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 4701–4750 of 10420 papers

TitleStatusHype
AUSN: Approximately Uniform Quantization by Adaptively Superimposing Non-uniform Distribution for Deep Neural Networks—0
Improving Performance of Semi-Supervised Learning by Adversarial Attacks—0
Improving Robustness and Reliability in Medical Image Classification with Latent-Guided Diffusion and Nested-Ensembles—0
Improving Feature Stability during Upsampling -- Spectral Artifacts and the Importance of Spatial Context—0
Data-Free Group-Wise Fully Quantized Winograd Convolution via Learnable Scales—0
A Lightweight ReLU-Based Feature Fusion for Aerial Scene Classification—0
Dense Depth Distillation with Out-of-Distribution Simulated Images—0
Data-Free Black-Box Federated Learning via Zeroth-Order Gradient Estimation—0
A Universal Model for Cross Modality Mapping by Relational Reasoning—0
Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation—0
Improving Machine Reading Comprehension via Adversarial Training—0
Improving Label Error Detection and Elimination with Uncertainty Quantification—0
Adapting OpenAI's CLIP Model for Few-Shot Image Inspection in Manufacturing Quality Control: An Expository Case Study with Multiple Application Examples—0
Improving Layer-wise Adaptive Rate Methods using Trust Ratio Clipping—0
Improving Model Accuracy for Imbalanced Image Classification Tasks by Adding a Final Batch Normalization Layer: An Empirical Study—0
Data Dropout: Optimizing Training Data for Convolutional Neural Networks—0
A Lightweight Neural Architecture Search Model for Medical Image Classification—0
Time-Varying Propensity Score to Bridge the Gap between the Past and Present—0
A Unified View of Long-Sequence Models towards Modeling Million-Scale Dependencies—0
Improving Image Recognition by Retrieving from Web-Scale Image-Text Data—0
Data downlink prioritization using image classification on-board a 6U CubeSat—0
A Unified Scheme of ResNet and Softmax—0
A Unified Plug-and-Play Framework for Effective Data Denoising and Robust Abstention—0
A Light-weight Deep Learning Model for Remote Sensing Image Classification—0
Improving Image Classification with Location Context—0
Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters—0
Improving Model Performance and Removing the Class Imbalance Problem Using Augmentation—0
A Unified Framework with Meta-dropout for Few-shot Learning—0
Data Consistency for Weakly Supervised Learning—0
Algorithms for Hyper-Parameter Optimization—0
Data-Centric Diet: Effective Multi-center Dataset Pruning for Medical Image Segmentation—0
Data-Centric Debugging: mitigating model failures via targeted data collection—0
A Unified Framework to Enforce, Discover, and Promote Symmetry in Machine Learning—0
Algorithmic progress in computer vision—0
Database Meets Deep Learning: Challenges and Opportunities—0
Data-aware customization of activation functions reduces neural network error—0
A Unified Framework to Analyze and Design the Nonlocal Blocks for Neural Networks—0
Data augmentation with Symbolic-to-Real Image Translation GANs for Traffic Sign Recognition—0
A Unified Deep Speaker Embedding Framework for Mixed-Bandwidth Speech Data—0
Improving Image Classification of Knee Radiographs: An Automated Image Labeling Approach—0
Data Augmentation Vision Transformer for Fine-grained Image Classification—0
Data Augmentation using Feature Generation for Volumetric Medical Images—0
A Unified Approximation Framework for Compressing and Accelerating Deep Neural Networks—0
Data Augmentation Revisited: Rethinking the Distribution Gap between Clean and Augmented Data—0
Data Augmentation Policy Search for Long-Term Forecasting—0
Algorithmic Probability-guided Supervised Machine Learning on Non-differentiable Spaces—0
Data Augmentation in Training CNNs: Injecting Noise to Images—0
Data Augmentation in Training CNNs: Injecting Noise to Images—0
AUKT: Adaptive Uncertainty-Guided Knowledge Transfer with Conformal Prediction—0
Data Augmentation for Visual Question Answering—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