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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Xiangchao Yan
About me
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Posts
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Blog Post number 4
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Blog Post number 1
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portfolio
Photometric stereo vision-based defect detection for grease hoods
From Jan.2020 to Nov.2020, we were previously responsible for the defect detection workstation project of the grease hood of Boss Electric Group Hangzhou Smart Manufacturing Factory, with the aim of inspecting the product quality of the assembly line in real time. I was mainly responsible for the design of the visual inspection station and the design and deployment of the defect detection algorithm.
Micro-optical components defect detection based on light field microscope imaging
In order to solve the limitation of light field microscope imaging, this paper carried out a detailed study and proposed a complete set of micro-optical components defect detection scheme based on light field microscope imaging and deep learning.
Semi-Supervised Domain Adaptation for 3D Point Cloud Object Detection
In the field of 3D point cloud object detection, the parameters of different lidar sensors are very different, therefore the training parameters of different models cannot be adapted. In addition, the large amount of unlabeled data also has an important role.
publications
Cooperative Light-Field Image Super-Resolution Based on Multi-Modality Embedding and Fusion With Frequency Attention
Published in IEEE Signal Processing Letters, 2021
In this work, we propose a cooperative network to super-resolve LF sub-aperture images based on the multi-modality fusion. Specifically, in order to fully explore the LF information, we adopt various modalities and extract corresponding features to emphasise diverse LF characteristics. Then, we design a multi-scale fusion module to effectively integrate global and local LF features and apply frequency-aware attention mechanism to adaptively reinforce fused features.
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Bi3D: Bi-domain Active Learning for Cross-domain 3D Object Detection
Published in CVPR-2023, 2023
Unsupervised Domain Adaptation (UDA) technique has been explored in 3D cross-domain tasks recently. Though preliminary progress has been made, the performance gap between the UDA-based 3D model and the supervised one trained with fully annotated target domain is still large. This motivates us to consider selecting partial-yetimportant target data and labeling them at a minimum cost, to achieve a good trade-off between high performance and low annotation cost. To this end, we propose a Bi-domain active learning approach, namely Bi3D, to solve the crossdomain 3D object detection task. The Bi3D first develops a domainness-aware source sampling strategy, which identifies target-domain-like samples from the source domain to avoid the model being interfered by irrelevant source data. Then a diversity-based target sampling strategy is developed, which selects the most informative subset of target domain to improve the model adaptability to the target domain using as little annotation budget as possible. Experiments are conducted on typical cross-domain adaptation scenarios including cross-LiDAR-beam, cross-country, and crosssensor, where Bi3D achieves a promising target-domain detection accuracy (89.63% on KITTI) compared with UDAbased work (84.29%), even surpassing the detector trained on the full set of the labeled target domain (88.98%).
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talks
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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Portfolio item number 2
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