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 6551–6600 of 10420 papers

TitleStatusHype
Adaptive Smooth Activation for Improved Disease Diagnosis and Organ Segmentation from Radiology Scans—0
MultiCore+TPU Accelerated Multi-Modal TinyML for Livestock Behaviour Recognition—0
Learning Deep Context-Network Architectures for Image Annotation—0
Delay Differential Neural Networks—0
Learning Data Teaching Strategies Via Knowledge Tracing—0
Déjà Vu Memorization in Vision-Language Models—0
Benchmarks for Image Classification and Other High-dimensional Pattern Recognition Problems—0
Benchmarking the Robustness of Semantic Segmentation Models—0
Analysis of convolutional neural network image classifiers in a rotationally symmetric model—0
Learning cross space mapping via DNN using large scale click-through logs—0
Learning Cross-domain Generalizable Features by Representation Disentanglement—0
Deformation-Invariant Neural Network and Its Applications in Distorted Image Restoration and Analysis—0
Learning Continually from Low-shot Data Stream—0
Multi-Domain Learning with Modulation Adapters—0
Learning Contextual Dependencies with Convolutional Hierarchical Recurrent Neural Networks—0
Multi-Evidence Filtering and Fusion for Multi-Label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learning—0
Learning Consistent Deep Generative Models from Sparse Data via Prediction Constraints—0
Multi-frequency PolSAR Image Fusion Classification Based on Semantic Interactive Information and Topological Structure—0
Learning Consistent Deep Generative Models from Sparsely Labeled Data—0
MultiFusionNet: Multilayer Multimodal Fusion of Deep Neural Networks for Chest X-Ray Image Classification—0
Deformable Gabor Feature Networks for Biomedical Image Classification—0
Benchmarking Test-Time Unsupervised Deep Neural Network Adaptation on Edge Devices—0
Analysis of convolutional neural network image classifiers in a hierarchical max-pooling model with additional local pooling—0
Adaptive Sharpness-Aware Pruning for Robust Sparse Networks—0
Learning Connectivity of Neural Networks from a Topological Perspective—0
DeformableFormer: Classification of Endoscopic Ultrasound Guided Fine Needle Biopsy in Pancreatic Diseases—0
DefMamba: Deformable Visual State Space Model—0
Learning CNN filters from user-drawn image markers for coconut-tree image classification—0
Learning Class-to-Image Distance with Object Matchings—0
Multi-Instance Visual-Semantic Embedding—0
Multi-Label Adversarial Perturbations—0
Defining Quantum Neural Networks via Quantum Time Evolution—0
Deferred Poisoning: Making the Model More Vulnerable via Hessian Singularization—0
Learning Binary Codes and Binary Weights for Efficient Classification—0
Defensive Tensorization: Randomized Tensor Parametrization for Robust Neural Networks—0
Learning-Based Data Storage [Vision] (Technical Report)—0
Learning Augmentation Network via Influence Functions—0
Defensive Tensorization—0
Benchmarking PathCLIP for Pathology Image Analysis—0
Multi-Label Image Classification with Contrastive Learning—0
Learning and Transferring Mid-Level Image Representations using Convolutional Neural Networks—0
Learning and Interpreting Multi-Multi-Instance Learning Networks—0
Learning and Exploiting Interclass Visual Correlations for Medical Image Classification—0
Learning a Fourier Transform for Linear Relative Positional Encodings in Transformers—0
Benchmarking Multi-Domain Active Learning on Image Classification—0
Analysis of Catastrophic Forgetting for Random Orthogonal Transformation Tasks in the Overparameterized Regime—0
Adaptive Selection of Deep Learning Models on Embedded Systems—0
Multilayer deep feature extraction for visual texture recognition—0
Learning a Deep ConvNet for Multi-label Classification with Partial Labels—0
Defending Malware Classification Networks Against Adversarial Perturbations with Non-Negative Weight Restrictions—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
5DaViT-HTop 1 Accuracy90.2—Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified