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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5901–5925 of 10420 papers

TitleStatusHype
Diffusion-Based Representation Learning—0
FoveaTer: Foveated Transformer for Image Classification—0
A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image ClassificationCode1
EDDA: Explanation-driven Data Augmentation to Improve Explanation Faithfulness—0
ResT: An Efficient Transformer for Visual RecognitionCode1
AutoSampling: Search for Effective Data Sampling Schedules—0
A systematic review of transfer learning based approaches for diabetic retinopathy detection—0
Learning Structures for Deep Neural Networks—0
Hamiltonian Deep Neural Networks Guaranteeing Non-vanishing Gradients by DesignCode1
Training Classifiers that are Universally Robust to All Label Noise LevelsCode0
Training With Data Dependent Dynamic Learning Rates—0
Encoders and Ensembles for Task-Free Continual Learning—0
Drawing Multiple Augmentation Samples Per Image During Training Efficiently Decreases Test Error—0
Dynamic Probabilistic Pruning: A general framework for hardware-constrained pruning at different granularities—0
Predict then Interpolate: A Simple Algorithm to Learn Stable ClassifiersCode1
ViPTT-Net: Video pretraining of spatio-temporal model for tuberculosis type classification from chest CT scansCode0
Nested Hierarchical Transformer: Towards Accurate, Data-Efficient and Interpretable Visual UnderstandingCode1
Semi-supervised learning of images with strong rotational disorder: assembling nanoparticle librariesCode0
FedScale: Benchmarking Model and System Performance of Federated Learning at ScaleCode1
StructuralLM: Structural Pre-training for Form Understanding—0
Improved OOD Generalization via Adversarial Training and Pre-training—0
Pulmonary embolism identification in computerized tomography pulmonary angiography scans with deep learning technologies in COVID-19 patients—0
PLM: Partial Label Masking for Imbalanced Multi-label Classification—0
AutoLRS: Automatic Learning-Rate Schedule by Bayesian Optimization on the FlyCode1
ActCooLR – High-Level Learning Rate Schedules using Activation Pattern Temperature—0
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All datasetsImageNetCIFAR-10CIFAR-100STL-10ObjectNetMNISTSVHNiNaturalist 2018ImageNet ReaLFlowers-102Clothing1Mmini WebVision 1.0

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91—Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98—Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94—Unverified
4DaViT-GTop 1 Accuracy90.4—Unverified
5DaViT-HTop 1 Accuracy90.2—Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2—Unverified
7SwinV2-GTop 1 Accuracy90.17—Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1—Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05—Unverified
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified