scholarly journals Unbalanced Feature Transport for Exemplar-based Image Translation

2021 ◽  
Author(s):  
Fangneng Zhan
Keyword(s):  
Feature Matching ◽  
Optimal Transport ◽  
Semantic Segmentation ◽  
Grand Challenge ◽  
Great Success ◽  
Translation Process ◽  
Image Translation ◽  
Realistic Images ◽  
Normalization Scheme ◽  
Feature Alignment

Despite the great success of GANs in images translation with different conditioned inputs such as semantic segmentation and edge maps, generating high-fidelity realistic images with reference styles remains a grand challenge in conditional image-to-image translation. This paper presents a general image translation framework that incorporates optimal transport for feature alignment between conditional inputs and style exemplars in image translation. The introduction of optimal transport mitigates the constraint of many-to-one feature matching significantly while building up accurate semantic correspondences between conditional inputs and exemplars. We design a novel unbalanced optimaltransport to address the transport between features with deviational distributions which exists widely between conditional inputs and exemplars. In addition, we design a semantic-activation normalization scheme that injects stylefeatures of exemplars into the image translation process successfully. Extensive experiments over multiple image translation tasks show that our method achieves superior image translation qualitatively and quantitatively as comparedwith the state-of-the-art.

scholarly journals Convolutional Neural Networks for Semantic Segmentation as a Tool for Multiclass Face Analysis in Thermal Infrared

2021 ◽  
Vol 40 (1) ◽  
Author(s):  
David Müller ◽  
Andreas Ehlen ◽  
Bernd Valeske
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Semantic Segmentation ◽  
Thermal Infrared ◽  
Relative Temperature ◽  
Face Analysis ◽  
Image Translation ◽  
The Face ◽  
Live Image ◽  
Quantitative Thermography

AbstractConvolutional neural networks were used for multiclass segmentation in thermal infrared face analysis. The principle is based on existing image-to-image translation approaches, where each pixel in an image is assigned to a class label. We show that established networks architectures can be trained for the task of multiclass face analysis in thermal infrared. Created class annotations consisted of pixel-accurate locations of different face classes. Subsequently, the trained network can segment an acquired unknown infrared face image into the defined classes. Furthermore, face classification in live image acquisition is shown, in order to be able to display the relative temperature in real-time from the learned areas. This allows a pixel-accurate temperature face analysis e.g. for infection detection like Covid-19. At the same time our approach offers the advantage of concentrating on the relevant areas of the face. Areas of the face irrelevant for the relative temperature calculation or accessories such as glasses, masks and jewelry are not considered. A custom database was created to train the network. The results were quantitatively evaluated with the intersection over union (IoU) metric. The methodology shown can be transferred to similar problems for more quantitative thermography tasks like in materials characterization or quality control in production.

scholarly journals A Novel Building Temperature Simulation Approach Driven by Expanding Semantic Segmentation Training Datasets with Synthetic Aerial Thermal Images

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 353
Author(s):  
Yu Hou ◽  
Rebekka Volk ◽  
Lucio Soibelman
Keyword(s):  
Semantic Segmentation ◽  
Sensor Data ◽  
University Campus ◽  
City Center ◽  
Thermal Images ◽  
Complex Envelope ◽  
Virtual Camera ◽  
Image Translation ◽  
Rgb Images ◽  
Synthetic Datasets

Multi-sensor imagery data has been used by researchers for the image semantic segmentation of buildings and outdoor scenes. Due to multi-sensor data hunger, researchers have implemented many simulation approaches to create synthetic datasets, and they have also synthesized thermal images because such thermal information can potentially improve segmentation accuracy. However, current approaches are mostly based on the laws of physics and are limited to geometric models’ level of detail (LOD), which describes the overall planning or modeling state. Another issue in current physics-based approaches is that thermal images cannot be aligned to RGB images because the configurations of a virtual camera used for rendering thermal images are difficult to synchronize with the configurations of a real camera used for capturing RGB images, which is important for segmentation. In this study, we propose an image translation approach to directly convert RGB images to simulated thermal images for expanding segmentation datasets. We aim to investigate the benefits of using an image translation approach for generating synthetic aerial thermal images and compare those approaches with physics-based approaches. Our datasets for generating thermal images are from a city center and a university campus in Karlsruhe, Germany. We found that using the generating model established by the city center to generate thermal images for campus datasets performed better than using the latter to generate thermal images for the former. We also found that using a generating model established by one building style to generate thermal images for datasets with the same building styles performed well. Therefore, we suggest using training datasets with richer and more diverse building architectural information, more complex envelope structures, and similar building styles to testing datasets for an image translation approach.

scholarly journals Multi-Attribute Transfer via Disentangled Representation

2019 ◽  
Vol 33 ◽  
pp. 9195-9202 ◽  
Author(s):  
Jianfu Zhang ◽  
Yuanyuan Huang ◽  
Yaoyi Li ◽  
Weijie Zhao ◽  
Liqing Zhang
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Generative Adversarial Networks ◽  
Significant Progress ◽  
Target Domain ◽  
Adversarial Networks ◽  
Proposed Model ◽  
Image Translation ◽  
Realistic Images ◽  
Novel Model

Recent studies show significant progress in image-to-image translation task, especially facilitated by Generative Adversarial Networks. They can synthesize highly realistic images and alter the attribute labels for the images. However, these works employ attribute vectors to specify the target domain which diminishes image-level attribute diversity. In this paper, we propose a novel model formulating disentangled representations by projecting images to latent units, grouped feature channels of Convolutional Neural Network, to disassemble the information between different attributes. Thanks to disentangled representation, we can transfer attributes according to the attribute labels and moreover retain the diversity beyond the labels, namely, the styles inside each image. This is achieved by specifying some attributes and swapping the corresponding latent units to “swap” the attributes appearance, or applying channel-wise interpolation to blend different attributes. To verify the motivation of our proposed model, we train and evaluate our model on face dataset CelebA. Furthermore, the evaluation of another facial expression dataset RaFD demonstrates the generalizability of our proposed model.

scholarly journals Self-Ensembling Attention Networks: Addressing Domain Shift for Semantic Segmentation

2019 ◽  
Vol 33 ◽  
pp. 5581-5588 ◽  
Author(s):  
Yonghao Xu ◽  
Bo Du ◽  
Lefei Zhang ◽  
Qian Zhang ◽  
Guoli Wang ◽  
...  
Keyword(s):  
Domain Adaptation ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Great Success ◽  
Learning Models ◽  
Target Domain ◽  
Attention Networks ◽  
Source Domain ◽  
Benchmark Datasets ◽  
Different Levels

Recent years have witnessed the great success of deep learning models in semantic segmentation. Nevertheless, these models may not generalize well to unseen image domains due to the phenomenon of domain shift. Since pixel-level annotations are laborious to collect, developing algorithms which can adapt labeled data from source domain to target domain is of great significance. To this end, we propose self-ensembling attention networks to reduce the domain gap between different datasets. To the best of our knowledge, the proposed method is the first attempt to introduce selfensembling model to domain adaptation for semantic segmentation, which provides a different view on how to learn domain-invariant features. Besides, since different regions in the image usually correspond to different levels of domain gap, we introduce the attention mechanism into the proposed framework to generate attention-aware features, which are further utilized to guide the calculation of consistency loss in the target domain. Experiments on two benchmark datasets demonstrate that the proposed framework can yield competitive performance compared with the state of the art methods.

scholarly journals Multimodal Structure-Consistent Image-to-Image Translation

2020 ◽  
Vol 34 (07) ◽  
pp. 11490-11498
Author(s):  
Che-Tsung Lin ◽  
Yen-Yi Wu ◽  
Po-Hao Hsu ◽  
Shang-Hong Lai
Keyword(s):  
Data Augmentation ◽  
Network Models ◽  
Detection Accuracy ◽  
Target Domain ◽  
Generative Adversarial Network ◽  
Image Objects ◽  
Perceptual Distance ◽  
Adversarial Network ◽  
Image Translation ◽  
Realistic Images

Unpaired image-to-image translation is proven quite effective in boosting a CNN-based object detector for a different domain by means of data augmentation that can well preserve the image-objects in the translated images. Recently, multimodal GAN (Generative Adversarial Network) models have been proposed and were expected to further boost the detector accuracy by generating a diverse collection of images in the target domain, given only a single/labelled image in the source domain. However, images generated by multimodal GANs would achieve even worse detection accuracy than the ones by a unimodal GAN with better object preservation. In this work, we introduce cycle-structure consistency for generating diverse and structure-preserved translated images across complex domains, such as between day and night, for object detector training. Qualitative results show that our model, Multimodal AugGAN, can generate diverse and realistic images for the target domain. For quantitative comparisons, we evaluate other competing methods and ours by using the generated images to train YOLO, Faster R-CNN and FCN models and prove that our model achieves significant improvement and outperforms other methods on the detection accuracies and the FCN scores. Also, we demonstrate that our model could provide more diverse object appearances in the target domain through comparison on the perceptual distance metric.

scholarly journals Accurate and Consistent Image-to-Image Conditional Adversarial Network

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 395 ◽  
Author(s):  
Naeem Ul Islam ◽  
Sungmin Lee ◽  
Jaebyung Park
Keyword(s):  
Image Representation ◽  
Terrain Analysis ◽  
Generative Adversarial Network ◽  
Training Strategy ◽  
Adversarial Network ◽  
Image Translation ◽  
Successful Approach ◽  
Realistic Image ◽  
Potential Impact ◽  
Realistic Images

Image-to-image translation based on deep learning has attracted interest in the robotics and vision community because of its potential impact on terrain analysis and image representation, interpretation, modification, and enhancement. Currently, the most successful approach for generating a translated image is a conditional generative adversarial network (cGAN) for training an autoencoder with skip connections. Despite its impressive performance, it has low accuracy and a lack of consistency; further, its training is imbalanced. This paper proposes a balanced training strategy for image-to-image translation, resulting in an accurate and consistent network. The proposed approach uses two generators and a single discriminator. The generators translate images from one domain to another. The discriminator takes the input of three different configurations and guides both the generators to generate realistic images in their corresponding domains while ensuring high accuracy and consistency. Experiments are conducted on different datasets. In particular, the proposed approach outperforms the cGAN in realistic image translation in terms of accuracy and consistency in training.

Enhanced Feature Alignment for Unsupervised Domain Adaptation of Semantic Segmentation

2021 ◽  
pp. 1-1
Author(s):  
Tao Chen ◽  
Shuihua Wang ◽  
Qiong Wang ◽  
Zheng Zhang ◽  
Guosen Xie ◽  
...  
Keyword(s):  
Domain Adaptation ◽  
Semantic Segmentation ◽  
Unsupervised Domain Adaptation ◽  
Feature Alignment

scholarly journals 3D Reconstruction of a Complex Grid Structure Combining UAS Images and Deep Learning

2020 ◽  
Vol 12 (19) ◽  
pp. 3128
Author(s):  
Vladimir A. Knyaz ◽  
Vladimir V. Kniaz ◽  
Fabio Remondino ◽  
Sergey Y. Zheltov ◽  
Armin Gruen
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
3D Reconstruction ◽  
Network Model ◽  
Neural Network Model ◽  
Feature Matching ◽  
Semantic Segmentation ◽  
Unmanned Aerial Systems ◽  
Grid Structure ◽  
Large Size

The latest advances in technical characteristics of unmanned aerial systems (UAS) and their onboard sensors opened the way for smart flying vehicles exploiting new application areas and allowing to perform missions seemed to be impossible before. One of these complicated tasks is the 3D reconstruction and monitoring of large-size, complex, grid-like structures as radio or television towers. Although image-based 3D survey contains a lot of visual and geometrical information useful for making preliminary conclusions on construction health, standard photogrammetric processing fails to perform dense and robust 3D reconstruction of complex large-size mesh structures. The main problem of such objects is repeated and self-occlusive similar elements resulting in false feature matching. This paper presents a method developed for an accurate Multi-View Stereo (MVS) dense 3D reconstruction of the Shukhov Radio Tower in Moscow (Russia) based on UAS photogrammetric survey. A key element for the successful image-based 3D reconstruction is the developed WireNetV2 neural network model for robust automatic semantic segmentation of wire structures. The proposed neural network provides high matching quality due to an accurate masking of the tower elements. The main contributions of the paper are: (1) a deep learning WireNetV2 convolutional neural network model that outperforms the state-of-the-art results of semantic segmentation on a dataset containing images of grid structures of complicated topology with repeated elements, holes, self-occlusions, thus providing robust grid structure masking and, as a result, accurate 3D reconstruction, (2) an advanced image-based pipeline aided by a neural network for the accurate 3D reconstruction of the large-size and complex grid structured, evaluated on UAS imagery of Shukhov radio tower in Moscow.

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