Search Results for author:
Found 25 papers, 6 papers with code
Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks
Factorization Machines (FMs) are a supervised learning approach that enhances the linear regression model by incorporating the second-order feature interactions.
Arbitrary-Oriented Scene Text Detection via Rotation Proposals
This paper introduces a novel rotation-based framework for arbitrary-oriented text detection in natural scene images.
Spectrum Sharing in Vehicular Networks Based on Multi-Agent Reinforcement Learning
This paper investigates the spectrum sharing problem in vehicular networks based on multi-agent reinforcement learning, where multiple vehicle-to-vehicle (V2V) links reuse the frequency spectrum preoccupied by vehicle-to-infrastructure (V2I) links.
Information Theory Information Theory
Modeling Spatial-Temporal Clues in a Hybrid Deep Learning Framework for Video Classification
In this paper, we propose a hybrid deep learning framework for video classification, which is able to model static spatial information, short-term motion, as well as long-term temporal clues in the videos.
Adaptive Scenario Discovery for Crowd Counting
Crowd counting, i. e., estimation number of the pedestrian in crowd images, is emerging as an important research problem with the public security applications.
Aperture Diffraction for Compact Snapshot Spectral Imaging
We demonstrate a compact, cost-effective snapshot spectral imaging system named Aperture Diffraction Imaging Spectrometer (ADIS), which consists only of an imaging lens with an ultra-thin orthogonal aperture mask and a mosaic filter sensor, requiring no additional physical footprint compared to common RGB cameras.
Precise Temporal Action Localization by Evolving Temporal Proposals
The performances of existing action localization approaches remain unsatisfactory in precisely determining the beginning and the end of an action.
Toward Intelligent Vehicular Networks: A Machine Learning Framework
As wireless networks evolve towards high mobility and providing better support for connected vehicles, a number of new challenges arise due to the resulting high dynamics in vehicular environments and thus motive rethinking of traditional wireless design methodologies.
Evolving Boxes for Fast Vehicle Detection
We perform fast vehicle detection from traffic surveillance cameras.
Fusing Multi-Stream Deep Networks for Video Classification
A multi-stream framework is proposed to fully utilize the rich multimodal information in videos.
Evaluating Two-Stream CNN for Video Classification
In this paper, we conduct an in-depth study to investigate important implementation options that may affect the performance of deep nets on video classification.
Crowd Counting with Density Adaption Networks
Crowd counting is one of the core tasks in various surveillance applications.
Channel Agnostic End-to-End Learning based Communication Systems with Conditional GAN
In this article, we use deep neural networks (DNNs) to develop a wireless end-to-end communication system, in which DNNs are employed for all signal-related functionalities, such as encoding, decoding, modulation, and equalization.
Information Theory Information Theory
Detecting Curve Text with Local Segmentation Network and Curve Connection
Curve text or arbitrary shape text is very common in real-world scenarios.
Deep Learning based End-to-End Wireless Communication Systems with Conditional GAN as Unknown Channel
We propose to use a conditional generative adversarial net (GAN) to represent channel effects and to bridge the transmitter DNN and the receiver DNN so that the gradient of the transmitter DNN can be back-propagated from the receiver DNN.
Information Theory Information Theory
Edge-Aware Deep Image Deblurring
In this paper, we resort to human visual demands of sharp edges and propose a two-phase edge-aware deep network to improve deep image deblurring.
Fast Video Crowd Counting with a Temporal Aware Network
Crowd counting aims to count the number of instantaneous people in a crowded space, and many promising solutions have been proposed for single image crowd counting.
Deep Learning based Wireless Resource Allocation with Application to Vehicular Networks
The traditional wisdom is to explicitly formulate resource allocation as an optimization problem and then exploit mathematical programming to solve the problem to a certain level of optimality.
Learn to Allocate Resources in Vehicular Networks
Meanwhile, there exists an optimal number of continuous feedback and binary feedback, respectively.
Learn to Compress CSI and Allocate Resources in Vehicular Networks
The centralized decision unit employs a deep Q-network to allocate resources and then sends the decision results to all vehicles.
Scene Text Recognition with Temporal Convolutional Encoder
In this paper, we study text recognition framework by considering the long-term temporal dependencies in the encoder stage.
Cascaded Detail-Preserving Networks for Super-Resolution of Document Images
The accuracy of OCR is usually affected by the quality of the input document image and different kinds of marred document images hamper the OCR results.
Federated Learning and Wireless Communications
In contrast to other machine learning tools that require no communication resources, federated learning exploits communications between the central server and the distributed local clients to train and optimize a machine learning model.
Information Theory Signal Processing Information Theory
Document Image Layout Analysis via Explicit Edge Embedding Network
Layout analysis from a document image plays an important role in document content understanding and information extraction systems.
Metric Distribution to Vector: Constructing Data Representation via Broad-Scale Discrepancies
Graph embedding provides a feasible methodology to conduct pattern classification for graph-structured data by mapping each data into the vectorial space.
