The long-term goal of artificial intelligence （AI） is to make machines learn and think like human beings. Due to the high levels of uncertainty and vulnerability in human life and the open-ended nature of problems t...The long-term goal of artificial intelligence （AI） is to make machines learn and think like human beings. Due to the high levels of uncertainty and vulnerability in human life and the open-ended nature of problems that humans are facing, no matter how intelligent machines are, they are unable to completely replace humans. Therefore, it is necessary to introduce human cognitive capabilities or human-like cognitive models into AI systems to develop a new form of AI, that is, hybrid-augmented intelligence. This form of AI or machine intelligence is a feasible and important developing model. Hybrid-augmented intelligence can be divided into two basic models： one is human-in-the-loop augmented intelligence with human-computer collaboration, and the other is cognitive computing based augmented intelligence, in which a cognitive model is embedded in the machine learning system. This survey describes a basic framework for human-computer collaborative hybrid-augmented intelligence, and the basic elements of hybrid-augmented intelligence based on cognitive computing. These elements include intuitive reasoning, causal models, evolution of memory and knowledge, especially the role and basic principles of intuitive reasoning for complex problem solving, and the cognitive learning framework for visual scene understanding based on memory and reasoning. Several typical applications of hybrid-augmented intelligence in related fields are given.展开更多
复杂视觉场景下存在过暗或者过曝的光照、恶劣的天气、严重遮挡、行人尺寸差别大以及图像模糊等问题,大大增加了行人检测的难度。因此,针对复杂视觉场景下行人检测准确度低、漏检严重的问题,提出了改进的YOLOv4算法以增强复杂视觉场景...复杂视觉场景下存在过暗或者过曝的光照、恶劣的天气、严重遮挡、行人尺寸差别大以及图像模糊等问题,大大增加了行人检测的难度。因此,针对复杂视觉场景下行人检测准确度低、漏检严重的问题,提出了改进的YOLOv4算法以增强复杂视觉场景下的行人检测效果。首先,构建复杂视觉场景下的行人数据集。然后,在主干网中加入混合空洞卷积,提高网络对行人特征的提取能力。最后,提出空间锯齿空洞卷积结构,代替空间金字塔池化结构,获取更多细节特征。实验表明,在本文构建的行人数据集上,改进后的YOLOv4算法的平均精度(average precision,AP)达到了90.08%,相比原YOLOv4算法提高了7.2%,对数平均漏检率(log-average miss rate,LAMR)降低了13.69%。展开更多
目的 传统视觉场景识别(visual place recognition,VPR)算法的性能依赖光学图像的成像质量,因此高速和高动态范围场景导致的图像质量下降会进一步影响视觉场景识别算法的性能。针对此问题,提出一种融合事件相机的视觉场景识别算法,利用...目的 传统视觉场景识别(visual place recognition,VPR)算法的性能依赖光学图像的成像质量,因此高速和高动态范围场景导致的图像质量下降会进一步影响视觉场景识别算法的性能。针对此问题,提出一种融合事件相机的视觉场景识别算法,利用事件相机的低延时和高动态范围的特性,提升视觉场景识别算法在高速和高动态范围等极端场景下的识别性能。方法 本文提出的方法首先使用图像特征提取模块提取质量良好的参考图像的特征,然后使用多模态特征融合模块提取查询图像及其曝光区间事件信息的多模态融合特征,最后通过特征匹配查找与查询图像最相似的参考图像。结果 在MVSEC(multi-vehicle stereo event camera dataset)和RobotCar两个数据集上的实验表明,本文方法对比现有视觉场景识别算法在高速和高动态范围场景下具有明显优势。在高速高动态范围场景下,本文方法在MVSEC数据集上相较对比算法最优值在召回率与精度上分别提升5.39%和8.55%,在RobotCar数据集上相较对比算法最优值在召回率与精度上分别提升3.36%与4.41%。结论 本文提出了融合事件相机的视觉场景识别算法,利用了事件相机在高速和高动态范围场景的成像优势,有效提升了视觉场景识别算法在高速和高动态范围场景下的场景识别性能。展开更多
基金Project supported by the Chinese Academy of Engi- neering, the National Natural Science Foundation of China （No. L1522023）, the National Basic Research Program （973） of China （No. 2015CB351703）, and the National Key Research and Development Plan （Nos. 2016YFB1001004 and 2016YFB1000903）
文摘The long-term goal of artificial intelligence （AI） is to make machines learn and think like human beings. Due to the high levels of uncertainty and vulnerability in human life and the open-ended nature of problems that humans are facing, no matter how intelligent machines are, they are unable to completely replace humans. Therefore, it is necessary to introduce human cognitive capabilities or human-like cognitive models into AI systems to develop a new form of AI, that is, hybrid-augmented intelligence. This form of AI or machine intelligence is a feasible and important developing model. Hybrid-augmented intelligence can be divided into two basic models： one is human-in-the-loop augmented intelligence with human-computer collaboration, and the other is cognitive computing based augmented intelligence, in which a cognitive model is embedded in the machine learning system. This survey describes a basic framework for human-computer collaborative hybrid-augmented intelligence, and the basic elements of hybrid-augmented intelligence based on cognitive computing. These elements include intuitive reasoning, causal models, evolution of memory and knowledge, especially the role and basic principles of intuitive reasoning for complex problem solving, and the cognitive learning framework for visual scene understanding based on memory and reasoning. Several typical applications of hybrid-augmented intelligence in related fields are given.
文摘复杂视觉场景下存在过暗或者过曝的光照、恶劣的天气、严重遮挡、行人尺寸差别大以及图像模糊等问题,大大增加了行人检测的难度。因此,针对复杂视觉场景下行人检测准确度低、漏检严重的问题,提出了改进的YOLOv4算法以增强复杂视觉场景下的行人检测效果。首先,构建复杂视觉场景下的行人数据集。然后,在主干网中加入混合空洞卷积,提高网络对行人特征的提取能力。最后,提出空间锯齿空洞卷积结构,代替空间金字塔池化结构,获取更多细节特征。实验表明,在本文构建的行人数据集上,改进后的YOLOv4算法的平均精度(average precision,AP)达到了90.08%,相比原YOLOv4算法提高了7.2%,对数平均漏检率(log-average miss rate,LAMR)降低了13.69%。
