Attention-based Assisted Excitation for Salient Object Detection

• URL: http://arxiv.org/abs/2003.14194v2
• Date: Mon, 11 May 2020 05:42:16 GMT
• Title: Attention-based Assisted Excitation for Salient Object Detection
• Authors: Saeed Masoudnia, Melika Kheirieh, Abdol-Hossein Vahabie, Babak Nadjar Araabi
• Abstract summary: We introduce a mechanism for modification of activations in feature maps of CNNs inspired by object-based attention in brain. Similar to brain, we use the idea to address two challenges in salient object detection: gathering object interior parts while segregation from background with concise boundaries. We implement the object-based attention in the U-net model using different architectures in the encoder parts, including AlexNet, VGG, and ResNet.
• Score: 3.238929552408813
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual attention brings significant progress for Convolution Neural Networks (CNNs) in various applications. In this paper, object-based attention in human visual cortex inspires us to introduce a mechanism for modification of activations in feature maps of CNNs. In this mechanism, the activations of object locations are excited in feature maps. This mechanism is specifically inspired by attention-based gain modulation in object-based attention in brain. It facilitates figure-ground segregation in the visual cortex. Similar to brain, we use the idea to address two challenges in salient object detection: gathering object interior parts while segregation from background with concise boundaries. We implement the object-based attention in the U-net model using different architectures in the encoder parts, including AlexNet, VGG, and ResNet. The proposed method was examined on three benchmark datasets: HKU-IS, MSRB, and PASCAL-S. Experimental results showed that our inspired method could significantly improve the results in terms of mean absolute error and F-measure. The results also showed that our proposed method better captured not only the boundary but also the object interior. Thus, it can tackle the mentioned challenges.

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