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开放世界物体识别与检测系统:现状、挑战与展望

聂晖, 王瑞平, 陈熙霖

聂晖, 王瑞平, 陈熙霖. 开放世界物体识别与检测系统:现状、挑战与展望[J]. 计算机研究与发展, 2024, 61(9): 2128-2141. DOI: 10.7544/issn1000-1239.202440054
引用本文: 聂晖, 王瑞平, 陈熙霖. 开放世界物体识别与检测系统:现状、挑战与展望[J]. 计算机研究与发展, 2024, 61(9): 2128-2141. DOI: 10.7544/issn1000-1239.202440054
Nie Hui, Wang Ruiping, Chen Xilin. Open World Object Recognition and Detection Systems: Landscapes, Challenges and Prospects[J]. Journal of Computer Research and Development, 2024, 61(9): 2128-2141. DOI: 10.7544/issn1000-1239.202440054
Citation: Nie Hui, Wang Ruiping, Chen Xilin. Open World Object Recognition and Detection Systems: Landscapes, Challenges and Prospects[J]. Journal of Computer Research and Development, 2024, 61(9): 2128-2141. DOI: 10.7544/issn1000-1239.202440054
聂晖, 王瑞平, 陈熙霖. 开放世界物体识别与检测系统:现状、挑战与展望[J]. 计算机研究与发展, 2024, 61(9): 2128-2141. CSTR: 32373.14.issn1000-1239.202440054
引用本文: 聂晖, 王瑞平, 陈熙霖. 开放世界物体识别与检测系统:现状、挑战与展望[J]. 计算机研究与发展, 2024, 61(9): 2128-2141. CSTR: 32373.14.issn1000-1239.202440054
Nie Hui, Wang Ruiping, Chen Xilin. Open World Object Recognition and Detection Systems: Landscapes, Challenges and Prospects[J]. Journal of Computer Research and Development, 2024, 61(9): 2128-2141. CSTR: 32373.14.issn1000-1239.202440054
Citation: Nie Hui, Wang Ruiping, Chen Xilin. Open World Object Recognition and Detection Systems: Landscapes, Challenges and Prospects[J]. Journal of Computer Research and Development, 2024, 61(9): 2128-2141. CSTR: 32373.14.issn1000-1239.202440054

开放世界物体识别与检测系统:现状、挑战与展望

基金项目: 科技创新2030 —“新一代人工智能”重大项目(2021ZD0111901);国家自然科学基金项目(U21B2025,U19B2036)
详细信息
    作者简介:

    聂晖: 1996年生. 博士研究生. 主要研究方向为开放世界物体检测、计算机视觉

    王瑞平: 1981年生. 博士,教授,博士生导师. 主要研究方向为计算机视觉、模式识别、机器学习

    陈熙霖: 1965年生. 博士,教授,博士生导师. 主要研究方向为计算机视觉、模式识别、图像处理、多模式人机接口

    通讯作者:

    王瑞平(wangruiping@ict.ac.cn

  • 中图分类号: TP391

Open World Object Recognition and Detection Systems: Landscapes, Challenges and Prospects

Funds: This work was supported by the National Key Research and Development Program of China (2021ZD0111901) and the National Natural Science Foundation of China (U21B2025, U19B2036).
More Information
    Author Bio:

    Nie Hui: born in 1996. PhD candidate. His main research interests include open world object detection and computer vision

    Wang Ruiping: born in 1981. PhD, professor, PhD supervisor. His main research interests include computer vision, pattern recognition, and machine learning

    Chen Xilin: born in 1965. PhD, professor, PhD supervisor. His main research interests include computer vision, pattern recognition, image processing, and multimodal interfaces

  • 摘要:

    探究了从封闭环境到开放世界环境的转变及其对视觉感知(集中于物体识别和检测)与深度学习领域的影响. 在开放世界环境中,系统软件需适应不断变化的环境和需求,这为深度学习方法带来新挑战. 特别是,开放世界视觉感知要求系统理解和处理训练阶段未见的环境和物体,这超出了传统封闭系统的能力. 首先讨论了技术进步带来的动态、自适应系统需求,突出了开放系统相较封闭系统的优势. 接着,深入探讨了开放世界的定义和现有工作,涵盖开集学习、零样本学习、小样本学习、长尾学习、增量学习等5个开放维度. 在开放世界物体识别方面,分析了每个维度的核心挑战,并为每个任务数据集提供了量化的评价指标. 对于开放世界物体检测,讨论了检测相比识别的新增挑战,如遮挡、尺度、姿态、共生关系、背景干扰等,并强调了仿真环境在构建开放世界物体检测数据集中的重要性. 最后,强调开放世界概念为深度学习带来的新视角和机遇,是推动技术进步和深入理解世界的机会,为未来研究提供参考.

    Abstract:

    We explore the transition from closed environments to open world environments and its impact on visual perception (focusing on object recognition and detection) and the field of deep learning. In open world environments, software systems need to adapt to constantly changing conditions and demands, presenting new challenges for deep learning methods. In particular, open world visual perception requires systems to understand and process environments and objects not seen during the training phase, which exceeds the capabilities of traditional closed systems. We first discuss the dynamic and adaptive system requirements brought about by technological advances, highlighting the advantages of open systems over closed systems. Then we delve into the definition of the open world and existing work, covering five dimensions of openness: open set learning, zero-shot learning, few-shot learning, long-tail learning, and incremental learning. In terms of open world recognition, we analyze the core challenges of each dimension and provide quantified evaluation metrics for each task dataset. For open world object detection, we discuss additional challenges compared with recognition, such as occlusion, scale, posture, symbiotic relationships, background interference, etc., and emphasize the importance of simulation environments in constructing open world object detection datasets. Finally, we underscore the new perspectives and opportunities that the concept of the open world brings to deep learning, acting as a catalyst for technological advancement and deeper understanding of the realistic environment challenges, offering a reference for future research.

  • 图  1   封闭环境和开放环境的对比

    Figure  1.   Comparison between closed and open environments

    图  2   开放世界识别方法分类

    Figure  2.   Classification of open world recognition methods

    图  3   开放世界检测方法分类

    Figure  3.   Classification of open world detection methods

    图  4   从不同角度模拟真实世界的开放性

    Figure  4.   Simulating the openness of the real scenes from different perspectives

    图  5   开放性维度及其核心难度指标

    Figure  5.   Openness dimensions and their core difficulty metrics

    图  6   在COCO数据中存在许多未标注物体

    Figure  6.   There are many unannotated objects in COCO dataset

    图  7   在仿真环境中控制光照、纹理和位姿变化

    Figure  7.   Controlling lighting, texture, and pose variations in simulation environments

    图  8   仿真环境自带的多样化标注

    Figure  8.   Diverse annotations provided by the simulation environment

    表  1   变化迁移性指标的零样本物体检测实验结果

    Table  1   Experimental Results of Zero-shot Object Detection with Varying Transferability Metrics

    方法 $ {{M}}_{{\mathrm{tran}}} $ AP50s AP50u
    DPIF 0.30 51.5 2.7
    0.46 57.2 3.4
    0.57 52.6 4.5
    RRFS 0.30 53.5 1.6
    0.46 44.8 1.9
    0.57 49.9 3.9
    ZSDSCR 0.30 53.6 0.7
    0.46 44.8 1.2
    0.57 50.0 3.5
    下载: 导出CSV

    表  2   变化不均衡性指标的长尾物体检测实验结果

    Table  2   Experimental Results of Long-tailed Object Detection with Varying Imbalance Metrics

    方法 $ {{M}}_{{\mathrm{imb}}} $ AP AP50
    EQLV2 0.1 50.3 64.0
    0.5 30.8 39.5
    0.9 17.2 21.7
    Seesaw 0.1 51.2 64.9
    0.5 32.9 42.0
    0.9 17.6 22.1
    下载: 导出CSV

    表  3   变化迁移性指标的零样本物体检测实验结果

    Table  3   Experimental Results of Zero-shot Object Detection with Varying Transferability Metrics

    方法 $ {{M}}_{{\mathrm{tran}}} $ AP50s AP50u
    DPIF 0.25 44.4 0.8
    0.45 37.6 21.8
    0.67 32.3 38.3
    RRFS 0.25 70.3 0.3
    0.45 62.6 7.7
    0.67 59.4 19.4
    ZSDSCR 0.25 70.3 0.3
    0.45 62.6 6.6
    0.67 59.4 16.9
    下载: 导出CSV
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出版历程
  • 收稿日期:  2024-01-28
  • 修回日期:  2024-06-05
  • 网络出版日期:  2024-06-16
  • 刊出日期:  2024-08-31

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