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Found 16 papers, 0 papers with code
RECOME: a New Density-Based Clustering Algorithm Using Relative KNN Kernel Density
Furthermore, we discover that the number of clusters computed by RECOME is a step function of the $\alpha$ parameter with jump discontinuity on a small collection of values.
Databases
Logographic Subword Model for Neural Machine Translation
A novel logographic subword model is proposed to reinterpret logograms as abstract subwords for neural machine translation.
Ubiquitous Acoustic Sensing on Commodity IoT Devices: A Survey
This framework encompasses three layers, i. e., physical layer, core technique layer, and application layer.
SensorDrop: A Reinforcement Learning Framework for Communication Overhead Reduction on the Edge
As such decisions are application dependent and may change over time, they should be learned during the operation of the system, for that we propose a method based on Advantage Actor-Critic (A2C) reinforcement learning which gradually learns which sensor's data is cost-effective to be sent to the central node.
CacheNet: A Model Caching Framework for Deep Learning Inference on the Edge
Inference of uncompressed large scale DNN models can only run in the cloud with extra communication latency back and forth between cloud and end devices, while compressed DNN models achieve real-time inference on end devices at the price of lower predictive accuracy.
Invariant Feature Learning for Sensor-based Human Activity Recognition
In recent years, many deep models have been applied to HAR problems.
DeepScale: Online Frame Size Adaptation for Multi-object Tracking on Smart Cameras and Edge Servers
In the training stage, we incorporate detectability scores into a one-shot tracker architecture so that DeepScale can learn representation estimations for different frame sizes in a self-supervised manner.
Efficient Action Recognition Using Confidence Distillation
Modern neural networks are powerful predictive models.
Ranked #67 on
Action Recognition
on UCF101
CROMOSim: A Deep Learning-based Cross-modality Inertial Measurement Simulator
With the prevalence of wearable devices, inertial measurement unit (IMU) data has been utilized in monitoring and assessment of human mobility such as human activity recognition (HAR).
AttTrack: Online Deep Attention Transfer for Multi-object Tracking
Multi-object tracking (MOT) is a vital component of intelligent video analytics applications such as surveillance and autonomous driving.
Edge Video Analytics: A Survey on Applications, Systems and Enabling Techniques
Video, as a key driver in the global explosion of digital information, can create tremendous benefits for human society.
VALERIAN: Invariant Feature Learning for IMU Sensor-based Human Activity Recognition in the Wild
In this work, we examine two in-the-wild HAR datasets and DivideMix, a state-of-the-art learning with noise labels (LNL) method to understand the extent and impacts of noisy labels in training data.
Pi-ViMo: Physiology-inspired Robust Vital Sign Monitoring using mmWave Radars
In this paper, we address these limitations and present "Pi-ViMo", a non-contact Physiology-inspired Robust Vital Sign Monitoring system, using mmWave radars.
Efficient Rotating Synthetic Aperture Radar Imaging via Robust Sparse Array Synthesis
Due to its non-linear track, the Back-Projection Algorithm (BPA) is commonly used to generate SAR images in ROSAR.
MirrorCalib: Utilizing Human Pose Information for Mirror-based Virtual Camera Calibration
To address this issue, prior knowledge of a human body and 2D joint locations are utilized to estimate the camera extrinsic parameters when a person is in front of a mirror.
Learning Online Policies for Person Tracking in Multi-View Environments
The MVSparse system is comprised of a carefully orchestrated pipeline, combining edge server-based models with distributed lightweight Reinforcement Learning (RL) agents operating on individual cameras.
