Search Results for author: Xiaoming Chen

Found 21 papers, 5 papers with code

VIPTR: A Vision Permutable Extractor for Fast and Efficient Scene Text Recognition

1 code implementation18 Jan 2024 Xianfu Cheng, Weixiao Zhou, Xiang Li, Xiaoming Chen, Jian Yang, Tongliang Li, Zhoujun Li

In this work, we propose the VIsion Permutable extractor for fast and efficient scene Text Recognition (VIPTR), which achieves an impressive balance between high performance and rapid inference speeds in the domain of STR.

Scene Text Recognition

E2HQV: High-Quality Video Generation from Event Camera via Theory-Inspired Model-Aided Deep Learning

1 code implementation16 Jan 2024 Qiang Qu, Yiran Shen, Xiaoming Chen, Yuk Ying Chung, Tongliang Liu

In this work, we propose \textbf{E2HQV}, a novel E2V paradigm designed to produce high-quality video frames from events.

Video Generation

Beyond Subspace Isolation: Many-to-Many Transformer for Light Field Image Super-resolution

no code implementations1 Jan 2024 Zeke Zexi Hu, Xiaoming Chen, Vera Yuk Ying Chung, Yiran Shen

The effective extraction of spatial-angular features plays a crucial role in light field image super-resolution (LFSR) tasks, and the introduction of convolution and Transformers leads to significant improvement in this area.

Image Super-Resolution

Mean Field Game-based Waveform Precoding Design for Mobile Crowd Integrated Sensing, Communication, and Computation Systems

no code implementations6 Sep 2023 Dezhi Wang, Chongwen Huang, Jiguang He, Xiaoming Chen, Wei Wang, Zhaoyang Zhang, Zhu Han, Mérouane Debbah

In this paper, we consider the environment sensing problem in the large-scale mobile crowd ISCC systems and propose an efficient waveform precoding design algorithm based on the mean field game~(MFG).

Dense Voxel 3D Reconstruction Using a Monocular Event Camera

no code implementations1 Sep 2023 Haodong Chen, Vera Chung, Li Tan, Xiaoming Chen

Our preliminary results demonstrate that the proposed method can produce visually distinguishable dense 3D reconstructions directly without requiring pipelines like those used by existing methods.

3D Reconstruction Semantic Segmentation

From Data-driven Learning to Physics-inspired Inferring: A Novel Mobile MIMO Channel Prediction Scheme Based on Neural ODE

no code implementations9 Apr 2023 Zhuoran Xiao, Zhaoyang Zhang, Zirui Chen, Zhaohui Yang, Chongwen Huang, Xiaoming Chen

Then, we design a novel physics-inspired spatial channel gradient network (SCGnet), which represents the derivative process of channel varying as a special neural network and can obtain the gradients at any relative displacement needed for the ODE solving.

LFACon: Introducing Anglewise Attention to No-Reference Quality Assessment in Light Field Space

1 code implementation20 Mar 2023 Qiang Qu, Xiaoming Chen, Yuk Ying Chung, Weidong Cai

In this paper, we propose a novel concept of "anglewise attention" by introducing a multihead self-attention mechanism to the angular domain of an LFI.

Image Quality Assessment

Reconfigurable Intelligent Surface-Aided 6G Massive Access: Coupled Tensor Modeling and Sparse Bayesian Learning

no code implementations11 Jun 2022 Xiaodan Shao, Lei Cheng, Xiaoming Chen, Chongwen Huang, Derrick Wing Kwan Ng

Then, by associating the data sequences to multiple rank-one tensors and exploiting the angular sparsity of the RIS-BS channel, the detection problem is cast as a high-order coupled tensor decomposition problem without the need of exploiting pilot sequences.

Tensor Decomposition Variational Inference

Target Sensing with Intelligent Reflecting Surface: Architecture and Performance

no code implementations22 Jan 2022 Xiaodan Shao, Changsheng You, Wenyan Ma, Xiaoming Chen, Rui Zhang

Intelligent reflecting surface (IRS) has emerged as a promising technology to reconfigure the radio propagation environment by dynamically controlling wireless signal's amplitude and/or phase via a large number of reflecting elements.

C-GRBFnet: A Physics-Inspired Generative Deep Neural Network for Channel Representation and Prediction

no code implementations5 Dec 2021 Zhuoran Xiao, Zhaoyang Zhang, Chongwen Huang, Xiaoming Chen, Caijun Zhong, Mérouane Debbah

Specifically, we first use a forward deep neural network to infer the positions of all possible images of the source reflected by the surrounding scatterers within that environment, and then use the well-known Gaussian Radial Basis Function network (GRBF) to approximate the amplitudes of all possible propagation paths.

Texture-enhanced Light Field Super-resolution with Spatio-Angular Decomposition Kernels

no code implementations7 Nov 2021 Zexi Hu, Xiaoming Chen, Henry Wing Fung Yeung, Yuk Ying Chung, Zhibo Chen

Despite the recent progress in light field super-resolution (LFSR) achieved by convolutional neural networks, the correlation information of light field (LF) images has not been sufficiently studied and exploited due to the complexity of 4D LF data.

Material Recognition Super-Resolution

Efficient Light Field Reconstruction via Spatio-Angular Dense Network

2 code implementations8 Aug 2021 Zexi Hu, Henry Wing Fung Yeung, Xiaoming Chen, Yuk Ying Chung, Haisheng Li

As an image sensing instrument, light field images can supply extra angular information compared with monocular images and have facilitated a wide range of measurement applications.


Fronthaul Compression and Passive Beamforming Design for Intelligent Reflecting Surface-aided Cloud Radio Access Networks

no code implementations25 Feb 2021 Yu Zhang, Xuelu Wu, Hong Peng, Caijun Zhong, Xiaoming Chen

This letter studies a cloud radio access network (C-RAN) with multiple intelligent reflecting surfaces (IRS) deployed between users and remote radio heads (RRH).


Distributed ADMM with Synergetic Communication and Computation

no code implementations29 Sep 2020 Zhuojun Tian, Zhaoyang Zhang, Jue Wang, Xiaoming Chen, Wei Wang, Huaiyu Dai

In this paper, we propose a novel distributed alternating direction method of multipliers (ADMM) algorithm with synergetic communication and computation, called SCCD-ADMM, to reduce the total communication and computation cost of the system.

FTT-NAS: Discovering Fault-Tolerant Convolutional Neural Architecture

no code implementations20 Mar 2020 Xuefei Ning, Guangjun Ge, Wenshuo Li, Zhenhua Zhu, Yin Zheng, Xiaoming Chen, Zhen Gao, Yu Wang, Huazhong Yang

By inspecting the discovered architectures, we find that the operation primitives, the weight quantization range, the capacity of the model, and the connection pattern have influences on the fault resilience capability of NN models.

Neural Architecture Search Quantization

Graph-augmented Convolutional Networks on Drug-Drug Interactions Prediction

no code implementations8 Dec 2019 Yi Zhong, Xueyu Chen, Yu Zhao, Xiaoming Chen, Tingfang Gao, Zuquan Weng

We propose an end-to-end model to predict drug-drug interactions (DDIs) by employing graph-augmented convolutional networks.

Drug Discovery

Communication Lower Bound in Convolution Accelerators

no code implementations8 Nov 2019 Xiaoming Chen, Yinhe Han, Yu Wang

Evaluations based on the 65nm technology demonstrate that the proposed architecture nearly reaches the theoretical minimum communication in a three-level memory hierarchy and it is computation dominant.

Distributed, Parallel, and Cluster Computing Hardware Architecture

Fast Light Field Reconstruction With Deep Coarse-To-Fine Modeling of Spatial-Angular Clues

1 code implementation ECCV 2018 Henry Wing Fung Yeung, Junhui Hou, Jie Chen, Yuk Ying Chung, Xiaoming Chen

Specifically, our end-to-end model first synthesizes a set of intermediate novel sub-aperture images (SAIs) by exploring the coarse characteristics of the sparsely-sampled LF input with spatial-angular alternating convolutions.

Deploy Large-Scale Deep Neural Networks in Resource Constrained IoT Devices with Local Quantization Region

no code implementations24 May 2018 Yi Yang, Andy Chen, Xiaoming Chen, Jiang Ji, Zhenyang Chen, Yan Dai

Implementing large-scale deep neural networks with high computational complexity on low-cost IoT devices may inevitably be constrained by limited computation resource, making the devices hard to respond in real-time.


A Deep Learning Approach for Blind Drift Calibration of Sensor Networks

no code implementations16 Jun 2017 Yuzhi Wang, Anqi Yang, Xiaoming Chen, Pengjun Wang, Yu Wang, Huazhong Yang

Temporal drift of sensory data is a severe problem impacting the data quality of wireless sensor networks (WSNs).

Optimizing Memory Efficiency for Convolution Kernels on Kepler GPUs

no code implementations29 May 2017 Xiaoming Chen, Jianxu Chen, Danny Z. Chen, Xiaobo Sharon Hu

The high computation throughput and memory bandwidth of graphics processing units (GPUs) make GPUs a natural choice for accelerating convolution operations.

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