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 3101–3150 of 10419 papers

TitleStatusHype
Patch-wise Features for Blur Image Classification—0
Robust Neural Architecture Search—0
Source-free Domain Adaptation Requires Penalized Diversity—0
Adopting Two Supervisors for Efficient Use of Large-Scale Remote Deep Neural NetworksCode0
SMPConv: Self-moving Point Representations for Continuous ConvolutionCode1
A Certified Radius-Guided Attack Framework to Image Segmentation ModelsCode0
Self-Supervised Siamese Autoencoders—0
High-fidelity Pseudo-labels for Boosting Weakly-Supervised SegmentationCode0
Efficient CNNs via Passive Filter Pruning—0
Context-Aware Classification of Legal Document Pages—0
Multi-Class Unlearning for Image Classification via Weight Filtering—0
Adaptive Ensemble Learning: Boosting Model Performance through Intelligent Feature Fusion in Deep Neural Networks—0
Uncertainty estimation in Deep Learning for Panoptic segmentation—0
VNE: An Effective Method for Improving Deep Representation by Manipulating Eigenvalue DistributionCode1
Strong Baselines for Parameter Efficient Few-Shot Fine-tuning—0
Cross-modulated Few-shot Image Generation for Colorectal Tissue ClassificationCode1
EGC: Image Generation and Classification via a Diffusion Energy-Based ModelCode1
Learning to Name Classes for Vision and Language Models—0
Astroformer: More Data Might not be all you need for ClassificationCode1
Knowledge Accumulation in Continually Learned Representations and the Issue of Feature ForgettingCode0
Online Algorithms for Hierarchical Inference in Deep Learning applications at the Edge—0
Personalized Federated Learning with Local Attention—0
Resolution-Invariant Image Classification based on Fourier Neural OperatorsCode0
CNNs with Multi-Level Attention for Domain Generalization—0
Parents and Children: Distinguishing Multimodal DeepFakes from Natural ImagesCode1
Video Pretraining Advances 3D Deep Learning on Chest CT TasksCode1
Multimodal Hyperspectral Image Classification via Interconnected Fusion—0
ConvBLS: An Effective and Efficient Incremental Convolutional Broad Learning System for Image Classification—0
Predictive Heterogeneity: Measures and Applications—0
Vision Transformers with Mixed-Resolution TokenizationCode1
DIME-FM: DIstilling Multimodal and Efficient Foundation Models—0
LaCViT: A Label-aware Contrastive Fine-tuning Framework for Vision TransformersCode0
Benchmarking FedAvg and FedCurv for Image Classification Tasks—0
Rethinking Local Perception in Lightweight Vision TransformerCode1
PMatch: Paired Masked Image Modeling for Dense Geometric MatchingCode1
Soft Neighbors are Positive Supporters in Contrastive Visual Representation Learning—0
Mole Recruitment: Poisoning of Image Classifiers via Selective Batch SamplingCode0
InceptionNeXt: When Inception Meets ConvNeXtCode4
Polarity is all you need to learn and transfer fasterCode0
Beyond Empirical Risk Minimization: Local Structure Preserving Regularization for Improving Adversarial Robustness—0
Nearest Neighbor Based Out-of-Distribution Detection in Remote Sensing Scene Classification—0
Towards Understanding the Effect of Pretraining Label Granularity—0
Provable Robustness for Streaming Models with a Sliding Window—0
On the Local Cache Update Rules in Streaming Federated Learning—0
Fully Hyperbolic Convolutional Neural Networks for Computer VisionCode1
Your Diffusion Model is Secretly a Zero-Shot ClassifierCode2
Automated wildlife image classification: An active learning tool for ecological applicationsCode0
Iteratively Coupled Multiple Instance Learning from Instance to Bag Classifier for Whole Slide Image ClassificationCode1
Exploring Deep Learning Methods for Classification of SAR Images: Towards NextGen Convolutions via Transformers—0
Learning Expressive Prompting With Residuals for Vision 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
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