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 15761600 of 10419 papers

TitleStatusHype
Breast Cancer Histopathology Image Classification and Localization using Multiple Instance LearningCode1
Lookahead Optimizer: k steps forward, 1 step backCode1
Deblurring Masked Autoencoder is Better Recipe for Ultrasound Image RecognitionCode1
Combating noisy labels by agreement: A joint training method with co-regularizationCode1
Low-Rank Rescaled Vision Transformer Fine-Tuning: A Residual Design ApproachCode1
LRSCLIP: A Vision-Language Foundation Model for Aligning Remote Sensing Image with Longer TextCode1
BRECQ: Pushing the Limit of Post-Training Quantization by Block ReconstructionCode1
DCN-T: Dual Context Network with Transformer for Hyperspectral Image ClassificationCode1
Combining GANs and AutoEncoders for Efficient Anomaly DetectionCode1
Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective AdaptationCode1
A General Regret Bound of Preconditioned Gradient Method for DNN TrainingCode1
DCT-CryptoNets: Scaling Private Inference in the Frequency DomainCode1
Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural NetworksCode1
DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic AugmentationCode1
Mamba2D: A Natively Multi-Dimensional State-Space Model for Vision TasksCode1
Reviving the Context: Camera Trap Species Classification as Link Prediction on Multimodal Knowledge GraphsCode1
DataMUX: Data Multiplexing for Neural NetworksCode1
Data Feedback Loops: Model-driven Amplification of Dataset BiasesCode1
BSRBF-KAN: A combination of B-splines and Radial Basis Functions in Kolmogorov-Arnold NetworksCode1
Dataset Condensation with Contrastive SignalsCode1
DC3DCD: unsupervised learning for multiclass 3D point cloud change detectionCode1
Masked Frequency Modeling for Self-Supervised Visual Pre-TrainingCode1
Decision Stream: Cultivating Deep Decision TreesCode1
Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP)Code1
Data Augmentation with norm-VAE for Unsupervised Domain AdaptationCode1
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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