MASON: A Model AgnoStic ObjectNess Framework

Balasubramanian, Vineeth N and Chunilal Patel, Rajiv and Srivastava, Amit and et al, . (2019) MASON: A Model AgnoStic ObjectNess Framework. In: 15th European Conference on Computer Vision, ECCV, 8-14 September 2018, Germany.

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This paper proposes a simple, yet very effective method to localize dominant foreground objects in an image, to pixel-level precision. The proposed method ‘MASON’ (Model-AgnoStic ObjectNess) uses a deep convolutional network to generate category-independent and model-agnostic heat maps for any image. The network is not explicitly trained for the task, and hence, can be used off-the-shelf in tandem with any other network or task. We show that this framework scales to a wide variety of images, and illustrate the effectiveness of MASON in three varied application contexts.

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Balasubramanian, Vineeth NUNSPECIFIED
Item Type: Conference or Workshop Item (Paper)
Subjects: Computer science
Divisions: Department of Computer Science & Engineering
Depositing User: Library Staff
Date Deposited: 28 Oct 2019 11:28
Last Modified: 28 Oct 2019 11:28
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