11 citations · 21 across the 18 of their papers we have counts for
10 papers · 1 filter
The 1st Solution for 7th LSVOS RVOS Track: SaSaSa2VA
Quanzhu Niu, Dengxian Gong, Shihao Chen +6
Referring video object segmentation (RVOS) requires segmenting and tracking objects in videos conditioned on natural-language expressions, demanding fine-grained understanding of b…
LSVOS 2025 Challenge Report: Recent Advances in Complex Video Object Segmentation
Chang Liu, Henghui Ding, Kaining Ying +46
This report presents an overview of the 7th Large-scale Video Object Segmentation (LSVOS) Challenge held in conjunction with ICCV 2025. Besides the two traditional tracks of LSVOS…
DeH4R: A Decoupled and Hybrid Method for Road Network Graph Extraction
Dengxian Gong, Shunping Ji
The automated extraction of complete and precise road network graphs from remote sensing imagery remains a critical challenge in geospatial computer vision. Segmentation-based appr…
Beyond Appearance: Geometric Cues for Robust Video Instance Segmentation
Quanzhu Niu, Yikang Zhou, Shihao Chen +2
Video Instance Segmentation (VIS) fundamentally struggles with pervasive challenges including object occlusions, motion blur, and appearance variations during temporal association.…
VectorLLM: Human-like Extraction of Structured Building Contours vis Multimodal LLMs
Tao Zhang, Shiqing Wei, Shihao Chen +3
Automatically extracting vectorized building contours from remote sensing imagery is crucial for urban planning, population estimation, and disaster assessment. Current state-of-th…
SIU3R: Simultaneous Scene Understanding and 3D Reconstruction Beyond Feature Alignment
Qi Xu, Dongxu Wei, Lingzhe Zhao +4
Simultaneous understanding and 3D reconstruction plays an important role in developing end-to-end embodied intelligent systems. To achieve this, recent approaches resort to 2D-to-3…
Dense360: Dense Understanding from Omnidirectional Panoramas
Yikang Zhou, Tao Zhang, Dizhe Zhang +3
Multimodal Large Language Models (MLLMs) require comprehensive visual inputs to achieve dense understanding of the physical world. While existing MLLMs demonstrate impressive world…
Pixel-SAIL: Single Transformer For Pixel-Grounded Understanding
Tao Zhang, Xiangtai Li, Zilong Huang +6
Multimodal Large Language Models (MLLMs) achieve remarkable performance for fine-grained pixel-level understanding tasks. However, all the works rely heavily on extra components, s…
Are They the Same? Exploring Visual Correspondence Shortcomings of Multimodal LLMs
Yikang Zhou, Tao Zhang, Shilin Xu +7
Recent advancements in multimodal large language models (MLLM) have shown a strong ability in visual perception, reasoning abilities, and vision-language understanding. However, th…
Sa2VA: Marrying SAM2 with MLLM for Dense Grounded Understanding of Images and Videos
Haobo Yuan, Xiangtai Li, Tao Zhang +8
This work presents Sa2VA, the first comprehensive, unified model for dense grounded understanding of both images and videos. Unlike existing multi-modal large language models, whic…