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 14511500 of 10419 papers

TitleStatusHype
Benchmarking Knowledge-driven Zero-shot LearningCode1
Co^2L: Contrastive Continual LearningCode1
Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic SparsityCode1
Multi-Compound Transformer for Accurate Biomedical Image SegmentationCode1
R-Drop: Regularized Dropout for Neural NetworksCode1
RAILS: A Robust Adversarial Immune-inspired Learning SystemCode1
Can An Image Classifier Suffice For Action Recognition?Code1
Spectral-Spatial Global Graph Reasoning for Hyperspectral Image ClassificationCode1
PVT v2: Improved Baselines with Pyramid Vision TransformerCode1
SITTA: Single Image Texture Translation for Data AugmentationCode1
VOLO: Vision Outlooker for Visual RecognitionCode1
Multi-layered Semantic Representation Network for Multi-label Image ClassificationCode1
P2T: Pyramid Pooling Transformer for Scene UnderstandingCode1
TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?Code1
Secure Distributed Training at ScaleCode1
Stateful ODE-Nets using Basis Function ExpansionsCode1
Exploring Vision Transformers for Fine-grained ClassificationCode1
T-vMF Similarity for Regularizing Intra-Class Feature DistributionCode1
Contrastive Learning of Generalized Game RepresentationsCode1
Equivariance-bridged SO(2)-Invariant Representation Learning using Graph Convolutional NetworkCode1
How to train your ViT? Data, Augmentation, and Regularization in Vision TransformersCode1
Deep Subdomain Adaptation Network for Image ClassificationCode1
Multi-Label Learning from Single Positive LabelsCode1
Positional Contrastive Learning for Volumetric Medical Image SegmentationCode1
Federated Semi-supervised Medical Image Classification via Inter-client Relation MatchingCode1
Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective AdaptationCode1
SAR Image Classification Based on Spiking Neural Network through Spike-Time Dependent Plasticity and Gradient DescentCode1
MLPerf Tiny BenchmarkCode1
Variational Quanvolutional Neural Networks with enhanced image encodingCode1
pix2rule: End-to-end Neuro-symbolic Rule LearningCode1
Partial success in closing the gap between human and machine visionCode1
The Backpropagation Algorithm Implemented on Spiking Neuromorphic HardwareCode1
Entropy-based Logic Explanations of Neural NetworksCode1
HR-NAS: Searching Efficient High-Resolution Neural Architectures with Lightweight TransformersCode1
MlTr: Multi-label Classification with TransformerCode1
AugNet: End-to-End Unsupervised Visual Representation Learning with Image AugmentationCode1
PeCLR: Self-Supervised 3D Hand Pose Estimation from monocular RGB via Equivariant Contrastive LearningCode1
Scaling Vision with Sparse Mixture of ExpertsCode1
CLCC: Contrastive Learning for Color ConstancyCode1
Salient Positions based Attention Network for Image ClassificationCode1
Rethinking Transfer Learning for Medical Image ClassificationCode1
Cervical Cytology Classification Using PCA & GWO Enhanced Deep Features SelectionCode1
CoAtNet: Marrying Convolution and Attention for All Data SizesCode1
Self-Supervised Learning with Data Augmentations Provably Isolates Content from StyleCode1
Benchmarking Bias Mitigation Algorithms in Representation Learning through Fairness MetricsCode1
To Smooth or Not? When Label Smoothing Meets Noisy LabelsCode1
ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive BiasCode1
Mixture Outlier Exposure: Towards Out-of-Distribution Detection in Fine-grained EnvironmentsCode1
Shuffle Transformer: Rethinking Spatial Shuffle for Vision TransformerCode1
Refiner: Refining Self-attention for Vision TransformersCode1
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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
5DaViT-HTop 1 Accuracy90.2Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90Unverified