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 4501–4550 of 10420 papers

TitleStatusHype
Locally Differentially Private Distributed Online Learning with Guaranteed Optimality—0
Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy ImitationCode0
Meta-Gating Framework for Fast and Continuous Resource Optimization in Dynamic Wireless Environments—0
Towards quantum enhanced adversarial robustness in machine learning—0
To Spike or Not to Spike? A Quantitative Comparison of SNN and CNN FPGA Implementations—0
Data-Free Backbone Fine-Tuning for Pruned Neural NetworksCode0
A Comprehensive Study on the Robustness of Image Classification and Object Detection in Remote Sensing: Surveying and Benchmarking—0
Efficient Deep Spiking Multi-Layer Perceptrons with Multiplication-Free InferenceCode0
Annotating Ambiguous Images: General Annotation Strategy for High-Quality Data with Real-World Biomedical ValidationCode0
Benchmark data to study the influence of pre-training on explanation performance in MR image classification—0
Balanced Mixture of SuperNets for Learning the CNN Pooling ArchitectureCode0
Comparative Evaluation of Recent Universal Adversarial Perturbations in Image Classification—0
No Wrong Turns: The Simple Geometry Of Neural Networks Optimization PathsCode0
Shape Guided Gradient Voting for Domain Generalization—0
RaViTT: Random Vision Transformer Tokens—0
Pre-Pruning and Gradient-Dropping Improve Differentially Private Image Classification—0
Continual Adaptation of Vision Transformers for Federated LearningCode0
Scaling Open-Vocabulary Object DetectionCode0
Label-noise-tolerant medical image classification via self-attention and self-supervised learning—0
Enlarged Large Margin Loss for Imbalanced ClassificationCode0
A Comparison of Self-Supervised Pretraining Approaches for Predicting Disease Risk from Chest Radiograph Images—0
Modularity Trumps Invariance for Compositional RobustnessCode0
High-performance deep spiking neural networks with 0.3 spikes per neuron—0
Noise Stability Optimization for Finding Flat Minima: A Hessian-based Regularization ApproachCode0
I See Dead People: Gray-Box Adversarial Attack on Image-To-Text Models—0
Safeguarding Data in Multimodal AI: A Differentially Private Approach to CLIP TrainingCode0
Resource Efficient Neural Networks Using Hessian Based Pruning—0
Rotational augmentation techniques: a new perspective on ensemble learning for image classification—0
Scale-Rotation-Equivariant Lie Group Convolution Neural Networks (Lie Group-CNNs)—0
Augmenting Zero-Shot Detection Training with Image Labels—0
Active Globally Explainable Learning for Medical Images via Class Association Embedding and Cyclic Adversarial Generation—0
Neural Architecture Design and Robustness: A Dataset—0
Computational and Storage Efficient Quadratic Neurons for Deep Neural Networks—0
Higher Chest X-ray Resolution Improves Classification Performance—0
Hidden Classification Layers: Enhancing linear separability between classes in neural networks layers—0
Understanding the Effect of the Long Tail on Neural Network Compression—0
Leveraging Large Language Models for Scalable Vector Graphics-Driven Image UnderstandingCode0
Contrastive Learning for Predicting Cancer Prognosis Using Gene Expression ValuesCode0
LayerAct: Advanced Activation Mechanism for Robust Inference of CNNsCode0
A Melting Pot of Evolution and Learning—0
Regularizing with Pseudo-Negatives for Continual Self-Supervised LearningCode0
T-ADAF: Adaptive Data Augmentation Framework for Image Classification Network based on Tensor T-product Operator—0
GeoDiffusion: Text-Prompted Geometric Control for Object Detection Data Generation—0
Quantitative Analysis of Primary Attribution Explainable Artificial Intelligence Methods for Remote Sensing Image ClassificationCode0
Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How—0
Human-imperceptible, Machine-recognizable ImagesCode0
Input-gradient space particle inference for neural network ensemblesCode0
Semantically-Prompted Language Models Improve Visual Descriptions—0
Continual Learning with Pretrained Backbones by Tuning in the Input Space—0
Resilient Constrained Learning—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