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20172026
most citedBrain Inspired Cognitive Model with Attention for Self-Driving Cars

11 citations · 15 across the 9 of their papers we have counts for

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6 papers · 1 filter

cs.CV2025

StructVPR++: Distill Structural and Semantic Knowledge with Weighting Samples for Visual Place Recognition

Yanqing Shen, Sanping Zhou, Jingwen Fu +3

Visual place recognition is a challenging task for autonomous driving and robotics, which is usually considered as an image retrieval problem. A commonly used two-stage strategy in…

cs.CV2024★ 2 cited

Leveraging Anchor-based LiDAR 3D Object Detection via Point Assisted Sample Selection

Shitao Chen, Haolin Zhang, Nanning Zheng

3D object detection based on LiDAR point cloud and prior anchor boxes is a critical technology for autonomous driving environment perception and understanding. Nevertheless, an ove…

cs.CV2023★ 1 cited

MLF-DET: Multi-Level Fusion for Cross-Modal 3D Object Detection

Zewei Lin, Yanqing Shen, Sanping Zhou +2

In this paper, we propose a novel and effective Multi-Level Fusion network, named as MLF-DET, for high-performance cross-modal 3D object DETection, which integrates both the featur…

cs.CV2022★ 1 cited

StructVPR: Distill Structural Knowledge with Weighting Samples for Visual Place Recognition

Yanqing Shen, Sanping Zhou, Jingwen Fu +3

Visual place recognition (VPR) is usually considered as a specific image retrieval problem. Limited by existing training frameworks, most deep learning-based works cannot extract s…

cs.CV2018

Feature Selective Small Object Detection via Knowledge-based Recurrent Attentive Neural Network

Kai Yi, Zhiqiang Jian, Shitao Chen +1

At present, the performance of deep neural network in general object detection is comparable to or even surpasses that of human beings. However, due to the limitations of deep lear…

cs.CV2017★ 11 cited

Brain Inspired Cognitive Model with Attention for Self-Driving Cars

Shitao Chen, Songyi Zhang, Jinghao Shang +2

Perception-driven approach and end-to-end system are two major vision-based frameworks for self-driving cars. However, it is difficult to introduce attention and historical informa…