{"type":"ScholarlyArticle","title":"Matting Moments: A Unified Data-Driven Matting Engine for Mobile AIGC in Photo Gallery","authors":[{"name":"Yanhao Zhang","slug":"yanhao-zhang","given":"Yanhao","family":"Zhang","affiliation":"OPPO Research Institute"},{"name":"Fanyi Wang","slug":"fanyi-wang","given":"Fanyi","family":"Wang","affiliation":"OPPO Research Institute"},{"name":"Weixuan Sun","slug":"weixuan-sun","given":"Weixuan","family":"Sun","affiliation":"OPPO Research Institute; Australian National University"},{"name":"Jingwen Su","slug":"jingwen-su","given":"Jingwen","family":"Su","affiliation":"OPPO Research Institute"},{"name":"Peng Liu","slug":"peng-liu","given":"Peng","family":"Liu","affiliation":"OPPO Research Institute"},{"name":"Yaqian Li","slug":"yaqian-li","given":"Yaqian","family":"Li","affiliation":"OPPO Research Institute"},{"name":"Xinjie Feng","slug":"xinjie-feng","given":"Xinjie","family":"Feng","affiliation":"OPPO Research Institute"},{"name":"Zhengxia Zou","slug":"zhengxia-zou","given":"Zhengxia","family":"Zou","affiliation":"Beihang University"}],"abstract":"Image matting is a fundamental technique in visual understanding and has become one of the most significant capabilities in mobile phones. Despite the development of mobile storage and computing power, achieving diverse mobile Artificial Intelligence Generated Content (AIGC) applications remains a great challenge. To address this issue, we present an innovative demonstration of an automatic system called \"Matting Moments\" that enables automatic image editing based on matting models in different scenarios. Coupled with accurate and refined matting subjects, our system provides visual element editing abilities and backend services for distribution and recommendation that respond to emotional expressions. Our system comprises three components: 1) photo content structuring, 2) data-driven matting engine, and 3) AIGC functions for generation, which automatically achieve diverse photo beautification in the gallery. This system offers a unified framework that guides consumers to obtain intelligent recommendations with beautifully generated contents, helping them enjoy the moments and memories of their present life.","doi":"10.24963/ijcai.2023/845","url":"https://ijcai.org/proceedings/2023/845","pdf":"https://ijcai.org/proceedings/2023/0845.pdf","markdown":"https://ijcai.org/proceedings/2023/845.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/845","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Demo Track","track":"Demo Track","number":845,"pages":{"first":7183,"last":7186},"keywords":["Computer Vision: CV: Applications","Computer Vision: CV: Segmentation","Computer Vision: CV: Other"],"videos":[],"references":[{"position":1,"raw":"[Bao et al., 2022] Yunqing Bao, Hang Dai, and Abdulmo- taleb Elsaddik. Semi-supervised cross-modal salient ob- ject detection with u-structure networks. arXiv preprint arXiv:2208.04361, 2022.","title":"and Abdulmotaleb Elsaddik. Semi-supervised cross-modal salient object detection with u-structure networks","authors":"Bao","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Chen et al., 2022] Guowei Chen, Yi Liu, Jian Wang, Jun- cai Peng, Yuying Hao, Lutao Chu, Shiyu Tang, Zewu Wu, Zeyu Chen, Zhiliang Yu, et al. Pp-matting: High-accuracy natural image matting. arXiv preprint arXiv:2204.09433, 2022.","title":"Pp-matting: High-accuracy natural image matting","authors":"Guowei Chen, Yi Liu, Jian Wang, Juncai Peng, Yuying Hao, Lutao Chu, Shiyu Tang, Zewu Wu, Zeyu Chen, Zhiliang Yu","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chiu and Gurari, 2022] Tai-Yin Chiu and Danna Gurari. Pca-based knowledge distillation towards lightweight and content-style balanced photorealistic style transfer models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 18-24, 2022, pages 7834-7843. IEEE, 2022.","title":"Pca-based knowledge distillation towards lightweight and content-style balanced photorealistic style transfer models","authors":"Gurari ; ] Tai-Yin Chiu, Danna Chiu, Gurari","year":"2022","venue":"IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Cong et al., 2022] Runmin Cong, Qi Qin, Chen Zhang, Qi- uping Jiang, Shiqi Wang, Yao Zhao, and Sam Kwong. A weakly supervised learning framework for salient object detection via hybrid labels. IEEE Transactions on Circuits and Systems for Video Technology, 33(2):534-548, 2022.","title":"A weakly supervised learning framework for salient object detection via hybrid labels","authors":"Cong","year":"2022","venue":"IEEE Transactions on Circuits and Systems for Video Technology","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Du et al., 2023] Hongyang Du, Zonghang Li, Dusit Niyato, Jiawen Kang, Zehui Xiong, Dong In Kim, et al. Enabling ai-generated content (aigc) services in wireless edge net- works. arXiv preprint arXiv:2301.03220, 2023.","title":"Enabling ai-generated content (aigc) services in wireless edge networks","authors":"Hongyang Du, Zonghang Li, Dusit Niyato, Jiawen Kang, Zehui Xiong, Dong In Kim","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Hu et al., 2023] Liangpeng Hu, Yating Kong, Jide Li, and Xiaoqiang Li. Effective local-global transformer for nat- ural image matting. IEEE Transactions on Circuits and Systems for Video Technology, 2023.","title":"Effective local-global transformer for natural image matting","authors":"Hu","year":"2023","venue":"IEEE Transactions on Circuits and Systems for Video Technology","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Li et al., 2022] Jiachen Li, Vidit Goel, Marianna Ohanyan, Shant Navasardyan, Yunchao Wei, and Humphrey Shi. Vmformer: End-to-end video matting with transformer. arXiv preprint arXiv:2208.12801, 2022.","title":"Vmformer: End-to-end video matting with transformer","authors":"Li","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Liang et al., 2022] Yanhua Liang, Guihe Qin, Minghui Sun, Jun Qin, Jie Yan, and Zhonghan Zhang. Multi-modal interactive attention and dual progressive decoding net- work for rgb-d/t salient object detection. Neurocomputing, 490:132-145, 2022.","title":"Multi-modal interactive attention and dual progressive decoding network for rgb-d/t salient object detection","authors":"Yanhua Liang, Guihe Qin, Minghui Sun, Jun Qin, Jie Yan, Zhonghan Zhang","year":"2022","venue":"Neurocomputing","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Liu et al., 2022] Jiang-Jiang Liu, Qibin Hou, Zhi-Ang Liu, and Ming-Ming Cheng. Poolnet+: Exploring the potential of pooling for salient object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(1):887- 904, 2022.","title":"Poolnet+: Exploring the potential of pooling for salient object detection","authors":"Jiang-Jiang Liu, Qibin Hou, Zhi-Ang Liu, Ming-Ming Cheng","year":"2022","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Lv et al., 2022] Yunqiu Lv, Bowen Liu, Jing Zhang, Yuchao Dai, Aixuan Li, and Tong Zhang. Semi-supervised active salient object detection. Pattern Recognition, 123:108364, 2022.","title":"Semi-supervised active salient object detection","authors":"Lv","year":"2022","venue":"Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Ouali et al., 2020] Yassine Ouali, Céline Hudelot, and Myr- iam Tami. Semi-supervised semantic segmentation with cross-consistency training. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020, pages 12671-12681, 2020.","title":"Semi-supervised semantic segmentation with cross-consistency training","authors":"Yassine Ouali, Céline Hudelot, Myriam Tami","year":"2020","venue":"IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"Piao et al., ] Yongri Piao, Chenyang Lu, Miao Zhang, and Huchuan Lu. Semi-supervised video salient object detec- tion based on uncertainty-guided pseudo labels. In Ad- vances in Neural Information Processing Systems.","title":"Semi-supervised video salient object detection based on uncertainty-guided pseudo labels","authors":"Piao","year":null,"venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Qin et al., 2020] Xuebin Qin, Zichen Zhang, Chenyang Huang, Masood Dehghan, Osmar R. Zaïane, and Mar- tin Jägersand. U 2 -net: Going deeper with nested u- structure for salient object detection. Pattern Recognit., 106:107404, 2020.","title":"Zaïane, and Martin Jägersand. U 2 -net: Going deeper with nested ustructure for salient object detection","authors":"Qin","year":"2020","venue":"Pattern Recognit","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Radford et al., 2021] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agar- wal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning transferable visual models from natural language supervi- sion. In Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Vir- tual Event, volume 139 of Proceedings of Machine Learn- ing Research, pages 8748-8763, 2021.","title":"Learning transferable visual models from natural language supervision","authors":"Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever","year":"2021","venue":"Proceedings of the 38th International Conference on Machine Learning, ICML 2021","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Shin et al., 2022] Gyungin Shin, Samuel Albanie, and Weidi Xie. Unsupervised salient object detection with spectral cluster voting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3971-3980, 2022.","title":"Unsupervised salient object detection with spectral cluster voting","authors":"Shin","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Sun et al., 2022] Yanan Sun, Chi-Keung Tang, and Yu- Wing Tai. Human instance matting via mutual guid- ance and multi-instance refinement. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2647-2656, 2022.","title":"Human instance matting via mutual guidance and multi-instance refinement","authors":"Yanan Sun, Chi-Keung Tang, Yu-Wing Tai","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Zeng et al., 2021] Yu Zeng, Zhe Lin, Huchuan Lu, and Vishal M. Patel. Cr-fill: Generative image inpainting with auxiliary contextual reconstruction. In 2021 IEEE/CVF In- ternational Conference on Computer Vision, ICCV 2021, Montreal, QC, Canada, October 10-17, 2021, pages 14144-14153. IEEE, 2021.","title":"Cr-fill: Generative image inpainting with auxiliary contextual reconstruction","authors":"Yu Zeng, Zhe Lin, Huchuan Lu, Vishal M Patel","year":"2021","venue":"IEEE/CVF International Conference on Computer Vision, ICCV 2021","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Zhang et al., 2022] Jin Zhang, Yanjiao Shi, Qing Zhang, Liu Cui, Ying Chen, and Yugen Yi. Attention guided con- textual feature fusion network for salient object detection. Image and Vision Computing, 117:104337, 2022.","title":"Attention guided contextual feature fusion network for salient object detection","authors":"Jin Zhang, Yanjiao Shi, Qing Zhang, Liu Cui, Ying Chen, Yugen Yi","year":"2022","venue":"Image and Vision Computing","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}