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 851–900 of 10419 papers

TitleStatusHype
ExCon: Explanation-driven Supervised Contrastive Learning for Image ClassificationCode1
Expediting Large-Scale Vision Transformer for Dense Prediction without Fine-tuningCode1
AdvCLIP: Downstream-agnostic Adversarial Examples in Multimodal Contrastive LearningCode1
Explainable Deep Learning Methods in Medical Image Classification: A SurveyCode1
Explaining Latent Representations with a Corpus of ExamplesCode1
Explaining Predictions of Non-Linear Classifiers in NLPCode1
Explanation as a Watermark: Towards Harmless and Multi-bit Model Ownership Verification via Watermarking Feature AttributionCode1
Exploiting Label Skews in Federated Learning with Model ConcatenationCode1
A Comprehensive Approach to Unsupervised Embedding Learning based on AND AlgorithmCode1
Exploring Vision Transformers for Fine-grained ClassificationCode1
A Novel Approach for detecting Normal, COVID-19 and Pneumonia patient using only binary classifications from chest CT-ScansCode1
Extremely Lightweight Quantization Robust Real-Time Single-Image Super Resolution for Mobile DevicesCode1
CoDeNet: Efficient Deployment of Input-Adaptive Object Detection on Embedded FPGAsCode1
CODE-CL: Conceptor-Based Gradient Projection for Deep Continual LearningCode1
Collaborative Transformers for Grounded Situation RecognitionCode1
A Novel Convolutional Neural Network Architecture with a Continuous SymmetryCode1
A Comprehensive Empirical Evaluation on Online Continual LearningCode1
Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive ProcessesCode1
Fast AutoAugmentCode1
Fast Convex Optimization for Two-Layer ReLU Networks: Equivalent Model Classes and Cone DecompositionsCode1
A Survey on Transferability of Adversarial Examples across Deep Neural NetworksCode1
Fast Fishing: Approximating BAIT for Efficient and Scalable Deep Active Image ClassificationCode1
Fast Hierarchical Games for Image ExplanationsCode1
Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary CellsCode1
CoCa: Contrastive Captioners are Image-Text Foundation ModelsCode1
CoAtNet: Marrying Convolution and Attention for All Data SizesCode1
Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label CorrectionCode1
FatNet: High Resolution Kernels for Classification Using Fully Convolutional Optical Neural NetworksCode1
FDFtNet: Facing Off Fake Images using Fake Detection Fine-tuning NetworkCode1
Feature-based Federated Transfer Learning: Communication Efficiency, Robustness and PrivacyCode1
Feature Guided Masked Autoencoder for Self-supervised Learning in Remote SensingCode1
Feature Interaction Interpretability: A Case for Explaining Ad-Recommendation Systems via Neural Interaction DetectionCode1
FedCV: A Federated Learning Framework for Diverse Computer Vision TasksCode1
FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and CorrectionCode1
Adversarial Attacks on ML Defense Models CompetitionCode1
Federated Learning via Input-Output Collaborative DistillationCode1
FedMLP: Federated Multi-Label Medical Image Classification under Task HeterogeneityCode1
An Overview of Deep Learning Architectures in Few-Shot Learning DomainCode1
Combating Label Noise in Deep Learning Using AbstentionCode1
FedIIC: Towards Robust Federated Learning for Class-Imbalanced Medical Image ClassificationCode1
FedSoup: Improving Generalization and Personalization in Federated Learning via Selective Model InterpolationCode1
FewSAR: A Few-shot SAR Image Classification BenchmarkCode1
Few-shot Image Classification: Just Use a Library of Pre-trained Feature Extractors and a Simple ClassifierCode1
Few-shot Learning with Class-Covariance Metric for Hyperspectral Image ClassificationCode1
CNN Filter DB: An Empirical Investigation of Trained Convolutional FiltersCode1
Fine-grained Classes and How to Find ThemCode1
Adversarial AutoMixupCode1
Mixture Outlier Exposure: Towards Out-of-Distribution Detection in Fine-grained EnvironmentsCode1
Fine-Grained Self-Supervised Learning with Jigsaw Puzzles for Medical Image ClassificationCode1
CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep LearningCode1
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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