William Thong

I am a research scientist at Sony AI, focusing on algorithmic fairness in computer vision.

I did my PhD at the Video & Image Sense lab of the University of Amsterdam, under the supervision of Cees Snoek. My PhD dissertation involved visual similarity, learning with limited labels, and model biases.

Previously, I received a B.Eng. and an M.Sc. in Biomedical Engineering from Polytechnique Montréal, and an M.Sc. in Bioimaging from Télécom Paris. I completed my Master's thesis under the supervision of Samuel Kadoury (MedICAL lab) and Chris Pal (Mila) on the classification of biomedical images with deep learning.

[Email] [LinkedIn] [Google Scholar] [Twitter] [Github]

Conference & Journal publicatons

	Diversely-Supervised Visual Product Search William Thong and Cees G. M. Snoek ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 2022 [paper] [arxiv] [code] We create a diverse set of labels from instance, attribute and category similarities for visual product search.
	Feature and Label Embedding Spaces Matter in Addressing Image Classifier Bias William Thong, Cees G. M. Snoek British Machine Vision Conference (BMVC), 2021 [paper] [arxiv] [code] We identify and mitigate biases in both feature and label embedding spaces in image classifiers.
	Object Priors for Classifying and Localizing Unseen Actions Pascal Mettes, William Thong, Cees G. M. Snoek International Journal of Computer Vision (IJCV), 2021 [paper] [arxiv] [code] We derive spatial and semantic priors to recognize unseen actions in videos with zero training sample.
	Bias-Awareness for Zero-Shot Learning the Seen and Unseen William Thong and Cees G. M. Snoek British Machine Vision Conference (BMVC), 2020 [paper] [arxiv] [code] [video] We mitigate the classifier bias towards classes seen during training in generalized zero-shot learning.
	Open Cross-Domain Visual Search William Thong, Pascal Mettes, Cees G.M. Snoek Computer Vision and Image Understanding (CVIU), 2020 [paper] [arxiv] [code] We search for categories from any source domain to any target domain in a common semantic space.
	A Layer-Based Sequential Framework for Scene Generation with GANs Mehmet O. Turkoglu, William Thong, Luuk Spreeuwers, Berkay Kicanaoglu AAAI Conference on Artificial Intelligence (AAAI), 2019 [paper] [arxiv] [poster] [code] We compose a scene layer-by-layer, with an explicit control over the generation of all scene elements.
	Convolutional Networks for Kidney Segmentation in Contrast-Enhanced CT Scans William Thong, Samuel Kadoury, Nicolas Piché, Christopher J. Pal CMBBE: Imaging & Visualization, 2018 [paper] – initially presented at MICCAI-DLMIA 2015 We segment healthy and abnormal kidneys in CT scans with a patch-based ConvNet.
	Three-dimensional Morphology Study of Surgical Adolescent Idiopathic Scoliosis Patient from Encoded Geometric Models William Thong, Stefan Parent, James Wu, Carl-Éric Aubin, Hubert Labelle, Samuel Kadoury European Spine Journal (ESJ), 2016 [paper] We cluster scoliotic spine deformations in 3D representations with a stacked auto-encoder.
	Automatic Labeling of Vertebral Levels using a Robust Template-Based Approach Eugénie Ullmann, Jean François Pelletier Paquette, William Thong*, Julien Cohen-Adad International Journal of Biomedical Imaging (IJBI)*, 2014 [paper] We build a template to predict vertebral levels in MRI images.

Workshop & Abstract publications

NTIRE 2021 challenge on perceptual image quality assessment
Gu et al. CVPR workshops, 2021.

Interactive Exploration of Journalistic Video Footage through Multimodal Semantic Matching
Ibrahimi et al. ACM Multimedia (Demo track), 2019.

Stacked Auto-Encoders for Classification of 3D Spine Models in Adolescent Idiopathic Scoliosis
Thong et al. MICCAI-CSI workshop, 2014.

Spinal Cord Toolbox: an Open-Source Framework for Processing Spinal Cord MRI Data
Cohen-Adad et al. OHBM, 2014.

Academic service

Reviewer for CVPR, ECCV, ICCV, NeurIPS, WACV, BMVC.

Outstanding reviewer awards at CVPR'21 and BMVC'20 and 21.

Webpage template from Jon Barron.