Description

Book Synopsis

.- Anomaly Detection with Foundation Models.
.- GPT-4V-AD: Exploring Grounding Potential of VQA-oriented GPT-4V for Zero-shot Anomaly Detection.
.- CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detection.
.- DDPM-MoCo: Advancing Industrial Surface Defect Generation and Detection with Generative and Contrastive Learning.
.- Dual Memory-guided Probabilistic Model for Weakly-supervised Anomaly Detection.
.- Deep Learning for Human Activity Recognition.
.- Real-Time Human Action Prediction via Pose Kinematics.
.- Uncertainty Awareness for Unsupervised Domain Adaptation on Human Activity Recognition.
.- Deep Interaction Feature Fusion for Robust Human Activity Recognition.
.- How effective are Self-Supervised models for Contact Identification in Videos.
.- A Wearable Multi-Modal Edge-Computing System for Real-Time Kitchen Activity Recognition.

Human Activity Recognition and Anomaly Detection

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    £54.99

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    Order before 4pm tomorrow for delivery by Wed 17 Jun 2026.

    A Paperback by Kuan-Chuan Peng

    15 in stock


      View other formats and editions of Human Activity Recognition and Anomaly Detection by Kuan-Chuan Peng

      Publisher: Springer
      Publication Date: 17/11/2024
      ISBN13: 9789819790029, 978-9819790029
      ISBN10:
      Also in:
      Artificial intelligence (AI)

      Description

      Book Synopsis

      .- Anomaly Detection with Foundation Models.
      .- GPT-4V-AD: Exploring Grounding Potential of VQA-oriented GPT-4V for Zero-shot Anomaly Detection.
      .- CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detection.
      .- DDPM-MoCo: Advancing Industrial Surface Defect Generation and Detection with Generative and Contrastive Learning.
      .- Dual Memory-guided Probabilistic Model for Weakly-supervised Anomaly Detection.
      .- Deep Learning for Human Activity Recognition.
      .- Real-Time Human Action Prediction via Pose Kinematics.
      .- Uncertainty Awareness for Unsupervised Domain Adaptation on Human Activity Recognition.
      .- Deep Interaction Feature Fusion for Robust Human Activity Recognition.
      .- How effective are Self-Supervised models for Contact Identification in Videos.
      .- A Wearable Multi-Modal Edge-Computing System for Real-Time Kitchen Activity Recognition.

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