Cross-Modality Person Re-Identification Based on Dual-Path Multi-Branch Network
Why this work is in the frame
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Bibliographic record
Abstract
Person re-identification is an important surveillance task of searching and identifying pedestrian across different images or video frames. Despite a significant progress has been made in person re-identification based on RGB image sensors, few work focus on the person re-identification between RGB and infrared images, which is a challenging cross-modality problem and has been widely encountered in a dark environment or at night. In addition to the challenges for the same identity associated with variations in camera viewpoints and person poses, there is a non-negligible shift across different sensor modalities since the visual characteristics from RGB and infrared images are heterogeneous. In this paper, we propose a novel end-to-end dual-path multi-branch network for RGB-infrared cross-modality person re-identification, which introduces the multi-branch deep network architecture. The experimental results obtained with SYSU-MM01 datasets indicate that the proposed method can successfully transfer descriptive visual characteristic between RGB and infrared sensor modality. It can significantly outperform state-of-the-art conventional methods and convolutional neural network methods.
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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 | 0.004 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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