Search Results for author:
Found 9 papers, 3 papers with code
GaussianTalker: Speaker-specific Talking Head Synthesis via 3D Gaussian Splatting
In this paper, we propose GaussianTalker, a novel method for audio-driven talking head synthesis based on 3D Gaussian Splatting.
Segment Any Events via Weighted Adaptation of Pivotal Tokens
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.
Ranked #1 on
Event-based Object Segmentation
on DSEC-SEG
Ecsnet: Spatio-temporal feature learning for event camera
To fully exploit their inherent sparsity with reconciling the spatio-temporal information, we introduce a compact event representation, namely 2D-1T event cloud sequence (2D-1T ECS).
Ranked #1 on
Event data classification
on N-CARS
Control theoretically explainable application of autoencoder methods to fault detection in nonlinear dynamic systems
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.
Context Attention Network for Skeleton Extraction
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.
Spatial-temporal associations representation and application for process monitoring using graph convolution neural network
Thank you very much for the attention and concern of colleagues and scholars in this work.
Graph neural network-based fault diagnosis: a review
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.
Simple Baseline for Single Human Motion Forecasting
Global human motion forecasting is important in many fields, which is the combination of global human trajectory prediction and local human pose prediction.
High-resolution Depth Maps Imaging via Attention-based Hierarchical Multi-modal Fusion
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.
