| --- |
| license: mit |
| library_name: pytorch |
| pipeline_tag: video-classification |
| tags: |
| - video-anomaly-detection |
| - weakly-supervised-learning |
| - transfer-learning |
| - clip |
| --- |
| |
| # Model Card for PATT-Net |
|
|
| ## Model Details
|
| - **Architecture:** ProtectedTransferVAD (dual-branch NativeTemporalBackbone)
|
| - **Base Feature Extractor:** OpenAI CLIP ViT-L/14 (768-dim)
|
| - **Temporal Modeling:** Bi-directional LSTM with Masked Temporal Dilated Convolutions
|
| - **Intended Use:** Video Anomaly Detection (VAD) research and controlled ablation studies.
|
|
|
| ## Training Data
|
| - **Pre-training (Temporal Branch):** PreVAD dataset (35,279 videos, distinct from XD-Violence).
|
| - **Target Fine-tuning:** UCF-Crime dataset.
|
|
|
| ## Evaluation and Validation
|
| - **Metric:** Frame-level AUC and AP, evaluated via center-crop (stride-16) visual-only features.
|
| - **Selection Criteria:** Best validation video AUC.
|
| - **Scientific Limits:** The P-B contrast represents the whole system effect (branch mechanism + pretraining). Within this matched frozen-branch design, P-R isolates PreVAD rather than random source initialization. The P-R differences are small and not statistically significant across five seeds (AUC exact two-sided sign-flip `p = 0.1875`). |
|
|
| ## Ethical Considerations and Licenses
|
| - The repository code is provided under the MIT License. Third-party dataset and model terms continue to apply to data-derived artifacts. |
| - Users must comply with the licenses of the underlying third-party datasets (UCF-Crime and PreVAD) and the OpenAI CLIP feature extractor. The repository authors are not responsible for the misuse of these models in surveillance or real-world classification scenarios without appropriate safeguards.
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