Thanapong Intharah

Orcid: 0000-0003-0965-680X

Affiliations:
  • University College London, UK (PhD 2018)


According to our database1, Thanapong Intharah authored at least 12 papers between 2015 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of five.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2024
CONG: Yet Another Human Tracking Dataset, but with a Little Secret.
Proceedings of the 16th International Conference on Knowledge and Smart Technology, 2024

Deeptoothduo: Multi-Task Age-Sex Estimation and Understanding Via Panoramic Radiograph.
Proceedings of the IEEE International Symposium on Biomedical Imaging, 2024

2023
BiTNet: Hybrid deep convolutional model for ultrasound image analysis of human biliary tract and its applications.
Artif. Intell. Medicine, May, 2023

DeepDDM: A Compact Deep-Learning Assisted Platform for Micro-Rheological Assessment of Micro-Volume Fluids.
IEEE Access, 2023

2019
HILC: Domain-Independent PbD System Via Computer Vision and Follow-Up Questions.
ACM Trans. Interact. Intell. Syst., 2019

Construction of A Mobile Video Retrieval Dataset in the Cloud: Dos, Don'ts, and the Analysis.
Proceedings of the 19th International Symposium on Communications and Information Technologies, 2019

2018
Learn to automate GUI tasks from demonstration.
PhD thesis, 2018

DeepLogger: Extracting User Input Logs From 2D Gameplay Videos.
Proceedings of the Annual Symposium on Computer-Human Interaction in Play, 2018

RecurBot: Learn to Auto-complete GUI Tasks From Human Demonstrations.
Proceedings of the Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems, 2018

2017
Help, It Looks Confusing: GUI Task Automation Through Demonstration and Follow-up Questions.
Proceedings of the 22nd International Conference on Intelligent User Interfaces, 2017

2016
Demonstration-based GUI Task Automation Through Interactive Training.
CoRR, 2016

2015
Context Tricks for Cheap Semantic Segmentation.
CoRR, 2015


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