Similarity Regulation and Calibration Alignment for Weakly Supervised Text-Based Person Re-Identification
Bibliographic record
Abstract
Traditional text-based person re-identification relies on identity labels. However, it is impossible to annotate large datasets, since identity annotation is expensive and time-consuming. Weakly supervised text-based person re-identification, where only text–image pairs are available without annotation of identities, is very practical in real life. While dealing with the weakly supervised person re-identification, two issues should be strengthed, i.e., alignment caused by different modal, and cross-modal matching ambiguity caused by the lack of identity labels. In this article, we propose a similarity regulation and calibration alignment (SRCA) framework, which consists of two unimodal encoders for images and text, respectively, and a multi-modal encoder for the masked language modeling task. First, a similarity regulation (SR) strategy is proposed to relax the strict one-to-one constraints for the local similarities between different pairs by introducing a novel soft objective. The soft objective can adjust hard objectives to achieve soft cross-modal alignment by establishing a many-to-many relationship between two modalities. Second, the calibration alignment (CA) module is proposed to improve intra-class compactness by modeling pseudo-label assignment as optimal transport. The ambiguity of cross-modal matching can be reduced by aligning features and pseudo-labels of different modalities and gradually calibrating the distribution of pseudo-labels. Experimental results show that our method has achieved obvious advantages compared with existing methods and also demonstrated competitive performance compared with fully supervised methods.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".