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 5101–5125 of 10420 papers

TitleStatusHype
An Image Patch is a Wave: Phase-Aware Vision MLPCode0
Improved Fine-Tuning by Better Leveraging Pre-Training Data—0
PeCo: Perceptual Codebook for BERT Pre-training of Vision TransformersCode1
MorphMLP: An Efficient MLP-Like Backbone for Spatial-Temporal Representation LearningCode0
Multi-label Iterated Learning for Image Classification with Label AmbiguityCode0
Inducing Functions through Reinforcement Learning without Task Specification—0
AutoDC: Automated data-centric processingCode1
CoDiM: Learning with Noisy Labels via Contrastive Semi-Supervised Learning—0
Using mixup as regularization and tuning hyper-parameters for ResNetsCode0
Focal and Global Knowledge Distillation for DetectorsCode1
Learning Consistent Deep Generative Models from Sparsely Labeled Data—0
Linearised Laplace Inference in Networks with Normalisation Layers and the Neural g-Prior—0
Broad Adversarial Training with Data Augmentation in the Output Space—0
Metamorphic Adversarial Detection Pipeline for Face Recognition Systems—0
Revisiting Adversarial Robustness of Classifiers With a Reject Option—0
DBIA: Data-free Backdoor Injection Attack against Transformer NetworksCode0
FedCV: A Federated Learning Framework for Diverse Computer Vision TasksCode1
Nanorobot queue: Cooperative treatment of cancer based on team member communication and image processing—0
Adversarial Examples on Segmentation Models Can be Easy to Transfer—0
SSR: An Efficient and Robust Framework for Learning with Unknown Label NoiseCode1
MiNet: A Convolutional Neural Network for Identifying and Categorising Minerals—0
Deep Learning Based Automated COVID-19 Classification from Computed Tomography ImagesCode0
Semi-Supervised Vision TransformersCode1
MetaFormer Is Actually What You Need for VisionCode2
Florence: A New Foundation Model for Computer VisionCode1
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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