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 2851–2900 of 10419 papers

TitleStatusHype
Inception-inspired LSTM for Next-frame Video PredictionCode0
An Architecture Combining Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for Image ClassificationCode0
A Data-Driven Measure of Relative Uncertainty for Misclassification DetectionCode0
Improvising the Learning of Neural Networks on Hyperspherical ManifoldCode0
Biased Importance Sampling for Deep Neural Network TrainingCode0
Biased Attention: Do Vision Transformers Amplify Gender Bias More than Convolutional Neural Networks?Code0
An Analysis of Unsupervised Pre-training in Light of Recent AdvancesCode0
Improving the repeatability of deep learning models with Monte Carlo dropoutCode0
Improving the trustworthiness of image classification models by utilizing bounding-box annotationsCode0
Beyond Uniform Query Distribution: Key-Driven Grouped Query AttentionCode0
Improving the Efficiency of Human-in-the-Loop Systems: Adding Artificial to Human ExpertsCode0
Improving the Gating Mechanism of Recurrent Neural NetworksCode0
Improving Transferability of Adversarial Examples with Input DiversityCode0
Improving robustness to corruptions with multiplicative weight perturbationsCode0
An analysis of over-sampling labeled data in semi-supervised learning with FixMatchCode0
Beyond One-Hot-Encoding: Injecting Semantics to Drive Image ClassifiersCode0
Improving Shift Invariance in Convolutional Neural Networks with Translation Invariant Polyphase SamplingCode0
Improving Pre-Trained Weights Through Meta-Heuristics Fine-TuningCode0
Improving Pairwise Ranking for Multi-label Image ClassificationCode0
Improving Prototypical Visual Explanations with Reward Reweighing, Reselection, and RetrainingCode0
Improving Nonlinear Projection Heads using Pretrained Autoencoder EmbeddingsCode0
DeSparsify: Adversarial Attack Against Token Sparsification Mechanisms in Vision TransformersCode0
Improving Neural Architecture Search Image Classifiers via Ensemble LearningCode0
Improving Random-Sampling Neural Architecture Search by Evolving the Proxy Search SpaceCode0
Improving singing voice separation with the Wave-U-Net using Minimum Hyperspherical EnergyCode0
Improving k-Means Clustering Performance with Disentangled Internal RepresentationsCode0
Design of Kernels in Convolutional Neural Networks for Image ClassificationCode0
Improving Intervention Efficacy via Concept Realignment in Concept Bottleneck ModelsCode0
Improving Generalization of Batch Whitening by Convolutional Unit OptimizationCode0
Designing Neural Network Architectures using Reinforcement LearningCode0
Improving Generalization and Convergence by Enhancing Implicit RegularizationCode0
SynerMix: Synergistic Mixup Solution for Enhanced Intra-Class Cohesion and Inter-Class Separability in Image ClassificationCode0
Improving Memory Efficiency for Training KANs via Meta LearningCode0
Improving Ensemble Distillation With Weight Averaging and Diversifying PerturbationCode0
Improving Fairness in Image Classification via SketchingCode0
Improving Deep Neural Network Random Initialization Through Neuronal RewiringCode0
Improving Classification Neural Networks by using Absolute activation function (MNIST/LeNET-5 example)Code0
Improving Confident-Classifiers For Out-of-distribution DetectionCode0
Designing Stable Neural Networks using Convex Analysis and ODEsCode0
Beyond Cats and Dogs: Semi-supervised Classification of fuzzy labels with overclusteringCode0
Improving Calibration by Relating Focal Loss, Temperature Scaling, and PropernessCode0
Improving Generalizability of Kolmogorov-Arnold Networks via Error-Correcting Output CodesCode0
Improving model calibration with accuracy versus uncertainty optimizationCode0
Improving Long-tailed Object Detection with Image-Level Supervision by Multi-Task Collaborative LearningCode0
Adaptive Stochastic Weight AveragingCode0
Depth and Representation in Vision ModelsCode0
BR-NPA: A Non-Parametric High-Resolution Attention Model to improve the Interpretability of AttentionCode0
Deployment of Image Analysis Algorithms under Prevalence ShiftsCode0
Beyond Accuracy: Metrics that Uncover What Makes a 'Good' Visual DescriptorCode0
Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to ATARI gamesCode0
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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