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 2451–2475 of 10420 papers

TitleStatusHype
Gall Bladder Cancer Detection from US Images with Only Image Level LabelsCode0
Class-Incremental Grouping Network for Continual Audio-Visual LearningCode1
SABLE: Secure And Byzantine robust LEarning—0
SparseSwin: Swin Transformer with Sparse Transformer BlockCode1
Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated LearningCode1
DiffAug: Enhance Unsupervised Contrastive Learning with Domain-Knowledge-Free Diffusion-based Data AugmentationCode1
When to Learn What: Model-Adaptive Data Augmentation CurriculumCode1
HAct: Out-of-Distribution Detection with Neural Net Activation Histograms—0
RR-CP: Reliable-Region-Based Conformal Prediction for Trustworthy Medical Image Classification—0
TMComposites: Plug-and-Play Collaboration Between Specialized Tsetlin MachinesCode0
Decoding visual brain representations from electroencephalography through Knowledge Distillation and latent diffusion modelsCode0
Adversarial attacks on hybrid classical-quantum Deep Learning models for Histopathological Cancer Detection—0
Privacy Preserving Federated Learning with Convolutional Variational Bottlenecks—0
Representation Synthesis by Probabilistic Many-Valued Logic Operation in Self-Supervised Learning—0
Improving Resnet-9 Generalization Trained on Small Datasets—0
How adversarial attacks can disrupt seemingly stable accurate classifiers—0
Signatures of Bayesian inference emerge from energy efficient synapsesCode0
Improving Image Classification of Knee Radiographs: An Automated Image Labeling Approach—0
Multiclass Alignment of Confidence and Certainty for Network Calibration—0
Federated Learning Over Images: Vertical Decompositions and Pre-Trained Backbones Are Difficult to Beat—0
AdaPlus: Integrating Nesterov Momentum and Precise Stepsize Adjustment on AdamW BasisCode0
Dynamic Early Exiting Predictive Coding Neural Networks—0
Compressing Vision Transformers for Low-Resource Visual LearningCode0
Building a Winning Team: Selecting Source Model Ensembles using a Submodular Transferability Estimation Approach—0
Locality-Aware Hyperspectral ClassificationCode1
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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
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2—Unverified
6DaViT-HTop 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
10RevCol-HTop 1 Accuracy90—Unverified