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 3051–3075 of 10420 papers

TitleStatusHype
Experimental Observations of the Topology of Convolutional Neural Network Activations—0
Explanatory Masks for Neural Network Interpretability—0
Computational and Storage Efficient Quadratic Neurons for Deep Neural Networks—0
Feature Channel Adaptive Enhancement for Fine-Grained Visual Classification—0
Boosting Few-Shot Learning with Disentangled Self-Supervised Learning and Meta-Learning for Medical Image Classification—0
Discriminative Pattern Mining for Breast Cancer Histopathology Image Classification via Fully Convolutional Autoencoder—0
Evidential Federated Learning for Skin Lesion Image Classification—0
Discriminative Nonlinear Analysis Operator Learning: When Cosparse Model Meets Image Classification—0
Discriminative models for robust image classification—0
Addressing Uncertainty in Imbalanced Histopathology Image Classification of HER2 Breast Cancer: An interpretable Ensemble Approach with Threshold Filtered Single Instance Evaluation (SIE)—0
Discriminative Localization in CNNs for Weakly-Supervised Segmentation of Pulmonary Nodules—0
Discriminative Learning of Sum-Product Networks—0
Evidence-empowered Transfer Learning for Alzheimer's Disease—0
Discriminative Label Consistent Domain Adaptation—0
Discriminative k-shot learning using probabilistic models—0
Boosting Discriminative Visual Representation Learning with Scenario-Agnostic Mixup—0
Improving Deep Hyperspectral Image Classification Performance with Spectral Unmixing—0
Discriminative Robust Deep Dictionary Learning for Hyperspectral Image Classification—0
Discriminative Transfer Learning with Tree-based Priors—0
Boosting Few-Shot Text Classification via Distribution Estimation—0
Discriminative Distillation to Reduce Class Confusion in Continual Learning—0
ACLS: Adaptive and Conditional Label Smoothing for Network Calibration—0
Evaluation, Tuning and Interpretation of Neural Networks for Meteorological Applications—0
Disentangled Deep Autoencoding Regularization for Robust Image Classification—0
Discriminative and Geometry Aware Unsupervised Domain Adaptation—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