RPNet: Gait Recognition With Relationships Between Each Body-Parts
Why this work is in the frame
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Bibliographic record
Abstract
At present, many studies have shown that partitioning the gait sequence and its feature map can improve the accuracy of gait recognition. However, most models just cut the feature map at a fixed single scale, which loses the dependence between various parts. So, our paper proposes a structure called Part Feature Relationship Extractor (PFRE) to discover all of the relationships between each parts for gait recognition. The paper uses PFRE and a Convolutional Neural Network (CNN) to form the RPNet. PFRE is divided into two parts. One part that we call the Total-Partial Feature Extractor (TPFE) is used to extract the features of different scale blocks, and the other part, called the Adjacent Feature Relation Extractor (AFRE), is used to find the relationships between each block. At the same time, the paper adjusts the number of input frames during training to perform quantitative experiments and finds the rule between the number of input frames and the performance of the model. Our model is tested on three public gait datasets, CASIA-B, OU-LP and OU-MVLP. It exhibits a significant level of robustness to occlusion situations, and achieves accuracies of 92.82% and 80.26% on CASIA-B under BG # and CL # conditions, respectively. The results show that our method reaches the top level among state-of-the-art methods.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it