Papers › LoTE-Animal: A Long Time-span Dataset for Endangered Animal Behavior Understanding
LoTE-Animal: A Long Time-span Dataset for Endangered Animal Behavior Understanding
Dan Liu, Jin Hou, Shaoli Huang, Jing Liu, Yuxin He, Bochuan Zheng, Jifeng Ning, Jingdong Zhang
Understanding and analyzing animal behavior is increasingly essential to protect endangered animal species. However, the application of advanced computer vision techniques in this regard is minimal, which boils down to lacking large and diverse datasets for training deep models. To break the deadlock, we present LoTE-Animal, a large-scale endangered animal dataset collected over 12 years, to foster the application of deep learning in rare species conservation. The collected data contains vast variations such as ecological seasons, weather conditions, periods, viewpoints, and habitat scenes. So far, we retrieved at least 500K videos and 1.2 million images. Specifically, we selected and annotated 11 endangered animals for behavior understanding, including 10K video sequences for the action recognition task, 28K images for object detection, instance segmentation, and pose estimation tasks. In addition, we gathered 7K web images of the same species as source domain data for the domain adaptation task. We provide evaluation results of representative vision understanding approaches and cross-domain experiments. LoTE-Animal dataset would facilitate the community to research more advanced machine learning models and explore new tasks to aid endangered animal conservation. Our dataset will be released with the paper.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Action Recognition | LoTE-Animal | SlowOnly r50 | Accuracy (Top-1) | 79.39 | #1 of 4 | Archive leaderboard | report |
| Action Recognition | LoTE-Animal | SlowFast r50 | Accuracy (Top-1) | 71.79 | #2 of 4 | Archive leaderboard | report |
| Action Recognition | LoTE-Animal | TimeSformer | Accuracy (Top-1) | 70.24 | #3 of 4 | Archive leaderboard | report |
| Action Recognition | LoTE-Animal | SlowOnly r101 | Accuracy (Top-1) | 68.98 | #4 of 4 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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