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arXiv · 2407.01851

Meerkat: Audio-Visual Large Language Model for Grounding in Space and Time

Sanjoy Chowdhury·Sayan Nag·Subhrajyoti Dasgupta·Jun Chen·Mohamed Elhoseiny·Ruohan Gao·Dinesh Manocha
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Abstract

Leveraging Large Language Models' remarkable proficiency in text-based tasks, recent works on Multi-modal LLMs (MLLMs) extend them to other modalities like vision and audio. However, the progress in these directions has been mostly focused on tasks that only require a coarse-grained understanding of the audio-visual semantics. We present Meerkat, an audio-visual LLM equipped with a fine-grained understanding of image and audio both spatially and temporally. With a new modality alignment module based on optimal transport and a cross-attention module that enforces audio-visual consistency, Meerkat can tackle challenging tasks such as audio referred image grounding, image guided audio temporal localization, and audio-visual fact-checking. Moreover, we carefully curate a large dataset AVFIT that comprises 3M instruction tuning samples collected from open-source datasets, and introduce MeerkatBench that unifies five challenging audio-visual tasks. We achieve state-of-the-art performance on all these downstream tasks with a relative improvement of up to 37.12%.

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BibTeXRIS

Sanjoy Chowdhury, Sayan Nag, Subhrajyoti Dasgupta, Jun Chen, Mohamed Elhoseiny, Ruohan Gao, Dinesh Manocha. 2024-07-03. Meerkat: Audio-Visual Large Language Model for Grounding in Space and Time. https://arxiv.org/abs/2407.01851

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