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 5701–5725 of 10420 papers

TitleStatusHype
A Hybrid Generative and Discriminative PointNet on Unordered Point Sets—0
Learning from Attacks: Attacking Variational Autoencoder for Improving Image Classification—0
Guiding the retraining of convolutional neural networks against adversarial inputs—0
Creating Scalable AGI: the Open General Intelligence Framework—0
Demystifying What Code Summarization Models Learned—0
Lean classical-quantum hybrid neural network model for image classification—0
Locally-Transferred Fisher Vectors for Texture Classification—0
Learning from Exemplary Explanations—0
Learning from Few Samples: A Survey—0
Learning from Large-scale Noisy Web Data with Ubiquitous Reweighting for Image Classification—0
Local Magnification for Data and Feature Augmentation—0
LoCa: Logit Calibration for Knowledge Distillation—0
Learning from Matured Dumb Teacher for Fine Generalization—0
Learning from Mistakes based on Class Weighting with Application to Neural Architecture Search—0
GUIDED MCMC FOR SPARSE BAYESIAN MODELS TO DETECT RARE EVENTS IN IMAGES SANS LABELED DATA—0
Locally Differentially Private Gradient Tracking for Distributed Online Learning over Directed Graphs—0
Learning from Noisy Labels with Coarse-to-Fine Sample Credibility Modeling—0
Attention-Aware Noisy Label Learning for Image Classification—0
Locally optimal detection of stochastic targeted universal adversarial perturbations—0
gSwin: Gated MLP Vision Model with Hierarchical Structure of Shifted Window—0
Learning from Teaching Regularization: Generalizable Correlations Should be Easy to Imitate—0
Learning from Web Data: the Benefit of Unsupervised Object Localization—0
Growing Efficient Deep Networks by Structured Continuous Sparsification—0
Visually Consistent Hierarchical Image Classification—0
A Hybrid GA-PSO Method for Evolving Architecture and Short Connections of Deep Convolutional Neural Networks—0
Show:102550
← PrevPage 229 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
5DaViT-HTop 1 Accuracy90.2—Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified