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 7301–7325 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
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
Perception Improvement for Free: Exploring Imperceptible Black-box Adversarial Attacks on Image Classification—0
Loss re-scaling VQA: Revisiting the LanguagePrior Problem from a Class-imbalance ViewCode0
Why Do Better Loss Functions Lead to Less Transferable Features?—0
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
Scalable Bayesian neural networks by layer-wise input augmentationCode0
Structural Prior Driven Regularized Deep Learning for Sonar Image Classification—0
Peak Detection On Data Independent Acquisition Mass Spectrometry Data With Semisupervised Convolutional Transformers—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
5DaViT-HTop 1 Accuracy90.2—Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified