The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
BASNet: Boundary-Aware Salient Object Detection
Abstract
Deep Convolutional Neural Networks have been adopted for salient object detection and achieved the state-of-the-art performance. Most of the previous works however focus on region accuracy but not on the boundary quality. In this paper, we propose a predict-refine architecture, BASNet, and a new hybrid loss for Boundary-Aware Salient object detection. Specifically, the architecture is composed of a densely supervised Encoder-Decoder network and a residual refinement module, which are respectively in charge of saliency prediction and saliency map refinement. The hybrid loss guides the network to learn the transformation between the input image and the ground truth in a three-level hierarchy -- pixel-, patch- and map- level -- by fusing Binary Cross Entropy (BCE), Structural SIMilarity (SSIM) and Intersection-over-Union (IoU) losses. Equipped with the hybrid loss, the proposed predict-refine architecture is able to effectively segment the salient object regions and accurately predict the fine structures with clear boundaries. Experimental results on six public datasets show that our method outperforms the state-of-the-art methods both in terms of regional and boundary evaluation measures. Our method runs at over 25 fps on a single GPU. The code is available at: https://github.com/NathanUA/BASNet.
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Cites
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BackgroundInfluentialRoad Extraction by Deep Residual U-Net
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ResultSelf-citationLarge Kernel Matters — Improve Semantic Segmentation by Global Convolutional Network
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Cited by
Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images
Fan, D.-P., Zhou, T., Ji, G.-P., Zhou, Y., Chen, G., Fu, H., Shen, J., & Shao, L. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2996645 · Cited by 1,259
DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion
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UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders
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Salient Object Detection via Integrity Learning
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Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals
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MobileSal: Extremely Efficient RGB-D Salient Object Detection
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Volumetric memory network for interactive medical image segmentation
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Densely nested top-down flows for salient object detection
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Deep attentive fusion network for flood detection on uni-temporal Sentinel-1 data
Yadav, R., Nascetti, A., & Ban, Y. (2022) · Frontiers in Remote Sensing · doi:10.3389/frsen.2022.1060144 · Cited by 21
Metadata
- Authors
- Qin, X., Zhang, Z., Huang, C., Gao, C., Dehghan, M., & Jagersand, M.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 7471-7481
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2961348656
- Cited by
- 1,565
- References
- 10
- FWCI
- 56.93
- Type
- conference paper
- Language
- en
- Open access
- closed
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