AVCap: Leveraging Audio-Visual Features as Text Tokens for Captioning

• URL: http://arxiv.org/abs/2407.07801v2
• Date: Thu, 11 Jul 2024 02:38:14 GMT
• Title: AVCap: Leveraging Audio-Visual Features as Text Tokens for Captioning
• Authors: Jongsuk Kim, Jiwon Shin, Junmo Kim,
• Abstract summary: We propose AVCap, an Audio-Visual Captioning framework. AVCap utilizes audio-visual features as text tokens. Our method outperforms existing audio-visual captioning methods across all metrics.
• Score: 24.608569008975497
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In recent years, advancements in representation learning and language models have propelled Automated Captioning (AC) to new heights, enabling the generation of human-level descriptions. Leveraging these advancements, we propose AVCap, an Audio-Visual Captioning framework, a simple yet powerful baseline approach applicable to audio-visual captioning. AVCap utilizes audio-visual features as text tokens, which has many advantages not only in performance but also in the extensibility and scalability of the model. AVCap is designed around three pivotal dimensions: the exploration of optimal audio-visual encoder architectures, the adaptation of pre-trained models according to the characteristics of generated text, and the investigation into the efficacy of modality fusion in captioning. Our method outperforms existing audio-visual captioning methods across all metrics and the code is available on https://github.com/JongSuk1/AVCap

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