Search Results for author: Yue He

Found 24 papers, 12 papers with code

Bilateral Unsymmetrical Graph Contrastive Learning for Recommendation

no code implementations22 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.

Collaborative Filtering Contrastive Learning +1

Full Bayesian Significance Testing for Neural Networks

1 code implementation24 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.

Self-Supervised Deconfounding Against Spatio-Temporal Shifts: Theory and Modeling

1 code implementation21 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.

Rethinking the Evaluation Protocol of Domain Generalization

no code implementations24 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.

Domain Generalization Model Selection

Exploring and Exploiting Data Heterogeneity in Recommendation

no code implementations21 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.

Recommendation Systems

MAP: Towards Balanced Generalization of IID and OOD through Model-Agnostic Adapters

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.

Bilevel Optimization Inductive Bias

CFCG: Semi-Supervised Semantic Segmentation via Cross-Fusion and Contour Guidance Supervision

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.

Semi-Supervised Semantic Segmentation

Stable Learning via Sparse Variable Independence

no code implementations2 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.

Variable Selection

TransVCL: Attention-enhanced Video Copy Localization Network with Flexible Supervision

2 code implementations23 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.

Retrieval Video Retrieval

Repainting and Imitating Learning for Lane Detection

no code implementations11 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.

Lane Detection

SoccerNet 2022 Challenges Results

7 code implementations5 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.

Action Spotting Camera Calibration +3

StylizedNeRF: Consistent 3D Scene Stylization as Stylized NeRF via 2D-3D Mutual Learning

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.

Image Stylization

NICO++: Towards Better Benchmarking for Domain Generalization

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.

Benchmarking Domain Generalization +2

The Gerber-Shiu discounted penalty function: A review from practical perspectives

no code implementations21 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.

CausPref: Causal Preference Learning for Out-of-Distribution Recommendation

1 code implementation8 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.

Recommendation Systems

Visual Semantics Allow for Textual Reasoning Better in Scene Text Recognition

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)

Language Modelling Scene Text Recognition

Towards Out-Of-Distribution Generalization: A Survey

no code implementations31 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.

Out-of-Distribution Generalization Representation Learning

Good Practices and A Strong Baseline for Traffic Anomaly Detection

1 code implementation9 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.

Anomaly Detection Management +1

Deep Stable Learning for Out-Of-Distribution Generalization

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.

Domain Generalization Out-of-Distribution Generalization

Sample Balancing for Improving Generalization under Distribution Shifts

no code implementations1 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.

Domain Adaptation Object Recognition

Counterfactual Prediction for Bundle Treatment

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.

counterfactual Decision Making +2

One-shot Face Reenactment

2 code implementations5 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.

Decoder Face Reconstruction +1

Towards Non-I.I.D. Image Classification: A Dataset and Baselines

no code implementations7 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.

Classification General Classification +1

Merge or Not? Learning to Group Faces via Imitation Learning

1 code implementation13 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.

Clustering Imitation Learning

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