This paper focuses on classifying images displayed on the websites by incremental learning classifier with Deep Convolutional Neural Network (DCNN) especially for Context Aware Advertisement (CAA) framework.
no code implementations • 18 Aug 2020 • Yuqian Zhou, Michael Kwan, Kyle Tolentino, Neil Emerton, Sehoon Lim, Tim Large, Lijiang Fu, Zhihong Pan, Baopu Li, Qirui Yang, Yihao Liu, Jigang Tang, Tao Ku, Shibin Ma, Bingnan Hu, Jiarong Wang, Densen Puthussery, Hrishikesh P. S, Melvin Kuriakose, Jiji C. V, Varun Sundar, Sumanth Hegde, Divya Kothandaraman, Kaushik Mitra, Akashdeep Jassal, Nisarg A. Shah, Sabari Nathan, Nagat Abdalla Esiad Rahel, Dafan Chen, Shichao Nie, Shuting Yin, Chengconghui Ma, Haoran Wang, Tongtong Zhao, Shanshan Zhao, Joshua Rego, Huaijin Chen, Shuai Li, Zhenhua Hu, Kin Wai Lau, Lai-Man Po, Dahai Yu, Yasar Abbas Ur Rehman, Yiqun Li, Lianping Xing
The results in the paper are state-of-the-art restoration performance of Under-Display Camera Restoration.
In industrial inspection tasks, it is common to capture abundant defect-free image samples but very limited anomalous ones.
One pivot challenge for image anomaly (AD) detection is to learn discriminative information only from normal class training images.
no code implementations • 9 Nov 2019 • Ramanpreet Singh Pahwa, Jin Chao, Jestine Paul, Yiqun Li, Ma Tin Lay Nwe, Shudong Xie, Ashish James, ArulMurugan Ambikapathi, Zeng Zeng, Vijay Ramaseshan Chandrasekhar
In this paper, a multi-phase deep learning based technique is proposed to perform accurate fault detection of rail-valves.