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Found 6 papers, 1 papers with code
Deep MSFOP: Multiple Spectral filter Operators Preservation in Deep Functional Maps for Unsupervised Shape Matching
We propose a novel constraint called Multiple Spectral filter Operators Preservation (MSFOR) to compute functional maps and based on it, develop an efficient deep functional map architecture called Deep MSFOP for shape matching.
Efficient Deformable Shape Correspondence via Multiscale Spectral Manifold Wavelets Preservation
The functional map framework has proven to be extremely effective for representing dense correspondences between deformable shapes.
Memory-Efficient Modeling and Slicing of Large-Scale Adaptive Lattice Structures
The presented methods have been validated by a series of case studies with large number (up to 100M) of struts to demonstrate its applicability to large-scale lattice structures.
Computational Geometry
Visual Attack and Defense on Text
Modifying characters of a piece of text to their visual similar ones often ap-pear in spam in order to fool inspection systems and other conditions, which we regard as a kind of adversarial attack to neural models.
Learning to predict crisp boundaries
Recent methods for boundary or edge detection built on Deep Convolutional Neural Networks (CNNs) typically suffer from the issue of predicted edges being thick and need post-processing to obtain crisp boundaries.
Relative Depth Order Estimation Using Multi-scale Densely Connected Convolutional Networks
We study the problem of estimating the relative depth order of point pairs in a monocular image.
