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Found 33 papers, 2 papers with code
High-Performance Transmission Mechanism Design of Multi-Stream Carrier Aggregation for 5G Non-Standalone Network
Multi-stream carrier aggregation is a key technology to expand bandwidth and improve the throughput of the fifth-generation wireless communication systems.
Prediction-based Hybrid Slicing Framework for Service Level Agreement Guarantee in Mobility Scenarios: A Deep Learning Approach
In the proposal, LSTM networks are employed to predict traffic demand and the location of each user in a slicing window level.
Joint Optimization of DNN Inference Delay and Energy under Accuracy Constraints for AR Applications
The high computational complexity and high energy consumption of artificial intelligence (AI) algorithms hinder their application in augmented reality (AR) systems.
TRIG: Transformer-Based Text Recognizer with Initial Embedding Guidance
We propose a 1-D split to address the challenges of complexity and replace the CNN with the transformer encoder to reduce the need for a context modeling module.
Multi-Task and Multi-Modal Learning for RGB Dynamic Gesture Recognition
Our framework is trained to learn a representation for multi-task learning: gesture segmentation and gesture recognition.
HENet: Forcing a Network to Think More for Font Recognition
Although lots of progress were made in Text Recognition/OCR in recent years, the task of font recognition is remaining challenging.
Ranked #1 on
Font Recognition
on Explor_all
(Top 1 Accuracy metric)
Deep Image Matting with Flexible Guidance Input
In this paper, we propose a matting method that use Flexible Guidance Input as user hint, which means our method can use trimap, scribblemap or clickmap as guidance information or even work without any guidance input.
High Precision Indoor Localization with Dummy Antennas -- An Experimental Study
With the rising demand for indoor localization, high precision technique-based fingerprints became increasingly important nowadays.
IFR: Iterative Fusion Based Recognizer For Low Quality Scene Text Recognition
In this paper, we propose an Iterative Fusion based Recognizer (IFR) for low quality scene text recognition, taking advantage of refined text images input and robust feature representation.
RW-Resnet: A Novel Speech Anti-Spoofing Model Using Raw Waveform
In recent years, synthetic speech generated by advanced text-to-speech (TTS) and voice conversion (VC) systems has caused great harms to automatic speaker verification (ASV) systems, urging us to design a synthetic speech detection system to protect ASV systems.
SGTBN: Generating Dense Depth Maps from Single-Line LiDAR
Therefore, we propose a method to tackle the problem of single-line depth completion, in which we aim to generate a dense depth map from the single-line LiDAR info and the aligned RGB image.
A Novel GCN based Indoor Localization System with Multiple Access Points
With the rapid development of indoor location-based services (LBSs), the demand for accurate localization keeps growing as well.
Arbitrary-Shaped Text Detection withAdaptive Text Region Representation
In this paper, we propose a novel text regionrepresentation method, with a robust pipeline, which can precisely detect dense adjacent text instances witharbitrary shapes.
A Dataset and Benchmark Towards Multi-Modal Face Anti-Spoofing Under Surveillance Scenarios
Besides, we propose an Attention based Face Anti-spoofing network with Feature Augment (AFA) to solve the FAS towards low-quality face images.
Tracking Based Semi-Automatic Annotation for Scene Text Videos
Moreover, a paired low-quality scene text video dataset named Text-RBL is proposed, consisting of raw videos, blurry videos, and low-resolution videos, labeled by the proposed convenient semi-automatic labeling strategy.
Self-Calibrating Indoor Localization with Crowdsourcing Fingerprints and Transfer Learning
Precise indoor localization is one of the key requirements for fifth Generation (5G) and beyond, concerning various wireless communication systems, whose applications span different vertical sectors.
SIRI: Spatial Relation Induced Network For Spatial Description Resolution
Spatial Description Resolution, as a language-guided localization task, is proposed for target location in a panoramic street view, given corresponding language descriptions.
Energy-Efficient NOMA Multicasting System for 5G Cellular V2X Communications with Imperfect CSI
The proposed system multicast the information through low complexity optimal power allocation algorithms used under channel outage probability constraint of vehicles with imperfect CSI, QoS constraints of vehicles, and transmit power limits constraint of RSUs.
Finding Action Tubes with a Sparse-to-Dense Framework
The task of spatial-temporal action detection has attracted increasing attention among researchers.
Ranked #3 on
Action Detection
on UCF Sports
(Video-mAP 0.2 metric)
Joint Visual and Wireless Signal Feature based Approach for High-Precision Indoor Localization
The existing localization systems for indoor applications basically rely on wireless signal.
CFAD: Coarse-to-Fine Action Detector for Spatiotemporal Action Localization
Most current pipelines for spatio-temporal action localization connect frame-wise or clip-wise detection results to generate action proposals, where only local information is exploited and the efficiency is hindered by dense per-frame localization.
High Accurate Time-of-Arrival Estimation with Fine-Grained Feature Generation for Internet-of-Things Applications
Conventional schemes often require extra reference signals or more complicated algorithms to improve the time-of-arrival (TOA) estimation accuracy.
Cooling-Aware Resource Allocation and Load Management for Mobile Edge Computing Systems
Driven by explosive computation demands of Internet of Things (IoT), mobile edge computing (MEC) provides a promising technique to enhance the computation capability for mobile users.
Age of Information Optimized MAC in V2X Sidelink via Piggyback-Based Collaboration
Real-time status update in future vehicular networks is vital to enable control-level cooperative autonomous driving.
Information Theory Networking and Internet Architecture Information Theory
Deep Reinforcement Learning-Based Beam Tracking for Low-Latency Services in Vehicular Networks
Ultra-Reliable and Low-Latency Communications (URLLC) services in vehicular networks on millimeter-wave bands present a significant challenge, considering the necessity of constantly adjusting the beam directions.
Sub-Spectrogram Segmentation for Environmental Sound Classification via Convolutional Recurrent Neural Network and Score Level Fusion
Environmental Sound Classification (ESC) is an important and challenging problem, and feature representation is a critical and even decisive factor in ESC.
A Study on Angular Based Embedding Learning for Text-independent Speaker Verification
Learning a good speaker embedding is important for many automatic speaker recognition tasks, including verification, identification and diarization.
Two-stage Training for Chinese Dialect Recognition
In this paper, we present a two-stage language identification (LID) system based on a shallow ResNet14 followed by a simple 2-layer recurrent neural network (RNN) architecture, which was used for Xunfei (iFlyTek) Chinese Dialect Recognition Challenge and won the first place among 110 teams.
Triplet Based Embedding Distance and Similarity Learning for Text-independent Speaker Verification
The improvements are both based on triplet cause the training stage and the evaluation stage of the baseline x-vector system focus on different aims.
Attention based Convolutional Recurrent Neural Network for Environmental Sound Classification
In order to deal with this, we employ a frame-level attention model to focus on the semantically relevant frames and salient frames.
Passive TCP Identification for Wired and WirelessNetworks: A Long-Short Term Memory Approach
TCP congestion control algorithm identification (TCP identification) can be used to significantly improve network efficiency.
How many labeled license plates are needed?
As a large amount of labeled data is typically difficult to collect and even more difficult to annotate, data augmentation and data generation are widely used in the process of training deep neural networks.
Monocular Depth Estimation with Augmented Ordinal Depth Relationships
In this paper, we propose to improve the performance of metric depth estimation with relative depths collected from stereo movie videos using existing stereo matching algorithm.
