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Your search keyword '"Gu, Chaochen"' showing total 26 results
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26 results on '"Gu, Chaochen"'

4. An Oriented Object Detector towards Diatoms

Author

Gong, Song, primary, Wu, Kaijie, additional, Xia, Zhiying, additional, Ran, Lihua, additional, Gu, Chaochen, additional, Lu, Changsheng, additional, Guan, Tongkun, additional, and Zhao, Yudi, additional

Published
2023
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8. Complementary Patch for Weakly Supervised Semantic Segmentation

Author

Zhang, Fei, Gu, Chaochen, Zhang, Chenyue, and Dai, Yuchao

Subjects
FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition
Abstract

Weakly Supervised Semantic Segmentation (WSSS) based on image-level labels has been greatly advanced by exploiting the outputs of Class Activation Map (CAM) to generate the pseudo labels for semantic segmentation. However, CAM merely discovers seeds from a small number of regions, which may be insufficient to serve as pseudo masks for semantic segmentation. In this paper, we formulate the expansion of object regions in CAM as an increase in information. From the perspective of information theory, we propose a novel Complementary Patch (CP) Representation and prove that the information of the sum of the CAMs by a pair of input images with complementary hidden (patched) parts, namely CP Pair, is greater than or equal to the information of the baseline CAM. Therefore, a CAM with more information related to object seeds can be obtained by narrowing down the gap between the sum of CAMs generated by the CP Pair and the original CAM. We propose a CP Network (CPN) implemented by a triplet network and three regularization functions. To further improve the quality of the CAMs, we propose a Pixel-Region Correlation Module (PRCM) to augment the contextual information by using object-region relations between the feature maps and the CAMs. Experimental results on the PASCAL VOC 2012 datasets show that our proposed method achieves a new state-of-the-art in WSSS, validating the effectiveness of our CP Representation and CPN., Comment: 10 pages, accepted in ICCV2021

Published
2021

10. Industrial Scene Text Detection With Refined Feature-Attentive Network.

Author

Guan, Tongkun, Gu, Chaochen, Lu, Changsheng, Tu, Jingzheng, Feng, Qi, Wu, Kaijie, and Guan, Xinping

Subjects
*INDUSTRIAL metals, *LOCALIZATION (Mathematics), *TEXT recognition, *FEATURE extraction, *INDUSTRIAL research, *IMAGE segmentation
Abstract

Detecting the marking characters of industrial metal parts remains challenging due to low visual contrast, uneven illumination, corroded surfaces, and cluttered background of metal part images. Affected by these factors, bounding boxes generated by most existing methods could not locate low-contrast text areas very well. In this paper, we propose a refined feature-attentive network (RFN) to solve the inaccurate localization problem. Specifically, we first design a parallel feature integration mechanism to construct an adaptive feature representation from multi-resolution features, which enhances the perception of multi-scale texts at each scale-specific level to generate a high-quality attention map. Then, an attentive proposal refinement module is developed by the attention map to rectify the location deviation of candidate boxes. Besides, a re-scoring mechanism is designed to select text boxes with the best rectified location. To promote the research towards industrial scene text detection, we contribute two industrial scene text datasets, including a total of 102156 images and 1948809 text instances with various character structures and metal parts. Extensive experiments on our dataset and four public datasets demonstrate that our proposed method achieves the state-of-the-art performance. Both code and dataset are available at: https://github.com/TongkunGuan/RFN. [ABSTRACT FROM AUTHOR]

Published
2022
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