Towards Explainable Person Re- Identification
Bibliographic record
Abstract
Visually recognizing an individual in a crowded area using a distributed camera network is essential for a range of biometric and security applications. We propose a shift in perspective of the ongoing re-identification studies, towards creating more explainable and coherent models that are applicable in real-world engineering problems, even if this comes with a slight decrease in performance. The proposed explainable model uses attribute classification to perform the task of re-identification. This method steps away from intrusive and controversial techniques such as facial recognition to improve public acceptance of re-identification models. Current methods of person re-identification do not explain the importance of each attribute in determining the results, and often use complicated and esoteric algorithms to improve the performance on closed-world datasets which may not represent more realistic open-world scenarios. We applied our approach to the Market-1501 dataset and examined the impact of careful selection of backbone outputs for each individual attribute in our experiment. Our simple model is capable of performing attribute classification for 0-shot re-identification that is explainable and less intrusive when compared to state-of-the-art models focused on re-identification.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".