no code implementations • EANCS 2021 • Yi Huang, Junlan Feng, Xiaoting Wu, Xiaoyu Du
Our findings are: the performance variance of generative DSTs is not only due to the model structure itself, but can be attributed to the distribution of cross-domain values.
1 code implementation • 23 Jan 2023 • Fei Shen, Xiaoyu Du, Liyan Zhang, Jinhui Tang
For the inter-class instance, a hybrid contrastive loss (HCL) re-defines the sample correlations by approaching the positive cluster features and leaving the all negative instance features.
no code implementations • 5 Dec 2022 • Yixin Yang, Zhongzheng Peng, Xiaoyu Du, Zhulin Tao, Jinhui Tang, Jinshan Pan
To overcome this problem, we further develop a mixed expert block to extract semantic information for modeling the object boundaries of frames so that the semantic image prior can better guide the colorization process for better performance.
1 code implementation • 31 May 2022 • Fei Shen, Zhe Wang, Zijun Wang, Xiaode Fu, Jiayi Chen, Xiaoyu Du, Jinhui Tang
Vision-based pattern identification (such as face, fingerprint, iris etc.)
no code implementations • 2 Dec 2020 • Xiaoyu Du, Quan Le, Mark Scanlon
This is due to an ever-growing number of cases requiring digital forensic investigation coupled with the growing volume of data to be processed per case.
no code implementations • 2 Dec 2020 • Xiaoyu Du, Chris Hargreaves, John Sheppard, Felix Anda, Asanka Sayakkara, Nhien-An Le-Khac, Mark Scanlon
Multi-year digital forensic backlogs have become commonplace in law enforcement agencies throughout the globe.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Yi Huang, Junlan Feng, Shuo Ma, Xiaoyu Du, Xiaoting Wu
In this paper, we propose a meta-learning based semi-supervised explicit dialogue state tracker (SEDST) for neural dialogue generation, denoted as MEDST.
no code implementations • ACL 2020 • Yi Huang, Junlan Feng, Min Hu, Xiaoting Wu, Xiaoyu Du, Shuo Ma
The state-of-the-art accuracy for DST is below 50{\%} for a multi-domain dialogue task.
no code implementations • 2 Jul 2019 • Xiaoyu Du, Mark Scanlon
In this paper, a methodology for the automatic prioritisation of suspicious file artefacts (i. e., file artefacts that are pertinent to the investigation) is proposed to reduce the manual analysis effort required.
1 code implementation • 26 Jun 2019 • Xiaoyu Du, Xiangnan He, Fajie Yuan, Jinhui Tang, Zhiguang Qin, Tat-Seng Chua
In this work, we emphasize on modeling the correlations among embedding dimensions in neural networks to pursue higher effectiveness for CF.
1 code implementation • 11 Nov 2018 • Xiangnan He, Jinhui Tang, Xiaoyu Du, Richang Hong, Tongwei Ren, Tat-Seng Chua
This poses an imbalanced learning problem, since the scale of missing entries is usually much larger than that of observed entries, but they cannot be ignored due to the valuable negative signal.
1 code implementation • 19 Sep 2018 • Jinhui Tang, Xiaoyu Du, Xiangnan He, Fajie Yuan, Qi Tian, Tat-Seng Chua
To this end, we propose a novel solution named Adversarial Multimedia Recommendation (AMR), which can lead to a more robust multimedia recommender model by using adversarial learning.
Information Retrieval Multimedia
1 code implementation • 12 Aug 2018 • Xiangnan He, Zhankui He, Xiaoyu Du, Tat-Seng Chua
Extensive experiments on three public real-world datasets demonstrate the effectiveness of APR --- by optimizing MF with APR, it outperforms BPR with a relative improvement of 11. 2% on average and achieves state-of-the-art performance for item recommendation.
1 code implementation • 12 Aug 2018 • Xiangnan He, Xiaoyu Du, Xiang Wang, Feng Tian, Jinhui Tang, Tat-Seng Chua
In this work, we contribute a new multi-layer neural network architecture named ONCF to perform collaborative filtering.