MS2CAST: A Video Anomaly Detection Framework via Multi-Scale Spatial Cross-Attention and Efficient Spatiotemporal Networks
Video anomaly detection (VAD) is a challenging task for surveillance systems due to the scarcity and diversity of anomalous events.
ProofPaper ↗
Key points
- While semi-supervised VAD approaches have advanced the field, many rely on computationally intensive optical flow networks to capture motion dynamics.
- Convolutional neural networks (CNNs) are also widely used for spatial encoding, but their effective receptive field is highly local.
- To address these limitations, we introduce MS2CAST, an end-to-end semi-supervised predictive VAD framework.
- To model spatiotemporal dynamics without optical flow estimation, we integrate two interchangeable spatiotemporal modules in the latent space.
Sources (1)
- [1]MS2CAST: A Video Anomaly Detection Framework via Multi-Scale Spatial Cross-Attention and Efficient Spatiotemporal NetworksarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 12:29 PM
Video anomaly detection (VAD) is a challenging task for surveillance systems due to the scarcity and diversity of anomalous events.
While semi-supervised VAD approaches have advanced the field, many rely on computationally intensive optical flow networks to capture motion dynamics.
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