Browsing by Author "Hsu, Cheng-Chun"

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Weakly Supervised Instance Segmentation using the Bounding Box Tightness Prior
(2019-01-01)
Hsu, Cheng-Chun
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Hsu, Kuang-Jui
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Tsai, Chung-Chi
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Lin, Yen-Yu
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Chuang, Yung-Yu
This paper presents a weakly supervised instance segmentation method that consumes training data with tight bounding box annotations. The major difficulty lies in the uncertain figure-ground separation within each bounding box since there is no supervisory signal about it. We address the difficulty by formulating the problem as a multiple instance learning (MIL) task, and generate positive and negative bags based on the sweeping lines of each bounding box. The proposed deep model integrates MIL into a fully supervised instance segmentation network, and can be derived by the objective consisting of two terms, i.e., the unary term and the pairwise term. The former estimates the foreground and background areas of each bounding box while the latter maintains the unity of the estimated object masks. The experimental results show that our method performs favorably against existing weakly supervised methods and even surpasses some fully supervised methods for instance segmentation on the PASCAL VOC dataset. The code is available at https://github.com/chengchunhsu/WSIS_BBTP.
What Dress Fits Me Best? Fashion Recommendation on the Clothing Style for Personal Body Shape
(2018-01-01)
Hidayati, Shintami Chusnul
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Hsu, Cheng-Chun
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Chang, Yu-Ting
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Hua, Kai-Lung
;
Fu, Jianlong
Clothing is an integral part of life. Also, it is always an uneasy task for people to make decisions on what to wear. An essential style tip is to dress for the body shape, i.e., knowing one's own body shape (e.g., hourglass, rectangle, round and inverted triangle) and selecting the types of clothes that will accentuate the body's good features. In the literature, although various fashion recommendation systems for clothing items have been developed, none of them had explicitly taken the user's basic body shape into consideration. In this paper, therefore, we proposed a first framework for learning the compatibility of clothing styles and body shapes from social big data, with the goal to recommend a user about what to wear better in relation to his/her essential body attributes. The experimental results demonstrate the superiority of our proposed approach, leading to a new aspect for research into fashion recommendation.