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import gradio as gr |
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perception_papers = [ |
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{ |
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"title": "CoSDH: Communication-Efficient Collaborative Perception", |
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"venue": "CVPR 2025", |
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"description": "Novel approach for efficient collaborative perception using supply-demand awareness.", |
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"link": "https://arxiv.org/abs/2503.03430" |
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}, |
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{ |
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"title": "V2X-R: Cooperative LiDAR-4D Radar Fusion", |
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"venue": "CVPR 2025", |
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"description": "Cooperative fusion of LiDAR and 4D radar sensors for enhanced 3D object detection.", |
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"link": "https://arxiv.org/abs/2411.08402" |
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}, |
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{ |
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"title": "Where2comm: Efficient Collaborative Perception via Spatial Confidence Maps", |
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"venue": "NeurIPS 2022", |
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"description": "Groundbreaking work on efficient collaborative perception using spatial confidence maps.", |
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"link": "https://openreview.net/forum?id=dLL4KXzKUpS" |
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} |
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] |
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datasets_data = [ |
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["DAIR-V2X", "2022", "Real-world", "V2I", "71K frames", "3D boxes, Infrastructure"], |
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["V2V4Real", "2023", "Real-world", "V2V", "20K frames", "Real V2V, Highway"], |
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["OPV2V", "2022", "Simulation", "V2V", "Large-scale", "CARLA, Multi-agent"], |
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["V2X-Sim", "2021", "Simulation", "Multi", "Scalable", "Multi-agent, Collaborative"] |
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] |
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def create_paper_card(paper): |
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return f""" |
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<div style="border: 1px solid #ddd; border-radius: 10px; padding: 20px; margin: 10px 0; background: white;"> |
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<div style="background: #667eea; color: white; padding: 5px 10px; border-radius: 15px; display: inline-block; font-size: 0.8em; margin-bottom: 10px;"> |
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{paper['venue']} |
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</div> |
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<h3 style="color: #333; margin: 10px 0;">{paper['title']}</h3> |
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<p style="color: #666; line-height: 1.5; margin-bottom: 15px;">{paper['description']}</p> |
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<a href="{paper['link']}" target="_blank" style="background: #667eea; color: white; padding: 8px 15px; border-radius: 5px; text-decoration: none; font-size: 0.9em;"> |
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📄 Read Paper |
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</a> |
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</div> |
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""" |
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custom_css = """ |
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.gradio-container { |
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max-width: 1200px !important; |
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} |
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.main-header { |
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text-align: center; |
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background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); |
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color: white; |
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padding: 40px 20px; |
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border-radius: 15px; |
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margin-bottom: 30px; |
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} |
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.stats-grid { |
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display: grid; |
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grid-template-columns: repeat(auto-fit, minmax(200px, 1fr)); |
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gap: 20px; |
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margin: 20px 0; |
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} |
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.stat-card { |
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background: rgba(255,255,255,0.1); |
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padding: 20px; |
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border-radius: 10px; |
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text-align: center; |
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} |
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""" |
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with gr.Blocks( |
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title="🤖 Awesome Multi-Agent Collaborative Perception", |
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theme=gr.themes.Soft(), |
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css=custom_css |
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) as demo: |
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gr.HTML(""" |
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<div class="main-header"> |
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<h1 style="font-size: 2.5rem; margin-bottom: 10px;">🤖 Awesome Multi-Agent Collaborative Perception</h1> |
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<p style="font-size: 1.2rem; opacity: 0.9;">Explore cutting-edge resources for Multi-Agent Collaborative Perception, Prediction, and Planning</p> |
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<div style="display: flex; justify-content: center; gap: 30px; margin-top: 20px; flex-wrap: wrap;"> |
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<div style="background: rgba(255,255,255,0.2); padding: 10px 20px; border-radius: 25px;"> |
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<div style="font-size: 1.5rem; font-weight: bold;">200+</div> |
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<div>Papers</div> |
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</div> |
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<div style="background: rgba(255,255,255,0.2); padding: 10px 20px; border-radius: 25px;"> |
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<div style="font-size: 1.5rem; font-weight: bold;">25+</div> |
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<div>Datasets</div> |
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</div> |
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<div style="background: rgba(255,255,255,0.2); padding: 10px 20px; border-radius: 25px;"> |
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<div style="font-size: 1.5rem; font-weight: bold;">50+</div> |
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<div>Code Repos</div> |
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</div> |
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</div> |
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</div> |
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""") |
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with gr.Tabs(): |
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with gr.Tab("🔍 Perception"): |
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gr.Markdown("## Multi-Agent Collaborative Perception Papers") |
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papers_html = "".join([create_paper_card(paper) for paper in perception_papers]) |
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gr.HTML(papers_html) |
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with gr.Tab("📊 Datasets"): |
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gr.Markdown("## Datasets & Benchmarks") |
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gr.Dataframe( |
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value=datasets_data, |
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headers=["Dataset", "Year", "Type", "Agents", "Size", "Features"], |
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datatype=["str", "str", "str", "str", "str", "str"], |
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interactive=False |
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) |
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gr.Markdown(""" |
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### Notable Datasets: |
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- **DAIR-V2X**: First real-world V2I collaborative perception dataset |
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- **V2V4Real**: Real vehicle-to-vehicle communication dataset |
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- **OPV2V**: Large-scale simulation benchmark in CARLA |
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- **V2X-Sim**: Comprehensive multi-agent simulation platform |
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""") |
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with gr.Tab("📍 Tracking"): |
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gr.Markdown("## Multi-Object Tracking & State Estimation") |
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gr.HTML(""" |
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<div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(300px, 1fr)); gap: 20px;"> |
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<div style="border: 1px solid #ddd; border-radius: 10px; padding: 20px; background: white;"> |
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<h3>MOT-CUP</h3> |
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<p>Multi-Object Tracking with Conformal Uncertainty Propagation</p> |
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<a href="https://arxiv.org/abs/2303.14346" target="_blank" style="color: #667eea;">📄 Paper</a> |
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</div> |
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<div style="border: 1px solid #ddd; border-radius: 10px; padding: 20px; background: white;"> |
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<h3>DMSTrack</h3> |
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<p>Probabilistic 3D Multi-Object Cooperative Tracking (ICRA 2024)</p> |
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<a href="https://arxiv.org/abs/2309.14655" target="_blank" style="color: #667eea;">📄 Paper</a> |
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</div> |
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</div> |
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""") |
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with gr.Tab("🔮 Prediction"): |
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gr.Markdown("## Trajectory Forecasting & Motion Prediction") |
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gr.HTML(""" |
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<div style="background: #f8f9fa; border-radius: 10px; padding: 20px; margin: 20px 0;"> |
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<h3>🧠 Key Approaches:</h3> |
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<ul style="line-height: 1.8;"> |
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<li><strong>Graph Neural Networks</strong>: Modeling agent interactions</li> |
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<li><strong>Transformer Architectures</strong>: Attention-based prediction</li> |
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<li><strong>Multi-Modal Fusion</strong>: Combining different sensor modalities</li> |
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<li><strong>Uncertainty Quantification</strong>: Reliable confidence estimation</li> |
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</ul> |
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</div> |
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""") |
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with gr.Tab("🏛️ Conferences"): |
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gr.Markdown("## Top Venues & Publication Trends") |
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conference_data = [ |
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["CVPR 2025", "5+", "End-to-end systems, robustness"], |
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["ICLR 2025", "3+", "Learning representations, scalability"], |
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["AAAI 2025", "4+", "AI applications, defense mechanisms"], |
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["ICRA 2025", "6+", "Robotics applications, real-world deployment"], |
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["NeurIPS 2024", "2+", "Theoretical foundations, novel architectures"] |
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] |
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gr.Dataframe( |
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value=conference_data, |
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headers=["Conference", "Papers", "Focus Areas"], |
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datatype=["str", "str", "str"], |
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interactive=False |
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) |
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gr.HTML(""" |
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<div style="text-align: center; margin-top: 40px; padding: 30px; background: #f8f9fa; border-radius: 10px;"> |
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<h3>🤝 Contributing</h3> |
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<p>We welcome contributions! Please submit papers, datasets, and code repositories via GitHub.</p> |
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<div style="margin-top: 20px;"> |
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<a href="https://github.com/your-username/awesome-multi-agent-collaborative-perception" target="_blank" |
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style="background: #667eea; color: white; padding: 10px 20px; border-radius: 5px; text-decoration: none; margin: 5px;"> |
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📚 GitHub Repository |
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</a> |
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<a href="https://huggingface.co/spaces/your-username/awesome-multi-agent-collaborative-perception" target="_blank" |
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style="background: #ff6b6b; color: white; padding: 10px 20px; border-radius: 5px; text-decoration: none; margin: 5px;"> |
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🤗 Hugging Face Space |
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</a> |
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</div> |
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</div> |
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""") |
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if __name__ == "__main__": |
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demo.launch() |
