构建深海生物视觉识别数据集，助力海洋探索与生态研究
FathomVerse: A community science dataset for ocean animal discovery
- 收集深海12类形态动物图像，含8092个边界框标注
- 涵盖罕见生物如吸血鬼鱿鱼、海蜘蛛，挑战视觉识别极限
- 适合研究细粒度迁移学习与新物种发现的科研人员
计算机视觉能否助力海洋探索？当前计算机视觉主要识别人类日常接触的物体与动物，而深海中极少与人类接触的生物带来了全新的视觉挑战。本文提出FathomVerse v0检测数据集，包含3843张深海底栖图像，覆盖12种不同形态类群，共8092个边界框标注，数据采集自两个对计算机视觉而言全新且未被充分研究的深海区域。该数据集包含极具迷惑性的视觉场景，如章鱼缠绕海星，以及易混淆类别如吸血鬼鱿鱼和海蜘蛛。该数据集可推动细粒度迁移学习、新类别发现、物种分布建模及碳循环分析等前沿研究，对地球生态保护具有重要意义。
原文摘要 · Abstract (English)
Can computer vision help us explore the ocean? The ultimate challenge for computer vision is to recognize any visual phenomena, more than only the objects and animals humans encounter in their terrestrial lives. Previous datasets have explored everyday objects and fine-grained categories humans see frequently. We present the FathomVerse v0 detection dataset to push the limits of our field by exploring animals that rarely come in contact with people in the deep sea. These animals present a novel vision challenge. The FathomVerse v0 dataset consists of 3843 images with 8092 bounding boxes from 12 distinct morphological groups recorded at two locations on the deep seafloor that are new to computer vision. It features visually perplexing scenarios such as an octopus intertwined with a sea star, and confounding categories like vampire squids and sea spiders. This dataset can push forward research on topics like fine-grained transfer learning, novel category discovery, species distribution modeling, and carbon cycle analysis, all of which are important to the care and husbandry of our planet.
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