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 5226–5250 of 10420 papers

TitleStatusHype
Efficient Automatic Machine Learning via Design GraphsCode0
Imbalanced Classification in Medical Imaging via Regrouping—0
Iterative collaborative routing among equivariant capsules for transformation-robust capsule networks—0
Visual-Semantic Contrastive Alignment for Few-Shot Image Classification—0
Robustcaps: a transformation-robust capsule network for image classification—0
Similarity of Neural Architectures using Adversarial Attack Transferability—0
Analysing Training-Data Leakage from Gradients through Linear Systems and Gradient MatchingCode0
G2NetPL: Generic Game-Theoretic Network for Partial-Label Image Classification—0
Efficient, probabilistic analysis of combinatorial neural codes—0
Training set cleansing of backdoor poisoning by self-supervised representation learning—0
Sustainable Personalisation and Explainability in Dyadic Data Systems—0
A Unified View of Masked Image ModelingCode0
On effects of Knowledge Distillation on Transfer Learning—0
Layer-wise Relevance Propagation for Echo State Networks applied to Earth System Variability—0
Few-Shot Learning of Compact Models via Task-Specific Meta Distillation—0
A Novel Membership Inference Attack against Dynamic Neural Networks by Utilizing Policy Networks Information—0
Packed-Ensembles for Efficient Uncertainty Estimation—0
Early Diagnosis of Retinal Blood Vessel Damage via Deep Learning-Powered Collective Intelligence ModelsCode0
Signal Processing for Implicit Neural Representations—0
Bootstrapping the Relationship Between Images and Their Clean and Noisy LabelsCode0
Explaining Image Classification with Visual DebatesCode0
Self-Supervised Learning Through Efference CopiesCode0
Histopathological Image Classification based on Self-Supervised Vision Transformer and Weak LabelsCode0
Thinking Two Moves Ahead: Anticipating Other Users Improves Backdoor Attacks in Federated LearningCode0
Federated Learning with Privacy-Preserving Ensemble Attention Distillation—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