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 9751–9800 of 10420 papers

TitleStatusHype
Classification of Quantitative Light-Induced Fluorescence Images Using Convolutional Neural Network—0
Robust Data Geometric Structure Aligned Close yet Discriminative Domain Adaptation—0
Self-supervised learning of visual features through embedding images into text topic spaces—0
Attention-based Natural Language Person Retrieval—0
Continual Learning with Deep Generative ReplayCode0
Patchnet: Interpretable Neural Networks for Image Classification—0
An Out-of-the-box Full-network Embedding for Convolutional Neural Networks—0
CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral FiltersCode0
Classification and Retrieval of Digital Pathology Scans: A New Dataset—0
Sparse Coding on Stereo Video for Object Detection—0
WebVision Challenge: Visual Learning and Understanding With Web Data—0
LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems—0
Revisiting IM2GPS in the Deep Learning Era—0
A Feature Embedding Strategy for High-level CNN representations from Multiple ConvNets—0
Incremental Learning Through Deep Adaptation—0
Cross-label Suppression: A Discriminative and Fast Dictionary Learning with Group Regularization—0
Convolutional Sequence to Sequence LearningCode1
A Design Methodology for Efficient Implementation of Deconvolutional Neural Networks on an FPGACode0
Residual Squeeze VGG16—0
ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax DiseasesCode1
DeepCorrect: Correcting DNN models against Image DistortionsCode0
Towards well-specified semi-supervised model-based classifiers via structural adaptation—0
Hyperspectral Image Classification with Markov Random Fields and a Convolutional Neural NetworkCode0
Discriminative Nonlinear Analysis Operator Learning: When Cosparse Model Meets Image Classification—0
Deep Learning in the Automotive Industry: Applications and Tools—0
Deep Multi-view Models for Glitch Classification—0
Decision Stream: Cultivating Deep Decision TreesCode1
Residual Attention Network for Image ClassificationCode0
Solar Power Plant Detection on Multi-Spectral Satellite Imagery using Weakly-Supervised CNN with Feedback Features and m-PCNN FusionCode0
Understanding the Mechanisms of Deep Transfer Learning for Medical Images—0
Skeleton based action recognition using translation-scale invariant image mapping and multi-scale deep cnn—0
Universal Adversarial Perturbations Against Semantic Image Segmentation—0
Sparse Communication for Distributed Gradient DescentCode1
Integrating Scene Text and Visual Appearance for Fine-Grained Image Classification—0
Close Yet Distinctive Domain Adaptation—0
ApproxDBN: Approximate Computing for Discriminative Deep Belief Networks—0
Unsupervised part learning for visual recognition—0
Cutting the Error by Half: Investigation of Very Deep CNN and Advanced Training Strategies for Document Image ClassificationCode0
Improving Pairwise Ranking for Multi-label Image ClassificationCode0
Fine-graind Image Classification via Combining Vision and Language—0
Using convolutional networks and satellite imagery to identify patterns in urban environments at a large scaleCode0
Supervised Infinite Feature SelectionCode0
Enhancing Robustness of Machine Learning Systems via Data Transformations—0
Encoder Based Lifelong Learning—0
Object-Part Attention Model for Fine-grained Image ClassificationCode0
On Generalization and Regularization in Deep Learning—0
Classification of Diabetic Retinopathy Images Using Multi-Class Multiple-Instance Learning Based on Color Correlogram Features—0
The Relative Performance of Ensemble Methods with Deep Convolutional Neural Networks for Image ClassificationCode0
Truncating Wide Networks using Binary Tree ArchitecturesCode0
A Genetic Programming Approach to Designing Convolutional Neural Network ArchitecturesCode0
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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