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 2501–2550 of 10419 papers

TitleStatusHype
Interpretable and Interactive Deep Multiple Instance Learning for Dental Caries Classification in Bitewing X-raysCode0
Classifying Textual Data with Pre-trained Vision Models through Transfer Learning and Data TransformationsCode0
A Parameterized Generative Adversarial Network Using Cyclic Projection for Explainable Medical Image ClassificationCode0
Adversarial Defense of Image Classification Using a Variational Auto-EncoderCode0
Adversarial Defense by Suppressing High-frequency ComponentsCode0
Classifying high-dimensional Gaussian mixtures: Where kernel methods fail and neural networks succeedCode0
Interferometric Neural NetworksCode0
Classifying a specific image region using convolutional nets with an ROI mask as inputCode0
Interlocking Backpropagation: Improving depthwise model-parallelismCode0
InterpNET: Neural Introspection for Interpretable Deep LearningCode0
Interpret Your Decision: Logical Reasoning Regularization for Generalization in Visual ClassificationCode0
Class-Conditioned Transformation for Enhanced Robust Image ClassificationCode0
Instilling Inductive Biases with SubnetworksCode0
Classification-Specific Parts for Improving Fine-Grained Visual CategorizationCode0
Instance Temperature Knowledge DistillationCode0
Integrating kNN with Foundation Models for Adaptable and Privacy-Aware Image ClassificationCode0
Classification robustness to common optical aberrationsCode0
Instance Enhancement Batch Normalization: an Adaptive Regulator of Batch NoiseCode0
Instance-dependent Label Distribution Estimation for Learning with Label NoiseCode0
Intelligent Multi-View Test Time AugmentationCode0
Input Invex Neural NetworkCode0
Input-gradient space particle inference for neural network ensemblesCode0
Initialization Matters for Adversarial Transfer LearningCode0
In-Place Activated BatchNorm for Memory-Optimized Training of DNNsCode0
Instance-based Label Smoothing For Better Calibrated Classification NetworksCode0
Information Competing Process for Learning Diversified RepresentationsCode0
Inference via Sparse Coding in a Hierarchical Vision ModelCode0
Influence of Image Classification Accuracy on Saliency Map EstimationCode0
An Intelligent Remote Sensing Image Quality Inspection SystemCode0
Intra-class Patch Swap for Self-DistillationCode0
Kannada-MNIST: A new handwritten digits dataset for the Kannada languageCode0
Learnable Adaptive Cosine Estimator (LACE) for Image ClassificationCode0
Understanding Intrinsic Robustness Using Label UncertaintyCode0
AntidoteRT: Run-time Detection and Correction of Poison Attacks on Neural NetworksCode0
Increasing-Margin Adversarial (IMA) Training to Improve Adversarial Robustness of Neural NetworksCode0
Classification Metrics for Image Explanations: Towards Building Reliable XAI-EvaluationsCode0
Adversarial Augmentation for Enhancing Classification of Mammography ImagesCode0
A Comprehensive Study of Image Classification Model Sensitivity to Foregrounds, Backgrounds, and Visual AttributesCode0
Inception-inspired LSTM for Next-frame Video PredictionCode0
Classification Beats Regression: Counting of Cells from Greyscale Microscopic Images based on Annotation-free Training SamplesCode0
Improving Transferability of Adversarial Examples with Input DiversityCode0
Improving the repeatability of deep learning models with Monte Carlo dropoutCode0
Improving the Gating Mechanism of Recurrent Neural NetworksCode0
Improving the trustworthiness of image classification models by utilizing bounding-box annotationsCode0
Improvising the Learning of Neural Networks on Hyperspherical ManifoldCode0
Revisiting 16-bit Neural Network Training: A Practical Approach for Resource-Limited LearningCode0
A Consensual Collaborative Learning Method for Remote Sensing Image Classification Under Noisy Multi-LabelsCode0
Improving singing voice separation with the Wave-U-Net using Minimum Hyperspherical EnergyCode0
Improving robustness to corruptions with multiplicative weight perturbationsCode0
Improving Random-Sampling Neural Architecture Search by Evolving the Proxy Search SpaceCode0
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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