Video-Teller: Enhancing Cross-Modal Generation with Fusion and Decoupling

• URL: http://arxiv.org/abs/2310.04991v3
• Date: Wed, 11 Oct 2023 07:20:32 GMT
• Title: Video-Teller: Enhancing Cross-Modal Generation with Fusion and Decoupling
• Authors: Haogeng Liu, Qihang Fan, Tingkai Liu, Linjie Yang, Yunzhe Tao, Huaibo Huang, Ran He, Hongxia Yang
• Abstract summary: Video-Teller is a video-language foundation model that leverages multi-modal fusion and fine-grained modality alignment. Video-Teller boosts the training efficiency by utilizing frozen pretrained vision and language modules. It capitalizes on the robust linguistic capabilities of large language models, enabling the generation of both concise and elaborate video descriptions.
• Score: 79.49128866877922
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper proposes Video-Teller, a video-language foundation model that leverages multi-modal fusion and fine-grained modality alignment to significantly enhance the video-to-text generation task. Video-Teller boosts the training efficiency by utilizing frozen pretrained vision and language modules. It capitalizes on the robust linguistic capabilities of large language models, enabling the generation of both concise and elaborate video descriptions. To effectively integrate visual and auditory information, Video-Teller builds upon the image-based BLIP-2 model and introduces a cascaded Q-Former which fuses information across frames and ASR texts. To better guide video summarization, we introduce a fine-grained modality alignment objective, where the cascaded Q-Former's output embedding is trained to align with the caption/summary embedding created by a pretrained text auto-encoder. Experimental results demonstrate the efficacy of our proposed video-language foundation model in accurately comprehending videos and generating coherent and precise language descriptions. It is worth noting that the fine-grained alignment enhances the model's capabilities (4% improvement of CIDEr score on MSR-VTT) with only 13% extra parameters in training and zero additional cost in inference.

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