Advances in semi and self-supervised learning techniques for cross-domain visual inspection tools

A Comprehensive Survey on Self-Supervised Learning on Computer Vision: Moving Beyond the Supervised Paradigm

This survey presents a seven-category taxonomy of self-supervised learning methods covering: 1) input reconstruction or restoration, 2) context prediction, 3) contrastive learning, 4) feature clustering, 5) self-distillation-based feature reconstruction, 6) redundancy reduction, and 7) masked image modeling.

Lowering the Barrier to AI-Driven Inspection: A No-Code Workflow for Automated Structural Defect Detection

Structural health monitoring (SHM) is essential in modern engineering, providing data for condition-based maintenance, lifecycle assessment, and predictive decision-making. Traditionally, SHM relied on visual inspection to detect defects such as cracks and deformations. Early computer vision (CV) methods, including thr...

Domain-Specific Self-Supervised Representation Learning for Retinal Fundus Classification

The results show that tailoring augmentation strategies to the characteristics of retinal images plays a critical role in improving performance, and even under constrained settings, lightweight SSL frameworks can learn transferable representations that reduce dependence on large annotated datasets and achieve competiti...

Semi-Supervised Surgical Tool Detection via Curated Pseudo-Labeling

Dynamic adaptation and boundary-aware learning for semi-supervised medical image segmentation.

DABAL is proposed, a semi-supervised framework designed to improve both supervision reliability and contour localization and introduces a Dynamic-static Domain Adaptive Adapter (DDAA) into the Segment Anything Model (SAM) encoder to preserve stable structural priors while providing input-dependent feature compensation...

Learning Where to Focus: Self-Supervised Multi-Scale ViTs for Histopathology

CRAFT (Coarse-to-fine Region-Adaptive Feature Tokenization), a DINO-based framework that learns image-dependent mixed-scale representations by using self-supervised attention to selectively refine informative regions while preserving coarse context, together with a symmetric cross-scale regularization objective that en...