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 7051–7100 of 10420 papers

TitleStatusHype
Overview: Computer vision and machine learning for microstructural characterization and analysis—0
A Novel Site-Agnostic Multimodal Deep Learning Model to Identify Pro-Eating Disorder Content on Social Media—0
On-Off Pattern Encoding and Path-Count Encoding as Deep Neural Network Representations—0
On Parameter Tuning in Meta-learning for Computer Vision—0
Ownership Verification of DNN Models Using White-Box Adversarial Attacks with Specified Probability Manipulation—0
PACE: Posthoc Architecture-Agnostic Concept Extractor for Explaining CNNs—0
Fusing Deep Convolutional Networks for Large Scale Visual Concept Classification—0
A General Multiple Data Augmentation Based Framework for Training Deep Neural Networks—0
On Space Folds of ReLU Neural Networks—0
On Study of the Binarized Deep Neural Network for Image Classification—0
FUSECAPS: Investigating Feature Fusion Based Framework for Capsule Endoscopy Image Classification—0
On the ability of CNNs to extract color invariant intensity based features for image classification—0
FUNN: Flexible Unsupervised Neural Network—0
Enabling Deep Learning on Edge Devices through Filter Pruning and Knowledge Transfer—0
The Effects of Spectral Dimensionality Reduction on Hyperspectral Pixel Classification: A Case Study—0
On the benefits of robust models in modulation recognition—0
On the Calibration of Pre-trained Language Models using Mixup Guided by Area Under the Margin and Saliency—0
On the Computational Inefficiency of Large Batch Sizes for Stochastic Gradient Descent—0
On the Confidence of Neural Network Predictions for some NLP Tasks—0
On the Convergence of Continual Learning with Adaptive Methods—0
Over-parameterization: A Necessary Condition for Models that Extrapolate—0
Fundamental Limits of Transfer Learning in Binary Classifications—0
On the Cost of Model-Serving Frameworks: An Experimental Evaluation—0
EncodeNet: A Framework for Boosting DNN Accuracy with Entropy-driven Generalized Converting Autoencoder—0
Encoder Based Lifelong Learning—0
On the Effectiveness of Deep Ensembles for Small Data Tasks—0
Function-Space Variational Inference for Deep Bayesian Classification—0
On the Effectiveness of Neural Ensembles for Image Classification with Small Datasets—0
On the Effectiveness of Regularization Against Membership Inference Attacks—0
On the Effects of Different Types of Label Noise in Multi-Label Remote Sensing Image Classification—0
Encoding Hierarchical Information in Neural Networks helps in Subpopulation Shift—0
On the Eigenvalues of Global Covariance Pooling for Fine-grained Visual Recognition—0
Continual Learning with Bayesian Model based on a Fixed Pre-trained Feature Extractor—0
On the Evaluation of User Privacy in Deep Neural Networks using Timing Side Channel—0
Encoding High Dimensional Local Features by Sparse Coding Based Fisher Vectors—0
On the generalization capabilities of FSL methods through domain adaptation: a case study in endoscopic kidney stone image classification—0
Function-Space Regularization for Deep Bayesian Classification—0
A study of the effect of JPG compression on adversarial images—0
Adapted and Oversegmenting Graphs: Application to Geometric Deep Learning—0
A Framework for Generalizing Critical Heat Flux Detection Models Using Unsupervised Image-to-Image Translation—0
Active Generative Adversarial Network for Image Classification—0
On the Importance of Normalisation Layers in Deep Learning with Piecewise Linear Activation Units—0
Fully Hyperbolic Convolutional Neural Networks—0
An Optimization and Generalization Analysis for Max-Pooling Networks—0
Fully Convolutional Neural Networks to Detect Clinical Dermoscopic Features—0
On the Initial Behavior Monitoring Issues in Federated Learning—0
On the interplay of adversarial robustness and architecture components: patches, convolution and attention—0
End-to-End Optimization of JPEG-Based Deep Learning Process for Image Classification—0
A Study of Image Analysis with Tangent Distance—0
Unsupervised Continual Learning Via Pseudo Labels—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