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首页> 外文期刊>Cognitive computation >Biased Competition in Visual Processing Hierarchies: A Learning Approach Using Multiple Cues
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Biased Competition in Visual Processing Hierarchies: A Learning Approach Using Multiple Cues

机译：视觉处理层次结构中的有偏竞争：一种使用多种线索的学习方法

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摘要

In this contribution, we present a large-scale hierarchical system for object detection fusing bottom-up (signal-driven) processing results with top-down (model or task-driven) attentional modulation. Specifically, we focus on the question of how the autonomous learning of invariant models can be embedded into a performing system and how such models can be used to define object-specific attentional modulation signals. Our system implements bi-directional data flow in a processing hierarchy. The bottom-up data flow proceeds from a preprocessing level to the hypothesis level where object hypotheses created by exhaustive object detection algorithms are represented in a roughly retinotopic way. A competitive selection mechanism is used to determine the most confident hypotheses, which are used on the system level to train multimodal models that link object identity to invariant hypothesis properties. The top-down data flow originates at the system level, where the trained multimodal models are used to obtain space- and feature-based attentional modulation signals, providing biases for the competitive selection process at the hypothesis level. This results in object-specific hypothesis facilitation/suppression in certain image regions which we show to be applicable to different object detection mechanisms. In order to demonstrate the benefits of this approach, we apply the system to the detection of cars in a variety of challenging traffic videos. Evaluating our approach on a publicly available dataset containing approximately 3,500 annotated video images from more than 1 h of driving, we can show strong increases in performance and generalization when compared to object detection in isolation. Furthermore, we compare our results to a late hypothesis rejection approach, showing that early coupling of top-down and bottom-up information is a favorable approach especially when processing resources are constrained.
机译：在此贡献中，我们提出了一种用于对象检测的大型分层系统，将自下而上（信号驱动）的处理结果与自上而下（模型或任务驱动）的注意力调制融合在一起。具体而言，我们关注以下问题：如何将不变模型的自主学习嵌入到执行系统中，以及如何将这些模型用于定义对象特定的注意调制信号。我们的系统在处理层次结构中实现双向数据流。自下而上的数据流从预处理级别前进到假设级别，在该级别上，穷举物体检测算法创建的物体假设以大致视网膜的方式表示。竞争性选择机制用于确定最可信的假设，这些假设在系统级别用于训练将对象身份与不变假设属性联系起来的多峰模型。自上而下的数据流起源于系统级别，在该级别上，训练有素的多峰模型用于获得基于空间和特征的注意力调制信号，从而为假设级别的竞争性选择过程提供了偏差。这导致在某些图像区域中特定于对象的假设得以简化/抑制，我们证明了该图像适用于不同的对象检测机制。为了证明这种方法的好处，我们将该系统应用于各种具有挑战性的交通视频中的汽车检测。在一个公开的数据集上评估我们的方法，该数据集包含来自驾驶1小时以上的大约3500个带注释的视频图像，与孤立地进行对象检测相比，我们可以显示出性能和泛化能力的大幅提高。此外，我们将我们的结果与后期假设拒绝方法进行了比较，结果表明，自上而下和自下而上的信息的早期耦合是一种有利的方法，尤其是在处理资源受限的情况下。

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