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 4701–4750 of 10420 papers

TitleStatusHype
Self Pre-training with Masked Autoencoders for Medical Image Classification and SegmentationCode1
Multiscale Convolutional Transformer with Center Mask Pretraining for Hyperspectral Image Classification—0
Renyi Fair Information Bottleneck for Image Classification—0
Uni4Eye: Unified 2D and 3D Self-supervised Pre-training via Masked Image Modeling Transformer for Ophthalmic Image Classification—0
Active Self-Semi-Supervised Learning for Few Labeled Samples—0
ParC-Net: Position Aware Circular Convolution with Merits from ConvNets and TransformerCode2
Dynamic Group Transformer: A General Vision Transformer Backbone with Dynamic Group Attention—0
Graph Attention Transformer Network for Multi-Label Image ClassificationCode1
PASS: Part-Aware Self-Supervised Pre-Training for Person Re-IdentificationCode1
Discriminability-Transferability Trade-Off: An Information-Theoretic PerspectiveCode0
Selective-Supervised Contrastive Learning with Noisy LabelsCode1
Art-Attack: Black-Box Adversarial Attack via Evolutionary Art—0
Explaining Classifiers by Constructing Familiar ConceptsCode0
Dynamic MLP for Fine-Grained Image Classification by Leveraging Geographical and Temporal InformationCode1
Dynamic ConvNets on Tiny Devices via Nested Sparsity—0
WaveMix: Resource-efficient Token Mixing for ImagesCode1
Graph Neural Networks for Image Classification and Reinforcement Learning using Graph representations—0
Fidelity of Interpretability Methods and Perturbation Artifacts in Neural Networks—0
MetaFormer: A Unified Meta Framework for Fine-Grained RecognitionCode2
Dynamic Backdoors with Global Average Pooling—0
FairPrune: Achieving Fairness Through Pruning for Dermatological Disease Diagnosis—0
Class-Aware Contrastive Semi-Supervised LearningCode1
DiT: Self-supervised Pre-training for Document Image TransformerCode1
Semi-supervised Learning using Robust LossCode0
Ensembles of Vision Transformers as a New Paradigm for Automated Classification in EcologyCode0
Random Quantum Neural Networks (RQNN) for Noisy Image RecognitionCode1
AdaFamily: A family of Adam-like adaptive gradient methods—0
Exploring Hierarchical Graph Representation for Large-Scale Zero-Shot Image ClassificationCode1
MIAShield: Defending Membership Inference Attacks via Preemptive Exclusion of Members—0
Aggregated Pyramid Vision Transformer: Split-transform-merge Strategy for Image Recognition without Convolutions—0
Continuous-Time Meta-Learning with Forward Mode Differentiation—0
ADVISE: ADaptive Feature Relevance and VISual Explanations for Convolutional Neural NetworksCode0
Visual Feature Encoding for GNNs on Road Networks—0
Semi-supervised Deep Learning for Image Classification with Distribution Mismatch: A Survey—0
Amortized Proximal Optimization—0
Evaluating the Adversarial Robustness of Adaptive Test-time DefensesCode1
ESW Edge-Weights : Ensemble Stochastic Watershed Edge-Weights for Hyperspectral Image Classification—0
Attribute Descent: Simulating Object-Centric Datasets on the Content Level and BeyondCode1
Synergistic Network Learning and Label Correction for Noise-robust Image Classification—0
Neuro-Inspired Deep Neural Networks with Sparse, Strong ActivationsCode0
Relational Surrogate Loss LearningCode1
QOC: Quantum On-Chip Training with Parameter Shift and Gradient PruningCode3
Accelerating Neural Architecture Exploration Across Modalities Using Genetic Algorithms—0
TeachAugment: Data Augmentation Optimization Using Teacher KnowledgeCode1
Monogenic Wavelet Scattering Network for Texture Image Classification—0
Goal-Oriented Communication for Edge Learning based on the Information Bottleneck—0
RRL:Regional Rotation Layer in Convolutional Neural Networks—0
SUTD-PRCM Dataset and Neural Architecture Search Approach for Complex Metasurface Design—0
New Benchmark for Household Garbage Image Recognition—0
Self-Training: A Survey—0
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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