SiamCLIM: Text-Based Pedestrian Search Via Multi-Modal Siamese Contrastive Learning
Bibliographic record
Abstract
Text-based pedestrian search (TBPS) aims at retrieving target persons from the image gallery through descriptive text queries. Despite remarkable progress in recent state-of-the-art approaches, previous works still struggle to efficiently extract discriminative features from multi-modal data. To address the problem of cross-modal fine-grained text-to-image, we proposed a novel Siamese Contrastive Language-Image Model (SiamCLIM). The model implements textual description and target-person interaction through deep bilateral projection, and siamese network structure to capture the relationship between text and image. Experiments show that our model significantly outperforms the state-of-the-art methods on cross-modal fine-grained matching tasks. We conduct the downstream task experiments on the benchmark dataset CUHK-PEDES and the experimental results demonstrate that our model is state-of-the-art and outperforms the current methods by 11.55%, 11.02%, and 7.76% in terms of top-1, top-5, and top-10 accuracy, respectively.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".