  This paper focuses on the challenge of answering questions in scenarios that
are composed of rich and complex dynamic audio-visual components. Although
existing Multimodal Large Language Models (MLLMs) can respond to audio-visual
content, these responses are sometimes ambiguous and fail to describe specific
audio-visual events. To overcome this limitation, we introduce the CAT, which
enhances MLLM in three ways: 1) besides straightforwardly bridging audio and
video, we design a clue aggregator that aggregates question-related clues in
dynamic audio-visual scenarios to enrich the detailed knowledge required for
large language models. 2) CAT is trained on a mixed multimodal dataset,
allowing direct application in audio-visual scenarios. Notably, we collect an
audio-visual joint instruction dataset named AVinstruct, to further enhance the
capacity of CAT to model cross-semantic correlations. 3) we propose AI-assisted
ambiguity-aware direct preference optimization, a strategy specialized in
retraining the model to favor the non-ambiguity response and improve the
ability to localize specific audio-visual objects. Extensive experimental
results demonstrate that CAT outperforms existing methods on multimodal tasks,
especially in Audio-Visual Question Answering (AVQA) tasks. The codes and the
collected instructions are released at https://github.com/rikeilong/Bay-CAT.
