Search Results for author:
Found 14 papers, 3 papers with code
VideoTrack: Learning To Track Objects via Video Transformer
Existing Siamese tracking methods, which are built on pair-wise matching between two single frames, heavily rely on additional sophisticated mechanism to exploit temporal information among successive video frames, hindering them from high efficiency and industrial deployments.
Multi-level Contrast Network for Wearables-based Joint Activity Segmentation and Recognition
Human activity recognition (HAR) with wearables is promising research that can be widely adopted in many smart healthcare applications.
Self-Supervised Image Representation Learning with Geometric Set Consistency
We propose a method for self-supervised image representation learning under the guidance of 3D geometric consistency.
Self-Supervised Point Cloud Registration with Deep Versatile Descriptors
In this paper, we propose to jointly use both global and local descriptors to register point clouds in a self-supervised manner, which is motivated by a critical observation that all local geometries of point clouds are transformed consistently under the same transformation.
Learning Efficient Representations for Enhanced Object Detection on Large-scene SAR Images
It is a challenging problem to detect and recognize targets on complex large-scene Synthetic Aperture Radar (SAR) images.
Unsupervised Shape Completion via Deep Prior in the Neural Tangent Kernel Perspective
We present a novel approach for completing and reconstructing 3D shapes from incomplete scanned data by using deep neural networks.
MARS: Mixed Virtual and Real Wearable Sensors for Human Activity Recognition with Multi-Domain Deep Learning Model
Together with the rapid development of the Internet of Things (IoT), human activity recognition (HAR) using wearable Inertial Measurement Units (IMUs) becomes a promising technology for many research areas.
A Robust and Reliable Point Cloud Recognition Network Under Rigid Transformation
We prove that CAT is a rotation and translation-invariant transformation based on the theoretical analysis.
A Deep Learning Method for Complex Human Activity Recognition Using Virtual Wearable Sensors
The experimental results show that the proposed method can surprisingly converge in a few iterations and achieve an accuracy of 91. 15% on a real IMU dataset, demonstrating the efficiency and effectiveness of the proposed method.
LEMO: Learn to Equalize for MIMO-OFDM Systems with Low-Resolution ADCs
This paper develops a new deep neural network optimized equalization framework for massive multiple input multiple output orthogonal frequency division multiplexing (MIMOOFDM) systems that employ low-resolution analog-to-digital converters (ADCs) at the base station (BS).
A Survey on Nonconvex Regularization Based Sparse and Low-Rank Recovery in Signal Processing, Statistics, and Machine Learning
In recent, nonconvex regularization based sparse and low-rank recovery is of considerable interest and it in fact is a main driver of the recent progress in nonconvex and nonsmooth optimization.
Efficient Nonlinear Precoding for Massive MU-MIMO Downlink Systems with 1-Bit DACs
The power consumption of digital-to-analog converters (DACs) constitutes a significant proportion of the total power consumption in a massive multiuser multiple-input multiple-output (MU-MIMO) base station (BS).
Signal Processing Optimization and Control
An Accurate and Real-time Self-blast Glass Insulator Location Method Based On Faster R-CNN and U-net with Aerial Images
The location of broken insulators in aerial images is a challenging task.
Individual Recognition in Schizophrenia using Deep Learning Methods with Random Forest and Voting Classifiers: Insights from Resting State EEG Streams
Recently, there has been a growing interest in monitoring brain activity for individual recognition system.
