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 9151–9175 of 10420 papers

TitleStatusHype
Feature Aligning Few shot Learning Method Using Local Descriptors Weighted Rules—0
Feature Alignment and Representation Transfer in Knowledge Distillation for Large Language Models—0
Feature Augmentation for Self-supervised Contrastive Learning: A Closer Look—0
Feature-based Graph Attention Networks Improve Online Continual Learning—0
Feature CAM: Interpretable AI in Image Classification—0
Feature Channel Adaptive Enhancement for Fine-Grained Visual Classification—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
Feature Extraction of Hyperspectral Images With Image Fusion and Recursive Filtering—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
Feature-level augmentation to improve robustness of deep neural networks to affine transformations—0
Feature Losses for Adversarial Robustness—0
Feature Map Convergence Evaluation for Functional Module—0
Feature Preserving Shrinkage on Bayesian Neural Networks via the R2D2 Prior—0
Feature Representation in Convolutional Neural Networks—0
Features based Mammogram Image Classification using Weighted Feature Support Vector Machine—0
Feature selection of neural networks is skewed towards the less abstract cue—0
Feature Space Augmentation for Long-Tailed Data—0
Feature Weaken: Vicinal Data Augmentation for Classification—0
Feature Whitening via Gradient Transformation for Improved Convergence—0
FedAvg with Fine Tuning: Local Updates Lead to Representation Learning—0
FedBABU: Toward Enhanced Representation for Federated 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
5DaViT-HTop 1 Accuracy90.2—Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 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