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 20012025 of 10420 papers

TitleStatusHype
The Devil is in the Margin: Margin-based Label Smoothing for Network CalibrationCode1
The Dual Form of Neural Networks Revisited: Connecting Test Time Predictions to Training Patterns via Spotlights of AttentionCode1
The First to Know: How Token Distributions Reveal Hidden Knowledge in Large Vision-Language Models?Code1
The Heterogeneity Hypothesis: Finding Layer-Wise Differentiated Network ArchitecturesCode1
The Information Pathways Hypothesis: Transformers are Dynamic Self-EnsemblesCode1
The Large Labelled Logo Dataset (L3D): A Multipurpose and Hand-Labelled Continuously Growing DatasetCode1
Compressing Features for Learning with Noisy LabelsCode1
Deep Hyperspectral Unmixing using Transformer NetworkCode1
Theory-Inspired Path-Regularized Differential Network Architecture SearchCode1
The Reversible Residual Network: Backpropagation Without Storing ActivationsCode1
Complementary-Label Learning for Arbitrary Losses and ModelsCode1
GANterfactual - Counterfactual Explanations for Medical Non-Experts using Generative Adversarial LearningCode1
This Looks Like That... Does it? Shortcomings of Latent Space Prototype Interpretability in Deep NetworksCode1
TiC: Exploring Vision Transformer in ConvolutionCode1
A Fast Knowledge Distillation Framework for Visual RecognitionCode1
All Tokens Matter: Token Labeling for Training Better Vision TransformersCode1
DenoiseRep: Denoising Model for Representation LearningCode1
Is Less More? Exploring Token Condensation as Training-free Adaptation for CLIPCode1
Depth Uncertainty in Neural NetworksCode1
Topological structure of complex predictionsCode1
Compositional Explanations of NeuronsCode1
Delving into Out-of-Distribution Detection with Medical Vision-Language ModelsCode1
ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image ClassificationCode1
Towards Accurate and Interpretable Neuroblastoma Diagnosis via Contrastive Multi-scale Pathological Image AnalysisCode1
Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response JacobiansCode1
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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