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Full Description
The multi-volume set LNCS 15623 until LNCS 15646 constitutes the proceedings of the workshops that were held in conjunction with the 18th European Conference on Computer Vision, ECCV 2024, which took place in Milan, Italy, during September 29-October 4, 2024.
These LNCS volumes contain 574 accepted papers from 53 of the 73 workshops. The list of workshops and distribution of the workshop papers in the LNCS volumes can be found in the preface that is freely accessible online.
Contents
.- On the Application of Egocentric Computer Vision to Industrial Inspection.
.- NeuroSymbolic Visual Transform based on Logic Tensor Network for Defect Detection.
.- Multimodal computer vision techniques for wooden utility pole density esti mation with contact-free sensing.
.- Dynamic Label Injection for Imbalanced Industrial Defect Segmentation.
.- XAI-guided Insulator Anomaly Detection for Imbalanced Datasets.
.- Exploring Multi-modal Neural Scene Representations With Applications on Thermal Imaging.
.- Foreground-Aware Knowledge Distillation for Enhanced Damage Detection.
.- AnomalyFactory: Regard Anomaly Generation as Unsupervised Anomaly Localization.
.- Interactive Explainable Anomaly Detection for Industrial Settings.
.- DAS3D: Dual-modality Anomaly Synthesis for 3D Anomaly Detection.
.- SQUAD: Scalar Quantized representation learning for Unsupervised Anomaly Detection and localization.
.- Deep Unsupervised Segmentation of Log Point Clouds.
.- A Computer Vision System for Automatic Edge Detection of Magnetic Grain Profile.
.- Find the Assembly Mistakes: Error Segmentation for Industrial Applications.
.- EM Based Nano-Scale Defect Analysis in Semiconductor Man ufacturing for Advanced IC Nodes.
.- On The Relationship between Visual Anomaly-free and Anomalous Representations.
.- DIE-VIS: an Automated Visual Inspection System for Cardboard Box Manufacturing.
.- When the Small-Loss Trick is Not Enough: Multi-Label Image Classification with Noisy Labels Applied to CCTV Sewer Inspections.
.- AnomalousPatchCore: Exploring the Use of Anomalous Samples in Industrial Anomaly Detection.
.- Self-supervised Models are Strong Industrial Few-shot Classification Learners.
.- Hyperspectral Imaging and Computer Vision Based Remote Monitoring of SO2 Emissions in Maritime Vessels.
.- Temporal-consistent CAMs for Weakly Supervised Video Segmentation in Waste Sorting.
.- Sequential PatchCore: Anomaly Detection for Surface Inspection using Synthetic Impurities.
.- SplatPose+: Real Time Image-Based Pose-Agnostic 3D Anomaly Detection.
.- BBD-Polyp: Weakly Supervised Polyp Segmentation via Bounding Box and
Depth Map.
.- ENSTRECT: A Stage-based Approach to 2.5D Structural Damage Detection.
.- An Augmentation-based Model Re-adaptation Framework for Robust Image Segmentation.
.- Meta Learning-Driven Iterative Refinement for Robust Anomaly Detection in Industrial Inspection.
