19 citations · 26 across the 12 of their papers we have counts for
7 papers · 1 filter
VELoRA: A Low-Rank Adaptation Approach for Efficient RGB-Event based Recognition
Lan Chen, Haoxiang Yang, Pengpeng Shao +5
Pattern recognition leveraging both RGB and Event cameras can significantly enhance performance by deploying deep neural networks that utilize a fine-tuning strategy. Inspired by t…
Retain, Blend, and Exchange: A Quality-aware Spatial-Stereo Fusion Approach for Event Stream Recognition
Lan Chen, Dong Li, Xiao Wang +5
Existing event stream-based pattern recognition models usually represent the event stream as the point cloud, voxel, image, etc., and design various deep neural networks to learn t…
Mamba-FETrack: Frame-Event Tracking via State Space Model
Ju Huang, Shiao Wang, Shuai Wang +3
RGB-Event based tracking is an emerging research topic, focusing on how to effectively integrate heterogeneous multi-modal data (synchronized exposure video frames and asynchronous…
State Space Model for New-Generation Network Alternative to Transformers: A Survey
Xiao Wang, Shiao Wang, Yuhe Ding +13
In the post-deep learning era, the Transformer architecture has demonstrated its powerful performance across pre-trained big models and various downstream tasks. However, the enorm…
Long-Term Visual Object Tracking with Event Cameras: An Associative Memory Augmented Tracker and A Benchmark Dataset
Xiao Wang, Xufeng Lou, Shiao Wang +3
Existing event stream based trackers undergo evaluation on short-term tracking datasets, however, the tracking of real-world scenarios involves long-term tracking, and the performa…
CRSOT: Cross-Resolution Object Tracking using Unaligned Frame and Event Cameras
Yabin Zhu, Xiao Wang, Chenglong Li +5
Existing datasets for RGB-DVS tracking are collected with DVS346 camera and their resolution () is low for practical applications. Actually, only visible cameras ar…
Uncertainty-aware Bridge based Mobile-Former Network for Event-based Pattern Recognition
Haoxiang Yang, Chengguo Yuan, Yabin Zhu +3
The mainstream human activity recognition (HAR) algorithms are developed based on RGB cameras, which are easily influenced by low-quality images (e.g., low illumination, motion blu…