Scribble-Supervised Segmentation of Aerial Building Footprints Using Adversarial Learning

Wu, W.; Qi, Huan; Rong, Zhenrui; Liu, Liang; Su, Hongye

doi:10.1109/access.2018.2874544

Cited by 27 publications

(17 citation statements)

References 39 publications

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“…Therefore, we set the number of positive scribbles N s pos to 1. To sample negative scribbles, [29] proposed a "background scribble generation" method via Random Walks. Different from their works, we simply cut the background scribble into several pieces after inverting the ground truth mask.…”

Section: Simulating User Samplingmentioning

confidence: 99%

“…Since SE-Net [41] only adopts the global average pooling [43] (pp. [29][30][31][32][33][34][35][36][37][38][39], it can encode the entire spatial feature on a channel as a global feature, which is effectively used for standard image segmentation. However, the most important channels in an interactive segmentation task are mostly decided by the user-interactions.…”

Section: Attention-guided Convolution (Agc) Modulementioning

confidence: 99%

“…First, we applied a global average pooling [43] (pp. [29][30][31][32][33][34][35][36][37][38][39] and a global max pooling [43] (pp. [29][30][31][32][33][34][35][36][37][38][39], respectively, to squeeze global spatial information, and obtained the output squeeze gap ∈ R C,1,1 and squeeze gmp ∈ R C,1,1 .…”

Section: Attention-guided Convolution (Agc) Modulementioning

confidence: 99%

“…2020, 12, 789 3 of 23 interactive extraction of objects in natural images, we must take lots of impacts into consideration in more complex scenes. However, only a few frameworks have been proposed to apply CNNs to interactive satellite imagery interpretation [29].…”

Section: Introductionmentioning

confidence: 99%

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Attention-Guided Multi-Scale Segmentation Neural Network for Interactive Extraction of Region Objects from High-Resolution Satellite Imagery

Jiang

et al. 2020

Remote Sensing

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Automatic extraction of region objects from high-resolution satellite imagery presents a great challenge, because there may be very large variations of the objects in terms of their size, texture, shape, and contextual complexity in the image. To handle these issues, we present a novel, deep-learning-based approach to interactively extract non-artificial region objects, such as water bodies, woodland, farmland, etc., from high-resolution satellite imagery. First, our algorithm transforms user-provided positive and negative clicks or scribbles into guidance maps, which consist of a relevance map modified from Euclidean distance maps, two geodesic distance maps (for positive and negative, respectively), and a sampling map. Then, feature maps are extracted by applying a VGG convolutional neural network pre-trained on the ImageNet dataset to the image X, and they are then upsampled to the resolution of X. Image X, guidance maps, and feature maps are integrated as the input tensor. We feed the proposed attention-guided, multi-scale segmentation neural network (AGMSSeg-Net) with the input tensor above to obtain the mask that assigns a binary label to each pixel. After a post-processing operation based on a fully connected Conditional Random Field (CRF), we extract the selected object boundary from the segmentation result. Experiments were conducted on two typical datasets with diverse region object types from complex scenes. The results demonstrate the effectiveness of the proposed method, and our approach outperforms existing methods for interactive image segmentation.

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Section: Simulating User Samplingmentioning

confidence: 99%

Section: Attention-guided Convolution (Agc) Modulementioning

confidence: 99%

Section: Attention-guided Convolution (Agc) Modulementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Attention-Guided Multi-Scale Segmentation Neural Network for Interactive Extraction of Region Objects from High-Resolution Satellite Imagery

Jiang

et al. 2020

Remote Sensing

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“…The use of scribbles is a viable alternative to full annotations due to its simplicity. A combination of scribbledsupervised learning and object dependent regularization has shown success in semantic segmentation of natural images (20)(21)(22). Scribble2Label (23) was recently proposed in the context of cell segmentation, outperforming methods such as (20,24,25).…”

Section: Introductionmentioning

confidence: 99%

ImPartial: Partial Annotations for Cell Instance Segmentation

Martínez

Sapiro

Tannenbaum

et al. 2021

Preprint

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Segmenting noisy multiplex spatial tissue images constitutes a challenging task, since the characteristics of both the noise and the biology being imaged differs significantly across tissues and modalities; this is compounded by the high monetary and time costs associated with manual annotations. It is therefore imperative to build algorithms that can accurately segment the noisy images based on a small number of annotations. Recently techniques to derive such an algorithm from a few scribbled annotations have been proposed, mostly relying on the refinement and estimation of pseudo-labels. Other techniques leverage the success of self-supervised denoising as a parallel task to potentially improve the segmentation objective when few annotations are available. In this paper, we propose a method that augments the segmentation objective via self-supervised multi-channel quantized imputation, meaning that each class of the segmentation objective can be characterized by a mixture of distributions. This approach leverages the observation that perfect pixel-wise reconstruction or denoising of the image is not needed for accurate segmentation, and introduces a self-supervised classification objective that better aligns with the overall segmentation goal. We demonstrate the superior performance of our approach for a variety of cancer datasets acquired with different highly-multiplexed imaging modalities in real clinical settings. Code for our method along with a benchmarking dataset is available at https://github.com/natalialmg/ImPartial.

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Scribble2Label: Scribble-Supervised Cell Segmentation via Self-generating Pseudo-Labels with Consistency

Lee

Jeong

2020

Lecture Notes in Computer Science

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Scribble-Supervised Segmentation of Aerial Building Footprints Using Adversarial Learning

Cited by 27 publications

References 39 publications

Attention-Guided Multi-Scale Segmentation Neural Network for Interactive Extraction of Region Objects from High-Resolution Satellite Imagery

Attention-Guided Multi-Scale Segmentation Neural Network for Interactive Extraction of Region Objects from High-Resolution Satellite Imagery

ImPartial: Partial Annotations for Cell Instance Segmentation

Scribble2Label: Scribble-Supervised Cell Segmentation via Self-generating Pseudo-Labels with Consistency

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