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 751775 of 10419 papers

TitleStatusHype
Towards Effective Visual Representations for Partial-Label LearningCode1
DPMLBench: Holistic Evaluation of Differentially Private Machine LearningCode1
Learning Semi-supervised Gaussian Mixture Models for Generalized Category DiscoveryCode1
CAMIL: Context-Aware Multiple Instance Learning for Cancer Detection and Subtyping in Whole Slide ImagesCode1
DC3DCD: unsupervised learning for multiclass 3D point cloud change detectionCode1
Reduction of Class Activation Uncertainty with Background InformationCode1
Long-Tailed Recognition by Mutual Information Maximization between Latent Features and Ground-Truth LabelsCode1
CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual RepresentationsCode1
POUF: Prompt-oriented unsupervised fine-tuning for large pre-trained modelsCode1
Multistage Relation Network With Dual-Metric for Few-Shot Hyperspectral Image ClassificationCode1
From Association to Generation: Text-only Captioning by Unsupervised Cross-modal MappingCode1
Deep Fast Vision: Accelerated Deep Transfer Learning Vision Prototyping and BeyondCode1
ESPT: A Self-Supervised Episodic Spatial Pretext Task for Improving Few-Shot LearningCode1
Bayesian Optimization Meets Self-DistillationCode1
AwesomeMeta+: A Mixed-Prototyping Meta-Learning System Supporting AI Application Design AnywhereCode1
Function-Consistent Feature DistillationCode1
MixPro: Data Augmentation with MaskMix and Progressive Attention Labeling for Vision TransformerCode1
Learning Partial Correlation based Deep Visual Representation for Image ClassificationCode1
Learning Bottleneck Concepts in Image ClassificationCode1
DCN-T: Dual Context Network with Transformer for Hyperspectral Image ClassificationCode1
Hyperbolic Image-Text RepresentationsCode1
TransHP: Image Classification with Hierarchical PromptingCode1
Remote Sensing Change Detection With Transformers Trained from ScratchCode1
Boosting Convolutional Neural Networks with Middle Spectrum Grouped ConvolutionCode1
SpectralDiff: A Generative Framework for Hyperspectral Image Classification with Diffusion ModelsCode1
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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
5DaViT-HTop 1 Accuracy90.2Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90Unverified