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 84768500 of 10420 papers

TitleStatusHype
EPRNet: Efficient Pyramid Representation Network for Real-Time Street Scene SegmentationCode0
12 mJ per Class On-Device Online Few-Shot Class-Incremental LearningCode0
Epigenetic evolution of deep convolutional modelsCode0
Controlling Participation in Federated Learning with FeedbackCode0
Contrastive Learning for Predicting Cancer Prognosis Using Gene Expression ValuesCode0
EPIC: Explanation of Pretrained Image Classification Networks via PrototypeCode0
Contrastive Learning for OOD in Object detectionCode0
GhostImage: Remote Perception Attacks against Camera-based Image Classification SystemsCode0
Masked Image Residual Learning for Scaling Deeper Vision TransformersCode0
Ensembles of Vision Transformers as a New Paradigm for Automated Classification in EcologyCode0
PSSCL: A progressive sample selection framework with contrastive loss designed for noisy labelsCode0
GhostShiftAddNet: More Features from Energy-Efficient OperationsCode0
Ontology-driven Event Type Classification in ImagesCode0
Contrastive-center loss for deep neural networksCode0
giMLPs: Gate with Inhibition Mechanism in MLPsCode0
Contrastive Bi-Projector for Unsupervised Domain AdaptionCode0
Ensemble of ConvNeXt V2 and MaxViT for Long-Tailed CXR Classification with View-Based AggregationCode0
Contrastive-Based Deep Embeddings for Label Noise-Resilient Histopathology Image ClassificationCode0
Ensemble learning in CNN augmented with fully connected subnetworksCode0
Enlarged Large Margin Loss for Imbalanced ClassificationCode0
Masking: A New Perspective of Noisy SupervisionCode0
Continuous Meta-Learning without TasksCode0
GLiRA: Black-Box Membership Inference Attack via Knowledge DistillationCode0
Enhancing Multimodal Medical Image Classification using Cross-Graph Modal Contrastive LearningCode0
Global Attention Mechanism: Retain Information to Enhance Channel-Spatial InteractionsCode0
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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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90Unverified