May 15, 2024, 4:46 a.m. | Zeqing Wang, Wentao Wan, Qiqing Lao, Runmeng Chen, Minjie Lang, Keze Wang, Liang Lin

cs.CV updates on arXiv.org arxiv.org

arXiv:2311.17331v2 Announce Type: replace
Abstract: Recently, several methods have been proposed to augment large Vision Language Models (VLMs) for Visual Question Answering (VQA) simplicity by incorporating external knowledge from knowledge bases or visual clues derived from question decomposition. Although having achieved promising results, these methods still suffer from the challenge that VLMs cannot inherently understand the incorporated knowledge and might fail to generate the optimal answers. Contrarily, human cognition engages visual questions through a top-down reasoning process, systematically exploring relevant …

abstract agent arxiv augment cs.cv knowledge language language models multi-agent question question answering reasoning replace results simplicity type vision visual vlms vqa

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