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 7001–7050 of 10420 papers

TitleStatusHype
Zen-NAS: A Zero-Shot NAS for High-Performance Deep Image Recognition—0
A Novel Approach for Classification and Forecasting of Time Series in Particle Accelerators—0
Few-shot Image Classification with Multi-Facet Prototypes—0
Classification of Shoulder X-Ray Images with Deep Learning Ensemble Models—0
[Re] Can gradient clipping mitigate label noise?—0
Spectral Roll-off Points Variations: Exploring Useful Information in Feature Maps by Its Variations—0
[Re] A Reproduction of Ensemble Distribution DistillationCode0
Ultrasound Image Classification using ACGAN with Small Training DatasetCode0
Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysis—0
The Deep Radial Basis Function Data Descriptor (D-RBFDD) Network: A One-Class Neural Network for Anomaly Detection—0
CORL: Compositional Representation Learning for Few-Shot Classification—0
Information contraction in noisy binary neural networks and its implications—0
CNN with large memory layers—0
Learning task-agnostic representation via toddler-inspired learning—0
Spatio-temporal Data Augmentation for Visual Surveillance—0
Deep Learning Generalization and the Convex Hull of Training Sets—0
Hyperspectral Image Classification: Artifacts of Dimension Reduction on Hybrid CNNCode0
Cross Knowledge-based Generative Zero-Shot Learning Approach with Taxonomy Regularization—0
DenseNet for Breast Tumor Classification in Mammographic ImagesCode0
MinConvNets: A new class of multiplication-less Neural Networks—0
Learning degraded image classification with restoration data fidelity—0
A Comprehensive Survey on Hardware-Aware Neural Architecture Search—0
FedNS: Improving Federated Learning for collaborative image classification on mobile clients—0
Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data—0
Beyond Fine-tuning: Classifying High Resolution Mammograms using Function-Preserving Transformations—0
Riemannian Manifold Optimization for Discriminant Subspace Learning—0
Initialization Using Perlin Noise for Training Networks with a Limited Amount of Data—0
Dissonance Between Human and Machine Understanding—0
What Do Deep Nets Learn? Class-wise Patterns Revealed in the Input Space—0
Machine learning with limited data—0
Benchmarking Perturbation-based Saliency Maps for Explaining Atari AgentsCode0
Knowledge Distillation Methods for Efficient Unsupervised Adaptation Across Multiple Domains—0
A Layer-Wise Information Reinforcement Approach to Improve Learning in Deep Belief Networks—0
Should Ensemble Members Be Calibrated?—0
Advancing Eosinophilic Esophagitis Diagnosis and Phenotype Assessment with Deep Learning Computer Vision—0
Deep learning based prediction of Alzheimer's disease from magnetic resonance images—0
3D-ANAS: 3D Asymmetric Neural Architecture Search for Fast Hyperspectral Image ClassificationCode0
Explaining the Black-box Smoothly- A Counterfactual Approach—0
Spherical Transformer: Adapting Spherical Signal to CNNs—0
Resolution-Based Distillation for Efficient Histology Image Classification—0
Lesion2Vec: Deep Metric Learning for Few-Shot Multiple Lesions Recognition in Wireless Capsule Endoscopy Video—0
Combining pretrained CNN feature extractors to enhance clustering of complex natural images—0
Who's a Good Boy? Reinforcing Canine Behavior in Real-Time using Machine LearningCode0
Practical Evaluation of Out-of-Distribution Detection Methods for Image Classification—0
Contextual Classification Using Self-Supervised Auxiliary Models for Deep Neural NetworksCode0
LightLayers: Parameter Efficient Dense and Convolutional Layers for Image ClassificationCode0
Self Supervision for Attention NetworksCode0
AutoDropout: Learning Dropout Patterns to Regularize Deep Networks—0
Fast Ensemble Learning Using Adversarially-Generated Restricted Boltzmann MachinesCode0
Recommending Accurate and Diverse Items Using Bilateral Branch Network—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