most citedMeViS: A Multi-Modal Dataset for Referring Motion Expression Video Segmentation

13 citations · 13 across the 8 of their papers we have counts for

collaborators
Showing cs.CVShow all

12 papers · 1 filter

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

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…

cs.CV202513 cited

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…

cs.CV2025

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…