13 citations · 21 across the 11 of their papers we have counts for
12 papers
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…
WBench: A Comprehensive Multi-turn Benchmark for Interactive Video World Model Evaluation
Kaining Ying, Hengrui Hu, Siyu Ren +6
Interactive world models are advancing rapidly, yet existing benchmarks cover only part of the required competencies, leaving no unified standard for systematic evaluation. To fill…
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)…
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…
ConvBench: A Multi-Turn Conversation Evaluation Benchmark with Hierarchical Capability for Large Vision-Language Models
Shuo Liu, Kaining Ying, Hao Zhang +8
This paper presents ConvBench, a novel multi-turn conversation evaluation benchmark tailored for Large Vision-Language Models (LVLMs). Unlike existing benchmarks that assess indivi…
MMT-Bench: A Comprehensive Multimodal Benchmark for Evaluating Large Vision-Language Models Towards Multitask AGI
Kaining Ying, Fanqing Meng, Jin Wang +19
Large Vision-Language Models (LVLMs) show significant strides in general-purpose multimodal applications such as visual dialogue and embodied navigation. However, existing multimod…
Human-to-Human Interaction Detection
Zhenhua Wang, Kaining Ying, Jiajun Meng +1
A comprehensive understanding of interested human-to-human interactions in video streams, such as queuing, handshaking, fighting and chasing, is of immense importance to the survei…
CTVIS: Consistent Training for Online Video Instance Segmentation
Kaining Ying, Qing Zhong, Weian Mao +7
The discrimination of instance embeddings plays a vital role in associating instances across time for online video instance segmentation (VIS). Instance embedding learning is direc…