activity
20142023
most citedTruly Proximal Policy Optimization

31 citations · 106 across the 14 of their papers we have counts for

collaborators

14 papers

cs.CV2023★ 11 cited

MNet: Multilevel, Mixed and Multistage Attention Network for Salient Object Detection

Yao Yuan, Pan Gao, XiaoYang Tan

Most existing salient object detection methods mostly use U-Net or feature pyramid structure, which simply aggregates feature maps of different scales, ignoring the uniqueness and…

cs.CV2023★ 5 cited

ProxyFormer: Proxy Alignment Assisted Point Cloud Completion with Missing Part Sensitive Transformer

Shanshan Li, Pan Gao, Xiaoyang Tan +1

Problems such as equipment defects or limited viewpoints will lead the captured point clouds to be incomplete. Therefore, recovering the complete point clouds from the partial ones…

cs.LG2020★ 1 cited

Stabilizing Q Learning Via Soft Mellowmax Operator

Yaozhong Gan, Zhe Zhang, Xiaoyang Tan

Learning complicated value functions in high dimensional state space by function approximation is a challenging task, partially due to that the max-operator used in temporal differ…

cs.CV2020

Deep Robust Multilevel Semantic Cross-Modal Hashing

Ge Song, Jun Zhao, Xiaoyang Tan

Hashing based cross-modal retrieval has recently made significant progress. But straightforward embedding data from different modalities into a joint Hamming space will inevitably…

cs.MA2019

SMIX(): Enhancing Centralized Value Functions for Cooperative Multi-Agent Reinforcement Learning

Xinghu Yao, Chao Wen, Yuhui Wang +1

Learning a stable and generalizable centralized value function (CVF) is a crucial but challenging task in multi-agent reinforcement learning (MARL), as it has to deal with the issu…

cs.LG2019★ 31 cited

Truly Proximal Policy Optimization

Yuhui Wang, Hao He, Chao Wen +1

Proximal policy optimization (PPO) is one of the most successful deep reinforcement-learning methods, achieving state-of-the-art performance across a wide range of challenging task…

cs.LG2019★ 9 cited

Robust Reinforcement Learning in POMDPs with Incomplete and Noisy Observations

Yuhui Wang, Hao He, Xiaoyang Tan

In real-world scenarios, the observation data for reinforcement learning with continuous control is commonly noisy and part of it may be dynamically missing over time, which violat…

cs.CV2019★ 2 cited

Pornographic Image Recognition via Weighted Multiple Instance Learning

Jin Xin, Wang Yuhui, Tan Xiaoyang

In the era of Internet, recognizing pornographic images is of great significance for protecting children's physical and mental health. However, this task is very challenging as the…

cs.LG2019★ 30 cited

Trust Region-Guided Proximal Policy Optimization

Yuhui Wang, Hao He, Xiaoyang Tan +1

Proximal policy optimization (PPO) is one of the most popular deep reinforcement learning (RL) methods, achieving state-of-the-art performance across a wide range of challenging ta…

cs.CV2016★ 1 cited

A Unified Gender-Aware Age Estimation

Qing Tian, Songcan Chen, Xiaoyang Tan

Human age estimation has attracted increasing researches due to its wide applicability in such as security monitoring and advertisement recommendation. Although a variety of method…

cs.CV2016★ 4 cited

Face Alignment In-the-Wild: A Survey

Xin Jin, Xiaoyang Tan

Over the last two decades, face alignment or localizing fiducial facial points has received increasing attention owing to its comprehensive applications in automatic face analysis.…

cs.CV2016

Bayesian Neighbourhood Component Analysis

Dong Wang, Xiaoyang Tan

Learning a good distance metric in feature space potentially improves the performance of the KNN classifier and is useful in many real-world applications. Many metric learning algo…

cs.CV2015★ 9 cited

Tri-Subject Kinship Verification: Understanding the Core of A Family

Xiaoqian Qin, Xiaoyang Tan, Songcan Chen

One major challenge in computer vision is to go beyond the modeling of individual objects and to investigate the bi- (one-versus-one) or tri- (one-versus-two) relationship among mu…

cs.CV2014★ 3 cited

Unsupervised Feature Learning with C-SVDDNet

Dong Wang, Xiaoyang Tan

In this paper, we investigate the problem of learning feature representation from unlabeled data using a single-layer K-means network. A K-means network maps the input data into a…