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 9801–9850 of 10420 papers

TitleStatusHype
Improving CNN classifiers by estimating test-time priors—0
Improving Deep Learning through Automatic Programming—0
Improving Deep Neural Networks with Probabilistic Maxout Units—0
Improving End-to-End Memory Networks with Unified Weight Tying—0
Improving Explainability of Image Classification in Scenarios with Class Overlap: Application to COVID-19 and Pneumonia—0
Improving Few-Shot Image Classification Using Machine- and User-Generated Natural Language Descriptions—0
Enhancing Few-Shot Image Classification with Unlabelled Examples—0
Improving Few-Shot Visual Classification with Unlabelled Examples—0
Improving Data-Efficient Fossil Segmentation via Model Editing—0
Improving Global Adversarial Robustness Generalization With Adversarially Trained GAN—0
Improving greedy core-set configurations for active learning with uncertainty-scaled distances—0
Improving High Resolution Histology Image Classification with Deep Spatial Fusion Network—0
Improving Human-AI Collaboration With Descriptions of AI Behavior—0
Improving Hyperbolic Representations via Gromov-Wasserstein Regularization—0
Improving Image Classification of Knee Radiographs: An Automated Image Labeling Approach—0
Improving Image Classification Robustness through Selective CNN-Filters Fine-Tuning—0
Improving Image Classification with Location Context—0
Improving Image Recognition by Retrieving from Web-Scale Image-Text Data—0
Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters—0
Improving Label Error Detection and Elimination with Uncertainty Quantification—0
Improving Layer-wise Adaptive Rate Methods using Trust Ratio Clipping—0
Improving Machine Reading Comprehension via Adversarial Training—0
Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation—0
Improving Model Accuracy for Imbalanced Image Classification Tasks by Adding a Final Batch Normalization Layer: An Empirical Study—0
Improving Model Performance and Removing the Class Imbalance Problem Using Augmentation—0
Improving Multi-fidelity Optimization with a Recurring Learning Rate for Hyperparameter Tuning—0
Improving Normalization with the James-Stein Estimator—0
Improving Object Detection with Selective Self-supervised Self-training—0
Improving Performance of Semi-Supervised Learning by Adversarial Attacks—0
Improving plant disease classification by adaptive minimal ensembling—0
Improving Quaternion Neural Networks with Quaternionic Activation Functions—0
Improving Resnet-9 Generalization Trained on Small Datasets—0
Robust Contrastive Active Learning with Feature-guided Query Strategies—0
Improving Robustness and Reliability in Medical Image Classification with Latent-Guided Diffusion and Nested-Ensembles—0
Improving Robustness and Uncertainty Modelling in Neural Ordinary Differential Equations—0
Improving Sample Complexity with Observational Supervision—0
Improving Semantic Embedding Consistency by Metric Learning for Zero-Shot Classification—0
Improving Feature Stability during Upsampling -- Spectral Artifacts and the Importance of Spatial Context—0
Improving STDP-based Visual Feature Learning with Whitening—0
Improving Strong-Scaling of CNN Training by Exploiting Finer-Grained Parallelism—0
Improving Tail-Class Representation with Centroid Contrastive Learning—0
Improving the Accuracy of Learning Example Weights for Imbalance Classification—0
Improving the accuracy of neural networks in analog computing-in-memory systems by a generalized quantization method—0
Improving the Deployment of Recycling Classification through Efficient Hyper-Parameter Analysis—0
Improving the Effectiveness of Deep Generative Data—0
Improving the Reliability for Confidence Estimation—0
Improving training of deep neural networks via Singular Value Bounding—0
Improving Transferability of Deep Neural Networks—0
Improving Whole Slide Segmentation Through Visual Context - A Systematic Study—0
IMWA: Iterative Model Weight Averaging Benefits Class-Imbalanced Learning Tasks—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