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 7726–7750 of 10420 papers

TitleStatusHype
CRL: Class Representative Learning for Image Classification—0
ARMA Nets: Expanding Receptive Field for Dense PredictionCode0
Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning—0
Multi-Task Multicriteria Hyperparameter Optimization—0
Manifold-based Test Generation for Image Classifiers—0
Graph-propagation based Correlation Learning for Weakly Supervised Fine-grained Image Classification—0
BionoiNet: ligand-binding site classification with off-the-shelf deep neural networkCode1
CBIR using features derived by Deep LearningCode0
Scalable and Practical Natural Gradient for Large-Scale Deep LearningCode1
The use of Convolutional Neural Networks for signal-background classification in Particle Physics experiments—0
Object Detection on Single Monocular Images through Canonical Correlation Analysis—0
Superpixel Image Classification with Graph Attention NetworksCode1
Regularizing activations in neural networks via distribution matching with the Wasserstein metric—0
Cross-Iteration Batch NormalizationCode1
A Simple Framework for Contrastive Learning of Visual RepresentationsCode2
Retrain or not retrain? -- efficient pruning methods of deep CNN networks—0
Over-the-Air Adversarial Flickering Attacks against Video Recognition NetworksCode1
Capsules with Inverted Dot-Product Attention RoutingCode1
Task-Robust Model-Agnostic Meta-Learning—0
Learnable Bernoulli Dropout for Bayesian Deep Learning—0
On Parameter Tuning in Meta-learning for Computer Vision—0
The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image ClassificationCode1
Regularized Evolutionary Population-Based Training—0
Best of Both Worlds: AutoML Codesign of a CNN and its Hardware Accelerator—0
Invariant Risk Minimization GamesCode1
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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