Search Results for author: Hung-Jen Chen

Found 7 papers, 3 papers with code

Character-Preserving Coherent Story Visualization

2 code implementations ECCV 2020 Yun-Zhu Song, Zhi Rui Tam, Hung-Jen Chen, Huiao-Han Lu, Hong-Han Shuai

Different from video generation that focuses on maintaining the continuity of generated images (frames), story visualization emphasizes preserving the global consistency of characters and scenes across different story pictures, which is very challenging since story sentences only provide sparse signals for generating images.

Ranked #2 on Story Visualization on Pororo (using extra training data)

Representation Learning Sentence +1

Self-Supervised Robustifying Guidance for Monocular 3D Face Reconstruction

1 code implementation29 Dec 2021 Hitika Tiwari, Min-Hung Chen, Yi-Min Tsai, Hsien-Kai Kuo, Hung-Jen Chen, Kevin Jou, K. S. Venkatesh, Yong-Sheng Chen

Therefore, we propose a Self-Supervised RObustifying GUidancE (ROGUE) framework to obtain robustness against occlusions and noise in the face images.

3D Face Reconstruction

Network Space Search for Pareto-Efficient Spaces

no code implementations22 Apr 2021 Min-Fong Hong, Hao-Yun Chen, Min-Hung Chen, Yu-Syuan Xu, Hsien-Kai Kuo, Yi-Min Tsai, Hung-Jen Chen, Kevin Jou

We propose an NSS method to directly search for efficient-aware network spaces automatically, reducing the manual effort and immense cost in discovering satisfactory ones.

Neural Architecture Search

Mitigating Forgetting in Online Continual Learning via Instance-Aware Parameterization

no code implementations NeurIPS 2020 Hung-Jen Chen, An-Chieh Cheng, Da-Cheng Juan, Wei Wei, Min Sun

To preserve the knowledge we learn from previous instances, we proposed a method to protect the path by restricting the gradient updates of one instance from overriding past updates calculated from previous instances if these instances are not similar.

Continual Learning

BeautyGlow: On-Demand Makeup Transfer Framework With Reversible Generative Network

no code implementations CVPR 2019 Hung-Jen Chen, Ka-Ming Hui, Szu-Yu Wang, Li-Wu Tsao, Hong-Han Shuai, Wen-Huang Cheng

To facilitate on-demand makeup transfer, in this work, we propose BeautyGlow that decompose the latent vectors of face images derived from the Glow model into makeup and non-makeup latent vectors.

Cannot find the paper you are looking for? You can Submit a new open access paper.