Attentive Feedback Network for Boundary-Aware Salient Object Detection
Recent deep learning based salient object detection methods achieve gratifying performance built upon Fully Convolutional Neural Networks (FCNs). However, most of them have suffered from the boundary challenge. The state-of-the-art methods employ feature aggregation tech- nique and can precisely find out wherein the salient object, but they often fail to segment out the entire object with fine boundaries, especially those raised narrow stripes. So there is still a large room for improvement over the FCN based models. In this paper, we design the Attentive Feedback Modules (AFMs) to better explore the structure of objects. A Boundary-Enhanced Loss (BEL) is further employed for learning exquisite boundaries. Our proposed deep model produces satisfying results on the object boundaries and achieves state-of-the-art performance on five widely tested salient object detection benchmarks. The network is in a fully convolutional fashion running at a speed of 26 FPS and does not need any post-processing.
Code (1)
Tasks
Objectobject-detectionObject DetectionRGB Salient Object DetectionSalient Object DetectionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Scribble-based Boundary-aware Network for Weakly Supervised Salient Object Detection in Remote Sensing Images
Existing CNNs-based salient object detection (SOD) heavily depends on the large-scale pixel-level annotations, which is labor-intensive, time-consuming, and expensive. By contrast, the sparse annotations become appealing…
Objectobject-detectionObject DetectionSalient Object DetectionContour Loss: Boundary-Aware Learning for Salient Object Segmentation
We present a learning model that makes full use of boundary information for salient object segmentation. Specifically, we come up with a novel loss function, i.e., Contour Loss, which leverages object contours to guide m…
GPUObjectSaliency DetectionSemantic SegmentationBoundary-semantic collaborative guidance network with dual-stream feedback mechanism for salient object detection in optical remote sensing imagery
With the increasing application of deep learning in various domains, salient object detection in optical remote sensing images (ORSI-SOD) has attracted significant attention. However, most existing ORSI-SOD methods predo…
Decoderobject-detectionObject DetectionSalient Object DetectionPosition-Aware Relation Learning for RGB-Thermal Salient Object Detection
RGB-Thermal salient object detection (SOD) combines two spectra to segment visually conspicuous regions in images. Most existing methods use boundary maps to learn the sharp boundary. These methods ignore the interaction…
DecoderObjectobject-detectionObject Detection+3Selectivity or Invariance: Boundary-aware Salient Object Detection
Typically, a salient object detection (SOD) model faces opposite requirements in processing object interiors and boundaries. The features of interiors should be invariant to strong appearance change so as to pop-out the …
Objectobject-detectionObject DetectionRGB Salient Object Detection+1