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 2951–3000 of 10419 papers

TitleStatusHype
Better Teacher Better Student: Dynamic Prior Knowledge for Knowledge DistillationCode0
Better Self-training for Image Classification through Self-supervisionCode0
Densely Connected Search Space for More Flexible Neural Architecture SearchCode0
Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture SearchCode0
Improving singing voice separation with the Wave-U-Net using Minimum Hyperspherical EnergyCode0
Dense and Diverse Capsule Networks: Making the Capsules Learn BetterCode0
Is user feedback always informative? Retrieval Latent Defending for Semi-Supervised Domain Adaptation without Source DataCode0
Diagnosing Model Performance Under Distribution ShiftCode0
Improving Random-Sampling Neural Architecture Search by Evolving the Proxy Search SpaceCode0
Improving robustness to corruptions with multiplicative weight perturbationsCode0
Better May Not Be Fairer: A Study on Subgroup Discrepancy in Image ClassificationCode0
Improving Pre-Trained Weights Through Meta-Heuristics Fine-TuningCode0
Beta-Rank: A Robust Convolutional Filter Pruning Method For Imbalanced Medical Image AnalysisCode0
Improving Pairwise Ranking for Multi-label Image ClassificationCode0
Improving Prototypical Visual Explanations with Reward Reweighing, Reselection, and RetrainingCode0
Improving Neural Architecture Search Image Classifiers via Ensemble LearningCode0
Improving Memory Efficiency for Training KANs via Meta LearningCode0
Why gradient clipping accelerates training: A theoretical justification for adaptivityCode0
Improving model calibration with accuracy versus uncertainty optimizationCode0
DiCENet: Dimension-wise Convolutions for Efficient NetworksCode0
Improving Nonlinear Projection Heads using Pretrained Autoencoder EmbeddingsCode0
Improving Long-tailed Object Detection with Image-Level Supervision by Multi-Task Collaborative LearningCode0
Improving Intervention Efficacy via Concept Realignment in Concept Bottleneck ModelsCode0
Improving k-Means Clustering Performance with Disentangled Internal RepresentationsCode0
Improving Shift Invariance in Convolutional Neural Networks with Translation Invariant Polyphase SamplingCode0
Delving into Transferable Adversarial Examples and Black-box AttacksCode0
BioLCNet: Reward-modulated Locally Connected Spiking Neural NetworksCode0
Improving Generalizability of Kolmogorov-Arnold Networks via Error-Correcting Output CodesCode0
Improving Fairness in Image Classification via SketchingCode0
Improving Generalization and Convergence by Enhancing Implicit RegularizationCode0
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet ClassificationCode0
Improving Deep Neural Network Random Initialization Through Neuronal RewiringCode0
Delayed Memory Unit: Modelling Temporal Dependency Through Delay GateCode0
Improving Ensemble Distillation With Weight Averaging and Diversifying PerturbationCode0
Improving Classification Neural Networks by using Absolute activation function (MNIST/LeNET-5 example)Code0
Improving Calibration by Relating Focal Loss, Temperature Scaling, and PropernessCode0
Improving Confident-Classifiers For Out-of-distribution DetectionCode0
Differentially Private Image Classification from FeaturesCode0
Improving (α, f)-Byzantine Resilience in Federated Learning via layerwise aggregation and cosine distanceCode0
Classification Utility, Fairness, and Compactness via Tunable Information Bottleneck and Rényi MeasuresCode0
Towards Difficulty-Agnostic Efficient Transfer Learning for Vision-Language ModelsCode0
Deformable Kernels: Adapting Effective Receptive Fields for Object DeformationCode0
Differential Privacy Has Disparate Impact on Model AccuracyCode0
BiRA-Net: Bilinear Attention Net for Diabetic Retinopathy GradingCode0
DiffExplainer: Unveiling Black Box Models Via Counterfactual GenerationCode0
DiffFormer: a Differential Spatial-Spectral Transformer for Hyperspectral Image ClassificationCode0
FTBNN: Rethinking Non-linearity for 1-bit CNNs and Going BeyondCode0
Improving Generalization of Batch Whitening by Convolutional Unit OptimizationCode0
Improved Training Speed, Accuracy, and Data Utilization Through Loss Function OptimizationCode0
Benchmarking Robust Self-Supervised Learning Across Diverse Downstream TasksCode0
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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