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 10761100 of 10419 papers

TitleStatusHype
Evaluating histopathology transfer learning with ChampKitCode1
Masked Autoencoders are Robust Data AugmentorsCode1
Disentangled Ontology Embedding for Zero-shot LearningCode1
S3Net: Spectral–Spatial Siamese Network for Few-Shot Hyperspectral Image ClassificationCode1
FixCaps: An Improved Capsules Network for Diagnosis of Skin CancerCode1
Localizing Semantic Patches for Accelerating Image ClassificationCode1
Masked Unsupervised Self-training for Label-free Image ClassificationCode1
Tackling covariate shift with node-based Bayesian neural networksCode1
JigsawHSI: a network for Hyperspectral Image classificationCode1
Optimizing Relevance Maps of Vision Transformers Improves RobustnessCode1
A memory-efficient neural ODE framework based on high-level adjoint differentiationCode1
Efficient Self-supervised Vision Pretraining with Local Masked ReconstructionCode1
CLIP4IDC: CLIP for Image Difference CaptioningCode1
Going Beyond One-Hot Encoding in Classification: Can Human Uncertainty Improve Model Performance?Code1
Revisiting the Importance of Amplifying Bias for DebiasingCode1
A Closer Look at Self-Supervised Lightweight Vision TransformersCode1
WaveMix: A Resource-efficient Neural Network for Image AnalysisCode1
Failure Detection in Medical Image Classification: A Reality Check and Benchmarking TestbedCode1
DLTTA: Dynamic Learning Rate for Test-time Adaptation on Cross-domain Medical ImagesCode1
Dual-Perspective Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial LabelsCode1
Trainable Weight Averaging: A General Approach for Subspace TrainingCode1
MixMAE: Mixed and Masked Autoencoder for Efficient Pretraining of Hierarchical Vision TransformersCode1
Boosting Multi-Label Image Classification with Complementary Parallel Self-DistillationCode1
Knowledge Distillation from A Stronger TeacherCode1
Vision Transformers in 2022: An Update on Tiny ImageNetCode1
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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