Poster

Learning to Compose Visual Relations

Nan Liu · Shuang Li · Yilun Du · Josh Tenenbaum · Antonio Torralba

Keywords: [ Deep Learning ] [ Graph Learning ]

[ Abstract ]
[ OpenReview
Fri 10 Dec 8:30 a.m. PST — 10 a.m. PST
 
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Abstract:

The visual world around us can be described as a structured set of objects and their associated relations. An image of a room may be conjured given only the description of the underlying objects and their associated relations. While there has been significant work on designing deep neural networks which may compose individual objects together, less work has been done on composing the individual relations between objects. A principal difficulty is that while the placement of objects is mutually independent, their relations are entangled and dependent on each other. To circumvent this issue, existing works primarily compose relations by utilizing a holistic encoder, in the form of text or graphs. In this work, we instead propose to represent each relation as an unnormalized density (an energy-based model), enabling us to compose separate relations in a factorized manner. We show that such a factorized decomposition allows the model to both generate and edit scenes that have multiple sets of relations more faithfully. We further show that decomposition enables our model to effectively understand the underlying relational scene structure.

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