Modern medical imaging increasingly relies on artificial intelligence to support detection, diagnosis, and prognostic ...

For many computer vision tasks (e.g., image classification, object detection), existing deep learning-based models usually suffer from significant performance degradation when directly applying them ...

microCLIP is a lightweight self-training framework that adapts CLIP for fine-grained image classification without requiring labeled data. While CLIP is strong in zero-shot transfer, it primarily ...

Army Capt. Jeremy Kuykendall and his wife Kate, cradle their youngest daughter Isabella at their new home in Fort Leavenworth, Kansas, on April 7, 2024. Bella was abused at a military day care center ...

In response to the problem of inadequate utilization of local information in PolSAR image classification using Vision Transformer in existing studies, this paper proposes a Vision Transformer method ...

Ilya Sutskever, co-founder of OpenAI, explains why unsupervised learning works and how it relates to supervised learning. The core concept is compression - good compressors can become good predictors.

Learning from limited exemplars (few-shot learning) is a fundamental, unsolved problem that has been laboriously explored in the machine learning community. However, current few-shot learners are ...

Abstract: Unsupervised classification plays an important role in understanding polarimetric synthetic aperture radar (PolSAR) images. One of the typical representations of PolSAR data is in the form ...