Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Spatio-Temporal Analysis of Facial Actions using Lifecycle-Aware Capsule Networks
Date
2021-12-26
Author
Churamani, Nikhil
Kalkan, Sinan
Güneş, Hatice
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
101
views
0
downloads
Cite This
URI
http://iab-rubric.org/fg2021/pdfs/FG2021_program.pdf
https://hdl.handle.net/11511/96016
Conference Name
IEEE International Conference on Automatic Face and Gesture Recognition
Collections
Department of Computer Engineering, Conference / Seminar
Suggestions
OpenMETU
Core
Spatio-Temporal Analysis of Facial Actions using Lifecycle-Aware Capsule Networks
Churamani, Nikhil; Kalkan, Sinan; Güneş, Hatice (2021-12-26)
Most state-of-the-art approaches for Facial Action Unit (AU) detection rely on evaluating static frames, encoding a snapshot of heightened facial activity. In real-world interactions, however, facial expressions are more subtle and evolve over time requiring AU detection models to learn spatial as well as temporal information. In this work, we focus on both spatial and spatio-temporal features encoding the temporal evolution of facial AU activation. We propose the Action Unit Lifecycle-Aware Capsule Network...
Spatio-temporal querying in video databases
Koprulu, M; Cicekli, NK; Yazıcı, Adnan (Elsevier BV, 2004-03-22)
A video data model that supports spatio-temporal querying in videos is presented. The data model is focused on the semantic content of video streams. Objects, events, activities, and spatial properties of objects are main interests of the model. The data model enables the user to query fuzzy spatio-temporal relationships between video objects and also trajectories of moving objects. A prototype of the proposed model has been implemented.
Spatio-temporal querying in video databases
Köprülü, Mesru; Çiçekli, Nihan Kesim; Yazıcı, Adnan; Department of Computer Engineering (2001)
Spatio-temporal Characteristics of Point and Field Sources in Wireless Sensor Networks
Vuran, Mehmet C.; Akan, Ozgur B. (2006-06-15)
Wireless Sensor Networks (WSN) are comprised of densely deployed sensor nodes collaboratively observing and communicating extracted information about a physical phenomenon. Dense deployment of sensor nodes makes the sensor observations highly correlated in the space domain. In addition, consecutive samples obtained by a sensor node are also temporally correlated for the applications involving the observation of the variation of a physical phenomenon. Based on the physical characteristics and dispersion patt...
Spatiotemporal data mining for situation awareness in microblogs
Özdikiş, Özer; Karagöz, Pınar; Oğuztüzün, Mehmet Halit S.; Department of Computer Engineering (2016)
Detection of real-world events using messages posted in microblogs has been the motivation of numerous recent studies. In this thesis, we study spatiotemporal data mining techniques to improve situation awareness by detecting events and estimating their locations using the content in microblogs, particularly in Twitter. We present an enhancement to the clustering techniques in the literature by measuring associations between terms in tweets in a temporal context and using these associations in a vector expa...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
N. Churamani, S. Kalkan, and H. Güneş, “Spatio-Temporal Analysis of Facial Actions using Lifecycle-Aware Capsule Networks,” presented at the IEEE International Conference on Automatic Face and Gesture Recognition, Hindistan, 2021, Accessed: 00, 2022. [Online]. Available: http://iab-rubric.org/fg2021/pdfs/FG2021_program.pdf.