Search Results for author: Suya You

Found 21 papers, 3 papers with code

DefakeHop++: An Enhanced Lightweight Deepfake Detector

no code implementations30 Apr 2022 Hong-Shuo Chen, Shuowen Hu, Suya You, C. -C. Jay Kuo

Second, for discriminant features selection, DefakeHop uses an unsupervised approach while DefakeHop++ adopts a more effective approach with supervision, called the Discriminant Feature Test (DFT).

Face Swapping

Unsupervised Lightweight Single Object Tracking with UHP-SOT++

no code implementations15 Nov 2021 Zhiruo Zhou, Hongyu Fu, Suya You, C. -C. Jay Kuo

Based on the experimental results, we compare pros and cons of supervised and unsupervised trackers and provide a new perspective to understand the performance gap between supervised and unsupervised methods, which is the third contribution of this work.

Object Tracking Trajectory Modeling

Geo-DefakeHop: High-Performance Geographic Fake Image Detection

no code implementations19 Oct 2021 Hong-Shuo Chen, Kaitai Zhang, Shuowen Hu, Suya You, C. -C. Jay Kuo

A robust fake satellite image detection method, called Geo-DefakeHop, is proposed in this work.

Fake Image Detection

Meta-UDA: Unsupervised Domain Adaptive Thermal Object Detection using Meta-Learning

no code implementations7 Oct 2021 Vibashan VS, Domenick Poster, Suya You, Shuowen Hu, Vishal M. Patel

Though thermal cameras are widely used for military applications and increasingly for commercial applications, there is a lack of robust algorithms to robustly exploit the thermal imagery due to the limited availability of labeled thermal data.

Meta-Learning Object Detection +1

UHP-SOT: An Unsupervised High-Performance Single Object Tracker

no code implementations5 Oct 2021 Zhiruo Zhou, Hongyu Fu, Suya You, Christoph C. Borel-Donohue, C. -C. Jay Kuo

An unsupervised online object tracking method that exploits both foreground and background correlations is proposed and named UHP-SOT (Unsupervised High-Performance Single Object Tracker) in this work.

Object Tracking

GTNet:Guided Transformer Network for Detecting Human-Object Interactions

1 code implementation2 Aug 2021 A S M Iftekhar, Satish Kumar, R. Austin McEver, Suya You, B. S. Manjunath

For detecting HOI, it is important to utilize relative spatial configurations and object semantics to find salient spatial regions of images that highlight the interactions between human object pairs.

Human-Object Interaction Detection

Evaluation of Multimodal Semantic Segmentation using RGB-D Data

no code implementations31 Mar 2021 Jiesi Hu, Ganning Zhao, Suya You, C. C. Jay Kuo

Our goal is to develop stable, accurate, and robust semantic scene understanding methods for wide-area scene perception and understanding, especially in challenging outdoor environments.

Scene Understanding Semantic Segmentation

CalibDNN: Multimodal Sensor Calibration for Perception Using Deep Neural Networks

no code implementations27 Mar 2021 Ganning Zhao, Jiesi Hu, Suya You, C. -C. Jay Kuo

Current perception systems often carry multimodal imagers and sensors such as 2D cameras and 3D LiDAR sensors.

Low-Resolution Face Recognition In Resource-Constrained Environments

no code implementations23 Nov 2020 Mozhdeh Rouhsedaghat, Yifan Wang, Shuowen Hu, Suya You, C. -C. Jay Kuo

A non-parametric low-resolution face recognition model for resource-constrained environments with limited networking and computing is proposed in this work.

Active Learning Face Recognition

Constructing Multilayer Perceptrons as Piecewise Low-Order Polynomial Approximators: A Signal Processing Approach

no code implementations15 Oct 2020 Ruiyuan Lin, Suya You, Raghuveer Rao, C. -C. Jay Kuo

Through the construction, a one-to-one correspondence between the approximation of an MLP and that of a piecewise low-order polynomial is established.

From Two-Class Linear Discriminant Analysis to Interpretable Multilayer Perceptron Design

no code implementations9 Sep 2020 Ruiyuan Lin, Zhiruo Zhou, Suya You, Raghuveer Rao, C. -C. Jay Kuo

Besides input layer $l_{in}$ and output layer $l_{out}$, the MLP of interest consists of two intermediate layers, $l_1$ and $l_2$.

FaceHop: A Light-Weight Low-Resolution Face Gender Classification Method

no code implementations18 Jul 2020 Mozhdeh Rouhsedaghat, Yifan Wang, Xiou Ge, Shuowen Hu, Suya You, C. -C. Jay Kuo

For gray-scale face images of resolution $32 \times 32$ in the LFW and the CMU Multi-PIE datasets, FaceHop achieves correct gender classification rates of 94. 63% and 95. 12% with model sizes of 16. 9K and 17. 6K parameters, respectively.

Classification General Classification

Object Detection on Single Monocular Images through Canonical Correlation Analysis

no code implementations13 Feb 2020 Zifan Yu, Suya You

In this report, we propose a two-dimensional CCA(canonical correlation analysis) framework to fuse monocular images and corresponding predicted depth images for basic computer vision tasks like image classification and object detection.

General Classification Image Classification +1

PixelHop++: A Small Successive-Subspace-Learning-Based (SSL-based) Model for Image Classification

no code implementations8 Feb 2020 Yueru Chen, Mozhdeh Rouhsedaghat, Suya You, Raghuveer Rao, C. -C. Jay Kuo

In PixelHop++, one can control the learning model size of fine-granularity, offering a flexible tradeoff between the model size and the classification performance.

Classification General Classification +1

Deep RGB-D Canonical Correlation Analysis For Sparse Depth Completion

1 code implementation NeurIPS 2019 Yiqi Zhong, Cho-Ying Wu, Suya You, Ulrich Neumann

Such a transformation enables CFCNet to predict features and reconstruct data of missing depth measurements according to their corresponding, transformed RGB features.

Depth Completion

Robustness Of Saak Transform Against Adversarial Attacks

no code implementations7 Feb 2019 Thiyagarajan Ramanathan, Abinaya Manimaran, Suya You, C-C Jay Kuo

This work investigates the robustness of Saak transform against adversarial attacks towards high performance image classification.

Adversarial Robustness Classification +3

Learning to Prune Filters in Convolutional Neural Networks

no code implementations23 Jan 2018 Qiangui Huang, Kevin Zhou, Suya You, Ulrich Neumann

Specifically, we introduce a "try-and-learn" algorithm to train pruning agents that remove unnecessary CNN filters in a data-driven way.

Semantic Segmentation

Shape Inpainting using 3D Generative Adversarial Network and Recurrent Convolutional Networks

no code implementations ICCV 2017 Weiyue Wang, Qiangui Huang, Suya You, Chao Yang, Ulrich Neumann

The 3D-ED-GAN is a 3D convolutional neural network trained with a generative adversarial paradigm to fill missing 3D data in low-resolution.

Scene Labeling using Gated Recurrent Units with Explicit Long Range Conditioning

no code implementations22 Nov 2016 Qiangui Huang, Weiyue Wang, Kevin Zhou, Suya You, Ulrich Neumann

A novel neural network architecture is built for scene labeling tasks where one of the variants of the new RNN unit, Gated Recurrent Unit with Explicit Long-range Conditioning (GRU-ELC), is used to model multi scale contextual dependencies in images.

Scene Labeling

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