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In the paper See the Glass Half Full: Reasoning about Liquid Containers, their Volume and Content, one of the inputs to the model is "a bounding box mask smoothed by a Gaussian kernel". I'm not sure I understood what a "bounding box mask" is and how it is derived given the 4 coordinates (x1, x2, y1, y2) of the object's region in the image. Based on Figure 3, is this simply done by creating a black rectangle of the same dimensions as the image, and marking the region denoted by the coordinates with white pixels and then applying Gaussian smoothing to the entire image?

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