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 8051–8100 of 10420 papers

TitleStatusHype
Automatic Error Detection in Integrated Circuits Image Segmentation: A Data-driven Approach—0
PAWS-VMK: A Unified Approach To Semi-Supervised Learning And Out-of-Distribution Detection—0
Frustratingly Easy Uncertainty Estimation for Distribution Shift—0
Improved OOD Generalization via Adversarial Training and Pre-training—0
Deep Active Learning in the Presence of Label Noise: A Survey—0
Rethinking the Zigzag Flattening for Image Reading—0
Rethinking Two Consensuses of the Transferability in Deep Learning—0
Automatic discovery of discriminative parts as a quadratic assignment problem—0
Rethinking VLMs and LLMs for Image Classification—0
Improved Multi-Source Domain Adaptation by Preservation of Factors—0
Deep Active Learning in the Open World—0
Improved Mix-up with KL-Entropy for Learning From Noisy Labels—0
Does Deep Active Learning Work in the Wild?—0
Retrain or not retrain? -- efficient pruning methods of deep CNN networks—0
Improved Image Classification with Token Fusion—0
Retrieval-enriched zero-shot image classification in low-resource domains—0
Improved Image Classification with Manifold Neural Networks—0
Deep Active Ensemble Sampling For Image Classification—0
Automatic Discovery and Optimization of Parts for Image Classification—0
Automatic Detection and Image Recognition of Precision Agriculture for Citrus Diseases—0
Reveal of Vision Transformers Robustness against Adversarial Attacks—0
Improved Fine-Tuning by Better Leveraging Pre-Training Data—0
Reversed Active Learning based Atrous DenseNet for Pathological Image Classification—0
Reverse engineering adversarial attacks with fingerprints from adversarial examples—0
Improved Few-Shot Visual Classification—0
Unauthorized AI cannot Recognize Me: Reversible Adversarial Example—0
Decoupling Feature Extraction and Classification Layers for Calibrated Neural Networks—0
Improved Few-Shot Image Classification Through Multiple-Choice Questions—0
Automatic Dataset Construction (ADC): Sample Collection, Data Curation, and Beyond—0
Improved EATFormer: A Vision Transformer for Medical Image Classification—0
Improved Deep Neural Network Generalization Using m-Sharpness-Aware Minimization—0
Improved Conformer-based End-to-End Speech Recognition Using Neural Architecture Search—0
All-You-Can-Fit 8-Bit Flexible Floating-Point Format for Accurate and Memory-Efficient Inference of Deep Neural Networks—0
Improved Algorithms for Agnostic Pool-based Active Classification—0
Revisiting Adversarial Robustness of Classifiers With a Reject Option—0
Revisiting a kNN-based Image Classification System with High-capacity Storage—0
ImpressLearn: Continual Learning via Combined Task Impressions—0
Decoupled Greedy Learning of CNNs for Synchronous and Asynchronous Distributed Learning—0
Importance of the Mathematical Foundations of Machine Learning Methods for Scientific and Engineering Applications—0
Importance of Preprocessing in Histopathology Image Classification Using Deep Convolutional Neural Network—0
Decomposition-Based Transfer Distance Metric Learning for Image Classification—0
Automatic Classification of Human Epithelial Type 2 Cell Indirect Immunofluorescence Images using Cell Pyramid Matching—0
Revisiting ensembling for improving the performance of deep learning models—0
Revisiting IM2GPS in the Deep Learning Era—0
All-Transfer Learning for Deep Neural Networks and its Application to Sepsis Classification—0
Access Control with Encrypted Feature Maps for Object Detection Models—0
AI-Augmented Thyroid Scintigraphy for Robust Classification—0
Decomposing Convolutional Neural Networks into Reusable and Replaceable Modules—0
Implications of Noise in Resistive Memory on Deep Neural Networks for Image Classification—0
Automatic Classification and Segmentation of Tunnel Cracks Based on Deep Learning and Visual Explanations—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