3 citations · 8 across the 16 of their papers we have counts for
7 papers · 1 filter
Unveiling Visual Perception in Language Models: An Attention Head Analysis Approach
Jing Bi, Junjia Guo, Yunlong Tang +3
Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated remarkable progress in visual understanding. This impressive leap raises a compelling question: ho…
MMCOMPOSITION: Revisiting the Compositionality of Pre-trained Vision-Language Models
Hang Hua, Yunlong Tang, Ziyun Zeng +5
The advent of large Vision-Language Models (VLMs) has significantly advanced multimodal understanding, enabling more sophisticated and accurate integration of visual and textual in…
EAGLE: Egocentric AGgregated Language-video Engine
Jing Bi, Yunlong Tang, Luchuan Song +3
The rapid evolution of egocentric video analysis brings new insights into understanding human activities and intentions from a first-person perspective. Despite this progress, the…
CaRDiff: Video Salient Object Ranking Chain of Thought Reasoning for Saliency Prediction with Diffusion
Yolo Yunlong Tang, Gen Zhan, Li Yang +2
Video saliency prediction aims to identify the regions in a video that attract human attention and gaze, driven by bottom-up features from the video and top-down processes like mem…
Do More Details Always Introduce More Hallucinations in LVLM-based Image Captioning?
Mingqian Feng, Yunlong Tang, Zeliang Zhang +1
Large Vision-Language Models (LVLMs) excel in integrating visual and linguistic contexts to produce detailed content, facilitating applications such as image captioning. However, u…
V2Xum-LLM: Cross-Modal Video Summarization with Temporal Prompt Instruction Tuning
Hang Hua, Yolo Yunlong Tang, Chenliang Xu +1
Video summarization aims to create short, accurate, and cohesive summaries of longer videos. Despite the existence of various video summarization datasets, a notable limitation is…
Empowering LLMs with Pseudo-Untrimmed Videos for Audio-Visual Temporal Understanding
Yolo Yunlong Tang, Daiki Shimada, Jing Bi +3
Large language models (LLMs) have demonstrated remarkable capabilities in natural language and multimodal domains. By fine-tuning multimodal LLMs with temporal annotations from wel…