Autonomous Vehicle

• INT Towards Infinite-frames 3D Detection with An Efficient Framework
• LIFT Learning 4D LiDAR Image Fusion Transformer for 3D Object Detection
• MPPNet Multi-Frame Feature Intertwining with Proxy Points for 3D Temporal Object Detection
• TransPillars Coarse-to-Fine Aggregation for Multi-Frame 3D Object Detection
• Spatial-Temporal Transformer for 3D Point Cloud Sequences
• No Pain, Big Gain Classify Dynamic Point Cloud Sequences with Static Models by Fitting Feature-level Space-time Surfaces
• SpOT Spatiotemporal Modeling for 3D Object Tracking
• 3D-MAN 3D Multi-frame Attention Network for Object Detection
• LiDAR-based Online 3D Video Object Detection with Graph-based Message Passing and Spatiotemporal Transformer Attention
• Robust Collaborative 3D Object Detection in Presence of Pose Errors
• CoBEVT Cooperative Bird’s Eye View Semantic Segmentation with Sparse Transformers
• Learning for Vehicle-to-Vehicle Cooperative Perception under Lossy Communication
• BEVFormer Learning Bird’s-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers
• TransFusion Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers
• DeepInteraction 3D Object Detection via Modality Interaction
• BEVFusion A Simple and Robust LiDAR-Camera Fusion Framework
• Where2comm Communication-Efficient Collaborative Perception via Spatial Confidence Maps
• V2X-ViT Vehicle-to-Everything Cooperative Perception with Vision Transformer
• COOPERNAUT End-to-End Driving with Cooperative Perception for Networked Vehicles
• Bridging the Domain Gap for Multi-Agent Perception
• Model-Agnostic Multi-Agent Perception Framework
• F-Cooper Feature based Cooperative Perception for Autonomous Vehicle Edge Computing System Using 3D Point Clouds
• Cooper Cooperative Perception for Connected Autonomous Vehicles based on 3D Point Clouds
• A Survey of Autonomous Driving Common Practices and Emerging Technologies
• OPV2V An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication

Federated Learning

• Data-Efficient Structured Pruning via Submodular Optimization
• FedorAS federated architecture search under system heterogeneity
• Efficient split-mix federated learning for on-demand and in-situ customization
• FjORD Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout
• ZEROFL EFFICIENT ON-DEVICE TRAINING FOR FEDERATED LEARNING WITH LOCAL SPARSITY
• HeteroFL computation and communication efficient federated learning for heterogeneous clients
• Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization