8 citations · 10 across the 16 of their papers we have counts for
17 papers
Knowledge Poisoning Attacks on Medical Multi-Modal Retrieval-Augmented Generation
Peiru Yang, Haoran Zheng, Tong Ju +6
Retrieval-augmented generation (RAG) is a widely adopted paradigm for enhancing LLMs in medical applications by incorporating expert multimodal knowledge during generation. However…
ReCoVR: Closing the Loop in Interactive Composed Video Retrieval
Bingqing Zhang, Yi Zhang, Zhuo Cao +4
Composed video retrieval (CoVR) searches for target videos using a reference video and a modification text, but existing methods are restricted to a single interaction round and ca…
Robust Test-time Video-Text Retrieval: Benchmarking and Adapting for Query Shifts
Bingqing Zhang, Zhuo Cao, Heming Du +4
Modern video-text retrieval (VTR) models excel on in-distribution benchmarks but are highly vulnerable to real-world query shifts, where the distribution of query data deviates fro…
Beyond Static LLM Policies: Imitation-Enhanced Reinforcement Learning for Recommendation
Yi Zhang, Lili Xie, Ruihong Qiu +2
Recommender systems (RecSys) have become critical tools for enhancing user engagement by delivering personalized content across diverse digital platforms. Recent advancements in la…
MARCO: A Cooperative Knowledge Transfer Framework for Personalized Cross-domain Recommendations
Lili Xie, Yi Zhang, Ruihong Qiu +2
Recommender systems frequently encounter data sparsity issues, particularly when addressing cold-start scenarios involving new users or items. Multi-source cross-domain recommendat…
Quantifying and Narrowing the Unknown: Interactive Text-to-Video Retrieval via Uncertainty Minimization
Bingqing Zhang, Zhuo Cao, Heming Du +4
Despite recent advances, Text-to-video retrieval (TVR) is still hindered by multiple inherent uncertainties, such as ambiguous textual queries, indistinct text-video mappings, and…
Stable Preference Optimization: A Bilevel Approach to Catastrophic Preference Shift
Chengtao Jian, Kai Yang, Tianhao Gao +5
Direct Preference Learning has emerged as a dominant offline paradigm for preference optimization. Most of these methods are based on the Bradley-Terry (BT) model for pairwise pref…
RePaViT: Scalable Vision Transformer Acceleration via Structural Reparameterization on Feedforward Network Layers
Xuwei Xu, Yang Li, Yudong Chen +2
We reveal that feedforward network (FFN) layers, rather than attention layers, are the primary contributors to Vision Transformer (ViT) inference latency, with their impact signify…
DARLR: Dual-Agent Offline Reinforcement Learning for Recommender Systems with Dynamic Reward
Yi Zhang, Ruihong Qiu, Xuwei Xu +2
Model-based offline reinforcement learning (RL) has emerged as a promising approach for recommender systems, enabling effective policy learning by interacting with frozen world mod…
TokenBinder: Text-Video Retrieval with One-to-Many Alignment Paradigm
Bingqing Zhang, Zhuo Cao, Heming Du +4
Text-Video Retrieval (TVR) methods typically match query-candidate pairs by aligning text and video features in coarse-grained, fine-grained, or combined (coarse-to-fine) manners.…
Navigating Beyond Instructions: Vision-and-Language Navigation in Obstructed Environments
Haodong Hong, Sen Wang, Zi Huang +2
Real-world navigation often involves dealing with unexpected obstructions such as closed doors, moved objects, and unpredictable entities. However, mainstream Vision-and-Language N…
ROLeR: Effective Reward Shaping in Offline Reinforcement Learning for Recommender Systems
Yi Zhang, Ruihong Qiu, Jiajun Liu +1
Offline reinforcement learning (RL) is an effective tool for real-world recommender systems with its capacity to model the dynamic interest of users and its interactive nature. Mos…
Why Only Text: Empowering Vision-and-Language Navigation with Multi-modal Prompts
Haodong Hong, Sen Wang, Zi Huang +2
Current Vision-and-Language Navigation (VLN) tasks mainly employ textual instructions to guide agents. However, being inherently abstract, the same textual instruction can be assoc…
No Token Left Behind: Efficient Vision Transformer via Dynamic Token Idling
Xuwei Xu, Changlin Li, Yudong Chen +3
Vision Transformers (ViTs) have demonstrated outstanding performance in computer vision tasks, yet their high computational complexity prevents their deployment in computing resour…
GTP-ViT: Efficient Vision Transformers via Graph-based Token Propagation
Xuwei Xu, Sen Wang, Yudong Chen +3
Vision Transformers (ViTs) have revolutionized the field of computer vision, yet their deployments on resource-constrained devices remain challenging due to high computational dema…
Understanding the Effects of Projectors in Knowledge Distillation
Yudong Chen, Sen Wang, Jiajun Liu +4
Conventionally, during the knowledge distillation process (e.g. feature distillation), an additional projector is often required to perform feature transformation due to the dimens…
Plug n' Play: Channel Shuffle Module for Enhancing Tiny Vision Transformers
Xuwei Xu, Sen Wang, Yudong Chen +1
Vision Transformers (ViTs) have demonstrated remarkable performance in various computer vision tasks. However, the high computational complexity hinders ViTs' applicability on devi…