Vivienne Huiling Wang
I am a Postdoctoral Researcher in the Aalto Robot Learning Lab working with Joni Pajarinen. I did my PhD in the Robot Learning Lab of Aalto University and Computer Vision Group of Tampere University, supervised by Joni Pajarinen and Joni Kämäräinen. My recent research mainly explores novel algorithms for Reinforcement Learning.
During 2015-2016, I worked as research assistant in Deep Learning and Bayesian Modeling group of Aalto University, supervised by Tapani Raiko and Juha Karhunen. I worked as research intern in Nokia Research Center in 2013-2014. I have published on top venues such as ICML, ICLR, AAAI, IJCAI and one US patent in the related fields, and received one IEEE best paper award in ICME 2015. My research publications in collaboration with Nokia scientists have won Nokia Labs Award in 2016 and 2017. I serve as senior reviewer of RLC 2025, PC Member and reviewer of AAAI 2020-2025, ICLR 2024-2025, NeurIPS 2022-2025 and ICML 2022-2025 etc.
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Research
I'm generally interested in reinforcement learning, deep learning, 2D/3D computer vision.
Selected papers and patent are listed as follows. Full list of publications can be found in my Google Scholar page.
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Probabilistic Subgoal Representations for Hierarchical Reinforcement Learning
Vivienne Huiling Wang,
Tinghuai Wang,
Wenyan Yang,
Joni-Kristian Kämäräinen,
Joni Pajarinen
The Forty-first International Conference on Machine Learning (ICML), 2024
We propose a new Gaussian processes (GPs) based method for learning probabilistic subgoal representations in Hierarchical Reinforcement Learning (HRL).
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State-Conditioned Adversarial Subgoal Generation
Vivienne Huiling Wang,
Joni Pajarinen,
Tinghuai Wang,
Joni-Kristian Kämäräinen
Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI), 2023
pdf
We propose a novel adversarially guided subgoal generation framework for goal-conditioned HRL to mitigate the issue of non-stationarity in off-policy training.
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Cross-Granularity Graph Inference for Semantic Video Object Segmentation
Huiling Wang,
Tinghuai Wang,
Ke Chen,
Joni-Kristian Kämäräinen
International Joint Conference on Artificial Intelligence (IJCAI), 2017
pdf
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video
We address semantic video object segmentation
via a novel cross-granularity hierarchical graphical
model to integrate tracklet and object proposal reasoning with superpixel labeling.
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Primary Object Discovery and Segmentation in Videos via Graph-Based Transductive Inference
Huiling Wang,
Tinghuai Wang
Computer Vision and Image Understanding (CVIU), 2016
pdf
We present a novel algorithm that detects recurring primary object and learns cohort object proposals over space-time in video. Our core contribution is a graph transduction process that exploits both appearance cues learned from rudimentary detections of object-like regions, and the intrinsic structures within video data.
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Semi-Supervised Domain Adaptation for Weakly Labeled Semantic Video Object Segmentation
Huiling Wang,
Tapani Raiko,
Lasse Lensu,
Tinghuai Wang,
Juha Karhunen
Asian Conference on Computer Vision (ACCV), 2016
pdf
We propose a semi-supervised approach to adapting CNN image recognition model trained from labeled image data to the target domain exploiting both semantic evidence learned from CNN, and the intrinsic structures of video data.
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A Weakly Supervised Geodesic Level Set Framework for Interactive Image Segmentation
Tinghuai Wang,
Huiling Wang,
Lixin Fan
Neurocomputing, 2015
pdf
We combine geodesic distance information with the flexibility of level set methods in energy minimization, leveraging the complementary strengths of each to promote accurate boundary placement and strong region connectivity while requiring less user interaction.
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Reviewer, NeurIPS 2022-2024
Reviewer, ICML 2022-2024
Reviewer, ICLR 2024
Program Committee, AAAI 2020-2023
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