1 code implementation • CVPR 2022 • Chenxi Xie, Changqun Xia, Mingcan Ma, Zhirui Zhao, Xiaowu Chen, Jia Li
An attention-based Cross-Model Grafting Module (CMGM) is proposed to enable CNN branch to combine broken detailed information more holistically, guided by different source feature during decoding process.
Ranked #5 on RGB Salient Object Detection on UHRSD (using extra training data)
no code implementations • 15 Oct 2021 • Mingcan Ma, Changqun Xia, Chenxi Xie, Xiaowu Chen, Jia Li
Moreover, Unlike multi-path parallel training, MHB randomly selects one branch each time for gradient back propagation in a boosting way.
1 code implementation • 18 Dec 2019 • Jia Li, Jinming Su, Changqun Xia, Mingcan Ma, Yonghong Tian
Through these two attentions, we use the Purificatory Mechanism to impose strict weights with different regions of the whole salient objects and purify results from hard-to-distinguish regions, thus accurately predicting the locations and details of salient objects.