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 9151–9200 of 10420 papers

TitleStatusHype
Geometric Scattering for Graph Data Analysis—0
Deep convolutional Gaussian processesCode0
Learning Compressed Transforms with Low Displacement RankCode0
CINIC-10 is not ImageNet or CIFAR-10Code0
Ancient Coin Classification Using Graph Transduction Games—0
Target Aware Network Adaptation for Efficient Representation Learning—0
CUNI System for the WMT18 Multimodal Translation Task—0
Extended Bit-Plane Compression for Convolutional Neural Network AcceleratorsCode0
A Probabilistic Model for Joint Learning of Word Embeddings from Texts and Images—0
Accurate Dictionary Learning with Direct Sparsity ControlCode0
Improving Bag-of-Visual-Words Towards Effective Facial Expressive Image Classification—0
Model-Agnostic Meta-Learning for Multimodal Task Distributions—0
Dropout Distillation for Efficiently Estimating Model Confidence—0
Adaptive Mixture of Low-Rank Factorizations for Compact Neural Modeling—0
Faster Training by Selecting Samples Using Embeddings—0
Using Word Embeddings to Explore the Learned Representations of Convolutional Neural Networks—0
Weakly-Supervised Localization and Classification of Proximal Femur Fractures—0
Smooth Inter-layer Propagation of Stabilized Neural Networks for Classification—0
Deep Neural Networks for Pattern Recognition—0
Combined convolutional and recurrent neural networks for hierarchical classification of images—0
Global Weighted Average Pooling Bridges Pixel-level Localization and Image-level Classification—0
Playing the Game of Universal Adversarial Perturbations—0
Deep Generative Classifiers for Thoracic Disease Diagnosis with Chest X-ray ImagesCode0
Removing the Feature Correlation Effect of Multiplicative NoiseCode0
From BOP to BOSS and Beyond: Time Series Classification with Dictionary Based Classifiers—0
Revisit Multinomial Logistic Regression in Deep Learning: Data Dependent Model Initialization for Image Recognition—0
FermiNets: Learning generative machines to generate efficient neural networks via generative synthesis—0
Study and Observation of the Variations of Accuracies for Handwritten Digits Recognition with Various Hidden Layers and Epochs using Convolutional Neural Network—0
Hyperspectral Image Classification in the Presence of Noisy LabelsCode0
Searching for Efficient Multi-Scale Architectures for Dense Image Prediction—0
Temporal-Spatial Mapping for Action Recognition—0
Fast and Efficient Information Transmission with Burst Spikes in Deep Spiking Neural Networks—0
Improved Techniques for Adversarial Discriminative Domain Adaptation—0
Learning to Zoom: a Saliency-Based Sampling Layer for Neural NetworksCode0
Finding Better Topologies for Deep Convolutional Neural Networks by Evolution—0
Instance-based Deep Transfer Learning—0
Structure-Preserving Transformation: Generating Diverse and Transferable Adversarial ExamplesCode0
A proof that artificial neural networks overcome the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations—0
Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings—0
Modified Diversity of Class Probability Estimation Co-training for Hyperspectral Image Classification—0
Identifying Land Patterns from Satellite Imagery in Amazon Rainforest using Deep Learning—0
Domain transfer through deep activation matching—0
Evaluating Capability of Deep Neural Networks for Image Classification via Information Plane—0
Data Dropout: Optimizing Training Data for Convolutional Neural Networks—0
Correcting the Triplet Selection Bias for Triplet LossCode0
Geolocation Estimation of Photos using a Hierarchical Model and Scene ClassificationCode0
DetNet: Design Backbone for Object Detection—0
Open Set Learning with Counterfactual Images—0
Dynamic Conditional Networks for Few-Shot Learning—0
Grassmann Pooling as Compact Homogeneous Bilinear Pooling for Fine-Grained Visual 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