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 6801–6850 of 10420 papers

TitleStatusHype
Suppressing Mislabeled Data via Grouping and Self-AttentionCode1
Permute, Quantize, and Fine-tune: Efficient Compression of Neural NetworksCode1
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
Image Representations Learned With Unsupervised Pre-Training Contain Human-like BiasesCode1
How Does the Task Landscape Affect MAML Performance?—0
μNAS: Constrained Neural Architecture Search for MicrocontrollersCode1
Active Learning for Noisy Data Streams Using Weak and Strong Labelers—0
Real-Time Edge Classification: Optimal Offloading under Token Bucket ConstraintsCode0
Scalable Bayesian neural networks by layer-wise input augmentationCode0
Peak Detection On Data Independent Acquisition Mass Spectrometry Data With Semisupervised Convolutional Transformers—0
Structural Prior Driven Regularized Deep Learning for Sonar Image Classification—0
Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response JacobiansCode1
Smartphone-Based Test and Predictive Models for Rapid, Non-Invasive, and Point-of-Care Monitoring of Ocular and Cardiovascular Complications Related to Diabetes—0
An empirical study of domain-agnostic semi-supervised learning via energy-based models: joint-training and pre-training—0
Multi-task Supervised Learning via Cross-learning—0
Scale-, shift- and rotation-invariant diffractive optical networks—0
Fusion of Dual Spatial Information for Hyperspectral Image ClassificationCode1
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
Restoring Negative Information in Few-Shot Object DetectionCode1
Adversarial Attacks on Binary Image Recognition Systems—0
Learning Invariances in Neural NetworksCode1
Ultimate Limits of Thermal Pattern Recognition—0
Boosting Gradient for White-Box Adversarial Attacks—0
What is Wrong with Continual Learning in Medical Image Segmentation?—0
Adaptive Pixel-wise Structured Sparse Network for Efficient CNNs—0
Learning Curves for Analysis of Deep NetworksCode0
Synthesis of COVID-19 Chest X-rays using Unpaired Image-to-Image TranslationCode1
Deep Low-Shot Learning for Biological Image Classification and Visualization from Limited Training SamplesCode0
Cross-Modal Information Maximization for Medical Imaging: CMIM—0
AdaBelief Optimizer: Adapting Stepsizes by theBelief in Observed Gradients—0
Lung Nodule Classification Using Biomarkers, Volumetric Radiomics and 3D CNNs—0
Failure Prediction by Confidence Estimation of Uncertainty-Aware Dirichlet Networks—0
RobustBench: a standardized adversarial robustness benchmarkCode1
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
FTBNN: Rethinking Non-linearity for 1-bit CNNs and Going BeyondCode0
PseudoSeg: Designing Pseudo Labels for Semantic SegmentationCode1
Deep and interpretable regression models for ordinal outcomesCode0
A general approach to compute the relevance of middle-level input features—0
Active Domain Adaptation via Clustering Uncertainty-weighted EmbeddingsCode1
Performance evaluation and application of computation based low-cost homogeneous machine learning model algorithm for image classification—0
Why Are Convolutional Nets More Sample-Efficient than Fully-Connected Nets?—0
The Deep Bootstrap Framework: Good Online Learners are Good Offline GeneralizersCode1
A Comparative Study on Efficiencies of Variants of Convolutional Neural Networks based on Image Classification TaskCode0
Why Layer-Wise Learning is Hard to Scale-up and a Possible Solution via Accelerated Downsampling—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