61 citations · 68 across the 6 of their papers we have counts for
6 papers
InstructionBench: An Instructional Video Understanding Benchmark
Haiwan Wei, Yitian Yuan, Xiaohan Lan +2
Despite progress in video large language models (Video-LLMs), research on instructional video understanding, crucial for enhancing access to instructional content, remains insuffic…
TimeMarker: A Versatile Video-LLM for Long and Short Video Understanding with Superior Temporal Localization Ability
Shimin Chen, Xiaohan Lan, Yitian Yuan +2
Rapid development of large language models (LLMs) has significantly advanced multimodal large language models (LMMs), particularly in vision-language tasks. However, existing video…
VidCompress: Memory-Enhanced Temporal Compression for Video Understanding in Large Language Models
Xiaohan Lan, Yitian Yuan, Zequn Jie +1
Video-based multimodal large language models (Video-LLMs) possess significant potential for video understanding tasks. However, most Video-LLMs treat videos as a sequential set of…
A Closer Look at Debiased Temporal Sentence Grounding in Videos: Dataset, Metric, and Approach
Xiaohan Lan, Yitian Yuan, Xin Wang +4
Temporal Sentence Grounding in Videos (TSGV), which aims to ground a natural language sentence in an untrimmed video, has drawn widespread attention over the past few years. Howeve…
A Survey on Temporal Sentence Grounding in Videos
Xiaohan Lan, Yitian Yuan, Xin Wang +2
Temporal sentence grounding in videos(TSGV), which aims to localize one target segment from an untrimmed video with respect to a given sentence query, has drawn increasing attentio…
A Closer Look at Temporal Sentence Grounding in Videos: Dataset and Metric
Yitian Yuan, Xiaohan Lan, Xin Wang +3
Temporal Sentence Grounding in Videos (TSGV), i.e., grounding a natural language sentence which indicates complex human activities in a long and untrimmed video sequence, has recei…