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

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Showing 5051–5100 of 10420 papers

TitleStatusHype
How does promoting the minority fraction affect generalization? A theoretical study of the one-hidden-layer neural network on group imbalance—0
Automatic Classification and Segmentation of Tunnel Cracks Based on Deep Learning and Visual Explanations—0
How Does Diverse Interpretability of Textual Prompts Impact Medical Vision-Language Zero-Shot Tasks?—0
Revisiting Classification Perspective on Scene Text Recognition—0
How do Convolutional Neural Networks Learn Design?—0
How Compact?: Assessing Compactness of Representations through Layer-Wise Pruning—0
Adam^+: A Stochastic Method with Adaptive Variance Reduction—0
Importance of the Mathematical Foundations of Machine Learning Methods for Scientific and Engineering Applications—0
ImpressLearn: Continual Learning via Combined Task Impressions—0
Automatic Classification of Human Epithelial Type 2 Cell Indirect Immunofluorescence Images using Cell Pyramid Matching—0
Improved Algorithms for Agnostic Pool-based Active Classification—0
How adversarial attacks can disrupt seemingly stable accurate classifiers—0
Homogenizing Non-IID datasets via In-Distribution Knowledge Distillation for Decentralized Learning—0
CSIT-Free Model Aggregation for Federated Edge Learning via Reconfigurable Intelligent Surface—0
Attribute Recognition by Joint Recurrent Learning of Context and Correlation—0
Improved EATFormer: A Vision Transformer for Medical Image Classification—0
Hierarchical Auxiliary Learning—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
Keep your distance: learning dispersed embeddings on S_m—0
CSIFT Based Locality-constrained Linear Coding for Image Classification—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
HOG feature extraction from encrypted images for privacy-preserving machine learning—0
Improved Multi-Source Domain Adaptation by Preservation of Factors—0
AI visualization in Nanoscale Microscopy—0
HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach—0
Attribute Prototype Network for Any-Shot Learning—0
Frustratingly Easy Uncertainty Estimation for Distribution Shift—0
PAWS-VMK: A Unified Approach To Semi-Supervised Learning And Out-of-Distribution Detection—0
Keep Learning: Self-supervised Meta-learning for Learning from Inference—0
Historical Test-time Prompt Tuning for Vision Foundation Models—0
CSA: Data-efficient Mapping of Unimodal Features to Multimodal Features—0
Automatic Error Detection in Integrated Circuits Image Segmentation: A Data-driven Approach—0
Histopathology Image Classification using Deep Manifold Contrastive Learning—0
Attribute Mix: Semantic Data Augmentation for Fine Grained Recognition—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
Automatic estimation of heading date of paddy rice using deep learning—0
Improved Training Speed, Accuracy, and Data Utilization via Loss Function Optimization—0
Keep Your AI-es on the Road: Tackling Distracted Driver Detection with Convolutional Neural Networks and Targeted Data Augmentation—0
Identify ambiguous tasks combining crowdsourced labels by weighting Areas Under the Margin—0
KerCNNs: biologically inspired lateral connections for classification of corrupted images—0
Improvement Strategies for Few-Shot Learning in OCT Image Classification of Rare Retinal Diseases—0
Kernel Reconstruction ICA for Sparse Representation—0
Knowledge Distillation for Object Detection via Rank Mimicking and Prediction-guided Feature Imitation—0
Improve Unsupervised Domain Adaptation with Mixup Training—0
Label Consistent Transform Learning for Hyperspectral Image Classification—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
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2—Unverified
6DaViT-HTop 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