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 4001–4050 of 10420 papers

TitleStatusHype
Feature CAM: Interpretable AI in Image Classification—0
Feature Channel Adaptive Enhancement for Fine-Grained Visual Classification—0
Comparison of Methods Generalizing Max- and Average-Pooling—0
Feature Density Estimation for Out-of-Distribution Detection via Normalizing Flows—0
Feature Embedding by Template Matching as a ResNet Block—0
Feature-EndoGaussian: Feature Distilled Gaussian Splatting in Surgical Deformable Scene Reconstruction—0
Adaptive Test-Time Augmentation for Low-Power CPU—0
Graph Based Convolutional Neural Network—0
CORL: Compositional Representation Learning for Few-Shot Classification—0
Graph Clustering With Missing Data: Convex Algorithms and Analysis—0
Design Space Exploration of Hardware Spiking Neurons for Embedded Artificial Intelligence—0
Feature Kernel Distillation—0
Feature Learning beyond the Lazy-Rich Dichotomy: Insights from Representational Geometry—0
Feature Learning to Automatically Assess Radiographic Knee Osteoarthritis Severity—0
Design of Supervision-Scalable Learning Systems: Methodology and Performance Benchmarking—0
Beyond Image Classification: A Video Benchmark and Dual-Branch Hybrid Discrimination Framework for Compositional Zero-Shot Learning—0
Feature Map Convergence Evaluation for Functional Module—0
Competing Ratio Loss for Discriminative Multi-class Image Classification—0
Feature Preserving Shrinkage on Bayesian Neural Networks via the R2D2 Prior—0
Design of Real-time Semantic Segmentation Decoder for Automated Driving—0
Feature Representation in Convolutional Neural Networks—0
Features based Mammogram Image Classification using Weighted Feature Support Vector Machine—0
Analyzing the Dependency of ConvNets on Spatial Information—0
Grafting Vision Transformers—0
A fast dynamic graph convolutional network and CNN parallel network for hyperspectral image classification—0
Feature Weaken: Vicinal Data Augmentation for Classification—0
Feature Whitening via Gradient Transformation for Improved Convergence—0
FedAvg with Fine Tuning: Local Updates Lead to Representation Learning—0
FedBABU: Toward Enhanced Representation for Federated Image Classification—0
Design of Image Matched Non-Separable Wavelet using Convolutional Neural Network—0
Beyond Fine-tuning: Classifying High Resolution Mammograms using Function-Preserving Transformations—0
Adaptive Temperature Scaling with Conformal Prediction—0
Beyond Entropy: Style Transfer Guided Single Image Continual Test-Time Adaptation—0
Analyzing Images for Music Recommendation—0
FedDiff: Diffusion Model Driven Federated Learning for Multi-Modal and Multi-Clients—0
FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning—0
FedDropoutAvg: Generalizable federated learning for histopathology image classification—0
Compositional Attribute Imbalance in Vision Datasets—0
Grafit: Learning fine-grained image representations with coarse labels—0
Designing Extremely Memory-Efficient CNNs for On-device Vision Tasks—0
Federated Deep Learning with Bayesian Privacy—0
Federated Distillation for Medical Image Classification: Towards Trustworthy Computer-Aided Diagnosis—0
Beyond Empirical Risk Minimization: Local Structure Preserving Regularization for Improving Adversarial Robustness—0
Designing Adaptive Neural Networks for Energy-Constrained Image Classification—0
Federated Learning for Commercial Image Sources—0
Federated Learning for Medical Image Classification: A Comprehensive Benchmark—0
Descriptive analysis of computational methods for automating mammograms with practical applications—0
Beyond cross-entropy: learning highly separable feature distributions for robust and accurate classification—0
Federated Learning Over Images: Vertical Decompositions and Pre-Trained Backbones Are Difficult to Beat—0
Analyzing Filters Toward Efficient ConvNet—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