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 6151–6200 of 10420 papers

TitleStatusHype
Learning with convolution and pooling operations in kernel methods—0
Two-step adversarial debiasing with partial learning -- medical image case-studies—0
Improved Robustness of Vision Transformer via PreLayerNorm in Patch Embedding—0
ReCLIP: A Strong Zero-Shot Baseline for Referring Expression Comprehension—0
Learning Data Teaching Strategies Via Knowledge Tracing—0
Image Classification with Consistent Supporting Evidence—0
Full-attention based Neural Architecture Search using Context Auto-regression—0
Nonlinear Tensor Ring Network—0
Selective Synthetic Augmentation with HistoGAN for Improved Histopathology Image Classification—0
A Relational Model for One-Shot Classification—0
Hybrid BYOL-ViT: Efficient approach to deal with small datasets—0
Multi-Fake Evolutionary Generative Adversarial Networks for Imbalance Hyperspectral Image Classification—0
Crowdsourcing with Meta-Workers: A New Way to Save the Budget—0
Learning of Time-Frequency Attention Mechanism for Automatic Modulation Recognition—0
First steps on Gamification of Lung Fluid Cells Annotations in the Flower Domain—0
Intrusion Detection: Machine Learning Baseline Calculations for Image Classification—0
Virus-MNIST: Machine Learning Baseline Calculations for Image Classification—0
Fitness Landscape Footprint: A Framework to Compare Neural Architecture Search Problems—0
Combating Noise: Semi-supervised Learning by Region Uncertainty Quantification—0
Deep AUC Maximization for Medical Image Classification: Challenges and Opportunities—0
Data-Efficient Language Shaped Few-shot Image ClassificationCode0
Hierarchical Image Classification with A Literally Toy Dataset—0
Revealing and Protecting Labels in Distributed TrainingCode0
Smart(Sampling)Augment: Optimal and Efficient Data Augmentation for Semantic Segmentation—0
Approximation properties of Residual Neural Networks for Kolmogorov PDEs—0
RMSMP: A Novel Deep Neural Network Quantization Framework with Row-wise Mixed Schemes and Multiple Precisions—0
Dynamic Differential-Privacy Preserving SGD—0
MFNet: Multi-class Few-shot Segmentation Network with Pixel-wise Metric Learning—0
Training Integrable Parameterizations of Deep Neural Networks in the Infinite-Width LimitCode0
UDIS: Unsupervised Discovery of Bias in Deep Visual Recognition ModelsCode0
Domain Agnostic Few-Shot Learning For Document Intelligence—0
Scalable Unidirectional Pareto Optimality for Multi-Task Learning with Constraints—0
Eigencurve: Optimal Learning Rate Schedule for SGD on Quadratic Objectives with Skewed Hessian SpectrumsCode0
Diversity Matters When Learning From Ensembles—0
Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers—0
Can't Fool Me: Adversarially Robust Transformer for Video Understanding—0
Addressing out-of-distribution label noise in webly-labelled data—0
Defensive Tensorization—0
Deep Integrated Pipeline of Segmentation Guided Classification of Breast Cancer from Ultrasound Images—0
VAC-CNN: A Visual Analytics System for Comparative Studies of Deep Convolutional Neural Networks—0
MUSE: Feature Self-Distillation with Mutual Information and Self-Information—0
Some like it tough: Improving model generalization via progressively increasing the training difficultyCode0
Progressively Select and Reject Pseudo-labelled Samples for Open-Set Domain Adaptation—0
Exploring Gradient Flow Based Saliency for DNN Model CompressionCode0
Attend and Guide (AG-Net): A Keypoints-driven Attention-based Deep Network for Image RecognitionCode0
Generalized Resubstitution for Classification Error Estimation—0
Game of Gradients: Mitigating Irrelevant Clients in Federated LearningCode0
Federated Unlearning via Class-Discriminative PruningCode0
Logical Activation Functions: Logit-space equivalents of Probabilistic Boolean Operators—0
Signature-Graph Networks—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