Deep convolutional neural networks (CNNs) have revolutionized the computer vision research and have seen unprecedented adoption for multiple tasks, such as classification, detection, and caption generation. However, they offer little transparency into their inner workings and are often treated as black boxes that deliver excellent performance.
Defocus blur detection is an important and challenging task in computer vision and digital imaging fields. Previous work on defocus blur detection has put a lot of effort into designing local sharpness metric maps.
A novel scheme of edge detection based on the physical law of diffusion is presented in this paper. Though the most current studies are using data…
Read moreRetrieving specific persons with various types of queries, e.g., a set of attributes or a portrait photo has great application potential in large…
Read moreImage annotation aims to annotate a given image with a variable number of class labels corresponding to diverse visual concepts. In this paper, we…
Read moreZero-shot learning (ZSL) for visual recognition aims to accurately recognize the objects of unseen classes through mapping the visual feature to an…
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