​​ow we aim to utilize DensePose pseudo-labels obtained on the unlabeled frames for retraining the network.
6. To be able to select good point-wise predictions our model has to know how to estimate it's uncertainty for every pixel and for every task which we are solving. We introduce a novel Auto-Calibrated version of DensePose-RCNN which can estimate the uncertainty of its predictions for every pixel and every task. We propose to model: (a) Classification uncertainty (for object classification and segmentation) using temperature scaling in the softmax layer and; (b) regression uncertainty (for bounding box proposals and DensePose UV-maps) by prediction of a Gaussian distribution instead of a single target value. The higher the predicted variance the higher the uncertainty.
7. Now given pixel-wise uncertainties we can sample for the second round of training only those foreground points from the selected 100k points which have the highest confidence. We have experimented with different sampling strategies and show that sampling based on the confidences from fine-grained tasks (24-way body part segmentation, UV-maps) results in the bests performance.
8. The network retrained on the augmented data (existing human annotations + pseudo-labeled animals) show a significant performance boost on the hold out manually annotated DensePose-Chimps dataset.
The video demonstration of the self-trained model: youtu.be/OU3Ayg_l4QM
9. We also show that the proposed Auto-Calibrated RCNN improves over the baseline even without self-training on standard DensePose-Human, detection and segmentation tasks. This is due to the higher robustness of the proposed model to unseen data distributions at test time.

✔️Conclusion:
- Studied the problem of extending dense body pose recognition to animal species and suggested that doing this at scale requires learning from unlabelled data;
- demonstrated that existing detection, segmentation, and dense pose labeling models can transfer very well to a proximal animal class such as chimpanzee despite significant inter-class differences;
- introduced Auto-Calibrated DensePose-RCNN which can estimate the uncertainty of its predictions;
- introduced novel DensePose-Chips dataset for benchmarking dense pose prediction for Chimpanzees;
- showed that substantial improvements can be obtained by carefully selecting which categories to use to pre-train the model, by using a class-agnostic architecture to integrate different sources of information;
- and by modeling labeling uncertainty to grade pseudo-labels for self-training;
- achieved excellent performance without using a single labeled image of the target class for training.