73 citations · 155 across the 10 of their papers we have counts for
14 papers
Forbid Your Attention: Fooling Multimodal Large Language Models by Selectively Removing Intrinsic Focus in Spectral Domain
Daizong Liu, Junhao Dong, Zhiyuan Ma +6
Multimodal large language models (MLLMs) have extended the capability of large language models (LLMs) to process more contextual multimodal information, showing remarkable progress…
Annotations Are Not All You Need: A Cross-modal Knowledge Transfer Network for Unsupervised Temporal Sentence Grounding
Xiang Fang, Daizong Liu, Wanlong Fang +4
This paper addresses the task of temporal sentence grounding (TSG). Although many respectable works have made decent achievements in this important topic, they severely rely on mas…
Not All Inputs Are Valid: Towards Open-Set Video Moment Retrieval Using Language
Xiang Fang, Wanlong Fang, Daizong Liu +8
Video Moment Retrieval (VMR) targets to retrieve the specific moment corresponding to a sentence query from an untrimmed video. Although recent works have made remarkable progress…
Fewer Steps, Better Performance: Efficient Cross-Modal Clip Trimming for Video Moment Retrieval Using Language
Xiang Fang, Daizong Liu, Wanlong Fang +5
Given an untrimmed video and a sentence query, video moment retrieval using language (VMR) aims to locate a target query-relevant moment. Since the untrimmed video is overlong, alm…
Rethinking Weakly-supervised Video Temporal Grounding From a Game Perspective
Xiang Fang, Zeyu Xiong, Wanlong Fang +7
This paper addresses the challenging task of weakly-supervised video temporal grounding. Existing approaches are generally based on the moment proposal selection framework that uti…
Multi-Pair Temporal Sentence Grounding via Multi-Thread Knowledge Transfer Network
Xiang Fang, Wanlong Fang, Changshuo Wang +5
Given some video-query pairs with untrimmed videos and sentence queries, temporal sentence grounding (TSG) aims to locate query-relevant segments in these videos. Although previous…
You Can Ground Earlier than See: An Effective and Efficient Pipeline for Temporal Sentence Grounding in Compressed Videos
Xiang Fang, Daizong Liu, Pan Zhou +1
Given an untrimmed video, temporal sentence grounding (TSG) aims to locate a target moment semantically according to a sentence query. Although previous respectable works have made…
Hypotheses Tree Building for One-Shot Temporal Sentence Localization
Daizong Liu, Xiang Fang, Pan Zhou +3
Given an untrimmed video, temporal sentence localization (TSL) aims to localize a specific segment according to a given sentence query. Though respectable works have made decent ac…
Multi-Modal Cross-Domain Alignment Network for Video Moment Retrieval
Xiang Fang, Daizong Liu, Pan Zhou +1
As an increasingly popular task in multimedia information retrieval, video moment retrieval (VMR) aims to localize the target moment from an untrimmed video according to a given la…
Hierarchical Local-Global Transformer for Temporal Sentence Grounding
Xiang Fang, Daizong Liu, Pan Zhou +2
This paper studies the multimedia problem of temporal sentence grounding (TSG), which aims to accurately determine the specific video segment in an untrimmed video according to a g…
V3H: View Variation and View Heredity for Incomplete Multi-view Clustering
Xiang Fang, Yuchong Hu, Pan Zhou +1
Real data often appear in the form of multiple incomplete views. Incomplete multi-view clustering is an effective method to integrate these incomplete views. Previous methods only…
Double Self-weighted Multi-view Clustering via Adaptive View Fusion
Xiang Fang, Yuchong Hu
Multi-view clustering has been applied in many real-world applications where original data often contain noises. Some graph-based multi-view clustering methods have been proposed t…
ANIMC: A Soft Framework for Auto-weighted Noisy and Incomplete Multi-view Clustering
Xiang Fang, Yuchong Hu, Pan Zhou +1
Multi-view clustering has wide applications in many image processing scenarios. In these scenarios, original image data often contain missing instances and noises, which is ignored…
Unbalanced Incomplete Multi-view Clustering via the Scheme of View Evolution: Weak Views are Meat; Strong Views do Eat
Xiang Fang, Yuchong Hu, Pan Zhou +1
Incomplete multi-view clustering is an important technique to deal with real-world incomplete multi-view data. Previous works assume that all views have the same incompleteness, i.…