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.
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 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 • 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.
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.
no code implementations • 16 Sep 2023 • Xin Jiang, Hao Tang, Junyao Gao, Xiaoyu Du, Shengfeng He, Zechao Li
In this paper, we aim to fully exploit the capabilities of cross-modal description to tackle FGVC tasks and propose a novel multimodal prompting solution, denoted as MP-FGVC, based on the contrastive language-image pertaining (CLIP) model.
Ranked #6 on Fine-Grained Image Classification on NABirds
no code implementations • 10 Jan 2024 • Luanyuan Dai, Xiaoyu Du, Hanwang Zhang, Jinhui Tang
To obtain information integrating implicit and explicit local graphs, we construct local graphs from implicit and explicit aspects and combine them effectively, which is used to build a global graph.
1 code implementation • 28 Nov 2023 • Xiaoyu Du, Kun Qian, Yunshan Ma, Xinguang Xiang
In this paper, we propose a novel approach EBRec, short of Enhanced Bundle Recommendation, which incorporates two enhanced modules to explore inherent item-level bundle representations.
1 code implementation • 23 Jan 2023 • Fei Shen, Xiaoyu Du, Liyan Zhang, Xiangbo Shu, Jinhui Tang
To address this problem, in this paper, we propose a simple Triplet Contrastive Representation Learning (TCRL) framework which leverages cluster features to bridge the part features and global features for unsupervised vehicle re-identification.
1 code implementation • 28 Nov 2023 • Yunshan Ma, Yingzhi He, Xiang Wang, Yinwei Wei, Xiaoyu Du, Yuyangzi Fu, Tat-Seng Chua
It does, however, have two limitations: 1) the two-view formulation does not fully exploit all the heterogeneous relations among users, bundles and items; and 2) the "early contrast and late fusion" framework is less effective in capturing user preference and difficult to generalize to multiple views.
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 • 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.)
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.
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 • 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.