ProCo: Prototype-Aware Contrastive Learning for Long-Tailed Medical Image Classification

Contrastive Learning for Image Captioning

Image captioning, a popular topic in computer vision, has achieved substantial progress in recent years. However, the distinctiveness of natural descriptions is often overlooked in previous work. It is closely related to the quality of captions, as distinctive captions are more likely to describe images with their unique aspects. In this work, we propose a new learning method, Contrastive Learn...

Local Color Contrastive Descriptor for Image Classification

Image representation and classification are two fundamental tasks towards multimedia content retrieval and understanding. The idea that shape and texture information (e.g. edge or orientation) are the key features for visual representation is ingrained and dominated in current multimedia and computer vision communities. A number of low-level features have been proposed by computing local gradie...

Prototype Discriminative Learning for Face Image Set Classification

Medical Image Classification by Supervised Machine Learning

In this paper, Support Vector Machine (SVM) was used to learn image feature characteristics for image classification. Several image visual features describe the shape, edge, and texture of image (including histogram, spatial layout, coherence moment and gabor features) have been employed in this paper to categorize the 500 test images into 46 classes. The result shows that the spatial relations...

Investigation Necessary Competencies for Life Long Learning in Medical Students

Introduction: Lifelong learning is a process in which individuals develop their knowledge, skills, and interest through learning opportunities in their life. This paper aimed to determine the required competencies for training medical students to acquire lifelong learning. Methods: In this qualitative study, purposeful sampling method and open-ended semi-structured interview were employed for ...