  Computer vision tasks typically involve describing what is present in an
image (e.g. classification, detection, segmentation, and captioning). We study
a visual common sense task that requires understanding what is not present.
Specifically, given an image (e.g. of a living room) and name of an object
("cushion"), a vision system is asked to predict semantically-meaningful
regions (masks or bounding boxes) in the image where that object could be
placed or is likely be placed by humans (e.g. on the sofa). We call this task:
Semantic Placement (SP) and believe that such common-sense visual understanding
is critical for assitive robots (tidying a house), and AR devices
(automatically rendering an object in the user's space). Studying the invisible
is hard. Datasets for image description are typically constructed by curating
relevant images and asking humans to annotate the contents of the image;
neither of those two steps are straightforward for objects not present in the
image. We overcome this challenge by operating in the opposite direction: we
start with an image of an object in context from web, and then remove that
object from the image via inpainting. This automated pipeline converts
unstructured web data into a dataset comprising pairs of images with/without
the object. Using this, we collect a novel dataset, with ${\sim}1.3$M images
across $9$ object categories, and train a SP prediction model called CLIP-UNet.
CLIP-UNet outperforms existing VLMs and baselines that combine semantic priors
with object detectors on real-world and simulated images. In our user studies,
we find that the SP masks predicted by CLIP-UNet are favored $43.7\%$ and
$31.3\%$ times when comparing against the $4$ SP baselines on real and
simulated images. In addition, we demonstrate leveraging SP mask predictions
from CLIP-UNet enables downstream applications like building tidying robots in
indoor environments.
