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 10511100 of 10419 papers

TitleStatusHype
COVID-CXNet: Detecting COVID-19 in Frontal Chest X-ray Images using Deep LearningCode1
Counterfactual Visual ExplanationsCode1
CoV-TI-Net: Transferred Initialization with Modified End Layer for COVID-19 DiagnosisCode1
Co-Tuning for Transfer LearningCode1
Attention-Based Adaptive Spectral-Spatial Kernel ResNet for Hyperspectral Image ClassificationCode1
Counterfactual Generative NetworksCode1
CPrune: Compiler-Informed Model Pruning for Efficient Target-Aware DNN ExecutionCode1
CorGAN: Correlation-Capturing Convolutional Generative Adversarial Networks for Generating Synthetic Healthcare RecordsCode1
Attentional-Biased Stochastic Gradient DescentCode1
CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasksCode1
Attention based Dual-Branch Complex Feature Fusion Network for Hyperspectral Image ClassificationCode1
Attentional Feature FusionCode1
Attention-Challenging Multiple Instance Learning for Whole Slide Image ClassificationCode1
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy LabelsCode1
CrAM: A Compression-Aware MinimizerCode1
CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual RepresentationsCode1
Convolutional Sequence to Sequence LearningCode1
Convolutional Spiking Neural Networks for Spatio-Temporal Feature ExtractionCode1
ConvMLP: Hierarchical Convolutional MLPs for VisionCode1
Convolutional Channel-wise Competitive Learning for the Forward-Forward AlgorithmCode1
Convolutional Xformers for VisionCode1
Contrastive Masked Autoencoders are Stronger Vision LearnersCode1
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy LabelsCode1
AIO-P: Expanding Neural Performance Predictors Beyond Image ClassificationCode1
Making Convolutional Networks Shift-Invariant AgainCode1
Controllable Orthogonalization in Training DNNsCode1
Convolution-enhanced Evolving Attention NetworksCode1
AIDeveloper: deep learning image classification in life science and beyondCode1
Contrastive Deep SupervisionCode1
A Hybrid Neural Coding Approach for Pattern Recognition with Spiking Neural NetworksCode1
Contrastive Learning Improves Model Robustness Under Label NoiseCode1
Contrastive Learning of Generalized Game RepresentationsCode1
Continual Learning with Scaled Gradient ProjectionCode1
Boosting Memory Efficiency in Transfer Learning for High-Resolution Medical Image ClassificationCode1
ConTNet: Why not use convolution and transformer at the same time?Code1
Attribute Descent: Simulating Object-Centric Datasets on the Content Level and BeyondCode1
ActMAD: Activation Matching to Align Distributions for Test-Time-TrainingCode1
Continual Learning Using a Kernel-Based Method Over Foundation ModelsCode1
Contrasting Intra-Modal and Ranking Cross-Modal Hard Negatives to Enhance Visio-Linguistic Compositional UnderstandingCode1
Contrastive Learning of Medical Visual Representations from Paired Images and TextCode1
CoProNN: Concept-based Prototypical Nearest Neighbors for Explaining Vision ModelsCode1
CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped WindowsCode1
DeepGleason: a System for Automated Gleason Grading of Prostate Cancer using Deep Neural NetworksCode1
Contextual Diversity for Active LearningCode1
Contextual Convolutional Neural NetworksCode1
Contextual Squeeze-and-Excitation for Efficient Few-Shot Image ClassificationCode1
Content-aware Token Sharing for Efficient Semantic Segmentation with Vision TransformersCode1
Container: Context Aggregation NetworkCode1
Contextual Transformer Networks for Visual RecognitionCode1
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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