OptiFlex: video-based animal pose estimation using deep learning enhanced by optical flow
Bibliographic record
Abstract
Abstract Deep learning based animal pose estimation tools have greatly improved animal behaviour quantification. However, those tools all make predictions on individual video frames and do not account for variability of animal body shape in their model designs. Here, we introduce the first video-based animal pose estimation architecture, referred to as OptiFlex, which integrates a flexible base model to account for variability in animal body shape with an optical flow model to incorporate temporal context from nearby video frames. This approach can be combined with multi-view information, generating prediction enhancement using all four dimensions (3D space and time). To evaluate OptiFlex, we adopted datasets of four different lab animal species (mouse, fruit fly, zebrafish, and monkey) and proposed a more intuitive evaluation metric - percentage of correct key points (aPCK). Our evaluations show that OptiFlex provides the best prediction accuracy amongst current deep learning based tools, and that it can be readily applied to analyse a wide range of behaviours.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".