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

Showing 1120 of 308 papers

TitleStatusHype
Effective Dual-Region Augmentation for Reduced Reliance on Large Amounts of Labeled DataCode0
Instruction-augmented Multimodal Alignment for Image-Text and Element Matching0
Diffusion Models for Robotic Manipulation: A Survey0
Enhance Then Search: An Augmentation-Search Strategy with Foundation Models for Cross-Domain Few-Shot Object DetectionCode2
An Empirical Study of Validating Synthetic Data for Text-Based Person RetrievalCode0
Enhancing Pavement Crack Classification with Bidirectional Cascaded Neural Networks0
Anomaly Detection Using Computer Vision: A Comparative Analysis of Class Distinction and Performance Metrics0
Semi-supervised Semantic Segmentation with Multi-Constraint Consistency LearningCode0
DynASyn: Multi-Subject Personalization Enabling Dynamic Action Synthesis0
Exploring Token-Level Augmentation in Vision Transformer for Semi-Supervised Semantic SegmentationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0Unverified