Image Classification

Image classification, the task of assigning predefined labels to images, aims to develop robust and accurate algorithms for diverse applications. Current research emphasizes improving generalization to unseen data and handling challenges like data scarcity, class imbalance, and adversarial attacks, often employing deep learning models such as convolutional neural networks (CNNs), vision transformers (ViTs), and large language models (LLMs) integrated with techniques like self-supervised learning, data augmentation, and uncertainty quantification. These advancements are crucial for various fields, including medical diagnosis, autonomous driving, and environmental monitoring, where reliable and efficient image analysis is paramount.

Papers

February 17, 2023

Random Padding Data Augmentation
Nan Yang, Laicheng Zhong, Fan Huang, Dong Yuan, Wei Bao
Convolutional Neural Network Image Classification CNN Model Zero Padding Random Data Augmentation

February 16, 2023

February 6, 2023

CHiLS: Zero-Shot Image Classification with Hierarchical Label Sets
Zachary Novack, Julian McAuley, Zachary C. Lipton, Saurabh Garg
Image Classification Open Vocabulary Zero Shot Classification Label Hierarchy Hierarchical Label Semantic Hierarchy

February 4, 2023

Semantic-Guided Generative Image Augmentation Method with Diffusion Models for Image Classification
Bohan Li, Xiao Xu, Xinghao Wang, Yutai Hou, Yunlong Feng, Feng Wang, Xuanliang Zhang, Qingfu Zhu, Wanxiang Che
Diffusion Model Image Classification Image Augmentation Diverse Image

February 3, 2023

January 30, 2023

January 28, 2023

Anticipate, Ensemble and Prune: Improving Convolutional Neural Networks via Aggregated Early Exits
Simone Sarti, Eugenio Lomurno, Matteo Matteucci
Neural Network Convolutional Neural Network Image Classification Multi Layer Diverse Ensemble Pruning Method Early eXit Aggregated Data

January 26, 2023

Universal Domain Adaptation for Remote Sensing Image Scene Classification
Qingsong Xu, Yilei Shi, Xin Yuan, Xiao Xiang Zhu
Domain Adaptation Image Classification Scene Classification Universal Domain Adaptation Remote Sensing Scene Classification

January 25, 2023

Navigating the Pitfalls of Active Learning Evaluation: A Systematic Framework for Meaningful Performance Assessment
Carsten T. Lüth, Till J. Bungert, Lukas Klein, Paul F. Jaeger
LeArning Abstract Active Learning Image Classification Unlabeled Data Common Pitfall Performance Evaluation

January 23, 2023

Combined Use of Federated Learning and Image Encryption for Privacy-Preserving Image Classification with Vision Transformer
Teru Nagamori, Hitoshi Kiya
Deep Learning Vision Transformer Image Classification Privacy Preserving Privacy Preserving Image Cifar 10 Image Encryption

January 21, 2023

Counterfactual Explanation and Instance-Generation using Cycle-Consistent Generative Adversarial Networks
Tehseen Zia, Zeeshan Nisar, Shakeeb Murtaza
Generative Adversarial Network Image Classification Counterfactual Explanation Counterfactual Example Cycle Consistent Generative Adversarial Network Instance Generation

January 14, 2023

Gated Self-supervised Learning For Improving Supervised Learning
Erland Hilman Fuadi, Aristo Renaldo Ruslim, Putu Wahyu Kusuma Wardhana, Novanto Yudistira
Self Supervised Learning Image Classification Supervised Learning Self Supervised Transformer Mixture Density Network Channel Data Augmentation

January 10, 2023

January 4, 2023

MoBYv2AL: Self-supervised Active Learning for Image Classification
Razvan Caramalau, Binod Bhattarai, Danail Stoyanov, Tae-Kyun Kim
Deep Learning Model Active Learning Image Classification Active Learning Framework

Image Classification

Papers

Random Padding Data Augmentation

Efficiency 360: Efficient Vision Transformers

Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification

CHiLS: Zero-Shot Image Classification with Hierarchical Label Sets

Semantic-Guided Generative Image Augmentation Method with Diffusion Models for Image Classification

SoK: A Systematic Evaluation of Backdoor Trigger Characteristics in Image Classification

Cluster-CAM: Cluster-Weighted Visual Interpretation of CNNs' Decision in Image Classification

DAFD: Domain Adaptation via Feature Disentanglement for Image Classification

Massively Scaling Heteroscedastic Classifiers

NeSyFOLD: Neurosymbolic Framework for Interpretable Image Classification

Lateralized Learning for Multi-Class Visual Classification Tasks

Anticipate, Ensemble and Prune: Improving Convolutional Neural Networks via Aggregated Early Exits

Universal Domain Adaptation for Remote Sensing Image Scene Classification

Navigating the Pitfalls of Active Learning Evaluation: A Systematic Framework for Meaningful Performance Assessment

Combined Use of Federated Learning and Image Encryption for Privacy-Preserving Image Classification with Vision Transformer

Counterfactual Explanation and Instance-Generation using Cycle-Consistent Generative Adversarial Networks

Gated Self-supervised Learning For Improving Supervised Learning

Semi-Supervised Learning with Pseudo-Negative Labels for Image Classification

A Privacy Preserving Method with a Random Orthogonal Matrix for ConvMixer Models

MoBYv2AL: Self-supervised Active Learning for Image Classification