Understanding is the essence of any intelligent system.We revise our four machine understanding paradigms which are:(i)basic understanding,(ii)rich understanding,(iii)exploratory understanding,and(iv)theory-based unde...Understanding is the essence of any intelligent system.We revise our four machine understanding paradigms which are:(i)basic understanding,(ii)rich understanding,(iii)exploratory understanding,and(iv)theory-based understanding;and we elaborate on the first two of them.We then introduce the concept of two-stage(or deep)machine understanding which provides descriptive understandings,as well as evaluations of these understandings.After a brief systematization of emotions,we cover the following paradigms for agents with two-stage(deep)understanding abilities for emotional intelligence simulation:(i)basic understanding,(ii)rich-understanding,and(iii)switchable understanding.展开更多
Digital cartoon production requires extensive manual labor to colorize sketches with visually pleasant color composition and color shading.During colorization,the artist usually takes an existing cartoon image as colo...Digital cartoon production requires extensive manual labor to colorize sketches with visually pleasant color composition and color shading.During colorization,the artist usually takes an existing cartoon image as color guidance,particularly when colorizing related characters or an animation sequence.Reference-guided colorization is more intuitive than colorization with other hints,such as color points or scribbles,or text-based hints.Unfortunately,reference-guided colorization is challenging since the style of the colorized image should match the style of the reference image in terms of both global color composition and local color shading.In this paper,we propose a novel learning-based framework which colorizes a sketch based on a color style feature extracted from a reference color image.Our framework contains a color style extractor to extract the color feature from a color image,a colorization network to generate multi-scale output images by combining a sketch and a color feature,and a multi-scale discriminator to improve the reality of the output image.Extensive qualitative and quantitative evaluations show that our method outperforms existing methods,providing both superior visual quality and style reference consistency in the task of reference-based colorization.展开更多
文摘Understanding is the essence of any intelligent system.We revise our four machine understanding paradigms which are:(i)basic understanding,(ii)rich understanding,(iii)exploratory understanding,and(iv)theory-based understanding;and we elaborate on the first two of them.We then introduce the concept of two-stage(or deep)machine understanding which provides descriptive understandings,as well as evaluations of these understandings.After a brief systematization of emotions,we cover the following paradigms for agents with two-stage(deep)understanding abilities for emotional intelligence simulation:(i)basic understanding,(ii)rich-understanding,and(iii)switchable understanding.
基金supported in part by a CIHE Institutional Development Grant No.IDG200107the National Natural Science Foundation of China under Grant No.61973221the Natural Science Foundation of Guangdong Province of China under Grant Nos.2018A030313381 and 2019A1515011165.
文摘Digital cartoon production requires extensive manual labor to colorize sketches with visually pleasant color composition and color shading.During colorization,the artist usually takes an existing cartoon image as color guidance,particularly when colorizing related characters or an animation sequence.Reference-guided colorization is more intuitive than colorization with other hints,such as color points or scribbles,or text-based hints.Unfortunately,reference-guided colorization is challenging since the style of the colorized image should match the style of the reference image in terms of both global color composition and local color shading.In this paper,we propose a novel learning-based framework which colorizes a sketch based on a color style feature extracted from a reference color image.Our framework contains a color style extractor to extract the color feature from a color image,a colorization network to generate multi-scale output images by combining a sketch and a color feature,and a multi-scale discriminator to improve the reality of the output image.Extensive qualitative and quantitative evaluations show that our method outperforms existing methods,providing both superior visual quality and style reference consistency in the task of reference-based colorization.
