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 8551–8600 of 10420 papers

TitleStatusHype
Deep Neural Networks for Marine Debris Detection in Sonar Images—0
Hierarchically Structured Meta-learningCode0
Multi-Agent Image Classification via Reinforcement LearningCode0
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable FeaturesCode1
A novel statistical metric learning for hyperspectral image classification—0
Understanding and Utilizing Deep Neural Networks Trained with Noisy LabelsCode1
Breast cancer image classification on WSI with spatial correlationsCode0
Generative Adversarial Networks and Conditional Random Fields for Hyperspectral Image Classification—0
Budgeted Training: Rethinking Deep Neural Network Training Under Resource ConstraintsCode0
Multitask Deep Learning with Spectral Knowledge for Hyperspectral Image ClassificationCode0
Breast Tumor Classification and Segmentation using Convolutional Neural Networks—0
Single-Path NAS: Device-Aware Efficient ConvNet Design—0
Learning Loss for Active LearningCode1
Spatial-Spectral Feature Extraction via Deep ConvLSTM Neural Networks for Hyperspectral Image Classification—0
Improving Discrete Latent Representations With Differentiable Approximation Bridges—0
S4L: Self-Supervised Semi-Supervised LearningCode0
Seesaw-Net: Convolution Neural Network With Uneven Group ConvolutionCode0
What Do Single-view 3D Reconstruction Networks Learn?—0
Enhancing Cross-task Transferability of Adversarial Examples with Dispersion ReductionCode0
Fast-DENSER++: Evolving Fully-Trained Deep Artificial Neural Networks—0
AutoAssist: A Framework to Accelerate Training of Deep Neural NetworksCode1
Advancements in Image Classification using Convolutional Neural Network—0
Skin Lesion Classification Using CNNs with Patch-Based Attention and Diagnosis-Guided Loss WeightingCode0
High Frequency Residual Learning for Multi-Scale Image Classification—0
Locality and Structure Regularized Low Rank Representation for Hyperspectral Image Classification—0
Image Matters: Scalable Detection of Offensive and Non-Compliant Content / Logo in Product Images—0
Learning Optimal Data Augmentation Policies via Bayesian Optimization for Image Classification TasksCode0
MixMatch: A Holistic Approach to Semi-Supervised LearningCode1
Searching for MobileNetV3Code1
Deep Visual City Recognition VisualizationCode0
Edge-labeling Graph Neural Network for Few-shot LearningCode0
Bilinear discriminant feature line analysis for image feature extraction—0
DisplaceNet: Recognising Displaced People from Images by Exploiting Dominance LevelCode0
Parity Models: A General Framework for Coding-Based Resilience in ML Inference—0
Billion-scale semi-supervised learning for image classificationCode1
Directing DNNs Attention for Facial Attribution Classification using Gradient-weighted Class Activation Mapping—0
On Expected Accuracy—0
Fast AutoAugmentCode1
Probabilistic Model-Based Dynamic Architecture Search—0
Radial Basis Feature Transformation to Arm CNNs Against Adversarial Attacks—0
Probabilistic Federated Neural Matching—0
Graph Classification with Geometric Scattering—0
A Synaptic Neural Network and Synapse LearningCode0
Asynchronous SGD without gradient delay for efficient distributed training—0
HIGHLY EFFICIENT 8-BIT LOW PRECISION INFERENCE OF CONVOLUTIONAL NEURAL NETWORKS—0
FAST OBJECT LOCALIZATION VIA SENSITIVITY ANALYSIS—0
Contextual Recurrent Convolutional Model for Robust Visual Learning—0
Model Compression with Generative Adversarial Networks—0
Deep Ensemble Bayesian Active Learning : Adressing the Mode Collapse issue in Monte Carlo dropout via Ensembles—0
Causal importance of orientation selectivity for generalization in image recognitionCode0
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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