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 8901–8950 of 10420 papers

TitleStatusHype
One-Class Feature Learning Using Intra-Class Splitting—0
DAC: Data-free Automatic Acceleration of Convolutional NetworksCode0
An Empirical Evaluation of Sketched SVD and its Application to Leverage Score Ordering—0
Semi-Supervised Deep Learning for Abnormality Classification in Retinal ImagesCode1
Toward Multimodal Model-Agnostic Meta-Learning—0
TOP-GAN: Label-Free Cancer Cell Classification Using Deep Learning with a Small Training Set—0
The Recognition Of Persian Phonemes Using PPNet—0
Not Using the Car to See the Sidewalk: Quantifying and Controlling the Effects of Context in Classification and Segmentation—0
Attending Category Disentangled Global Context for Image Classification—0
Efficient Super Resolution Using Binarized Neural Network—0
Pre-Trained Convolutional Neural Network Features for Facial Expression Recognition—0
Flatten-T Swish: a thresholded ReLU-Swish-like activation function for deep learningCode0
Evolutionary Neural Architecture Search for Image Restoration—0
Rethinking Layer-wise Feature Amounts in Convolutional Neural Network ArchitecturesCode0
Learning to Learn from Noisy Labeled DataCode0
Generating Hard Examples for Pixel-wise Classification—0
Impact of Data Normalization on Deep Neural Network for Time Series Forecasting—0
Thwarting Adversarial Examples: An L_0-RobustSparse Fourier Transform—0
Reproduction Report on "Learn to Pay Attention"Code0
A Main/Subsidiary Network Framework for Simplifying Binary Neural Network—0
ECG Arrhythmia Classification Using Transfer Learning from 2-Dimensional Deep CNN Features—0
Layer-Parallel Training of Deep Residual Neural NetworksCode0
Proximal Mean-field for Neural Network QuantizationCode1
Defending Against Universal Perturbations With Shared Adversarial Training—0
Feature Denoising for Improving Adversarial RobustnessCode0
FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture SearchCode1
Detecting Adversarial Examples in Convolutional Neural Networks—0
Variational Saccading: Efficient Inference for Large Resolution ImagesCode0
Adversarial Defense of Image Classification Using a Variational Auto-EncoderCode0
Optimizing speed/accuracy trade-off for person re-identification via knowledge distillation—0
Pseudo-Rehearsal: Achieving Deep Reinforcement Learning without Catastrophic ForgettingCode0
Fooling Network Interpretation in Image Classification—0
Blockchain Enabled Trustless API Marketplace—0
Few-shot Object Detection via Feature ReweightingCode1
Teacher-Student Compression with Generative Adversarial NetworksCode0
Auto-tuning TensorFlow Threading Model for CPU Backend—0
Energy Efficient Hardware for On-Device CNN Inference via Transfer Learning—0
Bag of Tricks for Image Classification with Convolutional Neural NetworksCode1
Ladder Networks for Semi-Supervised Hyperspectral Image Classification—0
Prototype-based Neural Network Layers: Incorporating Vector Quantization—0
Efficient Attention: Attention with Linear ComplexitiesCode1
Parameter Re-Initialization through Cyclical Batch Size Schedules—0
Singing Voice Separation Using a Deep Convolutional Neural Network Trained by Ideal Binary Mask and Cross EntropyCode0
Deep Learning for Classical Japanese LiteratureCode0
Protection Against Reconstruction and Its Applications in Private Federated Learning—0
Deep Hierarchical Machine: a Flexible Divide-and-Conquer Architecture—0
Knowledge Distillation with Feature Maps for Image Classification—0
Identification and Recognition of Rice Diseases and Pests Using Convolutional Neural Networks—0
Adversarial Domain Randomization—0
Universal Perturbation Attack Against Image Retrieval—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