44 citations · 89 across the 20 of their papers we have counts for
21 papers
EgoTwin: Dreaming Body and View in First Person
Jingqiao Xiu, Fangzhou Hong, Yicong Li +5
While exocentric video synthesis has achieved great progress, egocentric video generation remains largely underexplored, which requires modeling first-person view content along wit…
VINCIE: Unlocking In-context Image Editing from Video
Leigang Qu, Feng Cheng, Ziyan Yang +7
In-context image editing aims to modify images based on a contextual sequence comprising text and previously generated images. Existing methods typically depend on task-specific pi…
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…
Predicting the Road Ahead: A Knowledge Graph based Foundation Model for Scene Understanding in Autonomous Driving
Hongkuan Zhou, Stefan Schmid, Yicong Li +3
The autonomous driving field has seen remarkable advancements in various topics, such as object recognition, trajectory prediction, and motion planning. However, current approaches…
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…
Causality Model for Semantic Understanding on Videos
Li Yicong
After a decade of prosperity, the development of video understanding has reached a critical juncture, where the sole reliance on massive data and complex architectures is no longer…
Understanding Long Videos via LLM-Powered Entity Relation Graphs
Meng Chu, Yicong Li, Tat-Seng Chua
The analysis of extended video content poses unique challenges in artificial intelligence, particularly when dealing with the complexity of tracking and understanding visual elemen…
Safe + Safe = Unsafe? Exploring How Safe Images Can Be Exploited to Jailbreak Large Vision-Language Models
Chenhang Cui, Gelei Deng, An Zhang +5
Recent advances in Large Vision-Language Models (LVLMs) have showcased strong reasoning abilities across multiple modalities, achieving significant breakthroughs in various real-wo…
Research on Personalized Compression Algorithm for Pre-trained Models Based on Homomorphic Entropy Increase
Yicong Li, Xing Guo, Haohua Du
In this article, we explore the challenges and evolution of two key technologies in the current field of AI: Vision Transformer model and Large Language Model (LLM). Vision Transfo…
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…
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…
Redundancy-aware Transformer for Video Question Answering
Yicong Li, Xun Yang, An Zhang +3
This paper identifies two kinds of redundancy in the current VideoQA paradigm. Specifically, the current video encoders tend to holistically embed all video clues at different gran…
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)…
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…
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…
Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagation
Sixiao Zhang, Hongxu Chen, Xiangguo Sun +2
Graph contrastive learning is the state-of-the-art unsupervised graph representation learning framework and has shown comparable performance with supervised approaches. However, ev…
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…
Hyperbolic Hypergraphs for Sequential Recommendation
Yicong Li, Hongxu Chen, Xiangguo Sun +5
Hypergraphs have been becoming a popular choice to model complex, non-pairwise, and higher-order interactions for recommender system. However, compared with traditional graph-based…