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 39013925 of 10420 papers

TitleStatusHype
AHA! an 'Artificial Hippocampal Algorithm' for Episodic Machine Learning0
FACET: Fairness in Computer Vision Evaluation Benchmark0
GenMix: Combining Generative and Mixture Data Augmentation for Medical Image Classification0
Geometric Scattering for Graph Data Analysis0
Detecting Hypo-plastic Left Heart Syndrome in Fetal Ultrasound via Disease-specific Atlas Maps0
Factorized Adversarial Networks for Unsupervised Domain Adaptation0
Beyond the Attention: Distinguish the Discriminative and Confusable Features For Fine-grained Image Classification0
Factors of Influence for Transfer Learning across Diverse Appearance Domains and Task Types0
FACTUAL: A Novel Framework for Contrastive Learning Based Robust SAR Image Classification0
Generative Negative Text Replay for Continual Vision-Language Pretraining0
Failure Prediction by Confidence Estimation of Uncertainty-Aware Dirichlet Networks0
Fair Comparison: Quantifying Variance in Resultsfor Fine-grained Visual Categorization0
Beyond Size and Class Balance: Alpha as a New Dataset Quality Metric for Deep Learning0
FairDD: Fair Dataset Distillation via Synchronized Matching0
Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging0
Adaptive Weighting Scheme for Automatic Time-Series Data Augmentation0
Combining Spiking Neural Network and Artificial Neural Network for Enhanced Image Classification0
Fairness Analysis of CLIP-Based Foundation Models for X-Ray Image Classification0
Fairness-Aware Meta-Learning via Nash Bargaining0
Fairness Testing of Deep Image Classification with Adequacy Metrics0
FairPrune: Achieving Fairness Through Pruning for Dermatological Disease Diagnosis0
FairREAD: Re-fusing Demographic Attributes after Disentanglement for Fair Medical Image Classification0
FairSAM: Fair Classification on Corrupted Data Through Sharpness-Aware Minimization0
Fair-VPT: Fair Visual Prompt Tuning for Image Classification0
Generative NeuroEvolution for Deep Learning0
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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