  This paper addresses the challenge of learning a local visual pattern of an
object from one image, and generating images depicting objects with that
pattern. Learning a localized concept and placing it on an object in a target
image is a nontrivial task, as the objects may have different orientations and
shapes. Our approach builds upon recent advancements in visual concept
learning. It involves acquiring a visual concept (e.g., an ornament) from a
source image and subsequently applying it to an object (e.g., a chair) in a
target image. Our key idea is to perform in-context concept learning, acquiring
the local visual concept within the broader context of the objects they belong
to. To localize the concept learning, we employ soft masks that contain both
the concept within the mask and the surrounding image area. We demonstrate our
approach through object generation within an image, showcasing plausible
embedding of in-context learned concepts. We also introduce methods for
directing acquired concepts to specific locations within target images,
employing cross-attention mechanisms, and establishing correspondences between
source and target objects. The effectiveness of our method is demonstrated
through quantitative and qualitative experiments, along with comparisons
against baseline techniques.
