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 601–650 of 10419 papers

TitleStatusHype
Contextual Convolutional Neural NetworksCode1
Achieving Fairness Through Channel Pruning for Dermatological Disease DiagnosisCode1
Contextual Diversity for Active LearningCode1
Content-aware Token Sharing for Efficient Semantic Segmentation with Vision TransformersCode1
Container: Context Aggregation NetworkCode1
Contextual Squeeze-and-Excitation for Efficient Few-Shot Image ClassificationCode1
Continual Learning with Scaled Gradient ProjectionCode1
Confidence-aware multi-modality learning for eye disease screeningCode1
Anytime Dense Prediction with Confidence AdaptivityCode1
Confidence Regularized Self-TrainingCode1
Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional NetworksCode1
AdaptiveMix: Improving GAN Training via Feature Space ShrinkageCode1
CondenseNet V2: Sparse Feature Reactivation for Deep NetworksCode1
Conformer: Local Features Coupling Global Representations for Visual RecognitionCode1
Compressive Visual RepresentationsCode1
Adaptive Mask Sampling and Manifold to Euclidean Subspace Learning with Distance Covariance Representation for Hyperspectral Image ClassificationCode1
Concept Learners for Few-Shot LearningCode1
Stateful ODE-Nets using Basis Function ExpansionsCode1
Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural NetworkCode1
Compressing Features for Learning with Noisy LabelsCode1
Learning Hierarchical Image Segmentation For Recognition and By RecognitionCode1
Consistency-based Active Learning for Object DetectionCode1
ConTNet: Why not use convolution and transformer at the same time?Code1
Communication-Efficient and Privacy-Preserving Feature-based Federated Transfer LearningCode1
Combining Metric Learning and Attention Heads For Accurate and Efficient Multilabel Image ClassificationCode1
Communication-Efficient Federated Learning Based on Explanation-Guided Pruning for Remote Sensing Image ClassificationCode1
Combining GANs and AutoEncoders for Efficient Anomaly DetectionCode1
Combating noisy labels by agreement: A joint training method with co-regularizationCode1
Combining Human Predictions with Model Probabilities via Confusion Matrices and CalibrationCode1
Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge DistillationCode1
CoDeNet: Efficient Deployment of Input-Adaptive Object Detection on Embedded FPGAsCode1
CODE-CL: Conceptor-Based Gradient Projection for Deep Continual LearningCode1
Collaborative Transformers for Grounded Situation RecognitionCode1
Adaptive Edge Offloading for Image Classification Under Rate LimitCode1
CoCa: Contrastive Captioners are Image-Text Foundation ModelsCode1
Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label CorrectionCode1
Combating Label Noise in Deep Learning Using AbstentionCode1
Complementary-Label Learning for Arbitrary Losses and ModelsCode1
Adaptive DropBlock Enhanced Generative Adversarial Networks for Hyperspectral Image ClassificationCode1
CNN Filter DB: An Empirical Investigation of Trained Convolutional FiltersCode1
Co^2L: Contrastive Continual LearningCode1
ClusterFormer: Clustering As A Universal Visual LearnerCode1
Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image ClassificationCode1
Systematic comparison of semi-supervised and self-supervised learning for medical image classificationCode1
Clusterability as an Alternative to Anchor Points When Learning with Noisy LabelsCode1
CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep LearningCode1
Co2L: Contrastive Continual LearningCode1
CLIP the Gap: A Single Domain Generalization Approach for Object DetectionCode1
FocusNet: Classifying Better by Focusing on Confusing ClassesCode1
An Analysis on Ensemble Learning optimized Medical Image Classification with Deep Convolutional Neural NetworksCode1
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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