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

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Showing 2501–2550 of 10419 papers

TitleStatusHype
A Novel Multi-scale Attention Feature Extraction Block for Aerial Remote Sensing Image Classification—0
Semi-Supervised Learning in the Few-Shot Zero-Shot Scenario—0
Pruning the Unlabeled Data to Improve Semi-Supervised Learning—0
Label Denoising through Cross-Model Agreement—0
A Dual-Direction Attention Mixed Feature Network for Facial Expression RecognitionCode1
PCNN: Probable-Class Nearest-Neighbor Explanations Improve Fine-Grained Image Classification Accuracy for AIs and HumansCode0
GRASP: A Rehearsal Policy for Efficient Online Continual Learning—0
REFT: Resource-Efficient Federated Training Framework for Heterogeneous and Resource-Constrained Environments—0
Efficient Annotation for Medical Image Analysis: A One-Pass Selective Annotation Approach—0
Dual-Activated Lightweight Attention ResNet50 for Automatic Histopathology Breast Cancer Image Classification—0
Black-box Unsupervised Domain Adaptation with Bi-directional Atkinson-Shiffrin MemoryCode0
Data-Side Efficiencies for Lightweight Convolutional Neural Networks—0
Don't Look into the Sun: Adversarial Solarization Attacks on Image ClassifiersCode0
Asymmetric Co-Training with Explainable Cell Graph Ensembling for Histopathological Image Classification—0
FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated LearningCode1
Masking Strategies for Background Bias Removal in Computer Vision ModelsCode1
ACLS: Adaptive and Conditional Label Smoothing for Network Calibration—0
Relational Concept Bottleneck ModelsCode0
DR-Tune: Improving Fine-tuning of Pretrained Visual Models by Distribution Regularization with Semantic CalibrationCode1
Open-set Face Recognition with Neural Ensemble, Maximal Entropy Loss and Feature AugmentationCode0
Integrated Image and Location Analysis for Wound Classification: A Deep Learning ApproachCode1
Concept Bottleneck with Visual Concept Filtering for Explainable Medical Image Classification—0
Food Image Classification and Segmentation with Attention-based Multiple Instance Learning—0
GrowCLIP: Data-aware Automatic Model Growing for Large-scale Contrastive Language-Image Pre-training—0
Fairness Explainability using Optimal Transport with Applications in Image ClassificationCode0
Knowledge-Aware Prompt Tuning for Generalizable Vision-Language Models—0
Protect Federated Learning Against Backdoor Attacks via Data-Free Trigger Generation—0
Development of a Novel Quantum Pre-processing Filter to Improve Image Classification Accuracy of Neural Network ModelsCode0
Diffusion Model as Representation LearnerCode1
Measuring the Effect of Causal Disentanglement on the Adversarial Robustness of Neural Network Models—0
Seeing the Intangible: Survey of Image Classification into High-Level and Abstract Categories—0
Image-free Classifier Injection for Zero-Shot ClassificationCode1
Foundation Model-oriented Robustness: Robust Image Model Evaluation with Pretrained Models—0
Unlocking Accuracy and Fairness in Differentially Private Image ClassificationCode1
LDCSF: Local depth convolution-based Swim framework for classifying multi-label histopathology imagesCode0
CoNe: Contrast Your Neighbours for Supervised Image ClassificationCode0
Quantile-based Maximum Likelihood Training for Outlier DetectionCode0
A Comprehensive Empirical Evaluation on Online Continual LearningCode1
Partition-and-Debias: Agnostic Biases Mitigation via A Mixture of Biases-Specific ExpertsCode0
ASPIRE: Language-Guided Data Augmentation for Improving Robustness Against Spurious CorrelationsCode0
Latent State Models of Training DynamicsCode0
The Impact of Background Removal on Performance of Neural Networks for Fashion Image Classification and Segmentation—0
Which Transformer to Favor: A Comparative Analysis of Efficiency in Vision TransformersCode1
Learning Through Guidance: Knowledge Distillation for Endoscopic Image Classification—0
How Does Pruning Impact Long-Tailed Multi-Label Medical Image Classifiers?Code1
How To Overcome Confirmation Bias in Semi-Supervised Image Classification By Active Learning—0
ResBuilder: Automated Learning of Depth with Residual Structures—0
Vision-Language Dataset DistillationCode1
Unified Data-Free Compression: Pruning and Quantization without Fine-Tuning—0
Towards Open-Set Test-Time Adaptation Utilizing the Wisdom of Crowds in Entropy MinimizationCode1
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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
5DaViT-HTop 1 Accuracy90.2—Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified