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
Discover and Mitigate Unknown Biases with Debiasing Alternate Networks
Zhiheng Li, Anthony Hoogs, Chenliang Xu
Rectifying Open-set Object Detection: A Taxonomy, Practical Applications, and Proper Evaluation
Yusuke Hosoya, Masanori Suganuma, Takayuki Okatani
Towards Accurate and Robust Classification in Continuously Transitioning Industrial Sprays with Mixup
Hongjiang Li, Huanyi Shui, Alemayehu Admasu, Praveen Narayanan, Devesh Upadhyay
Current Trends in Deep Learning for Earth Observation: An Open-source Benchmark Arena for Image Classification
Ivica Dimitrovski, Ivan Kitanovski, Dragi Kocev, Nikola Simidjievski
Tree Structure-Aware Few-Shot Image Classification via Hierarchical Aggregation
Min Zhang, Siteng Huang, Wenbin Li, Donglin Wang