ArticleProceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition2022
Self-Supervised Keypoint Discovery in Behavioral Videos.
Article in Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Embodied postural dynamics underlie behavioral diversity in a benthic sessile chordate.iScience · 2026Article
- Mapping the landscape of social behavior.Cell · 2025Article
- Detecting keypoints with semantic labels on skull point cloud for plastic surgery.Quantitative imaging in medicine and surgery · 2025Article
- Mapping the landscape of social behavior.bioRxiv : the preprint server for biology · 2024Article
- DAMM for the detection and tracking of multiple animals within complex social and environmental settings.Scientific reports · 2024Article
- Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics.Nature methods · 2024Article
- Lightning Pose: improved animal pose estimation via semi-supervised learning, Bayesian ensembling and cloud-native open-source tools.Nature methods · 2024Article
- SuperAnimal pretrained pose estimation models for behavioral analysis.Nature communications · 2024Article
- Lightning Pose: improved animal pose estimation via semi-supervised learning, Bayesian ensembling, and cloud-native open-source tools.bioRxiv : the preprint server for biology · 2024Article
- DAMM for the detection and tracking of multiple animals within complex social and environmental settings.bioRxiv : the preprint server for biology · 2024Article
- Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics.bioRxiv : the preprint server for biology · 2023Article
- Review
- Integrated software for multi-dimensional analysis of motion using tracking, electrophysiology, and sensor signals.Frontiers in bioengineering and biotechnology · 2023Article
- Identifying behavioral structure from deep variational embeddings of animal motion.Communications biology · 2022Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
We propose a method for learning the posture and structure of agents from unlabelled behavioral videos. Starting from the observation that behaving agents are generally the main sources of movement in behavioral videos, our method, Behavioral Keypoint Discovery (B-KinD), uses an encoder-decoder architecture with a geometric bottleneck to reconstruct the spatiotemporal difference between video frames. By focusing only on regions of movement, our approach works directly on input videos without requiring manual annotations. Experiments on a variety of agent types (mouse, fly, human, jellyfish, and trees) demonstrate the generality of our approach and reveal that our discovered keypoints represent semantically meaningful body parts, which achieve state-of-the-art performance on keypoint regression among self-supervised methods. Additionally, B-KinD achieve comparable performance to supervised keypoints on downstream tasks, such as behavior classification, suggesting that our method can dramatically reduce model training costs vis-a-vis supervised methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.