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 16511675 of 10420 papers

TitleStatusHype
Multi-Scale Vision Longformer: A New Vision Transformer for High-Resolution Image EncodingCode1
Dual-Branch Subpixel-Guided Network for Hyperspectral Image ClassificationCode1
On the Robustness of Vision Transformers to Adversarial ExamplesCode1
DualConv: Dual Convolutional Kernels for Lightweight Deep Neural NetworksCode1
Astroformer: More Data Might not be all you need for ClassificationCode1
OODformer: Out-Of-Distribution Detection TransformerCode1
DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of EnsemblesCode1
DUET: Cross-modal Semantic Grounding for Contrastive Zero-shot LearningCode1
Active Finetuning: Exploiting Annotation Budget in the Pretraining-Finetuning ParadigmCode1
Capsule Network based Contrastive Learning of Unsupervised Visual RepresentationsCode1
Open Vocabulary Semantic Segmentation with Patch Aligned Contrastive LearningCode1
Open-World Semi-Supervised LearningCode1
4-bit Shampoo for Memory-Efficient Network TrainingCode1
Dynamic Graph Message Passing Networks for Visual RecognitionCode1
Class-Balanced Distillation for Long-Tailed Visual RecognitionCode1
Dynamic Group Convolution for Accelerating Convolutional Neural NetworksCode1
Fixing Localization Errors to Improve Image ClassificationCode1
Overinterpretation reveals image classification model pathologiesCode1
P2T: Pyramid Pooling Transformer for Scene UnderstandingCode1
Dynamic MLP for Fine-Grained Image Classification by Leveraging Geographical and Temporal InformationCode1
fKAN: Fractional Kolmogorov-Arnold Networks with trainable Jacobi basis functionsCode1
A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network CalibrationCode1
Dynamic Routing Between CapsulesCode1
Age Estimation Using Expectation of Label Distribution LearningCode1
Firefly Neural Architecture Descent: a General Approach for Growing Neural NetworksCode1
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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