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 6801–6825 of 10420 papers

TitleStatusHype
Augmenting Deep Classifiers with Polynomial Neural NetworksCode0
Semisupervised Manifold Alignment of Multimodal Remote Sensing Images—0
Out-of-Distribution Detection for Dermoscopic Image Classification—0
All-You-Can-Fit 8-Bit Flexible Floating-Point Format for Accurate and Memory-Efficient Inference of Deep Neural Networks—0
A survey of active learning algorithms for supervised remote sensing image classification—0
A fast and Accurate Similarity-constrained Subspace Clustering Framework for Unsupervised Hyperspectral Image Classification—0
Annealing Knowledge DistillationCode0
Unsupervised Continual Learning Via Pseudo Labels—0
Anomaly Detection in Image Datasets Using Convolutional Neural Networks, Center Loss, and Mahalanobis Distance—0
Epigenetic evolution of deep convolutional modelsCode0
Domain Adaptive Monocular Depth Estimation With Semantic Information—0
Improved Conformer-based End-to-End Speech Recognition Using Neural Architecture Search—0
Scalable Marginal Likelihood Estimation for Model Selection in Deep LearningCode0
Combined Depth Space based Architecture Search For Person Re-identificationCode0
Unsupervised Class-Incremental Learning Through Confusion—0
Class-Wise Principal Component Analysis for hyperspectral image feature extraction—0
eGAN: Unsupervised approach to class imbalance using transfer learningCode0
Reinforced Attention for Few-Shot Learning and Beyond—0
Image Segmentation, Compression and Reconstruction from Edge Distribution Estimation with Random Field and Random Cluster Theories—0
Out-of-distribution detection in satellite image classification—0
Self-Weighted Ensemble Method to Adjust the Influence of Individual Models based on Reliability—0
Robust Training of Social Media Image Classification Models for Rapid Disaster Response—0
Tensor Network for Supervised Learning at Finite Temperature—0
Few-Shot Action Recognition with Compromised Metric via Optimal Transport—0
Prototypical Region Proposal Networks for Few-Shot Localization and 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
5DaViT-HTop 1 Accuracy90.2—Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 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