18,863 research outputs found
A Bayesian Approach to Manifold Topology Reconstruction
In this paper, we investigate the problem of statistical reconstruction of piecewise linear manifold topology. Given a noisy, probably undersampled point cloud from a one- or two-manifold, the algorithm reconstructs an approximated most likely mesh in a Bayesian sense from which the sample might have been taken. We incorporate statistical priors on the object geometry to improve the reconstruction quality if additional knowledge about the class of original shapes is available. The priors can be formulated analytically or learned from example geometry with known manifold tessellation. The statistical objective function is approximated by a linear programming / integer programming problem, for which a globally optimal solution is found. We apply the algorithm to a set of 2D and 3D reconstruction examples, demon-strating that a statistics-based manifold reconstruction is feasible, and still yields plausible results in situations where sampling conditions are violated
Grounding semantics in robots for Visual Question Answering
In this thesis I describe an operational implementation of an object detection and description system that incorporates in an end-to-end Visual Question Answering system and evaluated it on two visual question answering datasets for compositional language and elementary visual reasoning
Are Elephants Bigger than Butterflies? Reasoning about Sizes of Objects
Human vision greatly benefits from the information about sizes of objects.
The role of size in several visual reasoning tasks has been thoroughly explored
in human perception and cognition. However, the impact of the information about
sizes of objects is yet to be determined in AI. We postulate that this is
mainly attributed to the lack of a comprehensive repository of size
information. In this paper, we introduce a method to automatically infer object
sizes, leveraging visual and textual information from web. By maximizing the
joint likelihood of textual and visual observations, our method learns reliable
relative size estimates, with no explicit human supervision. We introduce the
relative size dataset and show that our method outperforms competitive textual
and visual baselines in reasoning about size comparisons.Comment: To appear in AAAI 201
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