29 citations · 30 across the 4 of their papers we have counts for
8 papers
Open Multimodal Retrieval-Augmented Factual Image Generation
Yang Tian, Fan Liu, Jingyuan Zhang +3
Large Multimodal Models (LMMs) have achieved remarkable progress in generating photorealistic and prompt-aligned images, but they often produce outputs that contradict verifiable k…
CoRe-MMRAG: Cross-Source Knowledge Reconciliation for Multimodal RAG
Yang Tian, Fan Liu, Jingyuan Zhang +3
Multimodal Retrieval-Augmented Generation (MMRAG) has been introduced to enhance Multimodal Large Language Models by incorporating externally retrieved multimodal knowledge, but it…
Mitigating Hallucination Through Theory-Consistent Symmetric Multimodal Preference Optimization
Wenqi Liu, Xuemeng Song, Jiaxi Li +4
Direct Preference Optimization (DPO) has emerged as an effective approach for mitigating hallucination in Multimodal Large Language Models (MLLMs). Although existing methods have a…
FineCIR: Explicit Parsing of Fine-Grained Modification Semantics for Composed Image Retrieval
Zixu Li, Zhiheng Fu, Yupeng Hu +3
Composed Image Retrieval (CIR) facilitates image retrieval through a multimodal query consisting of a reference image and modification text. The reference image defines the retriev…
Fine-grained Textual Inversion Network for Zero-Shot Composed Image Retrieval
Haoqiang Lin, Haokun Wen, Xuemeng Song +3
Composed Image Retrieval (CIR) allows users to search target images with a multimodal query, comprising a reference image and a modification text that describes the user's modifica…
A Comprehensive Survey on Composed Image Retrieval
Xuemeng Song, Haoqiang Lin, Haokun Wen +3
Composed Image Retrieval (CIR) is an emerging yet challenging task that allows users to search for target images using a multimodal query, comprising a reference image and a modifi…