Yue Jin
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Yue Jin has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Chunyang Zhao
School of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, China
1 shared publication - Pengfei Liu
Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110169, China
1 shared publication - Qijun Feng
School of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, China
1 shared publication - Wanglin Tian
School of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, China
1 shared publication - Zhichao Zhang
School of International Studies, Northeast Normal University, Changchun 130024, China
1 shared publication
Latest Publications
- Open Access
LST-BEV: Generating a Long-Term Spatial–Temporal Bird’s-Eye-View Feature for Multi-View 3D Object Detection
This paper presents a novel multi-view 3D object detection framework, Long-Term Spatial–Temporal Bird’s-Eye View (LST-BEV), designed to improve performance in autonomous driving. Traditional 3D detection relies on sensors like LiDAR, but visual perception using multi-camera systems is emerging as a more cost-effective solution. Existing methods struggle with capturing long-range dependencies and cross-task information due to limitations in attention mechanisms. To address this, we propose a Long-Range Cross-Task Detection Head (LRCH) to capture these dependencies and integrate cross-task information for accurate predictions. Additionally, we introduce the Long-Term Temporal Perception Module (LTPM), which efficiently extracts temporal features by combining Mamba and linear attention, overcoming challenges in temporal frame extraction. Experimental results in the nuScenes dataset demonstrate that our proposed LST-BEV outperforms its baseline (SA-BEVPool) by 2.1% mAP and 2.7% NDS, indicating a significant performance improvement.
Open AccessLST-BEV: Generating a Long-Term Spatial–Temporal Bird’s-Eye-View Feature for Multi-View 3D Object Detection
This paper presents a novel multi-view 3D object detection framework, Long-Term Spatial–Temporal Bird’s-Eye View (LST-BEV), designed to improve performance in autonomous driving. Traditional 3D detection relies on sensors like LiDAR, but visual perception using multi-camera systems is emerging as a more cost-effective solution. Existing methods struggle with capturing long-range dependencies and cross-task information due to limitations in attention mechanisms. To address this, we propose a Long-Range Cross-Task Detection Head (LRCH) to capture these dependencies and integrate cross-task information for accurate predictions. Additionally, we introduce the Long-Term Temporal Perception Module (LTPM), which efficiently extracts temporal features by combining Mamba and linear attention, overcoming challenges in temporal frame extraction. Experimental results in the nuScenes dataset demonstrate that our proposed LST-BEV outperforms its baseline (SA-BEVPool) by 2.1% mAP and 2.7% NDS, indicating a significant performance improvement.