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).
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.
In the industrial interior design process, professional designers plan the furniture layout to achieve a satisfactory 3D design for selling.
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.
We conduct our experiments on the proposed real-world interior layout dataset that contains $191208$ designs from the professional designers.
In this paper, we propose a multiple-domain model for producing a custom-size furniture layout in the interior scene.
We present a class of methods for robust, personalized federated learning, called Fed+, that unifies many federated learning algorithms.
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.
In this paper, we propose an adversarial model for producing furniture layout for interior scene synthesis when the interior room is rotated.
In order to reduce the loss, we extend the GNNs frameworks by exploring the aggregation and iteration scheme in the methodology of mutual information.
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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.
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.
It has been demonstrated that deep neural networks are prone to noisy examples particular adversarial samples during inference process.
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.