To address data scarcity and imbalance in MPP, some studies have adopted Graph Neural Networks (GNN) as an encoder to extract commonalities from molecular graphs.
Unmanned aerial vehicles (UAVs) as aerial relays are practically appealing for assisting Internet of Things (IoT) network.
For UTTOP, we first introduce a pretreatment method, and then use an improved particle swarm optimization with Normal distribution initialization, Genetic mechanism, Differential mechanism and Pursuit operator (PSO-NGDP) to deal with this sub optimization problem.
Wireless rechargeable sensor networks with a charging unmanned aerial vehicle (CUAV) have the broad application prospects in the power supply of the rechargeable sensor nodes (SNs).
Unmanned aerial vehicles (UAVs) play an increasingly important role in assisting fast-response post-disaster rescue due to their fast deployment, flexible mobility, and low cost.
Human-object interaction (HOI) detection is an important part of understanding human activities and visual scenes.
UACTN decouples the representation learning of sketches and 3D shapes into two separate tasks: classification-based sketch uncertainty learning and 3D shape feature transfer.
In this paper, we advance them to a more practical setting called Described Object Detection (DOD) by expanding category names to flexible language expressions for OVD and overcoming the limitation of REC only grounding the pre-existing object.
The experimental results show that our method achieves centimeter-level localization accuracy, and outperforms existing methods using vectorized maps by a large margin.
Unlike most previous HOI methods that focus on learning better human-object features, we propose a novel and complementary approach called category query learning.
Ranked #8 on Human-Object Interaction Detection on HICO-DET
Finally, the proposed ISSA is utilized to solve the objective function.
Temporal knowledge graph (TKG) representation learning aims to project entities and relations in TKG to low-dimensional vector space while preserving the evolutionary nature of TKG.
Due to this one-to-many dilemma, enlarged action space and ignoring logical relationship between entity and relation increase the difficulty of learning.
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.
We also introduce a new U6DA-Linemod dataset for robustness study of the 6D pose estimation task.
These scenarios indeed correspond to the vulnerabilities of the under-test driving policies, thus are meaningful for their further improvements.
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.
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.
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.
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
Neural Architecture Search (NAS) has received extensive attention due to its capability to discover neural network architectures in an automated manner.
We also design the micro-level search space to strengthen the information flow for BNN.
A major challenge in NAS is to conduct a fast and accurate evaluation of neural architectures.
Conducting efficient performance estimations of neural architectures is a major challenge in neural architecture search (NAS).
Recently, 3D face reconstruction and face alignment tasks are gradually combined into one task: 3D dense face alignment.
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.
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.
State-of-the-art human pose estimation methods are based on heat map representation.
Ranked #23 on Pose Estimation on MPII Human Pose
A central problem is that the structural information in the pose is not well exploited in the previous regression methods.
Ranked #36 on Pose Estimation on MPII Human Pose
In this work, we propose to directly embed a kinematic object model into the deep neutral network learning for general articulated object pose estimation.
Ranked #302 on 3D Human Pose Estimation on Human3.6M
Hierarchical segmentation based object proposal methods have become an important step in modern object detection paradigm.
We extends the previous 2D cascaded object pose regression work  in two aspects so that it works better for 3D articulated objects.
However, their usage of boundary prior is very simple, fragile, and the integration with other cues is mostly heuristic.