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 7951–8000 of 10420 papers

TitleStatusHype
MaxUp: A Simple Way to Improve Generalization of Neural Network TrainingCode0
Scalable Second Order Optimization for Deep LearningCode0
Byzantine-resilient Decentralized Stochastic Gradient Descent—0
A survey on Semi-, Self- and Unsupervised Learning for Image Classification—0
Deep regularization and direct training of the inner layers of Neural Networks with Kernel FlowsCode0
Interpreting Interpretations: Organizing Attribution Methods by Criteria—0
Towards Query-Efficient Black-Box Adversary with Zeroth-Order Natural Gradient DescentCode0
TensorShield: Tensor-based Defense Against Adversarial Attacks on Images—0
A Modified Perturbed Sampling Method for Local Interpretable Model-agnostic Explanation—0
Uncertainty Estimation in Autoregressive Structured Prediction—0
Photonic convolutional neural networks using integrated diffractive optics—0
CRL: Class Representative Learning for Image Classification—0
ARMA Nets: Expanding Receptive Field for Dense PredictionCode0
Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning—0
Multi-Task Multicriteria Hyperparameter Optimization—0
Manifold-based Test Generation for Image Classifiers—0
Graph-propagation based Correlation Learning for Weakly Supervised Fine-grained Image Classification—0
Regularizing activations in neural networks via distribution matching with the Wasserstein metric—0
The use of Convolutional Neural Networks for signal-background classification in Particle Physics experiments—0
Object Detection on Single Monocular Images through Canonical Correlation Analysis—0
CBIR using features derived by Deep LearningCode0
Retrain or not retrain? -- efficient pruning methods of deep CNN networks—0
Task-Robust Model-Agnostic Meta-Learning—0
Learnable Bernoulli Dropout for Bayesian Deep Learning—0
Regularized Evolutionary Population-Based Training—0
On Parameter Tuning in Meta-learning for Computer Vision—0
Best of Both Worlds: AutoML Codesign of a CNN and its Hardware Accelerator—0
Privacy-Preserving Image Classification in the Local Setting—0
Learning efficient structured dictionary for image classification—0
Convolutional Neural Network Pruning Using Filter Attenuation—0
PixelHop++: A Small Successive-Subspace-Learning-Based (SSL-based) Model for Image Classification—0
RAID: Randomized Adversarial-Input Detection for Neural Networks—0
CIFAR-10 Image Classification Using Feature EnsemblesCode0
Learning Hyperspectral Feature Extraction and Classification with ResNeXt Network—0
Impact of ImageNet Model Selection on Domain AdaptationCode0
Fixed smooth convolutional layer for avoiding checkerboard artifacts in CNNs—0
Understanding the Decision Boundary of Deep Neural Networks: An Empirical Study—0
Analyzing the Dependency of ConvNets on Spatial Information—0
Machine Learning Techniques to Detect and Characterise Whistler Radio Waves—0
Unsupervised Domain Adaptive Object Detection using Forward-Backward Cyclic Adaptation—0
AdvJND: Generating Adversarial Examples with Just Noticeable Difference—0
Learning to Detect Malicious Clients for Robust Federated Learning—0
Asymmetric Distribution Measure for Few-shot Learning—0
Localizing Interpretable Multi-scale informative Patches Derived from Media Classification Task—0
Adversarial Incremental Learning—0
Non-Determinism in TensorFlow ResNets—0
A CNN With Multi-scale Convolution for Hyperspectral Image Classification using Target-Pixel-Orientation scheme—0
Learn to Predict Sets Using Feed-Forward Neural Networks—0
Evaluating the Progress of Deep Learning for Visual Relational Concepts—0
Black Box Explanation by Learning Image Exemplars in the Latent Feature Space—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
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