Search Results for author:
Found 22 papers, 6 papers with code
Gan-Based Joint Activity Detection and Channel Estimation For Grant-free Random Access
Joint activity detection and channel estimation (JADCE) for grant-free random access is a critical issue that needs to be addressed to support massive connectivity in IoT networks.
Adversarial samples for deep monocular 6D object pose estimation
We also introduce a new U6DA-Linemod dataset for robustness study of the 6D pose estimation task.
Multi-Agent Vulnerability Discovery for Autonomous Driving with Hazard Arbitration Reward
These scenarios indeed correspond to the vulnerabilities of the under-test driving policies, thus are meaningful for their further improvements.
Sparse Signal Processing for Massive Connectivity via Mixed-Integer Programming
Although an enhanced estimation performance in terms of the mean squared error (MSE) can be achieved, the weighted $l_1$-norm minimization algorithm is still a convex relaxation of the original group-sparse matrix estimation problem, yielding a suboptimal solution.
One-step, Wash-free, Nanoparticle Clustering-based Magnetic Particle Spectroscopy (MPS) Bioassay Method for Detection of SARS-CoV-2 Spike and Nucleocapsid Proteins in Liquid Phase
With the ongoing global pandemic of coronavirus disease 2019 (COVID-19), there is an increasing quest for more accessible, easy-to-use, rapid, inexpensive, and high accuracy diagnostic tools.
A hybrid deep learning framework for Covid-19 detection via 3D Chest CT Images
In this paper, we present a hybrid deep learning framework named CTNet which combines convolutional neural network and transformer together for the detection of COVID-19 via 3D chest CT images.
Realizing Majorana fermion modes in the Kitaev model
We study the possibility to realize Majorana zero mode that's robust and may be easily manipulated for braiding in quantum computing in the ground state of the Kitaev model in this work.
Strongly Correlated Electrons
aw_nas: A Modularized and Extensible NAS framework
Neural Architecture Search (NAS) has received extensive attention due to its capability to discover neural network architectures in an automated manner.
BARS: Joint Search of Cell Topology and Layout for Accurate and Efficient Binary ARchitectures
We also design the micro-level search space to strengthen the information flow for BNN.
A Surgery of the Neural Architecture Evaluators
A major challenge in NAS is to conduct a fast and accurate evaluation of neural architectures.
Hardware Accelerator for Multi-Head Attention and Position-Wise Feed-Forward in the Transformer
Designing hardware accelerators for deep neural networks (DNNs) has been much desired.
Evaluating Efficient Performance Estimators of Neural Architectures
Conducting efficient performance estimations of neural architectures is a major challenge in neural architecture search (NAS).
3D Dense Face Alignment via Graph Convolution Networks
Recently, 3D face reconstruction and face alignment tasks are gradually combined into one task: 3D dense face alignment.
DNNVM : End-to-End Compiler Leveraging Heterogeneous Optimizations on FPGA-based CNN Accelerators
On the Xilinx ZU2 @330 MHz and ZU9 @330 MHz, we achieve equivalently state-of-the-art performance on our benchmarks by na\"ive implementations without optimizations, and the throughput is further improved up to 1. 26x by leveraging heterogeneous optimizations in DNNVM.
Pseudo Mask Augmented Object Detection
In this work, we present a novel and effective framework to facilitate object detection with the instance-level segmentation information that is only supervised by bounding box annotation.
Integral Human Pose Regression
State-of-the-art human pose estimation methods are based on heat map representation.
Ranked #19 on
Pose Estimation
on MPII Human Pose
Compositional Human Pose Regression
A central problem is that the structural information in the pose is not well exploited in the previous regression methods.
Deep Kinematic Pose Regression
In this work, we propose to directly embed a kinematic object model into the deep neutral network learning for general articulated object pose estimation.
Attribute Recognition from Adaptive Parts
Previous part-based attribute recognition approaches perform part detection and attribute recognition in separate steps.
Object Proposal by Multi-Branch Hierarchical Segmentation
Hierarchical segmentation based object proposal methods have become an important step in modern object detection paradigm.
Cascaded Hand Pose Regression
We extends the previous 2D cascaded object pose regression work [9] in two aspects so that it works better for 3D articulated objects.
Saliency Optimization from Robust Background Detection
However, their usage of boundary prior is very simple, fragile, and the integration with other cues is mostly heuristic.
