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 10101–10150 of 10420 papers

TitleStatusHype
Learning and Exploiting Interclass Visual Correlations for Medical Image Classification—0
Learning and Interpreting Multi-Multi-Instance Learning Networks—0
Learning and Transferring Mid-Level Image Representations using Convolutional Neural Networks—0
Learning Augmentation Network via Influence Functions—0
Learning-Based Data Storage [Vision] (Technical Report)—0
Learning Binary Codes and Binary Weights for Efficient Classification—0
Learning Class-to-Image Distance with Object Matchings—0
Learning CNN filters from user-drawn image markers for coconut-tree image classification—0
Learning Connectivity of Neural Networks from a Topological Perspective—0
Learning Consistent Deep Generative Models from Sparsely Labeled Data—0
Learning Consistent Deep Generative Models from Sparse Data via Prediction Constraints—0
Learning Contextual Dependencies with Convolutional Hierarchical Recurrent Neural Networks—0
Learning Continually from Low-shot Data Stream—0
Learning Cross-domain Generalizable Features by Representation Disentanglement—0
Learning cross space mapping via DNN using large scale click-through logs—0
Learning Data Teaching Strategies Via Knowledge Tracing—0
Learning Deep Context-Network Architectures for Image Annotation—0
Learning Deep NBNN Representations for Robust Place Categorization—0
Learning Deep Optimal Embeddings with Sinkhorn Divergences—0
Learning degraded image classification with restoration data fidelity—0
Learning Dependency Structures for Weak Supervision Models—0
Learning Discriminative Features Via Weights-biased Softmax Loss—0
Learning Discriminative Multilevel Structured Dictionaries for Supervised Image Classification—0
Learning Discriminative Representation via Metric Learning for Imbalanced Medical Image Classification—0
Learning Disentangled Representations of Satellite Image Time Series—0
Diverse Knowledge Distillation (DKD): A Solution for Improving The Robustness of Ensemble Models Against Adversarial Attacks—0
Learning efficient structured dictionary for image classification—0
Learning Embeddings for Image Clustering: An Empirical Study of Triplet Loss Approaches—0
Learning Expressive Prompting With Residuals for Vision Transformers—0
Learning Filter Pruning Criteria for Deep Convolutional Neural Networks Acceleration—0
Learning Fine-grained Features via a CNN Tree for Large-scale Classification—0
Learning from Attacks: Attacking Variational Autoencoder for Improving Image Classification—0
Learning from Crowds with Sparse and Imbalanced Annotations—0
Learning from Exemplary Explanations—0
Learning from Few Samples: A Survey—0
Learning from Large-scale Noisy Web Data with Ubiquitous Reweighting for Image Classification—0
Learning From Less Data: Diversified Subset Selection and Active Learning in Image Classification Tasks—0
Learning From Massive Noisy Labeled Data for Image Classification—0
Learning from Matured Dumb Teacher for Fine Generalization—0
Learning from Mistakes based on Class Weighting with Application to Neural Architecture Search—0
Learning from multiscale wavelet superpixels using GNN with spatially heterogeneous pooling—0
Learning from Noisy Labels with Coarse-to-Fine Sample Credibility Modeling—0
Learning from Noisy Labels with Noise Modeling Network—0
Learning from Teaching Regularization: Generalizable Correlations Should be Easy to Imitate—0
Learning from Web Data: the Benefit of Unsupervised Object Localization—0
Learning Graph Structure for Multi-Label Image Classification via Clique Generation—0
Visually Consistent Hierarchical Image Classification—0
Learning Hyperspectral Feature Extraction and Classification with ResNeXt Network—0
Learning Identity Mappings with Residual Gates—0
Learning Image Conditioned Label Space for Multilabel 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
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