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

Showing 21012150 of 10419 papers

TitleStatusHype
Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAMLCode1
3D U^2-Net: A 3D Universal U-Net for Multi-Domain Medical Image SegmentationCode1
Confidence Regularized Self-TrainingCode1
Learning Filter Basis for Convolutional Neural Network CompressionCode1
Dynamic Graph Message Passing NetworksCode1
Demystifying Learning Rate Policies for High Accuracy Training of Deep Neural NetworksCode1
Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised LearningCode1
On the Variance of the Adaptive Learning Rate and BeyondCode1
Model Agnostic Defence against Backdoor Attacks in Machine LearningCode1
Incremental Learning Techniques for Semantic SegmentationCode1
Grid Saliency for Context Explanations of Semantic SegmentationCode1
MixConv: Mixed Depthwise Convolutional KernelsCode1
Lookahead Optimizer: k steps forward, 1 step backCode1
Sparse Networks from Scratch: Faster Training without Losing PerformanceCode1
Invariant Risk MinimizationCode1
Learning Data Augmentation Strategies for Object DetectionCode1
Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial VehiclesCode1
XNAS: Neural Architecture Search with Expert AdviceCode1
Unsupervised Learning of Object Keypoints for Perception and ControlCode1
Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive ProcessesCode1
A Partially Reversible U-Net for Memory-Efficient Volumetric Image SegmentationCode1
Presence-Only Geographical Priors for Fine-Grained Image ClassificationCode1
When Does Label Smoothing Help?Code1
Combating Label Noise in Deep Learning Using AbstentionCode1
Derivative Manipulation for General Example WeightingCode1
Shredder: Learning Noise Distributions to Protect Inference PrivacyCode1
ProbAct: A Probabilistic Activation Function for Deep Neural NetworksCode1
DIANet: Dense-and-Implicit Attention NetworkCode1
Understanding and Utilizing Deep Neural Networks Trained with Noisy LabelsCode1
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable FeaturesCode1
Learning Loss for Active LearningCode1
AutoAssist: A Framework to Accelerate Training of Deep Neural NetworksCode1
MixMatch: A Holistic Approach to Semi-Supervised LearningCode1
Searching for MobileNetV3Code1
Billion-scale semi-supervised learning for image classificationCode1
Fast AutoAugmentCode1
Harmonic Networks with Limited Training SamplesCode1
Unsupervised Data Augmentation for Consistency TrainingCode1
Transformers with convolutional context for ASRCode1
Making Convolutional Networks Shift-Invariant AgainCode1
Counterfactual Visual ExplanationsCode1
Deep CNNs Meet Global Covariance Pooling: Better Representation and GeneralizationCode1
Weakly Supervised Learning of Instance Segmentation with Inter-pixel RelationsCode1
Hyperbolic Image EmbeddingsCode1
Augmented Neural ODEsCode1
Res2Net: A New Multi-scale Backbone ArchitectureCode1
Variational Adversarial Active LearningCode1
IMAE for Noise-Robust Learning: Mean Absolute Error Does Not Treat Examples Equally and Gradient Magnitude's Variance MattersCode1
Wasserstein Adversarial Examples via Projected Sinkhorn IterationsCode1
Meta-Weight-Net: Learning an Explicit Mapping For Sample WeightingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94Unverified
4DaViT-GTop 1 Accuracy90.4Unverified
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
6DaViT-HTop 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10RevCol-HTop 1 Accuracy90Unverified