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 28012825 of 10420 papers

TitleStatusHype
Improving singing voice separation with the Wave-U-Net using Minimum Hyperspherical EnergyCode0
Improving Prototypical Visual Explanations with Reward Reweighing, Reselection, and RetrainingCode0
Improving Random-Sampling Neural Architecture Search by Evolving the Proxy Search SpaceCode0
Improving robustness to corruptions with multiplicative weight perturbationsCode0
BlockQNN: Efficient Block-wise Neural Network Architecture GenerationCode0
An Efficient Quantum Classifier Based on Hamiltonian RepresentationsCode0
Improving Nonlinear Projection Heads using Pretrained Autoencoder EmbeddingsCode0
An Efficient Framework for Enhancing Discriminative Models via Diffusion TechniquesCode0
Blind Knowledge Distillation for Robust Image ClassificationCode0
Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural NetworkCode0
An Efficient Confidence Measure-Based Evaluation Metric for Breast Cancer Screening Using Bayesian Neural NetworksCode0
Black-box Unsupervised Domain Adaptation with Bi-directional Atkinson-Shiffrin MemoryCode0
Fairness Explainability using Optimal Transport with Applications in Image ClassificationCode0
Improving Pairwise Ranking for Multi-label Image ClassificationCode0
An Effective Weight Initialization Method for Deep Learning: Application to Satellite Image ClassificationCode0
Improving Memory Efficiency for Training KANs via Meta LearningCode0
Improving model calibration with accuracy versus uncertainty optimizationCode0
Improving k-Means Clustering Performance with Disentangled Internal RepresentationsCode0
3D-ANAS: 3D Asymmetric Neural Architecture Search for Fast Hyperspectral Image ClassificationCode0
Improving Long-tailed Object Detection with Image-Level Supervision by Multi-Task Collaborative LearningCode0
Bivariate Beta-LSTMCode0
Improving Intervention Efficacy via Concept Realignment in Concept Bottleneck ModelsCode0
Improving Neural Architecture Search Image Classifiers via Ensemble LearningCode0
Improving Pre-Trained Weights Through Meta-Heuristics Fine-TuningCode0
Improving Generalizability of Kolmogorov-Arnold Networks via Error-Correcting Output CodesCode0
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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