📢AnomalyCLIP: Harnessing CLIP for Weakly-Supervised Video Anomaly Recognition In this week’s deep dive, we explore AnomalyCLIP, the first method to adapt CLIP’s vision–language latent space for Video Anomaly Recognition (VAR) under weak supervision. We break down how it learns a normality prototype, uses semantic directions via context optimized prompts, and then leverages an Axial Transformer to jointly detect and classify anomalies across frames. We also share our hands-on implementations - from .npy feature pipelines to raw video inference - including frame saving, per-class probabilities, and majority-vote anomaly recognition. 👉🏼What’s Covered? ☑️Why AnomalyCLIP? The gap between Video Anomaly Detection (VAD) and Recognition (VAR) ☑️AnomalyCLIP for Video Anomaly Recognition Framework ☑️Semantic Multiple Instance Learning with CoOp Prompt Learning ☑️Training objectives: directionality, sparsity, and smoothness losses ☑️Troubleshooting common dependency issues and environment setup ☑️Our dual implementation: .npy features vs raw video + checkpoint pipeline This blog post is a complete, practical guide to AnomalyCLIP - bridging research insights and reproducible code for real-world video anomaly recognition. 👉🏼Read More: learnopencv.com/anomalyclip-vi… #AnomalyCLIP #CLIP #VideoAnomalyDetection #VideoAnomalyRecognition #WeaklySupervisedLearning #ComputerVision #SurveillanceAI #MultimodalAI #Transformer #CoOp #DeepLearning