| 1. |
- Fu, Keren, et al.
(författare)
-
Deepside: A general deep framework for salient object detection
- 2019
-
Ingår i: Neurocomputing. - : Elsevier BV. - 0925-2312 .- 1872-8286. ; 356, s. 69-82
-
Tidskriftsartikel (refereegranskat)abstract
- Deep learning-based salient object detection techniques have shown impressive results compared to con- ventional saliency detection by handcrafted features. Integrating hierarchical features of Convolutional Neural Networks (CNN) to achieve fine-grained saliency detection is a current trend, and various deep architectures are proposed by researchers, including “skip-layer” architecture, “top-down” architecture, “short-connection” architecture and so on. While these architectures have achieved progressive improve- ment on detection accuracy, it is still unclear about the underlying distinctions and connections between these schemes. In this paper, we review and draw underlying connections between these architectures, and show that they actually could be unified into a general framework, which simply just has side struc- tures with different depths. Based on the idea of designing deeper side structures for better detection accuracy, we propose a unified framework called Deepside that can be deeply supervised to incorporate hierarchical CNN features. Additionally, to fuse multiple side outputs from the network, we propose a novel fusion technique based on segmentation-based pooling, which severs as a built-in component in the CNN architecture and guarantees more accurate boundary details of detected salient objects. The effectiveness of the proposed Deepside scheme against state-of-the-art models is validated on 8 benchmark datasets.
|
|
| 2. |
- Fu, Keren, 1988, et al.
(författare)
-
Refinet: A Deep Segmentation Assisted Refinement Network for Salient Object Detection
- 2019
-
Ingår i: IEEE Transactions on Multimedia. - 1520-9210. ; 21:2, s. 457-469
-
Tidskriftsartikel (refereegranskat)abstract
- Deep convolutional neural networks (CNNs) recently have been successfully applied to saliency detection with improved performance on locating salient objects, as comparing to conventional saliency detection by handcrafted features. Unfortunately, due to repeated sub-sampling operations inside CNNs such as pooling and convolution, many CNN-based saliency models fail to maintain fine-grained spatial details and boundary structures of objects. To remedy this issue, this paper proposes a novel end-to-end deep learning-based refinement model named Refinet, which is based on fully convolutional network augmented with segmentation hypotheses. Intermediate saliency maps which are edge-aware are computed from segmentation-based pooling and then cancatenating two streams into a fully convolutional network for effective fusion and refinement, leading to more precise object details and boundaries. In addition, the resolution of feature maps in the proposed Refinet is carefully designed to guarantee sufficient boundary clarity of the refined saliency output. Compared to widely employed dense conditional random field (CRF), Refinet is able to enhance coarse saliency maps generated by existing models with more accurate spatial details, and its effectiveness is demonstrated by experimental results on 7 benchmark datasets.
|
|
