no code implementations • 1 Aug 2023 • Biao Liu, Congyu Qiao, Ning Xu, Xin Geng, Ziran Zhu, Jun Yang
In order to fully exploit the inherent spatial label-correlation between neighboring grids, we propose a novel approach, {\ours}, i. e., VAriational Label-Correlation Enhancement for Congestion Prediction, which considers the local label-correlation in the congestion map, associating the estimated congestion value of each grid with a local label-correlation weight influenced by its surrounding grids.
no code implementations • 2 Jun 2022 • Ning Xu, Biao Liu, Jiaqi Lv, Congyu Qiao, Xin Geng
Partial label learning (PLL) aims to train multiclass classifiers from the examples each annotated with a set of candidate labels where a fixed but unknown candidate label is correct.
1 code implementation • 1 Jun 2022 • Ning Xu, Congyu Qiao, Jiaqi Lv, Xin Geng, Min-Ling Zhang
To cope with the challenge, we investigate single-positive multi-label learning (SPMLL) where each example is annotated with only one relevant label, and show that one can successfully learn a theoretically grounded multi-label classifier for the problem.
1 code implementation • 8 Apr 2022 • Congyu Qiao, Ning Xu, Xin Geng
Most existing PLL approaches assume that the incorrect labels in each training example are randomly picked as the candidate labels and model the generation process of the candidate labels in a simple way.
1 code implementation • NeurIPS 2021 • Ning Xu, Congyu Qiao, Xin Geng, Min-Ling Zhang
In this paper, we consider instance-dependent PLL and assume that each example is associated with a latent label distribution constituted by the real number of each label, representing the degree to each label describing the feature.
no code implementations • 10 Nov 2020 • Andrey Ignatov, Radu Timofte, Ming Qian, Congyu Qiao, Jiamin Lin, Zhenyu Guo, Chenghua Li, Cong Leng, Jian Cheng, Juewen Peng, Xianrui Luo, Ke Xian, Zijin Wu, Zhiguo Cao, Densen Puthussery, Jiji C V, Hrishikesh P S, Melvin Kuriakose, Saikat Dutta, Sourya Dipta Das, Nisarg A. Shah, Kuldeep Purohit, Praveen Kandula, Maitreya Suin, A. N. Rajagopalan, Saagara M B, Minnu A L, Sanjana A R, Praseeda S, Ge Wu, Xueqin Chen, Tengyao Wang, Max Zheng, Hulk Wong, Jay Zou
This paper reviews the second AIM realistic bokeh effect rendering challenge and provides the description of the proposed solutions and results.
1 code implementation • 4 Nov 2020 • Ming Qian, Congyu Qiao, Jiamin Lin, Zhenyu Guo, Chenghua Li, Cong Leng, Jian Cheng
A photo captured with bokeh effect often means objects in focus are sharp while the out-of-focus areas are all blurred.