Search Results for author: Hsien-Tzu Cheng

Found 9 papers, 1 papers with code

Interactive Radiotherapy Target Delineation with 3D-Fused Context Propagation

no code implementations12 Dec 2020 Chun-Hung Chao, Hsien-Tzu Cheng, Tsung-Ying Ho, Le Lu, Min Sun

The proposed method is evaluated on two published radiotherapy target contouring datasets of nasopharyngeal and esophageal cancer.

Self-similarity Student for Partial Label Histopathology Image Segmentation

no code implementations ECCV 2020 Hsien-Tzu Cheng, Chun-Fu Yeh, Po-Chen Kuo, Andy Wei, Keng-Chi Liu, Mong-Chi Ko, Kuan-Hua Chao, Yu-Ching Peng, Tyng-Luh Liu

Following this similarity learning, our similarity ensemble merges similar patches' ensembled predictions as the pseudo-label of a given patch to counteract its noisy label.

Semantic Segmentation whole slide images

Radiotherapy Target Contouring with Convolutional Gated Graph Neural Network

no code implementations5 Apr 2019 Chun-Hung Chao, Yen-Chi Cheng, Hsien-Tzu Cheng, Chi-Wen Huang, Tsung-Ying Ho, Chen-Kan Tseng, Le Lu, Min Sun

Instead, inspired by the treating methodology of considering meaningful information across slices, we used Gated Graph Neural Network to frame this problem more efficiently.

Self-Supervised Learning of Depth and Camera Motion from 360° Videos

no code implementations13 Nov 2018 Fu-En Wang, Hou-Ning Hu, Hsien-Tzu Cheng, Juan-Ting Lin, Shang-Ta Yang, Meng-Li Shih, Hung-Kuo Chu, Min Sun

We propose a novel self-supervised learning approach for predicting the omnidirectional depth and camera motion from a 360{\deg} video.

Depth And Camera Motion Motion Estimation +2

Cube Padding for Weakly-Supervised Saliency Prediction in 360° Videos

no code implementations CVPR 2018 Hsien-Tzu Cheng, Chun-Hung Chao, Jin-Dong Dong, Hao-Kai Wen, Tyng-Luh Liu, Min Sun

Then, we concatenate all six faces while utilizing the connectivity between faces on the cube for image padding (i. e., Cube Padding) in convolution, pooling, convolutional LSTM layers.

Saliency Prediction

Cube Padding for Weakly-Supervised Saliency Prediction in 360° Videos

no code implementations CVPR 2018 Hsien-Tzu Cheng, Chun-Hung Chao, Jin-Dong Dong, Hao-Kai Wen, Tyng-Luh Liu, Min Sun

Then, we concatenate all six faces while utilizing the connectivity between faces on the cube for image padding (i. e., Cube Padding) in convolution, pooling, convolutional LSTM layers.

Saliency Prediction

Deep 360 Pilot: Learning a Deep Agent for Piloting Through 360deg Sports Videos

no code implementations CVPR 2017 Hou-Ning Hu, Yen-Chen Lin, Ming-Yu Liu, Hsien-Tzu Cheng, Yung-Ju Chang, Min Sun

Given the main object and previously selected viewing angles, our method regresses a shift in viewing angle to move to the next one.

Deep 360 Pilot: Learning a Deep Agent for Piloting through 360° Sports Video

1 code implementation CVPR 2017 Hou-Ning Hu, Yen-Chen Lin, Ming-Yu Liu, Hsien-Tzu Cheng, Yung-Ju Chang, Min Sun

Watching a 360{\deg} sports video requires a viewer to continuously select a viewing angle, either through a sequence of mouse clicks or head movements.

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