no code implementations • 5 Mar 2024 • Chenqiang Gao, Chuandong Liu, Jun Shu, Fangcen Liu, Jiang Liu, Luyu Yang, Xinbo Gao, Deyu Meng
Current state-of-the-art (SOTA) 3D object detection methods often require a large amount of 3D bounding box annotations for training.
no code implementations • 1 Mar 2024 • Junjie Guo, Chenqiang Gao, Fangcen Liu, Deyu Meng, Xinbo Gao
To effectively mine the complementary information and adapt to misalignment situations, we propose a Multispectral Deformable Cross-attention module to adaptively sample and aggregate multi-semantic level features of infrared and visible images for each object.
no code implementations • 1 Feb 2024 • Fangcen Liu, Chenqiang Gao, Yaming Zhang, Junjie Guo, Jinhao Wang, Deyu Meng
Finally, based on the fact that infrared images do not have a lot of details and texture information, we design an infrared decoder module, which further improves the performance of downstream tasks.
1 code implementation • CVPR 2023 • Chuandong Liu, Chenqiang Gao, Fangcen Liu, Pengcheng Li, Deyu Meng, Xinbo Gao
State-of-the-art 3D object detectors are usually trained on large-scale datasets with high-quality 3D annotations.
no code implementations • CVPR 2022 • Chuandong Liu, Chenqiang Gao, Fangcen Liu, Jiang Liu, Deyu Meng, Xinbo Gao
In the meantime, we design a reliable background mining module and a point cloud filling data augmentation strategy to generate the confident data for iteratively learning with reliable supervision.
no code implementations • 29 Sep 2021 • Fangcen Liu, Chenqiang Gao, Fang Chen, Deyu Meng, WangMeng Zuo, Xinbo Gao
We adopt the self-attention mechanism of the transformer to learn the interaction information of image features in a larger range.
1 code implementation • 13 Aug 2021 • Fang Chen, Chenqiang Gao, Fangcen Liu, Yue Zhao, Yuxi Zhou, Deyu Meng, WangMeng Zuo
A local patch network (LPNet) with global attention is proposed in this paper to detect small targets by jointly considering the global and local properties of infrared small target images.