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arXiv 2023年

作者： Si, Lingyu Dong, Hongwei Qiang, Wenwen Yu, Junzhi Zhai, Wenlong Zheng, Changwen Xu, Fanjiang Sun, Fuchun Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences China College of Engineering Peking University China Department of Computer Science and Technology Tsinghua University China

Due to limitations in data quality, some essential visual tasks are difficult to perform independently. Introducing previously unavailable information to transfer informative dark knowledge has been a common way to solve such hard tasks. However, research on why transferred knowledge works has not been extensively explored. To address this issue, in this paper, we discover the correlation between feature discriminability and dimensional structure (DS) by analyzing and observing features extracted from simple and hard tasks. On this basis, we express DS using deep channel-wise correlation and intermediate spatial distribution, and propose a novel cross-modal knowledge distillation (CMKD) method for better supervised cross-modal learning (CML) performance. The proposed method enforces output features to be channel-wise independent and intermediate ones to be uniformly distributed, thereby learning semantically irrelevant features from the hard task to boost its accuracy. This is especially useful in specific applications where the performance gap between dual modalities is relatively large. Furthermore, we collect a real-world CML dataset to promote community development. The dataset contains more than 10, 000 paired optical and radar images and is continuously being updated. Experimental results on real-world and benchmark datasets validate the effectiveness of the proposed method. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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CtrlAvatar: Controllable Avatars Generation via Disentangled Invertible Networks 39

CtrlAvatar: Controllable Avatars Generation via Disentangled...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Song, Wenfeng Ding, Yang Hou, Fei Li, Shuai Hao, Aimin Hou, Xia College of Computer Science Beijing Information Science and Technology University China Key Laboratory of System Software (CAS) State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China University of Chinese Academy of Sciences China State Key Laboratory of Virtual Reality Technology and Systems Beihang University China Zhongguancun Laboratory China

ISBN: (纸本)157735897X

As virtual experiences grow in popularity, the demand for realistic, personalized, and animatable human avatars increases. Traditional methods, relying on fixed templates, often produce costly avatars that lack expressiveness and realism. To overcome these challenges, we introduce Controllable Avatars generation via disentangled invertible networks (CtrlAvatar), a real-time framework for generating lifelike and customizable avatars. CtrlAvatar uses disentangled invertible networks to separate the deformation process into implicit body geometry and explicit texture components. This approach eliminates the need for repeated occupancy reconstruction, enabling detailed and coherent animations. The body geometry component ensures anatomical accuracy, while the texture component allows for complex, artifact-free clothing customization. This architecture ensures smooth integration between body movements and surface details. By optimizing transformations with position-varying offsets from the avatar’s initial Linear Blend Skinning vertices, CtrlAvatar achieves flexible, natural deformations that adapt to various scenarios. Extensive experiments show that CtrlAvatar outperforms other methods in quality, diversity, controllability, and cost-efficiency, marking a significant advancement in avatar generation. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Geometry

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Hierarchical Topology Isomorphism Expertise Embedded Graph Contrastive Learning

arXiv

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arXiv 2023年

作者： Li, Jiangmeng Jin, Yifan Gao, Hang Qiang, Wenwen Zheng, Changwen Sun, Fuchun Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences China State Key Laboratory of Intelligent Game China University of Chinese Academy of Sciences China Tsinghua University China

Graph contrastive learning (GCL) aims to align the positive features while differentiating the negative features in the latent space by minimizing a pair-wise contrastive loss. As the embodiment of an outstanding discriminative unsupervised graph representation learning approach, GCL achieves impressive successes in various graph benchmarks. However, such an approach falls short of recognizing the topology isomorphism of graphs, resulting in that graphs with relatively homogeneous node features cannot be sufficiently discriminated. By revisiting classic graph topology recognition works, we disclose that the corresponding expertise intuitively complements GCL methods. To this end, we propose a novel hierarchical topology isomorphism expertise embedded graph contrastive learning, which introduces knowledge distillations to empower GCL models to learn the hierarchical topology isomorphism expertise, including the graph-tier and subgraph-tier. On top of this, the proposed method holds the feature of plug-and-play, and we empirically demonstrate that the proposed method is universal to multiple state-of-the-art GCL models. The solid theoretical analyses are further provided to prove that compared with conventional GCL methods, our method acquires the tighter upper bound of Bayes classification error. We conduct extensive experiments on real-world benchmarks to exhibit the performance superiority of our method over candidate GCL methods, e.g., for the real-world graph representation learning experiments, the proposed method beats the state-of-the-art method by 0.23% on unsupervised representation learning setting, 0.43% on transfer learning setting. Our code is available at https://***/jyf123/HTML. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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Cross-Domain Requirements Linking via Adversarial-based Domain Adaptation

Cross-Domain Requirements Linking via Adversarial-based Doma...

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International Conference on software Engineering (ICSE)

作者： Zhiyuan Chang Mingyang Li Qing Wang Shoubin Li Junjie Wang Science and Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China State Key Laboratory of Intelligent Game Beijing China University of Chinese Academy of Sciences Beijing China

Requirements linking is the core of software system maintenance and evolution, and it is critical to assuring software quality. In practice, however, the requirements links are frequently absent or incorrectly labeled, and reconstructing such ties is time-consuming and error-prone. Numerous learning-based approaches have been put forth to address the problem. However, these approaches will lose effectiveness for the Cold-Start projects with few labeled samples. To this end, we propose RADIATION, an adversarial-based domain adaptation approach for cross-domain requirements linking. Generally, RADIATION firstly adopts an IDF-based Masking strategy to filter the domain-specific features. Then it pre-trains a linking model in the source domain with sufficient labeled samples and adapts the model to target domains using a distance-enhanced adversarial technique without using any labeled target samples. Evaluation on five public datasets shows that RADIATION could achieve 66.4% precision, 89.2% recall, and significantly outperform state-of-the-art baselines by 13.4% -42.9% F1. In addition, the designed components, i.e., IDF-based Masking and Distance-enhanced Loss, could significantly improve performance.

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Fuzzing with Sequence Diversity Inference for Sequential Decision-making Model Testing

Fuzzing with Sequence Diversity Inference for Sequential Dec...

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International Symposium on software Reliability Engineering (ISSRE)

作者： Kairui Wang Yawen Wang Junjie Wang Qing Wang State Key Laboratory of Intelligent Game Beijing China Science and Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

Nowadays increasing AI techniques, e.g., reinforcement learning, imitation learning, etc., are applied to solve sequential decision-making problems by modeling them as Markov Decision Process (MDP), and achieve superior performance in areas, such as video games, robotics and autonomous driving etc. The reliability of such models is facing severe challenges especially in some safety-critical areas, where failures would bring intolerable disasters. Existing works testing episodic decision-making models are not workable, since they neglect the nature of sequentiality and interactivity in MDP. While other works testing sequential decision-making models are challenged by low testing efficiency because the interaction of MDP is time-consuming. In this paper, we propose an optimized fuzzing framework SeqDivFuzz which infers the sequence diversity during the MDP interaction process to effectively and efficiently test sequential decision-making models in blackbox settings. It adapts the existing fuzzing framework, including Seed Selection, Seed Mutation, Feedback Analysis and integrating a module of Diversity Inference to accelerate the fuzzing procedure. The module learns historical in-process information to check the diversity of test cases when running up to checkPoint in the course of MDP, and early terminating those non-diverse ones. We conduct experimental evaluation with four models involving three simulation environments. The results reflect that SeqDivFuzz exposes 12.3%~49.1% more crashes during a 12-hour testing procedure in four pairs of models and environments compared with the state-of-the-art fuzzing framework. The idea of in-process terminating can potentially boost other techniques for testing sequential decision-making models.

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UAV navigation in high dynamic environments:A deep reinforcement learning approach

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Chinese Journal of Aeronautics 2021年第2期34卷 479-489页

作者： Tong GUO Nan JIANG Biyue LI Xi ZHU Ya WANG Wenbo DU School of Electronic and Information Engineering Beihang UniversityBejing 100083China Key Laboratory of Advanced Technology of Near Space Information System(Beihang University) Ministry of Industry and Information Technology of ChinaBeijing 100083China Research Institute of Frontier Science Beihang UniversityBeijing 100083China College of Software Beihang UniversityBeijing 100083China State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100083China

Unmanned Aerial Vehicle(UAV) navigation is aimed at guiding a UAV to the desired destinations along a collision-free and efficient path without human interventions, and it plays a crucial role in autonomous missions in harsh environments. The recently emerging Deep Reinforcement Learning(DRL) methods have shown promise for addressing the UAV navigation problem,but most of these methods cannot converge due to the massive amounts of interactive data when a UAV is navigating in high dynamic environments, where there are numerous obstacles moving *** this work, we propose an improved DRL-based method to tackle these fundamental *** be specific, we develop a distributed DRL framework to decompose the UAV navigation task into two simpler sub-tasks, each of which is solved through the designed Long Short-Term Memory(LSTM) based DRL network by using only part of the interactive data. Furthermore, a clipped DRL loss function is proposed to closely stack the two sub-solutions into one integral for the UAV navigation problem. Extensive simulation results are provided to corroborate the superiority of the proposed method in terms of the convergence and effectiveness compared with those of the state-of-the-art DRL methods.

关键词： Autonomous vehicles Deep learning Motion planning Navigation Reinforcement learning Unmanned Aerial Vehicle(UAV)

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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning 22

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Cao, Weipeng Yao, Xuyang Xu, Zhiwu Pan, Yinghui Sun, Yixuan Li, Dachuan Qiu, Bohua Wei, Muheng Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9798331527471

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model’s capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods. © 2024 IEEE.

关键词： Zero-shot learning

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Unsupervised Social Event Detection via Hybrid Graph Contrastive Learning and Reinforced Incremental Clustering

arXiv

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arXiv 2023年

作者： Guo, Yuanyuan Zang, Zehua Gao, Hang Xu, Xiao Wang, Rui Liu, Lixiang Li, Jiangmeng Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China National Defense University Beijing China

Detecting events from social media data streams is gradually attracting researchers. The innate challenge for detecting events is to extract discriminative information from social media data thereby assigning the data into different events. Due to the excessive diversity and high updating frequency of social data, using supervised approaches to detect events from social messages is hardly achieved. To this end, recent works explore learning discriminative information from social messages by leveraging graph contrastive learning (GCL) and embedding clustering in an unsupervised manner. However, two intrinsic issues exist in benchmark methods: conventional GCL can only roughly explore partial attributes, thereby insufficiently learning the discriminative information of social messages;for benchmark methods, the learned embeddings are clustered in the latent space by taking advantage of certain specific prior knowledge, which conflicts with the principle of unsupervised learning paradigm. In this paper, we propose a novel unsupervised social media event detection method via hybrid graph contrastive learning and reinforced incremental clustering (HCRC), which uses hybrid graph contrastive learning to comprehensively learn semantic and structural discriminative information from social messages and reinforced incremental clustering to perform efficient clustering in a solidly unsupervised manner. We conduct comprehensive experiments to evaluate HCRC on the Twitter and Maven datasets. The experimental results demonstrate that our approach yields consistent significant performance boosts. In traditional incremental setting, semi-supervised incremental setting and solidly unsupervised setting, the model performance has achieved maximum improvements of 53%, 45%, and 37%, respectively. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Understanding Individual Agent Importance in Multi-Agent system via Counterfactual Reasoning

arXiv

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arXiv 2024年

作者： Chen, Jianming Wang, Yawen Wang, Junjie Xie, Xiaofei Hu, Jun Wang, Qing Xu, Fanjiang Institute of Software Chinese Academy of Sciences Beijing China Science & Technology on Integrated Information System Laboratory Beijing China State Key Laboratory of Intelligent Game Beijing China University of Chinese Academy of Sciences Beijing China Singapore Management University Singapore

Explaining multi-agent systems (MAS) is urgent as these systems become increasingly prevalent in various applications. Previous work has provided explanations for the actions or states of agents, yet falls short in understanding the black-boxed agent's importance within a MAS and the overall team strategy. To bridge this gap, we propose EMAI, a novel agent-level explanation approach that evaluates the individual agent's importance. Inspired by counterfactual reasoning, a larger change in reward caused by the randomized action of agent indicates its higher importance. We model it as a MARL problem to capture interactions across agents. Utilizing counterfactual reasoning, EMAI learns the masking agents to identify important agents. Specifically, we define the optimization function to minimize the reward difference before and after action randomization and introduce sparsity constraints to encourage the exploration of more action randomization of agents during training. The experimental results in seven multi-agent tasks demonstrate that EMAI achieves higher fidelity in explanations than baselines and provides more effective guidance in practical applications concerning understanding policies, launching attacks, and patching policies. © 2024, CC BY.

关键词： Multi agent systems

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Image De-occlusion via Event-enhanced Multi-modal Fusion Hybrid Network

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Machine Intelligence Research 2022年第4期19卷 307-318页

作者： Si-Qi Li Yue Gao Qiong-Hai Dai Beijing National Research Center for Information Science and Technology Tsinghua UniversityBeijing 100084China Institute for Brain and Cognitive Sciences Tsinghua UniversityBeijing 100084China Beijing Laboratory of Brain and Cognitive Intelligence Beijing Municipal Education CommissionTsinghua UniversityBeijing 100084China Key Laboratory for Information System Security School of SoftwareTsinghua UniversityBeijing 100084China Department of Automation Tsinghua UniversityBeijing 100084China

Seeing through dense occlusions and reconstructing scene images is an important but challenging *** framebased image de-occlusion methods may lead to fatal errors when facing extremely dense occlusions due to the lack of valid information available from the limited input occluded *** cameras are bio-inspired vision sensors that record the brightness changes at each pixel asynchronously with high temporal ***,synthesizing images solely from event streams is ill-posed since only the brightness changes are recorded in the event stream,and the initial brightness is *** this paper,we propose an event-enhanced multi-modal fusion hybrid network for image de-occlusion,which uses event streams to provide complete scene information and frames to provide color and texture *** event stream encoder based on the spiking neural network(SNN)is proposed to encode and denoise the event stream efficiently.A comparison loss is proposed to generate clearer *** results on a largescale event-based and frame-based image de-occlusion dataset demonstrate that our proposed method achieves state-of-the-art performance.

关键词： Event camera multi-modal fusion image de-occlusion spiking neural network(SNN) image reconstruction

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