5 citations · 5 across the 5 of their papers we have counts for
5 papers · 1 filter
DenseStep2M: A Scalable, Training-Free Pipeline for Dense Instructional Video Annotation
Mingji Ge, Qirui Chen, Zeqian Li +1
Long-term video understanding requires interpreting complex temporal events and reasoning over procedural activities. While instructional video corpora, like HowTo100M, offer rich…
Universal Video Temporal Grounding with Generative Multi-modal Large Language Models
Zeqian Li, Shangzhe Di, Zhonghua Zhai +3
This paper presents a computational model for universal video temporal grounding, which accurately localizes temporal moments in videos based on natural language queries (e.g., que…
Learning Streaming Video Representation via Multitask Training
Yibin Yan, Jilan Xu, Shangzhe Di +6
Understanding continuous video streams plays a fundamental role in real-time applications including embodied AI and autonomous driving. Unlike offline video understanding, streamin…
Multi-Sentence Grounding for Long-term Instructional Video
Zeqian Li, Qirui Chen, Tengda Han +3
In this paper, we aim to establish an automatic, scalable pipeline for denoising the large-scale instructional dataset and construct a high-quality video-text dataset with multiple…
Multi-modal Prompting for Low-Shot Temporal Action Localization
Chen Ju, Zeqian Li, Peisen Zhao +5
In this paper, we consider the problem of temporal action localization under low-shot (zero-shot & few-shot) scenario, with the goal of detecting and classifying the action instanc…