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 4801–4825 of 10420 papers

TitleStatusHype
Combining Two Adversarial Attacks Against Person Re-Identification Systems—0
Fairness Testing of Deep Image Classification with Adequacy Metrics—0
Fairness-Aware Meta-Learning via Nash Bargaining—0
Combining Supervised Learning and Reinforcement Learning for Multi-Label Classification Tasks with Partial Labels—0
A Residual Multi-task Network for Joint Classification and Regression in Medical Imaging—0
AdvIT: Adversarial Frames Identifier Based on Temporal Consistency in Videos—0
Fairness Analysis of CLIP-Based Foundation Models for X-Ray Image Classification—0
Combining Spiking Neural Network and Artificial Neural Network for Enhanced Image Classification—0
Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging—0
Combining Similarity and Adversarial Learning to Generate Visual Explanation: Application to Medical Image Classification—0
Are Sample-Efficient NLP Models More Robust?—0
FairDD: Fair Dataset Distillation via Synchronized Matching—0
Combining Recurrent, Convolutional, and Continuous-time Models with Linear State Space Layers—0
Fair Comparison: Quantifying Variance in Resultsfor Fine-grained Visual Categorization—0
Failure Prediction by Confidence Estimation of Uncertainty-Aware Dirichlet Networks—0
Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers—0
Are pre-trained CNNs good feature extractors for anomaly detection in surveillance videos?—0
FACTUAL: A Novel Framework for Contrastive Learning Based Robust SAR Image Classification—0
Combining pretrained CNN feature extractors to enhance clustering of complex natural images—0
Factors of Influence for Transfer Learning across Diverse Appearance Domains and Task Types—0
Combining multiscale features for classification of hyperspectral images: a sequence based kernel approach—0
Factorized Adversarial Networks for Unsupervised Domain Adaptation—0
Fact or Artifact? Revise Layer-wise Relevance Propagation on various ANN Architectures—0
Combining multiple resolutions into hierarchical representations for kernel-based image classification—0
Are Out-of-Distribution Detection Methods Effective on Large-Scale Datasets?—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
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2—Unverified
6DaViT-HTop 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
10RevCol-HTop 1 Accuracy90—Unverified