no code implementations • 28 Apr 2024 • YuHan Liu, Yongjian Deng, Hao Chen, Bochen Xie, Youfu Li, Zhen Yang
Moreover, given that event data can provide accurate visual references at scene edges between consecutive frames, we introduce a learned visibility map derived from event data to adaptively mitigate the occlusion problem in the warping refinement process.
1 code implementation • 19 Mar 2023 • Qianang Zhou, Junlin Xiong, Youfu Li
As a result, the performance of event-based corner detectors is limited.
no code implementations • 7 Mar 2023 • Bochen Xie, Yongjian Deng, Zhanpeng Shao, Hai Liu, Qingsong Xu, Youfu Li
To fit the sparse nature of events and sufficiently explore the relationship between them, we develop a novel attention-aware model named Event Voxel Set Transformer (EVSTr) for spatiotemporal representation learning on event streams.
no code implementations • 8 Feb 2023 • Yongjian Deng, Hao Chen, Bochen Xie, Hai Liu, Youfu Li
Recent advances in event-based research prioritize sparsity and temporal precision.
1 code implementation • CVPR 2023 • Cheng Zhang, Hai Liu, Yongjian Deng, Bochen Xie, Youfu Li
To leverage the observed findings, we propose a novel critical minority relationship-aware method based on the Transformer architecture in which the facial part relationships can be learned.
1 code implementation • 15 Sep 2022 • Zhanpeng Shao, Wen Zhou, Wuzhen Wang, Jianyu Yang, Youfu Li
By this recurrent architecture, we can explicitly model not only the sequential but also non-sequential geometric consistency across time steps to accumulate information from previous frames to recover the entire human bodies, achieving a stable and accurate human pose estimation from event data.
no code implementations • CVPR 2022 • Yongjian Deng, Hao Chen, Hai Liu, Youfu Li
This study aims to address the core problem of balancing accuracy and model complexity for event-based classification models.
no code implementations • 20 Sep 2019 • Hao Chen, Youfu Li
The goal of this work is to present a systematic solution for RGB-D salient object detection, which addresses the following three aspects with a unified framework: modal-specific representation learning, complementary cue selection and cross-modal complement fusion.
no code implementations • 18 Sep 2019 • Ariyam Das, Youfu Li, Jin Wang, Mingda Li, Carlo Zaniolo
In the past, the semantic issues raised by the non-monotonic nature of aggregates often prevented their use in the recursive statements of logic programs and deductive databases.
1 code implementation • CVPR 2018 • Hao Chen, Youfu Li
In this paper, we answer this question from two perspectives: (1) We argue that if the complementary part can be modelled more explicitly, the cross-modal complement is likely to be better captured.
Ranked #23 on RGB-D Salient Object Detection on NJU2K
no code implementations • 18 Jun 2016 • Yao Guo, Youfu Li, Zhanpeng Shao
Motion behaviors of a rigid body can be characterized by a 6-dimensional motion trajectory, which contains position vectors of a reference point on the rigid body and rotations of this rigid body over time.