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 6401–6425 of 10420 papers

TitleStatusHype
Visually Consistent Hierarchical Image Classification—0
Learning Graph Structure for Multi-Label Image Classification via Clique Generation—0
MMF: Multi-Task Multi-Structure Fusion for Hierarchical Image Classification—0
MMFormer: Multimodal Transformer Using Multiscale Self-Attention for Remote Sensing Image Classification—0
m-mix: Generating hard negatives via multiple samples mixing for contrastive learning—0
MM-UNet: A Mixed MLP Architecture for Improved Ophthalmic Image Segmentation—0
MMViT: Multiscale Multiview Vision Transformers—0
Better (pseudo-)labels for semi-supervised instance segmentation—0
Learning from Web Data: the Benefit of Unsupervised Object Localization—0
Learning from Teaching Regularization: Generalizable Correlations Should be Easy to Imitate—0
Learning from Noisy Labels with Noise Modeling Network—0
Dense Bag-of-Temporal-SIFT-Words for Time Series Classification—0
Analysis of the rate of convergence of an over-parametrized convolutional neural network image classifier learned by gradient descent—0
Learning from Noisy Labels with Coarse-to-Fine Sample Credibility Modeling—0
Learning from multiscale wavelet superpixels using GNN with spatially heterogeneous pooling—0
Denoising Mutual Knowledge Distillation in Bi-Directional Multiple Instance Learning—0
Learning from Mistakes based on Class Weighting with Application to Neural Architecture Search—0
Learning from Matured Dumb Teacher for Fine Generalization—0
Learning From Massive Noisy Labeled Data for Image Classification—0
Learning From Less Data: Diversified Subset Selection and Active Learning in Image Classification Tasks—0
Denoised Labels for Financial Time-Series Data via Self-Supervised Learning—0
A Simple Probabilistic Method for Deep Classification under Input-Dependent Label Noise—0
Modality-bridge Transfer Learning for Medical Image Classification—0
MoDE: CLIP Data Experts via Clustering—0
Adaptive Step Sizes for Preconditioned Stochastic Gradient Descent—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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified