19 citations · 33 across the 6 of their papers we have counts for
6 papers
Depth-Aware Generative Adversarial Network for Talking Head Video Generation
Fa-Ting Hong, Longhao Zhang, Li Shen +1
Talking head video generation aims to produce a synthetic human face video that contains the identity and pose information respectively from a given source image and a driving vide…
Cross-modal Consensus Network for Weakly Supervised Temporal Action Localization
Fa-Ting Hong, Jia-Chang Feng, Dan Xu +2
Weakly supervised temporal action localization (WS-TAL) is a challenging task that aims to localize action instances in the given video with video-level categorical supervision. Bo…
MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection
Jia-Chang Feng, Fa-Ting Hong, Wei-Shi Zheng
Weakly supervised video anomaly detection (WS-VAD) is to distinguish anomalies from normal events based on discriminative representations. Most existing works are limited in insuff…
MINI-Net: Multiple Instance Ranking Network for Video Highlight Detection
Fa-Ting Hong, Xuanteng Huang, Wei-Hong Li +1
We address the weakly supervised video highlight detection problem for learning to detect segments that are more attractive in training videos given their video event label but wit…
Learning to Detect Important People in Unlabelled Images for Semi-supervised Important People Detection
Fa-Ting Hong, Wei-Hong Li, Wei-Shi Zheng
Important people detection is to automatically detect the individuals who play the most important roles in a social event image, which requires the designed model to understand a h…
Learning to Learn Relation for Important People Detection in Still Images
Wei-Hong Li, Fa-Ting Hong, Wei-Shi Zheng
Humans can easily recognize the importance of people in social event images, and they always focus on the most important individuals. However, learning to learn the relation betwee…