Tsung-Yu Lin

Research Assistant
Computer Science
University of Massachusetts Amherst
tsungyulin at cs.umass.edu

me
UMass Logo Academia Sinica Logo NTHU Logo

I am a fourth year Computer Science PhD student at University of Massachusetts Amherst. I am working at Computer Vision Lab led by Prof. Erik Learned-Miller and Prof. Subhransu Maji. My research interests are in computer vision and machine learning. Specifically, I am interested in extracting high-level visual information for image recognition problem.

I had been wokring as intern under the supervision of R. Manmatha and Deva Ranmanan at Amazon AI. I had been working at Academia Sinica with Dr. Tyng-Luh Liu before I joined UMass. Before that, I completed my MS degree at National Tsing Hua University advised by Prof. Shang-Hong Lai. See more on my CV. Also, you can check out my Google scholar.



Home        

Research        

Publication        

CV


Projects
Dark Ecology: tracking bird migration with computer vision and deep learning techniques
Visualizing and Understanding Deep Texture Representations
Bilinear CNN for Fine-Grained Classification
People localization in a camera network
pdf
Shape Prior Non-Rigid SfM for deformable Surfaces
pdf
Multi-camera and multi-target surveillance tracking 3D Face Model Deformation


Publications

Improved Bilinear Pooling with CNNs

BMVC 2017 (Oral)    [project] [pdf] [arXiv]

Bilinear Convolutional Neural Networks for Fine-grained Visual Recognition

PAMI 2017   [project] [pdf]

Visualizing and Understanding Deep Texture Representations

CVPR 2016    [project] [pdf] [arXiv]

One-to-many face recognition with Bilinear CNNs

WACV 2016   [pdf]

Implicit Sparse Code Hashing

arXiv:1512.00130 , 2015    [arXiv]

Bilinear CNN Models for Fine-grained Visual Recognition

ICCV 2015  (Oral, Acceptance rate: 3.3%)    [project] [pdf] [pdf-supp] [arXiv] [slides] [poster] [bibtex]

Efficient binary codes for extremely high-dimensional data

ICIP 2014  [pdf]

People localization in a camera network combining background subtraction and scene-aware human detection

MMM 2011  [pdf]