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 4001–4025 of 10420 papers

TitleStatusHype
Feature CAM: Interpretable AI in Image Classification—0
Feature Channel Adaptive Enhancement for Fine-Grained Visual Classification—0
Comparison of Methods Generalizing Max- and Average-Pooling—0
Feature Density Estimation for Out-of-Distribution Detection via Normalizing Flows—0
Feature Embedding by Template Matching as a ResNet Block—0
Feature-EndoGaussian: Feature Distilled Gaussian Splatting in Surgical Deformable Scene Reconstruction—0
Beyond Fine-tuning: Classifying High Resolution Mammograms using Function-Preserving Transformations—0
Adaptive Temperature Scaling with Conformal Prediction—0
CORL: Compositional Representation Learning for Few-Shot Classification—0
Beyond Entropy: Style Transfer Guided Single Image Continual Test-Time Adaptation—0
Analyzing Images for Music Recommendation—0
Feature Kernel Distillation—0
Feature Learning beyond the Lazy-Rich Dichotomy: Insights from Representational Geometry—0
Feature Learning to Automatically Assess Radiographic Knee Osteoarthritis Severity—0
GQKVA: Efficient Pre-training of Transformers by Grouping Queries, Keys, and Values—0
Understanding Dropout as an Optimization Trick—0
Feature Map Convergence Evaluation for Functional Module—0
Competing Ratio Loss for Discriminative Multi-class Image Classification—0
Feature Preserving Shrinkage on Bayesian Neural Networks via the R2D2 Prior—0
Designing Extremely Memory-Efficient CNNs for On-device Vision Tasks—0
Feature Representation in Convolutional Neural Networks—0
Features based Mammogram Image Classification using Weighted Feature Support Vector Machine—0
Beyond Empirical Risk Minimization: Local Structure Preserving Regularization for Improving Adversarial Robustness—0
Feature Space Augmentation for Long-Tailed Data—0
Designing Adaptive Neural Networks for Energy-Constrained Image Classification—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