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机器智能研究（英文）
机器智能研究（英文）

谭铁牛　刘国平　胡豁生

双月刊

2731-538X

ijac@ia.ac.cn

010-62655893

100190

北京海淀区中关村东路95号2728信箱

机器智能研究（英文）/Journal Machine Intelligence ResearchCSCDCSTPCD北大核心EI
查看更多>>International Journal of Automation and computing is a publication of Institute of Automation, the Chinese Academy of Sciencs and Chinese Automation and computing Society in the United Kingdom. The Journal publishes papers on original theoretical and experimental research and development in automation and computing. The scope of the journal is extensive. Topics include; artificial intelligence, automatic control, bioinformatics, computer sciene, information technology, modeling and simulation, networks and communications, optimization and decision, pattern recognition, robotics, signal processing, and systems engineering.
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    ReChoreoNet:Repertoire-based Dance Re-choreography with Music-conditioned Temporal and Style Clues

    Ho Yin AuJie ChenJunkun JiangYike Guo...
    771-781页
    查看更多>>摘要：To generate dance that temporally and aesthetically matches the music is a challenging problem in three aspects.First,the generated motion should be beats-aligned to the local musical features.Second,the global aesthetic style should be matched between motion and music.And third,the generated motion should be diverse and non-self-repeating.To address these challenges,we propose ReChoreoNet,which re-choreographs high-quality dance motion for a given piece of music.A data-driven learning strategy is proposed to efficiently correlate the temporal connections between music and motion in a progressively learned cross-modality embedding space.The beats-aligned content motion will be subsequently used as autoregressive context and control signal to control a normalizing-flow model,which transfers the style of a prototype motion to the final generated dance.In addition,we present an aesthetically labelled mu-sic-dance repertoire(MDR)for both efficient learning of the cross-modality embedding,and understanding of the aesthetic connections between music and motion.We demonstrate that our repertoire-based framework is robustly extensible in both content and style.Both quantitative and qualitative experiments have been carried out to validate the efficiency of our proposed model.
      原文链接:
    • NETL
    • NSTL
    • 万方数据

    Learning Top-K Subtask Planning Tree Based on Discriminative Representation Pretraining for Decision-making

    Jingqing RuanKaishen WangQingyang ZhangDengpeng Xing...
    782-800页
    查看更多>>摘要：Decomposing complex real-world tasks into simpler subtasks and devising a subtask execution plan is critical for humans to achieve effective decision-making.However,replicating this process remains challenging for AI agents and naturally raises two ques-tions:1)How to extract discriminative knowledge representation from priors?2)How to develop a rational plan to decompose complex problems?To address these issues,we introduce a groundbreaking framework that incorporates two main contributions.First,our mul-tiple-encoder and individual-predictor regime goes beyond traditional architectures to extract nuanced task-specific dynamics from datasets,enriching the feature space for subtasks.Second,we innovate in planning by introducing a top-K subtask planning tree gener-ated through an attention mechanism,which allows for dynamic adaptability and forward-looking decision-making.Our framework is empirically validated against challenging benchmarks Baby AI including multiple combinatorially rich synthetic tasks(e.g.,GoToSeq,SynthSeq,BossLevel),where it not only outperforms competitive baselines but also demonstrates superior adaptability and effective-ness in complex task decomposition.
      原文链接:
    • NETL
    • NSTL
    • 万方数据

    A Deep Model for Partial Multi-label Image Classification with Curriculum-based Disambiguation

    Feng SunMing-Kun XieSheng-Jun Huang
    801-814页
    查看更多>>摘要：In this paper,we study the partial multi-label(PML)image classification problem,where each image is annotated with a candidate label set consisting of multiple relevant labels and other noisy labels.Existing PML methods typically design a disambigu-ation strategy to filter out noisy labels by utilizing prior knowledge with extra assumptions,which unfortunately is unavailable in many real tasks.Furthermore,because the objective function for disambiguation is usually elaborately designed on the whole training set,it can hardly be optimized in a deep model with stochastic gradient descent(SGD)on mini-batches.In this paper,for the first time,we propose a deep model for PML to enhance the representation and discrimination ability.On the one hand,we propose a novel cur-riculum-based disambiguation strategy to progressively identify ground-truth labels by incorporating the varied difficulties of different classes.On the other hand,consistency regularization is introduced for model training to balance fitting identified easy labels and ex-ploiting potential relevant labels.Extensive experimental results on the commonly used benchmark datasets show that the proposed method significantly outperforms the SOTA methods.
      原文链接:
    • NETL
    • NSTL
    • 万方数据