1 code implementation • 13 Jul 2022 • Bo Pang, Yifan Zhang, Yaoyi Li, Jia Cai, Cewu Lu
In this paper, we propose a genuine group-level contrastive visual representation learning method whose linear evaluation performance on ImageNet surpasses the vanilla supervised learning.
Ranked #38 on Self-Supervised Image Classification on <h2>oi</h2>
1 code implementation • 1 Dec 2021 • Weihao Jiang, Dongdong Yu, Zhaozhi Xie, Yaoyi Li, Zehuan Yuan, Hongtao Lu
For emerging content-based feature fusion, most existing matting methods only focus on local features which lack the guidance of a global feature with strong semantic information related to the interesting object.
Ranked #4 on Image Matting on Composition-1K
no code implementations • 4 Jun 2020 • Weihao Jiang, Zhaozhi Xie, Yaoyi Li, Chang Liu, Hongtao Lu
Many of these applications need to perform a real-time and efficient prediction for semantic segmentation with a light-weighted network.
no code implementations • 7 Apr 2020 • Yaoyi Li, Qingyao Xu, Hongtao Lu
Natural image matting is a fundamental problem in computational photography and computer vision.
1 code implementation • 13 Jan 2020 • Yaoyi Li, Hongtao Lu
Inspired by affinity-based method and the successes of contextual attention in inpainting, we develop a novel end-to-end approach for natural image matting with a guided contextual attention module, which is specifically designed for image matting.
no code implementations • 16 May 2019 • Yaoyi Li, Jianfu Zhang, Weijie Zhao, Hongtao Lu
A high efficient image matting method based on a weakly annotated mask is in demand for mobile applications.
no code implementations • 21 Sep 2016 • Yaoyi Li, Hongtao Lu
In this paper, we present a method dubbed Consensus Prior Constraint Propagation (CPCP), which can provide the prior knowledge of the robustness of each data instance and its neighborhood.
no code implementations • 13 Nov 2015 • Yaoyi Li, Junxuan Chen, Hongtao Lu
In this paper, we propose a novel method, dubbed Adaptive Affinity Matrix (AdaAM), to learn an adaptive affinity matrix and derive a distance metric from the affinity.