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
DenseMP: Unsupervised Dense Pre-training for Few-shot Medical Image Segmentation
Zhaoxin Fan, Puquan Pan, Zeren Zhang, Ce Chen, Tianyang Wang, Siyang Zheng, Min Xu
On the ability of CNNs to extract color invariant intensity based features for image classification
Pradyumna Elavarthi, James Lee, Anca Ralescu
In-Domain Self-Supervised Learning Improves Remote Sensing Image Scene Classification
Ivica Dimitrovski, Ivan Kitanovski, Nikola Simidjievski, Dragi Kocev
Mitigating Bias: Enhancing Image Classification by Improving Model Explanations
Raha Ahmadi, Mohammad Javad Rajabi, Mohammad Khalooie, Mohammad Sabokrou