{"type":"ScholarlyArticle","title":"Connecting Low-Level Image Processing and High-Level Vision via Deep Learning","authors":[{"name":"Ding Liu","slug":"ding-liu","given":"Ding","family":"Liu","affiliation":"University of Illinois at Urbana-Champaign , USA"}],"abstract":"The latest development of computer vision has made exciting progress and tremendous impact in our daily lives. In this exciting era of technological advances, deep learning has gained huge popularity as a powerful tool for solving a lot of computer vision problems, and has added a great boost to this\r\nalready rapidly developing field. Conventionally, the connection between different vision tasks is fragile. For example, low-level image processing and high-level vision tasks are usually coped with separately. However, the inherent relation of feature representations among various tasks should be effectively utilized rather than omitted. My research focuses on connecting low-level image processing and high-level vision via deep learning. Specifically, my goal is to design deep learning mechanisms that can efficiently and effectively learn features from low-level image processing and use them to improve the performance of high-level vision tasks.","doi":"10.24963/ijcai.2018/831","url":"https://ijcai.org/proceedings/2018/831","pdf":"https://ijcai.org/proceedings/2018/0831.pdf","markdown":"https://ijcai.org/proceedings/2018/831.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/831","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Doctoral Consortium","track":"Doctoral Consortium","number":831,"pages":{"first":5775,"last":5776},"keywords":["Machine Learning: Machine Learning","Machine Learning: Deep Learning"],"videos":[],"references":[{"position":1,"raw":"[Han et al., 2018] Wei Han, Shiyu Chang, Ding Liu, Michael Witbrock, and Thomas S Huang. Image super-resolution via dual-state recurrent neural networks. In IEEE Confer- ence on Computer Vision and Pattern Recognition, 2018.","title":"Image super-resolution via dual-state recurrent neural networks","authors":"Wei Han, Shiyu Chang, Ding Liu, Michael Witbrock, Thomas S Huang","year":"2018","venue":"IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Liu et al., 2016a] Ding Liu, Zhaowen Wang, Nasser Nasrabadi, and Thomas Huang. Learning a mixture of deep networks for single image super-resolution. In Asian Conference on Computer Vision, pages 145-156. Springer, 2016.","title":"Learning a mixture of deep networks for single image super-resolution","authors":"Liu","year":"2016","venue":"Asian Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Liu et al., 2016b] Ding Liu, Zhaowen Wang, Bihan Wen, Jianchao Yang, Wei Han, and Thomas S Huang. Ro- bust single image super-resolution via deep networks with sparse prior. IEEE Transactions on Image Processing, 25(7):3194-3207, 2016.","title":"Robust single image super-resolution via deep networks with sparse prior","authors":"Liu","year":"2016","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Liu et al., 2017a] Ding Liu, Bowen Cheng, Zhangyang Wang, Haichao Zhang, and Thomas S Huang. Enhance visual recognition under adverse conditions via deep net- works. arXiv preprint arXiv:1712.07732, 2017.","title":"Enhance visual recognition under adverse conditions via deep networks","authors":"Liu","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Liu et al., 2017b] Ding Liu, Zhaowen Wang, Yuchen Fan, Xianming Liu, Zhangyang Wang, Shiyu Chang, and Thomas Huang. Robust video super-resolution with learned temporal dynamics. In International Conference on Computer Vision, pages 2507-2515, 2017.","title":"Robust video super-resolution with learned temporal dynamics","authors":"Liu","year":"2017","venue":"International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Liu et al., 2017c] Ding Liu, Bihan Wen, Xianming Liu, and Thomas S Huang. When image denoising meets high-level vision tasks: A deep learning approach. arXiv preprint arXiv:1706.04284, 2017.","title":"When image denoising meets high-level vision tasks: A deep learning approach","authors":"Liu","year":"2017","venue":null,"doi":null,"url":null,"internal_url":"/proceedings/2018/117"},{"position":7,"raw":"[Liu* et al., 2018] Ding Liu*, Bowen Cheng*, Zhangyang Wang, Haichao Zhang, and Thomas S Huang. Visual recognition in very low-quality settings: Delving into the power of pre-training. In The Thirty-Second AAAI Confer- ence on Artificial Intelligence, 2018.","title":"Visual recognition in very low-quality settings: Delving into the power of pre-training","authors":"Ding Liu, *, Bowen Cheng, *, Zhangyang Wang, Haichao Zhang, Thomas S Huang","year":"2018","venue":"The Thirty-Second AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Wang et al., 2015] Zhaowen Wang, Ding Liu, Jianchao Yang, Wei Han, and Thomas Huang. Deep networks for image super-resolution with sparse prior. In International Conference on Computer Vision, pages 370-378, 2015.","title":"Deep networks for image super-resolution with sparse prior","authors":"Wang","year":"2015","venue":"International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Wang et al., 2016a] Zhangyang Wang, Shiyu Chang, Y- ingzhen Yang, Ding Liu, and Thomas S Huang. Studying very low resolution recognition using deep networks. In IEEE Conference on Computer Vision and Pattern Recog- nition, pages 4792-4800, 2016.","title":"Studying very low resolution recognition using deep networks","authors":"Wang","year":"2016","venue":"IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Wang et al., 2016b] Zhangyang Wang, Ding Liu, Shiyu Chang, Qing Ling, Yingzhen Yang, and Thomas S Huang. D3: Deep dual-domain based fast restoration of jpeg- compressed images. In IEEE Conference on Computer Vision and Pattern Recognition, pages 2764-2772, 2016.","title":"D3: Deep dual-domain based fast restoration of jpegcompressed images","authors":"Wang","year":"2016","venue":"IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}