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 626650 of 10419 papers

TitleStatusHype
NoisyNN: Exploring the Impact of Information Entropy Change in Learning SystemsCode1
Long-Tail Learning with Foundation Model: Heavy Fine-Tuning HurtsCode1
Interpretability-Aware Vision TransformerCode1
Keep It SimPool: Who Said Supervised Transformers Suffer from Attention Deficit?Code1
Language Models as Black-Box Optimizers for Vision-Language ModelsCode1
SparseSwin: Swin Transformer with Sparse Transformer BlockCode1
Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated LearningCode1
Class-Incremental Grouping Network for Continual Audio-Visual LearningCode1
Divergences in Color Perception between Deep Neural Networks and HumansCode1
DiffAug: Enhance Unsupervised Contrastive Learning with Domain-Knowledge-Free Diffusion-based Data AugmentationCode1
When to Learn What: Model-Adaptive Data Augmentation CurriculumCode1
Locality-Aware Hyperspectral ClassificationCode1
Traveling Waves Encode the Recent Past and Enhance Sequence LearningCode1
Fine-grained Recognition with Learnable Semantic Data AugmentationCode1
CEFHRI: A Communication Efficient Federated Learning Framework for Recognizing Industrial Human-Robot InteractionCode1
A Dual-Direction Attention Mixed Feature Network for Facial Expression RecognitionCode1
FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated LearningCode1
Masking Strategies for Background Bias Removal in Computer Vision ModelsCode1
DR-Tune: Improving Fine-tuning of Pretrained Visual Models by Distribution Regularization with Semantic CalibrationCode1
Integrated Image and Location Analysis for Wound Classification: A Deep Learning ApproachCode1
Image-free Classifier Injection for Zero-Shot ClassificationCode1
Diffusion Model as Representation LearnerCode1
Unlocking Accuracy and Fairness in Differentially Private Image ClassificationCode1
A Comprehensive Empirical Evaluation on Online Continual LearningCode1
Which Transformer to Favor: A Comparative Analysis of Efficiency in Vision TransformersCode1
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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