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
First Session Adaptation: A Strong Replay-Free Baseline for Class-Incremental Learning—0
A Simple and Generic Framework for Feature Distillation via Channel-wise Transformation—0
Exploring the Benefits of Visual Prompting in Differential PrivacyCode0
Unsupervised Domain Adaptation for Training Event-Based Networks Using Contrastive Learning and Uncorrelated Conditioning—0
LD-ZNet: A Latent Diffusion Approach for Text-Based Image Segmentation—0
SCALES: Boost Binary Neural Network for Image Super-Resolution with Efficient Scalings—0
Deployment of Image Analysis Algorithms under Prevalence ShiftsCode0
Boundary Unlearning—0
ViC-MAE: Self-Supervised Representation Learning from Images and Video with Contrastive Masked AutoencodersCode0
Machine Learning for Brain Disorders: Transformers and Visual Transformers—0
Creating Ensembles of Classifiers through UMDA for Aerial Scene Classification—0
Parameter-Free Channel Attention for Image Classification and Super-Resolution—0
Bias mitigation techniques in image classification: fair machine learning in human heritage collections—0
Supervision Interpolation via LossMix: Generalizing Mixup for Object Detection and Beyond—0
Extracting the Brain-like Representation by an Improved Self-Organizing Map for Image ClassificationCode0
Unsupervised domain adaptation by learning using privileged information—0
Conditional Synthetic Food Image Generation—0
Practicality of generalization guarantees for unsupervised domain adaptation with neural networks—0
Agnostic Multi-Robust Learning Using ERM—0
Visual Prompt Based Personalized Federated Learning—0
Knowledge Distillation from Single to Multi Labels: an Empirical StudyCode0
A Contrastive Knowledge Transfer Framework for Model Compression and Transfer LearningCode0
SMUG: Towards robust MRI reconstruction by smoothed unrollingCode0
Is forgetting less a good inductive bias for forward transfer?—0
Context Normalization Layer with Applications—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