Efficient Audio-Visual Fusion for Video Classification

Mahrukh Awan, Asmar Nadeem, Armin Mustafa
11/8/2024
cs.CV

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Abstract

We present Attend-Fusion, a novel and efficient approach for audio-visual fusion in video classification tasks. Our method addresses the challenge of exploiting both audio and visual modalities while maintaining a compact model architecture. Through extensive experiments on the YouTube-8M dataset, we demonstrate that our Attend-Fusion achieves competitive performance with significantly reduced model complexity compared to larger baseline models.

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Cite this paper

@article{awan2024efficient,
  title  = {Efficient Audio-Visual Fusion for Video Classification},
  author = {Mahrukh Awan and Asmar Nadeem and Armin Mustafa},
  year   = {2024},
  eprint = {2411.05603},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2411.05603v1}
}

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