Fine-grained detection of academic emotions with spatial temporal graph attention networks using facial landmarks
With the incidence of the Covid-19 pandemic, institutions have adopted online learning as the main lessondelivery channel. A common criticism of online learning is that sensing of learners’ affective states such asengagement is lacking which degrades the quality of teaching. In this study, we propos...
Saved in:
Main Author: | FWA, Hua Leong |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2022
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/7157 https://ink.library.smu.edu.sg/context/sis_research/article/8160/viewcontent/paper10_emotion_detection_STGAN_camera.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
Similar Items
-
A Fine-Grained Spatial-Temporal Attention Model for Video Captioning
by: Liu, A.-A., et al.
Published: (2021) -
Timespan-aware dynamic knowledge graph embedding by incorporating temporal evolution
by: Tang, Xiaoli, et al.
Published: (2021) -
Deep learning of facial embeddings and facial landmark points for the detection of academic emotions
by: FWA, Hua Leong
Published: (2020) -
MGAT: Multimodal Graph Attention Network for Recommendation
by: Zhulin Tao, et al.
Published: (2020) -
Tour the world: A technical demonstration of a web-scale landmark recognition engine
by: Zheng, Y.-T., et al.
Published: (2013)