16 citations · 45 across the 22 of their papers we have counts for
6 papers · 1 filter
On the Consistency of Video Large Language Models in Temporal Comprehension
Minjoon Jung, Junbin Xiao, Byoung-Tak Zhang +1
Video large language models (Video-LLMs) can temporally ground language queries and retrieve video moments. Yet, such temporal comprehension capabilities are neither well-studied n…
Scene-Text Grounding for Text-Based Video Question Answering
Sheng Zhou, Junbin Xiao, Xun Yang +5
Existing efforts in text-based video question answering (TextVideoQA) are criticized for their opaque decisionmaking and heavy reliance on scene-text recognition. In this paper, we…
Question-Answering Dense Video Events
Hangyu Qin, Junbin Xiao, Angela Yao
This paper presents question-answering on dense video events, a novel task that answers and grounds dense-event questions in long videos, thus challenging MLLMs to faithfully compr…
VideoQA in the Era of LLMs: An Empirical Study
Junbin Xiao, Nanxin Huang, Hangyu Qin +8
Video Large Language Models (Video-LLMs) are flourishing and has advanced many video-language tasks. As a golden testbed, Video Question Answering (VideoQA) plays pivotal role in V…
Video-Language Understanding: A Survey from Model Architecture, Model Training, and Data Perspectives
Thong Nguyen, Yi Bin, Junbin Xiao +6
Humans use multiple senses to comprehend the environment. Vision and language are two of the most vital senses since they allow us to easily communicate our thoughts and perceive t…
Abductive Ego-View Accident Video Understanding for Safe Driving Perception
Jianwu Fang, Lei-lei Li, Junfei Zhou +5
We present MM-AU, a novel dataset for Multi-Modal Accident video Understanding. MM-AU contains 11,727 in-the-wild ego-view accident videos, each with temporally aligned text descri…