13 citations · 13 across the 10 of their papers we have counts for
14 papers
SAM-MT: Real-Time Interactive Multi-Target Video Segmentation
Ruiqi Shen, Chang Liu, Henghui Ding
Modern Video Object Segmentation (VOS) involves tracking and segmenting user-specified targets. While recent approaches have achieved remarkable performance in single-target scenar…
SAM2Matting: Generalized Image and Video Matting
Ruiqi Shen, Guangquan Jie, Chang Liu +1
Despite impressive advances in image matting, video matting remains challenging due to the inherent gap between high-level tracking, which requires frame-wise understanding, and lo…
Unison: Benchmarking Unified Multimodal Models via Synergistic Understanding and Generation
Jinyu Liu, Xincheng Shuai, Henghui Ding +1
Unified multimodal models capable of both understanding and generation have achieved remarkable strides. However, despite their unified designs, existing evaluations typically asse…
FeVOS: Foresight Expression Video Object Segmentation
Kehan Lan, Kaining Ying, Henghui Ding
Existing Referring Video Object Segmentation tasks focus on referring expressions describing events, actions or appearances of relevant objects within the observed frames, lacking…
AVTrack: Audio-Visual Tracking in Human-centric Complex Scenes
Yaoting Wang, Yun Zhou, Zipei Zhang +1
Audio-visual speaker tracking aims to localize and track active speakers by leveraging auditory and visual cues, enabling fine-grained, human-centric scene understanding. This capa…
SAM3-DMS: Decoupled Memory Selection for Multi-target Video Segmentation of SAM3
Ruiqi Shen, Chang Liu, Henghui Ding
Segment Anything 3 (SAM3) has established a powerful foundation that robustly detects, segments, and tracks specified targets in videos. However, in its original implementation, it…
MeViS: A Multi-Modal Dataset for Referring Motion Expression Video Segmentation
Henghui Ding, Chang Liu, Shuting He +4
This paper proposes a large-scale multi-modal dataset for referring motion expression video segmentation, focusing on segmenting and tracking target objects in videos based on lang…
Segment Anything Across Shots: A Method and Benchmark
Hengrui Hu, Kaining Ying, Henghui Ding
This work focuses on multi-shot semi-supervised video object segmentation (MVOS), which aims at segmenting the target object indicated by an initial mask throughout a video with mu…
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…
MOSEv2: A More Challenging Dataset for Video Object Segmentation in Complex Scenes
Henghui Ding, Kaining Ying, Chang Liu +5
Video object segmentation (VOS) aims to segment specified target objects throughout a video. Although state-of-the-art methods have achieved impressive performance (e.g., 90+% J&F)…
Multimodal Referring Segmentation: A Survey
Henghui Ding, Song Tang, Shuting He +3
Multimodal referring segmentation aims to segment target objects in visual scenes, such as images, videos, and 3D scenes, based on referring expressions in text or audio format. Th…
Towards Omnimodal Expressions and Reasoning in Referring Audio-Visual Segmentation
Kaining Ying, Henghui Ding, Guangquan Jie +1
Referring audio-visual segmentation (RAVS) has recently seen significant advancements, yet challenges remain in integrating multimodal information and deeply understanding and reas…
MOVE: Motion-Guided Few-Shot Video Object Segmentation
Kaining Ying, Hengrui Hu, Henghui Ding
This work addresses motion-guided few-shot video object segmentation (FSVOS), which aims to segment dynamic objects in videos based on a few annotated examples with the same motion…
Progressive Scaling Visual Object Tracking
Jack Hong, Shilin Yan, Zehao Xiao +4
In this work, we propose a progressive scaling training strategy for visual object tracking, systematically analyzing the influence of training data volume, model size, and input r…