11 citations · 21 across the 18 of their papers we have counts for
20 papers
PixelEyes: Decoupling Perception and Reasoning for Pinpoint Visual Evidence Seeking
Dengxian Gong, Yuanzheng Wu, Haobo Yuan +11
This paper explores multi-turn visual reasoning and observes that MLLMs repeatedly fail to localize the target, leading to long, redundant trajectories. We attribute this failure t…
Towards One-to-Many Temporal Grounding
Qi Xu, Yue Tan, Shihao Chen +5
Temporal Grounding (TG) aims to localize video segments corresponding to a textual query. Prior research predominantly focuses on single-segment retrieval. Real-world scenarios, ho…
Report of the 5th PVUW Challenge: Towards More Diverse Modalities in Pixel-Level Understanding
Chang Liu, Henghui Ding, Nikhila Ravi +40
This report summarizes the objectives, datasets, and top-performing methodologies of the 2026 Pixel-level Video Understanding in the Wild (PVUW) Challenge, hosted at CVPR 2026, whi…
SaSaSaSa2VA: 2nd Place of the 5th PVUW MeViS-Text Track
Dengxian Gong, Quanzhu Niu, Shihao Chen +6
Referring video object segmentation (RVOS) commonly grounds targets in videos based on static textual cues. MeViS benchmark extends this by incorporating motion-centric expressions…
SAMTok: Representing Any Mask with Two Words
Yikang Zhou, Tao Zhang, Dengxian Gong +13
Pixel-wise capabilities are essential for building interactive intelligent systems. However, pixel-wise multi-modal LLMs (MLLMs) remain difficult to scale due to complex region-lev…
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…
OMG-LLaVA: Bridging Image-level, Object-level, Pixel-level Reasoning and Understanding
Tao Zhang, Xiangtai Li, Hao Fei +5
Current universal segmentation methods demonstrate strong capabilities in pixel-level image and video understanding. However, they lack reasoning abilities and cannot be controlled…
P2PFormer: A Primitive-to-polygon Method for Regular Building Contour Extraction from Remote Sensing Images
Tao Zhang, Shiqing Wei, Yikang Zhou +3
Extracting building contours from remote sensing imagery is a significant challenge due to buildings' complex and diverse shapes, occlusions, and noise. Existing methods often stru…
DVIS-DAQ: Improving Video Segmentation via Dynamic Anchor Queries
Yikang Zhou, Tao Zhang, Shunping Ji +2
Modern video segmentation methods adopt object queries to perform inter-frame association and demonstrate satisfactory performance in tracking continuously appearing objects despit…
Point Cloud Mamba: Point Cloud Learning via State Space Model
Tao Zhang, Haobo Yuan, Lu Qi +5
Recently, state space models have exhibited strong global modeling capabilities and linear computational complexity in contrast to transformers. This research focuses on applying s…
DVIS++: Improved Decoupled Framework for Universal Video Segmentation
Tao Zhang, Xingye Tian, Yikang Zhou +7
We present the \textbf{D}ecoupled \textbf{VI}deo \textbf{S}egmentation (DVIS) framework, a novel approach for the challenging task of universal video segmentation, including video…