Search Results for author: Fayao Liu

Found 23 papers, 4 papers with code

On Representation Knowledge Distillation for Graph Neural Networks

no code implementations9 Nov 2021 Chaitanya K. Joshi, Fayao Liu, Xu Xun, Jie Lin, Chuan-Sheng Foo

Past work on distillation for GNNs proposed the Local Structure Preserving loss (LSP), which matches local structural relationships across the student and teacher's node embedding spaces.

Contrastive Learning Knowledge Distillation

3D Pose Transfer with Correspondence Learning and Mesh Refinement

1 code implementation NeurIPS 2021 Chaoyue Song, Jiacheng Wei, Ruibo Li, Fayao Liu, Guosheng Lin

It aims to transfer the pose of a source mesh to a target mesh and keep the identity (e. g., body shape) of the target mesh.

Pose Transfer

LONG-TAILED RECOGNITION BY LEARNING FROM LATENT CATEGORIES

no code implementations29 Sep 2021 Weide Liu, Zhonghua Wu, Yiming Wang, Henghui Ding, Fayao Liu, Jie Lin, Guosheng Lin

In this work, we argue that there are common latent features between the head and tailed classes that can be used to give better feature representation.

Data Augmentation

Few-Shot Segmentation with Global and Local Contrastive Learning

1 code implementation11 Aug 2021 Weide Liu, Zhonghua Wu, Henghui Ding, Fayao Liu, Jie Lin, Guosheng Lin

To this end, we first propose a prior extractor to learn the query information from the unlabeled images with our proposed global-local contrastive learning.

Contrastive Learning Semantic Segmentation

Point Discriminative Learning for Unsupervised Representation Learning on 3D Point Clouds

no code implementations4 Aug 2021 Fayao Liu, Guosheng Lin, Chuan-Sheng Foo, Chaitanya K. Joshi, Jie Lin

In this work we propose a point discriminative learning method for unsupervised representation learning on 3D point clouds, which is specially designed for point cloud data and can learn local and global shape features.

3D Object Classification 3D Part Segmentation +4

Dense Supervision Propagation for Weakly Supervised Semantic Segmentation on 3D Point Clouds

no code implementations23 Jul 2021 Jiacheng Wei, Guosheng Lin, Kim-Hui Yap, Fayao Liu, Tzu-Yi Hung

While dense labeling on 3D data is expensive and time-consuming, only a few works address weakly supervised semantic point cloud segmentation methods to relieve the labeling cost by learning from simpler and cheaper labels.

Point Cloud Segmentation Scene Understanding +1

Feature Flow: In-network Feature Flow Estimation for Video Object Detection

no code implementations21 Sep 2020 Ruibing Jin, Guosheng Lin, Changyun Wen, Jianliang Wang, Fayao Liu

Optical flow, which expresses pixel displacement, is widely used in many computer vision tasks to provide pixel-level motion information.

Optical Flow Estimation Video Object Detection

Correlation Propagation Networks for Scene Text Detection

no code implementations30 Sep 2018 Zichuan Liu, Guosheng Lin, Wang Ling Goh, Fayao Liu, Chunhua Shen, Xiaokang Yang

In this work, we propose a novel hybrid method for scene text detection namely Correlation Propagation Network (CPN).

Scene Text Scene Text Detection

Structured Learning of Tree Potentials in CRF for Image Segmentation

no code implementations26 Mar 2017 Fayao Liu, Guosheng Lin, Ruizhi Qiao, Chunhua Shen

In this fashion, we easily achieve nonlinear learning of potential functions on both unary and pairwise terms in CRFs.

Semantic Segmentation

Structured Learning of Binary Codes with Column Generation

no code implementations22 Feb 2016 Guosheng Lin, Fayao Liu, Chunhua Shen, Jianxin Wu, Heng Tao Shen

Our column generation based method can be further generalized from the triplet loss to a general structured learning based framework that allows one to directly optimize multivariate performance measures.

Image Retrieval Information Retrieval

Discriminative Training of Deep Fully-connected Continuous CRF with Task-specific Loss

no code implementations28 Jan 2016 Fayao Liu, Guosheng Lin, Chunhua Shen

We exemplify the usefulness of the proposed model on multi-class semantic labelling (discrete) and the robust depth estimation (continuous) problems.

Depth Estimation Multi-class Classification

CRF Learning with CNN Features for Image Segmentation

no code implementations28 Mar 2015 Fayao Liu, Guosheng Lin, Chunhua Shen

The deep CNN is trained on the ImageNet dataset and transferred to image segmentations here for constructing potentials of superpixels.

Semantic Segmentation Superpixels

Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields

1 code implementation26 Feb 2015 Fayao Liu, Chunhua Shen, Guosheng Lin, Ian Reid

Therefore, here we present a deep convolutional neural field model for estimating depths from single monocular images, aiming to jointly explore the capacity of deep CNN and continuous CRF.

Depth Estimation

Deep Convolutional Neural Fields for Depth Estimation from a Single Image

no code implementations CVPR 2015 Fayao Liu, Chunhua Shen, Guosheng Lin

Therefore, we in this paper present a deep convolutional neural field model for estimating depths from a single image, aiming to jointly explore the capacity of deep CNN and continuous CRF.

Depth Estimation

Learning Deep Convolutional Features for MRI Based Alzheimer's Disease Classification

no code implementations13 Apr 2014 Fayao Liu, Chunhua Shen

In this work, we propose to learn deep convolutional image features using unsupervised and supervised learning.

Classification General Classification +1

From Kernel Machines to Ensemble Learning

no code implementations4 Jan 2014 Chunhua Shen, Fayao Liu

This finding not only enables us to design new ensemble learning methods directly from kernel methods, but also makes it possible to take advantage of those highly-optimized fast linear SVM solvers for ensemble learning.

Ensemble Learning Translation

Online Unsupervised Feature Learning for Visual Tracking

no code implementations7 Oct 2013 Fayao Liu, Chunhua Shen, Ian Reid, Anton Van Den Hengel

Feature encoding with respect to an over-complete dictionary learned by unsupervised methods, followed by spatial pyramid pooling, and linear classification, has exhibited powerful strength in various vision applications.

Dictionary Learning Visual Tracking

Multiple Kernel Learning in the Primal for Multi-modal Alzheimer's Disease Classification

no code implementations3 Oct 2013 Fayao Liu, Luping Zhou, Chunhua Shen, Jianping Yin

In this work, we propose a novel multiple kernel learning framework to combine multi-modal features for AD classification, which is scalable and easy to implement.

General Classification

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