Discovering and segmenting objects in videos is a challenging task due to large variations of objects in appearances, deformed shapes and cluttered backgrounds. In this paper, we propose to segment objects and understand their visual semantics from
Convolutional networks are the de-facto standard for analyzing spatio-temporal data such as images, videos, and 3D shapes. Whilst some of this data is naturally dense (e.g., photos), many other data sources are inherently sparse. Examples include 3D
Human parsing has been extensively studied recently (Yamaguchi et al. 2012; Xia et al. 2017) due to its wide applications in many important scenarios. Mainstream fashion parsing models (i.e., parsers) focus on parsing the high-resolution
and clean im
