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},
1 Citations

Weakly-Supervised Crack Segmentation via Scribble Annotations

For precise pixel-level crack extraction, a large amount of densely labeled data is usually required to train a deep neural network, which is time-consuming and laborious. Scribble annotations are



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.

Structure-Consistent Weakly Supervised Salient Object Detection with Local Saliency Coherence

This work proposes a one-round end-to-end training approach for weakly supervised salient object detection via scribble annotations without pre/post-processing operations or extra supervision data, and designs a saliency structure consistency loss as self-consistent mechanism to ensure consistent saliency maps are predicted with different scales of the same image as input.

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.

Learning to Detect Salient Objects with Image-Level Supervision

This paper develops a weakly supervised learning method for saliency detection using image-level tags only, which outperforms unsupervised ones with a large margin, and achieves comparable or even superior performance than fully supervised counterparts.

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 WeiShunping 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.