Dr. Supasorn Suwajanakorn

Educational Background

  • Ph.D. in Computer Science, University of Washington

Academic Experiences

  • Lecturer at School of Information Science and Technology (IST), Vidyasirimedhi Institute of Science and Technology, Rayong, Thailand
    • Supervising 9 Ph.D. students

Research Award

    Research Interest

    • 3D Reconstruction

      We explore the problem of view synthesis which allows for the generation of new views of objects or scenes given from a set of few input images. We focus on generating high-quality view extrapolations. This is challenging, it requires comprehensively understanding the 3D scene from 2D images. In this context, occlusions and depth uncertainty are also the most pressing issues, and worsen as the degree of extrapolation increases. We’re looking for a practical and robust deep learning solution for capturing and rendering novel views of complex real-world scenes.

      With the current state-of-the art technique for 3-D modeling it is not good enough many thing such as, glass, fluffy things, moving people. So, our group delve into new technique called Multiplane Images (MPIs) to tackle this problem.

    • Improve the quality of Time-Lapse Reconstruction from Internet Photos

      We want to try using GANs and other methods to improve the quality of Time-lapse generated from internet photos instead of running a camera at the same place for a long time. We aim to solve three main problems from the existing work. Firstly, the generated Time-lapse has low resolution. Secondly, sometimes thin objects disappear due to the different perspectives from varied photos. Lastly, we want to generate photos from the period that there are no photographs taken to reduce the “skip” feeling of Time-lapse video.

    • Unsupervised Landmark Detection via Spatial Reasoning

      Creating annotation for landmark detection requires high human labor. Hence, this work aims to create a network that can learn to detect landmarks of an object unsupervisedly. The network learns the spatial relationships between components within an object and tries to apply the acquired knowledge on the landmark detection task without human annotation.

    • Retrieving real video from DeepFake

      DeepFake can be used in harmful ways for example: delivery misleading political speech, cyberbullying, etc. What is DeepFake? Basically it is a video generated by an “AI”(Face2Face, FaceSwap, DeepFake). In order to generate a new DeepFake video it requires an original video as a base or source video for an AI. By retrieving an original video from a DeepFake we can show that the video is being manipulated by DeepFake and help combating DeepFake videos.

    • Set representation learning for retrieval task

      Set is a type of data that forms a collection of things. Such as set of points in 2-dimensional space, a set of points in 3-dimensional space or a point cloud, a set of objects in each image, etc. Unfortunately, applying deep learning with the set is a challenge issue because the set has a property called permutation invariant which isn't applicable with traditional neural network approaches that treat each sequence or position of vector differently (i.e., permutation equivariant). For this reason, we try to design a neural network that can represent the set correctly which can extend the ability to apply set to other several tasks like classification, retrieval, and segmentation.

    Recent Publication

    Dr. Supasorn Suwajanakorn, Faculty Member
    Conference Paper • Conference Proceeding
    Self-supervised deep metric learning for pointsets
    Arsomngern P., Long C., Suwajanakorn S., Nutanong S.
    Proceedings - International Conference on Data Engineering, 2021 pp. 2171-2176
    Article • Journal
    SleepPoseNet: Multi-View Learning for Sleep Postural Transition Recognition Using UWB
    Piriyajitakonkij, M., Warin, P., Lakhan, P., ...Mukhopadhyay, S.C., Wilaiprasitporn, T.
    IEEE Journal of Biomedical and Health Informatics, 2021 , vol. 25, no. 4, pp. 1305-1314
    Article • Journal
    Towards Pointsets Representation Learning via Self-Supervised Learning and Set Augmentation
    Arsomngern, P., Long, C., Suwajanakorn, S., Nutanong, S.
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
    Conference Paper • Conference Proceeding
    Repurposing GANs for One-shot Semantic Part Segmentation
    Tritrong, N., Rewatbowornwong, P., Suwajanakorn, S.
    Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2021 pp. 4473-4483
    Conference Paper • Conference Proceeding
    NeX: Real-time View Synthesis with Neural Basis Expansion
    Wizadwongsa, S., Phongthawee, P., Yenphraphai, J., Suwajanakorn, S.
    Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2021 pp. 8530-8539
    Conference Paper • Conference Proceeding
    Discovery of latent 3D keypoints via end-to-end geometric reasoning
    Suwajanakorn, S., Snavely, N., Tompson, J., Norouzi, M.
    Advances in Neural Information Processing Systems, 2018 pp. 2059-2070