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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 9776–9800 of 10420 papers

TitleStatusHype
Improved EATFormer: A Vision Transformer for Medical Image Classification—0
Improved Few-Shot Image Classification Through Multiple-Choice Questions—0
Improved Few-Shot Visual Classification—0
Improved Fine-Tuning by Better Leveraging Pre-Training Data—0
Improved Image Classification with Manifold Neural Networks—0
Improved Image Classification with Token Fusion—0
Improved Mix-up with KL-Entropy for Learning From Noisy Labels—0
Improved Multi-Source Domain Adaptation by Preservation of Factors—0
Improved OOD Generalization via Adversarial Training and Pre-training—0
Frustratingly Easy Uncertainty Estimation for Distribution Shift—0
PAWS-VMK: A Unified Approach To Semi-Supervised Learning And Out-of-Distribution Detection—0
Improved Robustness of Vision Transformer via PreLayerNorm in Patch Embedding—0
Improved Text Classification via Test-Time Augmentation—0
Improved texture image classification through the use of a corrosion-inspired cellular automaton—0
Improved Trainable Calibration Method for Neural Networks on Medical Imaging Classification—0
Improved Training Speed, Accuracy, and Data Utilization via Loss Function Optimization—0
Identify ambiguous tasks combining crowdsourced labels by weighting Areas Under the Margin—0
Improvement of image classification by multiple optical scattering—0
Improvement Strategies for Few-Shot Learning in OCT Image Classification of Rare Retinal Diseases—0
Improve Unsupervised Domain Adaptation with Mixup Training—0
Improved Techniques for Adversarial Discriminative Domain Adaptation—0
Improving Adversarially Robust Few-Shot Image Classification With Generalizable Representations—0
A Pseudo-labelling Auto-Encoder for unsupervised image classification—0
Improving Bag-of-Visual-Words Towards Effective Facial Expressive Image Classification—0
Improving Chest X-Ray Classification by RNN-based Patient Monitoring—0
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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
10RevCol-HTop 1 Accuracy90—Unverified