no code implementations • 29 Jul 2021 • Xuewen Yang, Yingru Liu, Xin Wang
To improve the quality of image captioning, we propose a novel architecture ReFormer -- a RElational transFORMER to generate features with relation information embedded and to explicitly express the pair-wise relationships between objects in the image.
no code implementations • 3 Oct 2020 • Jing Shi, Jing Bi, Yingru Liu, Chenliang Xu
The marriage of recurrent neural networks and neural ordinary differential networks (ODE-RNN) is effective in modeling irregularly-observed sequences.
2 code implementations • ECCV 2020 • Xuewen Yang, Heming Zhang, Di Jin, Yingru Liu, Chi-Hao Wu, Jianchao Tan, Dongliang Xie, Jue Wang, Xin Wang
The goal of this work is to develop a novel learning framework for accurate and expressive fashion captioning.
no code implementations • 11 Jun 2020 • Yingru Liu, Yucheng Xing, Xuewen Yang, Xin Wang, Jing Shi, Di Jin, Zhaoyue Chen
Learning continuous-time stochastic dynamics is a fundamental and essential problem in modeling sporadic time series, whose observations are irregular and sparse in both time and dimension.
no code implementations • 19 Nov 2019 • Yingru Liu, Xuewen Yang, Dongliang Xie, Xin Wang, Li Shen, Hao-Zhi Huang, Niranjan Balasubramanian
In this paper, we propose a novel deep learning model called Task Adaptive Activation Network (TAAN) that can automatically learn the optimal network architecture for MTL.
no code implementations • IJCNLP 2019 • Xuewen Yang, Yingru Liu, Dongliang Xie, Xin Wang, Niranjan Balasubramanian
In this work, we introduce a new latent variable model, LaSyn, that captures the co-dependence between syntax and semantics, while allowing for effective and efficient inference over the latent space.