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 3651–3675 of 10420 papers

TitleStatusHype
Feature Weaken: Vicinal Data Augmentation for Classification—0
Personalized Federated Learning with Hidden Information on Personalized Prior—0
Non-Coherent Over-the-Air Decentralized Gradient Descent—0
Towards Adversarial Robustness of Deep Vision Algorithms—0
Vision Transformers in Medical Imaging: A Review—0
TensAIR: Real-Time Training of Neural Networks from Data-streamsCode0
Invariant Learning via Diffusion Dreamed Distribution Shifts—0
Hyperbolic Sliced-Wasserstein via Geodesic and Horospherical ProjectionsCode0
A Transformer Framework for Data Fusion and Multi-Task Learning in Smart CitiesCode0
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
Efficient Feature Compression for Edge-Cloud SystemsCode0
FedFA: Federated Learning with Feature Anchors to Align Features and Classifiers for Heterogeneous DataCode1
Data-Centric Debugging: mitigating model failures via targeted data collection—0
Towards All-in-one Pre-training via Maximizing Multi-modal Mutual InformationCode1
DeepVoxNet2: Yet another CNN frameworkCode1
Improving the Computer-Aided Estimation of Ulcerative Colitis Severity According to Mayo Endoscopic Score by Using Regression-Based Deep LearningCode1
A Stable, Fast, and Fully Automatic Learning Algorithm for Predictive Coding Networks—0
Scalar Invariant Networks with Zero Bias—0
Masked Reconstruction Contrastive Learning with Information Bottleneck Principle—0
Probabilistic Deep Metric Learning for Hyperspectral Image ClassificationCode0
Bayesian Federated Neural Matching that Completes Full Information—0
Identifying Spurious Correlations and Correcting them with an Explanation-based Learning—0
Will Large-scale Generative Models Corrupt Future Datasets?Code0
Federated Adaptive Prompt Tuning for Multi-Domain Collaborative LearningCode1
Local Magnification for Data and Feature Augmentation—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