2 citations · 2 across the 3 of their papers we have counts for
5 papers
UniPixel: Unified Object Referring and Segmentation for Pixel-Level Visual Reasoning
Ye Liu, Zongyang Ma, Junfu Pu +4
Recent advances in Large Multi-modal Models (LMMs) have demonstrated their remarkable success as general-purpose multi-modal assistants, with particular focuses on holistic image-…
A Survey on Video Temporal Grounding with Multimodal Large Language Model
Jianlong Wu, Wei Liu, Ye Liu +4
The recent advancement in video temporal grounding (VTG) has significantly enhanced fine-grained video understanding, primarily driven by multimodal large language models (MLLMs).…
VideoMind: A Chain-of-LoRA Agent for Temporal-Grounded Video Reasoning
Ye Liu, Kevin Qinghong Lin, Chang Wen Chen +1
Videos, with their unique temporal dimension, demand precise grounded understanding, where answers are directly linked to visual, interpretable evidence. Despite significant breakt…
E.T. Bench: Towards Open-Ended Event-Level Video-Language Understanding
Ye Liu, Zongyang Ma, Zhongang Qi +3
Recent advances in Video Large Language Models (Video-LLMs) have demonstrated their great potential in general-purpose video understanding. To verify the significance of these mode…
-Tuning: Efficient Image-to-Video Transfer Learning for Video Temporal Grounding
Ye Liu, Jixuan He, Wanhua Li +4
Video temporal grounding (VTG) is a fine-grained video understanding problem that aims to ground relevant clips in untrimmed videos given natural language queries. Most existing VT…