Image Augmentation
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Benchmarks
Most implemented
AutoAugment: Learning Augmentation Policies from Data
Improved Regularization of Convolutional Neural Networks with Cutout
Unsupervised Data Augmentation for Consistency Training
Random Erasing Data Augmentation
Fast AutoAugment
Learning Data Augmentation Strategies for Object Detection
Papers
Empowering Local Agriculture: A Deep Learning-Powered Web System for Identifying Bangladeshi Mango Varieties
Mango variety identification in Bangladesh is challenging because closely related cultivars can have similar visual characteristics and images are often captured under varying real-world conditions. This work presents a …
Image AugmentationFailure-Informed Image Self-Augmentation for Multimodal Large Language Model Self-Improvement
Multimodal large language models (MLLMs) have achieved remarkable performance across vision-language tasks, but their progress depends heavily on large-scale, high-quality multimodal data that are costly to annotate. Sel…
Visual Question AnsweringImage AugmentationMedDiffuseMix: Preserving Diagnostic Evidence with Saliency-Aware Diffusion Medical Image Data Augmentatio
Limited data availability, class imbalance, and domain variability remain major barriers to reliable medical image classification. Conventional augmentation can improve training diversity but may distort diagnostically i…
Medical Image ClassificationImage AugmentationA welding penetration prediction model for laser welding process based on self-supervised learning using physics-informed neural networks
The laser welding full-penetration is of critical importance, as it constitutes one of the fundamental factors in achieving defect-free welded joints. Accurate prediction of the penetration state is therefore essential f…
Self-Supervised LearningContrastive LearningImage AugmentationFew-Shot LearningMaking the Discrete Continuous: Synthetic RAW Augmentations for Fine-Grained Evaluation of Person Detection Performance in Low Light
Real-world deployment of AI vision models is both fueled and limited by the data available for training and testing. Real datasets are sparse and uneven: long-tailed or unbalanced distributions hinder generalization, and…
Pedestrian DetectionImage AugmentationAutonomous DrivingObject DetectionCAD-Free Learning of Spacecraft Pose Estimators via NeRF-Based Augmentations
Spacecraft pose estimation networks require tens of thousands of CAD-rendered images to be trained. This reliance on synthetic CAD data (i) limits applicability to targets with reliable geometry prior, excluding uncooper…
Domain GeneralizationImage AugmentationPose Estimation