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
Prompt-Free Conditional Diffusion for Multi-object Image AugmentationCode1
rQdia: Regularizing Q-Value Distributions With Image Augmentation—0
GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models—0
Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention—0
Camera-based method for the detection of lifted truck axles using convolutional neural networks—0
When Large Multimodal Models Confront Evolving Knowledge:Challenges and PathwaysCode2
Automated Detection of Salvin's Albatrosses: Improving Deep Learning Tools for Aerial Wildlife Surveys—0
Fault Detection Method for Power Conversion Circuits Using Thermal Image and Convolutional Autoencoder—0
Language-Driven Dual Style Mixing for Single-Domain Generalized Object DetectionCode0
Batch Augmentation with Unimodal Fine-tuning for Multimodal LearningCode0
Effective Dual-Region Augmentation for Reduced Reliance on Large Amounts of Labeled DataCode0
Instruction-augmented Multimodal Alignment for Image-Text and Element Matching—0
Diffusion Models for Robotic Manipulation: A Survey—0
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 Networks—0
Anomaly Detection Using Computer Vision: A Comparative Analysis of Class Distinction and Performance Metrics—0
Semi-supervised Semantic Segmentation with Multi-Constraint Consistency LearningCode0
DynASyn: Multi-Subject Personalization Enabling Dynamic Action Synthesis—0
Exploring Token-Level Augmentation in Vision Transformer for Semi-Supervised Semantic SegmentationCode0
Tab2Visual: Overcoming Limited Data in Tabular Data Classification Using Deep Learning with Visual Representations—0
A framework for river connectivity classification using temporal image processing and attention based neural networks—0
Deep Ensembling with Multimodal Image Fusion for Efficient Classification of Lung Cancer—0
Image, Text, and Speech Data Augmentation using Multimodal LLMs for Deep Learning: A SurveyCode1
Multi-visual modality micro drone-based structural damage detection—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified