6 papers
TennisVAR: A Stroke-Evidence-Grounded Multimodal Large Language Model for Tactical Reasoning in Tennis Videos
Yifan Mei, Qingling Shi, Changli Wu +3
Sports-video understanding is moving beyond event recognition toward explaining how actions collectively shape match progression, however, existing tennis-video methods either perc…
PixDLM: A Dual-Path Multimodal Language Model for UAV Reasoning Segmentation
Shuyan Ke, Yifan Mei, Changli Wu +4
Reasoning segmentation has recently expanded from ground-level scenes to remote-sensing imagery, yet UAV data poses distinct challenges, including oblique viewpoints, ultra-high re…
MVGGT: Multimodal Visual Geometry Grounded Transformer for Multiview 3D Referring Expression Segmentation
Changli Wu, Haodong Wang, Jiayi Ji +5
Most existing 3D referring expression segmentation (3DRES) methods rely on dense, high-quality point clouds, while real-world agents such as robots and mobile phones operate with o…
3D-DRES: Detailed 3D Referring Expression Segmentation
Qi Chen, Changli Wu, Jiayi Ji +2
Current 3D visual grounding tasks only process sentence level detection or segmentation, which critically fails to leverage the rich compositional contextual reasonings within natu…
IPDN: Image-enhanced Prompt Decoding Network for 3D Referring Expression Segmentation
Qi Chen, Changli Wu, Jiayi Ji +3
3D Referring Expression Segmentation (3D-RES) aims to segment point cloud scenes based on a given expression. However, existing 3D-RES approaches face two major challenges: feature…
RG-SAN: Rule-Guided Spatial Awareness Network for End-to-End 3D Referring Expression Segmentation
Changli Wu, Qi Chen, Jiayi Ji +7
3D Referring Expression Segmentation (3D-RES) aims to segment 3D objects by correlating referring expressions with point clouds. However, traditional approaches frequently encounte…