no code implementations • 22 Mar 2024 • Jiaheng Yu, Jing Li, Yue He, Kai Zhu, Shuyi Zhang, Wen Hu
Recent methods utilize graph contrastive Learning within graph-structured user-item interaction data for collaborative filtering and have demonstrated their efficacy in recommendation tasks.
1 code implementation • 24 Jan 2024 • Zehua Liu, Zimeng Li, Jingyuan Wang, Yue He
Significance testing aims to determine whether a proposition about the population distribution is the truth or not given observations.
1 code implementation • 21 Nov 2023 • Jiahao Ji, Wentao Zhang, Jingyuan Wang, Yue He, Chao Huang
It first encodes traffic data into two disentangled representations for associating invariant and variant ST contexts.
no code implementations • 24 May 2023 • Han Yu, Xingxuan Zhang, Renzhe Xu, Jiashuo Liu, Yue He, Peng Cui
This paper examines the risks of test data information leakage from two aspects of the current evaluation protocol: supervised pretraining on ImageNet and oracle model selection.
no code implementations • 21 May 2023 • Zimu Wang, Jiashuo Liu, Hao Zou, Xingxuan Zhang, Yue He, Dongxu Liang, Peng Cui
In this work, we focus on exploring two representative categories of heterogeneity in recommendation data that is the heterogeneity of prediction mechanism and covariate distribution and propose an algorithm that explores the heterogeneity through a bilevel clustering method.
1 code implementation • ICCV 2023 • Min Zhang, Junkun Yuan, Yue He, Wenbin Li, Zhengyu Chen, Kun Kuang
To achieve this goal, we apply a bilevel optimization to explicitly model and optimize the coupling relationship between the OOD model and auxiliary adapter layers.
no code implementations • ICCV 2023 • Shuo Li, Yue He, Weiming Zhang , Wei zhang, Xiao Tan, Junyu Han, Errui Ding, Jingdong Wang
Current state-of-the-art semi-supervised semantic segmentation (SSSS) methods typically adopt pseudo labeling and consistency regularization between multiple learners with different perturbations.
no code implementations • 2 Dec 2022 • Han Yu, Peng Cui, Yue He, Zheyan Shen, Yong Lin, Renzhe Xu, Xingxuan Zhang
The problem of covariate-shift generalization has attracted intensive research attention.
2 code implementations • 23 Nov 2022 • Sifeng He, Yue He, Minlong Lu, Chen Jiang, Xudong Yang, Feng Qian, Xiaobo Zhang, Lei Yang, Jiandong Zhang
Previous methods typically start from frame-to-frame similarity matrix generated by cosine similarity between frame-level features of the input video pair, and then detect and refine the boundaries of copied segments on similarity matrix under temporal constraints.
no code implementations • 11 Oct 2022 • Yue He, Minyue Jiang, Xiaoqing Ye, Liang Du, Zhikang Zou, Wei zhang, Xiao Tan, Errui Ding
In this paper, we target at finding an enhanced feature space where the lane features are distinctive while maintaining a similar distribution of lanes in the wild.
7 code implementations • 5 Oct 2022 • Silvio Giancola, Anthony Cioppa, Adrien Deliège, Floriane Magera, Vladimir Somers, Le Kang, Xin Zhou, Olivier Barnich, Christophe De Vleeschouwer, Alexandre Alahi, Bernard Ghanem, Marc Van Droogenbroeck, Abdulrahman Darwish, Adrien Maglo, Albert Clapés, Andreas Luyts, Andrei Boiarov, Artur Xarles, Astrid Orcesi, Avijit Shah, Baoyu Fan, Bharath Comandur, Chen Chen, Chen Zhang, Chen Zhao, Chengzhi Lin, Cheuk-Yiu Chan, Chun Chuen Hui, Dengjie Li, Fan Yang, Fan Liang, Fang Da, Feng Yan, Fufu Yu, Guanshuo Wang, H. Anthony Chan, He Zhu, Hongwei Kan, Jiaming Chu, Jianming Hu, Jianyang Gu, Jin Chen, João V. B. Soares, Jonas Theiner, Jorge De Corte, José Henrique Brito, Jun Zhang, Junjie Li, Junwei Liang, Leqi Shen, Lin Ma, Lingchi Chen, Miguel Santos Marques, Mike Azatov, Nikita Kasatkin, Ning Wang, Qiong Jia, Quoc Cuong Pham, Ralph Ewerth, Ran Song, RenGang Li, Rikke Gade, Ruben Debien, Runze Zhang, Sangrok Lee, Sergio Escalera, Shan Jiang, Shigeyuki Odashima, Shimin Chen, Shoichi Masui, Shouhong Ding, Sin-wai Chan, Siyu Chen, Tallal El-Shabrawy, Tao He, Thomas B. Moeslund, Wan-Chi Siu, Wei zhang, Wei Li, Xiangwei Wang, Xiao Tan, Xiaochuan Li, Xiaolin Wei, Xiaoqing Ye, Xing Liu, Xinying Wang, Yandong Guo, YaQian Zhao, Yi Yu, YingYing Li, Yue He, Yujie Zhong, Zhenhua Guo, Zhiheng Li
The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team.
no code implementations • CVPR 2022 • Yi-Hua Huang, Yue He, Yu-Jie Yuan, Yu-Kun Lai, Lin Gao
We first pre-train a standard NeRF of the 3D scene to be stylized and replace its color prediction module with a style network to obtain a stylized NeRF.
2 code implementations • CVPR 2023 • Xingxuan Zhang, Yue He, Renzhe Xu, Han Yu, Zheyan Shen, Peng Cui
Most current evaluation methods for domain generalization (DG) adopt the leave-one-out strategy as a compromise on the limited number of domains.
no code implementations • 21 Mar 2022 • Yue He, Reiichiro Kawai, Yasutaka Shimizu, Kazutoshi Yamazaki
The Gerber-Shiu function provides a unified framework for the evaluation of a variety of risk quantities.
1 code implementation • 8 Feb 2022 • Yue He, Zimu Wang, Peng Cui, Hao Zou, Yafeng Zhang, Qiang Cui, Yong Jiang
In spite of the tremendous development of recommender system owing to the progressive capability of machine learning recently, the current recommender system is still vulnerable to the distribution shift of users and items in realistic scenarios, leading to the sharp decline of performance in testing environments.
1 code implementation • AAAI 2022 2021 • Yue He, Chen Chen, Jing Zhang, Juhua Liu, Fengxiang He, Chaoyue Wang, Bo Du
Technically, given the character segmentation maps predicted by a VR model, we construct a subgraph for each instance, where nodes represent the pixels in it and edges are added between nodes based on their spatial similarity.
Ranked #10 on Scene Text Recognition on ICDAR2015 (using extra training data)
no code implementations • 31 Aug 2021 • Jiashuo Liu, Zheyan Shen, Yue He, Xingxuan Zhang, Renzhe Xu, Han Yu, Peng Cui
This paper represents the first comprehensive, systematic review of OOD generalization, encompassing a spectrum of aspects from problem definition, methodological development, and evaluation procedures, to the implications and future directions of the field.
1 code implementation • 9 May 2021 • Yuxiang Zhao, Wenhao Wu, Yue He, YingYing Li, Xiao Tan, Shifeng Chen
In this paper, we propose a straightforward and efficient framework that includes pre-processing, a dynamic track module, and post-processing.
2 code implementations • CVPR 2021 • Xingxuan Zhang, Peng Cui, Renzhe Xu, Linjun Zhou, Yue He, Zheyan Shen
Approaches based on deep neural networks have achieved striking performance when testing data and training data share similar distribution, but can significantly fail otherwise.
Ranked #28 on Domain Generalization on VLCS
no code implementations • 1 Jan 2021 • Xingxuan Zhang, Peng Cui, Renzhe Xu, Yue He, Linjun Zhou, Zheyan Shen
We propose to address this problem by removing the dependencies between features via reweighting training samples, which results in a more balanced distribution and helps deep models get rid of spurious correlations and, in turn, concentrate more on the true connection between features and labels.
no code implementations • NeurIPS 2020 • Hao Zou, Peng Cui, Bo Li, Zheyan Shen, Jianxin Ma, Hongxia Yang, Yue He
Estimating counterfactual outcome of different treatments from observational data is an important problem to assist decision making in a variety of fields.
2 code implementations • 5 Aug 2019 • Yunxuan Zhang, Siwei Zhang, Yue He, Cheng Li, Chen Change Loy, Ziwei Liu
However, in real-world scenario end-users often only have one target face at hand, rendering existing methods inapplicable.
no code implementations • 7 Jun 2019 • Yue He, Zheyan Shen, Peng Cui
The experimental results demonstrate that NICO can well support the training of ConvNet model from scratch, and a batch balancing module can help ConvNets to perform better in Non-I. I. D.
1 code implementation • 13 Jul 2017 • Yue He, Kaidi Cao, Cheng Li, Chen Change Loy
Given a large number of unlabeled face images, face grouping aims at clustering the images into individual identities present in the data.