Search Results for author: Hao Shen

Found 27 papers, 5 papers with code

Unsupervised Domain Adaptation with Dynamics-Aware Rewards in Reinforcement Learning

no code implementations NeurIPS 2021 Jinxin Liu, Hao Shen, Donglin Wang, Yachen Kang, Qiangxing Tian

Unsupervised reinforcement learning aims to acquire skills without prior goal representations, where an agent automatically explores an open-ended environment to represent goals and learn the goal-conditioned policy.

Unsupervised Domain Adaptation Unsupervised Reinforcement Learning

Identifying Influential Users in Unknown Social Networks for Adaptive Incentive Allocation Under Budget Restriction

no code implementations13 Jul 2021 Shiqing Wu, Weihua Li, Hao Shen, Quan Bai

To tackle the aforementioned challenges, in this paper, we propose a novel algorithm for exploring influential users in unknown networks, which can estimate the influential relationships among users based on their historical behaviors and without knowing the topology of the network.

Recommendation Systems

Analysis and Optimisation of Bellman Residual Errors with Neural Function Approximation

no code implementations16 Jun 2021 Martin Gottwald, Sven Gronauer, Hao Shen, Klaus Diepold

First, we conduct a critical point analysis of the error function and provide technical insights on optimisation and design choices for neural networks.

Continuous Control

Large $N$ limit of the $O(N)$ linear sigma model in 3D

no code implementations4 Feb 2021 Hao Shen, Rongchan Zhu, Xiangchan Zhu

We prove tightness of the invariant measures in the large N limit.

Quantization Probability Mathematical Physics Analysis of PDEs Mathematical Physics

Dynamic Texture Recognition via Nuclear Distances on Kernelized Scattering Histogram Spaces

1 code implementation1 Feb 2021 Alexander Sagel, Julian Wörmann, Hao Shen

Distance-based dynamic texture recognition is an important research field in multimedia processing with applications ranging from retrieval to segmentation of video data.

Dynamic Texture Recognition General Classification

End-to-End Video Instance Segmentation with Transformers

2 code implementations CVPR 2021 Yuqing Wang, Zhaoliang Xu, Xinlong Wang, Chunhua Shen, Baoshan Cheng, Hao Shen, Huaxia Xia

Here, we propose a new video instance segmentation framework built upon Transformers, termed VisTR, which views the VIS task as a direct end-to-end parallel sequence decoding/prediction problem.

Instance Segmentation Semantic Segmentation +2

Attentional Separation-and-Aggregation Network for Self-supervised Depth-Pose Learning in Dynamic Scenes

no code implementations18 Nov 2020 Feng Gao, Jincheng Yu, Hao Shen, Yu Wang, Huazhong Yang

Learning depth and ego-motion from unlabeled videos via self-supervision from epipolar projection can improve the robustness and accuracy of the 3D perception and localization of vision-based robots.

Metapath- and Entity-aware Graph Neural Network for Recommendation

1 code implementation22 Oct 2020 Muhammad Umer Anwaar, Zhiwei Han, Shyam Arumugaswamy, Rayyan Ahmad Khan, Thomas Weber, Tianming Qiu, Hao Shen, Yuanting Liu, Martin Kleinsteuber

In this paper, we employ collaborative subgraphs (CSGs) and metapaths to form metapath-aware subgraphs, which explicitly capture sequential semantics in graph structures.

Link Prediction Recommendation Systems

3D Scene Geometry-Aware Constraint for Camera Localization with Deep Learning

no code implementations13 May 2020 Mi Tian, Qiong Nie, Hao Shen

Camera localization is a fundamental and key component of autonomous driving vehicles and mobile robots to localize themselves globally for further environment perception, path planning and motion control.

Autonomous Driving Camera Localization

CenterMask: single shot instance segmentation with point representation

no code implementations CVPR 2020 Yuqing Wang, Zhaoliang Xu, Hao Shen, Baoshan Cheng, Lirong Yang

Accordingly, we decompose the instance segmentation into two parallel subtasks: Local Shape prediction that separates instances even in overlapping conditions, and Global Saliency generation that segments the whole image in a pixel-to-pixel manner.

Instance Segmentation Semantic Segmentation

A New Dataset and Boundary-Attention Semantic Segmentation for Face Parsing

no code implementations Proceedings of the AAAI Conference on Artificial Intelligence 2020 Yinglu Liu, Hailin Shi, Hao Shen, Yue Si, Xiaobo Wang, Tao Mei

The dataset is publicly accessible to the community for boosting the advance of face parsing. 1 Second, a simple yet effective Boundary-Attention Semantic Segmentation (BASS) method is proposed for face parsing, which contains a three-branch network with elaborately developed loss functions to fully exploit the boundary information.

Face Parsing Image Generation +1

A High-Efficiency Framework for Constructing Large-Scale Face Parsing Benchmark

no code implementations13 May 2019 Yinglu Liu, Hailin Shi, Yue Si, Hao Shen, Xiaobo Wang, Tao Mei

Each image is provided with accurate annotation of a 11-category pixel-level label map along with coordinates of 106-point landmarks.

Face Alignment Face Detection +2

Grand Challenge of 106-Point Facial Landmark Localization

no code implementations9 May 2019 Yinglu Liu, Hao Shen, Yue Si, Xiaobo Wang, Xiangyu Zhu, Hailin Shi, Zhibin Hong, Hanqi Guo, Ziyuan Guo, Yanqin Chen, Bi Li, Teng Xi, Jun Yu, Haonian Xie, Guochen Xie, Mengyan Li, Qing Lu, Zengfu Wang, Shenqi Lai, Zhenhua Chai, Xiaoming Wei

However, previous competitions on facial landmark localization (i. e., the 300-W, 300-VW and Menpo challenges) aim to predict 68-point landmarks, which are incompetent to depict the structure of facial components.

Face Alignment Face Recognition +2

A Generative Map for Image-based Camera Localization

1 code implementation18 Feb 2019 Mingpan Guo, Stefan Matthes, Jiaojiao Ye, Hao Shen

For localization, we show that Generative Map achieves comparable performance with current regression models.

Camera Localization Visual Localization +1

A Differential Topological View of Challenges in Learning with Feedforward Neural Networks

no code implementations26 Nov 2018 Hao Shen

Among many unsolved puzzles in theories of Deep Neural Networks (DNNs), there are three most fundamental challenges that highly demand solutions, namely, expressibility, optimisability, and generalisability.

Representation Learning

Trace Quotient with Sparsity Priors for Learning Low Dimensional Image Representations

no code implementations8 Oct 2018 Xian Wei, Hao Shen, Martin Kleinsteuber

We propose a generic algorithmic framework, which leverages two classic representation learning paradigms, i. e., sparse representation and the trace quotient criterion.

Data Visualization Dimensionality Reduction +1

Dynamic Variational Autoencoders for Visual Process Modeling

1 code implementation20 Mar 2018 Alexander Sagel, Hao Shen

This work studies the problem of modeling visual processes by leveraging deep generative architectures for learning linear, Gaussian representations from observed sequences.

Towards a Mathematical Understanding of the Difficulty in Learning with Feedforward Neural Networks

no code implementations CVPR 2018 Hao Shen

Training deep neural networks for solving machine learning problems is one great challenge in the field, mainly due to its associated optimisation problem being highly non-convex.

Reinforcement Learning in Conflicting Environments for Autonomous Vehicles

no code implementations22 Oct 2016 Dominik Meyer, Johannes Feldmaier, Hao Shen

In this work, we investigate the application of Reinforcement Learning to two well known decision dilemmas, namely Newcomb's Problem and Prisoner's Dilemma.

Autonomous Vehicles

$\ell_1$ Regularized Gradient Temporal-Difference Learning

no code implementations5 Oct 2016 Dominik Meyer, Hao Shen, Klaus Diepold

In this paper, we study the Temporal Difference (TD) learning with linear value function approximation.

Texture Retrieval via the Scattering Transform

no code implementations12 Jan 2015 Alexander Sagel, Dominik Meyer, Hao Shen

Our approach employs a recently developed method, the so-called Scattering transform, for the process of feature extraction in texture retrieval.

Content-Based Image Retrieval Texture Classification

An Adaptive Dictionary Learning Approach for Modeling Dynamical Textures

no code implementations19 Dec 2013 Xian Wei, Hao Shen, Martin Kleinsteuber

Video representation is an important and challenging task in the computer vision community.

Dictionary Learning

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