Descriptive Caption Enhancement with Visual Specialists for Multimodal Perception

• URL: http://arxiv.org/abs/2412.14233v2
• Date: Sun, 19 Jan 2025 05:38:52 GMT
• Title: Descriptive Caption Enhancement with Visual Specialists for Multimodal Perception
• Authors: Yanpeng Sun, Jing Hao, Ke Zhu, Jiang-Jiang Liu, Yuxiang Zhao, Xiaofan Li, Gang Zhang, Zechao Li, Jingdong Wang,
• Abstract summary: Training Large Multimodality Models relies on descriptive image caption that connects image and language. We propose to leverage off-the-shelf visual specialists, which were trained from annotated images initially not for image captioning, for enhancing the image caption.
• Score: 42.432917056115166
• License:
• Abstract: Training Large Multimodality Models (LMMs) relies on descriptive image caption that connects image and language. Existing methods either distill the caption from the LMM models or construct the captions from the internet images or by human. We propose to leverage off-the-shelf visual specialists, which were trained from annotated images initially not for image captioning, for enhancing the image caption. Our approach, named DCE, explores object low-level and fine-grained attributes (e.g., depth, emotion and fine-grained categories) and object relations (e.g., relative location and human-object-interaction (HOI)), and combine the attributes into the descriptive caption. Experiments demonstrate that such visual specialists are able to improve the performance for visual understanding tasks as well as reasoning that benefits from more accurate visual understanding. We will release the source code and the pipeline so that other visual specialists are easily combined into the pipeline. The complete source code of DCE pipeline and datasets will be available at \url{https://github.com/syp2ysy/DCE}.

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