16 citations · 56 across the 25 of their papers we have counts for
6 papers · 1 filter
Mid-Training Language Models on Raw Video
Jaedong Hwang, Xiaoqian Shen, Ernie Chang +9
Multimodal large language models learn mostly from paired image-text data or annotated video, and raw web video is rarely used to further train an existing language model. We study…
Exploring Audio Hallucination in Egocentric Video Understanding
Ashish Seth, Xinhao Mei, Changsheng Zhao +9
Egocentric videos provide a distinctive setting in which sound serves as crucial cues to understand user activities and surroundings, particularly when visual information is unstab…
EgoAVU: Egocentric Audio-Visual Understanding
Ashish Seth, Xinhao Mei, Changsheng Zhao +9
Understanding egocentric videos plays a vital role for embodied intelligence. Recent multi-modal large language models (MLLMs) can accept both visual and audio inputs. However, due…
VideoAuto-R1: Video Auto Reasoning via Thinking Once, Answering Twice
Shuming Liu, Mingchen Zhuge, Changsheng Zhao +20
Chain-of-thought (CoT) reasoning has emerged as a powerful tool for multimodal large language models on video understanding tasks. However, its necessity and advantages over direct…
DepthLM: Metric Depth From Vision Language Models
Zhipeng Cai, Ching-Feng Yeh, Hu Xu +7
Vision language models (VLMs) can flexibly address various vision tasks through text interactions. Although successful in semantic understanding, state-of-the-art VLMs including GP…
LongVU: Spatiotemporal Adaptive Compression for Long Video-Language Understanding
Xiaoqian Shen, Yunyang Xiong, Changsheng Zhao +14
Multimodal Large Language Models (MLLMs) have shown promising progress in understanding and analyzing video content. However, processing long videos remains a significant challenge…