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Object Segmentation using Pixel-wise Adversarial Loss
[article]
2019
arXiv
pre-print
Preserved Fulltext
The key element of our method is to replace the commonly used binary adversarial loss with a high resolution pixel-wise loss. ...
We show, that this combination of pixel-wise adversarial training and weight averaging leads to significant and consistent gains in segmentation performance, compared to the baseline models. ...
In this paper, we address the task of object segmentation by proposing an adversarial learning scheme which adds a pixel-wise adversarial loss to the classical topology. ...
arXiv:1909.10341v1
fatcat:yp7sotlz5zgqjelotztutkd6r4
Adaptive Affinity Fields for Semantic Segmentation
[article]
2018
arXiv
pre-print
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Semantic segmentation has made much progress with increasingly powerful pixel-wise classifiers and incorporating structural priors via Conditional Random Fields (CRF) or Generative Adversarial Networks ...
We use adversarial learning to select the optimal affinity field size for each semantic category. ...
Summary We propose adaptive affinity fields (AAF) for semantic segmentation, which incorporate geometric regularities into segmentation models, and learn local relations with adaptive ranges through adversarial ...
arXiv:1803.10335v3
fatcat:2o74jzivonbarfafrvhfyeqopu
Adaptive Affinity Fields for Semantic Segmentation
[chapter]
2018
Lecture Notes in Computer Science
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Semantic segmentation has made much progress with increasingly powerful pixel-wise classifiers and incorporating structural priors via Conditional Random Fields (CRF) or Generative Adversarial Networks ...
We use adversarial learning to select the optimal affinity field size for each semantic category. ...
Summary We propose adaptive affinity fields (AAF) for semantic segmentation, which incorporate geometric regularities into segmentation models, and learn local relations with adaptive ranges through adversarial ...
doi:10.1007/978-3-030-01246-5_36
fatcat:icz2qozfrng57ocxi6as5vydlu
No More Discrimination: Cross City Adaptation of Road Scene Segmenters
[article]
2017
arXiv
pre-print
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By advancing a joint global and class-specific domain adversarial learning framework, adaptation of pre-trained segmenters to that city can be achieved without the need of any user annotation or interaction ...
By utilizing Google Street View and its time-machine feature, we can collect unannotated images for each road scene at different times, so that the associated static-object priors can be extracted accordingly ...
domain adversarial loss, respectively. ...
arXiv:1704.08509v1
fatcat:7ldqict6yzba5cm6j2aqsdqcu4
SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation
2018
Neuroinformatics
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2017 pre-print arXiv:1706.01805v1 fatcat:gz6nmfaa65b5jnhiy63br4upqaOther Versions
Instead, we use a fully convolutional neural network as the segmentor to generate segmentation label maps, and propose a novel adversarial critic network with a multi-scale L_1 loss function to force the ...
We tested our SegAN method using datasets from the MICCAI BRATS brain tumor segmentation challenge. ...
[9] used both the conventional adversarial loss of GAN and pixel-wise softmax loss against ground truth. ...
doi:10.1007/s12021-018-9377-x
pmid:29725916
fatcat:njcwonnnhzgmnhjydsoss7hpee
SROBB: Targeted Perceptual Loss for Single Image Super-Resolution
[article]
2019
arXiv
pre-print
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In particular, the proposed method leverages our proposed OBB (Object, Background and Boundary) labels, generated from segmentation labels, to estimate a suitable perceptual loss for boundaries, while ...
In this paper, we propose a novel method to benefit from perceptual loss in a more objective way. ...
The MSE and adversarial loss terms are defined as follows: • Pixel-wise loss It is by far the most commonly used loss function in SR. ...
arXiv:1908.07222v1
fatcat:ygnwxgjpwnctnb4omufprrdpt4
Defending From Physically-Realizable Adversarial Attacks Through Internal Over-Activation Analysis
[article]
2022
arXiv
pre-print
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The presented defense relies on specific Z-score analysis performed on the internal network features to detect and mask the pixels corresponding to adversarial objects in the input image. ...
The effectiveness of Z-Mask is evaluated with an extensive set of experiments carried out on models for both semantic segmentation and object detection. ...
In practice, a pixel-wise max function is used for F(•). ...
arXiv:2203.07341v1
fatcat:ti6xsuxybbabba2pldcngfjflq
Unsupervised Image Segmentation by Mutual Information Maximization and Adversarial Regularization
[article]
2021
arXiv
pre-print
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To customize an adversarial training scheme for the problem, we incorporate adversarial pixel noise along with spatial perturbations to impose photometrical and geometrical invariance on the deep neural ...
Our experiments demonstrate that our method achieves the state-of-the-art performance on two commonly used unsupervised semantic segmentation datasets, COCO-Stuff, and Potsdam. ...
pixel-wise label predictions. ...
arXiv:2107.00691v1
fatcat:y3wgpxrpqfapnk4otstxebc6pa
ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation
2019
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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In this work, we address the task of unsupervised domain adaptation in semantic segmentation with losses based on the entropy of the pixel-wise predictions. ...
To this end, we propose two novel, complementary methods using (i) an entropy loss and (ii) an adversarial loss respectively. ...
A similar approach of global and class-wise alignment is used in [5] with the class-wise alignment being done using adversarial training on grid-wise soft pseudolabels. ...
doi:10.1109/cvpr.2019.00262
dblp:conf/cvpr/VuJBCP19
fatcat:ywxufgupfvfejjfuzngweqcbeq
Unsupervised Domain Adaption for High-Resolution Coastal Land Cover Mapping with Category-Space Constrained Adversarial Network
2021
Remote Sensing
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Additionally, the low inter-class variances and intricate spatial details of coastal objects may entail poor presentation. ...
Coastal land cover mapping (CLCM) across image domains presents a fundamental and challenging segmentation task. ...
Alternate adversarial training is driven by the objective function L, in which the segmentation loss L S seg and adversarial loss L i adv , respectively, serve for the dense prediction and multi-level ...
doi:10.3390/rs13081493
fatcat:j2vpmd4smrcvvmpszk43bvt52a
ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation
[article]
2019
arXiv
pre-print
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In this work, we address the task of unsupervised domain adaptation in semantic segmentation with losses based on the entropy of the pixel-wise predictions. ...
To this end, we propose two novel, complementary methods using (i) entropy loss and (ii) adversarial loss respectively. ...
A similar approach of global and class-wise alignment is used in [5] with the class-wise alignment being done using adversarial training on grid-wise soft pseudolabels. ...
arXiv:1811.12833v2
fatcat:e7ox63x7svbzvlvnalfbleotp4
Adversarial Policy Gradient for Deep Learning Image Augmentation
[article]
2019
arXiv
pre-print
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The use of semantic segmentation for masking and cropping input images has proven to be a significant aid in medical imaging classification tasks by decreasing the noise and variance of the training dataset ...
Our method, Adversarial Policy Gradient Augmentation (APGA), shows promising results on Stanford's MURA dataset and on a hip fracture classification task with an increase in global accuracy of up to 7.33% ...
To mask out the image-level features that are less useful for the classification task, we use the segmentation model to produce the pixel-wise probability p k of the pixel being useful. ...
arXiv:1909.04108v1
fatcat:g2kxhgz2p5fk7bru5rvaohwfde
Structured Knowledge Distillation for Dense Prediction
[article]
2020
arXiv
pre-print
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Specifically, we study two structured distillation schemes: i) pair-wise distillation that distills the pair-wise similarities by building a static graph; and ii) holistic distillation that uses adversarial ...
The effectiveness of our knowledge distillation approaches is demonstrated by experiments on three dense prediction tasks: semantic segmentation, depth estimation and object detection. ...
Optimization The overall objective function consists of a standard multiclass cross-entropy loss mc (S) with pixel-wise and structured distillation terms 1 (S, D) = mc (S) + λ 1 ( pi (S) + pa (S)) − λ ...
arXiv:1903.04197v7
fatcat:vcwpcgffgndwfo3xye3kdjtm24
3D Scene Parsing via Class-Wise Adaptation
[article]
2019
arXiv
pre-print
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Especially in the case of semantic segmentation, annotating pixel by pixel takes a significant amount of time and often makes mistakes. ...
We propose the method that uses only computer graphics datasets to parse the real world 3D scenes. 3D scene parsing based on semantic segmentation is required to implement the categorical interaction in ...
Pixel-wise loss which uses local information for domain adaptation helps for local domain shift. Figure 2 shows the training procedure. ...
arXiv:1812.03622v2
fatcat:f3p3x42u5vcfre4d7tooh2hkoq
Pixel-by-Pixel Cross-Domain Alignment for Few-Shot Semantic Segmentation
[article]
2021
arXiv
pre-print
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We propose a novel pixel-by-pixel domain adversarial loss following three criteria: (i) align the source and the target domain for each pixel, (ii) avoid negative transfer on the correctly represented ...
In this context, aligning the domains is made more challenging by the pixel-wise class imbalance that is intrinsic in the segmentation and that leads to ignoring the underrepresented classes and overfitting ...
To do this, we introduce the Pixel-By-Pixel Cross-Domain Alignment framework (PixDA), that uses a novel pixel-wise discriminator and modulates the adversarial loss for each pixel to: (i) align pixel-wise ...
arXiv:2110.11650v1
fatcat:b2gusgf7pfgktmwgldvkf7btti
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