metadata
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.