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

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Showing 10151–10200 of 10420 papers

TitleStatusHype
Human Action Recognition using Factorized Spatio-Temporal Convolutional Networks—0
Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and TreeCode0
Mapping Generative Models onto a Network of Digital Spiking Neurons—0
Learning Wake-Sleep Recurrent Attention Models—0
Tensorizing Neural NetworksCode0
Medical Image Classification via SVM using LBP Features from Saliency-Based Folded Data—0
Model Accuracy and Runtime Tradeoff in Distributed Deep Learning:A Systematic StudyCode0
Learning Contextual Dependencies with Convolutional Hierarchical Recurrent Neural Networks—0
DeepSat - A Learning framework for Satellite ImageryCode0
Deep Attributes from Context-Aware Regional Neural Codes—0
Image Classification with Rejection using Contextual Information—0
Computational Integration of Human Vision and Natural Language through Bitext Alignment—0
Exemplar Based Deep Discriminative and Shareable Feature Learning for Scene Image Classification—0
Fisher Vectors Meet Neural Networks: A Hybrid Classification Architecture—0
Cost Sensitive Learning of Deep Feature Representations from Imbalanced Data—0
A Practical Guide to CNNs and Fisher Vectors for Image Instance Retrieval—0
Deep Boosting: Joint Feature Selection and Analysis Dictionary Learning in Hierarchy—0
INsight: A Neuromorphic Computing System for Evaluation of Large Neural Networks—0
Local Color Contrastive Descriptor for Image Classification—0
On the Importance of Normalisation Layers in Deep Learning with Piecewise Linear Activation Units—0
Flip-Rotate-Pooling Convolution and Split Dropout on Convolution Neural Networks for Image Classification—0
Neural NILM: Deep Neural Networks Applied to Energy DisaggregationCode0
Towards Effective Codebookless Model for Image Classification—0
Feature Representation in Convolutional Neural Networks—0
DCTNet : A Simple Learning-free Approach for Face Recognition—0
Batch Normalization: Accelerating Deep Network Training byReducing Internal Covariate Shift—0
Recipe recognition with large multimodal food datasetCode0
Polarimetric Hierarchical Semantic Model and Scattering Mechanism Based PolSAR Image Classification—0
Summarization of Multi-Document Topic Hierarchies using Submodular Mixtures—0
Learning to Detect Blue-white Structures in Dermoscopy Images with Weak Supervision—0
Online Learning to Sample—0
Network Representation Learning with Rich Text InformationCode0
Histopathological Image Classification using Discriminative Feature-oriented Dictionary LearningCode0
Multi-path Convolutional Neural Networks for Complex Image ClassificationCode0
On-the-Job Learning with Bayesian Decision TheoryCode0
Stacked What-Where Auto-encodersCode0
SVM and ELM: Who Wins? Object Recognition with Deep Convolutional Features from ImageNet—0
Hyperspectral Image Classification and Clutter Detection via Multiple Structural Embeddings and Dimension Reductions—0
Hyper-Class Augmented and Regularized Deep Learning for Fine-Grained Image Classification—0
From Dictionary of Visual Words to Subspaces: Locality-Constrained Affine Subspace Coding—0
DevNet: A Deep Event Network for Multimedia Event Detection and Evidence Recounting—0
Deep Multiple Instance Learning for Image Classification and Auto-Annotation—0
Saliency Detection by Multi-Context Deep Learning—0
Learning From Massive Noisy Labeled Data for Image Classification—0
Modeling Local and Global Deformations in Deep Learning: Epitomic Convolution, Multiple Instance Learning, and Sliding Window Detection—0
Learning Graph Structure for Multi-Label Image Classification via Clique Generation—0
From Categories to Subcategories: Large-Scale Image Classification With Partial Class Label Refinement—0
Exemplar SVMs as Visual Feature Encoders—0
Three Viewpoints Toward Exemplar SVM—0
Transformation-Invariant Convolutional Jungles—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