Junjie Hou's research while affiliated with Chinese Academy of Sciences and other places
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Publications (3)
The image harmonization task endeavors to adjust foreground information within an image synthesis process to achieve visual consistency by leveraging background information. In academic research, this task conventionally involves the utilization of simple synthesized images and matching masks as inputs. However, obtaining precise masks for image ha...
Arbitrary artistic style transfer has achieved great success with deep neural networks, but it is still difficult for existing methods to tackle the dilemma of content preservation and style translation due to the inherent content-and-style conflict. In this paper, we introduce content self-supervised learning and style contrastive learning to arbi...
Image harmonization is an interesting but challenging task in image processing. The task is to adjust the visual information between the foreground and background in the picture, so that the overall picture becomes a harmonious and unified process. Compared to traditional unsupervised methods for image processing, recent developments in deep learni...
Citations
... Subsequently, it iteratively optimizes the content image to match the style of the referenced image. Since then, numerous methods have been proposed to enhance various aspects of performance, such as controllability [19][20][21]23], text-driven [2,3,11,12] and quality [25][26][27][28][29]. Controllability: Babaeizadeh et al. [19] propose a method based on the feed-forward neural network to achieve a tradeoff between content and style by adjusting the weights of the corresponding losses. ...
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