no code implementations • 6 Jun 2023 • Qiuyu Peng, Zifei Jiang, Yan Huang, Jingliang Peng
By contrast, we explore in this work a unified framework that is trained once and then used to super-resolve input face images of varied low resolutions.
no code implementations • 22 Nov 2018 • Peng Jiang, Zhiyi Pan, Nuno Vasconcelos, Baoquan Chen, Jingliang Peng
Following this analysis, we propose super diffusion, a novel inclusive learning-based framework for salient object detection, which makes the optimum and robust performance by integrating a large pool of feature spaces, scales and even features originally computed for non-diffusion-based salient object detection.
no code implementations • ICCV 2015 • Peng Jiang, Nuno Vasconcelos, Jingliang Peng
In this work, we propose a generic scheme to promote any diffusion-based salient object detection algorithm by original ways to re-synthesize the diffusion matrix and construct the seed vector.