1 citations · 2 across the 15 of their papers we have counts for
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STAC: Selective Spatiotemporal Aggregation and Compression for Video Reasoning Segmentation
Syed Ariff Syed Hesham, Yun Liu, Guolei Sun +4
Video reasoning segmentation demands pixel-accurate object tracking across hundreds of frames under complex natural language queries, producing dense spatiotemporal tokens whose qu…
DCP-Prune: Ultra-Low Token Pruning with Distribution Consistency Preservation
Xifeng Xue, Xiaokang Wang, Zirui Li +2
Recent vision token pruning methods effectively preserve model performance under moderate token budgets but become unstable under ultra-low token budget. Our analysis shows that as…
Breaking Modality Heterogeneity in Low-Bit Quantization for Large Vision-Language Models
Yi Zhong, Haotong Qin, Xindong Zhang +2
Low-bit post-training quantization (PTQ) is a pivotal technique for deploying Vision-Language Models (VLMs) on resource-constrained devices. However, existing PTQ methods often deg…
EgoSound: Benchmarking Sound Understanding in Egocentric Videos
Bingwen Zhu, Yuqian Fu, Qiaole Dong +6
Multimodal Large Language Models (MLLMs) have recently achieved remarkable progress in vision-language understanding. Yet, human perception is inherently multisensory, integrating…
Evaluating SAM2 for Video Semantic Segmentation
Syed Hesham Syed Ariff, Yun Liu, Guolei Sun +4
The Segmentation Anything Model 2 (SAM2) has proven to be a powerful foundation model for promptable visual object segmentation in both images and videos, capable of storing object…
MedVSR: Medical Video Super-Resolution with Cross State-Space Propagation
Xinyu Liu, Guolei Sun, Cheng Wang +2
High-resolution (HR) medical videos are vital for accurate diagnosis, yet are hard to acquire due to hardware limitations and physiological constraints. Clinically, the collected l…
HiM2SAM: Enhancing SAM2 with Hierarchical Motion Estimation and Memory Optimization towards Long-term Tracking
Ruixiang Chen, Guolei Sun, Yawei Li +2
This paper presents enhancements to the SAM2 framework for video object tracking task, addressing challenges such as occlusions, background clutter, and target reappearance. We int…
A Comprehensive Survey on Video Scene Parsing:Advances, Challenges, and Prospects
Guohuan Xie, Syed Ariff Syed Hesham, Wenya Guo +4
Video Scene Parsing (VSP) studies dense video understanding, where every pixel in each frame must be segmented, each region must be named, and each object identity must remain cohe…
CamSAM2: Segment Anything Accurately in Camouflaged Videos
Yuli Zhou, Yawei Li, Yuqian Fu +3
Video camouflaged object segmentation (VCOS), aiming at segmenting camouflaged objects that seamlessly blend into their environment, is a fundamental vision task with various real-…
Generalized Few-shot 3D Point Cloud Segmentation with Vision-Language Model
Zhaochong An, Guolei Sun, Yun Liu +4
Generalized few-shot 3D point cloud segmentation (GFS-PCS) adapts models to new classes with few support samples while retaining base class segmentation. Existing GFS-PCS methods e…
SAM-Aware Graph Prompt Reasoning Network for Cross-Domain Few-Shot Segmentation
Shi-Feng Peng, Guolei Sun, Yong Li +2
The primary challenge of cross-domain few-shot segmentation (CD-FSS) is the domain disparity between the training and inference phases, which can exist in either the input data or…
Multimodality Helps Few-shot 3D Point Cloud Semantic Segmentation
Zhaochong An, Guolei Sun, Yun Liu +5
Few-shot 3D point cloud segmentation (FS-PCS) aims at generalizing models to segment novel categories with minimal annotated support samples. While existing FS-PCS methods have sho…
When SAM2 Meets Video Camouflaged Object Segmentation: A Comprehensive Evaluation and Adaptation
Yuli Zhou, Guolei Sun, Yawei Li +3
This study investigates the application and performance of the Segment Anything Model 2 (SAM2) in the challenging task of video camouflaged object segmentation (VCOS). VCOS involve…