J
Jun Yu
Researcher at Hangzhou Dianzi University
Publications - 193
Citations - 10327
Jun Yu is an academic researcher from Hangzhou Dianzi University. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 38, co-authored 179 publications receiving 7667 citations. Previous affiliations of Jun Yu include Xiamen University & Jiangnan University.
Papers
More filters
Proceedings ArticleDOI
Multi-modal Factorized Bilinear Pooling with Co-attention Learning for Visual Question Answering
TL;DR: A Multi-modal Factorized Bilinear (MFB) pooling approach to efficiently and effectively combine multi- modal features, which results in superior performance for VQA compared with other bilinear pooling approaches.
Journal ArticleDOI
Multimodal Deep Autoencoder for Human Pose Recovery
TL;DR: A novel pose recovery method using non-linear mapping with multi-layered deep neural network and back-propagation deep learning to obtain a unified feature description by standard eigen-decomposition of the hypergraph Laplacian matrix.
Journal ArticleDOI
Click Prediction for Web Image Reranking Using Multimodal Sparse Coding
Jun Yu,Yong Rui,Dacheng Tao +2 more
TL;DR: A multimodal hypergraph learning-based sparse coding method is proposed for image click prediction, and the obtained click data is applied to the reranking of images, which shows the use of click prediction is beneficial to improving the performance of prominent graph-based image reranking algorithms.
Journal ArticleDOI
Beyond Bilinear: Generalized Multimodal Factorized High-Order Pooling for Visual Question Answering
TL;DR: Zhang et al. as mentioned in this paper proposed a coattention mechanism using a deep neural network (DNN) architecture to jointly learn the attentions for both the image and the question, which can reduce the irrelevant features effectively and obtain more discriminative features for image and question representations.
Proceedings ArticleDOI
Deep Modular Co-Attention Networks for Visual Question Answering
TL;DR: In this article, a modular co-attention network (MCAN) is proposed, which consists of Modular Co-Attention (MCA) layers cascaded in depth.