17 citations · 36 across the 39 of their papers we have counts for
5 papers · 1 filter
Understanding Before Recommendation: Semantic Aspect-Aware Review Exploitation via Large Language Models
Fan Liu, Yaqi Liu, Huilin Chen +3
Recommendation systems harness user-item interactions like clicks and reviews to learn their representations. Previous studies improve recommendation accuracy and interpretability…
Attribute-driven Disentangled Representation Learning for Multimodal Recommendation
Zhenyang Li, Fan Liu, Yinwei Wei +3
Recommendation algorithms forecast user preferences by correlating user and item representations derived from historical interaction patterns. In pursuit of enhanced performance, m…
LION : Empowering Multimodal Large Language Model with Dual-Level Visual Knowledge
Gongwei Chen, Leyang Shen, Rui Shao +2
Multimodal Large Language Models (MLLMs) have endowed LLMs with the ability to perceive and understand multi-modal signals. However, most of the existing MLLMs mainly adopt vision…
Enhancing HOI Detection with Contextual Cues from Large Vision-Language Models
Yu-Wei Zhan, Fan Liu, Xin Luo +3
Human-Object Interaction (HOI) detection aims at detecting human-object pairs and predicting their interactions. However, conventional HOI detection methods often struggle to fully…
UNK-VQA: A Dataset and a Probe into the Abstention Ability of Multi-modal Large Models
Yangyang Guo, Fangkai Jiao, Zhiqi Shen +2
Teaching Visual Question Answering (VQA) models to refrain from answering unanswerable questions is necessary for building a trustworthy AI system. Existing studies, though have ex…