Improved Method for Pedestrian Recognition Based on Generative Adversarial Networks
Why this work is in the frame
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Bibliographic record
Abstract
Traditional supervisory person re-id technology learning methods mainly relies on pre-marked image data, but there are a lot of unlabeled data in actual security scene, which seriously limits the application of person re-id technology in security monitoring field. Therefore, it is very important to study the semi-supervised learning of unlabeled data generated by antagonistic network. In the process of using GAN to generate data, in this paper we use global and local information of pedestrian image to generate realistic pedestrian image conditionally, and trains robust feature representations for different intra-class changes of cameras, so as to improve the accuracy of person re-id. The experimental results show that this method is more effective than the benchmark method. The performance of dataset Market1501 and Duke MTMC-reID improved by 4% and 3% respectively.
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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 | 0.009 | 0.009 |
| 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 | 0.001 |
| Open science | 0.001 | 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