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

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Showing 6051–6075 of 10420 papers

TitleStatusHype
EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: the MonuMAI cultural heritage use caseCode1
Carrying out CNN Channel Pruning in a White BoxCode1
Research on the Detection Method of Breast Cancer Deep Convolutional Neural Network Based on Computer Aid—0
Mini-batch graphs for robust image classification—0
Complex-valued reservoir computing for aspect classification and slope-angle estimation with low computational cost and high resolution in interferometric synthetic aperture radar—0
Multiscale Vision TransformersCode1
ImageNet-21K Pretraining for the MassesCode1
All Tokens Matter: Token Labeling for Training Better Vision TransformersCode1
VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and TextCode1
Visual Analysis Motivated Rate-Distortion Model for Image Coding—0
MetricOpt: Learning to Optimize Black-Box Evaluation Metrics—0
Stateless Neural Meta-Learning using Second-Order GradientsCode0
BraidNet: procedural generation of neural networks for image classification problems using braid theory—0
Differentiable Model Compression via Pseudo Quantization NoiseCode1
MixDefense: A Defense-in-Depth Framework for Adversarial Example Detection Based on Statistical and Semantic Analysis—0
Gradient Matching for Domain GeneralizationCode1
A Framework using Contrastive Learning for Classification with Noisy Labels—0
Quantum algorithms for SVD-based data representation and analysisCode0
Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?Code1
Contrastive Learning Improves Model Robustness Under Label NoiseCode1
Texture Based Classification of High Resolution Remotely Sensed Imagery using Weber Local Descriptor—0
Filtering Empty Camera Trap Images in Embedded SystemsCode0
"BNN - BN = ?": Training Binary Neural Networks without Batch NormalizationCode1
Augmenting Deep Classifiers with Polynomial Neural NetworksCode0
Efficient and Generic 1D Dilated Convolution Layer for Deep LearningCode0
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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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified