1 code implementation • CVPRW 2023 • Marcos V. Conde, Manuel Kolmet, Tim Seizinger, Tom E. Bishop, Radu Timofte, Xiangyu Kong, Dafeng Zhang, Jinlong Wu, Fan Wang, Juewen Peng, Zhiyu Pan, Chengxin Liu, Xianrui Luo, Huiqiang Sun, Liao Shen, Zhiguo Cao, Ke Xian, Chaowei Liu, Zigeng Chen, Xingyi Yang, Songhua Liu, Yongcheng Jing, Michael Bi Mi, Xinchao Wang, Zhihao Yang, Wenyi Lian, Siyuan Lai, Haichuan Zhang, Trung Hoang, Amirsaeed Yazdani, Vishal Monga, Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao, Jens Sjölund, Thomas B. Schön, Yuxuan Zhao, Baoliang Chen, Yiqing Xu, JiXiang Niu
We present the new Bokeh Effect Transformation Dataset (BETD), and review the proposed solutions for this novel task at the NTIRE 2023 Bokeh Effect Transformation Challenge.
no code implementations • CVPR 2023 • Yongcheng Jing, Chongbin Yuan, Li Ju, Yiding Yang, Xinchao Wang, DaCheng Tao
In this paper, we explore a novel model reusing task tailored for graph neural networks (GNNs), termed as "deep graph reprogramming".
no code implementations • 23 Apr 2023 • Yongcheng Jing, Xinchao Wang, DaCheng Tao
The recent work known as Segment Anything (SA) has made significant strides in pushing the boundaries of semantic segmentation into the era of foundation models.
1 code implementation • 9 Apr 2023 • Wenxiang Xu, Yongcheng Jing, Linyun Zhou, Wenqi Huang, Lechao Cheng, Zunlei Feng, Mingli Song
This is specifically achieved by devising an elaborated ``prophetic'' teacher, termed as ``Propheter'', that aims to learn the potential class distributions.
1 code implementation • ICCV 2023 • Qihan Huang, Mengqi Xue, Wenqi Huang, Haofei Zhang, Jie Song, Yongcheng Jing, Mingli Song
Part-prototype networks (e. g., ProtoPNet, ProtoTree, and ProtoPool) have attracted broad research interest for their intrinsic interpretability and comparable accuracy to non-interpretable counterparts.
no code implementations • 24 Jul 2022 • Yongcheng Jing, Yining Mao, Yiding Yang, Yibing Zhan, Mingli Song, Xinchao Wang, DaCheng Tao
To this end, we develop an elaborated GNN model with content and style local patches as the graph vertices.
no code implementations • ICCV 2021 • Yongcheng Jing, Yiding Yang, Xinchao Wang, Mingli Song, DaCheng Tao
In this paper, we study a novel meta aggregation scheme towards binarizing graph neural networks (GNNs).
no code implementations • CVPR 2021 • Yongcheng Jing, Yiding Yang, Xinchao Wang, Mingli Song, DaCheng Tao
To this end, we propose an Event-based VSR framework (E-VSR), of which the key component is an asynchronous interpolation (EAI) module that reconstructs a high-frequency (HF) video stream with uniform and tiny pixel displacements between neighboring frames from an event stream.
1 code implementation • CVPR 2021 • Yongcheng Jing, Yiding Yang, Xinchao Wang, Mingli Song, DaCheng Tao
In this paper, we study a novel knowledge transfer task in the domain of graph neural networks (GNNs).
no code implementations • 16 Nov 2019 • Yongcheng Jing, Xiao Liu, Yukang Ding, Xinchao Wang, Errui Ding, Mingli Song, Shilei Wen
Prior normalization methods rely on affine transformations to produce arbitrary image style transfers, of which the parameters are computed in a pre-defined way.
no code implementations • 13 Jun 2018 • Zunlei Feng, Zhenyun Yu, Yezhou Yang, Yongcheng Jing, Junxiao Jiang, Mingli Song
In the supervised attributes module, multiple attributes labels are adopted to ensure that different parts of the overall embedding correspond to different attributes.
1 code implementation • ECCV 2018 • Yongcheng Jing, Yang Liu, Yezhou Yang, Zunlei Feng, Yizhou Yu, DaCheng Tao, Mingli Song
In this paper, we present a stroke controllable style transfer network that can achieve continuous and spatial stroke size control.
8 code implementations • 11 May 2017 • Yongcheng Jing, Yezhou Yang, Zunlei Feng, Jingwen Ye, Yizhou Yu, Mingli Song
We first propose a taxonomy of current algorithms in the field of NST.