Search Results for author:
Found 19 papers, 6 papers with code
Self-Supervised Multi-Frame Neural Scene Flow
Neural Scene Flow Prior (NSFP) and Fast Neural Scene Flow (FNSF) have shown remarkable adaptability in the context of large out-of-distribution autonomous driving.
Correlation-Embedded Transformer Tracking: A Single-Branch Framework
Thus, we reformulate the two-branch Siamese tracking as a conceptually simple, fully transformer-based Single-Branch Tracking pipeline, dubbed SBT.
Dynamic Inertial Poser (DynaIP): Part-Based Motion Dynamics Learning for Enhanced Human Pose Estimation with Sparse Inertial Sensors
This paper introduces a novel human pose estimation approach using sparse inertial sensors, addressing the shortcomings of previous methods reliant on synthetic data.
Timestamp-supervised Wearable-based Activity Segmentation and Recognition with Contrastive Learning and Order-Preserving Optimal Transport
To address these challenges, we propose a novel method for joint activity segmentation and recognition with timestamp supervision, in which only a single annotated sample is needed in each activity segment.
Exploiting Semantic Localization in Highly Dynamic Wireless Networks Using Deep Homoscedastic Domain Adaptation
We therefore consider the blockage by such dynamic objects as another semantic state.
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.
