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 8926–8950 of 10420 papers

TitleStatusHype
CMVAE: Causal Meta VAE for Unsupervised Meta-LearningCode0
ClusterFit: Improving Generalization of Visual RepresentationsCode0
Clustered Task-Aware Meta-Learning by Learning from Learning PathsCode0
Enhancing Neural Network Representations with Prior Knowledge-Based NormalizationCode0
CLOFAI: A Dataset of Real And Fake Image Classification Tasks for Continual LearningCode0
CLIP model is an Efficient Online Lifelong LearnerCode0
A Modular System for Enhanced Robustness of Multimedia Understanding Networks via Deep Parametric EstimationCode0
Attend and Guide (AG-Net): A Keypoints-driven Attention-based Deep Network for Image RecognitionCode0
Domain-Adaptive Pre-training of Self-Supervised Foundation Models for Medical Image Classification in Gastrointestinal EndoscopyCode0
Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language TasksCode0
Clinically-Inspired Hierarchical Multi-Label Classification of Chest X-rays with a Penalty-Based Loss FunctionCode0
Class Prototype-based Cleaner for Label Noise LearningCode0
A Transformer Framework for Data Fusion and Multi-Task Learning in Smart CitiesCode0
A Transfer Learning and Explainable Solution to Detect mpox from Smartphones imagesCode0
Perceptual Evaluation of Adversarial Attacks for CNN-based Image ClassificationCode0
Image-Caption Encoding for Improving Zero-Shot GeneralizationCode0
Model Accuracy and Runtime Tradeoff in Distributed Deep Learning:A Systematic StudyCode0
CLASS-M: Adaptive stain separation-based contrastive learning with pseudo-labeling for histopathological image classificationCode0
Image classification and retrieval with random depthwise signed convolutional neural networksCode0
Adversarial Robustness Assessment: Why both L_0 and L_ Attacks Are NecessaryCode0
A Training Framework for Optimal and Stable Training of Polynomial Neural NetworksCode0
A trainable monogenic ConvNet layer robust in front of large contrast changes in image classificationCode0
Class-Level Logit PerturbationCode0
Multi-head Spatial-Spectral Mamba for Hyperspectral Image ClassificationCode0
Low-Rank Subspace Override for Unsupervised Domain AdaptationCode0
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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