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 19011925 of 10419 papers

TitleStatusHype
Co2L: Contrastive Continual LearningCode1
DMT: Dynamic Mutual Training for Semi-Supervised LearningCode1
Delving into Out-of-Distribution Detection with Medical Vision-Language ModelsCode1
Dendritic Learning-incorporated Vision Transformer for Image RecognitionCode1
Sequential Graph Convolutional Network for Active LearningCode1
CoAtNet: Marrying Convolution and Attention for All Data SizesCode1
Depth-Wise Convolutions in Vision Transformers for Efficient Training on Small DatasetsCode1
CoCa: Contrastive Captioners are Image-Text Foundation ModelsCode1
Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label CorrectionCode1
Shapley Values-enabled Progressive Pseudo Bag Augmentation for Whole Slide Image ClassificationCode1
CODE-CL: Conceptor-Based Gradient Projection for Deep Continual LearningCode1
CoDeNet: Efficient Deployment of Input-Adaptive Object Detection on Embedded FPGAsCode1
On Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances and Million-AIDCode1
Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale TasksCode1
AQD: Towards Accurate Fully-Quantized Object DetectionCode1
DATA: Domain-Aware and Task-Aware Self-supervised LearningCode1
DeepVoxNet2: Yet another CNN frameworkCode1
Shuffle Transformer: Rethinking Spatial Shuffle for Vision TransformerCode1
DeepViT: Towards Deeper Vision TransformerCode1
Defending Against Unforeseen Failure Modes with Latent Adversarial TrainingCode1
Collaborative Transformers for Grounded Situation RecognitionCode1
Adversarial Robustness on In- and Out-Distribution Improves ExplainabilityCode1
UniUSNet: A Promptable Framework for Universal Ultrasound Disease Prediction and Tissue SegmentationCode1
Simplifying Graph Convolutional NetworksCode1
A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image ClassificationCode1
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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