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

Showing 28512875 of 10420 papers

TitleStatusHype
Influence of Image Classification Accuracy on Saliency Map EstimationCode0
Improving Neural Architecture Search Image Classifiers via Ensemble LearningCode0
An Architecture Combining Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for Image ClassificationCode0
Improving Memory Efficiency for Training KANs via Meta LearningCode0
A Data-Driven Measure of Relative Uncertainty for Misclassification DetectionCode0
Improving model calibration with accuracy versus uncertainty optimizationCode0
Improving Nonlinear Projection Heads using Pretrained Autoencoder EmbeddingsCode0
Biased Importance Sampling for Deep Neural Network TrainingCode0
Improving Long-tailed Object Detection with Image-Level Supervision by Multi-Task Collaborative LearningCode0
Biased Attention: Do Vision Transformers Amplify Gender Bias More than Convolutional Neural Networks?Code0
An Analysis of Unsupervised Pre-training in Light of Recent AdvancesCode0
SynerMix: Synergistic Mixup Solution for Enhanced Intra-Class Cohesion and Inter-Class Separability in Image ClassificationCode0
Improving Generalization of Batch Whitening by Convolutional Unit OptimizationCode0
Improving Intervention Efficacy via Concept Realignment in Concept Bottleneck ModelsCode0
Beyond Uniform Query Distribution: Key-Driven Grouped Query AttentionCode0
Improving Generalization and Convergence by Enhancing Implicit RegularizationCode0
Improving k-Means Clustering Performance with Disentangled Internal RepresentationsCode0
An analysis of over-sampling labeled data in semi-supervised learning with FixMatchCode0
Improving Ensemble Distillation With Weight Averaging and Diversifying PerturbationCode0
Beyond One-Hot-Encoding: Injecting Semantics to Drive Image ClassifiersCode0
Improving Deep Neural Network Random Initialization Through Neuronal RewiringCode0
Improving Fairness in Image Classification via SketchingCode0
Improving Confident-Classifiers For Out-of-distribution DetectionCode0
Improving Calibration by Relating Focal Loss, Temperature Scaling, and PropernessCode0
Improving Classification Neural Networks by using Absolute activation function (MNIST/LeNET-5 example)Code0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94Unverified
4DaViT-GTop 1 Accuracy90.4Unverified
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
6DaViT-HTop 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10RevCol-HTop 1 Accuracy90Unverified