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 9551–9600 of 10420 papers

TitleStatusHype
Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy—0
AOGNets: Compositional Grammatical Architectures for Deep LearningCode0
Spectral-spatial classification of hyperspectral images: three tricks and a new supervised learning settingCode0
Saliency-based Sequential Image Attention with Multiset Prediction—0
Decoupled Weight Decay RegularizationCode1
All-Transfer Learning for Deep Neural Networks and its Application to Sepsis Classification—0
Provably efficient neural network representation for image classification—0
Multi-label Image Recognition by Recurrently Discovering Attentional Regions—0
SIMILARnet: Simultaneous Intelligent Localization and Recognition NetworkCode0
Cortical microcircuits as gated-recurrent neural networks—0
Artificial Generation of Big Data for Improving Image Classification: A Generative Adversarial Network Approach on SAR DataCode0
HyperNetworks with statistical filtering for defending adversarial examples—0
Mitigating Adversarial Effects Through RandomizationCode0
Adversarial Dropout Regularization—0
Real-Time Document Image Classification using Deep CNN and Extreme Learning Machines—0
Structured Generative Adversarial NetworksCode0
The Achievement of Higher Flexibility in Multiple Choice-based Tests Using Image Classification Techniques—0
Learning with Latent LanguageCode0
Hierarchical Representations for Efficient Architecture SearchCode0
Countering Adversarial Images using Input TransformationsCode0
Generating Natural Adversarial ExamplesCode0
Conditional Variance Penalties and Domain Shift RobustnessCode0
Evolving Deep Convolutional Neural Networks for Image ClassificationCode0
A Supervised STDP-based Training Algorithm for Living Neural Networks—0
Multi-level Residual Networks from Dynamical Systems View—0
Dynamic Routing Between CapsulesCode1
InterpNET: Neural Introspection for Interpretable Deep LearningCode0
Deep Neural Networks—0
mixup: Beyond Empirical Risk MinimizationCode1
Incomplete Dot Products for Dynamic Computation Scaling in Neural Network Inference—0
Classification Driven Dynamic Image Enhancement—0
Deep Self-taught Learning for Remote Sensing Image Classification—0
Enhancing the Performance of Convolutional Neural Networks on Quality Degraded Datasets—0
VisDA: The Visual Domain Adaptation ChallengeCode1
Dropout Sampling for Robust Object Detection in Open-Set Conditions—0
Do Convolutional Neural Networks Learn Class Hierarchy?—0
Learning to Learn Image Classifiers with Visual Analogy—0
Entanglement Entropy of Target Functions for Image Classification and Convolutional Neural Network—0
Searching for Activation FunctionsCode0
Pushing the envelope in deep visual recognition for mobile platforms—0
Manifold regularization based on Nyström type subsampling—0
Arguing Machines: Human Supervision of Black Box AI Systems That Make Life-Critical DecisionsCode0
Convolutional Neural Networks for Histopathology Image Classification: Training vs. Using Pre-Trained Networks—0
Learning to Generalize: Meta-Learning for Domain GeneralizationCode1
Energy-efficient Amortized Inference with Cascaded Deep Classifiers—0
Application of Deep Learning in Neuroradiology: Automated Detection of Basal Ganglia Hemorrhage using 2D-Convolutional Neural Networks—0
Does Normalization Methods Play a Role for Hyperspectral Image Classification?—0
Deep Convolutional Neural Networks as Generic Feature Extractors—0
Energy-Based Spherical Sparse Coding—0
A concatenating framework of shortcut convolutional neural networks—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