Adaptive Metric Learning and Probe-Specific Reranking for Person Reidentification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this letter, we introduce an adaptive metric learning (AML) method for person reidentification. Different from conventional metric learning approaches, which treat all the negative samples equally, AML adaptively classifies the negative samples into three groups and pays different attention to them. By emphasizing the influence of hard negative samples, AML can better mine the discriminative information between positive and negative samples, and thus generate a more effective metric. Furthermore, we also propose a probe-specific reranking (PSR) algorithm to refine the initial ranking list measured by the learned metric. For each probe, PSR constructs a corresponding hypergraph to capture the neighborhood relationship between the probe and its top 100 ranked gallery images. Then, these images are reranked based on their neighborhood affinity in the hypergraph. Extensive experiments on three challenging datasets demonstrate the superiority of both AML and PSR.
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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 | 0.001 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 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