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 1017610200 of 10420 papers

TitleStatusHype
Semi-supervised dictionary learning with graph regularization and active pointsCode0
Sparse Bayesian approach for metric learning in latent spaceCode0
WOT-Class: Weakly Supervised Open-world Text ClassificationCode0
Using Wavelets and Spectral Methods to Study Patterns in Image-Classification DatasetsCode0
Transfer Learning for Illustration ClassificationCode0
Using Wavelets to Analyze Similarities in Image-Classification DatasetsCode0
Semi-Supervised Deep Learning with MemoryCode0
Semi-Supervised Brain Lesion Segmentation with an Adapted Mean Teacher ModelCode0
Transfer Learning with Convolutional Neural Networks for Rainfall Detection in Single ImagesCode0
Semantic Knowledge Distillation for Onboard Satellite Earth Observation Image ClassificationCode0
Semantic Cross Attention for Few-shot LearningCode0
Utilizing Synthetic Data for Medical Vision-Language Pre-training: Bypassing the Need for Real ImagesCode0
Transfer of Pretrained Model Weights Substantially Improves Semi-Supervised Image ClassificationCode0
Sound Randomized Smoothing in Floating-Point ArithmeticsCode0
Semantic-Aware Mixup for Domain GeneralizationCode0
Self-supervision for medical image classification: state-of-the-art performance with ~100 labeled training samples per classCode0
Task Vector Quantization for Memory-Efficient Model MergingCode0
Self Supervision for Attention NetworksCode0
VALE: A Multimodal Visual and Language Explanation Framework for Image Classifiers using eXplainable AI and Language ModelsCode0
Self-Supervised Pretraining for Differentially Private LearningCode0
(, δ) Considered Harmful: Best Practices for Reporting Differential Privacy GuaranteesCode0
Variable Importance Clouds: A Way to Explore Variable Importance for the Set of Good ModelsCode0
TaskSet: A Dataset of Optimization TasksCode0
SOOD-ImageNet: a Large-Scale Dataset for Semantic Out-Of-Distribution Image Classification and Semantic SegmentationCode0
WaveNets: Wavelet Channel Attention NetworksCode0
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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
10RevCol-HTop 1 Accuracy90Unverified