7 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…
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…
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…
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…
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…
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…