no code implementations • 19 Oct 2022 • Xinhan Di, Pengqian Yu
In this paper, we explore the furniture layout task as a Markov decision process (MDP) in virtual reality, which is solved by hierarchical reinforcement learning (HRL).
Hierarchical Reinforcement Learning
reinforcement-learning
+2
no code implementations • 21 Aug 2022 • Xinhan Di, Pengqian Yu
The first module is a lightweight feature attention module that extracts both local occlusion representation and global image patch representation in a coarse-to-fine manner.
1 code implementation • 18 Feb 2021 • Xinhan Di, Pengqian Yu
In the industrial interior design process, professional designers plan the furniture layout to achieve a satisfactory 3D design for selling.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
1 code implementation • 19 Jan 2021 • Xinhan Di, Pengqian Yu
In the industrial interior design process, professional designers plan the size and position of furniture in a room to achieve a satisfactory design for selling.
1 code implementation • 15 Dec 2020 • Xinhan Di, Pengqian Yu, Danfeng Yang, Hong Zhu, Changyu Sun, YinDong Liu
We conduct our experiments on the proposed real-world interior layout dataset that contains $191208$ designs from the professional designers.
no code implementations • 15 Dec 2020 • Xinhan Di, Pengqian Yu, Danfeng Yang, Hong Zhu, Changyu Sun, YinDong Liu
In this paper, we propose a multiple-domain model for producing a custom-size furniture layout in the interior scene.
no code implementations • 14 Sep 2020 • Achintya Kundu, Pengqian Yu, Laura Wynter, Shiau Hong Lim
We present a class of methods for robust, personalized federated learning, called Fed+, that unifies many federated learning algorithms.
no code implementations • 4 Aug 2020 • Xinhan Di, Pengqian Yu, Hong Zhu, Lei Cai, Qiuyan Sheng, Changyu Sun
In this paper, we propose an assistive model that supports professional interior designers to produce industrial interior decoration solutions and to meet the personalized preferences of the property owners.
no code implementations • 24 Jun 2020 • Xinhan Di, Pengqian Yu, Hong Zhu, Lei Cai, Qiuyan Sheng, Changyu Sun
In this paper, we propose an adversarial model for producing furniture layout for interior scene synthesis when the interior room is rotated.
2 code implementations • 21 May 2019 • Xinhan Di, Pengqian Yu, Rui Bu, Mingchao Sun
In order to reduce the loss, we extend the GNNs frameworks by exploring the aggregation and iteration scheme in the methodology of mutual information.
Ranked #1 on
Graph Classification
on Citeseer
no code implementations • 25 Jan 2019 • Pengqian Yu, Joon Sern Lee, Ilya Kulyatin, Zekun Shi, Sakyasingha Dasgupta
Dynamic portfolio optimization is the process of sequentially allocating wealth to a collection of assets in some consecutive trading periods, based on investors' return-risk profile.
no code implementations • 2 Jul 2018 • Xinhan Di, Pengqian Yu, Meng Tian
In this paper, we extend the ambient module to the hidden space of the generator, and provide the uniqueness condition and the corresponding strategy for the ambient hidden generator in the adversarial training process.
no code implementations • 1 Jul 2018 • Xinhan Di, Pengqian Yu, Meng Tian
It has been demonstrated that deep neural networks are prone to noisy examples particular adversarial samples during inference process.
no code implementations • 17 Jan 2017 • Xinhan Di, Pengqian Yu
While recent deep neural networks have achieved promising results for 3D reconstruction from a single-view image, these rely on the availability of RGB textures in images and extra information as supervision.