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 9951–10000 of 10420 papers

TitleStatusHype
Neural Networks Designing Neural Networks: Multi-Objective Hyper-Parameter Optimization—0
Does Distributionally Robust Supervised Learning Give Robust Classifiers?—0
Designing Neural Network Architectures using Reinforcement LearningCode0
Neural Architecture Search with Reinforcement LearningCode0
Learning Identity Mappings with Residual Gates—0
Eve: A Gradient Based Optimization Method with Locally and Globally Adaptive Learning RatesCode0
A New Distance Measure for Non-Identical Data with Application to Image Classification—0
Compressed Learning: A Deep Neural Network ApproachCode0
Doubly Convolutional Neural Networks—0
KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics—0
Fast Low-rank Shared Dictionary Learning for Image ClassificationCode0
Maxmin convolutional neural networks for image classification—0
Optimization on Submanifolds of Convolution Kernels in CNNs—0
Enhanced Object Detection via Fusion With Prior Beliefs from Image Classification—0
Safety Verification of Deep Neural NetworksCode0
Multi-view metric learning for multi-instance image classification—0
Spatio-Temporal Attention Models for Grounded Video Captioning—0
A Harmonic Mean Linear Discriminant Analysis for Robust Image Classification—0
Person Re-identification: Past, Present and Future—0
Deep Pyramidal Residual NetworksCode0
ILGNet: Inception Modules with Connected Local and Global Features for Efficient Image Aesthetic Quality Classification using Domain AdaptationCode0
QSGD: Communication-Efficient SGD via Gradient Quantization and EncodingCode0
Xception: Deep Learning with Depthwise Separable ConvolutionsCode0
Fast Image Classification by Boosting Fuzzy Classifiers—0
Kernel Methods on Approximate Infinite-Dimensional Covariance Operators for Image Classification—0
Graph Based Convolutional Neural Network—0
Image Retrieval with Fisher Vectors of Binary Features—0
Dropout with Expectation-linear Regularization—0
Matrix Variate RBM Model with Gaussian Distributions—0
Glasses Detection Using Convolutional Neural Networks—0
Making Deep Neural Networks Robust to Label Noise: a Loss Correction ApproachCode0
Associating Grasp Configurations with Hierarchical Features in Convolutional Neural Networks—0
An empirical study on the effects of different types of noise in image classification tasksCode0
Evolutionary Synthesis of Deep Neural Networks via Synaptic Cluster-driven Genetic Encoding—0
Deep Retinal Image UnderstandingCode0
Towards Automated Melanoma Screening: Exploring Transfer Learning Schemes—0
Sympathy for the Details: Dense Trajectories and Hybrid Classification Architectures for Action Recognition—0
Transfer Learning for Endoscopic Image Classification—0
A 4D Light-Field Dataset and CNN Architectures for Material Recognition—0
Lets keep it simple, Using simple architectures to outperform deeper and more complex architecturesCode0
Online Feature Selection with Group Structure Analysis—0
Deep Hashing: A Joint Approach for Image Signature Learning—0
Recurrent Neural Networks to Correct Satellite Image Classification Maps—0
Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks—0
Residual Networks of Residual Networks: Multilevel Residual Networks—0
Convolutional Oriented BoundariesCode0
Residual CNDS—0
Fusing Deep Convolutional Networks for Large Scale Visual Concept Classification—0
Play and Learn: Using Video Games to Train Computer Vision Models—0
A study of the effect of JPG compression on adversarial images—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