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Contextualizing Object Detection and Classification

机译:上下文化对象检测和分类

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We investigate how to iteratively and mutually boost object classification and detection performance by taking the outputs from one task as the context of the other one. While context models have been quite popular, previous works mainly concentrate onco-occurrence relationship within classes and few of them focus on contextualization from a top-down perspective, i.e. high-level task context. In this paper, our system adopts a new method for adaptive context modeling and iterative boosting. First, the contextualized support vector machine (Context-SVM) is proposed, where the context takes the role of dynamically adjusting the classification score based on the sample ambiguity, and thus the context-adaptive classifier is achieved. Then, an iterative training procedure is presented. In each step, Context-SVM, associated with the output context from one task (object classification or detection), is instantiated to boost the performance for the other task, whose augmented outputs are then further used to improve the former task by Context-SVM. The proposed solution is evaluated on the object classification and detection tasks of PASCAL Visual Object Classes Challenge (VOC) 2007, 2010 and SUN09 data sets, and achieves the state-of-the-art performance.
机译:我们研究如何通过将一项任务的输出作为另一项任务的上下文来迭代和相互促进对象分类和检测性能。尽管上下文模型非常流行,但以前的工作主要集中在类内的共现关系上,很少有人从上到下的角度(即高级任务上下文)关注上下文化。在本文中,我们的系统采用了一种新的方法来进行自适应上下文建模和迭代增强。首先,提出了一种上下文支持向量机(Context-SVM),其中上下文根据样本的模糊性承担动态调整分类得分的作用,从而实现了上下文自适应分类器。然后,提出了迭代训练程序。在每个步骤中,实例化与一个任务(对象分类或检测)的输出上下文关联的Context-SVM,以提高另一任务的性能,然后通过Context-SVM将其增强后的输出进一步用于改进前一个任务。对提出的解决方案进行了PASCAL视觉对象类别挑战(VOC)2007、2010和SUN09数据集的对象分类和检测任务评估,并获得了最新的性能。

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