3 code implementations • 7 Sep 2022 • Runmin Cong, Qi Qin, Chen Zhang, Qiuping Jiang, Shiqi Wang, Yao Zhao, Sam Kwong
In this paper, we focus on a new weakly-supervised SOD task under hybrid labels, where the supervision labels include a large number of coarse labels generated by the traditional unsupervised method and a small number of real labels.
Ranked #7 on RGB Salient Object Detection on PASCAL-S
no code implementations • NeurIPS 2021 • Qi Qin, Wenpeng Hu, Han Peng, Dongyan Zhao, Bing Liu
Continual learning (CL) of a sequence of tasks is often accompanied with the catastrophic forgetting(CF) problem.
1 code implementation • 31 Oct 2021 • Youjie Zhou, Yiming Wang, Fabio Poiesi, Qi Qin, Yi Wan
We compare our L3D-based loop closure approach with recent approaches on LiDAR data and achieve state-of-the-art loop closure detection accuracy.
1 code implementation • NeurIPS 2020 • Wenpeng Hu, Mengyu Wang, Qi Qin, Jinwen Ma, Bing Liu
Existing neural network based one-class learning methods mainly use various forms of auto-encoders or GAN style adversarial training to learn a latent representation of the given one class of data.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Qi Qin, Wenpeng Hu, Bing Liu
It proposes a new lifelong learning model (called L2PG) that can retain and selectively transfer the knowledge learned in the past to help learn the new task.
no code implementations • 23 Sep 2020 • Qi Qin, Wenpeng Hu, Bing Liu
In this paper, we propose a significantly more effective approach that converts the original problem to a pair-wise matching problem and then outputs how probable two instances belong to the same class.
no code implementations • ACL 2020 • Qi Qin, Wenpeng Hu, Bing Liu
In this paper, we propose a novel angle to further improve this representation learning, i. e., feature projection.
no code implementations • 22 Jan 2018 • Linbo Qiao, Tianyi Lin, Qi Qin, Xicheng Lu
In this paper, we propose a stochastic Primal-Dual Hybrid Gradient (PDHG) approach for solving a wide spectrum of regularized stochastic minimization problems, where the regularization term is composite with a linear function.