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Co-Saliency Detection via a Self-Paced Multiple-Instance Learning Framework

机译：通过自定进度的多实例学习框架进行共显着性检测

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摘要

As an interesting and emerging topic, co-saliency detection aims at simultaneously extracting common salient objects from a group of images. On one hand, traditional co-saliency detection approaches rely heavily on human knowledge for designing handcrafted metrics to possibly reflect the faithful properties of the co-salient regions. Such strategies, however, always suffer from poor generalization capability to flexibly adapt various scenarios in real applications. On the other hand, most current methods pursue cosaliency detection in unsupervised fashions. This, however, tends to weaken their performance in real complex scenarios because they are lack of robust learning mechanism to make full use of the weak labels of each image. To alleviate these two problems, this paper proposes a new SP-MIL framework for co-saliency detection, which integrates both multiple instance learning (MIL) and self-paced learning (SPL) into a unified learning framework. Specifically, for the first problem, we formulate the co-saliency detection problem as a MIL paradigm to learn the discriminative classifiers to detect the co-saliency object in the “instance-level”. The formulated MIL component facilitates our method capable of automatically producing the proper metrics to measure the intra-image contrast and the inter-image consistency for detecting co-saliency in a purely self-learning way. For the second problem, the embedded SPL paradigm is able to alleviate the data ambiguity under the weak supervision of co-saliency detection and guide a robust learning manner in complex scenarios. Experiments on benchmark datasets together with multiple extended computer vision applications demonstrate the superiority of the proposed framework beyond the state-of-the-arts.
机译：作为一个有趣且新兴的主题，共显着性检测旨在同时从一组图像中提取常见的显着对象。一方面，传统的共同显着性检测方法在很大程度上依赖于人类知识来设计手工制作的度量标准，以可能反映共同凸显区域的真实属性。然而，这样的策略总是遭受普遍性能力差的问题，无法灵活地适应实际应用中的各种情况。另一方面，大多数当前方法都以无监督的方式进行共性检测。但是，这会削弱它们在实际复杂场景中的性能，因为它们缺乏强大的学习机制来充分利用每个图像的弱标签。为了缓解这两个问题，本文提出了一种用于共显性检测的新SP-MIL框架，该框架将多实例学习（MIL）和自定进度学习（SPL）集成到一个统一的学习框架中。具体而言，对于第一个问题，我们将共显着性检测问题公式化为MIL范式，以学习判别式分类器，以在“实例级”中检测共显着性对象。制定的MIL组件有助于我们的方法能够自动生成适当的指标，以测量图像内对比度和图像间一致性，从而以纯自学习的方式检测共显着性。对于第二个问题，嵌入式SPL范式能够在共凸性检测的弱监督下减轻数据歧义，并在复杂的场景中提供一种鲁棒的学习方式。在基准数据集上进行的实验以及多个扩展的计算机视觉应用程序证明，所提出的框架具有超越现有技术的优越性。

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