9 citations · 24 across the 30 of their papers we have counts for
6 papers · 1 filter
Enhancing the Reasoning Capabilities of Small Language Models via Solution Guidance Fine-Tuning
Jing Bi, Yuting Wu, Weiwei Xing +1
Large language models (LLMs) have demonstrated remarkable performance across a wide range of tasks. Advances in prompt engineering and fine-tuning techniques have further enhanced…
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…
VidComposition: Can MLLMs Analyze Compositions in Compiled Videos?
Yolo Y. Tang, Junjia Guo, Hang Hua +9
The advancement of Multimodal Large Language Models (MLLMs) has enabled significant progress in multimodal understanding, expanding their capacity to analyze video content. However…
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…
OSCaR: Object State Captioning and State Change Representation
Nguyen Nguyen, Jing Bi, Ali Vosoughi +3
The capability of intelligent models to extrapolate and comprehend changes in object states is a crucial yet demanding aspect of AI research, particularly through the lens of human…
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…