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 6301–6325 of 10420 papers

TitleStatusHype
Learning Transferable Visual Models From Natural Language SupervisionCode2
GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical ImagingCode1
Class Knowledge Overlay to Visual Feature Learning for Zero-Shot Image Classification—0
A Universal Model for Cross Modality Mapping by Relational Reasoning—0
Highly Efficient Representation and Active Learning Framework and Its Application to Imbalanced Medical Image Classification—0
Web Table Classification based on Visual Features—0
Visual Word Embedding for Text ClassificationCode0
Do Input Gradients Highlight Discriminative Features?Code1
Robust Pollen Imagery Classification with Generative Modeling and Mixup Training—0
Transfer Learning with Convolutional Neural Networks for Rainfall Detection in Single ImagesCode0
Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without ConvolutionsCode1
Arguments for the Unsuitability of Convolutional Neural Networks for Non--Local Tasks—0
ASAM: Adaptive Sharpness-Aware Minimization for Scale-Invariant Learning of Deep Neural NetworksCode2
Probabilistic Spatial Analysis in Quantitative Microscopy with Uncertainty-Aware Cell Detection using Deep Bayesian Regression of Density Maps—0
Classifying high-dimensional Gaussian mixtures: Where kernel methods fail and neural networks succeedCode0
FINE Samples for Learning with Noisy LabelsCode1
Explainers in the Wild: Making Surrogate Explainers Robust to Distortions through Perception—0
Conditional Positional Encodings for Vision TransformersCode1
MetaDelta: A Meta-Learning System for Few-shot Image ClassificationCode1
Revisiting Classification Perspective on Scene Text Recognition—0
CSIT-Free Model Aggregation for Federated Edge Learning via Reconfigurable Intelligent Surface—0
The Uncanny Similarity of Recurrence and DepthCode0
Combining Spiking Neural Network and Artificial Neural Network for Enhanced Image Classification—0
A Hierarchical Conditional Random Field-based Attention Mechanism Approach for Gastric Histopathology Image Classification—0
Evolving Attention with Residual ConvolutionsCode1
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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