{"product_id":"computer-vision-eccv-2024-workshops-9783031928048","title":"Computer Vision  ECCV 2024 Workshops","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e.- On the Application of Egocentric Computer Vision to Industrial Inspection.\u003cbr\u003e.- NeuroSymbolic Visual Transform based on Logic Tensor Network for Defect Detection.\u003cbr\u003e.- Multimodal computer vision techniques for wooden utility pole density esti mation with contact-free sensing.\u003cbr\u003e.- Dynamic Label Injection for Imbalanced Industrial Defect Segmentation.\u003cbr\u003e.- XAI-guided Insulator Anomaly Detection for Imbalanced Datasets.\u003cbr\u003e.- Exploring Multi-modal Neural Scene Representations With Applications on Thermal Imaging.\u003cbr\u003e.- Foreground-Aware Knowledge Distillation for Enhanced Damage Detection.\u003cbr\u003e.- AnomalyFactory: Regard Anomaly Generation as Unsupervised Anomaly Localization.\u003cbr\u003e.- Interactive Explainable Anomaly Detection for Industrial Settings.\u003cbr\u003e.- DAS3D: Dual-modality Anomaly Synthesis for 3D Anomaly Detection.\u003cbr\u003e.- SQUAD: Scalar Quantized representation learning for Unsupervised Anomaly Detection and localization.\u003cbr\u003e.- Deep Unsupervised Segmentation of Log Point Clouds.\u003cbr\u003e.- A Computer Vision System for Automatic Edge Detection of Magnetic Grain Profile.\u003cbr\u003e.- Find the Assembly Mistakes: Error Segmentation for Industrial Applications.\u003cbr\u003e.- EM Based Nano-Scale Defect Analysis in Semiconductor Man ufacturing for Advanced IC Nodes.\u003cbr\u003e.- On The Relationship between Visual Anomaly-free and Anomalous Representations.\u003cbr\u003e.- DIE-VIS: an Automated Visual Inspection System for Cardboard Box Manufacturing.\u003cbr\u003e.- When the Small-Loss Trick is Not Enough: Multi-Label Image Classification with Noisy Labels Applied to CCTV Sewer Inspections.\u003cbr\u003e.- AnomalousPatchCore: Exploring the Use of Anomalous Samples in Industrial Anomaly Detection.\u003cbr\u003e.- Self-supervised Models are Strong Industrial Few-shot Classification Learners.\u003cbr\u003e.- Hyperspectral Imaging and Computer Vision Based Remote Monitoring of SO2 Emissions in Maritime Vessels.\u003cbr\u003e.- Temporal-consistent CAMs for Weakly Supervised Video Segmentation in Waste Sorting.\u003cbr\u003e.- Sequential PatchCore: Anomaly Detection for Surface Inspection using Synthetic Impurities.\u003cbr\u003e.- SplatPose+: Real Time Image-Based Pose-Agnostic 3D Anomaly Detection.\u003cbr\u003e.- BBD-Polyp: Weakly Supervised Polyp Segmentation via Bounding Box and\u003cbr\u003eDepth Map.\u003cbr\u003e.- ENSTRECT: A Stage-based Approach to 2.5D Structural Damage Detection.\u003cbr\u003e.- An Augmentation-based Model Re-adaptation Framework for Robust Image Segmentation.\u003cbr\u003e.- Meta Learning-Driven Iterative Refinement for Robust Anomaly Detection in Industrial Inspection.\u003c\/p\u003e","brand":"Springer","offers":[{"title":"Default Title","offer_id":53195483382103,"sku":"9783031928048","price":123.49,"currency_code":"GBP","in_stock":true}],"url":"https:\/\/bookcurl.com\/products\/computer-vision-eccv-2024-workshops-9783031928048","provider":"Book Curl","version":"1.0","type":"link"}