SOTAVerified

Image Augmentation

Image Augmentation is a data augmentation method that generates more training data from the existing training samples. Image Augmentation is especially useful in domains where training data is limited or expensive to obtain like in biomedical applications.

Source: Improved Image Augmentation for Convolutional Neural Networks by Copyout and CopyPairing

( Image credit: Kornia )

Papers

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Showing 1–25 of 308 papers

TitleStatusHype
Detectron2 Object Detection & Manipulating Images using CartoonizationCode4
UniMERNet: A Universal Network for Real-World Mathematical Expression RecognitionCode3
Differentiable Data Augmentation with KorniaCode3
AutoAugment: Learning Augmentation Policies from DataCode3
When Large Multimodal Models Confront Evolving Knowledge:Challenges and PathwaysCode2
Enhance Then Search: An Augmentation-Search Strategy with Foundation Models for Cross-Domain Few-Shot Object DetectionCode2
Diffusion-Enhanced Test-time Adaptation with Text and Image AugmentationCode2
XoFTR: Cross-modal Feature Matching TransformerCode2
A Survey on Data Augmentation in Large Model EraCode2
Deep PCB To COCO ConvertorCode2
CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documentsCode2
Random Erasing Data AugmentationCode2
Prompt-Free Conditional Diffusion for Multi-object Image AugmentationCode1
Image, Text, and Speech Data Augmentation using Multimodal LLMs for Deep Learning: A SurveyCode1
Swin Transformer with Enhanced Dropout and Layer-wise Unfreezing for Facial Expression Recognition in Mental Health DetectionCode1
Inversion Circle Interpolation: Diffusion-based Image Augmentation for Data-scarce ClassificationCode1
DIAGen: Diverse Image Augmentation with Generative ModelsCode1
AAPL: Adding Attributes to Prompt Learning for Vision-Language ModelsCode1
Diversified in-domain synthesis with efficient fine-tuning for few-shot classificationCode1
CLAP: Isolating Content from Style through Contrastive Learning with Augmented PromptsCode1
Diversify Your Vision Datasets with Automatic Diffusion-Based AugmentationCode1
MixPro: Data Augmentation with MaskMix and Progressive Attention Labeling for Vision TransformerCode1
CamDiff: Camouflage Image Augmentation via Diffusion ModelCode1
Improving the Transferability of Adversarial Samples by Path-Augmented MethodCode1
Intra-class Adaptive Augmentation with Neighbor Correction for Deep Metric LearningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified