Multi‐granularity re‐ranking for visible‐infrared person re‐identification
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
Abstract Visible‐infrared person re‐identification (VI‐ReID) is a supplementary task of single‐modality re‐identification, which makes up for the defect of conventional re‐identification under insufficient illumination. It is more challenging than single‐modality ReID because, in addition to difficulties in pedestrian posture, camera shooting angle and background change, there are also difficulties in the cross‐modality gap. Existing works only involve coarse‐grained global features in the re‐ranking calculation, which cannot effectively use fine‐grained features. However, fine‐grained features are particularly important due to the lack of information in cross‐modality re‐ID. To this end, the Q‐center Multi‐granularity K‐reciprocal Re‐ranking Algorithm (termed QCMR) is proposed, including a Q‐nearest neighbour centre encoder (termed QNC) and a Multi‐granularity K‐reciprocal Encoder (termed MGK) for a more comprehensive feature representation. QNC converts the probe‐corresponding modality features into gallery corresponding modality features through modality transfer to narrow the modality gap. MGK takes a coarse‐grained mutual nearest neighbour as the dominant and combines a fine‐grained nearest neighbour as a supplement for similarity measurement. Extensive experiments on two widely used VI‐ReID benchmarks, SYSU‐MM01 and RegDB have shown that our method achieves state‐of‐the‐art results. Especially, the mAP of SYSU‐MM01 is increased by 5.9% in all‐search mode.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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