Ocean scientists have been collecting visual data to study marine organisms
for decades. These images and videos are extremely valuable both for basic
science and environmental monitoring tasks. There are tools for automatically
processing these data, but none that are capable of handling the extreme
variability in sample populations, image quality, and habitat characteristics
that are common in visual sampling of the ocean. Such distribution shifts can
occur over very short physical distances and in narrow time windows. Creating
models that are able to recognize when an image or video sequence contains a
new organism, an unusual collection of animals, or is otherwise out-of-sample
is critical to fully leverage visual data in the ocean. The FathomNet2023
competition dataset presents a realistic scenario where the set of animals in
the target data differs from the training data. The challenge is both to
identify the organisms in a target image and assess whether it is
out-of-sample.

海洋科学家收集了图像数据进行研究海洋生物多年来，这些图像和视频在基础科学和环境监测任务中非常有价值。然而，目前没有能够处理海洋视觉采样中普遍存在的样本种群的极端变异性、图像质量和栖息地特征的工具。为了充分利用海洋中的视觉数据，创建能够识别图像或视频序列中是否包含新生物、非常规动物集合或者是否越界的模型至关重要。FathomNet2023 竞赛数据集提供了一个真实的情景，其中目标数据中的动物集合与训练数据不同。挑战是在目标图像中识别生物并评估其是否越界。