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 8726–8750 of 10420 papers

TitleStatusHype
Complex Scene Classification of PolSAR Imagery based on a Self-paced Learning Approach—0
Domain Generalization by Solving Jigsaw PuzzlesCode0
Improving Strong-Scaling of CNN Training by Exploiting Finer-Grained Parallelism—0
Selective Kernel NetworksCode0
Unsupervised Deep Transfer Feature Learning for Medical Image Classification—0
Superpixel Contracted Graph-Based Learning for Hyperspectral Image ClassificationCode0
Learning Dependency Structures for Weak Supervision Models—0
Improving Neural Architecture Search Image Classifiers via Ensemble LearningCode0
Signal Demodulation with Machine Learning Methods for Physical Layer Visible Light Communications: Prototype Platform, Open Dataset and Algorithms—0
DeepOBS: A Deep Learning Optimizer Benchmark SuiteCode0
All You Need is a Few Shifts: Designing Efficient Convolutional Neural Networks for Image ClassificationCode0
Image Classification base on PCA of Multi-view Deep Representation—0
Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection—0
Using satellite image classification and digital terrain modelling to assess forest species distribution on mountain slopes.A case study in Varatec Forest District—0
Spatial-Aware Non-Local Attention for Fashion Landmark Detection—0
Structured Knowledge Distillation for Dense PredictionCode0
A Hybrid GA-PSO Method for Evolving Architecture and Short Connections of Deep Convolutional Neural Networks—0
Sliced Wasserstein Discrepancy for Unsupervised Domain AdaptationCode0
Research on the pixel-based and object-oriented methods of urban feature extraction with GF-2 remote-sensing images—0
Inductive Transfer for Neural Architecture Optimization—0
Label Embedded Dictionary Learning for Image ClassificationCode0
Deep CNN-based Multi-task Learning for Open-Set RecognitionCode0
Analysis Dictionary Learning: An Efficient and Discriminative Solution—0
ViTOR: Learning to Rank Webpages Based on Visual Features—0
Weakly Supervised Complementary Parts Models for Fine-Grained Image Classification from the Bottom UpCode0
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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