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 4501–4550 of 10420 papers

TitleStatusHype
PolyLoss: A Polynomial Expansion Perspective of Classification Loss FunctionsCode1
Adaptive hybrid activation function for deep neural networksCode0
Do Users Benefit From Interpretable Vision? A User Study, Baseline, And DatasetCode0
Surpassing the Human Accuracy: Detecting Gallbladder Cancer from USG Images with Curriculum LearningCode1
OCFormer: One-Class Transformer Network for Image Classification—0
A Closer Look at Personalization in Federated Image Classification—0
iCAR: Bridging Image Classification and Image-text Alignment for Visual RecognitionCode0
Reinforcing Generated Images via Meta-learning for One-Shot Fine-Grained Visual Recognition—0
Sparsely-gated Mixture-of-Expert Layers for CNN Interpretability—0
Enhancing Core Image Classification Using Generative Adversarial Networks (GANs)—0
Energy-Efficient Classification at the Wireless Edge with Reliability Guarantees—0
Multiple EffNet/ResNet Architectures for Melanoma Classification—0
GAF-NAU: Gramian Angular Field encoded Neighborhood Attention U-Net for Pixel-Wise Hyperspectral Image Classification—0
A 3-stage Spectral-spatial Method for Hyperspectral Image Classification—0
K-LITE: Learning Transferable Visual Models with External KnowledgeCode2
Self-supervised Learning for Sonar Image ClassificationCode1
Robustness Testing of Data and Knowledge Driven Anomaly Detection in Cyber-Physical Systems—0
ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual ModelsCode4
Image Data Augmentation for Deep Learning: A Survey—0
Optical Remote Sensing Image Understanding with Weak Supervision: Concepts, Methods, and Perspectives—0
LayoutLMv3: Pre-training for Document AI with Unified Text and Image MaskingCode0
An Extendable, Efficient and Effective Transformer-based Object Detector—0
Learning with SignaturesCode1
Continual Hippocampus Segmentation with TransformersCode1
Privacy-Preserving Image Classification Using Isotropic Network—0
Towards Lightweight Transformer via Group-wise Transformation for Vision-and-Language TasksCode1
Searching Intrinsic Dimensions of Vision Transformers—0
Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a DifferenceCode1
Relaxing Equivariance Constraints with Non-stationary Continuous Filters—0
HASA: Hybrid Architecture Search with Aggregation Strategy for Echinococcosis Classification and Ovary Segmentation in Ultrasound Images—0
ViTOL: Vision Transformer for Weakly Supervised Object LocalizationCode1
Explainable Analysis of Deep Learning Methods for SAR Image Classification—0
Neighborhood Attention TransformerCode2
MiniViT: Compressing Vision Transformers with Weight MultiplexingCode3
Detection of Degraded Acacia tree species using deep neural networks on uav drone imagery—0
DeiT III: Revenge of the ViTCode1
Masked Siamese Networks for Label-Efficient LearningCode2
Out-Of-Distribution Detection In Unsupervised Continual Learning—0
On the Equity of Nuclear Norm Maximization in Unsupervised Domain Adaptation—0
Examining the Proximity of Adversarial Examples to Class Manifolds in Deep Networks—0
VisCUIT: Visual Auditor for Bias in CNN Image ClassifierCode0
ReCLIP: A Strong Zero-Shot Baseline for Referring Expression ComprehensionCode1
Adaptive Cross-Attention-Driven Spatial-Spectral Graph Convolutional Network for Hyperspectral Image Classification—0
SuperpixelGridCut, SuperpixelGridMean and SuperpixelGridMix Data AugmentationCode1
Comparison Analysis of Traditional Machine Learning and Deep Learning Techniques for Data and Image Classification—0
A Simple Approach to Adversarial Robustness in Few-shot Image ClassificationCode0
Effective Mutation Rate Adaptation through Group Elite Selection—0
Regularization-based Pruning of Irrelevant Weights in Deep Neural ArchitecturesCode0
No Token Left Behind: Explainability-Aided Image Classification and GenerationCode1
Machine Learning State-of-the-Art with UncertaintiesCode0
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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