This paper introduces CAT, a novel approach to enhance Multimodal Large Language Models for Audio-Visual Question Answering (AVQA). CAT improves detailed knowledge enrichment, multimodal training strategies, and ambiguity elimination to outperform existing methods in AVQA tasks. The study demonstrates the effectiveness of CAT through extensive experimental results and comparisons with state-of-the-art models.
The content discusses the challenges of answering questions in dynamic audio-visual scenarios and proposes CAT as a solution. It focuses on enhancing MLLMs through various strategies like clue aggregation, mixed multimodal training, and ambiguity-aware optimization. The paper presents detailed experiments and results showcasing the superiority of CAT in AVQA tasks.
Key points include the introduction of CAT to address challenges in AVQA tasks, the methodology involving clue aggregation and multimodal training strategies, and the successful application of AI-assisted ambiguity-aware direct preference optimization to improve responses. Extensive experiments demonstrate CAT's superior performance compared to existing methods in various AVQA scenarios.
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arxiv.org
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