activity
20172023
most citedTS-LSTM and Temporal-Inception: Exploiting Spatiotemporal Dynamics for Activity Recognition

39 citations · 46 across the 13 of their papers we have counts for

collaborators
Showing 2019Show all

6 papers · 1 filter

cs.CV2019

Interpretable Self-Attention Temporal Reasoning for Driving Behavior Understanding

Yi-Chieh Liu, Yung-An Hsieh, Min-Hung Chen +3

Performing driving behaviors based on causal reasoning is essential to ensure driving safety. In this work, we investigated how state-of-the-art 3D Convolutional Neural Networks (C…

cs.CV2019

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

Dogancan Temel, Min-Hung Chen, Ghassan AlRegib

Traffic signs are critical for maintaining the safety and efficiency of our roads. Therefore, we need to carefully assess the capabilities and limitations of automated traffic sign…

cs.CV2019

Temporal Attentive Alignment for Large-Scale Video Domain Adaptation

Min-Hung Chen, Zsolt Kira, Ghassan AlRegib +3

Although various image-based domain adaptation (DA) techniques have been proposed in recent years, domain shift in videos is still not well-explored. Most previous works only evalu…

cs.CV2019★ 1 cited

Image Captioning with Integrated Bottom-Up and Multi-level Residual Top-Down Attention for Game Scene Understanding

Jian Zheng, Sudha Krishnamurthy, Ruxin Chen +3

Image captioning has attracted considerable attention in recent years. However, little work has been done for game image captioning which has some unique characteristics and requir…

cs.CV2019★ 3 cited

Temporal Attentive Alignment for Video Domain Adaptation

Min-Hung Chen, Zsolt Kira, Ghassan AlRegib

Although various image-based domain adaptation (DA) techniques have been proposed in recent years, domain shift in videos is still not well-explored. Most previous works only evalu…

cs.CV2019

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

Dogancan Temel, Tariq Alshawi, Min-Hung Chen +1

State-of-the-art algorithms successfully localize and recognize traffic signs over existing datasets, which are limited in terms of challenging condition type and severity. Therefo…