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 7301–7350 of 10420 papers

TitleStatusHype
Neural Network Design: Learning from Neural Architecture SearchCode0
An Information-Geometric Distance on the Space of TasksCode0
Multi-Agent Mutual Learning at Sentence-Level and Token-Level for Neural Machine Translation—0
Brain Tumor Classification Using Medial Residual Encoder Layers—0
Fuzzy Pooling—0
MAD-VAE: Manifold Awareness Defense Variational AutoencoderCode0
A Survey on Contrastive Self-supervised Learning—0
C-Net: A Reliable Convolutional Neural Network for Biomedical Image ClassificationCode0
Perception Improvement for Free: Exploring Imperceptible Black-box Adversarial Attacks on Image Classification—0
Why Do Better Loss Functions Lead to Less Transferable Features?—0
Training EfficientNets at Supercomputer Scale: 83% ImageNet Top-1 Accuracy in One Hour—0
Mutual Information-based Disentangled Neural Networks for Classifying Unseen Categories in Different Domains: Application to Fetal Ultrasound Imaging—0
Loss re-scaling VQA: Revisiting the LanguagePrior Problem from a Class-imbalance ViewCode0
Can the state of relevant neurons in a deep neural networks serve as indicators for detecting adversarial attacks?—0
A Deep Convolutional Neural Network Applied to Ship Detection and Classification—0
Beyond cross-entropy: learning highly separable feature distributions for robust and accurate classification—0
Class-incremental learning: survey and performance evaluation on image classification—0
Classification Beats Regression: Counting of Cells from Greyscale Microscopic Images based on Annotation-free Training SamplesCode0
Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNetsCode0
Differentiable Channel Sparsity Search via Weight Sharing within Filters—0
How Does the Task Landscape Affect MAML Performance?—0
Active Learning for Noisy Data Streams Using Weak and Strong Labelers—0
Real-Time Edge Classification: Optimal Offloading under Token Bucket ConstraintsCode0
Peak Detection On Data Independent Acquisition Mass Spectrometry Data With Semisupervised Convolutional Transformers—0
Scalable Bayesian neural networks by layer-wise input augmentationCode0
Structural Prior Driven Regularized Deep Learning for Sonar Image Classification—0
An empirical study of domain-agnostic semi-supervised learning via energy-based models: joint-training and pre-training—0
Smartphone-Based Test and Predictive Models for Rapid, Non-Invasive, and Point-of-Care Monitoring of Ocular and Cardiovascular Complications Related to Diabetes—0
Multi-task Supervised Learning via Cross-learning—0
Scale-, shift- and rotation-invariant diffractive optical networks—0
Adversarial Attacks on Binary Image Recognition Systems—0
Efficient Scale-Permuted Backbone with Learned Resource Distribution—0
Malaria detection from RBC images using shallow Convolutional Neural Networks—0
Learning Loss for Test-Time Augmentation—0
Boosting Gradient for White-Box Adversarial Attacks—0
Adaptive Pixel-wise Structured Sparse Network for Efficient CNNs—0
Learning Curves for Analysis of Deep NetworksCode0
What is Wrong with Continual Learning in Medical Image Segmentation?—0
Ultimate Limits of Thermal Pattern Recognition—0
Deep Low-Shot Learning for Biological Image Classification and Visualization from Limited Training SamplesCode0
Cross-Modal Information Maximization for Medical Imaging: CMIM—0
Failure Prediction by Confidence Estimation of Uncertainty-Aware Dirichlet Networks—0
Multiclass Wound Image Classification using an Ensemble Deep CNN-based ClassifierCode0
Increasing Depth Leads to U-Shaped Test Risk in Over-parameterized Convolutional Networks—0
Lung Nodule Classification Using Biomarkers, Volumetric Radiomics and 3D CNNs—0
FTBNN: Rethinking Non-linearity for 1-bit CNNs and Going BeyondCode0
AdaBelief Optimizer: Adapting Stepsizes by theBelief in Observed Gradients—0
Performance evaluation and application of computation based low-cost homogeneous machine learning model algorithm for image classification—0
Deep and interpretable regression models for ordinal outcomesCode0
Why Are Convolutional Nets More Sample-Efficient than Fully-Connected Nets?—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