Unifying (Machine) Vision via Counterfactual World Modeling
Daniel M. Bear Kevin Feigelis Honglin Chen Wanhee Lee Rahul Venkatesh Klemen Kotar Alex Durango Daniel L. K. Yamins
Leading approaches in machine vision employ different architectures for
different tasks, trained on costly task-specific labeled datasets. This
complexity has held back progress in areas, such as robotics, where robust
task-general perception remains a bottleneck. In contrast, "foundation models"
of natural language have shown how large pre-trained neural networks can
provide zero-shot solutions to a broad spectrum of apparently distinct tasks.
Here we introduce Counterfactual World Modeling (CWM), a framework for
constructing a visual foundation model: a unified, unsupervised network that
can be prompted to perform a wide variety of visual computations. CWM has two
key components, which resolve the core issues that have hindered application of
the foundation model concept to vision. The first is structured masking, a
generalization of masked prediction methods that encourages a prediction model
to capture the low-dimensional structure in visual data. The model thereby
factors the key physical components of a scene and exposes an interface to them
via small sets of visual tokens. This in turn enables CWM's second main idea --
counterfactual prompting -- the observation that many apparently distinct
visual representations can be computed, in a zero-shot manner, by comparing the
prediction model's output on real inputs versus slightly modified
("counterfactual") inputs. We show that CWM generates high-quality readouts on
real-world images and videos for a diversity of tasks, including estimation of
keypoints, optical flow, occlusions, object segments, and relative depth. Taken
together, our results show that CWM is a promising path to unifying the
manifold strands of machine vision in a conceptually simple foundation.