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
Deep learning pipeline for image classification on mobile phones
Muhammad Muneeb, Samuel F. Feng, Andreas Henschel
FHIST: A Benchmark for Few-shot Classification of Histological Images
Fereshteh Shakeri, Malik Boudiaf, Sina Mohammadi, Ivaxi Sheth, Mohammad Havaei, Ismail Ben Ayed, Samira Ebrahimi Kahou
An Effective Fusion Method to Enhance the Robustness of CNN
Yating Ma, Zhichao Lian
Semi-Supervised Learning for Image Classification using Compact Networks in the BioMedical Context
Adrián Inés, Andrés Díaz-Pinto, César Domínguez, Jónathan Heras, Eloy Mata, Vico Pascual
Bayesian Convolutional Neural Networks for Limited Data Hyperspectral Remote Sensing Image Classification
Mohammad Joshaghani, Amirabbas Davari, Faezeh Nejati Hatamian, Andreas Maier, Christian Riess