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

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Showing 8251–8300 of 10420 papers

TitleStatusHype
A Semantics-Guided Class Imbalance Learning Model for Zero-Shot Classification—0
Multi-Path Learnable Wavelet Neural Network for Image Classification—0
Confidence Regularized Self-TrainingCode1
Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural NetworksCode0
Feature Learning to Automatically Assess Radiographic Knee Osteoarthritis Severity—0
Mish: A Self Regularized Non-Monotonic Activation FunctionCode0
Learning Filter Basis for Convolutional Neural Network CompressionCode1
Image Colorization By Capsule Networks—0
ColorNet -- Estimating Colorfulness in Natural ImagesCode0
Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial ExamplesCode0
DomainSiam: Domain-Aware Siamese Network for Visual Object Tracking—0
Density estimation in representation space to predict model uncertainty—0
Saccader: Improving Accuracy of Hard Attention Models for VisionCode0
Dynamic Graph Message Passing NetworksCode1
Representing text as abstract images enables image classifiers to also simultaneously classify text—0
Adversarial Defense by Suppressing High-frequency ComponentsCode0
Adaptative Inference Cost With Convolutional Neural Mixture Models—0
NLNL: Negative Learning for Noisy LabelsCode0
Demystifying Learning Rate Policies for High Accuracy Training of Deep Neural NetworksCode1
Verification of Neural Network Control Policy Under Persistent Adversarial Perturbation—0
Gradient Weighted Superpixels for Interpretability in CNNs—0
Symmetric Cross Entropy for Robust Learning with Noisy LabelsCode0
SCARLET-NAS: Bridging the Gap between Stability and Scalability in Weight-sharing Neural Architecture SearchCode0
Needles in Haystacks: On Classifying Tiny Objects in Large ImagesCode0
Sparse Bayesian approach for metric learning in latent spaceCode0
Improved Mix-up with KL-Entropy for Learning From Noisy Labels—0
DAPAS : Denoising Autoencoder to Prevent Adversarial attack in Semantic Segmentation—0
Benchmarking the Robustness of Semantic Segmentation Models—0
Mapping of Local and Global Synapses on Spiking Neuromorphic Hardware—0
Neural Plasticity NetworksCode0
Atlas: A Dataset and Benchmark for E-commerce Clothing Product CategorizationCode0
Instance Enhancement Batch Normalization: an Adaptive Regulator of Batch NoiseCode0
LIP: Local Importance-based PoolingCode0
PCGAN-CHAR: Progressively Trained Classifier Generative Adversarial Networks for Classification of Noisy Handwritten Bangla Characters—0
AutoGAN: Neural Architecture Search for Generative Adversarial NetworksCode0
Space-time error estimates for deep neural network approximations for differential equations—0
Recent Advances in Deep Learning for Object DetectionCode0
Neural Image Compression and ExplanationCode0
Bayesian Inference for Large Scale Image Classification—0
A Fast and Precise Method for Large-Scale Land-Use Mapping Based on Deep Learning—0
On the Variance of the Adaptive Learning Rate and BeyondCode1
Feature selection of neural networks is skewed towards the less abstract cue—0
NeuPDE: Neural Network Based Ordinary and Partial Differential Equations for Modeling Time-Dependent Data—0
Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised LearningCode1
Progressive Transfer LearningCode0
Explaining Convolutional Neural Networks using Softmax Gradient Layer-wise Relevance PropagationCode0
Deep Self-Learning From Noisy Labels—0
Model Agnostic Defence against Backdoor Attacks in Machine LearningCode1
NeuroMask: Explaining Predictions of Deep Neural Networks through Mask Learning—0
SqueezeNAS: Fast neural architecture search for faster semantic segmentationCode0
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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