VLDB 2026 Research / reviewers in the wild / expert
Thitirat Siriborvornratanakul
dblp:73/3955
· DBLP profile ↗
11ranked-venue papers
4as first author
8since 2021 · last 2026
0000-0002-6530-5302ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 9 · 3 first-author · 7 since 2021Artificial intelligence and machine learning · 2 · 1 first-author · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Advancing cat facial emotion interpretation: evaluating supervised and semi-supervised models using facial landmark and image data
Thanyakorn Hovongratana, Budsadee Sareerasart, Therarat Srisaswatakul, Wanlipa Chamchum, Thitirat Siriborvornratanakul |
Multim. Tools Appl. | 5 |
| 2026 | Real-time object detection and counting for inventory management using fine-tuned YOLOv11
Peaysararn Rapinrangchang, Krit Jamkachornkiat, Panupong Khamruen, Thitirat Siriborvornratanakul |
Multim. Tools Appl. | 4 |
| 2025 | Optimizing low-resource language encoders for text-to-image generation: a case study on Thai
Thitirat Siriborvornratanakul, Songpol Bunyang |
Multim. Syst. | 1 |
| 2025 | Few-shot signature verification with Double Siamese Network
Chalita Iamleelaporn, Ranakorn Boonsuankergchai, Tanwalai Yoongkieo, Nattawut Intanai, Patcharaporn Tuntino, Thitirat Siriborvornratanakul |
Multim. Tools Appl. | 6 |
| 2025 | 90s Thai music classification using audio-visual multimodal model
Kanis Charntaweekhun, Duangkamon Ketchanchai, Jittikan Narapan, Penprapa Wutthijuk, Sasithorn Sirintrawutthiwong, Thitirat Siriborvornratanakul |
Neural Comput. Appl. | 6 |
| 2024 | ThaiNutriChat: development of a Thai large language model-based chatbot for health food services
Thananan Luangaphirom, Lojrutai Jocknoi, Chalermchai Wunchum, Kittitee Chokerungreang, Thitirat Siriborvornratanakul |
Multim. Syst. | 5 |
| 2024 | Hair transplant assessment in Asian men with receding hairlines using images and computer vision techniques
Sorawit Sinlapanurak, Korawee Peerasantikul, Napat Phongvichian, Kruawun Jankaew, Pimchayanan Kusontramas, Thitirat Siriborvornratanakul |
Multim. Tools Appl. | 6 |
| 2024 | A lightweight image inpainting model for removing unwanted objects from residential real estate's indoor scenes
Srun Sompoppokasest, Thitirat Siriborvornratanakul |
Multim. Tools Appl. | 2 |
| 2016 | A Study of Virtual Reality Headsets and Physiological Extension Possibilities
Thitirat Siriborvornratanakul |
ICCSA (2) | 1 |
| 2008 | Clutter-aware dynamic projection system using a handheld projectorabstractWe propose a novel dynamic display approach using a handheld projector embedded with an ability to be aware of obstructing objects, called clutters. One camera is fixed on a projector to retrieve surface information. By integrating multiple target tracking knowledge using particle filters and gabor filters, the appearance and disappearance of unknown clutters are monitored. As a result, the unknown number of clutters can be tracked efficiently, while spurious objects are filtered out. No computation effort for tracking will be expended unless those clutters are persistently detected. At every time step, the projection target area adapts itself to suit with current situations of clutters and a projector. The biggest undistorted target area is placed on the clutter-free area adaptively with respect to the previous locations. The simulation results revealed that the system is effective for creating clutter-aware dynamic projection when a projector and clutters are moving in an unpredictable manner. Thitirat Siriborvornratanakul, Masanori Sugimoto |
ICARCV | 1 |
| 2008 | Clutter-aware adaptive projection inside a dynamic environmentabstractThis paper presents a framework for a computationally adaptive projection metaphor using a handheld projector inside a dynamic cluttered environment. In addition to conventional self-correcting projection features, the framework uses multiple clutter tracking and adaptive target generation to define the clutter-aware target area for projection in a reliable manner. Using a paired projector-camera system, the framework first builds high spatial frequency feature maps using a Laplacian pyramid approach. The feature maps are then passed to a rejection step to eliminate spurious features caused by contents of the projected image. After the resulting features representing clutters are processed by the appropriated design tracker, the target area for projection is generated. Finally, the desired information for projection is rendered and sent back to the projector. The framework can be used effectively for a clutter-aware handheld projector-based system without the need for a complex hardware setup or with any prior need to clean up the environment. Thitirat Siriborvornratanakul, Masanori Sugimoto |
VRST | 1 |