2,151 research outputs found
Nonlinear cross Gramians and gradient systems
We study the notion of cross Gramians for non-linear gradient systems, using the characterization in terms of prolongation and gradient extension associated to the system. The cross Gramian is given for the variational system associated to the original nonlinear gradient system. We obtain linearization results that precisely correspond to the notion of a cross Gramian for symmetric linear systems. Furthermore, first steps towards relations with the singular value functions of the nonlinear Hankel operator are studied and yield promising results.
Unsupervised Domain Adaptation for 3D Keypoint Estimation via View Consistency
In this paper, we introduce a novel unsupervised domain adaptation technique
for the task of 3D keypoint prediction from a single depth scan or image. Our
key idea is to utilize the fact that predictions from different views of the
same or similar objects should be consistent with each other. Such view
consistency can provide effective regularization for keypoint prediction on
unlabeled instances. In addition, we introduce a geometric alignment term to
regularize predictions in the target domain. The resulting loss function can be
effectively optimized via alternating minimization. We demonstrate the
effectiveness of our approach on real datasets and present experimental results
showing that our approach is superior to state-of-the-art general-purpose
domain adaptation techniques.Comment: ECCV 201
A Model of Heat Conduction
We define a deterministic ``scattering'' model for heat conduction which is
continuous in space, and which has a Boltzmann type flavor, obtained by a
closure based on memory loss between collisions. We prove that this model has,
for stochastic driving forces at the boundary, close to Maxwellians, a unique
non-equilibrium steady state
Integral Human Pose Regression
State-of-the-art human pose estimation methods are based on heat map
representation. In spite of the good performance, the representation has a few
issues in nature, such as not differentiable and quantization error. This work
shows that a simple integral operation relates and unifies the heat map
representation and joint regression, thus avoiding the above issues. It is
differentiable, efficient, and compatible with any heat map based methods. Its
effectiveness is convincingly validated via comprehensive ablation experiments
under various settings, specifically on 3D pose estimation, for the first time
Graph complexes in deformation quantization
Kontsevich's formality theorem and the consequent star-product formula rely
on the construction of an -morphism between the DGLA of polyvector
fields and the DGLA of polydifferential operators. This construction uses a
version of graphical calculus. In this article we present the details of this
graphical calculus with emphasis on its algebraic features. It is a morphism of
differential graded Lie algebras between the Kontsevich DGLA of admissible
graphs and the Chevalley-Eilenberg DGLA of linear homomorphisms between
polyvector fields and polydifferential operators. Kontsevich's proof of the
formality morphism is reexamined in this light and an algebraic framework for
discussing the tree-level reduction of Kontsevich's star-product is described.Comment: 39 pages; 3 eps figures; uses Xy-pic. Final version. Details added,
mainly concerning the tree-level approximation. Typos corrected. An abridged
version will appear in Lett. Math. Phy
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