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 5251–5300 of 10420 papers

TitleStatusHype
Compressive Image Classification using Deterministic Sensing Matrices—0
Providing Error Detection for Deep Learning Image Classifiers Using Self-Explainability—0
Attention Regularized Laplace Graph for Domain Adaptation—0
POGD: Gradient Descent with New Stochastic Rules—0
MEGCF: Multimodal Entity Graph Collaborative Filtering for Personalized RecommendationCode0
MergedNET: A simple approach for one-shot learning in siamese networks based on similarity layersCode0
Sequential Learning Of Neural Networks for Prequential MDL—0
CAP: Correlation-Aware Pruning for Highly-Accurate Sparse Vision Models—0
Confidence estimation of classification based on the distribution of the neural network output layer—0
Equi-Tuning: Group Equivariant Fine-Tuning of Pretrained Models—0
Improving the Reliability for Confidence Estimation—0
Are Sample-Efficient NLP Models More Robust?—0
On Divergence Measures for Bayesian PseudocoresetsCode0
Deep Combinatorial AggregationCode0
A Unified Framework with Meta-dropout for Few-shot Learning—0
Semantic Cross Attention for Few-shot LearningCode0
Image Projective Transformation Rectification with Synthetic Data for Smartphone-captured Chest X-ray Photos ClassificationCode0
Designing Robust Transformers using Robust Kernel Density Estimation—0
Edge-Cloud Cooperation for DNN Inference via Reinforcement Learning and Supervised Learning—0
Schedule-Robust Online Continual Learning—0
Improving Long-tailed Object Detection with Image-Level Supervision by Multi-Task Collaborative LearningCode0
Deep Active Ensemble Sampling For Image Classification—0
Energy-Efficient Deployment of Machine Learning Workloads on Neuromorphic Hardware—0
Continual Learning with Evolving Class Ontologies—0
Multi-Modal Fusion by Meta-InitializationCode0
Universal Adversarial Perturbations: Efficiency on a small image datasetCode0
Are All Vision Models Created Equal? A Study of the Open-Loop to Closed-Loop Causality Gap—0
A Differentiable Distance Approximation for Fairer Image ClassificationCode0
Fast-ParC: Capturing Position Aware Global Feature for ConvNets and ViTs—0
Improving Data-Efficient Fossil Segmentation via Model Editing—0
CLAD: A Contrastive Learning based Approach for Background DebiasingCode0
Deep Learning Mixture-of-Experts Approach for Cytotoxic Edema Assessment in Infants and Children—0
Topological Continual Learning with Wasserstein Distance and Barycenter—0
Gastrointestinal Disorder Detection with a Transformer Based Approach—0
Synthetic Dataset Generation for Privacy-Preserving Machine Learning—0
Misaligned orientations of 4f optical neural network for image classification accuracy on various datasets—0
ImpressLearn: Continual Learning via Combined Task Impressions—0
Why Random Pruning Is All We Need to Start SparseCode0
Multi-stream Fusion for Class Incremental Learning in Pill Image ClassificationCode0
CLINICAL: Targeted Active Learning for Imbalanced Medical Image Classification—0
Distance Based Image Classification: A solution to generative classification's conundrum?Code0
Time-Varying Propensity Score to Bridge the Gap between the Past and Present—0
Feature Embedding by Template Matching as a ResNet Block—0
Membership Inference Attacks Against Text-to-image Generation Models—0
Learning with Limited Samples -- Meta-Learning and Applications to Communication Systems—0
Systematic Generalization and Emergent Structures in Transformers Trained on Structured Tasks—0
A Smart Recycling Bin Using Waste Image Classification At The Edge—0
Efficient Multilingual Multi-modal Pre-training through Triple Contrastive Loss—0
Visual Prompt Tuning for Few-Shot Text Classification—0
Offline Handwritten Amharic Character Recognition Using Few-shot LearningCode0
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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
10RevCol-HTop 1 Accuracy90—Unverified