no code implementations • ICCV 2023 • Ke Xu, Gerhard Petrus Hancke, Rynson W.H. Lau
In this paper, we propose a novel neural approach to harmonize the image colors in a camera-independent color space, in which color values are proportional to the scene radiance.
no code implementations • ICCV 2023 • Jiaying Lin, Rynson W.H. Lau
Existing mirror detection methods require supervised ImageNet pre-training to obtain good general-purpose image features.
no code implementations • CVPR 2023 • Jiaying Lin, Xin Tan, Rynson W.H. Lau
However, detecting mirrors over dynamic scenes is still under-explored due to the lack of a high-quality dataset and an effective method for video mirror detection (VMD).
no code implementations • CVPR 2022 • Huankang Guan, Jiaying Lin, Rynson W.H. Lau
Inspired by this observation, we propose a model to exploit the semantic associations between the mirror and its surrounding objects for a reliable mirror localization.
no code implementations • CVPR 2022 • Xiaotian Qiao, Gerhard P. Hancke, Rynson W.H. Lau
We also show that our model enables a wide range of applications, including novel-view image synthesis, novel-view image editing, and amodal object estimation.
no code implementations • CVPR 2021 • Jiaying Lin, Zebang He, Rynson W.H. Lau
However, as it is only based on a general context integration operation and does not consider any specific glass surface properties, it gets confused when the images contain objects that are similar to glass surfaces and degenerates in challenging scenes with insufficient contexts.
1 code implementation • ICCV 2021 • Xiaotian Qiao, Gerhard P. Hancke, Rynson W.H. Lau
In particular, we first detect the light source regions and the flare regions separately, and then remove the flare artifacts based on the light source aware guidance.
no code implementations • ICCV 2021 • Lei Zhu, Ke Xu, Zhanghan Ke, Rynson W.H. Lau
These two phenomenons reveal that deep shadow detectors heavily depend on the intensity cue, which we refer to as intensity bias.
1 code implementation • ICCV 2021 • Avishek Siris, Jianbo Jiao, Gary K.L. Tam, Xianghua Xie, Rynson W.H. Lau
To our knowledge, such high-level semantic contextual information of image scenes is under-explored for saliency detection in the literature.