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Your search keyword '"Yan, Chenggang"' showing total 298 results
Start Over Author "Yan, Chenggang" Publication Type Academic Journals
298 results on '"Yan, Chenggang"'

31. SHREC’22 track: Open-Set 3D Object Retrieval

Author

Feng, Yifan, Gao, Yue, Zhao, Xibin, Guo, Yandong, Bagewadi, Nihar, Bui, Nhat-Tan, Dao, Hieu, Gangisetty, Shankar, Guan, Ripeng, Han, Xie, Hua, Cong, Hunakunti, Chidambar, Jiang, Yu, Jiao, Shichao, Ke, Yuqi, Kuang, Liqun, Liu, Anan, Nguyen, Dinh-Huan, Nguyen, Hai-Dang, Nie, Weizhi, Pham, Bang-Dang, Raikar, Karthik, Tang, Qingmei, Tran, Minh-Triet, Wan, Jialong, Yan, Chenggang, You, Haoxuan, and Zhu, Difei

Published
2022
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33. RGB‐guided hyperspectral image super‐resolution with deep progressive learning.

Author

Zhang, Tao, Fu, Ying, Huang, Liwei, Li, Siyuan, You, Shaodi, and Yan, Chenggang

Subjects
HIGH resolution imaging, DEEP learning, ARTIFICIAL neural networks, SUPERVISED learning, COMPUTER vision, IMAGE processing
Abstract

Due to hardware limitations, existing hyperspectral (HS) camera often suffer from low spatial/temporal resolution. Recently, it has been prevalent to super‐resolve a low resolution (LR) HS image into a high resolution (HR) HS image with a HR RGB (or multispectral) image guidance. Previous approaches for this guided super‐resolution task often model the intrinsic characteristic of the desired HR HS image using hand‐crafted priors. Recently, researchers pay more attention to deep learning methods with direct supervised or unsupervised learning, which exploit deep prior only from training dataset or testing data. In this article, an efficient convolutional neural network‐based method is presented to progressively super‐resolve HS image with RGB image guidance. Specifically, a progressive HS image super‐resolution network is proposed, which progressively super‐resolve the LR HS image with pixel shuffled HR RGB image guidance. Then, the super‐resolution network is progressively trained with supervised pre‐training and unsupervised adaption, where supervised pre‐training learns the general prior on training data and unsupervised adaptation generalises the general prior to specific prior for variant testing scenes. The proposed method can effectively exploit prior from training dataset and testing HS and RGB images with spectral‐spatial constraint. It has a good generalisation capability, especially for blind HS image super‐resolution. Comprehensive experimental results show that the proposed deep progressive learning method outperforms the existing state‐of‐the‐art methods for HS image super‐resolution in non‐blind and blind cases. [ABSTRACT FROM AUTHOR]

Published
2024
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47. Multi-Channel Hypergraph Collaborative Filtering with Attribute Inference.

Author

Jiang, Yutong, Gao, Yuhan, Sun, Yaoqi, Wang, Shuai, and Yan, Chenggang

Subjects
HYPERGRAPHS, DATA modeling
Abstract

In the field of collaborative filtering, attribute information is often integrated to improve recommendations. However, challenges remain unaddressed. Firstly, existing data modeling methods often fall short of appropriately handling attribute information. Secondly, attribute data are often sparse and can potentially impact recommendation performance due to the challenge of incomplete correspondence between the attribute information and the recommendations. To tackle these challenges, we propose a hypergraph collaborative filtering with attribute inference (HCFA) framework, which segregates attribute and user behavior information into distinct channels and leverages hypergraphs to capture high-order correlations among vertices, offering a more natural approach to modeling. Furthermore, we introduce behavior-based attribute confidence (BAC) for assessing the reliability of inferred attributes concerning the corresponding behaviors and update the most credible portions to enhance recommendation quality. Extensive experiments conducted on three public benchmarks demonstrate the superiority of our model. It consistently outperforms other state-of-the-art approaches, with ablation experiments further confirming the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

Published
2024
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