Search Results for author: Zhiwen Chen

Found 7 papers, 2 papers with code

Segment Any Events via Weighted Adaptation of Pivotal Tokens

1 code implementation24 Dec 2023 Zhiwen Chen, Zhiyu Zhu, Yifan Zhang, Junhui Hou, Guangming Shi, Jinjian Wu

One pivotal issue at the heart of this endeavor is the precise alignment and calibration of embeddings derived from event-centric data such that they harmoniously coincide with those originating from RGB imagery.

Semantic Segmentation

Control theoretically explainable application of autoencoder methods to fault detection in nonlinear dynamic systems

no code implementations2 Aug 2022 Linlin Li, Steven X. Ding, Ketian Liang, Zhiwen Chen, Ting Xue

The major efforts are made on the development of a control theoretic solution to the optimal fault detection problem, in which an analog concept to minimal sufficient statistic, the so-called lossless information compression, is introduced and proven for dynamic systems and fault detection specifications.

Anomaly Detection Fault Detection +1

Context Attention Network for Skeleton Extraction

no code implementations24 May 2022 Zixuan Huang, Yunfeng Wang, Zhiwen Chen, Xin Gao, Ruili Feng, Xiaobo Li

Skeleton extraction is a task focused on providing a simple representation of an object by extracting the skeleton from the given binary or RGB image.

Graph neural network-based fault diagnosis: a review

no code implementations16 Nov 2021 Zhiwen Chen, Jiamin Xu, Cesare Alippi, Steven X. Ding, Yuri Shardt, Tao Peng, Chunhua Yang

Graph neural network (GNN)-based fault diagnosis (FD) has received increasing attention in recent years, due to the fact that data coming from several application domains can be advantageously represented as graphs.

Graph Attention Time Series +1

Simple Baseline for Single Human Motion Forecasting

no code implementations14 Oct 2021 Chenxi Wang, Yunfeng Wang, Zixuan Huang, Zhiwen Chen

Global human motion forecasting is important in many fields, which is the combination of global human trajectory prediction and local human pose prediction.

Motion Forecasting Pose Prediction +1

High-resolution Depth Maps Imaging via Attention-based Hierarchical Multi-modal Fusion

1 code implementation4 Apr 2021 Zhiwei Zhong, Xianming Liu, Junjun Jiang, Debin Zhao, Zhiwen Chen, Xiangyang Ji

Specifically, to effectively extract and combine relevant information from LR depth and HR guidance, we propose a multi-modal attention based fusion (MMAF) strategy for hierarchical convolutional layers, including a feature enhance block to select valuable features and a feature recalibration block to unify the similarity metrics of modalities with different appearance characteristics.

Depth Map Super-Resolution

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