MASON: A Model AgnoStic ObjectNess Framework
K J Joseph, Vineeth N Balasubramanian

TL;DR
MASON is a versatile, model-agnostic deep learning framework that localizes foreground objects at pixel-level precision across diverse images without task-specific training.
Contribution
It introduces a simple, effective method for object localization that is category-independent and can be integrated with any existing network without additional training.
Findings
Works effectively across diverse image types
Achieves pixel-level object localization
Compatible with various neural network models
Abstract
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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Taxonomy
TopicsAdvanced Neural Network Applications · Visual Attention and Saliency Detection · Advanced Image and Video Retrieval Techniques
