• Corpus ID: 246652499

Scribble-based Boundary-aware Network for Weakly Supervised Salient Object Detection in Remote Sensing Images

  title={Scribble-based Boundary-aware Network for Weakly Supervised Salient Object Detection in Remote Sensing Images},
  author={Zhou Huang and Tianyi Xiang and Huaixin Chen and Hang Dai},
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 (e.g., image-level or scribble) become appealing to the salient object detection community. However, few efforts are devoted to learning salient object detection from sparse annotations, especially in the remote sensing field. In addition, the sparse annotation usually contains scanty… 


Weakly-Supervised Salient Object Detection via Scribble Annotations
This paper proposes a weakly-supervised salient object detection model to learn saliency from scribble annotations, and presents a new metric, termed saliency structure measure, as a complementary metric to evaluate sharpness of the prediction.
Weakly Supervised Salient Object Detection Using Image Labels
This paper proposes to use the combination of a coarse salient object activation map from the classification network and saliency maps generated from unsupervised methods as pixel-level annotation, and develops a simple yet very effective algorithm to train fully convolutional networks for salient object detection supervised by these noisy annotations.
Attentive Feedback Network for Boundary-Aware Salient Object Detection
The Attentive Feedback Modules (AFMs) are designed to better explore the structure of objects and produce satisfying results on the object boundaries and achieves state-of-the-art performance on five widely tested salient object detection benchmarks.
Semantic-Guided Attention Refinement Network for Salient Object Detection in Optical Remote Sensing Images
Quantitative and qualitative evaluations on two public RSI datasets and additional NSI datasets across five metrics show that the SARNet is superior to 14 state-of-the-art (SOTA) methods without any post-processing.
Weakly Supervised Video Salient Object Detection
An "Appearance-motion fusion module" and bidirectional ConvLSTM based framework are proposed to achieve effective multi-modal learning and long-term temporal context modeling based on the first weakly supervised video salient object detection model based on relabeled “fixation guided scribble annotations”.
Scribble-Based Weakly Supervised Deep Learning for Road Surface Extraction From Remote Sensing Images
  • Yao Wei, Shunping Ji
  • Environmental Science, Computer Science
    IEEE Transactions on Geoscience and Remote Sensing
  • 2022
A scribble-based weakly supervised road surface extraction method named ScRoadExtractor, which learns from easily accessible scribbles such as centerlines instead of densely annotated road surface ground truths to propagate semantic information from sparse scribbles to unlabeled pixels.
Semantic Segmentation of Remote Sensing Images With Sparse Annotations
Experimental results demonstrate that the exploitation of sparse annotations can significantly reduce labeling costs, while the proposed method can help improve the performance of semantic segmentation when training on such annotations.
Deeply Supervised Salient Object Detection with Short Connections
A new saliency method is proposed by introducing short connections to the skip-layer structures within the HED architecture, which produces state-of-the-art results on 5 widely tested salient object detection benchmarks, with advantages in terms of efficiency, effectiveness, and simplicity over the existing algorithms.
Weakly-supervised Salient Instance Detection
This paper proposes a novel weakly-supervised network with three branches: a Saliency Detection Branch leveraging class consistency information to locate candidate objects; a Boundary Detection Branch exploiting class discrepancy information to delineate object boundaries; and a Centroid Detection Branch using subitizing information to detect salient instance centroids.
Weakly-Supervised Salient Object Detection With Saliency Bounding Boxes
A novel form of weak supervision for salient object detection (SOD) based on saliency bounding boxes, which are minimum rectangular boxes enclosing the salient objects, which outperforms state-of-the-art weakly-supervised methods.