F
Feng Wu
Researcher at University of Science and Technology of China
Publications - 669
Citations - 19574
Feng Wu is an academic researcher from University of Science and Technology of China. The author has contributed to research in topics: Motion compensation & Data compression. The author has an hindex of 60, co-authored 645 publications receiving 15886 citations. Previous affiliations of Feng Wu include Center for Excellence in Education & Microsoft.
Papers
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Journal ArticleDOI
Efficient Measurement Generation and Pervasive Sparsity for Compressive Data Gathering
TL;DR: This paper investigates how to generate RIP (restricted isometry property) preserving measurements of sensor readings by taking multi-hop communication cost into account and discovers that a simple form of measurement matrix has good RIP, and the data gathering scheme that realizes this measurement matrix can further reduce the communication cost of CDG for both chain-type and tree-type topology.
Book ChapterDOI
A Convolutional Neural Network Approach for Post-Processing in HEVC Intra Coding
Yuanying Dai,Dong Liu,Feng Wu +2 more
TL;DR: In this article, a Variable-filter-size Residue-learning CNN (VRCNN) was proposed to improve the performance and to accelerate network training for High Efficiency Video Coding.
Proceedings ArticleDOI
Diverse Part Discovery: Occluded Person Re-identification with Part-Aware Transformer
TL;DR: Wang et al. as discussed by the authors proposed a Part-Aware Transformer (PAT) for occluded person Re-ID through diverse part discovery via a transformer encoder-decoder architecture, including a pixel context based transformer and a part prototype based transformer decoder.
Journal ArticleDOI
Towards Optimal Power Control via Ensembling Deep Neural Networks
TL;DR: In this article, a deep neural network (DNN) based power control method that aims at solving the non-convex optimization problem of maximizing the sum rate of a fading multi-user interference channel is proposed.
Proceedings ArticleDOI
Multi-Modality Cross Attention Network for Image and Sentence Matching
TL;DR: This work designs a novel cross-attention mechanism, which is able to exploit not only the intra-modality relationship within each modality, but also the inter- modality relationship between image regions and sentence words to complement and enhance each other for image and sentence matching.