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 8001–8050 of 10420 papers

TitleStatusHype
Enhancing Ship Classification in Optical Satellite Imagery: Integrating Convolutional Block Attention Module with ResNet for Improved Performance—0
ResNetX: a more disordered and deeper network architecture—0
Resolution-Based Distillation for Efficient Histology Image Classification—0
Feature CAM: Interpretable AI in Image Classification—0
REFT: Resource-Efficient Federated Training Framework for Heterogeneous and Resource-Constrained Environments—0
Resource Efficient Neural Networks Using Hessian Based Pruning—0
Comparison of Methods Generalizing Max- and Average-Pooling—0
Responsibility: An Example-based Explainable AI approach via Training Process Inspection—0
Few-shot Unsupervised Domain Adaptation with Image-to-class Sparse Similarity Encoding—0
Activation Atlas—0
Restricted Boltzmann Machines for galaxy morphology classification with a quantum annealer—0
RE-Tagger: A light-weight Real-Estate Image Classifier—0
RetailKLIP : Finetuning OpenCLIP backbone using metric learning on a single GPU for Zero-shot retail product image classification—0
Retaining Knowledge and Enhancing Long-Text Representations in CLIP through Dual-Teacher Distillation—0
Rethinking Two Consensuses of the Transferability in Deep Learning—0
Rethinking and Improving Relative Position Encoding for Vision Transformer—0
Retrain or not retrain? -- efficient pruning methods of deep CNN networks—0
Few-Shot Non-Parametric Learning with Deep Latent Variable Model—0
Few-shot medical image classification with simple shape and texture text descriptors using vision-language models—0
Rethinking Client Reweighting for Selfish Federated Learning—0
Concept Bottleneck with Visual Concept Filtering for Explainable Medical Image Classification—0
Rethinking Crowdsourcing Annotation: Partial Annotation with Salient Labels for Multi-Label Image Classification—0
Few-Shot Learning of Compact Models via Task-Specific Meta Distillation—0
AF-KAN: Activation Function-Based Kolmogorov-Arnold Networks for Efficient Representation Learning—0
Feature Kernel Distillation—0
Activate and Reject: Towards Safe Domain Generalization under Category Shift—0
Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST—0
Rethinking Generative Zero-Shot Learning: An Ensemble Learning Perspective for Recognising Visual Patches—0
Rethinking Hard-Parameter Sharing in Multi-Domain Learning—0
Rethinking Image Editing Detection in the Era of Generative AI Revolution—0
Computer Vision Pipeline for Automated Antarctic Krill Analysis—0
Few-shot Learning for Domain-specific Fine-grained Image Classification—0
Feature Losses for Adversarial Robustness—0
A Semantics-Guided Class Imbalance Learning Model for Zero-Shot Classification—0
A Multi-stage Transfer Learning Framework for Diabetic Retinopathy Grading on Small Data—0
Few-Shot Learning Approach on Tuberculosis Classification Based on Chest X-Ray Images—0
Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward—0
Few-shot Image Classification with Multi-Facet Prototypes—0
Few-Shot Image Classification via Contrastive Self-Supervised Learning—0
Feature Preserving Shrinkage on Bayesian Neural Networks via the R2D2 Prior—0
A Self-Supervised Learning Pipeline for Demographically Fair Facial Attribute Classification—0
Rethinking Persistent Homology for Visual Recognition—0
Rethinking Pseudo Labels for Semi-Supervised Object Detection—0
Rethinking Query, Key, and Value Embedding in Vision Transformer under Tiny Model Constraints—0
Compute Less to Get More: Using ORC to Improve Sparse Filtering—0
Rethinking Semi-Supervised Federated Learning: How to co-train fully-labeled and fully-unlabeled client imaging data—0
A Fistful of Words: Learning Transferable Visual Models from Bag-of-Words Supervision—0
Few-shot Image Classification based on Gradual Machine Learning—0
Rethinking Soft Label in Label Distribution Learning Perspective—0
Few-Shot Image Classification and Segmentation as Visual Question Answering Using Vision-Language Models—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