1 citations · 1 across the 9 of their papers we have counts for
9 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…
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…
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…
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…
3D-GRES: Generalized 3D Referring Expression Segmentation
Changli Wu, Yihang Liu, Jiayi Ji +6
3D Referring Expression Segmentation (3D-RES) is dedicated to segmenting a specific instance within a 3D space based on a natural language description. However, current approaches…
JM3D & JM3D-LLM: Elevating 3D Understanding with Joint Multi-modal Cues
Jiayi Ji, Haowei Wang, Changli Wu +3
The rising importance of 3D understanding, pivotal in computer vision, autonomous driving, and robotics, is evident. However, a prevailing trend, which straightforwardly resorted t…
3D-STMN: Dependency-Driven Superpoint-Text Matching Network for End-to-End 3D Referring Expression Segmentation
Changli Wu, Yiwei Ma, Qi Chen +4
In 3D Referring Expression Segmentation (3D-RES), the earlier approach adopts a two-stage paradigm, extracting segmentation proposals and then matching them with referring expressi…