no code implementations • 3 Apr 2024 • Xiwen Dengxiong, Xueting Wang, Shi Bai, Yunbo Zhang
Most existing 6-DoF robot grasping solutions depend on strong supervision on grasp pose to ensure satisfactory performance, which could be laborious and impractical when the robot works in some restricted area.
no code implementations • 13 Dec 2023 • Jiafeng Mao, Xueting Wang, Kiyoharu Aizawa
Text-to-image diffusion models allow users control over the content of generated images.
no code implementations • 11 Sep 2023 • Jiawei Qin, Xueting Wang
In this paper, we propose a method for improving the angular accuracy and photo-reality of gaze and head redirection in full-face images.
no code implementations • 8 Aug 2023 • Qianru Qiu, Xueting Wang, Mayu Otani
Additionally, it is applicable for another color recommendation task, full palette generation, which generates a complete color palette corresponding to the given text.
no code implementations • 5 May 2023 • Jiafeng Mao, Xueting Wang, Kiyoharu Aizawa
Diffusion models have the ability to generate high quality images by denoising pure Gaussian noise images.
no code implementations • 26 Apr 2023 • Jiafeng Mao, Xueting Wang
Current large-scale generative models have impressive efficiency in generating high-quality images based on text prompts.
no code implementations • 22 Sep 2022 • Qianru Qiu, Xueting Wang, Mayu Otani, Yuki Iwazaki
We train the model and build a color recommendation system on a large-scale dataset of vector graphic documents.
no code implementations • 1 Nov 2021 • Tetsu Kasanishi, Xueting Wang, Toshihiko Yamasaki
Graph Neural Networks (GNNs) are deep learning models that take graph data as inputs, and they are applied to various tasks such as traffic prediction and molecular property prediction.
no code implementations • 22 Dec 2020 • Jun Ikeda, Hiroyuki Seshime, Xueting Wang, Toshihiko Yamasaki
With expansion of the video advertising market, research to predict the effects of video advertising is getting more attention.
1 code implementation • 29 Oct 2020 • Li Tao, Xueting Wang, Toshihiko Yamasaki
It is convenient to treat PCL as a standard training strategy and apply it to many other works in self-supervised video feature learning.
Ranked #10 on Self-supervised Video Retrieval on UCF101
1 code implementation • 2 Oct 2020 • Nobukatsu Kajiura, Satoshi Kosugi, Xueting Wang, Toshihiko Yamasaki
In this study, we address image retargeting, which is a task that adjusts input images to arbitrary sizes.
2 code implementations • 6 Aug 2020 • Li Tao, Xueting Wang, Toshihiko Yamasaki
With the proposed Inter-Intra Contrastive (IIC) framework, we can train spatio-temporal convolutional networks to learn video representations.
Ranked #10 on Self-supervised Video Retrieval on HMDB51
no code implementations • 5 Jul 2020 • Lijie Wang, Xueting Wang, Toshihiko Yamasaki
The spread of social networking services has created an increasing demand for selecting, editing, and generating impressive images.
3 code implementations • 21 Jun 2020 • Li Tao, Xueting Wang, Toshihiko Yamasaki
In this paper, we propose a fast but effective way to extract motion features from videos utilizing residual frames as the input data in 3D ConvNets.
no code implementations • 3 Mar 2020 • Priyanto Hidayatullah, Xueting Wang, Toshihiko Yamasaki, Tati L. E. R. Mengko, Rinaldi Munir, Anggraini Barlian, Eros Sukmawati, Supraptono Supraptono
This study proposes an architecture, called DeepSperm, that solves the aforementioned challenges and is more accurate and faster than state-of-the-art architectures.
3 code implementations • 16 Jan 2020 • Li Tao, Xueting Wang, Toshihiko Yamasaki
Further analysis indicates that better motion features can be extracted using residual frames with 3D ConvNets, and our residual-frame-input path is a good supplement for existing RGB-frame-input models.
no code implementations • 12 Jan 2020 • Yiyan Chen, Li Tao, Xueting Wang, Toshihiko Yamasaki
For each subtask, the manager is trained to set a subgoal only by a task-level binary label, which requires much fewer labels than conventional approaches.
Hierarchical Reinforcement Learning reinforcement-learning +2