16 citations · 45 across the 22 of their papers we have counts for
23 papers
SafeGuard: A Multi-Agent Perception-Reasoning Framework for Social-Risk AI-Generated Video Detection
Wenlin Wu, Sheng Zhou, Peipei Song +3
As video generation paradigms evolve from localized manipulation to full-scene synthesis, AI-generated video detection becomes increasingly challenging, as forgeries exhibit cohere…
Causal-Entity Reflected Egocentric Traffic Accident Video Synthesis
Lei-lei Li, Jianwu Fang, Junbin Xiao +5
Egocentricly comprehending the causes and effects of car accidents is crucial for the safety of self-driving cars, and synthesizing causal-entity reflected accident videos can faci…
MUPA: Towards Multi-Path Agentic Reasoning for Grounded Video Question Answering
Jisheng Dang, Huilin Song, Junbin Xiao +6
Grounded Video Question Answering (Grounded VideoQA) requires aligning textual answers with explicit visual evidence. However, modern multimodal models often rely on linguistic pri…
Visual Intention Grounding for Egocentric Assistants
Pengzhan Sun, Junbin Xiao, Tze Ho Elden Tse +3
Visual grounding associates textual descriptions with objects in an image. Conventional methods target third-person image inputs and named object queries. In applications such as A…
EgoTextVQA: Towards Egocentric Scene-Text Aware Video Question Answering
Sheng Zhou, Junbin Xiao, Qingyun Li +6
We introduce EgoTextVQA, a novel and rigorously constructed benchmark for egocentric QA assistance involving scene text. EgoTextVQA contains 1.5K ego-view videos and 7K scene-text…
EgoBlind: Towards Egocentric Visual Assistance for the Blind
Junbin Xiao, Nanxin Huang, Hao Qiu +5
We present EgoBlind, the first egocentric VideoQA dataset collected from blind individuals to evaluate the assistive capabilities of contemporary multimodal large language models (…
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…
Can I Trust Your Answer? Visually Grounded Video Question Answering
Junbin Xiao, Angela Yao, Yicong Li +1
We study visually grounded VideoQA in response to the emerging trends of utilizing pretraining techniques for video-language understanding. Specifically, by forcing vision-language…
Discovering Spatio-Temporal Rationales for Video Question Answering
Yicong Li, Junbin Xiao, Chun Feng +2
This paper strives to solve complex video question answering (VideoQA) which features long video containing multiple objects and events at different time. To tackle the challenge,…
Contrastive Video Question Answering via Video Graph Transformer
Junbin Xiao, Pan Zhou, Angela Yao +4
We propose to perform video question answering (VideoQA) in a Contrastive manner via a Video Graph Transformer model (CoVGT). CoVGT's uniqueness and superiority are three-fold: 1)…
FakeSV: A Multimodal Benchmark with Rich Social Context for Fake News Detection on Short Video Platforms
Peng Qi, Yuyan Bu, Juan Cao +5
Short video platforms have become an important channel for news sharing, but also a new breeding ground for fake news. To mitigate this problem, research of fake news video detecti…
Equivariant and Invariant Grounding for Video Question Answering
Yicong Li, Xiang Wang, Junbin Xiao +1
Video Question Answering (VideoQA) is the task of answering the natural language questions about a video. Producing an answer requires understanding the interplay across visual sce…
Video Graph Transformer for Video Question Answering
Junbin Xiao, Pan Zhou, Tat-Seng Chua +1
This paper proposes a Video Graph Transformer (VGT) model for Video Quetion Answering (VideoQA). VGT's uniqueness are two-fold: 1) it designs a dynamic graph transformer module whi…
Invariant Grounding for Video Question Answering
Yicong Li, Xiang Wang, Junbin Xiao +2
Video Question Answering (VideoQA) is the task of answering questions about a video. At its core is understanding the alignments between visual scenes in video and linguistic seman…
Video Question Answering: Datasets, Algorithms and Challenges
Yaoyao Zhong, Junbin Xiao, Wei Ji +3
Video Question Answering (VideoQA) aims to answer natural language questions according to the given videos. It has earned increasing attention with recent research trends in joint…
Video as Conditional Graph Hierarchy for Multi-Granular Question Answering
Junbin Xiao, Angela Yao, Zhiyuan Liu +3
Video question answering requires the models to understand and reason about both the complex video and language data to correctly derive the answers. Existing efforts have been foc…
NExT-QA:Next Phase of Question-Answering to Explaining Temporal Actions
Junbin Xiao, Xindi Shang, Angela Yao +1
We introduce NExT-QA, a rigorously designed video question answering (VideoQA) benchmark to advance video understanding from describing to explaining the temporal actions. Based on…
Visual Relation Grounding in Videos
Junbin Xiao, Xindi Shang, Xun Yang +2
In this paper, we explore a novel task named visual Relation Grounding in Videos (vRGV). The task aims at spatio-temporally localizing the given relations in the form of subject-pr…