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.
1 code implementation • 19 Apr 2022 • Yue Zhao, Lingming Zhang, Yang Liu, Deyu Meng, Zhiming Cui, Chenqiang Gao, Xinbo Gao, Chunfeng Lian, Dinggang Shen
The state-of-the-art deep learning-based methods often simply concatenate the raw geometric attributes (i. e., coordinates and normal vectors) of mesh cells to train a single-stream network for automatic intra-oral scanner image segmentation.
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.
1 code implementation • 13 Sep 2019 • Minghan Li, Xiangyong Cao, Qian Zhao, Lei Zhang, Chenqiang Gao, Deyu Meng
Furthermore, a transformation operator imposed on the background scenes is further embedded into the proposed model, which finely conveys the dynamic background transformations, such as rotations, scalings and distortions, inevitably existed in a real video sequence.
1 code implementation • CVPR 2018 • Jiang Liu, Chenqiang Gao, Deyu Meng, Alexander G. Hauptmann
DecideNet starts with estimating the crowd density by generating detection and regression based density maps separately.
Ranked #10 on Crowd Counting on WorldExpo’10
no code implementations • ECCV 2018 • Lan Wang, Chenqiang Gao, Luyu Yang, Yue Zhao, WangMeng Zuo, Deyu Meng
As a result, using partial data channels to build a full representation of multi-modalities is clearly desired.
no code implementations • 7 Mar 2016 • Lan Wang, Chenqiang Gao, Jiang Liu, Deyu Meng
Detecting complex events in a large video collection crawled from video websites is a challenging task.
no code implementations • 12 Dec 2018 • Shisha Liao, Yongqing Sun, Chenqiang Gao, Pranav Shenoy K P, Song Mu, Jun Shimamura, Atsushi Sagata
Weakly-supervised instance segmentation, which could greatly save labor and time cost of pixel mask annotation, has attracted increasing attention in recent years.
no code implementations • CVPR 2014 • Yi Peng, Deyu Meng, Zongben Xu, Chenqiang Gao, Yi Yang, Biao Zhang
As compared to the conventional RGB or gray-scale images, multispectral images (MSI) can deliver more faithful representation for real scenes, and enhance the performance of many computer vision tasks.
no code implementations • 26 Dec 2020 • Lingming Zhang, Yue Zhao, Deyu Meng, Zhiming Cui, Chenqiang Gao, Xinbo Gao, Chunfeng Lian, Dinggang Shen
State-of-the-art methods directly concatenate the raw attributes of 3D inputs, namely coordinates and normal vectors of mesh cells, to train a single-stream network for fully-automated tooth segmentation.
no code implementations • CVPR 2021 • Lingming Zhang, Yue Zhao, Deyu Meng, Zhiming Cui, Chenqiang Gao, Xinbo Gao, Chunfeng Lian, Dinggang Shen
State-of-the-art methods directly concatenate the raw attributes of 3D inputs, namely coordinates and normal vectors of mesh cells, to train a single-stream network for fully-automated tooth segmentation.
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.
no code implementations • Pattern Recognition Letters 2021 • Yue Zhao, Lingming Zhang, Chongshi Yang, Yingyun Tan, Yang Liu, Pengcheng Li, Tianhao Huang, Chenqiang Gao
We have evaluated our network on a real-patient dataset of dental models acquired through 3D intraoral scanners, and experimental results show that our method outperforms state-of-the-art deep learning methods for 3D shape segmentation.
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 • 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.
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 • 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.