2 citations · 2 across the 12 of their papers we have counts for
14 papers
Revisiting Triplet Contrastive Learning for 3D Shape Understanding
Chaoyang Zhu, Zhe Liao, Jiayi Zhang +3
In multi-modal 3D pre-training, aligning 3D shapes, their 2D image counterparts, and language descriptions has made tremendous progress. However, we find that supervision from rend…
IVT-Guard: All-in-One Reasoning Model for AI-Generated Content Detection
Hongwei Niu, Yunpeng Luo, Hanjun Li +6
The rapid proliferation of highly realistic AI-Generated Content (AIGC) necessitates robust and interpretable detection mechanisms. However, existing detectors are predominantly co…
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…
GFR-SAM: Training-Free Referring Camouflaged Object Segmentation via Cross-Image Prompting
Yilong Yang, Jianxin Tian, Shengchuan Zhang +1
Referring Camouflaged Object Detection (Ref-COD) requires segmenting hidden targets guided by reference cues. While supervised methods are annotation-heavy and training-free approa…
FashionChameleon: Towards Real-Time and Interactive Human-Garment Video Customization
Quanjian Song, Yefeng Shen, Mengting Chen +5
Human-centric video customization, particularly at the garment level, has shown significant commercial value. However, existing approaches cannot support low-latency and interactiv…
PAR3D: A Unified 3D-MLLM with Part-Aware Representation for Scene Understanding
Shaohui Dai, Yansong Qu, You Shen +2
Recent advances in 3D multimodal large language models (3D-MLLMs) have enabled unified solutions for 3D scene understanding tasks, including visual question answering, captioning,…
HieraVid: Hierarchical Token Pruning for Fast Video Large Language Models
Yansong Guo, Chaoyang Zhu, Jiayi Ji +2
Video Large Language Models (VideoLLMs) have demonstrated impressive capabilities in video understanding, yet the massive number of input video tokens incurs a significant computat…
SEA-Vision: A Multilingual Benchmark for Comprehensive Document and Scene Text Understanding in Southeast Asia
Pengfei Yue, Xingran Zhao, Juntao Chen +6
Multilingual document and scene text understanding plays an important role in applications such as search, finance, and public services. However, most existing benchmarks focus on…
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…
Discover, Segment, and Select: A Progressive Mechanism for Zero-shot Camouflaged Object Segmentation
Yilong Yang, Jianxin Tian, Shengchuan Zhang +1
Current zero-shot Camouflaged Object Segmentation methods typically employ a two-stage pipeline (discover-then-segment): using MLLMs to obtain visual prompts, followed by SAM segme…
Test-Time Computing for Referring Multimodal Large Language Models
Mingrui Wu, Hao Chen, Jiayi Ji +5
We propose ControlMLLM++, a novel test-time adaptation framework that injects learnable visual prompts into frozen multimodal large language models (MLLMs) to enable fine-grained r…
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…
FastVGGT: Training-Free Acceleration of Visual Geometry Transformer
You Shen, Zhipeng Zhang, Yansong Qu +4
Foundation models for 3D vision have recently demonstrated remarkable capabilities in 3D perception. However, scaling these models to long-sequence image inputs remains a significa…
TextRefiner: Internal Visual Feature as Efficient Refiner for Vision-Language Models Prompt Tuning
Jingjing Xie, Yuxin Zhang, Jun Peng +2
Despite the efficiency of prompt learning in transferring vision-language models (VLMs) to downstream tasks, existing methods mainly learn the prompts in a coarse-grained manner wh…