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 10051–10100 of 10420 papers

TitleStatusHype
Towards Better Analysis of Deep Convolutional Neural Networks—0
Learning rotation invariant convolutional filters for texture classificationCode0
A Classifier-guided Approach for Top-down Salient Object Detection—0
Network of Experts for Large-Scale Image Categorization—0
Churn analysis using deep convolutional neural networks and autoencoders—0
CNN-RNN: A Unified Framework for Multi-label Image ClassificationCode0
Latent Model Ensemble with Auto-localization—0
Object Detection from Video Tubelets with Convolutional Neural NetworksCode0
Deep Residual Networks with Exponential Linear UnitCode0
Understanding How Image Quality Affects Deep Neural NetworksCode0
Going Deeper with Contextual CNN for Hyperspectral Image ClassificationCode0
A Classification Leveraged Object Detector—0
The Curious Robot: Learning Visual Representations via Physical Interactions—0
Sparse Activity and Sparse Connectivity in Supervised Learning—0
Evolution of active categorical image classification via saccadic eye movement—0
Deep Learning At Scale and At Ease—0
Joint Projection and Dictionary Learning using Low-rank Regularization and Graph Constraints—0
Revisiting Batch Normalization For Practical Domain AdaptationCode0
Learning Binary Codes and Binary Weights for Efficient Classification—0
Discriminative models for robust image classification—0
Variational Autoencoders for Semi-supervised Text Classification—0
Decision Forests, Convolutional Networks and the Models in-BetweenCode0
Convolutional Patch Representations for Image Retrieval: an Unsupervised Approach—0
On Complex Valued Convolutional Neural NetworksCode0
Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural NetworksCode0
On Study of the Binarized Deep Neural Network for Image Classification—0
Global Deconvolutional Networks for Semantic Segmentation—0
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning—0
Screen Content Image Segmentation Using Sparse Decomposition and Total Variation Minimization—0
Automatic and Quantitative evaluation of attribute discovery methods—0
Leveraging Mid-Level Deep Representations For Predicting Face Attributes in the Wild—0
Learning scale-variant and scale-invariant features for deep image classification—0
Unsupervised High-level Feature Learning by Ensemble Projection for Semi-supervised Image Classification and Image Clustering—0
What Can I Do Around Here? Deep Functional Scene Understanding for Cognitive Robots—0
Locally-Supervised Deep Hybrid Model for Scene Recognition—0
Compositional Model based Fisher Vector Coding for Image ClassificationCode0
Document image classification, with a specific view on applications of patent images—0
Learning Subclass Representations for Visually-varied Image Classification—0
Group Invariant Deep Representations for Image Instance Retrieval—0
Dense Bag-of-Temporal-SIFT-Words for Time Series Classification—0
Combined statistical and model based texture features for improved image classification—0
A Multiresolution Clinical Decision Support System Based on Fractal Model Design for Classification of Histological Brain Tumours—0
Convolutional Architecture Exploration for Action Recognition and Image Classification—0
Multi-Instance Visual-Semantic Embedding—0
Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels—0
Sparse Coding with Fast Image Alignment via Large Displacement Optical Flow—0
Kernel principal component analysis network for image classification—0
Blockout: Dynamic Model Selection for Hierarchical Deep Networks—0
Semantic-enriched Visual Vocabulary Construction in a Weakly Supervised Context—0
Deep Learning Algorithms with Applications to Video Analytics for A Smart City: A Survey—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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified