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 9001–9025 of 10420 papers

TitleStatusHype
Distributionally Robust Optimization with Probabilistic GroupCode0
MoGA: Searching Beyond MobileNetV3Code0
Distributionally Robust Classification on a Data BudgetCode0
ImageNot: A contrast with ImageNet preserves model rankingsCode0
PARTICLE: Part Discovery and Contrastive Learning for Fine-grained RecognitionCode0
Image Quality Assessment Guided Deep Neural Networks TrainingCode0
Mole Recruitment: Poisoning of Image Classifiers via Selective Batch SamplingCode0
Ablation study of self-supervised learning for image classificationCode0
Distributed Optimization of Multi-Class SVMsCode0
Moment Centralization based Gradient Descent Optimizers for Convolutional Neural NetworksCode0
Classification-Specific Parts for Improving Fine-Grained Visual CategorizationCode0
Classification robustness to common optical aberrationsCode0
Classification Metrics for Image Explanations: Towards Building Reliable XAI-EvaluationsCode0
Distributed Learning of Deep Neural Networks using Independent Subnet TrainingCode0
Classification Beats Regression: Counting of Cells from Greyscale Microscopic Images based on Annotation-free Training SamplesCode0
A Method of Moments Embedding Constraint and its Application to Semi-Supervised LearningCode0
Class2Str: End to End Latent Hierarchy LearningCode0
Partition-and-Debias: Agnostic Biases Mitigation via A Mixture of Biases-Specific ExpertsCode0
ReSpike: Residual Frames-based Hybrid Spiking Neural Networks for Efficient Action RecognitionCode0
Distributed Black-box Attack: Do Not Overestimate Black-box AttacksCode0
CLAD: A Contrastive Learning based Approach for Background DebiasingCode0
Radial Basis Function Networks for Convolutional Neural Networks to Learn Similarity Distance Metric and Improve InterpretabilityCode0
A Biologically Plausible Learning Rule for Deep Learning in the BrainCode0
iMixer: hierarchical Hopfield network implies an invertible, implicit and iterative MLP-MixerCode0
Asymmetric Masked Distillation for Pre-Training Small Foundation ModelsCode0
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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