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 4751–4800 of 10420 papers

TitleStatusHype
Increasing Shape Bias in ImageNet-Trained Networks Using Transfer Learning and Domain-Adversarial Methods—0
Indoor image representation by high-level semantic features—0
Data Augmentation for Image Classification using Generative AI—0
Data Augmentation for Electrocardiogram Classification with Deep Neural Network—0
AugOp: Inject Transformation into Neural Operator—0
Data Augmentation by Pairing Samples for Images Classification—0
Data Adaptive Traceback for Vision-Language Foundation Models in Image Classification—0
ALFA -- Leveraging All Levels of Feature Abstraction for Enhancing the Generalization of Histopathology Image Classification Across Unseen Hospitals—0
DASH: Visual Analytics for Debiasing Image Classification via User-Driven Synthetic Data Augmentation—0
DAS: A Deformable Attention to Capture Salient Information in CNNs—0
Augment & Valuate : A Data Enhancement Pipeline for Data-Centric AI—0
Dual-View Pyramid Pooling in Deep Neural Networks for Improved Medical Image Classification and Confidence Calibration—0
DARTS for Inverse Problems: a Study on Stability—0
Is Differentiable Architecture Search truly a One-Shot Method?—0
Augmenting Zero-Shot Detection Training with Image Labels—0
DART: Domain-Adversarial Residual-Transfer Networks for Unsupervised Cross-Domain Image Classification—0
Augmenting the Pathology Lab: An Intelligent Whole Slide Image Classification System for the Real World—0
ALERT: Accurate Learning for Energy and Timeliness—0
DARE: Diverse Visual Question Answering with Robustness Evaluation—0
DARC: Differentiable ARchitecture Compression—0
Augmenting Supervised Neural Networks with Unsupervised Objectives for Large-scale Image Classification—0
Adaptative Inference Cost With Convolutional Neural Mixture Models—0
Improving Whole Slide Segmentation Through Visual Context - A Systematic Study—0
DAPAS : Denoising Autoencoder to Prevent Adversarial attack in Semantic Segmentation—0
A Layer-Wise Information Reinforcement Approach to Improve Learning in Deep Belief Networks—0
Dangerous Cloaking: Natural Trigger based Backdoor Attacks on Object Detectors in the Physical World—0
Augmenting learning using symmetry in a biologically-inspired domain—0
Improving training of deep neural networks via Singular Value Bounding—0
Augmenting Image Question Answering Dataset by Exploiting Image Captions—0
Few-shot 1/a Anomalies Feedback : Damage Vision Mining Opportunity and Embedding Feature Imbalance—0
A large-scale field test on word-image classification in large historical document collections using a traditional and two deep-learning methods—0
Adapt Anything: Tailor Any Image Classifiers across Domains And Categories Using Text-to-Image Diffusion Models—0
Improving Transferability of Deep Neural Networks—0
DAF-NET: a saliency based weakly supervised method of dual attention fusion for fine-grained image classification—0
DAFD: Domain Adaptation via Feature Disentanglement for Image Classification—0
Adaptable image quality assessment using meta-reinforcement learning of task amenability—0
Improving the Effectiveness of Deep Generative Data—0
D^2: Decentralized Training over Decentralized Data—0
A Large Batch Optimizer Reality Check: Traditional, Generic Optimizers Suffice Across Batch Sizes—0
D^2: Decentralized Training over Decentralized Data—0
Human-interpretable model explainability on high-dimensional data—0
Augmented Equivariant Attention Networks for Microscopy Image Reconstruction—0
Accelerating Neural Network Inference by Overflow Aware Quantization—0
Improving the Reliability for Confidence Estimation—0
Augmented Conditioning Is Enough For Effective Training Image Generation—0
Cyclic orthogonal convolutions for long-range integration of features—0
Improving Strong-Scaling of CNN Training by Exploiting Finer-Grained Parallelism—0
Human Imperceptible Attacks and Applications to Improve Fairness—0
Human Face Recognition using Gabor based Kernel Entropy Component Analysis—0
Improving Tail-Class Representation with Centroid Contrastive Learning—0
Show:102550
← PrevPage 96 of 209Next →
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