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 81018125 of 10420 papers

TitleStatusHype
Compress image to patches for Vision TransformerCode0
Compressed learning based onboard semantic compression for remote sensing platformsCode0
Compressed Learning: A Deep Neural Network ApproachCode0
A Scalable Quantum Non-local Neural Network for Image ClassificationCode0
Reweighting Augmented Samples by Minimizing the Maximal Expected LossCode0
Federated Unlearning via Class-Discriminative PruningCode0
Federated Unlearning Made Practical: Seamless Integration via Negated Pseudo-GradientsCode0
Revisiting Unsupervised Meta-Learning via the Characteristics of Few-Shot TasksCode0
Revisiting the Calibration of Modern Neural NetworksCode0
Compound Figure Separation of Biomedical Images: Mining Large Datasets for Self-supervised LearningCode0
Revisiting Orthogonality Regularization: A Study for Convolutional Neural Networks in Image ClassificationCode0
Revisiting lp-constrained Softmax Loss: A Comprehensive StudyCode0
Revisiting Local Descriptor based Image-to-Class Measure for Few-shot LearningCode0
Federated Learning in Non-IID Settings Aided by Differentially Private Synthetic DataCode0
Compositional Model based Fisher Vector Coding for Image ClassificationCode0
Revisiting Data Augmentation for Ultrasound ImagesCode0
Revisiting CroPA: A Reproducibility Study and Enhancements for Cross-Prompt Adversarial Transferability in Vision-Language ModelsCode0
Revisiting Batch Normalization For Practical Domain AdaptationCode0
Federated learning compression designed for lightweight communicationsCode0
Federated Active Learning for Target Domain GeneralisationCode0
Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial RobustnessCode0
REVEL Framework to measure Local Linear Explanations for black-box models: Deep Learning Image Classification case of studyCode0
Revealing and Protecting Labels in Distributed TrainingCode0
FedDUAL: A Dual-Strategy with Adaptive Loss and Dynamic Aggregation for Mitigating Data Heterogeneity in Federated LearningCode0
Artificial intelligence based glaucoma and diabetic retinopathy detection using MATLAB — retrained AlexNet convolutional neural networkCode0
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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