4 papers
Parser-Free VLM Verification for Federated Weakly Supervised Video Anomaly Detection
Sébastien Thuau, Amira Gran, Siba Haidar +1
How can vision-language models help video anomaly detection (VAD) when surveillance data remain distributed, weakly labeled, and resource-constrained? Most weakly supervised VAD me…
Federated Binary Gating with Server-Side Vision-Language Inference for Surveillance Anomaly Classification
Côme-Alexis Puech, Sébastien Thuau, Amira Gran +3
Privacy-sensitive surveillance systems could benefit from large vision-language models (VLMs), but such models typically require centralized access to raw video. In federated learn…
Federated Learning for Video Violence Detection: Complementary Roles of Lightweight CNNs and Vision-Language Models for Energy-Efficient Use
Sébastien Thuau, Siba Haidar, Rachid Chelouah
Deep learning-based video surveillance increasingly demands privacy-preserving architectures with low computational and environmental overhead. Federated learning preserves privacy…
Frugal Federated Learning for Violence Detection: A Comparison of LoRA-Tuned VLMs and Personalized CNNs
Sébastien Thuau, Siba Haidar, Ayush Bajracharya +1
We examine frugal federated learning approaches to violence detection by comparing two complementary strategies: (i) zero-shot and federated fine-tuning of vision-language models (…