yanan Sun, Chi-Keung Tang, Yu-Wing Tai | Natural image matting separates the foreground from background in fractional occupancy which can be caused by highly transparent objects, complex foreground (e.g., net or tree), and/or objects containing very fine details (e.g., hairs). Although conventional matting formulation can be applied to all of the above cases, no previous work has attempted to reason the underlying causes of matting due to various foreground semantics. We show how to obtain better alpha mattes by incorporating into our framework semantic classification of matting regions. Specifically, we consider and learn 20 classes of matting patterns, and propose to extend the conventional trimap to semantic trimap. The proposed semantic trimap can be obtained automatically through patch structure analysis within trimap regions. Meanwhile, we learn a multi-class discriminator to regularize the alpha prediction at semantic level, and content-sensitive weights to balance different regularization losses. Experiments on multiple benchmarks show that our method outperforms other methods and has achieved the most competitive state-of-the-art performance. Finally, we contribute a large-scale Semantic Image Matting Dataset with careful consideration of data balancing across different semantic classes. Code and dataset will be released. | https://openaccess.thecvf.com/content/CVPR2021/papers/Sun_Semantic_Image_Matting_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.08201 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_Semantic_Image_Matting_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_Semantic_Image_Matting_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Sun_Semantic_Image_Matting_CVPR_2021_supplemental.pdf | null |
Semi-Supervised Semantic Segmentation With Cross Pseudo Supervision | Xiaokang Chen, Yuhui Yuan, Gang Zeng, Jingdong Wang | In this paper, we study the semi-supervised semantic segmentation problem via exploring both labeled data and extra unlabeled data. We propose a novel consistency regularization approach, called cross pseudo supervision (CPS). Our approach imposes the consistency on two segmentation networks perturbed with different initialization for the same input image. The pseudo one-hot label map, output from one perturbed segmentation network, is used to supervise the other segmentation network with the standard cross-entropy loss, and vice versa. The CPS consistency has two roles: encourage high similarity between the predictions of two perturbed networks for the same input image, and expand training data by using the unlabeled data with pseudo labels. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Semi-Supervised_Semantic_Segmentation_With_Cross_Pseudo_Supervision_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.01226 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Semi-Supervised_Semantic_Segmentation_With_Cross_Pseudo_Supervision_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Semi-Supervised_Semantic_Segmentation_With_Cross_Pseudo_Supervision_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Semi-Supervised_Semantic_Segmentation_CVPR_2021_supplemental.pdf | null |
Ranking Neural Checkpoints | Yandong Li, Xuhui Jia, Ruoxin Sang, Yukun Zhu, Bradley Green, Liqiang Wang, Boqing Gong | This paper is concerned with ranking many pre-trained deep neural networks (DNNs), called checkpoints, for the transfer learning to a downstream task. Thanks to the broad use of DNNs, we may easily collect hundreds of checkpoints from various sources. Which of them transfers the best to our downstream task of interest? Striving to answer this question thoroughly, we establish a neural checkpoint ranking benchmark (NeuCRaB) and study some intuitive ranking measures. These measures are generic, applying to the checkpoints of different output types without knowing how the checkpoints are pre-trained on which dataset. They also incur low computation cost, making them practically meaningful. Our results suggest that the linear separability of the features extracted by the checkpoints is a strong indicator of transferability. We also arrive at a new ranking measure, NLEEP, which gives rise to the best performance in the experiments. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Ranking_Neural_Checkpoints_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.11200 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Ranking_Neural_Checkpoints_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Ranking_Neural_Checkpoints_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Ranking_Neural_Checkpoints_CVPR_2021_supplemental.pdf | null |
SuperMix: Supervising the Mixing Data Augmentation | Ali Dabouei, Sobhan Soleymani, Fariborz Taherkhani, Nasser M. Nasrabadi | This paper presents a supervised mixing augmentation method termed SuperMix, which exploits the salient regions within input images to construct mixed training samples. SuperMix is designed to obtain mixed images rich in visual features and complying with realistic image priors. To enhance the efficiency of the algorithm, we develop a variant of the Newton iterative method, 65xfaster than gradient descent on this problem. We validate the effectiveness of SuperMix through extensive evaluations and ablation studies on two tasks of object classification and knowledge distillation. On the classification task, SuperMix provides comparable performance to the advanced augmentation methods, such as AutoAugment and RandAugment. In particular, combining SuperMix with RandAugment achieves 78.2% top-1 accuracy on ImageNet with ResNet50. On the distillation task, solely classifying images mixed using the teacher's knowledge achieves comparable performance to the state-of-the-art distillation methods. Furthermore, on average, incorporating mixed images into the distillation objective improves the performance by 3.4% and 3.1% on CIFAR-100 and ImageNet, respectively. The code is available at https://github.com/alldbi/SuperMix. | https://openaccess.thecvf.com/content/CVPR2021/papers/Dabouei_SuperMix_Supervising_the_Mixing_Data_Augmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2003.05034 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Dabouei_SuperMix_Supervising_the_Mixing_Data_Augmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Dabouei_SuperMix_Supervising_the_Mixing_Data_Augmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Dabouei_SuperMix_Supervising_the_CVPR_2021_supplemental.pdf | null |
Informative and Consistent Correspondence Mining for Cross-Domain Weakly Supervised Object Detection | Luwei Hou, Yu Zhang, Kui Fu, Jia Li | Cross-domain weakly supervised object detection aims to adapt object-level knowledge from a fully labeled source domain dataset (i.e. with object bounding boxes) to train object detectors for target domains that are weakly labeled (i.e. with image-level tags). Instead of domain-level distribution matching, as popularly adopted in the literature, we propose to learn pixel-wise cross-domain correspondences for more precise knowledge transfer. It is realized through a novel cross-domain co-attention scheme trained as region competition. In this scheme, the cross-domain correspondence module seeks for informative features on the target domain image, which after being warped to the source domain image, could best explain its annotations. Meanwhile, a collaborative mask generator competes to mask out the relevant target image region to make the remaining features uninformative. Such competitive learning strives to correlate the full foreground in cross-domain image pairs, revealing the accurate object extent in target domain. To alleviate the ambiguity of inter-domain correspondence learning, a domain-cycle consistency regularizer is futher proposed to leverage the more reliable intra-domain correspondence. The proposed approach achieves consistent improvements over existing approaches by a considerable margin, demonstrated by the experiments on various datasets. | https://openaccess.thecvf.com/content/CVPR2021/papers/Hou_Informative_and_Consistent_Correspondence_Mining_for_Cross-Domain_Weakly_Supervised_Object_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hou_Informative_and_Consistent_Correspondence_Mining_for_Cross-Domain_Weakly_Supervised_Object_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hou_Informative_and_Consistent_Correspondence_Mining_for_Cross-Domain_Weakly_Supervised_Object_CVPR_2021_paper.html | CVPR 2021 | null | null |
Inception Convolution With Efficient Dilation Search | Jie Liu, Chuming Li, Feng Liang, Chen Lin, Ming Sun, Junjie Yan, Wanli Ouyang, Dong Xu | As a variant of standard convolution, a dilated convolution can control effective receptive fields and handle large scale variance of objects without introducing additional computational costs. To fully explore the potential of dilated convolution, we proposed a new type of dilated convolution (referred to as inception convolution), where the convolution operations have independent dilation patterns among different axes, channels and layers. To develop a practical method for learning complex inception convolution based on the data, a simple but effective search algorithm, referred to as efficient dilation optimization (EDO), is developed. Based on statistical optimization, the EDO method operates in a low-cost manner and is extremely fast when it is applied on large scale datasets. Empirical results validate that our method achieves consistent performance gains for image recognition, object detection, instance segmentation, human detection, and human pose estimation. For instance, by simply replacing the 3 x 3 standard convolution in the ResNet-50 backbone with inception convolution, we significantly improve the AP of Faster R-CNN from 36.4% to 39.2% on MS COCO. | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Inception_Convolution_With_Efficient_Dilation_Search_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.13587 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Inception_Convolution_With_Efficient_Dilation_Search_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Inception_Convolution_With_Efficient_Dilation_Search_CVPR_2021_paper.html | CVPR 2021 | null | null |
Back to Event Basics: Self-Supervised Learning of Image Reconstruction for Event Cameras via Photometric Constancy | Federico Paredes-Valles, Guido C. H. E. de Croon | Event cameras are novel vision sensors that sample, in an asynchronous fashion, brightness increments with low latency and high temporal resolution. The resulting streams of events are of high value by themselves, especially for high speed motion estimation. However, a growing body of work has also focused on the reconstruction of intensity frames from the events, as this allows bridging the gap with the existing literature on appearance- and frame-based computer vision. Recent work has mostly approached this problem using neural networks trained with synthetic, ground-truth data. In this work we approach, for the first time, the intensity reconstruction problem from a self-supervised learning perspective. Our method, which leverages the knowledge of the inner workings of event cameras, combines estimated optical flow and the event-based photometric constancy to train neural networks without the need for any ground-truth or synthetic data. Results across multiple datasets show that the performance of the proposed self-supervised approach is in line with the state-of-the-art. Additionally, we propose a novel, lightweight neural network for optical flow estimation that achieves high speed inference with only a minor drop in performance. | https://openaccess.thecvf.com/content/CVPR2021/papers/Paredes-Valles_Back_to_Event_Basics_Self-Supervised_Learning_of_Image_Reconstruction_for_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Paredes-Valles_Back_to_Event_Basics_Self-Supervised_Learning_of_Image_Reconstruction_for_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Paredes-Valles_Back_to_Event_Basics_Self-Supervised_Learning_of_Image_Reconstruction_for_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Paredes-Valles_Back_to_Event_CVPR_2021_supplemental.pdf | null |
AdderSR: Towards Energy Efficient Image Super-Resolution | Dehua Song, Yunhe Wang, Hanting Chen, Chang Xu, Chunjing Xu, Dacheng Tao | This paper studies the single image super-resolution problem using adder neural networks (AdderNets). Compared with convolutional neural networks, AdderNets utilize additions to calculate the output features thus avoid massive energy consumptions of conventional multiplications. However, it is very hard to directly inherit the existing success of AdderNets on large-scale image classification to the image super-resolution task due to the different calculation paradigm. Specifically, the adder operation cannot easily learn the identity mapping, which is essential for image processing tasks. In addition, the functionality of high-pass filters cannot be ensured by AdderNets. To this end, we thoroughly analyze the relationship between an adder operation and the identity mapping and insert shortcuts to enhance the performance of SR models using adder networks. Then, we develop a learnable power activation for adjusting the feature distribution and refining details. Experiments conducted on several benchmark models and datasets demonstrate that, our image super-resolution models using AdderNets can achieve comparable performance and visual quality to that of their CNN baselines with an about 2.5x reduction on the energy consumption. The codes are available at: https://github.com/huawei-noah/AdderNet. | https://openaccess.thecvf.com/content/CVPR2021/papers/Song_AdderSR_Towards_Energy_Efficient_Image_Super-Resolution_CVPR_2021_paper.pdf | http://arxiv.org/abs/2009.08891 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Song_AdderSR_Towards_Energy_Efficient_Image_Super-Resolution_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Song_AdderSR_Towards_Energy_Efficient_Image_Super-Resolution_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Song_AdderSR_Towards_Energy_CVPR_2021_supplemental.pdf | null |
Semi-Supervised Domain Adaptation Based on Dual-Level Domain Mixing for Semantic Segmentation | Shuaijun Chen, Xu Jia, Jianzhong He, Yongjie Shi, Jianzhuang Liu | Data-driven based approaches, in spite of great success in many tasks, have poor generalization when applied to unseen image domains, and require expensive cost of annotation especially for dense pixel prediction tasks such as semantic segmentation. Recently, both unsupervised domain adaptation (UDA) from large amounts of synthetic data and semi-supervised learning (SSL) with small set of labeled data have been studied to alleviate this issue. However, there is still a large gap on performance compared to their supervised counterparts. We focus on a more practical setting of semi-supervised domain adaptation (SSDA) where both a small set of labeled target data and large amounts of labeled source data are available. To address the task of SSDA, a novel framework based on dual-level domain mixing is proposed. The proposed framework consists of three stages. First, two kinds of data mixing methods are proposed to reduce domain gap in both region-level and sample-level respectively. We can obtain two complementary domain-mixed teachers based on dual-level mixed data from holistic and partial views respectively. Then, a student model is learned by distilling knowledge from these two teachers. Finally, pseudo labels of unlabeled data are generated in a self-training manner for another few rounds of teachers training. Extensive experimental results have demonstrated the effectiveness of our proposed framework on synthetic-to-real semantic segmentation benchmarks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Semi-Supervised_Domain_Adaptation_Based_on_Dual-Level_Domain_Mixing_for_Semantic_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.04705 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Semi-Supervised_Domain_Adaptation_Based_on_Dual-Level_Domain_Mixing_for_Semantic_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Semi-Supervised_Domain_Adaptation_Based_on_Dual-Level_Domain_Mixing_for_Semantic_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Semi-Supervised_Domain_Adaptation_CVPR_2021_supplemental.pdf | null |
Connecting What To Say With Where To Look by Modeling Human Attention Traces | Zihang Meng, Licheng Yu, Ning Zhang, Tamara L. Berg, Babak Damavandi, Vikas Singh, Amy Bearman | We introduce a unified framework to jointly model images, text, and human attention traces. Our work is built on top of the recent Localized Narratives annotation framework, where each word of a given caption is paired with a mouse trace segment. We propose two novel tasks: (1) predict a trace given an image and caption (i.e., visual grounding), and (2) predict a caption and a trace given only an image. Learning the grounding of each word is challenging, due to noise in the human-provided traces and the presence of words that cannot be meaningfully visually grounded. We present a novel model architecture that is jointly trained on dual tasks (controlled trace generation and controlled caption generation). To evaluate the quality of the generated traces, we propose a local bipartite matching (LBM) distance metric which allows the comparison of two traces of different lengths. Extensive experiments show our model is robust to the imperfect training data and outperforms the baselines by a clear margin. Moreover, we demonstrate that our model pre-trained on the proposed tasks can be also beneficial to the downstream task of COCO's guided image captioning. Our code and project page are publicly available. | https://openaccess.thecvf.com/content/CVPR2021/papers/Meng_Connecting_What_To_Say_With_Where_To_Look_by_Modeling_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.05964 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Meng_Connecting_What_To_Say_With_Where_To_Look_by_Modeling_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Meng_Connecting_What_To_Say_With_Where_To_Look_by_Modeling_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Meng_Connecting_What_To_CVPR_2021_supplemental.pdf | null |
Shelf-Supervised Mesh Prediction in the Wild | Yufei Ye, Shubham Tulsiani, Abhinav Gupta | We aim to infer 3D shape and pose of objects from a single image and propose a learning-based approach that can train from unstructured image collections, using only segmentation outputs from off-the-shelf recognition systems as supervisory signal (i.e. 'shelf-supervised'). We first infer a volumetric representation in a canonical frame, along with the camera pose for the input image. We enforce the representation to be geometrically consistent with both appearance and masks, and also that the synthesized novel views are indistinguishable from image collections. The coarse volumetric prediction is then converted to a mesh-based representation, which is further refined in the predicted camera frame. These two steps allow both shape-pose factorization from unannotated images and reconstruction of per-instance shape in finer details. We report performance on both synthetic and real-world datasets and demonstrate the scalability of our approach on 50 categories in the wild, an order of magnitude more classes than existing works. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ye_Shelf-Supervised_Mesh_Prediction_in_the_Wild_CVPR_2021_paper.pdf | http://arxiv.org/abs/2102.06195 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ye_Shelf-Supervised_Mesh_Prediction_in_the_Wild_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ye_Shelf-Supervised_Mesh_Prediction_in_the_Wild_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ye_Shelf-Supervised_Mesh_Prediction_CVPR_2021_supplemental.pdf | null |
Learning To Filter: Siamese Relation Network for Robust Tracking | Siyuan Cheng, Bineng Zhong, Guorong Li, Xin Liu, Zhenjun Tang, Xianxian Li, Jing Wang | Despite the great success of Siamese-based trackers, their performance under complicated scenarios is still not satisfying, especially when there are distractors. To this end, we propose a novel Siamese relation network, which introduces two efficient modules, i.e. Relation Detector (RD) and Refinement Module (RM). RD performs in a meta-learning way to obtain a learning ability to filter the distractors from the background while RM aims to effectively integrate the proposed RD into the Siamese framework to generate accurate tracking result.Moreover, to further improve the discriminability and robustness of the tracker, we introduce a contrastive training strategy that attempts not only to learn matching the same target but also to learn how to distinguish the different objects. Therefore, our tracker can achieve accurate tracking results when facing background clutters, fast motion, and occlusion. Experimental results on five popular benchmarks, including VOT2018, VOT2019, OTB100, LaSOT, and UAV123, show that the proposed method is effective and can achieve state-of-the-art results. The code will be available at https://github.com/hqucv/siamrn | https://openaccess.thecvf.com/content/CVPR2021/papers/Cheng_Learning_To_Filter_Siamese_Relation_Network_for_Robust_Tracking_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00829 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Cheng_Learning_To_Filter_Siamese_Relation_Network_for_Robust_Tracking_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Cheng_Learning_To_Filter_Siamese_Relation_Network_for_Robust_Tracking_CVPR_2021_paper.html | CVPR 2021 | null | null |
Ensembling With Deep Generative Views | Lucy Chai, Jun-Yan Zhu, Eli Shechtman, Phillip Isola, Richard Zhang | Recent generative models can synthesize "views" of artificial images that mimic real-world variations, such as changes in color or pose, simply by learning from unlabeled image collections. Here, we investigate whether such views can be applied to real images to benefit downstream analysis tasks such as image classification. Using a pretrained generator, we first find the latent code corresponding to a given real input image. Applying perturbations to the code creates natural variations of the image, which can then be ensembled together at test-time. We use StyleGAN2 as the source of generative augmentations and investigate this setup on classification tasks involving facial attributes, cat faces, and cars. Critically, we find that several design decisions are required towards making this process work; the perturbation procedure, weighting between the augmentations and original image, and training the classifier on synthesized images can all impact the result. Currently, we find that while test-time ensembling with GAN-based augmentations can offer some small improvements, the remaining bottlenecks are the efficiency and accuracy of the GAN reconstructions, coupled with classifier sensitivities to artifacts in GAN-generated images. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chai_Ensembling_With_Deep_Generative_Views_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.14551 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chai_Ensembling_With_Deep_Generative_Views_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chai_Ensembling_With_Deep_Generative_Views_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chai_Ensembling_With_Deep_CVPR_2021_supplemental.pdf | null |
Accurate Few-Shot Object Detection With Support-Query Mutual Guidance and Hybrid Loss | Lu Zhang, Shuigeng Zhou, Jihong Guan, Ji Zhang | Most object detection methods require huge amounts of annotated data and can detect only the categories that appear in the training set. However, in reality acquiring massive annotated training data is both expensive and time-consuming. In this paper, we propose a novel two-stage detector for accurate few-shot object detection. In the first stage, we employ a support-query mutual guidance mechanism to generate more support-relevant proposals. Concretely, on the one hand, a query-guided support weighting module is developed for aggregating different supports to generate the support feature. On the other hand, a support-guided query enhancement module is designed by dynamic kernels. In the second stage, we score and filter proposals via multi-level feature comparison between each proposal and the aggregated support feature based on a distance metric learnt by an effective hybrid loss, which makes the embedding space of distance metric more discriminative. Extensive experiments on benchmark datasets show that our method substantially outperforms the existing methods and lifts the SOTA of FSOD task to a higher level. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Accurate_Few-Shot_Object_Detection_With_Support-Query_Mutual_Guidance_and_Hybrid_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Accurate_Few-Shot_Object_Detection_With_Support-Query_Mutual_Guidance_and_Hybrid_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Accurate_Few-Shot_Object_Detection_With_Support-Query_Mutual_Guidance_and_Hybrid_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Accurate_Few-Shot_Object_CVPR_2021_supplemental.pdf | null |
Cascaded Prediction Network via Segment Tree for Temporal Video Grounding | Yang Zhao, Zhou Zhao, Zhu Zhang, Zhijie Lin | Temporal video grounding aims to localize the target segment which is semantically aligned with the given sentence in an untrimmed video. Existing methods can be divided into two main categories, including proposal-based approaches and proposal-free approaches. However, the former ones suffer from the extra cost of generating proposals and inflexibility in determining fine-grained boundaries, and the latter ones usually attempt to decide the start and end timestamps directly, which brings about much difficulty and inaccuracy. In this paper, we convert this task into a multi-step decision problem and propose a novel Cascaded Prediction Network (CPN) to generate the grounding result in a coarse-to-fine manner. Concretely, we first encode video and query into the same latent space and fuse them into integrated representations. Afterwards, we construct a segment-tree-based structure and make predictions via decision navigation and signal decomposition in a cascaded way. We evaluate our proposed method on three large-scale publicly available benchmarks, namely ActivityNet Caption, Charades-STA and TACoS, where our CPN surpasses the performance of the state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhao_Cascaded_Prediction_Network_via_Segment_Tree_for_Temporal_Video_Grounding_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_Cascaded_Prediction_Network_via_Segment_Tree_for_Temporal_Video_Grounding_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_Cascaded_Prediction_Network_via_Segment_Tree_for_Temporal_Video_Grounding_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhao_Cascaded_Prediction_Network_CVPR_2021_supplemental.pdf | null |
Posterior Promoted GAN With Distribution Discriminator for Unsupervised Image Synthesis | Xianchao Zhang, Ziyang Cheng, Xiaotong Zhang, Han Liu | Sufficient real information in generator is a critical point for the generation ability of GAN. However, GAN and its variants suffer from lack of this point, resulting in brittle training processes. In this paper, we propose a novel variant of GAN, Posterior Promoted GAN (P2GAN), which promotes generator with the real information in the posterior distribution produced by discriminator. In our framework, different from other variants of GAN, the discriminator maps images to a multivariate Gaussian distribution and extracts real information. The generator employs the real information by AdaIN and a latent code regularizer. Besides, reparameterization trick and pretraining is applied to guarantee a stable training process in practice. The convergence of P2GAN is theoretically proved. Experimental results on typical high-dimensional multi-modal datasets demonstrate that P2GAN has achieved comparable results with the state-of-the-art variants of GAN on unsupervised image synthesis. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Posterior_Promoted_GAN_With_Distribution_Discriminator_for_Unsupervised_Image_Synthesis_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Posterior_Promoted_GAN_With_Distribution_Discriminator_for_Unsupervised_Image_Synthesis_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Posterior_Promoted_GAN_With_Distribution_Discriminator_for_Unsupervised_Image_Synthesis_CVPR_2021_paper.html | CVPR 2021 | null | null |
Toward Accurate and Realistic Outfits Visualization With Attention to Details | Kedan Li, Min Jin Chong, Jeffrey Zhang, Jingen Liu | Virtual try-on methods aim to generate images of fashion models wearing arbitrary combinations of garments. This is a challenging task because the generated image must appear realistic and accurately display the interaction between garments. Prior works produce images that are filled with artifacts and fail to capture important visual details necessary for commercial applications. We propose Outfit Visualization Net (OVNet) to capture these important details (e.g. buttons, shading, textures, realistic hemlines, and interactions between garments) and produce high quality multiple-garment virtual try-on images. OVNet consists of 1) a semantic layout generator and 2) an image generation pipeline using multiple coordinated warps. We train the warper to output multiple warps using a cascade loss, which refines each successive warp to focus on poorly generated regions of a previous warp and yields consistent improvements in detail. In addition, we introduce a method for matching outfits with the most suitable model and produce significant improvements for both our and other previous try-on methods. Through quantitative and qualitative analysis, we demonstrate our method generates substantially higher-quality studio images compared to prior works for multi-garment outfits. An interactive interface powered by this method has been deployed on fashion e-commerce websites and received overwhelmingly positive feedback. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Toward_Accurate_and_Realistic_Outfits_Visualization_With_Attention_to_Details_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.06593 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Toward_Accurate_and_Realistic_Outfits_Visualization_With_Attention_to_Details_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Toward_Accurate_and_Realistic_Outfits_Visualization_With_Attention_to_Details_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Toward_Accurate_and_CVPR_2021_supplemental.pdf | null |
Delving Deep Into Many-to-Many Attention for Few-Shot Video Object Segmentation | Haoxin Chen, Hanjie Wu, Nanxuan Zhao, Sucheng Ren, Shengfeng He | This paper tackles the task of Few-Shot Video Object Segmentation (FSVOS), i.e., segmenting objects in the query videos with certain class specified in a few labeled support images. The key is to model the relationship between the query videos and the support images for propagating the object information. This is a many-to-many problem and often relies on full-rank attention, which is computationally intensive. In this paper, we propose a novel Domain Agent Network (DAN), breaking down the full-rank attention into two smaller ones. We consider one single frame of the query video as the domain agent, bridging between the support images and the query video. Our DAN allows a linear space and time complexity as opposed to the original quadratic form with no loss of performance. In addition, we introduce a learning strategy by combining meta-learning with online learning to further improve the segmentation accuracy. We build a FSVOS benchmark on the Youtube-VIS dataset and conduct experiments to demonstrate that our method outperforms baselines on both computational cost and accuracy, achieving the state-of-the-art performance. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Delving_Deep_Into_Many-to-Many_Attention_for_Few-Shot_Video_Object_Segmentation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Delving_Deep_Into_Many-to-Many_Attention_for_Few-Shot_Video_Object_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Delving_Deep_Into_Many-to-Many_Attention_for_Few-Shot_Video_Object_Segmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Delving_Deep_Into_CVPR_2021_supplemental.pdf | null |
MongeNet: Efficient Sampler for Geometric Deep Learning | Leo Lebrat, Rodrigo Santa Cruz, Clinton Fookes, Olivier Salvado | Recent advances in geometric deep-learning introduce complex computational challenges for evaluating the distance between meshes. From a mesh model, point clouds are necessary along with a robust distance metric to assess surface quality or as part of the loss function for training models. Current methods often rely on a uniform random mesh discretization, which yields irregular sampling and noisy distance estimation. In this paper we introduce MongeNet, a fast and optimal transport based sampler that allows for an accurate discretization of a mesh with better approximation properties. We compare our method to the ubiquitous random uniform sampling and show that the approximation error is almost half with a very small computational overhead. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lebrat_MongeNet_Efficient_Sampler_for_Geometric_Deep_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.14554 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lebrat_MongeNet_Efficient_Sampler_for_Geometric_Deep_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lebrat_MongeNet_Efficient_Sampler_for_Geometric_Deep_Learning_CVPR_2021_paper.html | CVPR 2021 | null | null |
Gated Spatio-Temporal Attention-Guided Video Deblurring | Maitreya Suin, A. N. Rajagopalan | Video deblurring remains a challenging task due to the complexity of spatially and temporally varying blur. Most of the existing works depend on implicit or explicit alignment for temporal information fusion, which either increases the computational cost or results in suboptimal performance due to misalignment. In this work, we investigate two key factors responsible for deblurring quality: how to fuse spatio-temporal information and from where to collect it. We propose a factorized gated spatio-temporal attention module to perform non-local operations across space and time to fully utilize the available information without depending on alignment. First, we perform spatial aggregation followed by a temporal aggregation step. Next, we adaptively distribute the global spatio-temporal information to each pixel. It shows superior performance compared to existing non-local fusion techniques while being considerably more efficient. To complement the attention module, we propose a reinforcement learning-based framework for selecting keyframes from the neighborhood with the most complementary and useful information. Moreover, our adaptive approach can increase or decrease the frame usage at inference time, depending on the user's need. Extensive experiments on multiple datasets demonstrate the superiority of our method. | https://openaccess.thecvf.com/content/CVPR2021/papers/Suin_Gated_Spatio-Temporal_Attention-Guided_Video_Deblurring_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Suin_Gated_Spatio-Temporal_Attention-Guided_Video_Deblurring_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Suin_Gated_Spatio-Temporal_Attention-Guided_Video_Deblurring_CVPR_2021_paper.html | CVPR 2021 | null | null |
Learning Multi-Scale Photo Exposure Correction | Mahmoud Afifi, Konstantinos G. Derpanis, Bjorn Ommer, Michael S. Brown | Capturing photographs with wrong exposures remains a major source of errors in camera-based imaging. Exposure problems are categorized as either: (i) overexposed, where the camera exposure was too long, resulting in bright and washed-out image regions, or (ii) underexposed, where the exposure was too short, resulting in dark regions. Both under- and overexposure greatly reduce the contrast and visual appeal of an image. Prior work mainly focuses on underexposed images or general image enhancement. In contrast, our proposed method targets both over- and under-exposure errors in photographs. We formulate the exposure correction problem as two main sub-problems: (i) color enhancement and (ii) detail enhancement. Accordingly, we propose a coarse-to-fine deep neural network (DNN) model, trainable in an end-to-end manner, that addresses each sub-problem separately. A key aspect of our solution is a new dataset of over 24,000 images exhibiting the broadest range of exposure values to date with a corresponding properly exposed image. Our method achieves results on par with existing state-of-the-art methods on underexposed images and yields significant improvements for images suffering from overexposure errors. | https://openaccess.thecvf.com/content/CVPR2021/papers/Afifi_Learning_Multi-Scale_Photo_Exposure_Correction_CVPR_2021_paper.pdf | http://arxiv.org/abs/2003.11596 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Afifi_Learning_Multi-Scale_Photo_Exposure_Correction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Afifi_Learning_Multi-Scale_Photo_Exposure_Correction_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Afifi_Learning_Multi-Scale_Photo_CVPR_2021_supplemental.pdf | null |
Learning Semantic Person Image Generation by Region-Adaptive Normalization | Zhengyao Lv, Xiaoming Li, Xin Li, Fu Li, Tianwei Lin, Dongliang He, Wangmeng Zuo | Human pose transfer has received great attention due to its wide applications, yet is still a challenging task that is not well solved. Recent works have achieved great success to transfer the person image from the source to the target pose. However, most of them cannot well capture the semantic appearance, resulting in inconsistent and less realistic textures on the reconstructed results. To address this issue, we propose a new two-stage framework to handle the pose and appearance translation. In the first stage, we predict the target semantic parsing maps to eliminate the difficulties of pose transfer and further benefit the latter translation of per-region appearance style. In the second one, with the predicted target semantic maps, we suggest a new person image generation method by incorporating the region-adaptive normalization, in which it takes the per-region styles to guide the target appearance generation. Extensive experiments show that our proposed SPGNet can generate more semantic, consistent, and photo-realistic results and perform favorably against the state of the art methods in terms of quantitative and qualitative evaluation. The source code and model are available at https://github.com/cszy98/SPGNet.git. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lv_Learning_Semantic_Person_Image_Generation_by_Region-Adaptive_Normalization_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.06650 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lv_Learning_Semantic_Person_Image_Generation_by_Region-Adaptive_Normalization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lv_Learning_Semantic_Person_Image_Generation_by_Region-Adaptive_Normalization_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lv_Learning_Semantic_Person_CVPR_2021_supplemental.pdf | null |
Rethinking Class Relations: Absolute-Relative Supervised and Unsupervised Few-Shot Learning | Hongguang Zhang, Piotr Koniusz, Songlei Jian, Hongdong Li, Philip H. S. Torr | The majority of existing few-shot learning methods describe image relations with binary labels. However, such binary relations are insufficient to teach the network complicated real-world relations, due to the lack of decision smoothness. Furthermore, current few-shot learning models capture only the similarity via relation labels, but they are not exposed to class concepts associated with objects, which is likely detrimental to the classification performance due to underutilization of the available class labels. For instance, children learn the concept of tiger from a few of actual examples as well as from comparisons of tiger to other animals. Thus, we hypothesize that both similarity and class concept learning must be occurring simultaneously. With these observations at hand, we study the fundamental problem of simplistic class modeling in current few-shot learning methods. We rethink the relations between class concepts, and propose a novel Absolute-relative Learning paradigm to fully take advantage of label information to refine the image an relation representations in both supervised and unsupervised scenarios. Our proposed paradigm improves the performance of several state-of-the-art models on publicly available datasets. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Rethinking_Class_Relations_Absolute-Relative_Supervised_and_Unsupervised_Few-Shot_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2001.03919 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Rethinking_Class_Relations_Absolute-Relative_Supervised_and_Unsupervised_Few-Shot_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Rethinking_Class_Relations_Absolute-Relative_Supervised_and_Unsupervised_Few-Shot_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Rethinking_Class_Relations_CVPR_2021_supplemental.pdf | null |
Divergence Optimization for Noisy Universal Domain Adaptation | Qing Yu, Atsushi Hashimoto, Yoshitaka Ushiku | Universal domain adaptation (UniDA) has been proposed to transfer knowledge learned from a label-rich source domain to a label-scarce target domain without any constraints on the label sets. In practice, however, it is difficult to obtain a large amount of perfectly clean labeled data in a source domain with limited resources. Existing UniDA methods rely on source samples with correct annotations, which greatly limits their application in the real world. Hence, we consider a new realistic setting called Noisy UniDA, in which classifiers are trained with noisy labeled data from the source domain and unlabeled data with an unknown class distribution from the target domain. This paper introduces a two-head convolutional neural network framework to solve all problems simultaneously. Our network consists of one common feature generator and two classifiers with different decision boundaries. By optimizing the divergence between the two classifiers' outputs, we can detect noisy source samples, find "unknown" classes in the target domain, and align the distribution of the source and target domains. In an extensive evaluation of different domain adaptation settings, the proposed method outperformed existing methods by a large margin in most settings. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yu_Divergence_Optimization_for_Noisy_Universal_Domain_Adaptation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00246 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yu_Divergence_Optimization_for_Noisy_Universal_Domain_Adaptation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yu_Divergence_Optimization_for_Noisy_Universal_Domain_Adaptation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yu_Divergence_Optimization_for_CVPR_2021_supplemental.zip | null |
Learning Dynamic Alignment via Meta-Filter for Few-Shot Learning | Chengming Xu, Yanwei Fu, Chen Liu, Chengjie Wang, Jilin Li, Feiyue Huang, Li Zhang, Xiangyang Xue | Few-shot learning (FSL), which aims to recognise new classes by adapting the learned knowledge with extremely limited few-shot (support) examples, remains an important open problem in computer vision. Most of the existing methods for feature alignment in few-shot learning only consider image-level or spatial-level alignment while omitting the channel disparity. Our insight is that these methods would lead to poor adaptation with redundant matching, and leveraging channel-wise adjustment is the key to well adapting the learned knowledge to new classes. Therefore, in this paper, we propose to learn a dynamic alignment, which can effectively highlight both query regions and channels according to different local support information. Specifically, this is achieved by first dynamically sampling the neighbourhood of the feature position conditioned on the input few shot, based on which we further predict a both position-dependent and channel-dependent Dynamic Meta-filter. The filter is used to align the query feature with position-specific and channel-specific knowledge. Moreover, we adopt Neural Ordinary Differential Equation (ODE) to enable a more accurate control of the alignment. In such a sense our model is able to better capture fine-grained semantic context of the few-shot example and thus facilitates dynamical knowledge adaptation for few-shot learning. The resulting framework establishes the new state-of-the-arts on major few-shot visual recognition benchmarks, including miniImageNet and tieredImageNet. | https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_Learning_Dynamic_Alignment_via_Meta-Filter_for_Few-Shot_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.13582 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Learning_Dynamic_Alignment_via_Meta-Filter_for_Few-Shot_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Learning_Dynamic_Alignment_via_Meta-Filter_for_Few-Shot_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xu_Learning_Dynamic_Alignment_CVPR_2021_supplemental.pdf | null |
Unsupervised Learning of 3D Object Categories From Videos in the Wild | Philipp Henzler, Jeremy Reizenstein, Patrick Labatut, Roman Shapovalov, Tobias Ritschel, Andrea Vedaldi, David Novotny | Recently, numerous works have attempted to learn 3D reconstructors of textured 3D models of visual categories given a training set of annotated static images of objects. In this paper, we seek to decrease the amount of needed supervision by leveraging a collection of object-centric videos captured in-the-wild without requiring any manual 3D annotations. Since existing category-centric datasets are insufficient for this problem, we contribute with a large-scale crowd-sourced dataset of object-centric videos suitable for this task. We further propose a novel method that learns via differentiable rendering of a predicted implicit surface of the scene. Here, inspired by classic multi-view stereo methods, our key technical contribution is a novel warp-conditioned implicit shape function, which is robust to the noise in the SfM video reconstructions that supervise our learning. Our evaluation demonstrates performance improvements over several deep monocular reconstruction baselines on 2 existing benchmarks and on our novel dataset. | https://openaccess.thecvf.com/content/CVPR2021/papers/Henzler_Unsupervised_Learning_of_3D_Object_Categories_From_Videos_in_the_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16552 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Henzler_Unsupervised_Learning_of_3D_Object_Categories_From_Videos_in_the_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Henzler_Unsupervised_Learning_of_3D_Object_Categories_From_Videos_in_the_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Henzler_Unsupervised_Learning_of_CVPR_2021_supplemental.pdf | null |
Exploring Heterogeneous Clues for Weakly-Supervised Audio-Visual Video Parsing | Yu Wu, Yi Yang | We investigate the weakly-supervised audio-visual video parsing task, which aims to parse a video into temporal event segments and predict the audible or visible event categories. The task is challenging since there only exist video-level event labels for training, without indicating the temporal boundaries and modalities. Previous works take the overall event labels to supervise both audio and visual model predictions. However, we argue that such overall labels harm the model training due to the audio-visual asynchrony. For example, commentators speak in a basketball video, but we cannot visually find the speakers. In this paper, we tackle this issue by leveraging the cross-modal correspondence of audio and visual signals. We generate reliable event labels individually for each modality by swapping audio and visual tracks with other unrelated videos. If the original visual/audio data contain event clues, the event prediction from the newly assembled data would still be highly confident. In this way, we could protect our models from being misled by ambiguous event labels. In addition, we propose the cross-modal audio-visual contrastive learning to induce temporal difference on attention models within videos, i.e., urging the model to pick the current temporal segment from all context candidates. Experiments show we outperform state-of-the-art methods by a large margin. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wu_Exploring_Heterogeneous_Clues_for_Weakly-Supervised_Audio-Visual_Video_Parsing_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_Exploring_Heterogeneous_Clues_for_Weakly-Supervised_Audio-Visual_Video_Parsing_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_Exploring_Heterogeneous_Clues_for_Weakly-Supervised_Audio-Visual_Video_Parsing_CVPR_2021_paper.html | CVPR 2021 | null | null |
Dogfight: Detecting Drones From Drones Videos | Muhammad Waseem Ashraf, Waqas Sultani, Mubarak Shah | As airborne vehicles are becoming more autonomous and ubiquitous, it has become vital to develop the capability to detect the objects in their surroundings. This paper attempts to address the problem of drones detection from other flying drones. The erratic movement of the source and target drones, small size, arbitrary shape, large intensity variations, and occlusion make this problem quite challenging. In this scenario, region-proposal based methods are not able to capture sufficient discriminative foreground-background information. Also, due to the extremely small size and complex motion of the source and target drones, feature aggregation based methods are unable to perform well. To handle this, instead of using region-proposal based methods, we propose to use a two-stage segmentation-based approach employing spatio-temporal attention cues. During the first stage, given the overlapping frame regions, detailed contextual information is captured over convolution feature maps using pyramid pooling. After that pixel and channel-wise attention is enforced on the feature maps to ensure accurate drone localization. In the second stage, first stage detections are verified and new probable drone locations are explored. To discover new drone locations, motion boundaries are used. This is followed by tracking candidate drone detections for a few frames, cuboid formation, extraction of the 3D convolution feature map, and drones detection within each cuboid. The proposed approach is evaluated on two publicly available drone detection datasets and outperforms over several competitive baselines. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ashraf_Dogfight_Detecting_Drones_From_Drones_Videos_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.17242 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ashraf_Dogfight_Detecting_Drones_From_Drones_Videos_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ashraf_Dogfight_Detecting_Drones_From_Drones_Videos_CVPR_2021_paper.html | CVPR 2021 | null | null |
PAUL: Procrustean Autoencoder for Unsupervised Lifting | Chaoyang Wang, Simon Lucey | Recent success in casting Non-rigid Structure from Motion (NRSfM) as an unsupervised deep learning problem has raised fundamental questions about what novelty in NRSfM prior could the deep learning offer. In this paper we advocate for a 3D deep auto-encoder framework to be used explicitly as the NRSfM prior. The framework is unique as: (i) it learns the 3D auto-encoder weights solely from 2D projected measurements, and (ii) it is Procrustean in that it jointly resolves the unknown rigid pose for each shape instance. We refer to this architecture as a Procustean Autoencoder for Unsupervised Lifting (PAUL), and demonstrate state-of-the-art performance across a number of benchmarks in comparison to recent innovations such as Deep NRSfM and C3PDO. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_PAUL_Procrustean_Autoencoder_for_Unsupervised_Lifting_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16773 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_PAUL_Procrustean_Autoencoder_for_Unsupervised_Lifting_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_PAUL_Procrustean_Autoencoder_for_Unsupervised_Lifting_CVPR_2021_paper.html | CVPR 2021 | null | null |
Group Collaborative Learning for Co-Salient Object Detection | Qi Fan, Deng-Ping Fan, Huazhu Fu, Chi-Keung Tang, Ling Shao, Yu-Wing Tai | We present a novel group collaborative learning framework (GCNet) capable of detecting co-salient objects in real time (16ms), by simultaneously mining consensus representations at group level based on the two necessary criteria: 1) intra-group compactness to better formulate the consistency among co-salient objects by capturing their inherent shared attributes using our novel group affinity module; 2) inter-group separability to effectively suppress the influence of noisy objects on the output by introducing our new group collaborating module conditioning the inconsistent consensus. To learn a better embedding space without extra computational overhead, we explicitly employ auxiliary classification supervision. Extensive experiments on three challenging benchmarks, i.e., CoCA, CoSOD3k, and Cosal2015, demonstrate that our simple GCNet outperforms 10 cutting-edge models and achieves the new state-of-the-art. We demonstrate this paper's new technical contributions on a number of important downstream computer vision applications including content aware co-segmentation, co-localization based automatic thumbnails, etc. Our research code with two applications will be released. | https://openaccess.thecvf.com/content/CVPR2021/papers/Fan_Group_Collaborative_Learning_for_Co-Salient_Object_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.01108 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Fan_Group_Collaborative_Learning_for_Co-Salient_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Fan_Group_Collaborative_Learning_for_Co-Salient_Object_Detection_CVPR_2021_paper.html | CVPR 2021 | null | null |
RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening | Sungha Choi, Sanghun Jung, Huiwon Yun, Joanne T. Kim, Seungryong Kim, Jaegul Choo | Enhancing the generalization capability of deep neural networks to unseen domains is crucial for safety-critical applications in the real world such as autonomous driving. To address this issue, this paper proposes a novel instance selective whitening loss to improve the robustness of the segmentation networks for unseen domains. Our approach disentangles the domain-specific style and domain-invariant content encoded in higher-order statistics (i.e., feature covariance) of the feature representations and selectively removes only the style information causing domain shift. As shown in Fig. 1, our method provides reasonable predictions for (a) low-illuminated, (b) rainy, and (c) unseen structures. These types of images are not included in the training dataset, where the baseline shows a significant performance drop, contrary to ours. Being simple yet effective, our approach improves the robustness of various backbone networks without additional computational cost. We conduct extensive experiments in urban-scene segmentation and show the superiority of our approach to existing work. | https://openaccess.thecvf.com/content/CVPR2021/papers/Choi_RobustNet_Improving_Domain_Generalization_in_Urban-Scene_Segmentation_via_Instance_Selective_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.15597 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Choi_RobustNet_Improving_Domain_Generalization_in_Urban-Scene_Segmentation_via_Instance_Selective_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Choi_RobustNet_Improving_Domain_Generalization_in_Urban-Scene_Segmentation_via_Instance_Selective_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Choi_RobustNet_Improving_Domain_CVPR_2021_supplemental.pdf | null |
Monocular Real-Time Full Body Capture With Inter-Part Correlations | Yuxiao Zhou, Marc Habermann, Ikhsanul Habibie, Ayush Tewari, Christian Theobalt, Feng Xu | We present the first method for real-time full body capture that estimates shape and motion of body and hands together with a dynamic 3D face model from a single color image. Our approach uses a new neural network architecture that exploits correlations between body and hands at high computational efficiency. Unlike previous works, our approach is jointly trained on multiple datasets focusing on hand, body or face separately, without requiring data where all the parts are annotated at the same time, which is much more difficult to create at sufficient variety. The possibility of such multi-dataset training enables superior generalization ability. In contrast to earlier monocular full body methods, our approach captures more expressive 3D face geometry and color by estimating the shape, expression, albedo and illumination parameters of a statistical face model. Our method achieves competitive accuracy on public benchmarks, while being significantly faster and providing more complete face reconstructions. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_Monocular_Real-Time_Full_Body_Capture_With_Inter-Part_Correlations_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.06087 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Monocular_Real-Time_Full_Body_Capture_With_Inter-Part_Correlations_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Monocular_Real-Time_Full_Body_Capture_With_Inter-Part_Correlations_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhou_Monocular_Real-Time_Full_CVPR_2021_supplemental.pdf | null |
Pre-Trained Image Processing Transformer | Hanting Chen, Yunhe Wang, Tianyu Guo, Chang Xu, Yiping Deng, Zhenhua Liu, Siwei Ma, Chunjing Xu, Chao Xu, Wen Gao | As the computing power of modern hardware is increasing strongly, pre-trained deep learning models (e.g., BERT, GPT-3) learned on large-scale datasets have shown their effectiveness over conventional methods. The big progress is mainly contributed to the representation ability of transformer and its variant architectures. In this paper, we study the low-level computer vision task (e.g., denoising, super-resolution and deraining) and develop a new pre-trained model, namely, image processing transformer (IPT). To maximally excavate the capability of transformer, we present to utilize the well-known ImageNet benchmark for generating a large amount of corrupted image pairs. The IPT model is trained on these images with multi-heads and multi-tails. In addition, the constructive learning is introduced for well adapting to different image processing tasks. The pre-trained model can therefore efficiently employed on desired task after fine-tuning. With only one pre-trained model, IPT outperforms the current state-of-the-art methods on various low-level benchmarks. Code is available at https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/IPT | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Pre-Trained_Image_Processing_Transformer_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.00364 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Pre-Trained_Image_Processing_Transformer_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Pre-Trained_Image_Processing_Transformer_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Pre-Trained_Image_Processing_CVPR_2021_supplemental.pdf | null |
Robust and Accurate Object Detection via Adversarial Learning | Xiangning Chen, Cihang Xie, Mingxing Tan, Li Zhang, Cho-Jui Hsieh, Boqing Gong | Data augmentation has become a de facto component for training high-performance deep image classifiers, but its potential is under-explored for object detection. Noting that most state-of-the-art object detectors benefit from fine-tuning a pre-trained classifier, we first study how the classifiers' gains from various data augmentations transfer to object detection. The results are discouraging; the gains diminish after fine-tuning in terms of either accuracy or robustness. This work instead augments the fine-tuning stage for object detectors by exploring adversarial examples, which can be viewed as a model-dependent data augmentation. Our method dynamically selects the stronger adversarial images sourced from a detector's classification and localization branches and evolves with the detector to ensure the augmentation policy stays current and relevant. This model-dependent augmentation generalizes to different object detectors better than AutoAugment, a model-agnostic augmentation policy searched based on one particular detector. Our approach boosts the performance of state-of-the-art EfficientDets by +1.1 mAP on the COCO object detection benchmark. It also improves the detectors' robustness against natural distortions by +3.8 mAP and against domain shift by +1.3 mAP. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Robust_and_Accurate_Object_Detection_via_Adversarial_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.13886 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Robust_and_Accurate_Object_Detection_via_Adversarial_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Robust_and_Accurate_Object_Detection_via_Adversarial_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Robust_and_Accurate_CVPR_2021_supplemental.pdf | null |
Faster Meta Update Strategy for Noise-Robust Deep Learning | Youjiang Xu, Linchao Zhu, Lu Jiang, Yi Yang | It has been shown that deep neural networks are prone to overfitting on biased training data. Towards addressing this issue, meta-learning employs a meta model for correcting the training bias. Despite the promising performances, super slow training is currently the bottleneck in the meta learning approaches. In this paper, we introduce a novel Faster Meta Update Strategy (FaMUS) to replace the most expensive step in the meta gradient computation with a faster layer-wise approximation. We empirically find that FaMUS yields not only a reasonably accurate but also a low-variance approximation of the meta gradient. We conduct extensive experiments to verify the proposed method on two tasks. We show our method is able to save two-thirds of the training time while still maintaining the comparable or achieving even better generalization performance. In particular, our method achieves the state-of-the-art performance on both synthetic and realistic noisy labels, and obtains promising performance on long-tailed recognition on standard benchmarks. Code are released at https://github.com/youjiangxu/FaMUS. | https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_Faster_Meta_Update_Strategy_for_Noise-Robust_Deep_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.15092 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Faster_Meta_Update_Strategy_for_Noise-Robust_Deep_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Faster_Meta_Update_Strategy_for_Noise-Robust_Deep_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xu_Faster_Meta_Update_CVPR_2021_supplemental.pdf | null |
ContactOpt: Optimizing Contact To Improve Grasps | Patrick Grady, Chengcheng Tang, Christopher D. Twigg, Minh Vo, Samarth Brahmbhatt, Charles C. Kemp | Physical contact between hands and objects plays a critical role in human grasps. We show that optimizing the pose of a hand to achieve expected contact with an object can improve hand poses inferred via image-based methods. Given a hand mesh and an object mesh, a deep model trained on ground truth contact data infers desirable contact across the surfaces of the meshes. Then, ContactOpt efficiently optimizes the pose of the hand to achieve desirable contact using a differentiable contact model. Notably, our contact model encourages mesh interpenetration to approximate deformable soft tissue in the hand. In our evaluations, our methods result in grasps that better match ground truth contact, have lower kinematic error, and are significantly preferred by human participants. Code and models are available online. | https://openaccess.thecvf.com/content/CVPR2021/papers/Grady_ContactOpt_Optimizing_Contact_To_Improve_Grasps_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.07267 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Grady_ContactOpt_Optimizing_Contact_To_Improve_Grasps_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Grady_ContactOpt_Optimizing_Contact_To_Improve_Grasps_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Grady_ContactOpt_Optimizing_Contact_CVPR_2021_supplemental.pdf | null |
Panoptic-PolarNet: Proposal-Free LiDAR Point Cloud Panoptic Segmentation | Zixiang Zhou, Yang Zhang, Hassan Foroosh | Panoptic segmentation presents a new challenge in exploiting the merits of both detection and segmentation, with the aim of unifying instance segmentation and semantic segmentation in a single framework. However, an efficient solution for panoptic segmentation in the emerging domain of LiDAR point cloud is still an open research problem and is very much under-explored. In this paper, we present a fast and robust LiDAR point cloud panoptic segmentation framework, referred to as Panoptic-PolarNet. We learn both semantic segmentation and class-agnostic instance clustering in a single inference network using a polar Bird's Eye View (BEV) representation, enabling us to circumvent the issue of occlusion among instances in urban street scenes. To improve our network's learnability, we also propose an adapted instance augmentation technique and a novel adversarial point cloud pruning method. Our experiments show that Panoptic-PolarNet outperforms the baseline methods on SemanticKITTI and nuScenes datasets with an almost real-time inference speed. Panoptic-PolarNet achieved 54.1% PQ in the public SemanticKITTI panoptic segmentation leaderboard and leading performance for the validation set of nuScenes. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_Panoptic-PolarNet_Proposal-Free_LiDAR_Point_Cloud_Panoptic_Segmentation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Panoptic-PolarNet_Proposal-Free_LiDAR_Point_Cloud_Panoptic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Panoptic-PolarNet_Proposal-Free_LiDAR_Point_Cloud_Panoptic_Segmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhou_Panoptic-PolarNet_Proposal-Free_LiDAR_CVPR_2021_supplemental.pdf | null |
Source-Free Domain Adaptation for Semantic Segmentation | Yuang Liu, Wei Zhang, Jun Wang | Unsupervised Domain Adaptation (UDA) can tackle the challenge that convolutional neural network (CNN)-based approaches for semantic segmentation heavily rely on the pixel-level annotated data, which is labor-intensive. However, existing UDA approaches in this regard inevitably require the full access to source datasets to reduce the gap between the source and target domains during model adaptation, which are impractical in the real scenarios where the source datasets are private, and thus cannot be released along with the well-trained source models. To cope with this issue, we propose a source-free domain adaptation framework for semantic segmentation, namely SFDA, in which only a well-trained source model and an unlabeled target domain dataset are available for adaptation. SFDA not only enables to recover and preserve the source domain knowledge from the source model via knowledge transfer during model adaptation, but also distills valuable information from the target domain for self-supervised learning. The pixel- and patch-level optimization objectives tailored for semantic segmentation are seamlessly integrated in the framework. The extensive experimental results on numerous benchmark datasets highlight the effectiveness of our framework against the existing UDA approaches relying on source data. | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Source-Free_Domain_Adaptation_for_Semantic_Segmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16372 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Source-Free_Domain_Adaptation_for_Semantic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Source-Free_Domain_Adaptation_for_Semantic_Segmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Source-Free_Domain_Adaptation_CVPR_2021_supplemental.pdf | null |
Adaptive Weighted Discriminator for Training Generative Adversarial Networks | Vasily Zadorozhnyy, Qiang Cheng, Qiang Ye | Generative adversarial network (GAN) has become one of the most important neural network models for classical unsupervised machine learning. A variety of discriminator loss functions have been developed to train GAN's discriminators and they all have a common structure: a sum of real and fake losses that only depends on the actual and generated data respectively. One challenge associated with an equally weighted sum of two losses is that the training may benefit one loss but harm the other, which we show causes instability and mode collapse. In this paper, we introduce a new family of discriminator loss functions that adopts a weighted sum of real and fake parts, which we call adaptive weighted loss functions or aw-loss functions. Using the gradients of the real and fake parts of the loss, we can adaptively choose weights to train a discriminator in the direction that benefits the GAN's stability. Our method can be potentially applied to any discriminator model with a loss that is a sum of the real and fake parts. Experiments validated the effectiveness of our loss functions on unconditional and conditional image generation tasks, improving the baseline results by a significant margin on CIFAR-10, STL-10, and CIFAR-100 datasets in Inception Scores (IS) and Frechet Inception Distance (FID) metrics. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zadorozhnyy_Adaptive_Weighted_Discriminator_for_Training_Generative_Adversarial_Networks_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.03149 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zadorozhnyy_Adaptive_Weighted_Discriminator_for_Training_Generative_Adversarial_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zadorozhnyy_Adaptive_Weighted_Discriminator_for_Training_Generative_Adversarial_Networks_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zadorozhnyy_Adaptive_Weighted_Discriminator_CVPR_2021_supplemental.pdf | null |
Depth From Camera Motion and Object Detection | Brent A. Griffin, Jason J. Corso | This paper addresses the problem of learning to estimate the depth of detected objects given some measurement of camera motion (e.g., from robot kinematics or vehicle odometry). We achieve this by 1) designing a recurrent neural network (DBox) that estimates the depth of objects using a generalized representation of bounding boxes and uncalibrated camera movement and 2) introducing the Object Depth via Motion and Detection Dataset (ODMD). ODMD training data are extensible and configurable, and the ODMD benchmark includes 21,600 examples across four validation and test sets. These sets include mobile robot experiments using an end-effector camera to locate objects from the YCB dataset and examples with perturbations added to camera motion or bounding box data. In addition to the ODMD benchmark, we evaluate DBox in other monocular application domains, achieving state-of-the-art results on existing driving and robotics benchmarks and estimating the depth of objects using a camera phone. | https://openaccess.thecvf.com/content/CVPR2021/papers/Griffin_Depth_From_Camera_Motion_and_Object_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.01468 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Griffin_Depth_From_Camera_Motion_and_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Griffin_Depth_From_Camera_Motion_and_Object_Detection_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Griffin_Depth_From_Camera_CVPR_2021_supplemental.pdf | null |
PPR10K: A Large-Scale Portrait Photo Retouching Dataset With Human-Region Mask and Group-Level Consistency | Jie Liang, Hui Zeng, Miaomiao Cui, Xuansong Xie, Lei Zhang | Different from general photo retouching tasks, portrait photo retouching (PPR), which aims to enhance the visual quality of a collection of flat-looking portrait photos, has its special and practical requirements such as human-region priority (HRP) and group-level consistency (GLC). HRP requires that more attention should be paid to human regions, while GLC requires that a group of portrait photos should be retouched to a consistent tone. Models trained on existing general photo retouching datasets, however, can hardly meet these requirements of PPR. To facilitate the research on this high-frequency task, we construct a large-scale PPR dataset, namely PPR10K, which is the first of its kind to our best knowledge. PPR10K contains 1, 681 groups and 11, 161 high-quality raw portrait photos in total. High-resolution segmentation masks of human regions are provided. Each raw photo is retouched by three experts, while they elaborately adjust each group of photos to have consistent tones. We define a set of objective measures to evaluate the performance of PPR and propose strategies to learn PPR models with good HRP and GLC performance. The constructed PPR10K dataset provides a good benchmark for studying automatic PPR methods, and experiments demonstrate that the proposed learning strategies are effective to improve the retouching performance. Datasets and codes are available: https://github.com/csjliang/PPR10K. | https://openaccess.thecvf.com/content/CVPR2021/papers/Liang_PPR10K_A_Large-Scale_Portrait_Photo_Retouching_Dataset_With_Human-Region_Mask_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.09180 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liang_PPR10K_A_Large-Scale_Portrait_Photo_Retouching_Dataset_With_Human-Region_Mask_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liang_PPR10K_A_Large-Scale_Portrait_Photo_Retouching_Dataset_With_Human-Region_Mask_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liang_PPR10K_A_Large-Scale_CVPR_2021_supplemental.pdf | null |
Transformation Driven Visual Reasoning | Xin Hong, Yanyan Lan, Liang Pang, Jiafeng Guo, Xueqi Cheng | This paper defines a new visual reasoning paradigm by introducing an important factor, i.e. transformation. The motivation comes from the fact that most existing visual reasoning tasks, such as CLEVR in VQA, are solely defined to test how well the machine understands the concepts and relations within static settings, like one image. We argue that this kind of state driven visual reasoning approach has limitations in reflecting whether the machine has the ability to infer the dynamics between different states, which has been shown as important as state-level reasoning for human cognition in Piaget's theory. To tackle this problem, we propose a novel transformation driven visual reasoning task. Given both the initial and final states, the target is to infer the corresponding single-step or multi-step transformation, represented as a triplet (object, attribute, value) or a sequence of triplets, respectively. Following this definition, a new dataset namely TRANCE is constructed on the basis of CLEVR, including three levels of settings, i.e. Basic (single-step transformation), Event (multi-step transformation), and View (multi-step transformation with variant views). Experimental results show that the state-of-the-art visual reasoning models perform well on Basic, but are still far from human-level intelligence on Event and View. We believe the proposed new paradigm will boost the development of machine visual reasoning. More advanced methods and real data need to be investigated in this direction. The resource of TVR is available at https://hongxin2019.github.io/TVR. | https://openaccess.thecvf.com/content/CVPR2021/papers/Hong_Transformation_Driven_Visual_Reasoning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.13160 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hong_Transformation_Driven_Visual_Reasoning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hong_Transformation_Driven_Visual_Reasoning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hong_Transformation_Driven_Visual_CVPR_2021_supplemental.pdf | null |
Sparse R-CNN: End-to-End Object Detection With Learnable Proposals | Peize Sun, Rufeng Zhang, Yi Jiang, Tao Kong, Chenfeng Xu, Wei Zhan, Masayoshi Tomizuka, Lei Li, Zehuan Yuan, Changhu Wang, Ping Luo | We present Sparse R-CNN, a purely sparse method for object detection in images. Existing works on object detection heavily rely on dense object candidates, such as k anchor boxes pre-defined on all grids of image feature map of size HxW. In our method, however, a fixed sparse set of learned object proposals, total length of N, are provided to object recognition head to perform classification and location. By eliminating HWk (up to hundreds of thousands) hand-designed object candidates to N (e.g. 100) learnable proposals, Sparse R-CNN completely avoids all efforts related to object candidates design and many-to-one label assignment. More importantly, final predictions are directly output without non-maximum suppression post-procedure. Sparse R-CNN demonstrates accuracy, run-time and training convergence performance on par with the well-established detector baselines on the challenging COCO dataset, e.g., achieving 45.0 AP in standard 3x training schedule and running at 22 fps using ResNet-50 FPN model. We hope our work could inspire re-thinking the convention of dense prior in object detectors. The code is available at: https://github.com/PeizeSun/SparseR-CNN. | https://openaccess.thecvf.com/content/CVPR2021/papers/Sun_Sparse_R-CNN_End-to-End_Object_Detection_With_Learnable_Proposals_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_Sparse_R-CNN_End-to-End_Object_Detection_With_Learnable_Proposals_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_Sparse_R-CNN_End-to-End_Object_Detection_With_Learnable_Proposals_CVPR_2021_paper.html | CVPR 2021 | null | null |
Plan2Scene: Converting Floorplans to 3D Scenes | Madhawa Vidanapathirana, Qirui Wu, Yasutaka Furukawa, Angel X. Chang, Manolis Savva | We address the task of converting a floorplan and a set of associated photos of a residence into a textured 3D mesh model, a task which we call Plan2Scene. Our system 1) lifts a floorplan image to a 3D mesh model; 2) synthesizes surface textures based on the input photos; and 3) infers textures for unobserved surfaces using a graph neural network architecture. To train and evaluate our system we create indoor surface texture datasets, and augment a dataset of floorplans and photos from prior work with rectified surface crops and additional annotations. Our approach handles the challenge of producing tileable textures for dominant surfaces such as floors, walls, and ceilings from a sparse set of unaligned photos that only partially cover the residence. Qualitative and quantitative evaluations show that our system produces realistic 3D interior models, outperforming baseline approaches on a suite of texture quality metrics and as measured by a holistic user study. | https://openaccess.thecvf.com/content/CVPR2021/papers/Vidanapathirana_Plan2Scene_Converting_Floorplans_to_3D_Scenes_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.05375 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Vidanapathirana_Plan2Scene_Converting_Floorplans_to_3D_Scenes_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Vidanapathirana_Plan2Scene_Converting_Floorplans_to_3D_Scenes_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Vidanapathirana_Plan2Scene_Converting_Floorplans_CVPR_2021_supplemental.pdf | null |
Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges | Qingyong Hu, Bo Yang, Sheikh Khalid, Wen Xiao, Niki Trigoni, Andrew Markham | An essential prerequisite for unleashing the potential of supervised deep learning algorithms in the area of 3D scene understanding is the availability of large-scale and richly annotated datasets. However, publicly available datasets are either in relatively small spatial scales or have limited semantic annotations due to the expensive cost of data acquisition and data annotation, which severely limits the development of fine-grained semantic understanding in the context of 3D point clouds. In this paper, we present an urban-scale photogrammetric point cloud dataset with nearly three billion richly annotated points, which is three times the number of labeled points than the existing largest photogrammetric point cloud dataset. Our dataset consists of large areas from three UK cities, covering about 7.6 km^2 of the city landscape. In the dataset, each 3D point is labeled as one of 13 semantic classes. We extensively evaluate the performance of state-of-the-art algorithms on our dataset and provide a comprehensive analysis of the results. In particular, we identify several key challenges towards urban-scale point cloud understanding. The dataset is available at https://github.com/QingyongHu/SensatUrban. | https://openaccess.thecvf.com/content/CVPR2021/papers/Hu_Towards_Semantic_Segmentation_of_Urban-Scale_3D_Point_Clouds_A_Dataset_CVPR_2021_paper.pdf | http://arxiv.org/abs/2009.03137 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_Towards_Semantic_Segmentation_of_Urban-Scale_3D_Point_Clouds_A_Dataset_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_Towards_Semantic_Segmentation_of_Urban-Scale_3D_Point_Clouds_A_Dataset_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hu_Towards_Semantic_Segmentation_CVPR_2021_supplemental.pdf | null |
Towards Open World Object Detection | K J Joseph, Salman Khan, Fahad Shahbaz Khan, Vineeth N Balasubramanian | Humans have a natural instinct to identify unknown object instances in their environments. The intrinsic curiosity about these unknown instances aids in learning about them, when the corresponding knowledge is eventually available. This motivates us to propose a novel computer vision problem called: `Open World Object Detection', where a model is tasked to: 1) identify objects that have not been introduced to it as `unknown', without explicit supervision to do so, and 2) incrementally learn these identified unknown categories without forgetting previously learned classes, when the corresponding labels are progressively received. We formulate the problem, introduce a strong evaluation protocol and provide a novel solution, which we call OREO: Open World Object Detector, based on contrastive clustering and energy based unknown identification. Our experimental evaluation and ablation studies analyse the efficacy of OREO in achieving Open World objectives. As an interesting by-product, we find that identifying and characterising unknown instances helps to reduce confusion in an incremental object detection setting, where we achieve state-of-the-art performance, with no extra methodological effort. We hope that our work will attract further research into this newly identified, yet crucial research direction. | https://openaccess.thecvf.com/content/CVPR2021/papers/Joseph_Towards_Open_World_Object_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.02603 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Joseph_Towards_Open_World_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Joseph_Towards_Open_World_Object_Detection_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Joseph_Towards_Open_World_CVPR_2021_supplemental.pdf | null |
Conditional Bures Metric for Domain Adaptation | You-Wei Luo, Chuan-Xian Ren | As a vital problem in classification-oriented transfer, unsupervised domain adaptation (UDA) has attracted widespread attention in recent years. Previous UDA methods assume the marginal distributions of different domains are shifted while ignoring the discriminant information in the label distributions. This leads to classification performance degeneration in real applications. In this work, we focus on the conditional distribution shift problem which is of great concern to current conditional invariant models. We aim to seek a kernel covariance embedding for conditional distribution which remains yet unexplored. Theoretically, we propose the Conditional Kernel Bures (CKB) metric for characterizing conditional distribution discrepancy, and derive an empirical estimation for the CKB metric without introducing the implicit kernel feature map. It provides an interpretable approach to understand the knowledge transfer mechanism. The established consistency theory of the empirical estimation provides a theoretical guarantee for convergence. A conditional distribution matching network is proposed to learn the conditional invariant and discriminative features for UDA. Extensive experiments and analysis show the superiority of our proposed model. | https://openaccess.thecvf.com/content/CVPR2021/papers/Luo_Conditional_Bures_Metric_for_Domain_Adaptation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Luo_Conditional_Bures_Metric_for_Domain_Adaptation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Luo_Conditional_Bures_Metric_for_Domain_Adaptation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Luo_Conditional_Bures_Metric_CVPR_2021_supplemental.pdf | null |
DatasetGAN: Efficient Labeled Data Factory With Minimal Human Effort | Yuxuan Zhang, Huan Ling, Jun Gao, Kangxue Yin, Jean-Francois Lafleche, Adela Barriuso, Antonio Torralba, Sanja Fidler | We introduce DatasetGAN: an automatic procedure to generate massive datasets of high-quality semantically segmented images requiring minimal human effort. Current deep networks are extremely data-hungry, benefiting from training on large-scale datasets, which are time-consuming to annotate. Our method relies on the power of recent GANs to generate realistic images. We show how the GAN latent code can be decoded to produce a semantic segmentation of the image. Training the decoder only needs a few labeled examples to generalize to the rest of the latent space, resulting in an infinite annotated dataset generator! These generated datasets can then be used for training any computer vision architecture just as real datasets are. As only a few images need to be manually segmented, it becomes possible to annotate images in extreme detail and generate datasets with rich object and part segmentations. To showcase the power of our approach, we generated datasets for 7 image segmentation tasks which include pixel-level labels for 34 human face parts, and 32 car parts. Our approach outperforms all semi-supervised baselines significantly and is on par with fully supervised methods using labor-intensive annotations. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_DatasetGAN_Efficient_Labeled_Data_Factory_With_Minimal_Human_Effort_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.06490 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_DatasetGAN_Efficient_Labeled_Data_Factory_With_Minimal_Human_Effort_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_DatasetGAN_Efficient_Labeled_Data_Factory_With_Minimal_Human_Effort_CVPR_2021_paper.html | CVPR 2021 | null | null |
Repurposing GANs for One-Shot Semantic Part Segmentation | Nontawat Tritrong, Pitchaporn Rewatbowornwong, Supasorn Suwajanakorn | While GANs have shown success in realistic image generation, the idea of using GANs for other tasks unrelated to synthesis is underexplored. Do GANs learn meaningful structural parts of objects during their attempt to reproduce those objects? In this work, we test this hypothesis and propose a simple and effective approach based on GANs for semantic part segmentation that requires as few as one label example along with an unlabeled dataset. Our key idea is to leverage a trained GAN to extract a pixel-wise representation from the input image and use it as feature vectors for a segmentation network. Our experiments demonstrate that this GAN-derived representation is "readily discriminative" and produces surprisingly good results that are comparable to those from supervised baselines trained with significantly more labels. We believe this novel repurposing of GANs underlies a new class of unsupervised representation learning, which can generalize to many other tasks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Tritrong_Repurposing_GANs_for_One-Shot_Semantic_Part_Segmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.04379 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Tritrong_Repurposing_GANs_for_One-Shot_Semantic_Part_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Tritrong_Repurposing_GANs_for_One-Shot_Semantic_Part_Segmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tritrong_Repurposing_GANs_for_CVPR_2021_supplemental.pdf | null |
Semi-Supervised 3D Hand-Object Poses Estimation With Interactions in Time | Shaowei Liu, Hanwen Jiang, Jiarui Xu, Sifei Liu, Xiaolong Wang | Estimating 3D hand and object pose from a single image is an extremely challenging problem: hands and objects are often self-occluded during interactions, and the 3D annotations are scarce as even humans cannot directly label the ground-truths from a single image perfectly. To tackle these challenges, we propose a unified framework for estimating the 3D hand and object poses with semi-supervised learning. We build a joint learning framework where we perform explicit contextual reasoning between hand and object representations. Going beyond limited 3D annotations in a single image, we leverage the spatial-temporal consistency in large-scale hand-object videos as a constraint for generating pseudo labels in semi-supervised learning. Our method not only improves hand pose estimation in challenging real-world dataset, but also substantially improve the object pose which has fewer ground-truths per instance. By training with large-scale diverse videos, our model also generalizes better across multiple out-of-domain datasets. Project page and code: https://stevenlsw.github.io/Semi-Hand-Object | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Semi-Supervised_3D_Hand-Object_Poses_Estimation_With_Interactions_in_Time_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.05266 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Semi-Supervised_3D_Hand-Object_Poses_Estimation_With_Interactions_in_Time_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Semi-Supervised_3D_Hand-Object_Poses_Estimation_With_Interactions_in_Time_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Semi-Supervised_3D_Hand-Object_CVPR_2021_supplemental.pdf | null |
Cyclic Co-Learning of Sounding Object Visual Grounding and Sound Separation | Yapeng Tian, Di Hu, Chenliang Xu | There are rich synchronized audio and visual events in our daily life. Inside the events, audio scenes are associated with the corresponding visual objects; meanwhile, sounding objects can indicate and help to separate their individual sounds in the audio track. Based on this observation, in this paper, we propose a cyclic co-learning (CCoL) paradigm that can jointly learn sounding object visual grounding and audio-visual sound separation in a unified framework. Concretely, we can leverage grounded object-sound relations to improve the results of sound separation. Meanwhile, benefiting from discriminative information from separated sounds, we improve training example sampling for sounding object grounding, which builds a co-learning cycle for the two tasks and makes them mutually beneficial. Extensive experiments show that the proposed framework outperforms the compared recent approaches on both tasks, and they can benefit from each other with our cyclic co-learning. The source code and pre-trained models are released in https://github.com/YapengTian/CCOL-CVPR21. | https://openaccess.thecvf.com/content/CVPR2021/papers/Tian_Cyclic_Co-Learning_of_Sounding_Object_Visual_Grounding_and_Sound_Separation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.02026 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Tian_Cyclic_Co-Learning_of_Sounding_Object_Visual_Grounding_and_Sound_Separation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Tian_Cyclic_Co-Learning_of_Sounding_Object_Visual_Grounding_and_Sound_Separation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tian_Cyclic_Co-Learning_of_CVPR_2021_supplemental.pdf | null |
Digital Gimbal: End-to-End Deep Image Stabilization With Learnable Exposure Times | Omer Dahary, Matan Jacoby, Alex M. Bronstein | Mechanical image stabilization using actuated gimbals enables capturing long-exposure shots without suffering from blur due to camera motion. These devices, however, are often physically cumbersome and expensive, limiting their widespread use. In this work, we propose to digitally emulate a mechanically stabilized system from the input of a fast unstabilized camera. To exploit the trade-off between motion blur at long exposures and low SNR at short exposures, we train a CNN that estimates a sharp high-SNR image by aggregating a burst of noisy short-exposure frames, related by unknown motion. We further suggest learning the burst's exposure times in an end-to-end manner, thus balancing the noise and blur across the frames. We demonstrate this method's advantage over the traditional approach of deblurring a single image or denoising a fixed-exposure burst on both synthetic and real data. | https://openaccess.thecvf.com/content/CVPR2021/papers/Dahary_Digital_Gimbal_End-to-End_Deep_Image_Stabilization_With_Learnable_Exposure_Times_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.04515 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Dahary_Digital_Gimbal_End-to-End_Deep_Image_Stabilization_With_Learnable_Exposure_Times_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Dahary_Digital_Gimbal_End-to-End_Deep_Image_Stabilization_With_Learnable_Exposure_Times_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Dahary_Digital_Gimbal_End-to-End_CVPR_2021_supplemental.pdf | null |
Rethinking Text Segmentation: A Novel Dataset and a Text-Specific Refinement Approach | Xingqian Xu, Zhifei Zhang, Zhaowen Wang, Brian Price, Zhonghao Wang, Humphrey Shi | Text segmentation is a prerequisite in many real-world text-related tasks, e.g., text style transfer, and scene text removal. However, facing the lack of high-quality datasets and dedicated investigations, this critical prerequisite has been left as an assumption in many works, and has been largely overlooked by current research. To bridge this gap, we proposed TextSeg, a large-scale fine-annotated text dataset with six types of annotations: word- and character-wise bounding polygons, masks, and transcriptions. We also introduce Text Refinement Network (TexRNet), a novel text segmentation approach that adapts to the unique properties of text, e.g. non-convex boundary, diverse texture, etc., which often impose burdens on traditional segmentation models. In our TexRNet, we propose text-specific network designs to address such challenges, including key features pooling and attention-based similarity checking. We also introduce trimap and discriminator losses that show significant improvement in text segmentation. Extensive experiments are carried out on both our TextSeg dataset and other existing datasets. We demonstrate that TexRNet consistently improves text segmentation performance by nearly 2% compared to other state-of-the-art segmentation methods. Our dataset and code can be found at https://github.com/SHI-Labs/Rethinking-Text-Segmentation. | https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_Rethinking_Text_Segmentation_A_Novel_Dataset_and_a_Text-Specific_Refinement_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.14021 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Rethinking_Text_Segmentation_A_Novel_Dataset_and_a_Text-Specific_Refinement_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Rethinking_Text_Segmentation_A_Novel_Dataset_and_a_Text-Specific_Refinement_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xu_Rethinking_Text_Segmentation_CVPR_2021_supplemental.pdf | null |
SUTD-TrafficQA: A Question Answering Benchmark and an Efficient Network for Video Reasoning Over Traffic Events | Li Xu, He Huang, Jun Liu | Traffic event cognition and reasoning in videos is an important task that has a wide range of applications in intelligent transportation, assisted driving, and autonomous vehicles. In this paper, we create a novel dataset, SUTD-TrafficQA (Traffic Question Answering), which takes the form of video QA based on the collected 10,080 in-the-wild videos and annotated 62,535 QA pairs, for benchmarking the cognitive capability of causal inference and event understanding models in complex traffic scenarios. Specifically, we propose 6 challenging reasoning tasks corresponding to various traffic scenarios, so as to evaluate the reasoning capability over different kinds of complex yet practical traffic events. Moreover, we propose Eclipse, a novel Efficient glimpse network via dynamic inference, in order to achieve computation-efficient and reliable video reasoning. The experiments show that our method achieves superior performance while reducing the computation cost significantly. | https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_SUTD-TrafficQA_A_Question_Answering_Benchmark_and_an_Efficient_Network_for_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_SUTD-TrafficQA_A_Question_Answering_Benchmark_and_an_Efficient_Network_for_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_SUTD-TrafficQA_A_Question_Answering_Benchmark_and_an_Efficient_Network_for_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xu_SUTD-TrafficQA_A_Question_CVPR_2021_supplemental.pdf | null |
T2VLAD: Global-Local Sequence Alignment for Text-Video Retrieval | Xiaohan Wang, Linchao Zhu, Yi Yang | Text-video retrieval is a challenging task that aims to search relevant video contents based on natural language descriptions. The key to this problem is to measure text-video similarities in a joint embedding space. However, most existing methods only consider the global cross-modal similarity and overlook the local details. Some works incorporate the local comparisons through cross-modal local matching and reasoning. These complex operations introduce tremendous computation. In this paper, we design an efficient global-local alignment method. The multi-modal video sequences and text features are adaptively aggregated with a set of shared semantic centers. The local cross-modal similarities are computed between the video feature and text feature within the same center. This design enables the meticulous local comparison and reduces the computational cost of the interaction between each text-video pair. Moreover, a global alignment method is proposed to provide a global cross-modal measurement that is complementary to the local perspective. The global aggregated visual features also provide additional supervision, which is indispensable to the optimization of the learnable semantic centers. We achieve consistent improvements on three standard text-video retrieval benchmarks and outperform the state-of-the-art by a clear margin. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_T2VLAD_Global-Local_Sequence_Alignment_for_Text-Video_Retrieval_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.10054 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_T2VLAD_Global-Local_Sequence_Alignment_for_Text-Video_Retrieval_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_T2VLAD_Global-Local_Sequence_Alignment_for_Text-Video_Retrieval_CVPR_2021_paper.html | CVPR 2021 | null | null |
Privacy-Preserving Image Features via Adversarial Affine Subspace Embeddings | Mihai Dusmanu, Johannes L. Schonberger, Sudipta N. Sinha, Marc Pollefeys | Many computer vision systems require users to upload image features to the cloud for processing and storage. These features can be exploited to recover sensitive information about the scene or subjects, e.g., by reconstructing the appearance of the original image. To address this privacy concern, we propose a new privacy-preserving feature representation. The core idea of our work is to drop constraints from each feature descriptor by embedding it within an affine subspace containing the original feature as well as adversarial feature samples. Feature matching on the privacy-preserving representation is enabled based on the notion of subspace-to-subspace distance. We experimentally demonstrate the effectiveness of our method and its high practical relevance for the applications of visual localization and mapping as well as face authentication. Compared to the original features, our approach makes it significantly more difficult for an adversary to recover private information. | https://openaccess.thecvf.com/content/CVPR2021/papers/Dusmanu_Privacy-Preserving_Image_Features_via_Adversarial_Affine_Subspace_Embeddings_CVPR_2021_paper.pdf | http://arxiv.org/abs/2006.06634 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Dusmanu_Privacy-Preserving_Image_Features_via_Adversarial_Affine_Subspace_Embeddings_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Dusmanu_Privacy-Preserving_Image_Features_via_Adversarial_Affine_Subspace_Embeddings_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Dusmanu_Privacy-Preserving_Image_Features_CVPR_2021_supplemental.pdf | null |
StyleMeUp: Towards Style-Agnostic Sketch-Based Image Retrieval | Aneeshan Sain, Ayan Kumar Bhunia, Yongxin Yang, Tao Xiang, Yi-Zhe Song | Sketch-based image retrieval (SBIR) is a cross-modal matching problem which is typically solved by learning a joint embedding space where the semantic content shared between photo and sketch modalities are preserved. However, a fundamental challenge in SBIR has been largely ignored so far, that is, sketches are drawn by humans and considerable style variations exist amongst different users. An effective SBIR model needs to explicitly account for this style diversity, crucially, to generalise to unseen user styles. To this end, a novel style-agnostic SBIR model is proposed. Different from existing models, a cross-modal variational autoencoder (VAE) is employed to explicitly disentangle each sketch into a semantic content part shared with the corresponding photo, and a style part unique to the sketcher. Importantly, to make our model dynamically adaptable to any unseen user styles, we propose to meta-train our cross-modal VAE by adding two style-adaptive components: a set of feature transformation layers to its encoder and a regulariser to the disentangled semantic content latent code. With this meta-learning framework, our model can not only disentangle the cross-modal shared semantic content for SBIR, but can adapt the disentanglement to any unseen user style as well, making the SBIR model truly style-agnostic. Extensive experiments show that our style-agnostic model yields state-of-the-art performance for both category-level and instance-level SBIR. | https://openaccess.thecvf.com/content/CVPR2021/papers/Sain_StyleMeUp_Towards_Style-Agnostic_Sketch-Based_Image_Retrieval_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.15706 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Sain_StyleMeUp_Towards_Style-Agnostic_Sketch-Based_Image_Retrieval_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Sain_StyleMeUp_Towards_Style-Agnostic_Sketch-Based_Image_Retrieval_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Sain_StyleMeUp_Towards_Style-Agnostic_CVPR_2021_supplemental.pdf | null |
Embedding Transfer With Label Relaxation for Improved Metric Learning | Sungyeon Kim, Dongwon Kim, Minsu Cho, Suha Kwak | This paper presents a novel method for embedding transfer, a task of transferring knowledge of a learned embedding model to another. Our method exploits pairwise similarities between samples in the source embedding space as the knowledge, and transfers them through a loss used for learning target embedding models. To this end, we design a new loss called relaxed contrastive loss, which employs the pairwise similarities as relaxed labels for inter-sample relations. Our loss provides a rich supervisory signal beyond class equivalence, enables more important pairs to contribute more to training, and imposes no restriction on manifolds of target embedding spaces. Experiments on metric learning benchmarks demonstrate that our method largely improves performance, or reduces sizes and output dimensions of target models effectively. We further show that it can be also used to enhance quality of self-supervised representation and performance of classification models. In all the experiments, our method clearly outperforms existing embedding transfer techniques. | https://openaccess.thecvf.com/content/CVPR2021/papers/Kim_Embedding_Transfer_With_Label_Relaxation_for_Improved_Metric_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.14908 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_Embedding_Transfer_With_Label_Relaxation_for_Improved_Metric_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_Embedding_Transfer_With_Label_Relaxation_for_Improved_Metric_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kim_Embedding_Transfer_With_CVPR_2021_supplemental.pdf | null |
Beyond Static Features for Temporally Consistent 3D Human Pose and Shape From a Video | Hongsuk Choi, Gyeongsik Moon, Ju Yong Chang, Kyoung Mu Lee | Despite the recent success of single image-based 3D human pose and shape estimation methods, recovering temporally consistent and smooth 3D human motion from a video is still challenging. Several video-based methods have been proposed; however, they fail to resolve the single image-based methods' temporal inconsistency issue due to a strong dependency on a static feature of the current frame. In this regard, we present a temporally consistent mesh recovery system (TCMR). It effectively focuses on the past and future frames' temporal information without being dominated by the current static feature. Our TCMR significantly outperforms previous video-based methods in temporal consistency with better per-frame 3D pose and shape accuracy. We also release the codes. | https://openaccess.thecvf.com/content/CVPR2021/papers/Choi_Beyond_Static_Features_for_Temporally_Consistent_3D_Human_Pose_and_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.08627 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Choi_Beyond_Static_Features_for_Temporally_Consistent_3D_Human_Pose_and_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Choi_Beyond_Static_Features_for_Temporally_Consistent_3D_Human_Pose_and_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Choi_Beyond_Static_Features_CVPR_2021_supplemental.zip | null |
Layout-Guided Novel View Synthesis From a Single Indoor Panorama | Jiale Xu, Jia Zheng, Yanyu Xu, Rui Tang, Shenghua Gao | Existing view synthesis methods mainly focus on the perspective images and have shown promising results. However, due to the limited field-of-view of the pinhole camera, the performance quickly degrades when large camera movements are adopted. In this paper, we make the first attempt to generate novel views from a single indoor panorama and take the large camera translations into consideration. To tackle this challenging problem, we first use Convolutional Neural Networks (CNNs) to extract the deep features and estimate the depth map from the source-view image. Then, we leverage the room layout prior, a strong structural constraint of the indoor scene, to guide the generation of target views. More concretely, we estimate the room layout in the source view and transform it into the target viewpoint as guidance. Meanwhile, we also constrain the room layout of the generated target-view images to enforce geometric consistency. To validate the effectiveness of our method, we further build a large-scale photo-realistic dataset containing both small and large camera translations. The experimental results on our challenging dataset demonstrate that our method achieves state-of-the-art performance. The project page is at https://github.com/bluestyle97/PNVS. | https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_Layout-Guided_Novel_View_Synthesis_From_a_Single_Indoor_Panorama_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.17022 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Layout-Guided_Novel_View_Synthesis_From_a_Single_Indoor_Panorama_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Layout-Guided_Novel_View_Synthesis_From_a_Single_Indoor_Panorama_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xu_Layout-Guided_Novel_View_CVPR_2021_supplemental.pdf | null |
STMTrack: Template-Free Visual Tracking With Space-Time Memory Networks | Zhihong Fu, Qingjie Liu, Zehua Fu, Yunhong Wang | Boosting performance of the offline trained siamese trackers is getting harder nowadays since the fixed information of the template cropped from the first frame has been almost thoroughly mined, but they are poorly capable of resisting target appearance changes. Existing trackers with template updating mechanisms rely on time-consuming numerical optimization and complex hand-designed strategies to achieve competitive performance, hindering them from real-time tracking and practical applications. In this paper, we propose a novel tracking framework built on top of a space-time memory network that is competent to make full use of historical information related to the target for better adapting to appearance variations during tracking. Specifically, a novel memory mechanism is introduced, which stores the historical information of the target to guide the tracker to focus on the most informative regions in the current frame. Furthermore, the pixel-level similarity computation of the memory network enables our tracker to generate much more accurate bounding boxes of the target. Extensive experiments and comparisons with many competitive trackers on challenging large-scale benchmarks, OTB-2015, TrackingNet, GOT-10k, LaSOT, UAV123, and VOT2018, show that, without bells and whistles, our tracker outperforms all previous state-of-the-art real-time methods while running at 37 FPS. The code is available at https://github.com/fzh0917/STMTrack. | https://openaccess.thecvf.com/content/CVPR2021/papers/Fu_STMTrack_Template-Free_Visual_Tracking_With_Space-Time_Memory_Networks_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00324 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Fu_STMTrack_Template-Free_Visual_Tracking_With_Space-Time_Memory_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Fu_STMTrack_Template-Free_Visual_Tracking_With_Space-Time_Memory_Networks_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Fu_STMTrack_Template-Free_Visual_CVPR_2021_supplemental.pdf | null |
Reformulating HOI Detection As Adaptive Set Prediction | Mingfei Chen, Yue Liao, Si Liu, Zhiyuan Chen, Fei Wang, Chen Qian | Determining which image regions to concentrate is critical for Human-Object Interaction (HOI) detection. Conventional HOI detectors focus on either detected human and object pairs or pre-defined interaction locations, which limits learning of the effective features. In this paper, we reformulate HOI detection as an adaptive set prediction problem, with this novel formulation, we propose an Adaptive Set-based one-stage framework (AS-Net) with parallel instance and interaction branches. To attain this, we map a trainable interaction query set to an interaction prediction set with transformer. Each query adaptively aggregates the interaction-relevant features from global contexts through multi-head co-attention. Besides, the training process is supervised adaptively by matching each ground-truth with the interaction prediction. Furthermore, we design an effective instance-aware attention module to introduce instructive features from the instance branch into the interaction branch. Our method outperforms previous state-of-the-art methods without any extra human pose and language features on three challenging HOI detection datasets. Especially, we achieve over 31% relative improvement on a large scale HICO-DET dataset. Code is available at https://github.com/yoyomimi/AS-Net. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Reformulating_HOI_Detection_As_Adaptive_Set_Prediction_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.05983 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Reformulating_HOI_Detection_As_Adaptive_Set_Prediction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Reformulating_HOI_Detection_As_Adaptive_Set_Prediction_CVPR_2021_paper.html | CVPR 2021 | null | null |
Strengthen Learning Tolerance for Weakly Supervised Object Localization | Guangyu Guo, Junwei Han, Fang Wan, Dingwen Zhang | Weakly supervised object localization (WSOL) aims at learning to localize objects of interest by only using the image-level labels as the supervision. While numerous efforts have been made in this field, recent approaches still suffer from two challenges: one is the part domination issue while the other is the learning robustness issue. Specifically, the former makes the localizer prone to the local discriminative object regions rather than the desired whole object, and the latter makes the localizer over-sensitive to the variations of the input images so that one can hardly obtain localization results robust to the arbitrary visual stimulus. To solve these issues, we propose a novel framework to strengthen the learning tolerance, referred to as SLT-Net, for WSOL. Specifically, we consider two-fold learning tolerance strengthening mechanisms. One is the semantic tolerance strengthening mechanism, which allows the localizer to make mistakes for classifying similar semantics so that it will not concentrate too much on the discriminative local regions. The other is the visual stimuli tolerance strengthening mechanism, which enforces the localizer to be robust to different image transformations so that the prediction quality will not be sensitive to each specific input image. Finally, we implement comprehensive experimental comparisons on two widely-used datasets CUB and ILSVRC2012, which demonstrate the effectiveness of our proposed approach. | https://openaccess.thecvf.com/content/CVPR2021/papers/Guo_Strengthen_Learning_Tolerance_for_Weakly_Supervised_Object_Localization_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Guo_Strengthen_Learning_Tolerance_for_Weakly_Supervised_Object_Localization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Guo_Strengthen_Learning_Tolerance_for_Weakly_Supervised_Object_Localization_CVPR_2021_paper.html | CVPR 2021 | null | null |
Mesh Saliency: An Independent Perceptual Measure or a Derivative of Image Saliency? | Ran Song, Wei Zhang, Yitian Zhao, Yonghuai Liu, Paul L. Rosin | While mesh saliency aims to predict regional importance of 3D surfaces in agreement with human visual perception and is well researched in computer vision and graphics, latest work with eye-tracking experiments shows that state-of-the-art mesh saliency methods remain poor at predicting human fixations. Cues emerging prominently from these experiments suggest that mesh saliency might associate with the saliency of 2D natural images. This paper proposes a novel deep neural network for learning mesh saliency using image saliency ground truth to 1) investigate whether mesh saliency is an independent perceptual measure or just a derivative of image saliency and 2) provide a weakly supervised method for more accurately predicting mesh saliency. Through extensive experiments, we not only demonstrate that our method outperforms the current state-of-the-art mesh saliency method by 116% and 21% in terms of linear correlation coefficient and AUC respectively, but also reveal that mesh saliency is intrinsically related with both image saliency and object categorical information. Codes are available at https://github.com/rsong/MIMO-GAN. | https://openaccess.thecvf.com/content/CVPR2021/papers/Song_Mesh_Saliency_An_Independent_Perceptual_Measure_or_a_Derivative_of_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Song_Mesh_Saliency_An_Independent_Perceptual_Measure_or_a_Derivative_of_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Song_Mesh_Saliency_An_Independent_Perceptual_Measure_or_a_Derivative_of_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Song_Mesh_Saliency_An_CVPR_2021_supplemental.pdf | null |
Passive Inter-Photon Imaging | Atul Ingle, Trevor Seets, Mauro Buttafava, Shantanu Gupta, Alberto Tosi, Mohit Gupta, Andreas Velten | Digital camera pixels measure image intensities by converting incident light energy into an analog electrical current, and then digitizing it into a fixed-width binary representation. This direct measurement method, while conceptually simple, suffers from limited dynamic range and poor performance under extreme illumination --- electronic noise dominates under low illumination, and pixel full-well capacity results in saturation under bright illumination. We propose a novel intensity cue based on measuring inter-photon timing, defined as the time delay between detection of successive photons. Based on the statistics of inter-photon times measured by a time-resolved single-photon sensor, we develop theory and algorithms for a scene brightness estimator which works over extreme dynamic range; we experimentally demonstrate imaging scenes with a dynamic range of over ten million to one. The proposed techniques, aided by the emergence of single-photon sensors such as single-photon avalanche diodes (SPADs) with picosecond timing resolution, will have implications for a wide range of imaging applications: robotics, consumer photography, astronomy, microscopy and biomedical imaging. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ingle_Passive_Inter-Photon_Imaging_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00059 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ingle_Passive_Inter-Photon_Imaging_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ingle_Passive_Inter-Photon_Imaging_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ingle_Passive_Inter-Photon_Imaging_CVPR_2021_supplemental.pdf | null |
Domain Consensus Clustering for Universal Domain Adaptation | Guangrui Li, Guoliang Kang, Yi Zhu, Yunchao Wei, Yi Yang | In this paper, we investigate Universal Domain Adaptation (UniDA) problem, which aims to transfer the knowledge from source to target under unaligned label space. The main challenge of UniDA lies in how to separate common classes (i.e., classes shared across domains), from private classes (i.e., classes only exist in one domain). Previous works treat the private samples in the target as one generic class but ignore their intrinsic structure. Consequently, the resulting representations are not compact enough in the latent space and can be easily confused with common samples. To better exploit the intrinsic structure of the target domain, we propose Domain Consensus Clustering(DCC), which exploits the domain consensus knowledge to discover discriminative clusters on both common samples and private ones. Specifically, we draw the domain consensus knowledge from two aspects to facilitate the clustering and the private class discovery, i.e., the semantic-level consensus, which identifies the cycle-consistent clusters as the common classes, and the sample-level consensus, which utilizes the cross-domain classification agreement to determine the number of clusters and discover the private classes. Based on DCC, we are able to separate the private classes from the common ones, and differentiate the private classes themselves. Finally, we apply a class-aware alignment technique on identified common samples to minimize the distribution shift, and a prototypical regularizer to inspire discriminative target clusters. Experiments on four benchmarks demonstrate DCC significantly outperforms previous state-of-the-arts. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Domain_Consensus_Clustering_for_Universal_Domain_Adaptation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Domain_Consensus_Clustering_for_Universal_Domain_Adaptation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Domain_Consensus_Clustering_for_Universal_Domain_Adaptation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Domain_Consensus_Clustering_CVPR_2021_supplemental.pdf | null |
Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations | Umberto Michieli, Pietro Zanuttigh | Deep neural networks suffer from the major limitation of catastrophic forgetting old tasks when learning new ones. In this paper we focus on class incremental continual learning in semantic segmentation, where new categories are made available over time while previous training data is not retained. The proposed continual learning scheme shapes the latent space to reduce forgetting whilst improving the recognition of novel classes. Our framework is driven by three novel components which we also combine on top of existing techniques effortlessly. First, prototypes matching enforces latent space consistency on old classes, constraining the encoder to produce similar latent representation for previously seen classes in the subsequent steps. Second, features sparsification allows to make room in the latent space to accommodate novel classes. Finally, contrastive learning is employed to cluster features according to their semantics while tearing apart those of different classes. Extensive evaluation on the Pascal VOC2012 and ADE20K datasets demonstrates the effectiveness of our approach, significantly outperforming state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Michieli_Continual_Semantic_Segmentation_via_Repulsion-Attraction_of_Sparse_and_Disentangled_Latent_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.06342 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Michieli_Continual_Semantic_Segmentation_via_Repulsion-Attraction_of_Sparse_and_Disentangled_Latent_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Michieli_Continual_Semantic_Segmentation_via_Repulsion-Attraction_of_Sparse_and_Disentangled_Latent_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Michieli_Continual_Semantic_Segmentation_CVPR_2021_supplemental.pdf | null |
Audio-Driven Emotional Video Portraits | Xinya Ji, Hang Zhou, Kaisiyuan Wang, Wayne Wu, Chen Change Loy, Xun Cao, Feng Xu | Despite previous success in generating audio-driven talking heads, most of the previous studies focus on the correlation between speech content and the mouth shape. Facial emotion, which is one of the most important features on natural human faces, is always neglected in their methods. In this work, we present Emotional Video Portraits (EVP), a system for synthesizing high-quality video portraits with vivid emotional dynamics driven by audios. Specifically, we propose the Cross-Reconstructed Emotion Disentanglement technique to decompose speech into two decoupled spaces, i.e., a duration-independent emotion space and a duration dependent content space. With the disentangled features, dynamic 2D emotional facial landmarks can be deduced. Then we propose the Target-Adaptive Face Synthesis technique to generate the final high-quality video portraits, by bridging the gap between the deduced landmarks and the natural head poses of target videos. Extensive experiments demonstrate the effectiveness of our method both qualitatively and quantitatively. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ji_Audio-Driven_Emotional_Video_Portraits_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.07452 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ji_Audio-Driven_Emotional_Video_Portraits_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ji_Audio-Driven_Emotional_Video_Portraits_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ji_Audio-Driven_Emotional_Video_CVPR_2021_supplemental.zip | null |
Pareto Self-Supervised Training for Few-Shot Learning | Zhengyu Chen, Jixie Ge, Heshen Zhan, Siteng Huang, Donglin Wang | While few-shot learning (FSL) aims for rapid generalization to new concepts with little supervision, self-supervised learning (SSL) constructs supervisory signals directly computed from unlabeled data. Exploiting the complementarity of these two manners, few-shot auxiliary learning has recently drawn much attention to deal with few labeled data. Previous works benefit from sharing inductive bias between the main task (FSL) and auxiliary tasks (SSL), where the shared parameters of tasks are optimized by minimizing a linear combination of task losses. However, it is challenging to select a proper weight to balance tasks and reduce task conflict. To handle the problem as a whole, we propose a novel approach named as Pareto self-supervised training (PSST) for FSL. PSST explicitly decomposes the few-shot auxiliary problem into multiple constrained multi-objective subproblems with different trade-off preferences, and here a preference region in which the main task achieves the best performance is identified. Then, an effective preferred Pareto exploration is proposed to find a set of optimal solutions in such a preference region. Extensive experiments on several public benchmark datasets validate the effectiveness of our approach by achieving state-of-the-art performance. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Pareto_Self-Supervised_Training_for_Few-Shot_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.07841 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Pareto_Self-Supervised_Training_for_Few-Shot_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Pareto_Self-Supervised_Training_for_Few-Shot_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Pareto_Self-Supervised_Training_CVPR_2021_supplemental.zip | null |
EnD: Entangling and Disentangling Deep Representations for Bias Correction | Enzo Tartaglione, Carlo Alberto Barbano, Marco Grangetto | Artificial neural networks perform state-of-the-art in an ever-growing number of tasks, and nowadays they are used to solve an incredibly large variety of tasks. There are problems, like the presence of biases in the training data, which question the generalization capability of these models. In this work we propose EnD, a regularization strategy whose aim is to prevent deep models from learning unwanted biases. In particular, we insert an ""information bottleneck"" at a certain point of the deep neural network, where we disentangle the information about the bias, still letting the useful information for the training task forward-propagating in the rest of the model. One big advantage of EnD is that it does not require additional training complexity (like decoders or extra layers in the model), since it is a regularizer directly applied on the trained model. Our experiments show that EnD effectively improves the generalization on unbiased test sets, and it can be effectively applied on real-case scenarios, like removing hidden biases in the COVID-19 detection from radiographic images. | https://openaccess.thecvf.com/content/CVPR2021/papers/Tartaglione_EnD_Entangling_and_Disentangling_Deep_Representations_for_Bias_Correction_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.02023 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Tartaglione_EnD_Entangling_and_Disentangling_Deep_Representations_for_Bias_Correction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Tartaglione_EnD_Entangling_and_Disentangling_Deep_Representations_for_Bias_Correction_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tartaglione_EnD_Entangling_and_CVPR_2021_supplemental.pdf | null |
Recorrupted-to-Recorrupted: Unsupervised Deep Learning for Image Denoising | Tongyao Pang, Huan Zheng, Yuhui Quan, Hui Ji | Deep denoiser, the deep network for denoising, has been the focus of the recent development on image denoising. In the last few years, there is an increasing interest in developing unsupervised deep denoisers which only call unorganized noisy images without ground truth for training. Nevertheless, the performance of these unsupervised deep denoisers is not competitive to their supervised counterparts. Aiming at developing a more powerful unsupervised deep denoiser, this paper proposed a data augmentation technique, called recorrupted-to-recorrupted (R2R), to address the overfitting caused by the absence of truth images. For each noisy image, we showed that the cost function defined on the noisy/noisy image pairs constructed by the R2R method is statistically equivalent to its supervised counterpart defined on the noisy/truth image pairs. Extensive experiments showed that the proposed R2R method noticeably outperformed existing unsupervised deep denoisers, and is competitive to representative supervised deep denoisers. | https://openaccess.thecvf.com/content/CVPR2021/papers/Pang_Recorrupted-to-Recorrupted_Unsupervised_Deep_Learning_for_Image_Denoising_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Pang_Recorrupted-to-Recorrupted_Unsupervised_Deep_Learning_for_Image_Denoising_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Pang_Recorrupted-to-Recorrupted_Unsupervised_Deep_Learning_for_Image_Denoising_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Pang_Recorrupted-to-Recorrupted_Unsupervised_Deep_CVPR_2021_supplemental.pdf | null |
Reconsidering Representation Alignment for Multi-View Clustering | Daniel J. Trosten, Sigurd Lokse, Robert Jenssen, Michael Kampffmeyer | Aligning distributions of view representations is a core component of today's state of the art models for deep multi-view clustering. However, we identify several drawbacks with naively aligning representation distributions. We demonstrate that these drawbacks both lead to less separable clusters in the representation space, and inhibit the model's ability to prioritize views. Based on these observations, we develop a simple baseline model for deep multi-view clustering. Our baseline model avoids representation alignment altogether, while performing similar to, or better than, the current state of the art. We also expand our baseline model by adding a contrastive learning component. This introduces a selective alignment procedure that preserves the model's ability to prioritize views. Our experiments show that the contrastive learning component enhances the baseline model, improving on the current state of the art by a large margin on several datasets. | https://openaccess.thecvf.com/content/CVPR2021/papers/Trosten_Reconsidering_Representation_Alignment_for_Multi-View_Clustering_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.07738 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Trosten_Reconsidering_Representation_Alignment_for_Multi-View_Clustering_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Trosten_Reconsidering_Representation_Alignment_for_Multi-View_Clustering_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Trosten_Reconsidering_Representation_Alignment_CVPR_2021_supplemental.pdf | null |
Probabilistic Embeddings for Cross-Modal Retrieval | Sanghyuk Chun, Seong Joon Oh, Rafael Sampaio de Rezende, Yannis Kalantidis, Diane Larlus | Cross-modal retrieval methods build a common representation space for samples from multiple modalities, typically from the vision and the language domains. For images and their captions, the multiplicity of the correspondences makes the task particularly challenging. Given an image (respectively a caption), there are multiple captions (respectively images) that equally make sense. In this paper, we argue that deterministic functions are not sufficiently powerful to capture such one-to-many correspondences. Instead, we propose to use Probabilistic Cross-Modal Embedding (PCME), where samples from the different modalities are represented as probabilistic distributions in the common embedding space. Since common benchmarks such as COCO suffer from non-exhaustive annotations for cross-modal matches, we propose to additionally evaluate retrieval on the CUB dataset, a smaller yet clean database where all possible image-caption pairs are annotated. We extensively ablate PCME and demonstrate that it not only improves the retrieval performance over its deterministic counterpart but also provides uncertainty estimates that render the embeddings more interpretable. Code is available at https://github.com/naver-ai/pcme. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chun_Probabilistic_Embeddings_for_Cross-Modal_Retrieval_CVPR_2021_paper.pdf | http://arxiv.org/abs/2101.05068 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chun_Probabilistic_Embeddings_for_Cross-Modal_Retrieval_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chun_Probabilistic_Embeddings_for_Cross-Modal_Retrieval_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chun_Probabilistic_Embeddings_for_CVPR_2021_supplemental.pdf | null |
Cloud2Curve: Generation and Vectorization of Parametric Sketches | Ayan Das, Yongxin Yang, Timothy M. Hospedales, Tao Xiang, Yi-Zhe Song | Analysis of human sketches in deep learning has advanced immensely through the use of waypoint-sequences rather than raster-graphic representations. We further aim to model sketches as a sequence of low-dimensional parametric curves. To this end, we propose an inverse graphics framework capable of approximating a raster or waypoint based stroke encoded as a point-cloud with a variable-degree Bezier curve. Building on this module, we present Cloud2Curve, a generative model for scalable high-resolution vector sketches that can be trained end-to-end using point-cloud data alone. As a consequence, our model is also capable of deterministic vectorization which can map novel raster or waypoint based sketches to their corresponding high-resolution scalable Bezier equivalent. We evaluate the generation and vectorization capabilities of our model on Quick, Draw! and K-MNIST datasets. | https://openaccess.thecvf.com/content/CVPR2021/papers/Das_Cloud2Curve_Generation_and_Vectorization_of_Parametric_Sketches_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.15536 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Das_Cloud2Curve_Generation_and_Vectorization_of_Parametric_Sketches_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Das_Cloud2Curve_Generation_and_Vectorization_of_Parametric_Sketches_CVPR_2021_paper.html | CVPR 2021 | null | null |
TransFill: Reference-Guided Image Inpainting by Merging Multiple Color and Spatial Transformations | Yuqian Zhou, Connelly Barnes, Eli Shechtman, Sohrab Amirghodsi | Image inpainting is the task of plausibly restoring missing pixels within a hole region that is to be removed from a target image. Most existing technologies exploit patch similarities within the image, or leverage large-scale training data to fill the hole using learned semantic and texture information. However, due to the ill-posed nature of the inpainting task, such methods struggle to complete larger holes containing complicated scenes. In this paper, we propose TransFill, a multi-homography transformed fusion method to fill the hole by referring to another source image that shares scene contents with the target image. We first align the source image to the target image by estimating multiple homographies guided by different depth levels. We then learn to adjust the color and apply a pixel-level warping to each homography-warped source image to make it more consistent with the target. Finally, a pixel-level fusion module is learned to selectively merge the different proposals. Our method achieves state-of-the-art performance on pairs of images across a variety of wide baselines and color differences, and generalizes to user-provided image pairs. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_TransFill_Reference-Guided_Image_Inpainting_by_Merging_Multiple_Color_and_Spatial_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.15982 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_TransFill_Reference-Guided_Image_Inpainting_by_Merging_Multiple_Color_and_Spatial_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_TransFill_Reference-Guided_Image_Inpainting_by_Merging_Multiple_Color_and_Spatial_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhou_TransFill_Reference-Guided_Image_CVPR_2021_supplemental.pdf | null |
On Focal Loss for Class-Posterior Probability Estimation: A Theoretical Perspective | Nontawat Charoenphakdee, Jayakorn Vongkulbhisal, Nuttapong Chairatanakul, Masashi Sugiyama | The focal loss has demonstrated its effectiveness in many real-world applications such as object detection and image classification, but its theoretical understanding has been limited so far. In this paper, we first prove that the focal loss is classification-calibrated, i.e., its minimizer surely yields the Bayes-optimal classifier and thus the use of the focal loss in classification can be theoretically justified. However, we also prove a negative fact that the focal loss is not strictly proper, i.e., the confidence score of the classifier obtained by focal loss minimization does not match the true class-posterior probability. This may cause the trained classifier to give an unreliable confidence score, which can be harmful in critical applications. To mitigate this problem, we prove that there exists a particular closed-form transformation that can recover the true class-posterior probability from the outputs of the focal risk minimizer. Our experiments show that our proposed transformation successfully improves the quality of class-posterior probability estimation and improves the calibration of the trained classifier, while preserving the same prediction accuracy. | https://openaccess.thecvf.com/content/CVPR2021/papers/Charoenphakdee_On_Focal_Loss_for_Class-Posterior_Probability_Estimation_A_Theoretical_Perspective_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.09172 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Charoenphakdee_On_Focal_Loss_for_Class-Posterior_Probability_Estimation_A_Theoretical_Perspective_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Charoenphakdee_On_Focal_Loss_for_Class-Posterior_Probability_Estimation_A_Theoretical_Perspective_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Charoenphakdee_On_Focal_Loss_CVPR_2021_supplemental.pdf | null |
VIP-DeepLab: Learning Visual Perception With Depth-Aware Video Panoptic Segmentation | Siyuan Qiao, Yukun Zhu, Hartwig Adam, Alan Yuille, Liang-Chieh Chen | In this paper, we present ViP-DeepLab, a unified model attempting to tackle the long-standing and challenging inverse projection problem in vision, which we model as restoring the point clouds from perspective image sequences while providing each point with instance-level semantic interpretations. Solving this problem requires the vision models to predict the spatial location, semantic class, and temporally consistent instance label for each 3D point. ViP-DeepLab approaches it by jointly performing monocular depth estimation and video panoptic segmentation. We name this joint task as Depth-aware Video Panoptic Segmentation, and propose a new evaluation metric along with two derived datasets for it, which will be made available to the public. On the individual sub-tasks, ViP-DeepLab also achieves state-of-the-art results, outperforming previous methods by 5.1% VPQ on Cityscapes-VPS, ranking 1st on the KITTI monocular depth estimation benchmark, and 1st on KITTI MOTS pedestrian. The datasets and the evaluation codes are made publicly available. | https://openaccess.thecvf.com/content/CVPR2021/papers/Qiao_VIP-DeepLab_Learning_Visual_Perception_With_Depth-Aware_Video_Panoptic_Segmentation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Qiao_VIP-DeepLab_Learning_Visual_Perception_With_Depth-Aware_Video_Panoptic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Qiao_VIP-DeepLab_Learning_Visual_Perception_With_Depth-Aware_Video_Panoptic_Segmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Qiao_VIP-DeepLab_Learning_Visual_CVPR_2021_supplemental.pdf | null |
Sequence-to-Sequence Contrastive Learning for Text Recognition | Aviad Aberdam, Ron Litman, Shahar Tsiper, Oron Anschel, Ron Slossberg, Shai Mazor, R. Manmatha, Pietro Perona | We propose a framework for sequence-to-sequence contrastive learning (SeqCLR) of visual representations, which we apply to text recognition. To account for the sequence-to-sequence structure, each feature map is divided into different instances over which the contrastive loss is computed. This operation enables us to contrast in a sub-word level, where from each image we extract several positive pairs and multiple negative examples. To yield effective visual representations for text recognition, we further suggest novel augmentation heuristics, different encoder architectures and custom projection heads. Experiments on handwritten text and on scene text show that when a text decoder is trained on the learned representations, our method outperforms non-sequential contrastive methods. In addition, when the amount of supervision is reduced, SeqCLR significantly improves performance compared with supervised training, and when fine-tuned with 100% of the labels, our method achieves state-of-the-art results on standard handwritten text recognition benchmarks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Aberdam_Sequence-to-Sequence_Contrastive_Learning_for_Text_Recognition_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.10873 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Aberdam_Sequence-to-Sequence_Contrastive_Learning_for_Text_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Aberdam_Sequence-to-Sequence_Contrastive_Learning_for_Text_Recognition_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Aberdam_Sequence-to-Sequence_Contrastive_Learning_CVPR_2021_supplemental.pdf | null |
Prototype-Supervised Adversarial Network for Targeted Attack of Deep Hashing | Xunguang Wang, Zheng Zhang, Baoyuan Wu, Fumin Shen, Guangming Lu | Due to its powerful capability of representation learning and high-efficiency computation, deep hashing has made significant progress in large-scale image retrieval. However, deep hashing networks are vulnerable to adversarial examples, which is a practical secure problem but seldom studied in hashing-based retrieval field. In this paper, we propose a novel prototype-supervised adversarial network (ProS-GAN), which formulates a flexible generative architecture for efficient and effective targeted hashing attack. To the best of our knowledge, this is the first generation-based method to attack deep hashing networks. Generally, our proposed framework consists of three parts, i.e., a PrototypeNet, a generator and a discriminator. Specifically, the designed PrototypeNet embeds the target label into the semantic representation and learns the prototype code as the category-level representative of the target label. Moreover, the semantic representation and the original image are jointly fed into the generator for flexible targeted attack. Particularly, the prototype code is adopted to supervise the generator to construct the targeted adversarial example by minimizing the Hamming distance between the hash code of the adversarial example and the prototype code. Furthermore, the generator is against the discriminator to simultaneously encourage the adversarial examples visually realistic and the semantic representation informative. Extensive experiments verify that the proposed framework can efficiently produce adversarial examples with better targeted attack performance and transferability over state-of-the-art targeted attack methods of deep hashing. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Prototype-Supervised_Adversarial_Network_for_Targeted_Attack_of_Deep_Hashing_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.07553 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Prototype-Supervised_Adversarial_Network_for_Targeted_Attack_of_Deep_Hashing_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Prototype-Supervised_Adversarial_Network_for_Targeted_Attack_of_Deep_Hashing_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Prototype-Supervised_Adversarial_Network_CVPR_2021_supplemental.pdf | null |
PD-GAN: Probabilistic Diverse GAN for Image Inpainting | Hongyu Liu, Ziyu Wan, Wei Huang, Yibing Song, Xintong Han, Jing Liao | We propose PD-GAN, a probabilistic diverse GAN forimage inpainting. Given an input image with arbitrary holeregions, PD-GAN produces multiple inpainting results withdiverse and visually realistic content. Our PD-GAN is builtupon a vanilla GAN which generates images based on random noise. During image generation, we modulate deepfeatures of input random noise from coarse-to-fine by injecting an initially restored image and the hole regions inmultiple scales. We argue that during hole filling, the pixels near the hole boundary should be more deterministic(i.e., with higher probability trusting the context and initially restored image to create natural inpainting boundary), while those pixels lie in the center of the hole shouldenjoy more degrees of freedom (i.e., more likely to dependon the random noise for enhancing diversity). To this end, we propose spatially probabilistic diversity normalization(SPDNorm) inside the modulation to model the probabilityof generating a pixel conditioned on the context information. SPDNorm dynamically balances the realism and diversity inside the hole region, making the generated content more diverse towards the hole center and resembleneighboring image content more towards the hole boundary. Meanwhile, we propose a perceptual diversity loss tofurther empower PD-GAN for diverse content generation. Experiments on benchmark datasets including CelebA-HQ, Places2 and Paris Street View indicate that PD-GAN is ef-fective for diverse and visually realistic image restoration. | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_PD-GAN_Probabilistic_Diverse_GAN_for_Image_Inpainting_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_PD-GAN_Probabilistic_Diverse_GAN_for_Image_Inpainting_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_PD-GAN_Probabilistic_Diverse_GAN_for_Image_Inpainting_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_PD-GAN_Probabilistic_Diverse_CVPR_2021_supplemental.pdf | null |
Simple Copy-Paste Is a Strong Data Augmentation Method for Instance Segmentation | Golnaz Ghiasi, Yin Cui, Aravind Srinivas, Rui Qian, Tsung-Yi Lin, Ekin D. Cubuk, Quoc V. Le, Barret Zoph | Building instance segmentation models that are data-efficient and can handle rare object categories is an important challenge in computer vision. Leveraging data augmentations is a promising direction towards addressing this challenge. Here, we perform a systematic study of the Copy-Paste augmentation (e.g., [13, 12]) for instance segmentation where we randomly paste objects onto an image. Prior studies on Copy-Paste relied on modeling the surrounding visual context for pasting the objects. However, we find that the simple mechanism of pasting objects randomly is good enough and can provide solid gains on top of strong baselines. Furthermore, we show Copy-Paste is additive with semi-supervised methods that leverage extra data through pseudo labeling (eg. self-training). On COCO instance segmentation, we achieve 49.1 mask AP and 57.3 box AP, an improvement of +0.6 mask AP and +1.5 box AP over the previous state-of-the-art. We further demonstrate that Copy-Paste can lead to significant improvements on the LVIS benchmark. Our baseline model outperforms the LVIS 2020 Challenge winning entry by +3.6 mask AP on rare categories. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ghiasi_Simple_Copy-Paste_Is_a_Strong_Data_Augmentation_Method_for_Instance_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.07177 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ghiasi_Simple_Copy-Paste_Is_a_Strong_Data_Augmentation_Method_for_Instance_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ghiasi_Simple_Copy-Paste_Is_a_Strong_Data_Augmentation_Method_for_Instance_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ghiasi_Simple_Copy-Paste_Is_CVPR_2021_supplemental.pdf | null |
Learning Deep Latent Variable Models by Short-Run MCMC Inference With Optimal Transport Correction | Dongsheng An, Jianwen Xie, Ping Li | Learning latent variable models with deep top-down architectures typically requires inferring the latent variables for each training example based on the posterior distribution of these latent variables. The inference step typically relies on either time-consuming long run Markov chain Monte Caro (MCMC) or a separate inference model for variational learning. In this paper, we propose to use short run MCMC, such as Langevin dynamics, as an approximate inference engine, where the bias existing in the output distribution of the short run Langevin dynamics is corrected by optimal transport, which aims at minimizing the Wasserstein distance between the biased distribution produced by the finite step Langevin dynamics and the prior distribution. Our experiments show that the proposed strategy outperforms the variational auto-encoder (VAE) and alternating back-propagation algorithm (ABP) in terms of reconstruction error and synthesis quality. | https://openaccess.thecvf.com/content/CVPR2021/papers/An_Learning_Deep_Latent_Variable_Models_by_Short-Run_MCMC_Inference_With_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/An_Learning_Deep_Latent_Variable_Models_by_Short-Run_MCMC_Inference_With_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/An_Learning_Deep_Latent_Variable_Models_by_Short-Run_MCMC_Inference_With_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/An_Learning_Deep_Latent_CVPR_2021_supplemental.pdf | null |
MobileDets: Searching for Object Detection Architectures for Mobile Accelerators | Yunyang Xiong, Hanxiao Liu, Suyog Gupta, Berkin Akin, Gabriel Bender, Yongzhe Wang, Pieter-Jan Kindermans, Mingxing Tan, Vikas Singh, Bo Chen | Inverted bottleneck layers, which are built upon depthwise convolutions, have been the predominant building blocks in state-of-the-art object detection models on mobile devices. In this work, we investigate the optimality of this design pattern over a broad range of mobile accelerators by revisiting the usefulness of regular convolutions. We discover that regular convolutions are a potent component to boost the latency-accuracy trade-off for object detection on accelerators, provided that they are placed strategically in the network via neural architecture search. By incorporating regular convolutions in the search space and directly optimizing the network architectures for object detection, we obtain a family of object detection models, MobileDets, that achieve state-of-the-art results across mobile accelerators. On the COCO object detection task, MobileDets outperform MobileNetV3+SSDLite by 1.7 mAP at comparable mobile CPU inference latencies. MobileDets also outperform MobileNetV2+SSDLite by 1.9 mAP on mobile CPUs, 3.7 mAP on Google EdgeTPU, 3.4 mAP on Qualcomm Hexagon DSP and 2.7 mAP on Nvidia Jetson GPU without increasing latency. Moreover, MobileDets are comparable with the state-of-the-art MnasFPN on mobile CPUs even without using the feature pyramid, and achieve better mAP scores on both EdgeTPUs and DSPs with up to 2x speedup. Code and models are available in the TensorFlow Object Detection API: https://github.com/tensorflow/models/tree/master/research/object_detection. | https://openaccess.thecvf.com/content/CVPR2021/papers/Xiong_MobileDets_Searching_for_Object_Detection_Architectures_for_Mobile_Accelerators_CVPR_2021_paper.pdf | http://arxiv.org/abs/2004.14525 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xiong_MobileDets_Searching_for_Object_Detection_Architectures_for_Mobile_Accelerators_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xiong_MobileDets_Searching_for_Object_Detection_Architectures_for_Mobile_Accelerators_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xiong_MobileDets_Searching_for_CVPR_2021_supplemental.pdf | null |
Self-Supervised Geometric Perception | Heng Yang, Wei Dong, Luca Carlone, Vladlen Koltun | We present self-supervised geometric perception (SGP), the first general framework to learn a feature descriptor for correspondence matching without any ground-truth geometric model labels (e.g., camera poses, rigid transformations). Our first contribution is to formulate geometric perception as an optimization problem that jointly optimizes the feature descriptor and the geometric models given a large corpus of visual measurements (e.g., images, point clouds). Under this optimization formulation, we show that two important streams of research in vision, namely robust model fitting and deep feature learning, correspond to optimizing one block of the unknown variables while fixing the other block. This analysis naturally leads to our second contribution - the SGP algorithm that performs alternating minimization to solve the joint optimization. SGP iteratively executes two meta-algorithms: a teacher that performs robust model fitting given learned features to generate geometric pseudo-labels, and a student that performs deep feature learning under noisy supervision of the pseudo-labels. As a third contribution, we apply SGP to two perception problems on large-scale real datasets, namely relative camera pose estimation on MegaDepth and point cloud registration on 3DMatch. We demonstrate that SGP achieves state-of-the-art performance that is on-par or superior to the supervised oracles trained using ground-truth labels. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Self-Supervised_Geometric_Perception_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.03114 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Self-Supervised_Geometric_Perception_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Self-Supervised_Geometric_Perception_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_Self-Supervised_Geometric_Perception_CVPR_2021_supplemental.pdf | null |
CutPaste: Self-Supervised Learning for Anomaly Detection and Localization | Chun-Liang Li, Kihyuk Sohn, Jinsung Yoon, Tomas Pfister | We aim at constructing a high performance model for defect detection that detects unknown anomalous patterns of an image without anomalous data. To this end, we propose a two-stage framework for building anomaly detectors using normal training data only. We first learn self-supervised deep representations and then build a generative one-class classifier on learned representations. We learn representations by classifying normal data from the CutPaste, a simple data augmentation strategy that cuts an image patch and pastes at a random location of a large image. Our empirical study on MVTec anomaly detection dataset demonstrates the proposed algorithm is general to be able to detect various types of real-world defects. We bring the improvement upon previous arts by 3.1 AUCs when learning representations from scratch. By transfer learning on pretrained representations on ImageNet, we achieve a new state-of-the-art 96.6 AUC. Lastly, we extend the framework to learn and extract representations from patches to allow localizing defective areas without annotations during training. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_CutPaste_Self-Supervised_Learning_for_Anomaly_Detection_and_Localization_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.04015 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_CutPaste_Self-Supervised_Learning_for_Anomaly_Detection_and_Localization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_CutPaste_Self-Supervised_Learning_for_Anomaly_Detection_and_Localization_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_CutPaste_Self-Supervised_Learning_CVPR_2021_supplemental.pdf | null |
Open World Compositional Zero-Shot Learning | Massimiliano Mancini, Muhammad Ferjad Naeem, Yongqin Xian, Zeynep Akata | Compositional Zero-Shot learning (CZSL) requires to recognize state-object compositions unseen during training. In this work, instead of assuming prior knowledge about the unseen compositions, we operate in the open world setting, where the search space includes a large number of unseen compositions some of which might be unfeasible. In this setting, we start from the cosine similarity between visual features and compositional embeddings. After estimating the feasibility score of each composition, we use these scores to either directly mask the output space or as a margin for the cosine similarity between visual features and compositional embeddings during training. Our experiments on two standard CZSL benchmarks show that all the methods suffer severe performance degradation when applied in the open world setting. While our simple CZSL model achieves state-of-the-art performances in the closed world scenario, our feasibility scores boost the performance of our approach in the open world setting, clearly outperforming the previous state of the art. Code is available at: https://github.com/ExplainableML/czsl. | https://openaccess.thecvf.com/content/CVPR2021/papers/Mancini_Open_World_Compositional_Zero-Shot_Learning_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Mancini_Open_World_Compositional_Zero-Shot_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Mancini_Open_World_Compositional_Zero-Shot_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Mancini_Open_World_Compositional_CVPR_2021_supplemental.pdf | null |
Bi-GCN: Binary Graph Convolutional Network | Junfu Wang, Yunhong Wang, Zhen Yang, Liang Yang, Yuanfang Guo | Graph Neural Networks (GNNs) have achieved tremendous success in graph representation learning. Unfortunately, current GNNs usually rely on loading the entire attributed graph into network for processing. This implicit assumption may not be satisfied with limited memory resources, especially when the attributed graph is large. In this paper, we pioneer to propose a Binary Graph Convolutional Network (Bi-GCN), which binarizes both the network parameters and input node features. Besides, the original matrix multiplications are revised to binary operations for accelerations. According to the theoretical analysis, our Bi-GCN can reduce the memory consumption by an average of 30x for both the network parameters and input data, and accelerate the inference speed by an average of 47x, on the citation networks. Meanwhile, we also design a new gradient approximation based back-propagation method to train our Bi-GCN well. Extensive experiments have demonstrated that our Bi-GCN can give a comparable performance compared to the full-precision baselines. Besides, our binarization approach can be easily applied to other GNNs, which has been verified in the experiments. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Bi-GCN_Binary_Graph_Convolutional_Network_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Bi-GCN_Binary_Graph_Convolutional_Network_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Bi-GCN_Binary_Graph_Convolutional_Network_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Bi-GCN_Binary_Graph_CVPR_2021_supplemental.pdf | null |
Complementary Relation Contrastive Distillation | Jinguo Zhu, Shixiang Tang, Dapeng Chen, Shijie Yu, Yakun Liu, Mingzhe Rong, Aijun Yang, Xiaohua Wang | Knowledge distillation aims to transfer representation ability from a teacher model to a student model. Previous approaches focus on either individual representation distillation or inter-sample similarity preservation. While we argue that the inter-sample relation conveys abundant information and needs to be distilled in a more effective way. In this paper, we propose a novel knowledge distillation method, namely Complementary Relation Contrastive Distillation (CRCD), to transfer the structural knowledge from the teacher to the student. Specifically, we estimate the mutual relation in an anchor-based way and distill the anchor-student relation under the supervision of its corresponding anchor-teacher relation. To make it more robust, mutual relations are modeled by two complementary elements: the feature and its gradient. Furthermore, the low bound of mutual information between the anchor-teacher relation distribution and the anchor-student relation distribution is maximized via relation contrastive loss, which can distill both the sample representation and the inter-sample relations. Experiments on different benchmarks demonstrate the effectiveness of our proposed CRCD. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhu_Complementary_Relation_Contrastive_Distillation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16367 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_Complementary_Relation_Contrastive_Distillation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_Complementary_Relation_Contrastive_Distillation_CVPR_2021_paper.html | CVPR 2021 | null | null |
UnrealPerson: An Adaptive Pipeline Towards Costless Person Re-Identification | Tianyu Zhang, Lingxi Xie, Longhui Wei, Zijie Zhuang, Yongfei Zhang, Bo Li, Qi Tian | The main difficulty of person re-identification (ReID) lies in collecting annotated data and transferring the model across different domains. This paper presents UnrealPerson, a novel pipeline that makes full use of unreal image data to decrease the costs in both the training and deployment stages. Its fundamental part is a system that can generate synthesized images of high-quality and from controllable distributions. Instance-level annotation goes with the synthesized data and is almost free. We point out some details in image synthesis that largely impact the data quality. With 3,000 IDs and 120,000 instances, our method achieves a 38.5% rank-1 accuracy when being directly transferred to MSMT17. It almost doubles the former record using synthesized data and even surpasses previous direct transfer records using real data. This offers a good basis for unsupervised domain adaption, where our pre-trained model is easily plugged into the state-of-the-art algorithms towards higher accuracy. In addition, the data distribution can be flexibly adjusted to fit some corner ReID scenarios, which widens the application of our pipeline. We publish our data synthesis toolkit and synthesized data in https://github.com/FlyHighest/UnrealPerson. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_UnrealPerson_An_Adaptive_Pipeline_Towards_Costless_Person_Re-Identification_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.04268 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_UnrealPerson_An_Adaptive_Pipeline_Towards_Costless_Person_Re-Identification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_UnrealPerson_An_Adaptive_Pipeline_Towards_Costless_Person_Re-Identification_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_UnrealPerson_An_Adaptive_CVPR_2021_supplemental.pdf | null |
Iterative Filter Adaptive Network for Single Image Defocus Deblurring | Junyong Lee, Hyeongseok Son, Jaesung Rim, Sunghyun Cho, Seungyong Lee | We propose a novel end-to-end learning-based approach for single image defocus deblurring. The proposed approach is equipped with a novel Iterative Filter Adaptive Network (IFAN) that is specifically designed to handle spatially-varying and large defocus blur. For adaptively handling spatially-varying blur, IFAN predicts pixel-wise deblurring filters, which are applied to defocused features of an input image to generate deblurred features. For effectively managing large blur, IFAN models deblurring filters as stacks of small-sized separable filters. Predicted separable deblurring filters are applied to defocused features using a novel Iterative Adaptive Convolution (IAC) layer. We also propose a training scheme based on defocus disparity estimation and reblurring, which significantly boosts the deblurring quality. We demonstrate that our method achieves state-of-the-art performance both quantitatively and qualitatively on real-world images. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lee_Iterative_Filter_Adaptive_Network_for_Single_Image_Defocus_Deblurring_CVPR_2021_paper.pdf | https://arxiv.org/abs/2108.13610 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_Iterative_Filter_Adaptive_Network_for_Single_Image_Defocus_Deblurring_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_Iterative_Filter_Adaptive_Network_for_Single_Image_Defocus_Deblurring_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lee_Iterative_Filter_Adaptive_CVPR_2021_supplemental.pdf | https://openaccess.thecvf.com |
UPFlow: Upsampling Pyramid for Unsupervised Optical Flow Learning | Kunming Luo, Chuan Wang, Shuaicheng Liu, Haoqiang Fan, Jue Wang, Jian Sun | We present an unsupervised learning approach for optical flow estimation by improving the upsampling and learning of pyramid network. We design a self-guided upsample module to tackle the interpolation blur problem caused by bilinear upsampling between pyramid levels. Moreover, we propose a pyramid distillation loss to add supervision for intermediate levels via distilling the finest flow as pseudo labels. By integrating these two components together, our method achieves the best performance for unsupervised optical flow learning on multiple leading benchmarks, including MPI-SIntel, KITTI 2012 and KITTI 2015. In particular, we achieve EPE=1.4 on KITTI 2012 and F1=9.38% on KITTI 2015, which outperform the previous state-of-the-art methods by 22.2% and 15.7%, respectively. | https://openaccess.thecvf.com/content/CVPR2021/papers/Luo_UPFlow_Upsampling_Pyramid_for_Unsupervised_Optical_Flow_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.00212 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Luo_UPFlow_Upsampling_Pyramid_for_Unsupervised_Optical_Flow_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Luo_UPFlow_Upsampling_Pyramid_for_Unsupervised_Optical_Flow_Learning_CVPR_2021_paper.html | CVPR 2021 | null | null |
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