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 676700 of 10419 papers

TitleStatusHype
ConTNet: Why not use convolution and transformer at the same time?Code1
CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep LearningCode1
Contrastive Learning Improves Model Robustness Under Label NoiseCode1
Combining Human Predictions with Model Probabilities via Confusion Matrices and CalibrationCode1
Continual atlas-based segmentation of prostate MRICode1
Contrastive Masked Autoencoders are Stronger Vision LearnersCode1
Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy LabelsCode1
ConvMLP: Hierarchical Convolutional MLPs for VisionCode1
Convolutional Channel-wise Competitive Learning for the Forward-Forward AlgorithmCode1
Convolutional Spiking Neural Networks for Spatio-Temporal Feature ExtractionCode1
Convolutional Xformers for VisionCode1
Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised LearningCode1
Deeply Coupled Cross-Modal Prompt LearningCode1
Counterfactual Visual ExplanationsCode1
COVID-CXNet: Detecting COVID-19 in Frontal Chest X-ray Images using Deep LearningCode1
Achieving Fairness Through Channel Pruning for Dermatological Disease DiagnosisCode1
Can An Image Classifier Suffice For Action Recognition?Code1
CLCC: Contrastive Learning for Color ConstancyCode1
Cross-Domain Ensemble Distillation for Domain GeneralizationCode1
Class-Incremental Grouping Network for Continual Audio-Visual LearningCode1
An In-depth Study of Stochastic BackpropagationCode1
Adaptive Risk Minimization: Learning to Adapt to Domain ShiftCode1
Cross-Layer Retrospective Retrieving via Layer AttentionCode1
CLCNet: Rethinking of Ensemble Modeling with Classification Confidence NetworkCode1
Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODECode1
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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