Optimal Transport (OT) has recently emerged as a central tool in data sciences to compare
in a geometrically faithful way point clouds and more generally probability distributions. The …

Training on large-scale graphs has achieved remarkable results in graph representation
learning, but its cost and storage have attracted increasing concerns. Existing graph …

We propose and study a method called FLOT that estimates scene flow on point clouds. We
start the design of FLOT by noticing that scene flow estimation on point clouds reduces to …

Cross-domain alignment between two sets of entities (eg, objects in an image, words in a
sentence) is fundamental to both computer vision and natural language processing. Existing …

Cross-Domain Recommendation (CDR) has been popularly studied to utilize different
domain knowledge to solve the data sparsity and cold-start problem in recommender …

As large-scale graphs become increasingly more prevalent, it poses significant
computational challenges to process, extract and analyze large graph data. Graph …

Point clouds are naturally sparse, while image pixels are dense. The inconsistency limits
feature fusion from both modalities for point-wise scene flow estimation. Previous methods …

We present Wasserstein Embedding for Graph Learning (WEGL), a novel and fast
framework for embedding entire graphs in a vector space, in which various machine …

Interpreting the decisions of deep learning models has been actively studied since the
explosion of deep neural networks. One of the most convincing interpretation approaches is …

Aiming at obtaining structural information and 3D motion of dynamic scenes, scene flow
estimation has been an interest of research in computer vision and computer graphics for a …