103 citations · 157 across the 9 of their papers we have counts for
9 papers
Deep Graph Reprogramming
Yongcheng Jing, Chongbin Yuan, Li Ju +3
In this paper, we explore a novel model reusing task tailored for graph neural networks (GNNs), termed as "deep graph reprogramming". We strive to reprogram a pre-trained GNN, with…
Segment Anything in Non-Euclidean Domains: Challenges and Opportunities
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. The impact of SA h…
Evaluation and Improvement of Interpretability for Self-Explainable Part-Prototype Networks
Qihan Huang, Mengqi Xue, Wenqi Huang +4
Part-prototype networks (e.g., ProtoPNet, ProtoTree, and ProtoPool) have attracted broad research interest for their intrinsic interpretability and comparable accuracy to non-inter…
Learning Graph Neural Networks for Image Style Transfer
Yongcheng Jing, Yining Mao, Yiding Yang +4
State-of-the-art parametric and non-parametric style transfer approaches are prone to either distorted local style patterns due to global statistics alignment, or unpleasing artifa…
Meta-Aggregator: Learning to Aggregate for 1-bit Graph Neural Networks
Yongcheng Jing, Yiding Yang, Xinchao Wang +2
In this paper, we study a novel meta aggregation scheme towards binarizing graph neural networks (GNNs). We begin by developing a vanilla 1-bit GNN framework that binarizes both th…
Dynamic Instance Normalization for Arbitrary Style Transfer
Yongcheng Jing, Xiao Liu, Yukang Ding +4
Prior normalization methods rely on affine transformations to produce arbitrary image style transfers, of which the parameters are computed in a pre-defined way. Such manually-defi…
Interpretable Partitioned Embedding for Customized Fashion Outfit Composition
Zunlei Feng, Zhenyun Yu, Yezhou Yang +3
Intelligent fashion outfit composition becomes more and more popular in these years. Some deep learning based approaches reveal competitive composition recently. However, the unexp…
Stroke Controllable Fast Style Transfer with Adaptive Receptive Fields
Yongcheng Jing, Yang Liu, Yezhou Yang +4
The Fast Style Transfer methods have been recently proposed to transfer a photograph to an artistic style in real-time. This task involves controlling the stroke size in the styliz…
Neural Style Transfer: A Review
Yongcheng Jing, Yezhou Yang, Zunlei Feng +3
The seminal work of Gatys et al. demonstrated the power of Convolutional Neural Networks (CNNs) in creating artistic imagery by separating and recombining image content and style.…