Cross Attention Network fuses spatial and contextual features via a cross attention module to improve semantic segmentation performance and speed on Cityscapes and CamVid.
Dual Attention Network for Scene Segmentation
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abstract
In this paper, we address the scene segmentation task by capturing rich contextual dependencies based on the selfattention mechanism. Unlike previous works that capture contexts by multi-scale features fusion, we propose a Dual Attention Networks (DANet) to adaptively integrate local features with their global dependencies. Specifically, we append two types of attention modules on top of traditional dilated FCN, which model the semantic interdependencies in spatial and channel dimensions respectively. The position attention module selectively aggregates the features at each position by a weighted sum of the features at all positions. Similar features would be related to each other regardless of their distances. Meanwhile, the channel attention module selectively emphasizes interdependent channel maps by integrating associated features among all channel maps. We sum the outputs of the two attention modules to further improve feature representation which contributes to more precise segmentation results. We achieve new state-of-the-art segmentation performance on three challenging scene segmentation datasets, i.e., Cityscapes, PASCAL Context and COCO Stuff dataset. In particular, a Mean IoU score of 81.5% on Cityscapes test set is achieved without using coarse data. We make the code and trained model publicly available at https://github.com/junfu1115/DANet
years
2019 3verdicts
UNVERDICTED 3representative citing papers
OGNet proposes an output-guided attention module from multi-scale outputs and an intractable area F-measure loss to enhance salient object detection in edges and confusing areas while remaining lightweight.
cGAN with atrous convolutions and channel weighting segments breast tumors in ultrasound at 93.76% Dice and 88.82% IoU, then classifies benign vs malignant at 85% accuracy using boundary shape features.
citing papers explorer
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Cross Attention Network for Semantic Segmentation
Cross Attention Network fuses spatial and contextual features via a cross attention module to improve semantic segmentation performance and speed on Cityscapes and CamVid.
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OGNet: Salient Object Detection with Output-guided Attention Module
OGNet proposes an output-guided attention module from multi-scale outputs and an intractable area F-measure loss to enhance salient object detection in edges and confusing areas while remaining lightweight.
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An Efficient Solution for Breast Tumor Segmentation and Classification in Ultrasound Images Using Deep Adversarial Learning
cGAN with atrous convolutions and channel weighting segments breast tumors in ultrasound at 93.76% Dice and 88.82% IoU, then classifies benign vs malignant at 85% accuracy using boundary shape features.