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| Format: | Preprint |
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2024
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| Online-Zugang: | https://arxiv.org/abs/2412.15574 |
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| _version_ | 1866915072983433216 |
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| author | Yoshida, Takero Ito, Yuikazu Fujiwara, Yoshihiro Tsuchida, Shinji Sugiyama, Daisuke Matsuoka, Daisuke |
| author_facet | Yoshida, Takero Ito, Yuikazu Fujiwara, Yoshihiro Tsuchida, Shinji Sugiyama, Daisuke Matsuoka, Daisuke |
| contents | Japan Agency for Marine-Earth Science and Technology (JAMSTEC) has made available the JAMSTEC Earth Deep-sea Image (J-EDI), a deep-sea video and image archive (https://www.godac.jamstec.go.jp/jedi/e/index.html). This archive serves as a valuable resource for researchers and scholars interested in deep-sea imagery. The dataset comprises images and videos of deep-sea phenomena, predominantly of marine organisms, but also of the seafloor and physical processes. In this study, we propose J-EDI QA, a benchmark for understanding images of deep-sea organisms using a multimodal large language model (LLM). The benchmark is comprised of 100 images, accompanied by questions and answers with four options by JAMSTEC researchers for each image. The QA pairs are provided in Japanese, and the benchmark assesses the ability to understand deep-sea species in Japanese. In the evaluation presented in this paper, OpenAI o1 achieved a 50% correct response rate. This result indicates that even with the capabilities of state-of-the-art models as of December 2024, deep-sea species comprehension is not yet at an expert level. Further advances in deep-sea species-specific LLMs are therefore required. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_15574 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | J-EDI QA: Benchmark for deep-sea organism-specific multimodal LLM Yoshida, Takero Ito, Yuikazu Fujiwara, Yoshihiro Tsuchida, Shinji Sugiyama, Daisuke Matsuoka, Daisuke Computer Vision and Pattern Recognition Japan Agency for Marine-Earth Science and Technology (JAMSTEC) has made available the JAMSTEC Earth Deep-sea Image (J-EDI), a deep-sea video and image archive (https://www.godac.jamstec.go.jp/jedi/e/index.html). This archive serves as a valuable resource for researchers and scholars interested in deep-sea imagery. The dataset comprises images and videos of deep-sea phenomena, predominantly of marine organisms, but also of the seafloor and physical processes. In this study, we propose J-EDI QA, a benchmark for understanding images of deep-sea organisms using a multimodal large language model (LLM). The benchmark is comprised of 100 images, accompanied by questions and answers with four options by JAMSTEC researchers for each image. The QA pairs are provided in Japanese, and the benchmark assesses the ability to understand deep-sea species in Japanese. In the evaluation presented in this paper, OpenAI o1 achieved a 50% correct response rate. This result indicates that even with the capabilities of state-of-the-art models as of December 2024, deep-sea species comprehension is not yet at an expert level. Further advances in deep-sea species-specific LLMs are therefore required. |
| title | J-EDI QA: Benchmark for deep-sea organism-specific multimodal LLM |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.15574 |