Search Results for author:
Found 15 papers, 3 papers with code
An Empirical Study of Super-resolution on Low-resolution Micro-expression Recognition
Micro-expression recognition (MER) in low-resolution (LR) scenarios presents an important and complex challenge, particularly for practical applications such as group MER in crowded environments.
Toroidal Coordinates: Decorrelating Circular Coordinates With Lattice Reduction
The circular coordinates algorithm of de Silva, Morozov, and Vejdemo-Johansson takes as input a dataset together with a cohomology class representing a $1$-dimensional hole in the data; the output is a map from the data into the circle that captures this hole, and that is of minimum energy in a suitable sense.
Supervised Homogeneity Fusion: a Combinatorial Approach
Fusing regression coefficients into homogenous groups can unveil those coefficients that share a common value within each group.
Region attention and graph embedding network for occlusion objective class-based micro-expression recognition
More specifically, the backbone network aims at extracting feature representations from different facial regions, RI module computing an adaptive weight from the region itself based on attention mechanism with respect to the unobstructedness and importance for suppressing the influence of occlusion, and RR module exploiting the progressive interactions among these regions by performing graph convolutions.
A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources
We propose a tree-based model averaging approach to improve the estimation accuracy of conditional average treatment effects (CATE) at a target site by leveraging models derived from other potentially heterogeneous sites, without them sharing subject-level data.
Feature refinement: An expression-specific feature learning and fusion method for micro-expression recognition
It aims to obtain salient and discriminative features for specific expressions and also predict expression by fusing the expression-specific features.
Micro Expression Recognition
Micro-Expression Recognition
+1
Objective Class-based Micro-Expression Recognition through Simultaneous Action Unit Detection and Feature Aggregation
Specifically, we propose two new strategies in our AU detection module for more effective AU feature learning: the attention mechanism and the balanced detection loss function.
Pattern-Structure Diffusion for Multi-Task Learning
Inspired by the observation that pattern structures high-frequently recur within intra-task also across tasks, we propose a pattern-structure diffusion (PSD) framework to mine and propagate task-specific and task-across pattern structures in the task-level space for joint depth estimation, segmentation and surface normal prediction.
Ranked #9 on
Semantic Segmentation
on SUN-RGBD
(using extra training data)
Persistent Homotopy Groups of Metric Spaces
We pay particular attention to the case of fundamental groups, for which we obtain a more precise description.
Algebraic Topology Computational Geometry 53C23, 51F99, 55N35
Method of Contraction-Expansion (MOCE) for Simultaneous Inference in Linear Models
To establish a simultaneous post-model selection inference, we propose a method of contraction and expansion (MOCE) along the line of debiasing estimation that enables us to balance the bias-and-variance trade-off so that the super-sparsity assumption may be relaxed.
Understanding over-parameterized deep networks by geometrization
This can be regarded as a strong support of our proposal that geometrization is not only the bible for physics, it is also the key idea to understand deep learning systems.
Geometrization of deep networks for the interpretability of deep learning systems
By comparing the geometry of image matching and deep networks, we show that geometrization of deep networks can be used to understand existing deep learning systems and it may also help to solve the interpretability problem of deep learning systems.
Reducing Parameter Space for Neural Network Training
For neural networks (NNs) with rectified linear unit (ReLU) or binary activation functions, we show that their training can be accomplished in a reduced parameter space.
Demystifying AlphaGo Zero as AlphaGo GAN
The astonishing success of AlphaGo Zero\cite{Silver_AlphaGo} invokes a worldwide discussion of the future of our human society with a mixed mood of hope, anxiousness, excitement and fear.
How deep learning works --The geometry of deep learning
Why and how that deep learning works well on different tasks remains a mystery from a theoretical perspective.
