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 10001–10050 of 10420 papers

TitleStatusHype
A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning—0
Adversarial Perturbations Against Deep Neural Networks for Malware Classification—0
Convolutional Neural Fabrics—0
Selective Unsupervised Feature Learning with Convolutional Neural Network (S-CNN)—0
Integrated perception with recurrent multi-task neural networks—0
Unifying Geometric Features and Facial Action Units for Improved Performance of Facial Expression Analysis—0
Recursive Autoconvolution for Unsupervised Learning of Convolutional Neural NetworksCode0
Approximate Log-Hilbert-Schmidt Distances Between Covariance Operators for Image Classification—0
PPP: Joint Pointwise and Pairwise Image Label Prediction—0
On Benefits of Selection Diversity via Bilevel Exclusive Sparsity—0
Deep Decision Network for Multi-Class Image Classification—0
Region Ranking SVM for Image Classification—0
Hierarchical Gaussian Descriptor for Person Re-Identification—0
Conditional Graphical Lasso for Multi-Label Image Classification—0
Hyperspectral Image Classification with Support Vector Machines on Kernel Distribution Embeddings—0
FractalNet: Ultra-Deep Neural Networks without ResidualsCode0
Wide Residual NetworksCode1
Fine-to-coarse Knowledge Transfer For Low-Res Image Classification—0
End-to-End Kernel Learning with Supervised Convolutional Kernel Networks—0
On the Sampling Strategy for Evaluation of Spectral-spatial Methods in Hyperspectral Image Classification—0
MUST-CNN: A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-based Protein Structure PredictionCode0
Image Classification of Grapevine Buds using Scale-Invariant Features Transform, Bag of Features and Support Vector Machines—0
Distributed stochastic optimization for deep learning (thesis)—0
Image Colorization Using a Deep Convolutional Neural Network—0
Deep Learning with Eigenvalue Decay RegularizerCode0
Towards Better Analysis of Deep Convolutional Neural Networks—0
A Classifier-guided Approach for Top-down Salient Object Detection—0
Learning rotation invariant convolutional filters for texture classificationCode0
Network of Experts for Large-Scale Image Categorization—0
Churn analysis using deep convolutional neural networks and autoencoders—0
CNN-RNN: A Unified Framework for Multi-label Image ClassificationCode0
Latent Model Ensemble with Auto-localization—0
Object Detection from Video Tubelets with Convolutional Neural NetworksCode0
Understanding How Image Quality Affects Deep Neural NetworksCode0
Deep Residual Networks with Exponential Linear UnitCode0
Going Deeper with Contextual CNN for Hyperspectral Image ClassificationCode0
A Classification Leveraged Object Detector—0
The Curious Robot: Learning Visual Representations via Physical Interactions—0
Deep Networks with Stochastic DepthCode1
Sparse Activity and Sparse Connectivity in Supervised Learning—0
Evolution of active categorical image classification via saccadic eye movement—0
Deep Learning At Scale and At Ease—0
Joint Projection and Dictionary Learning using Low-rank Regularization and Graph Constraints—0
Identity Mappings in Deep Residual NetworksCode1
Revisiting Batch Normalization For Practical Domain AdaptationCode0
Learning Binary Codes and Binary Weights for Efficient Classification—0
Discriminative models for robust image classification—0
Variational Autoencoders for Semi-supervised Text Classification—0
Decision Forests, Convolutional Networks and the Models in-BetweenCode0
Convolutional Patch Representations for Image Retrieval: an Unsupervised Approach—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