Hand Pose Estimation through Semi-Supervised and Weakly-Supervised\n Learning
Bibliographic record
Abstract
We propose a method for hand pose estimation based on a deep regressor\ntrained on two different kinds of input. Raw depth data is fused with an\nintermediate representation in the form of a segmentation of the hand into\nparts. This intermediate representation contains important topological\ninformation and provides useful cues for reasoning about joint locations. The\nmapping from raw depth to segmentation maps is learned in a\nsemi/weakly-supervised way from two different datasets: (i) a synthetic dataset\ncreated through a rendering pipeline including densely labeled ground truth\n(pixelwise segmentations); and (ii) a dataset with real images for which ground\ntruth joint positions are available, but not dense segmentations. Loss for\ntraining on real images is generated from a patch-wise restoration process,\nwhich aligns tentative segmentation maps with a large dictionary of synthetic\nposes. The underlying premise is that the domain shift between synthetic and\nreal data is smaller in the intermediate representation, where labels carry\ngeometric and topological meaning, than in the raw input domain. Experiments on\nthe NYU dataset show that the proposed training method decreases error on\njoints over direct regression of joints from depth data by 15.7%.\n
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".