189 citations · 387 across the 34 of their papers we have counts for
10 papers · 2 filters
Camera-based 3D Semantic Scene Completion with Sparse Guidance Network
Jianbiao Mei, Yu Yang, Mengmeng Wang +5
Semantic scene completion (SSC) aims to predict the semantic occupancy of each voxel in the entire 3D scene from limited observations, which is an emerging and critical task for au…
Synchronize Feature Extracting and Matching: A Single Branch Framework for 3D Object Tracking
Teli Ma, Mengmeng Wang, Jimin Xiao +2
Siamese network has been a de facto benchmark framework for 3D LiDAR object tracking with a shared-parametric encoder extracting features from template and search region, respectiv…
Boosting Few-shot Action Recognition with Graph-guided Hybrid Matching
Jiazheng Xing, Mengmeng Wang, Yudi Ruan +6
Class prototype construction and matching are core aspects of few-shot action recognition. Previous methods mainly focus on designing spatiotemporal relation modeling modules or co…
MA-FSAR: Multimodal Adaptation of CLIP for Few-Shot Action Recognition
Jiazheng Xing, Chao Xu, Mengmeng Wang +5
Applying large-scale vision-language pre-trained models like CLIP to few-shot action recognition (FSAR) can significantly enhance both performance and efficiency. While several stu…
SSC-RS: Elevate LiDAR Semantic Scene Completion with Representation Separation and BEV Fusion
Jianbiao Mei, Yu Yang, Mengmeng Wang +3
Semantic scene completion (SSC) jointly predicts the semantics and geometry of the entire 3D scene, which plays an essential role in 3D scene understanding for autonomous driving s…
PANet: LiDAR Panoptic Segmentation with Sparse Instance Proposal and Aggregation
Jianbiao Mei, Yu Yang, Mengmeng Wang +3
Reliable LiDAR panoptic segmentation (LPS), including both semantic and instance segmentation, is vital for many robotic applications, such as autonomous driving. This work propose…