It is difficult if not impossible to appropriately and effectively select from among the vast pool of existing neural network machine learning predictive models for industrial incorporation or academic research explor...It is difficult if not impossible to appropriately and effectively select from among the vast pool of existing neural network machine learning predictive models for industrial incorporation or academic research exploration and enhancement. When all models outperform all the others under disparate circumstances, none of the models do. Selecting the ideal model becomes a matter of ill-supported opinion ungrounded on the extant real world environment. This paper proposes a novel grouping of the model pool grounded along a non-stationary real world data line into two groups: Permanent Data Learning and Reversible Data Learning. This paper further proposes a novel approach towards qualitatively and quantitatively demonstrating their significant differences based on how they alternatively approach dynamic and raw real world data vs static and prescient data mining biased laboratory data. The results across 2040 separate simulation runs using 15,600 data points in realistically operationally controlled data environments show that the two-group division is effective and significant with clear qualitative, quantitative and theoretical support. Results across the empirical and theoretical spectrum are internally and externally consistent yet demonstrative of why and how this result is non-obvious.展开更多
行人重识别(person re-identification, Person Re ID)技术对安全监控和个人跟踪等领域至关重要,但标注数据的稀缺性和高成本限制了其广泛应用。针对这一问题,设计一种基于身份导向自监督表示(Identity-seeking Self-supervised Represe...行人重识别(person re-identification, Person Re ID)技术对安全监控和个人跟踪等领域至关重要,但标注数据的稀缺性和高成本限制了其广泛应用。针对这一问题,设计一种基于身份导向自监督表示(Identity-seeking Self-supervised Representation, ISR)学习方法的寻人系统,能够从大规模无标注视频数据中学习并提取人员的特征表示。系统架构分为三个模块:首先,利用YOLOV8模型对视频流中的人物进行检测,并自动裁剪出galley图片;其次,通过ISR学习方法对galley图片进行特征提取,并构建特征数据库;最后,在特征数据库中,检索与查询图片相似的galley图片,并关联到对应的视频帧。实验结果证明,系统搜索准确高效,具有广泛的应用价值和实用潜力。展开更多
文摘It is difficult if not impossible to appropriately and effectively select from among the vast pool of existing neural network machine learning predictive models for industrial incorporation or academic research exploration and enhancement. When all models outperform all the others under disparate circumstances, none of the models do. Selecting the ideal model becomes a matter of ill-supported opinion ungrounded on the extant real world environment. This paper proposes a novel grouping of the model pool grounded along a non-stationary real world data line into two groups: Permanent Data Learning and Reversible Data Learning. This paper further proposes a novel approach towards qualitatively and quantitatively demonstrating their significant differences based on how they alternatively approach dynamic and raw real world data vs static and prescient data mining biased laboratory data. The results across 2040 separate simulation runs using 15,600 data points in realistically operationally controlled data environments show that the two-group division is effective and significant with clear qualitative, quantitative and theoretical support. Results across the empirical and theoretical spectrum are internally and externally consistent yet demonstrative of why and how this result is non-obvious.
文摘行人重识别(person re-identification, Person Re ID)技术对安全监控和个人跟踪等领域至关重要,但标注数据的稀缺性和高成本限制了其广泛应用。针对这一问题,设计一种基于身份导向自监督表示(Identity-seeking Self-supervised Representation, ISR)学习方法的寻人系统,能够从大规模无标注视频数据中学习并提取人员的特征表示。系统架构分为三个模块:首先,利用YOLOV8模型对视频流中的人物进行检测,并自动裁剪出galley图片;其次,通过ISR学习方法对galley图片进行特征提取,并构建特征数据库;最后,在特征数据库中,检索与查询图片相似的galley图片,并关联到对应的视频帧。实验结果证明,系统搜索准确高效,具有广泛的应用价值和实用潜力。