21 citations · 78 across the 19 of their papers we have counts for
19 papers
Multi-modal Learning with Missing Modality via Shared-Specific Feature Modelling
Hu Wang, Yuanhong Chen, Congbo Ma +3
The missing modality issue is critical but non-trivial to be solved by multi-modal models. Current methods aiming to handle the missing modality problem in multi-modal tasks, eithe…
M^4I: Multi-modal Models Membership Inference
Pingyi Hu, Zihan Wang, Ruoxi Sun +2
With the development of machine learning techniques, the attention of research has been moved from single-modal learning to multi-modal learning, as real-world data exist in the fo…
Document-aware Positional Encoding and Linguistic-guided Encoding for Abstractive Multi-document Summarization
Congbo Ma, Wei Emma Zhang, Pitawelayalage Dasun Dileepa Pitawela +3
One key challenge in multi-document summarization is to capture the relations among input documents that distinguish between single document summarization (SDS) and multi-document…
Uncertainty-aware Multi-modal Learning via Cross-modal Random Network Prediction
Hu Wang, Jianpeng Zhang, Yuanhong Chen +4
Multi-modal learning focuses on training models by equally combining multiple input data modalities during the prediction process. However, this equal combination can be detrimenta…
Risk and return prediction for pricing portfolios of non-performing consumer credit
Siyi Wang, Xing Yan, Bangqi Zheng +4
We design a system for risk-analyzing and pricing portfolios of non-performing consumer credit loans. The rapid development of credit lending business for consumers heightens the n…
Dependency Structure for News Document Summarization
Congbo Ma, Wei Emma Zhang, Hu Wang +2
In this work, we develop a neural network based model which leverages dependency parsing to capture cross-positional dependencies and grammatical structures. With the help of lingu…
Data Hiding with Deep Learning: A Survey Unifying Digital Watermarking and Steganography
Zihan Wang, Olivia Byrnes, Hu Wang +5
The advancement of secure communication and identity verification fields has significantly increased through the use of deep learning techniques for data hiding. By embedding infor…
Kernel Adversarial Learning for Real-world Image Super-resolution
Hu Wang, Congbo Ma, Jianpeng Zhang +2
Current deep image super-resolution (SR) approaches aim to restore high-resolution images from down-sampled images or by assuming degradation from simple Gaussian kernels and addit…
Oriole: Thwarting Privacy against Trustworthy Deep Learning Models
Liuqiao Chen, Hu Wang, Benjamin Zi Hao Zhao +2
Deep Neural Networks have achieved unprecedented success in the field of face recognition such that any individual can crawl the data of others from the Internet without their expl…
Delayed Rewards Calibration via Reward Empirical Sufficiency
Yixuan Liu, Hu Wang, Xiaowei Wang +3
Appropriate credit assignment for delay rewards is a fundamental challenge for reinforcement learning. To tackle this problem, we introduce a delay reward calibration paradigm insp…
Multi-intersection Traffic Optimisation: A Benchmark Dataset and a Strong Baseline
Hu Wang, Hao Chen, Qi Wu +3
The control of traffic signals is fundamental and critical to alleviate traffic congestion in urban areas. However, it is challenging since traffic dynamics are complicated in real…
Memory-Gated Recurrent Networks
Yaquan Zhang, Qi Wu, Nanbo Peng +3
The essence of multivariate sequential learning is all about how to extract dependencies in data. These data sets, such as hourly medical records in intensive care units and multi-…
Fully Quantized Image Super-Resolution Networks
Hu Wang, Peng Chen, Bohan Zhuang +1
With the rising popularity of intelligent mobile devices, it is of great practical significance to develop accurate, realtime and energy-efficient image Super-Resolution (SR) infer…
Robust Data Hiding Using Inverse Gradient Attention
Honglei Zhang, Hu Wang, Yuanzhouhan Cao +2
Data hiding is the procedure of encoding desired information into a certain types of cover media (e.g. images) to resist potential noises for data recovery, while ensuring the embe…
Multi-document Summarization via Deep Learning Techniques: A Survey
Congbo Ma, Wei Emma Zhang, Mingyu Guo +2
Multi-document summarization (MDS) is an effective tool for information aggregation that generates an informative and concise summary from a cluster of topic-related documents. Our…
Multi-label Thoracic Disease Image Classification with Cross-Attention Networks
Congbo Ma, Hu Wang, Steven C. H. Hoi
Automated disease classification of radiology images has been emerging as a promising technique to support clinical diagnosis and treatment planning. Unlike generic image classific…
Soft Expert Reward Learning for Vision-and-Language Navigation
Hu Wang, Qi Wu, Chunhua Shen
Vision-and-Language Navigation (VLN) requires an agent to find a specified spot in an unseen environment by following natural language instructions. Dominant methods based on super…
Randomized Online CP Decomposition
Congbo Ma, Xiaowei Yang, Hu Wang
CANDECOMP/PARAFAC (CP) decomposition has been widely used to deal with multi-way data. For real-time or large-scale tensors, based on the ideas of randomized-sampling CP decomposit…
ReNN: Rule-embedded Neural Networks
Hu Wang
The artificial neural network shows powerful ability of inference, but it is still criticized for lack of interpretability and prerequisite needs of big dataset. This paper propose…