Learning Optical Flow from a Few Matches

Shihao Jiang, Yao Lu, Hongdong Li, Richard Hartley
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021.

We show that high performance optical flow estimation can be achieved without using dense cost volumes.

Bidirectionally Self-Normalizing Neural Networks

Yao Lu, Stephen Gould, Thalaiyasingam Ajanthan
arXiv, 2020.

We theoretically solve the vanishing/exploding gradients problem in neural networks. Key idea: constrain signal norm in both directions via a new class of activation functions and orthogonal weight matrices.

Devon: Deformable Volume Network for Learning Optical Flow

Yao Lu, Jack Valmadre, Heng Wang, Juho Kannala, Mehrtash Harandi, Philip Torr
IEEE Winter Conference on Applications of Computer Vision (WACV), 2020.

We pointed out the problem of image warping in estimating optical flow and proposed the deformable cost volume to solve the problem.

Doubly Stochastic Neighbor Embedding on Spheres

Yao Lu, Jukka Corander, Zhirong Yang
Pattern Recognition Letters, 2019.

We present a new method for data visualization.
[paper] [project]

Learning Image Relations with Contrast Association Networks

Yao Lu, Zhirong Yang, Juho Kannala, Samuel Kaski
International Joint Conference on Neural Networks (IJCNN), 2019.

We proposed a new neural network module, Contrast Association Units, to model the relations between two sets of input variables.

Block Mean Approximation for Efficient Second Order Optimization

Yao Lu, Mehrtash Harandi, Richard Hartley, Razvan Pascanu
ICML workshop on Modern Trends in Nonconvex Optimization for Machine Learning, 2018.

We proposed a new matrix approximation method which allows efficient matrix inversion. We then applied the method to second order optimization algorithms for training neural networks.

Oblivious Neural Network Predictions via MiniONN Transformations

Jian Liu, Mika Juuti, Yao Lu, N. Asokan
ACM Conference on Computer and Communications Security (CCS), 2017.

We proposed a new method for privacy-preserving predictions with trained neural networks.

A Fast Projected Fixed-Point Algorithm for Large Graph Matching

Yao Lu, Kaizhu Huang, Cheng-Lin Liu
Pattern Recognition, 2016.

We designed a fast graph matching algorithm with time complexity O(n^3) per iteration, where n is the size of a graph. We proved its convergence rate. It takes within 10 seconds to match two graphs of 1000 nodes on a PC.

Unsupervised Learning on Neural Network Outputs

Yao Lu
International Joint Conference on Artificial Intelligence (IJCAI), 2016.

We found visual attributes of object classes can be unsuperisedly learned by applying Independent Component Analysis on the softmax outputs of a trained ConvNet. We showed such attributes can be useful for object recognition by performing zero-shot learning experiments on the ImageNet dataset of over 20,000 object classes.

An Algorithm for Maximum Common Subgraph of Planar Triangulation Graphs

Yao Lu, Horst Bunke, Cheng-Lin Liu
Graph-Based Representations in Pattern Recognition (GbR), 2013.

We designed a fast Maximum Common Subgraph (MCS) algorithm for Planar Triangulation Graphs. Its time complexity is O(mnk), where n is the size of one graph, m is the size of the other graph and k is the size of their MCS.

Traveling Bumps and Their Collisions in a Two-Dimensional Neural Field

Yao Lu, Yuzuru Sato, Shun-ichi Amari
Neural Computation, 2011.

We found a unique traveling bump solution, which was unknown to exist before, in a set of two-dimensional neural network equations.