1 code implementation • 1 Dec 2022 • Zhenwen Liang, Jipeng Zhang, Lei Wang, Yan Wang, Jie Shao, Xiangliang Zhang
In this paper, we design a new training framework for an MWP solver by introducing a solution buffer and a solution discriminator.
no code implementations • 23 Nov 2022 • Puzuo Wang, Wei Yao, Jie Shao
It considerably outperforms genuine scene-level weakly supervised methods by up to 25\% in terms of average F1 score and achieves competitive results against full supervision schemes.
2 code implementations • Knowledge-Based Systems 2022 • Jiasheng Zhang, Shuang Liang, Yongpan Sheng, Jie Shao
Temporal knowledge graph (TKG) representation learning aims to project entities and relations in TKG to low-dimensional vector space while preserving the evolutionary nature of TKG.
3 code implementations • Knowledge-Based Systems 2022 • Anjie Zhu, Deqiang Ouyang, Shuang Liang, Jie Shao
Due to this one-to-many dilemma, enlarged action space and ignoring logical relationship between entity and relation increase the difficulty of learning.
4 code implementations • IEEE International Conference on Multimedia and Expo 2022 2022 • Xiaoyang Tian, Jie Shao, Deqiang Ouyang, Anjie Zhu, Feiyu Chen.
Next, we simultaneously train dual conditional generative adversarial nets by taking the semantic segmentation images and converted images as input to synthesize the aerial image with ground view style.
no code implementations • 1 Apr 2022 • Yan Zhang, Changyu Li, Ivor W. Tsang, Hui Xu, Lixin Duan, Hongzhi Yin, Wen Li, Jie Shao
Motivated by the idea of meta-augmentation, in this paper, by treating a user's preference over items as a task, we propose a so-called Diverse Preference Augmentation framework with multiple source domains based on meta-learning (referred to as MetaDPA) to i) generate diverse ratings in a new domain of interest (known as target domain) to handle overfitting on the case of sparse interactions, and to ii) learn a preference model in the target domain via a meta-learning scheme to alleviate cold-start issues.
no code implementations • 27 Jan 2022 • Jie Shao, Wei Yao, Peng Wan, Lei Luo, Jiaxin Lyu, Wuming Zhang
Finally, we implement coarse alignment of ULS and ground LiDAR datasets by combining the results of ground alignment and image matching and finish fine registration.
1 code implementation • ICCV 2021 • Zhenchao Jin, Tao Gong, Dongdong Yu, Qi Chu, Jian Wang, Changhu Wang, Jie Shao
To address this, this paper proposes to mine the contextual information beyond individual images to further augment the pixel representations.
1 code implementation • Findings (NAACL) 2022 • Zhenwen Liang, Jipeng Zhang, Lei Wang, Wei Qin, Yunshi Lan, Jie Shao, Xiangliang Zhang
Math word problem (MWP) solving faces a dilemma in number representation learning.
Ranked #3 on Math Word Problem Solving on MathQA
no code implementations • 12 Jul 2021 • Kaixin Wang, Kuangqi Zhou, Qixin Zhang, Jie Shao, Bryan Hooi, Jiashi Feng
It enables learning high-quality Laplacian representations that faithfully approximate the ground truth.
1 code implementation • ICCV 2021 • Keyu Wen, Jin Xia, Yuanyuan Huang, Linyang Li, Jiayan Xu, Jie Shao
There are two key designs in it, one is the weight-sharing transformer on top of the visual and textual encoders to align text and image semantically, the other is three kinds of contrastive learning designed for sharing knowledge between different modalities.
no code implementations • ICCV 2021 • Kai Hu, Jie Shao, YuAn Liu, Bhiksha Raj, Marios Savvides, Zhiqiang Shen
To address this, we present a contrast-and-order representation (CORP) framework for learning self-supervised video representations that can automatically capture both the appearance information within each frame and temporal information across different frames.
1 code implementation • NeurIPS 2020 • Jie Shao, Kai Hu, Changhu Wang, xiangyang xue, Bhiksha Raj
In this paper, we study what would happen when normalization layers are removed from the network, and show how to train deep neural networks without normalization layers and without performance degradation.
1 code implementation • NeurIPS 2020 • Kaixin Wang, Bingyi Kang, Jie Shao, Jiashi Feng
Deep reinforcement learning (RL) agents trained in a limited set of environments tend to suffer overfitting and fail to generalize to unseen testing environments.
no code implementations • Applied Intelligence 2020 • Jie Shao, Qiyu Cheng
To effectively leverage the texture information of faces, we design a novel three-stream super-resolution network, which is embedded with an edge-enhancement block as one branch.
1 code implementation • 4 Aug 2020 • Jie Shao, Xin Wen, Bingchen Zhao, xiangyang xue
The current research focus on Content-Based Video Retrieval requires higher-level video representation describing the long-range semantic dependencies of relevant incidents, events, etc.
1 code implementation • ACL 2020 • Jipeng Zhang, Lei Wang, Roy Ka-Wei Lee, Yi Bin, Yan Wang, Jie Shao, Ee-Peng Lim
While the recent tree-based neural models have demonstrated promising results in generating solution expression for the math word problem (MWP), most of these models do not capture the relationships and order information among the quantities well.
Ranked #6 on Math Word Problem Solving on MAWPS
no code implementations • 29 Jun 2020 • Shu Wu, Feng Yu, Xueli Yu, Qiang Liu, Liang Wang, Tieniu Tan, Jie Shao, Fan Huang
The CTR (Click-Through Rate) prediction plays a central role in the domain of computational advertising and recommender systems.
Ranked #17 on Click-Through Rate Prediction on Criteo
no code implementations • 30 Sep 2019 • Dongdong Yu, Kai Su, Hengkai Guo, Jian Wang, Kaihui Zhou, Yuanyuan Huang, Minghui Dong, Jie Shao, Changhu Wang
Semi-supervised video object segmentation is an interesting yet challenging task in machine learning.
BIG-bench Machine Learning One-shot visual object segmentation +2
no code implementations • 12 Jun 2018 • Xiaoteng Zhang, Yixin Bao, Feiyun Zhang, Kai Hu, Yicheng Wang, Liang Zhu, Qinzhu He, Yining Lin, Jie Shao, Yao Peng
We also propose new non-local-based models for further improvement on the recognition accuracy.
no code implementations • 10 Jul 2017 • Lianli Gao, Jingkuan Song, Xingyi Liu, Junming Shao, Jiajun Liu, Jie Shao
Given the high dimensionality and the high complexity of multimedia data, it is important to investigate new machine learning algorithms to facilitate multimedia data analysis.
no code implementations • 16 Jun 2016 • Yang Yang, Wei-Lun Chen, Yadan Luo, Fumin Shen, Jie Shao, Heng Tao Shen
Supervised knowledge e. g. semantic labels or pair-wise relationship) associated to data is capable of significantly improving the quality of hash codes and hash functions.
no code implementations • 8 Dec 2015 • Jie Shao, Dequan Wang, xiangyang xue, Zheng Zhang
This paper proposes the problem of point-and-count as a test case to break the what-and-where deadlock.
no code implementations • ICCV 2015 • Dequan Wang, Zhiqiang Shen, Jie Shao, Wei zhang, xiangyang xue, Zheng Zhang
Fine-grained categorization, which aims to distinguish subordinate-level categories such as bird species or dog breeds, is an extremely challenging task.
no code implementations • 12 Sep 2013 • Li Wang, Jie Shao, Yaqin Zhong, Weisong Zhao, Reza Malekian
In Class-D Power Amplifiers (CDPAs), the power supply noise can intermodulate with the input signal, manifesting into power-supply induced intermodulation distortion (PS-IMD) and due to the memory effects of the system, there exist asymmetries in the PS-IMDs.