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Fei Yang; Luis Herranz; Joost Van de Weijer; Jose Antonio Iglesias; Antonio Lopez; Mikhail Mozerov |
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Title |
Variable Rate Deep Image Compression with Modulated Autoencoder |
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Journal Article |
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2020 |
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IEEE Signal Processing Letters |
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SPL |
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27 |
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331-335 |
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Variable rate is a requirement for flexible and adaptable image and video compression. However, deep image compression methods (DIC) are optimized for a single fixed rate-distortion (R-D) tradeoff. While this can be addressed by training multiple models for different tradeoffs, the memory requirements increase proportionally to the number of models. Scaling the bottleneck representation of a shared autoencoder can provide variable rate compression with a single shared autoencoder. However, the R-D performance using this simple mechanism degrades in low bitrates, and also shrinks the effective range of bitrates. To address these limitations, we formulate the problem of variable R-D optimization for DIC, and propose modulated autoencoders (MAEs), where the representations of a shared autoencoder are adapted to the specific R-D tradeoff via a modulation network. Jointly training this modulated autoencoder and the modulation network provides an effective way to navigate the R-D operational curve. Our experiments show that the proposed method can achieve almost the same R-D performance of independent models with significantly fewer parameters. |
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LAMP; ADAS; 600.141; 600.120; 600.118 |
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Admin @ si @ YHW2020 |
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3346 |
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Author |
Jiaolong Xu; Liang Xiao; Antonio Lopez |
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Title |
Self-supervised Domain Adaptation for Computer Vision Tasks |
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2019 |
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IEEE Access |
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ACCESS |
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7 |
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156694 - 156706 |
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Recent progress of self-supervised visual representation learning has achieved remarkable success on many challenging computer vision benchmarks. However, whether these techniques can be used for domain adaptation has not been explored. In this work, we propose a generic method for self-supervised domain adaptation, using object recognition and semantic segmentation of urban scenes as use cases. Focusing on simple pretext/auxiliary tasks (e.g. image rotation prediction), we assess different learning strategies to improve domain adaptation effectiveness by self-supervision. Additionally, we propose two complementary strategies to further boost the domain adaptation accuracy on semantic segmentation within our method, consisting of prediction layer alignment and batch normalization calibration. The experimental results show adaptation levels comparable to most studied domain adaptation methods, thus, bringing self-supervision as a new alternative for reaching domain adaptation. The code is available at this link. https://github.com/Jiaolong/self-supervised-da. |
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ADAS; 600.118 |
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Admin @ si @ XXL2019 |
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3302 |
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Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez |
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Title |
Hierarchical Adaptive Structural SVM for Domain Adaptation |
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2016 |
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International Journal of Computer Vision |
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IJCV |
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119 |
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2 |
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159-178 |
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Domain Adaptation; Pedestrian Detection |
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A key topic in classification is the accuracy loss produced when the data distribution in the training (source) domain differs from that in the testing (target) domain. This is being recognized as a very relevant problem for many
computer vision tasks such as image classification, object detection, and object category recognition. In this paper, we present a novel domain adaptation method that leverages multiple target domains (or sub-domains) in a hierarchical adaptation tree. The core idea is to exploit the commonalities and differences of the jointly considered target domains.
Given the relevance of structural SVM (SSVM) classifiers, we apply our idea to the adaptive SSVM (A-SSVM), which only requires the target domain samples together with the existing source-domain classifier for performing the desired adaptation. Altogether, we term our proposal as hierarchical A-SSVM (HA-SSVM).
As proof of concept we use HA-SSVM for pedestrian detection, object category recognition and face recognition. In the former we apply HA-SSVM to the deformable partbased model (DPM) while in the rest HA-SSVM is applied to multi-category classifiers. We will show how HA-SSVM is effective in increasing the detection/recognition accuracy with respect to adaptation strategies that ignore the structure of the target data. Since, the sub-domains of the target data are not always known a priori, we shown how HA-SSVM can incorporate sub-domain discovery for object category recognition. |
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Springer US |
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0920-5691 |
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ADAS; 600.085; 600.082; 600.076 |
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Admin @ si @ XRV2016 |
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2669 |
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Author |
Yi Xiao; Felipe Codevilla; Akhil Gurram; Onay Urfalioglu; Antonio Lopez |
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Title |
Multimodal end-to-end autonomous driving |
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2020 |
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IEEE Transactions on Intelligent Transportation Systems |
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TITS |
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1-11 |
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A crucial component of an autonomous vehicle (AV) is the artificial intelligence (AI) is able to drive towards a desired destination. Today, there are different paradigms addressing the development of AI drivers. On the one hand, we find modular pipelines, which divide the driving task into sub-tasks such as perception and maneuver planning and control. On the other hand, we find end-to-end driving approaches that try to learn a direct mapping from input raw sensor data to vehicle control signals. The later are relatively less studied, but are gaining popularity since they are less demanding in terms of sensor data annotation. This paper focuses on end-to-end autonomous driving. So far, most proposals relying on this paradigm assume RGB images as input sensor data. However, AVs will not be equipped only with cameras, but also with active sensors providing accurate depth information (e.g., LiDARs). Accordingly, this paper analyses whether combining RGB and depth modalities, i.e. using RGBD data, produces better end-to-end AI drivers than relying on a single modality. We consider multimodality based on early, mid and late fusion schemes, both in multisensory and single-sensor (monocular depth estimation) settings. Using the CARLA simulator and conditional imitation learning (CIL), we show how, indeed, early fusion multimodality outperforms single-modality. |
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ADAS |
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Call Number |
Admin @ si @ XCG2020 |
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3490 |
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Author |
Gabriel Villalonga; Joost Van de Weijer; Antonio Lopez |
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Title |
Recognizing new classes with synthetic data in the loop: application to traffic sign recognition |
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Journal Article |
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2020 |
Publication |
Sensors |
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SENS |
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20 |
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3 |
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583 |
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On-board vision systems may need to increase the number of classes that can be recognized in a relatively short period. For instance, a traffic sign recognition system may suddenly be required to recognize new signs. Since collecting and annotating samples of such new classes may need more time than we wish, especially for uncommon signs, we propose a method to generate these samples by combining synthetic images and Generative Adversarial Network (GAN) technology. In particular, the GAN is trained on synthetic and real-world samples from known classes to perform synthetic-to-real domain adaptation, but applied to synthetic samples of the new classes. Using the Tsinghua dataset with a synthetic counterpart, SYNTHIA-TS, we have run an extensive set of experiments. The results show that the proposed method is indeed effective, provided that we use a proper Convolutional Neural Network (CNN) to perform the traffic sign recognition (classification) task as well as a proper GAN to transform the synthetic images. Here, a ResNet101-based classifier and domain adaptation based on CycleGAN performed extremely well for a ratio∼ 1/4 for new/known classes; even for more challenging ratios such as∼ 4/1, the results are also very positive. |
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LAMP; ADAS; 600.118; 600.120 |
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Admin @ si @ VWL2020 |
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3405 |
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Author |
Gabriel Villalonga; Antonio Lopez |
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Title |
Co-Training for On-Board Deep Object Detection |
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Journal Article |
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Year |
2020 |
Publication |
IEEE Access |
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ACCESS |
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194441 - 194456 |
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Providing ground truth supervision to train visual models has been a bottleneck over the years, exacerbated by domain shifts which degenerate the performance of such models. This was the case when visual tasks relied on handcrafted features and shallow machine learning and, despite its unprecedented performance gains, the problem remains open within the deep learning paradigm due to its data-hungry nature. Best performing deep vision-based object detectors are trained in a supervised manner by relying on human-labeled bounding boxes which localize class instances (i.e. objects) within the training images. Thus, object detection is one of such tasks for which human labeling is a major bottleneck. In this article, we assess co-training as a semi-supervised learning method for self-labeling objects in unlabeled images, so reducing the human-labeling effort for developing deep object detectors. Our study pays special attention to a scenario involving domain shift; in particular, when we have automatically generated virtual-world images with object bounding boxes and we have real-world images which are unlabeled. Moreover, we are particularly interested in using co-training for deep object detection in the context of driver assistance systems and/or self-driving vehicles. Thus, using well-established datasets and protocols for object detection in these application contexts, we will show how co-training is a paradigm worth to pursue for alleviating object labeling, working both alone and together with task-agnostic domain adaptation. |
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ADAS; 600.118 |
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Admin @ si @ ViL2020 |
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3488 |
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Author |
Xavier Soria; Angel Sappa; Riad I. Hammoud |
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Title |
Wide-Band Color Imagery Restoration for RGB-NIR Single Sensor Images |
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Journal Article |
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Year |
2018 |
Publication |
Sensors |
Abbreviated Journal |
SENS |
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18 |
Issue |
7 |
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2059 |
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RGB-NIR sensor; multispectral imaging; deep learning; CNNs |
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Multi-spectral RGB-NIR sensors have become ubiquitous in recent years. These sensors allow the visible and near-infrared spectral bands of a given scene to be captured at the same time. With such cameras, the acquired imagery has a compromised RGB color representation due to near-infrared bands (700–1100 nm) cross-talking with the visible bands (400–700 nm).
This paper proposes two deep learning-based architectures to recover the full RGB color images, thus removing the NIR information from the visible bands. The proposed approaches directly restore the high-resolution RGB image by means of convolutional neural networks. They are evaluated with several outdoor images; both architectures reach a similar performance when evaluated in different
scenarios and using different similarity metrics. Both of them improve the state of the art approaches. |
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ADAS; MSIAU; 600.086; 600.130; 600.122; 600.118 |
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Admin @ si @ SSH2018 |
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3145 |
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Joan Serrat; Felipe Lumbreras; Idoia Ruiz |
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Title |
Learning to measure for preshipment garment sizing |
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Journal Article |
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2018 |
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Measurement |
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MEASURE |
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130 |
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327-339 |
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Apparel; Computer vision; Structured prediction; Regression |
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Clothing is still manually manufactured for the most part nowadays, resulting in discrepancies between nominal and real dimensions, and potentially ill-fitting garments. Hence, it is common in the apparel industry to manually perform measures at preshipment time. We present an automatic method to obtain such measures from a single image of a garment that speeds up this task. It is generic and extensible in the sense that it does not depend explicitly on the garment shape or type. Instead, it learns through a probabilistic graphical model to identify the different contour parts. Subsequently, a set of Lasso regressors, one per desired measure, can predict the actual values of the measures. We present results on a dataset of 130 images of jackets and 98 of pants, of varying sizes and styles, obtaining 1.17 and 1.22 cm of mean absolute error, respectively. |
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ADAS; MSIAU; 600.122; 600.118 |
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Admin @ si @ SLR2018 |
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3128 |
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Joan Serrat; Felipe Lumbreras; Antonio Lopez |
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Cost estimation of custom hoses from STL files and CAD drawings |
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2013 |
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Computers in Industry |
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COMPUTIND |
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64 |
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3 |
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299-309 |
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On-line quotation; STL format; Regression; Gaussian process |
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We present a method for the cost estimation of custom hoses from CAD models. They can come in two formats, which are easy to generate: a STL file or the image of a CAD drawing showing several orthogonal projections. The challenges in either cases are, first, to obtain from them a high level 3D description of the shape, and second, to learn a regression function for the prediction of the manufacturing time, based on geometric features of the reconstructed shape. The chosen description is the 3D line along the medial axis of the tube and the diameter of the circular sections along it. In order to extract it from STL files, we have adapted RANSAC, a robust parametric fitting algorithm. As for CAD drawing images, we propose a new technique for 3D reconstruction from data entered on any number of orthogonal projections. The regression function is a Gaussian process, which does not constrain the function to adopt any specific form and is governed by just two parameters. We assess the accuracy of the manufacturing time estimation by k-fold cross validation on 171 STL file models for which the time is provided by an expert. The results show the feasibility of the method, whereby the relative error for 80% of the testing samples is below 15%. |
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Elsevier |
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ADAS; 600.057; 600.054; 605.203 |
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Admin @ si @ SLL2013; ADAS @ adas @ |
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2161 |
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Joan Serrat; Felipe Lumbreras; Francisco Blanco; Manuel Valiente; Montserrat Lopez-Mesas |
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myStone: A system for automatic kidney stone classification |
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2017 |
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Expert Systems with Applications |
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ESA |
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89 |
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41-51 |
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Kidney stone; Optical device; Computer vision; Image classification |
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Kidney stone formation is a common disease and the incidence rate is constantly increasing worldwide. It has been shown that the classification of kidney stones can lead to an important reduction of the recurrence rate. The classification of kidney stones by human experts on the basis of certain visual color and texture features is one of the most employed techniques. However, the knowledge of how to analyze kidney stones is not widespread, and the experts learn only after being trained on a large number of samples of the different classes. In this paper we describe a new device specifically designed for capturing images of expelled kidney stones, and a method to learn and apply the experts knowledge with regard to their classification. We show that with off the shelf components, a carefully selected set of features and a state of the art classifier it is possible to automate this difficult task to a good degree. We report results on a collection of 454 kidney stones, achieving an overall accuracy of 63% for a set of eight classes covering almost all of the kidney stones taxonomy. Moreover, for more than 80% of samples the real class is the first or the second most probable class according to the system, being then the patient recommendations for the two top classes similar. This is the first attempt towards the automatic visual classification of kidney stones, and based on the current results we foresee better accuracies with the increase of the dataset size. |
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ADAS; MSIAU; 603.046; 600.122; 600.118 |
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Admin @ si @ SLB2017 |
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3026 |
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