Segmentation Guided Attention Networks for Human Pose Estimation
Bibliographic record
Abstract
Human pose estimation is an important and widely studied task in computer vision.One of the difficulties in human pose estimation is that the model is vulnerable to complex backgrounds when making predictions.In this paper, we propose a deep high-resolution network based on segmentation guided.A conceptually simple but computationally efficient segmentation guided module is used to generate segmentation maps.The obtained segmentation map will be used as a spatial attention map in the feature extraction stage.Since the skeletal point region is used as the foreground in the segmentation map, the model pays more attention to the key point region to effectively reduce the influence of complex background on the prediction results.The segmentation guided module provides a spatial attention map with a priori knowledge, unlike the traditional spatial attention mechanism.To verify the effectiveness of our method, we conducted a series of comparison experiments on the MPII human pose dataset and the COCO2017 keypoint detection dataset.The highest boosting effect of our model compared to HRNet on the COCO2017 dataset is up to 3%.The experimental results show that this segmentation guidance mechanism is effective in improving accuracy.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".