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 4701–4725 of 10420 papers

TitleStatusHype
Feature-EndoGaussian: Feature Distilled Gaussian Splatting in Surgical Deformable Scene Reconstruction—0
Feature Embedding by Template Matching as a ResNet Block—0
Comparison of Neural Models for X-ray Image Classification in COVID-19 Detection—0
Feature Density Estimation for Out-of-Distribution Detection via Normalizing Flows—0
Comparison of Methods Generalizing Max- and Average-Pooling—0
Feature Channel Adaptive Enhancement for Fine-Grained Visual Classification—0
Feature CAM: Interpretable AI in Image Classification—0
Comparison of fine-tuning strategies for transfer learning in medical image classification—0
Feature-based Graph Attention Networks Improve Online Continual Learning—0
Comparison of Batch Normalization and Weight Normalization Algorithms for the Large-scale Image Classification—0
Arithmetic Intensity Balancing Convolution for Hardware-aware Efficient Block Design—0
A Fast and Efficient Conditional Learning for Tunable Trade-Off between Accuracy and Robustness—0
A Convolutional Neural Network with Mapping Layers for Hyperspectral Image Classification—0
Feature Augmentation for Self-supervised Contrastive Learning: A Closer Look—0
Feature Alignment and Representation Transfer in Knowledge Distillation for Large Language Models—0
Feature Aligning Few shot Learning Method Using Local Descriptors Weighted Rules—0
Feature Activation Map: Visual Explanation of Deep Learning Models for Image Classification—0
Comparison Analysis of Traditional Machine Learning and Deep Learning Techniques for Data and Image Classification—0
Feasibility of Transfer Learning: A Mathematical Framework—0
Comparing LBP, HOG and Deep Features for Classification of Histopathology Images—0
Comparing concepts of quantum and classical neural network models for image classification task—0
FBNetV5: Neural Architecture Search for Multiple Tasks in One Run—0
Compare and Contrast: Learning Prominent Visual Differences—0
FAT: Training Neural Networks for Reliable Inference Under Hardware Faults—0
FATNN: Fast and Accurate Ternary Neural Networks—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