SOTAVerified

Image Classification

Image Classification is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike object detection, which involves classification and location of multiple objects within an image, image classification typically pertains to single-object images. When the classification becomes highly detailed or reaches instance-level, it is often referred to as image retrieval, which also involves finding similar images in a large database.

Source: Metamorphic Testing for Object Detection Systems

Papers

Showing 22762300 of 10420 papers

TitleStatusHype
Is user feedback always informative? Retrieval Latent Defending for Semi-Supervised Domain Adaptation without Source DataCode0
Investigation of Federated Learning Algorithms for Retinal Optical Coherence Tomography Image Classification with Statistical HeterogeneityCode0
Compound Figure Separation of Biomedical Images: Mining Large Datasets for Self-supervised LearningCode0
Investigating Weight-Perturbed Deep Neural Networks With Application in Iris Presentation Attack DetectionCode0
IPCL: Iterative Pseudo-Supervised Contrastive Learning to Improve Self-Supervised Feature RepresentationCode0
Spurious Feature Eraser: Stabilizing Test-Time Adaptation for Vision-Language Foundation ModelCode0
Invariant Shape Representation Learning For Image ClassificationCode0
Compositional Model based Fisher Vector Coding for Image ClassificationCode0
Investigating the Corruption Robustness of Image Classifiers with Random Lp-norm CorruptionsCode0
Artificial intelligence based glaucoma and diabetic retinopathy detection using MATLAB — retrained AlexNet convolutional neural networkCode0
Invariant backpropagation: how to train a transformation-invariant neural networkCode0
Artificial Generation of Big Data for Improving Image Classification: A Generative Adversarial Network Approach on SAR DataCode0
A Group-Theoretic Framework for Data AugmentationCode0
Intra-class Patch Swap for Self-DistillationCode0
Interpret Your Decision: Logical Reasoning Regularization for Generalization in Visual ClassificationCode0
Invariance encoding in sliced-Wasserstein space for image classification with limited training dataCode0
Is it enough to optimize CNN architectures on ImageNet?Code0
Joint Optimization Framework for Learning with Noisy LabelsCode0
Interpretable and Interactive Deep Multiple Instance Learning for Dental Caries Classification in Bitewing X-raysCode0
Complementing Representation Deficiency in Few-shot Image Classification: A Meta-Learning ApproachCode0
A Rotation Meanout Network with Invariance for Dermoscopy Image Classification and RetrievalCode0
InterpNET: Neural Introspection for Interpretable Deep LearningCode0
Competing Ratio Loss for Discriminative Multi-class Image ClassificationCode0
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)Code0
An Intelligent Remote Sensing Image Quality Inspection SystemCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94Unverified
4DaViT-GTop 1 Accuracy90.4Unverified
5DaViT-HTop 1 Accuracy90.2Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90Unverified