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 10051–10075 of 10420 papers

TitleStatusHype
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
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model sizeCode1
Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image AnnotationsCode1
Inception-v4, Inception-ResNet and the Impact of Residual Connections on LearningCode1
"Why Should I Trust You?": Explaining the Predictions of Any ClassifierCode1
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
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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