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 4051–4075 of 10420 papers

TitleStatusHype
Federated Learning System without Model Sharing through Integration of Dimensional Reduced Data Representations—0
Federated Learning Versus Classical Machine Learning: A Convergence Comparison—0
Designing Extremely Memory-Efficient CNNs for On-device Vision Tasks—0
Federated Learning with Bayesian Differential Privacy—0
Federated Learning with Downlink Device Selection—0
Federated Learning with Privacy-Preserving Ensemble Attention Distillation—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 Multi-Target Domain Adaptation—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
ARTxAI: Explainable Artificial Intelligence Curates Deep Representation Learning for Artistic Images using Fuzzy Techniques—0
Analyzing Filters Toward Efficient ConvNet—0
Dermoscopic Image Classification with Neural Style Transfer—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
Depthwise-STFT based separable Convolutional Neural Networks—0
Depthwise Separable Convolutions Allow for Fast and Memory-Efficient Spectral Normalization—0
Depthwise Non-local Module for Fast Salient Object Detection Using a Single Thread—0
Analyzing Classifiers: Fisher Vectors and Deep Neural Networks—0
FedRSClip: Federated Learning for Remote Sensing Scene Classification Using Vision-Language Models—0
Adaptive Pixel-wise Structured Sparse Network for Efficient CNNs—0
FedSLD: Federated Learning with Shared Label Distribution for Medical Image Classification—0
FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networks—0
GreedyNAS: Towards Fast One-Shot NAS with Greedy Supernet—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