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Learning Generalizable Agents via Saliency-Guided Features Decorrelation

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arXiv 2023年

作者： Huang, Sili Sun, Yanchao Hu, Jifeng Guo, Siyuan Chen, Hechang Chang, Yi Sun, Lichao Yang, Bo Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education China School of Artificial Intelligence Jilin University China Department of Computer Science University of Maryland College Park United States Lehigh University BethlehemPA United States

In visual-based Reinforcement Learning (RL), agents often struggle to generalize well to environmental variations in the state space that were not observed during training. The variations can arise in both task-irrelevant features, such as background noise, and task-relevant features, such as robot configurations, that are related to the optimal decisions. To achieve generalization in both situations, agents are required to accurately understand the impact of changed features on the decisions, i.e., establishing the true associations between changed features and decisions in the policy model. However, due to the inherent correlations among features in the state space, the associations between features and decisions become entangled, making it difficult for the policy to distinguish them. To this end, we propose Saliency-Guided Features Decorrelation (SGFD) to eliminate these correlations through sample reweighting. Concretely, SGFD consists of two core techniques: Random Fourier Functions (RFF) and the saliency map. RFF is utilized to estimate the complex non-linear correlations in high-dimensional images, while the saliency map is designed to identify the changed features. Under the guidance of the saliency map, SGFD employs sample reweighting to minimize the estimated correlations related to changed features, thereby achieving decorrelation in visual RL tasks. Our experimental results demonstrate that SGFD can generalize well on a wide range of test environments and significantly outperforms state-of-the-art methods in handling both task-irrelevant variations and task-relevant variations. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

HVIS: A Human-like Vision and Inference System for Human Motion Prediction

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arXiv 2025年

作者： Lyu, Kedi Chen, Haipeng Liu, Zhenguang Yin, Yifang Lin, Yukang Jiao, Yingying College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Institute of Blockchain and Data Security Hangzhou China A*STAR Singapore Tsinghua Shenzhen International Graduate School Nanshan District Shenzhen China

Grasping the intricacies of human motion, which involve perceiving spatio-temporal dependence and multi-scale effects, is essential for predicting human motion. While humans inherently possess the requisite skills to navigate this issue, it proves to be markedly more challenging for machines to emulate. To bridge the gap, we propose the Human-like Vision and Inference System (HVIS) for human motion prediction, which is designed to emulate human observation and forecast future movements. HVIS comprises two components: the human-like vision encode (HVE) module and the human-like motion inference (HMI) module. The HVE module mimics and refines the human visual process, incorporating a retina-analog component that captures spatiotemporal information separately to avoid unnecessary crosstalk. Additionally, a visual cortex-analogy component is designed to hierarchically extract and treat complex motion features, focusing on both global and local features of human poses. The HMI is employed to simulate the multi-stage learning model of the human brain. The spontaneous learning network simulates the neuronal fracture generation process for the adversarial generation of future motions. Subsequently, the deliberate learning network is optimized for hard-to-train joints to prevent misleading learning. Experimental results demonstrate that our method achieves new state-of-the-art performance, significantly outperforming existing methods by 19.8% on Human3.6M, 15.7% on CMU Mocap, and 11.1% on G3D. © 2025, CC BY.

关键词： Motion estimation

Structure- and Logic-Aware Heterogeneous Graph Learning for Recommendation

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Structure- and Logic-Aware Heterogeneous Graph Learning for ...

International Conference on Data engineering

作者： Anchen Li Bo Yang Huan Huo Farookh Khadeer Hussain Guandong Xu College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China Aalto University Espoo Finland School of Computer Science University of Technology Sydney Sydney Australia Education University of Hong Kong Hong Kong China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Recently, there has been a surge in recommendations based on heterogeneous information networks (HINs), attributed to their ability to integrate complex and rich semantics. Despite this advancement, most HIN-based recommenders overlook two critical aspects. First, they often fail to consider HIN's heterophily nature, hindering the capture of non-local structures in HINs. Second, most methods lack the capability for logical reasoning. In this paper, we propose a novel structure- and logic-aware heterogeneous graph learning framework for recommender systems (SLHRec). Our SLHRec contains a structure-aware module and a logic-aware module. The former uses network geometry to construct non-local neighborhoods for nodes in HINs, and then introduces a graph neural network to integrate constructed neighbors for modeling the heterophily of HINs. The logic-aware module uses the Markov logic network (MLN) to infuse logic rules into heterogeneous graph learning, thereby boosting logic reasoning in recommendations. Furthermore, we utilize contrastive learning to model cooperative signals between modules, enabling them to complement each other. In the prediction stage, both modules contribute to generating recommendations. Compared with several strong recommender baselines, our SLHRec achieves superior performance on four real-world datasets.

关键词： Geometry Semantics Contrastive learning Data engineering Boosting Cognition Graph neural networks

COVID-19 knowledge Graph for Drug and Vaccine Development

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COVID-19 Knowledge Graph for Drug and Vaccine Development

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Lan Huang Hongrui Guan Yanchun Liang Renchu Guan Xiaoyue Feng The Key Laboratory for Symbolic Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Changchun China Zhuhai Sub Laboratory Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education Zhuhai College of Science and Technology Zhuhai China

ISBN: (纸本)9781665429825

The worldwide spread of COVID-19 has made a severe impact on human health and life. It has shown rapid propagation, long in vitro survival, and a long incubation period. More seriously, COVID-19 is more susceptible to variation, as it is an RNA virus. Mutations of COVID-19 have been reported in multiple countries worldwide, which makes drug and vaccine development a significant challenge. To search for potential drugs and vaccines and reveal the atlas of COVID-19 evolution, we extract information from massive unstructured data and construct a COVID-19 knowledge graph using the COVID-19 data. Based on machine learning approaches, we infer and predict novel coronavirus pneumonia-related diseases, drug action targets, etc. to speculate on new and more effective treatment methods. In addition, to study transcriptome of SARS-CoV-2, new ideas can be provided to biomedical experts with flexible responses to viral variation. An in-depth analysis of the COVID-19 pathomechanism at the pharmaceutical, genetic, and protein levels provides effective means and tools for novel coronavirus pneumonia vaccines, drug development, and therapeutic program design.

关键词： COVID-19 Drugs Proteins Protein engineering Pulmonary diseases RNA Machine learning

An Enhanced Approach for Few-Shot Segmentation Via Smooth Downsampling Mask and Label Smoothing Loss

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SSRN

SSRN 2024年

作者： Jin, Hailong Li, Huiying Jilin University College of Computer Science and Technology Jilin Province Changchun China Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Changchun130012 China

Few-shot semantic segmentation aims to segment new categories with only a small number of annotated images. Previous methods mainly focused on exploiting the pixel-level correlation between the support image and the query image, combined with attention-based methods, resulting in significant advancements. In this paper, we introduce a new perspective to enhance few-shot segmentation. We identify that utilizing the bilinear interpolation method to downsample the mask leads to the loss of fine-grained information from the target features. To address this issue, we propose a Smooth Downsampling Mask (SDM) method. The SDM method is designed to retain more effective target semantic features by employing a cascaded downsampling approach with a smooth kernel for mask processing. Additionally, we propose a label smoothing loss to further enhance the performance, which provides direct guidance for low-resolution feature map optimization. Notably, our proposed method involves no additional learnable parameters and computes efficiently. To validate the effectiveness of our approach, we conduct extensive experiments on three public benchmarks, PASCAL-5i, COCO-20i, and FSS-1000, and achieve considerable improvements. © 2024, The Authors. All rights reserved.

关键词： Semantics

Feature Fusion Based Hand Gesture Recognition Method for Automotive Interfaces

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Chinese Journal of Electronics 2020年第6期29卷 1153-1164页

作者： XU Qianyi QIN Guihe SUN Minghui YAN Jie JIANG Huiming ZHANG Zhonghan College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computing and Knowledge Engineering Ministry of Education Jilin University College of Software Jilin University

Hand gesture recognition on the depth videos is a promising approach for automotive interfaces because it is less sensitive to light variation and more accurate than other traditional ***,video gestures recognition is still a challenging task since lots of interferences are induced by the uncorrelated gesture *** that if the displays are more relevant,the results will more accurate,so ResNext,a kind of compact and efficient neural network,is firstly used as feature extractor,then an improved weighted frame unification method is adopted to obtain the key frame samples,finally the Discriminant correlation analysis(DCA) is employed to fuse features for static data and dynamic data after conducting Feature embedding branch(FEB) on static *** public dataset named Depth based gesture recognition database(DGRD) is used in this paper,but the dataset is a little small and the class distribution is largely imbalance,and we find the performance of ResNext degrades badly in the condition of imbalance problem although it achieves excellent result at sufficient training *** order to conquer the disadvantages of limited dataset,a special loss function scheme combining the soft max loss and dice loss is *** of the algorithm performances in comparison with other state-of-the-art methods indicates that the proposed method is more practical for gesture recognition and may be widely adopted by automotive interfaces.

关键词： feature extraction gesture recognition learning (artificial intelligence) neural nets Depth based gesture recognition database Feature embedding branch dynamic data static data key frame samples improved weighted frame unification method feature extractor efficient neural network compact network uncorrelated gesture factors video gestures recognition depth videos automotive interfaces Feature fusion based hand gesture recognition method Weighted frame unification Discriminant correlation analysis (DCA) Feature embedding branch (FEB) Gesture recognition Loss function

Independently keypoint Learning for Small Object Semantic Correspondence

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arXiv 2024年

Semantic correspondence remains a challenging task for establishing correspondences between a pair of images with the same category or similar scenes due to the large intra-class appearance. In this paper, we introduce a novel problem called’Small Object Semantic Correspondence (SOSC).’ This problem is challenging due to the close proximity of keypoints associated with small objects, which results in the fusion of these respective features. It is difficult to identify the corresponding key points of the fused features, and it is also difficult to be recognized. To address this challenge, we propose the keypoint Bounding box-centered Cropping (KBC) method, which aims to increase the spatial separation between keypoints of small objects, thereby facilitating independent learning of these keypoints. The KBC method is seamlessly integrated into our proposed inference pipeline and can be easily incorporated into other methodologies, resulting in significant performance enhancements. Additionally, we introduce a novel framework, named KBCNet, which serves as our baseline model. KBCNet comprises a Cross-Scale Feature Alignment (CSFA) module and an efficient 4D convolutional decoder. The CSFA module is designed to align multi-scale features, enriching keypoint representations by integrating fine-grained features and deep semantic features. Meanwhile, the 4D convolutional decoder, based on efficient 4D convolution, ensures efficiency and rapid convergence. To empirically validate the effectiveness of our proposed methodology, extensive experiments are conducted on three widely used benchmarks: PF-PASCAL, PF-WILLOW, and SPair-71k. Our KBC method demonstrates a substantial performance improvement of 7.5% on the SPair-71K dataset, providing compelling evidence of its efficacy. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolution

A Multi-objective Optimization Approach for Secure Communications Based on Collaborative Beamforming in UAV Networks

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A Multi-objective Optimization Approach for Secure Communica...

IEEE Conference on Wireless Communications and Networking

作者： Xinrong Guo Fang Mei Geng Sun Tingting Zheng College of Software Jilin University Changchun China College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China College of Electrical and Computer Engineering University of Waterloo Waterloo Canada

With the rapid development of wireless communication, unmanned aerial vehicle (UAV) networks have received extensive attention and been applied in many fields, but some challenges exist in their applications, such as the issue of security when implementing communication. In this paper, a virtual antenna array (VAA) is formed in multiple UAV units using collaborative beamforming (CB) technology. Under the interference of multiple eavesdroppers, secure communication with the ground base station (BS) is achieved. To achieve better performance, we formulate a multi-objective optimization problem for UAV network security communication (SCMOP), and jointly optimize the positions and excitation current weights of UAVs to set the null values in the direction of known eavesdroppers, reduce the sidelobe levels (SLLs) and enhance the directivity of the main lobe (ML). Since the formulated SCMOP is an NP-hard problem, we propose an improved the third non-dominated sorting genetic algorithm (IMNSGA-III) with chaos operator and crossover and mutation operators to solve the problem in this paper. The simulation results show that the IMNSGA-III can solve the SCMOP well and obtain the best results compared with other benchmark algorithms.

关键词： Chaos Wireless communication Array signal processing Null value Collaboration Autonomous aerial vehicles Statistics

UAV-assisted Joint Mobile Edge Computing and Data Collection via Matching-enabled Deep Reinforcement Learning

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arXiv 2025年

作者： Wang, Boxiong Kang, Hui Li, Jiahui Sun, Geng Sun, Zemin Wang, Jiacheng Niyato, Dusit College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University 639798 Singapore College of Computing and Data Science Nanyang Technological University Singapore

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) and data collection (DC) have been popular research issues. Different from existing works that consider MEC and DC scenarios separately, this paper investigates a multi-UAV-assisted joint MEC-DC system. Specifically, we formulate a joint optimization problem to minimize the MEC latency and maximize the collected data volume. This problem can be classified as a non-convex mixed integer programming problem that exhibits long-term optimization and dynamics. Thus, we propose a deep reinforcement learning-based approach that jointly optimizes the UAV movement, user transmit power, and user association in real time to solve the problem efficiently. Specifically, we reformulate the optimization problem into an action space-reduced Markov decision process (MDP) and optimize the user association by using a two-phase matching-based association (TMA) strategy. Subsequently, we propose a soft actor-critic (SAC)-based approach that integrates the proposed TMA strategy (SAC-TMA) to solve the formulated joint optimization problem collaboratively. Simulation results demonstrate that the proposed SAC-TMA is able to coordinate the two subsystems and can effectively reduce the system latency and improve the data collection volume compared with other benchmark algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)