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 9151–9200 of 10420 papers

TitleStatusHype
Feature Aligning Few shot Learning Method Using Local Descriptors Weighted Rules—0
Feature Alignment and Representation Transfer in Knowledge Distillation for Large Language Models—0
Feature Augmentation for Self-supervised Contrastive Learning: A Closer Look—0
Feature-based Graph Attention Networks Improve Online Continual Learning—0
Feature CAM: Interpretable AI in Image Classification—0
Feature Channel Adaptive Enhancement for Fine-Grained Visual Classification—0
Feature Density Estimation for Out-of-Distribution Detection via Normalizing Flows—0
Feature Embedding by Template Matching as a ResNet Block—0
Feature-EndoGaussian: Feature Distilled Gaussian Splatting in Surgical Deformable Scene Reconstruction—0
Feature Extraction of Hyperspectral Images With Image Fusion and Recursive Filtering—0
Feature Kernel Distillation—0
Feature Learning beyond the Lazy-Rich Dichotomy: Insights from Representational Geometry—0
Feature Learning to Automatically Assess Radiographic Knee Osteoarthritis Severity—0
Feature-level augmentation to improve robustness of deep neural networks to affine transformations—0
Feature Losses for Adversarial Robustness—0
Feature Map Convergence Evaluation for Functional Module—0
Feature Preserving Shrinkage on Bayesian Neural Networks via the R2D2 Prior—0
Feature Representation in Convolutional Neural Networks—0
Features based Mammogram Image Classification using Weighted Feature Support Vector Machine—0
Feature selection of neural networks is skewed towards the less abstract cue—0
Feature Space Augmentation for Long-Tailed Data—0
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
FedBIAD: Communication-Efficient and Accuracy-Guaranteed Federated Learning with Bayesian Inference-Based Adaptive Dropout—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
Federated Active Learning (F-AL): an Efficient Annotation Strategy for Federated Learning—0
Federated Deep Learning with Bayesian Privacy—0
Federated Distillation for Medical Image Classification: Towards Trustworthy Computer-Aided Diagnosis—0
Federated Learning Across Decentralized and Unshared Archives for Remote Sensing Image Classification—0
Federated Learning for Commercial Image Sources—0
Federated Learning for Medical Image Classification: A Comprehensive Benchmark—0
Federated Learning of Shareable Bases for Personalization-Friendly Image Classification—0
Federated Learning Over Images: Vertical Decompositions and Pre-Trained Backbones Are Difficult to Beat—0
Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysis—0
Federated Learning System without Model Sharing through Integration of Dimensional Reduced Data Representations—0
Federated Learning Versus Classical Machine Learning: A Convergence Comparison—0
Federated Learning with Bayesian Differential Privacy—0
Federated Learning with Downlink Device Selection—0
Federated Learning with Privacy-Preserving Ensemble Attention Distillation—0
Federated Model Search via Reinforcement Learning—0
Federated Multi-Target Domain Adaptation—0
Federated Variational Inference: Towards Improved Personalization and Generalization—0
FedGEMS: Federated Learning of Larger Server Models via Selective Knowledge Fusion—0
FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise—0
FedNS: Improving Federated Learning for collaborative image classification on mobile clients—0
FedRSClip: Federated Learning for Remote Sensing Scene Classification Using Vision-Language Models—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
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