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 751–800 of 10419 papers

TitleStatusHype
A Closer Look at Self-Supervised Lightweight Vision TransformersCode1
DiagSet: a dataset for prostate cancer histopathological image classificationCode1
Addressing Failure Detection by Learning Model ConfidenceCode1
DIFFender: Diffusion-Based Adversarial Defense against Patch AttacksCode1
Discretization-Aware Architecture SearchCode1
Differentiable Top-k Classification LearningCode1
Understanding the Role of the Projector in Knowledge DistillationCode1
DiffMIC: Dual-Guidance Diffusion Network for Medical Image ClassificationCode1
Clusterability as an Alternative to Anchor Points When Learning with Noisy LabelsCode1
Grafting Transformer on Automatically Designed Convolutional Neural Network for Hyperspectral Image ClassificationCode1
Diffusion Visual Counterfactual ExplanationsCode1
DiG-IN: Diffusion Guidance for Investigating Networks - Uncovering Classifier Differences Neuron Visualisations and Visual Counterfactual ExplanationsCode1
Extending global-local view alignment for self-supervised learning with remote sensing imageryCode1
ClusterFormer: Clustering As A Universal Visual LearnerCode1
An Empirical Investigation of Representation Learning for ImitationCode1
An Empirical Investigation of the Role of Pre-training in Lifelong LearningCode1
Rethinking Depthwise Separable Convolutions: How Intra-Kernel Correlations Lead to Improved MobileNetsCode1
A Rainbow in Deep Network Black BoxesCode1
A deep active learning system for species identification and counting in camera trap imagesCode1
Discriminator-free Unsupervised Domain Adaptation for Multi-label Image ClassificationCode1
Disentangled Ontology Embedding for Zero-shot LearningCode1
Disentangling Label Distribution for Long-tailed Visual RecognitionCode1
Leveraging Vision-Language Models for Improving Domain Generalization in Image ClassificationCode1
Distilling Large Vision-Language Model with Out-of-Distribution GeneralizabilityCode1
AQD: Towards Accurate Fully-Quantized Object DetectionCode1
Distribution Alignment: A Unified Framework for Long-tail Visual RecognitionCode1
Layer-adaptive sparsity for the Magnitude-based PruningCode1
An Enhanced Scheme for Reducing the Complexity of Pointwise Convolutions in CNNs for Image Classification Based on Interleaved Grouped Filters without Divisibility ConstraintsCode1
Diverse Sample Generation: Pushing the Limit of Generative Data-free QuantizationCode1
Diversified in-domain synthesis with efficient fine-tuning for few-shot classificationCode1
An Ensemble of Simple Convolutional Neural Network Models for MNIST Digit RecognitionCode1
DivideMix: Learning with Noisy Labels as Semi-supervised LearningCode1
CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image ClassificationCode1
Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODECode1
A Neural Dirichlet Process Mixture Model for Task-Free Continual LearningCode1
DocXClassifier: High Performance Explainable Deep Network for Document Image ClassificationCode1
A Less Biased Evaluation of Out-of-distribution Sample DetectorsCode1
Do Input Gradients Highlight Discriminative Features?Code1
Domain-Adversarial Training of Neural NetworksCode1
Domain Generalization by Learning and Removing Domain-specific FeaturesCode1
3D Human Pose Estimation with Spatial and Temporal TransformersCode1
Arch-Net: Model Distillation for Architecture Agnostic Model DeploymentCode1
A New Benchmark: On the Utility of Synthetic Data with Blender for Bare Supervised Learning and Downstream Domain AdaptationCode1
Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse TrainingCode1
Do You Even Need Attention? A Stack of Feed-Forward Layers Does Surprisingly Well on ImageNetCode1
DPMLBench: Holistic Evaluation of Differentially Private Machine LearningCode1
Drastically Reducing the Number of Trainable Parameters in Deep CNNs by Inter-layer Kernel-sharingCode1
DR-Tune: Improving Fine-tuning of Pretrained Visual Models by Distribution Regularization with Semantic CalibrationCode1
Almost-Orthogonal Layers for Efficient General-Purpose Lipschitz NetworksCode1
CLR: Channel-wise Lightweight Reprogramming for Continual 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
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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified