出版物

Truncated Matrix Power Iteration for Differentiable DAG Learning

Recovering underlying Directed Acyclic Graph structures (DAG) from observational data is highly challenging due to the combinatorial …

Factor Graph Neural Network

Most of the successful deep neural network architectures are structured, often consisting of elements like convolutional neural …

Visual Relationship Detection with Low Rank Non-Negative Tensor Decomposition

We address the problem of Visual Relationship Detection (VRD) which aims to describe the relationships between pairs of objects in the …

Deep Graphical Feature Learning for the Feature Matching Problem

The feature matching problem is a fundamental problem in various areas of computer vision including image registration, tracking and …

Convolutional Sequence to Sequence Model for Human Dynamics

Human motion modeling is a classic problem in computer vision and graphics. Challenges in modeling human motion include high …

Dynamic Programming Bipartite Belief Propagation For Hyper Graph Matching

Hyper graph matching problems have drawn attention recently due to their robustness to noise, outliers, rotation and scaling variation. …

Solving Constrained Combinatorial Optimisation Problems Via MAP Inference Without High-order Penalties

Solving constrained combinatorial optimization problems via MAP inference is often achieved by introducing extra potential functions …

Pairwise Matching through Max-Weight Bipartite Belief Propagation

Feature matching is a key problem in computer vision and pattern recognition. One way to encode the essential interdependence between …

Joint Probabilistic Matching Using m-Best Solutions

Matching between two sets of objects is typically approached by finding the object pairs that collectively maximize the joint matching …

Joint Probabilistic Data Association Revisited

In this paper, we revisit the joint probabilistic data association (JPDA) technique and propose a novel solution based on recent …