Optimizing domain-generalizable ReID through non-parametric normalization
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Bibliographic record
Abstract
Optimizing deep neural networks to generalize effectively across diverse visual domains remains a key challenge in computer vision, especially in domain-generalizable person re-identification (ReID). The goal of domain-generalizable ReID is to develop robust deep learning (DL) models that are effective across both known (source) and unseen (target) domains. However, many top-performing ReID methods overfit to the source domain, impairing their generalization ability. Previous approaches have employed Instance Normalization (IN) with learnable parameters to generalize domains and eliminate source domain styles. Recently, some DL frameworks have adopted normalization techniques without learnable parameters. We critically examine non-parametric normalization techniques for optimizing the deep ReID model, emphasizing the advantages of using non-parametric instance normalization as a gating mechanism to extract style-independent features at various abstraction levels within both convolutional neural networks (CNNs) and Vision Transformers (ViT). Our framework offers strategic guidance on the optimal placement of non-parametric IN within the network architecture to ensure effective information flow management in subsequent layers. Additionally, we employ one-dimensional Batch Normalization (BN) without learnable parameters at deeper network levels to remove content-related biases from the source domain. Our integrated approach, termed DualNormNP , systematically optimizes the model’s capacity to generalize across varied domains. Comprehensive evaluations on multiple benchmark ReID datasets demonstrate that our approach surpasses current state-of-the-art ReID methods in terms of generalization performance. Code is available on Github: https://github.com/mdamranhossenbhuiyan/DualNormNP • Critical Insights: Non-parametric normalization improves model generalization in ReID. • Novel Framework: DualNormNP uses non-parametric IN and BN to optimize ReID models. • Optimal Layer Integration: Identifying layers for non-parametric IN to optimize gating. • ViT Extension: DualNormNP adapted for ViTs to improve domain-invariant feature extraction.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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