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 2651–2700 of 10419 papers

TitleStatusHype
FedSoup: Improving Generalization and Personalization in Federated Learning via Selective Model InterpolationCode1
The importance of feature preprocessing for differentially private linear optimization—0
Confidence Estimation Using Unlabeled DataCode0
Interpreting and Correcting Medical Image Classification with PIP-NetCode1
Class Attention to Regions of Lesion for Imbalanced Medical Image Recognition—0
What do neural networks learn in image classification? A frequency shortcut perspectiveCode1
Semantic-Aware Dual Contrastive Learning for Multi-label Image ClassificationCode1
Attacking by Aligning: Clean-Label Backdoor Attacks on Object DetectionCode0
As large as it gets: Learning infinitely large Filters via Neural Implicit Functions in the Fourier DomainCode0
Linearized Relative Positional EncodingCode0
Promoting Exploration in Memory-Augmented Adam using Critical MomentaCode0
Human Action Recognition in Still Images Using ConViT—0
PatchCT: Aligning Patch Set and Label Set with Conditional Transport for Multi-Label Image ClassificationCode0
Systematic comparison of semi-supervised and self-supervised learning for medical image classificationCode1
R-Cut: Enhancing Explainability in Vision Transformers with Relationship Weighted Out and Cut—0
Bridging the Gap: Multi-Level Cross-Modality Joint Alignment for Visible-Infrared Person Re-IdentificationCode1
Multi-Domain Learning with Modulation Adapters—0
Airway Label Prediction in Video Bronchoscopy: Capturing Temporal Dependencies Utilizing Anatomical Knowledge—0
Active Learning for Object Detection with Non-Redundant Informative Sampling—0
M-FLAG: Medical Vision-Language Pre-training with Frozen Language Models and Latent Space Geometry OptimizationCode1
Does Visual Pretraining Help End-to-End Reasoning?—0
Diffusion Models Beat GANs on Image ClassificationCode1
Fast Adaptation with Bradley-Terry Preference Models in Text-To-Image Classification and Generation—0
Spatial-Spectral Hyperspectral Classification based on Learnable 3D Group ConvolutionCode0
Machine learning for option pricing: an empirical investigation of network architectures—0
FedBIAD: Communication-Efficient and Accuracy-Guaranteed Federated Learning with Bayesian Inference-Based Adaptive Dropout—0
Multi-Dimensional Ability Diagnosis for Machine Learning AlgorithmsCode0
Multiplicative update rules for accelerating deep learning training and increasing robustness—0
On the ability of CNNs to extract color invariant intensity based features for image classification—0
DGCNet: An Efficient 3D-Densenet based on Dynamic Group Convolution for Hyperspectral Remote Sensing Image ClassificationCode0
Data Augmentation in Training CNNs: Injecting Noise to Images—0
The Whole Pathological Slide Classification via Weakly Supervised Learning—0
Learning from Exemplary Explanations—0
Function-Space Regularization for Deep Bayesian Classification—0
CellGAN: Conditional Cervical Cell Synthesis for Augmenting Cytopathological Image ClassificationCode1
Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and ResolutionCode6
Feature Activation Map: Visual Explanation of Deep Learning Models for Image Classification—0
Class Instance Balanced Learning for Long-Tailed Classification—0
OpenAL: An Efficient Deep Active Learning Framework for Open-Set Pathology Image ClassificationCode0
Text Descriptions are Compressive and Invariant Representations for Visual Learning—0
Search-time Efficient Device Constraints-Aware Neural Architecture Search—0
Hierarchical Semantic Tree Concept Whitening for Interpretable Image Classification—0
SPLAL: Similarity-based pseudo-labeling with alignment loss for semi-supervised medical image classification—0
CognitiveNet: Enriching Foundation Models with Emotions and Awareness—0
Class-Incremental Mixture of Gaussians for Deep Continual Learning—0
A Novel Explainable Artificial Intelligence Model in Image Classification problem—0
FILM: How can Few-Shot Image Classification Benefit from Pre-Trained Language Models?—0
Measuring the Success of Diffusion Models at Imitating Human Artists—0
Improving Prototypical Visual Explanations with Reward Reweighing, Reselection, and RetrainingCode0
End-to-End Supervised Multilabel Contrastive LearningCode0
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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