Search Results for author: Min-Hung Chen

Found 13 papers, 7 papers with code

Self-Supervised Robustifying Guidance for Monocular 3D Face Reconstruction

no code implementations29 Dec 2021 Hitika Tiwari, Min-Hung Chen, Yi-Min Tsai, Hsien-Kai Kuo, Hung-Jen Chen, Kevin Jou, K. S. Venkatesh, Yong-Sheng Chen

On the three variations of the test dataset of CelebA: rational occlusions, delusional occlusions, and noisy face images, our method outperforms the current state-of-the-art method by large margins (e. g., for the shape-based 3D vertex errors, a reduction from 0. 146 to 0. 048 for rational occlusions, from 0. 292 to 0. 061 for delusional occlusions and from 0. 269 to 0. 053 for the noise in the face images), demonstrating the effectiveness of the proposed approach.

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

Traffic Sign Detection under Challenging Conditions: A Deeper Look Into Performance Variations and Spectral Characteristics

2 code implementations29 Aug 2019 Dogancan Temel, Min-Hung Chen, Ghassan AlRegib

We investigate the effect of challenging conditions through spectral analysis and show that challenging conditions can lead to distinct magnitude spectrum characteristics.

Traffic Sign Detection Traffic Sign Recognition

Temporal Attentive Alignment for Large-Scale Video Domain Adaptation

5 code implementations ICCV 2019 Min-Hung Chen, Zsolt Kira, Ghassan AlRegib, Jaekwon Yoo, Ruxin Chen, Jian Zheng

Finally, we propose Temporal Attentive Adversarial Adaptation Network (TA3N), which explicitly attends to the temporal dynamics using domain discrepancy for more effective domain alignment, achieving state-of-the-art performance on four video DA datasets (e. g. 7. 9% accuracy gain over "Source only" from 73. 9% to 81. 8% on "HMDB --> UCF", and 10. 3% gain on "Kinetics --> Gameplay").

Domain Adaptation

Temporal Attentive Alignment for Video Domain Adaptation

5 code implementations26 May 2019 Min-Hung Chen, Zsolt Kira, Ghassan AlRegib

Finally, we propose Temporal Attentive Adversarial Adaptation Network (TA3N), which explicitly attends to the temporal dynamics using domain discrepancy for more effective domain alignment, achieving state-of-the-art performance on three video DA datasets.

Domain Adaptation

Challenging Environments for Traffic Sign Detection: Reliability Assessment under Inclement Conditions

2 code implementations19 Feb 2019 Dogancan Temel, Tariq Alshawi, Min-Hung Chen, Ghassan AlRegib

Experimental results show that benchmarked algorithms are highly sensitive to tested challenging conditions, which result in an average performance drop of 0. 17 in terms of precision and a performance drop of 0. 28 in recall under severe conditions.

Traffic Sign Detection

TS-LSTM and Temporal-Inception: Exploiting Spatiotemporal Dynamics for Activity Recognition

4 code implementations30 Mar 2017 Chih-Yao Ma, Min-Hung Chen, Zsolt Kira, Ghassan AlRegib

We demonstrate that using both RNNs (using LSTMs) and Temporal-ConvNets on spatiotemporal feature matrices are able to exploit spatiotemporal dynamics to improve the overall performance.

Action Classification Action Recognition +1

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