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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 6676–6700 of 10420 papers

TitleStatusHype
Nonuniform Defocus Removal for Image Classification—0
Stochastic Whitening Batch Normalization—0
TVDIM: Enhancing Image Self-Supervised Pretraining via Noisy Text Data—0
Semantic-Aware Contrastive Learning for Multi-object Medical Image Segmentation—0
Energy-Efficient Model Compression and Splitting for Collaborative Inference Over Time-Varying Channels—0
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels—0
Memory Wrap: a Data-Efficient and Interpretable Extension to Image Classification ModelsCode0
Reconciliation of Statistical and Spatial Sparsity For Robust Image and Image-Set ClassificationCode0
Rethinking Pseudo Labels for Semi-Supervised Object Detection—0
Learning to Learn Semantic Factors in Heterogeneous Image Classification—0
Fidelity Estimation Improves Noisy-Image Classification With Pretrained NetworksCode0
Energy-Efficient and Federated Meta-Learning via Projected Stochastic Gradient Ascent—0
Dual-stream Network for Visual Recognition—0
Bounded logit attention: Learning to explain image classifiersCode0
Analysis of convolutional neural network image classifiers in a hierarchical max-pooling model with additional local pooling—0
Scorpion detection and classification systems based on computer vision and deep learning for health security purposes—0
Drop Clause: Enhancing Performance, Interpretability and Robustness of the Tsetlin MachineCode0
High Performance Hyperspectral Image Classification using Graphics Processing Units—0
EDDA: Explanation-driven Data Augmentation to Improve Explanation Faithfulness—0
Diffusion-Based Representation Learning—0
FoveaTer: Foveated Transformer for Image Classification—0
AutoSampling: Search for Effective Data Sampling Schedules—0
A systematic review of transfer learning based approaches for diabetic retinopathy detection—0
Encoders and Ensembles for Task-Free Continual Learning—0
Drawing Multiple Augmentation Samples Per Image During Training Efficiently Decreases Test Error—0
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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