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 4026–4050 of 10420 papers

TitleStatusHype
Feature Weaken: Vicinal Data Augmentation for Classification—0
Feature Whitening via Gradient Transformation for Improved Convergence—0
FedAvg with Fine Tuning: Local Updates Lead to Representation Learning—0
FedBABU: Toward Enhanced Representation for Federated Image Classification—0
Design of Image Matched Non-Separable Wavelet using Convolutional Neural Network—0
Beyond Fine-tuning: Classifying High Resolution Mammograms using Function-Preserving Transformations—0
Adaptive Temperature Scaling with Conformal Prediction—0
Beyond Entropy: Style Transfer Guided Single Image Continual Test-Time Adaptation—0
Analyzing Images for Music Recommendation—0
FedDiff: Diffusion Model Driven Federated Learning for Multi-Modal and Multi-Clients—0
FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning—0
FedDropoutAvg: Generalizable federated learning for histopathology image classification—0
Compositional Attribute Imbalance in Vision Datasets—0
Grafit: Learning fine-grained image representations with coarse labels—0
Designing Extremely Memory-Efficient CNNs for On-device Vision Tasks—0
Federated Deep Learning with Bayesian Privacy—0
Federated Distillation for Medical Image Classification: Towards Trustworthy Computer-Aided Diagnosis—0
Beyond Empirical Risk Minimization: Local Structure Preserving Regularization for Improving Adversarial Robustness—0
Designing Adaptive Neural Networks for Energy-Constrained Image Classification—0
Federated Learning for Commercial Image Sources—0
Federated Learning for Medical Image Classification: A Comprehensive Benchmark—0
Descriptive analysis of computational methods for automating mammograms with practical applications—0
Beyond cross-entropy: learning highly separable feature distributions for robust and accurate classification—0
Federated Learning Over Images: Vertical Decompositions and Pre-Trained Backbones Are Difficult to Beat—0
Analyzing Filters Toward Efficient ConvNet—0
Show:102550
← PrevPage 162 of 417Next →
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