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Structural accuracy of segmentation is important for fine-scale structures in biomedical images. We propose a novel Topological-Attention ConvLSTM Network (TACLNet) for 3D anisotropic image segmentation with high structural accuracy. We adopt ConvLSTM to leverage contextual information from adjacent slices while achieving high efficiency. We propose a Spatial Topological-Attention (STA) module to effectively transfer topologically critical information across slices. Furthermore, we propose an Iterative Topological-Attention (ITA) module that provides a more stable topologically critical map for segmentation. Quantitative and qualitative results show that our proposed method outperforms various baselines in terms of topology-aware evaluation metrics.
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Contextual Attention for Hand Detection in the Wild
We present Hand-CNN, a novel convolutional network architecture for detecting hand masks and predicting hand orientations in unconstrained images. Hand-CNN extends MaskRCNN with a novel attention mechanism to incorporate contextual cues in the detection process. This attention mechanism can be implemented as an efficient network module that captures non-local dependencies between features. This network module can be inserted at different stages of an object detection network, and the entire detector can be trained end-to-end. We also introduce a large-scale annotated hand dataset containing hands in unconstrained images for training and evaluation. We show that Hand-CNN outperforms existing methods on several datasets, including our hand detection benchmark and the publicly available PASCAL VOC human layout challenge. We also conduct ablation studies on hand detection to show the effectiveness of the proposed contextual attention module.
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- Award ID(s):
- 1650499
- PAR ID:
- 10137864
- Date Published:
- Journal Name:
- IEEE International Conference on Computer Vision workshops
- ISSN:
- 2473-9936
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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