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Found 13 papers, 3 papers with code
Space-time Reinforcement Network for Video Object Segmentation
Recently, video object segmentation (VOS) networks typically use memory-based methods: for each query frame, the mask is predicted by space-time matching to memory frames.
Robust and Efficient Memory Network for Video Object Segmentation
For limitation 2, we first adaptively decide whether to update the memory features depending on the variation of foreground objects to reduce temporal redundancy.
Multi-Sem Fusion: Multimodal Semantic Fusion for 3D Object Detection
Most multi-modal 3D object detection frameworks integrate semantic knowledge from 2D images into 3D LiDAR point clouds to enhance detection accuracy.
Group frame neural network of moving object ghost imaging combined with frame merging algorithm
The nature of multiple samples to extract correlation information limits the applications of ghost imaging of moving objects.
PU-EVA: An Edge Vector based Approximation Solution for Flexible-scale Point Cloud Upsampling
In this work, the flexible upsampling rates are achieved via edge vector based affine combinations, and a novel design of Edge Vector based Approximation for Flexible-scale Point clouds Upsampling (PU-EVA) is proposed.
PU-EVA: An Edge-Vector Based Approximation Solution for Flexible-Scale Point Cloud Upsampling
In this work, the arbitrary point clouds upsampling rates are achieved via edge-vector based affine combinations, and a novel design of Edge-Vector based Approximation for Flexible-scale Point clouds Upsampling (PU-EVA) is proposed.
AMPSO: Artificial Multi-Swarm Particle Swarm Optimization
To guarantee the accuracy of the results, a novel diversity scheme based on the positions and fitness values of the particles is proposed to control the exploration, exploitation and convergence processes of the swarms.
Improving Semantic Analysis on Point Clouds via Auxiliary Supervision of Local Geometric Priors
Existing deep learning algorithms for point cloud analysis mainly concern discovering semantic patterns from global configuration of local geometries in a supervised learning manner.
Concise and Effective Network for 3D Human Modeling from Orthogonal Silhouettes
In this paper, we revisit the problem of 3D human modeling from two orthogonal silhouettes of individuals (i. e., front and side views).
Prognostics Estimations with Dynamic States
The health state assessment and remaining useful life (RUL) estimation play very important roles in prognostics and health management (PHM), owing to their abilities to reduce the maintenance and improve the safety of machines or equipment.
Robust Matrix Elastic Net based Canonical Correlation Analysis: An Effective Algorithm for Multi-View Unsupervised Learning
Rather than simply incorporating existing regularization minimization terms into CCA, this paper provides a new learning paradigm for CCA and is the first to derive a coupled feature selection based CCA algorithm that guarantees convergence.
A New Learning Paradigm for Random Vector Functional-Link Network: RVFL+
Furthermore, the statistical property of the proposed RVFL+ is investigated, and we present a sharp and high-quality generalization error bound based on the Rademacher complexity.
A Weight-coded Evolutionary Algorithm for the Multidimensional Knapsack Problem
A revised weight-coded evolutionary algorithm (RWCEA) is proposed for solving multidimensional knapsack problems.
