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Author Jose Luis Gomez; Gabriel Villalonga; Antonio Lopez edit   pdf
url  openurl
  Title Co-Training for Deep Object Detection: Comparing Single-Modal and Multi-Modal Approaches Type Journal Article
  Year (down) 2021 Publication Sensors Abbreviated Journal SENS  
  Volume 21 Issue 9 Pages 3185  
  Keywords co-training; multi-modality; vision-based object detection; ADAS; self-driving  
  Abstract Top-performing computer vision models are powered by convolutional neural networks (CNNs). Training an accurate CNN highly depends on both the raw sensor data and their associated ground truth (GT). Collecting such GT is usually done through human labeling, which is time-consuming and does not scale as we wish. This data-labeling bottleneck may be intensified due to domain shifts among image sensors, which could force per-sensor data labeling. In this paper, we focus on the use of co-training, a semi-supervised learning (SSL) method, for obtaining self-labeled object bounding boxes (BBs), i.e., the GT to train deep object detectors. In particular, we assess the goodness of multi-modal co-training by relying on two different views of an image, namely, appearance (RGB) and estimated depth (D). Moreover, we compare appearance-based single-modal co-training with multi-modal. Our results suggest that in a standard SSL setting (no domain shift, a few human-labeled data) and under virtual-to-real domain shift (many virtual-world labeled data, no human-labeled data) multi-modal co-training outperforms single-modal. In the latter case, by performing GAN-based domain translation both co-training modalities are on par, at least when using an off-the-shelf depth estimation model not specifically trained on the translated images.  
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  Notes ADAS; 600.118 Approved no  
  Call Number Admin @ si @ GVL2021 Serial 3562  
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Author Hannes Mueller; Andre Groeger; Jonathan Hersh; Andrea Matranga; Joan Serrat edit   pdf
url  doi
openurl 
  Title Monitoring war destruction from space using machine learning Type Journal Article
  Year (down) 2021 Publication Proceedings of the National Academy of Sciences of the United States of America Abbreviated Journal PNAS  
  Volume 118 Issue 23 Pages e2025400118  
  Keywords  
  Abstract Existing data on building destruction in conflict zones rely on eyewitness reports or manual detection, which makes it generally scarce, incomplete, and potentially biased. This lack of reliable data imposes severe limitations for media reporting, humanitarian relief efforts, human-rights monitoring, reconstruction initiatives, and academic studies of violent conflict. This article introduces an automated method of measuring destruction in high-resolution satellite images using deep-learning techniques combined with label augmentation and spatial and temporal smoothing, which exploit the underlying spatial and temporal structure of destruction. As a proof of concept, we apply this method to the Syrian civil war and reconstruct the evolution of damage in major cities across the country. Our approach allows generating destruction data with unprecedented scope, resolution, and frequency—and makes use of the ever-higher frequency at which satellite imagery becomes available.  
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  Notes ADAS; 600.118 Approved no  
  Call Number Admin @ si @ MGH2021 Serial 3584  
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Author Javier Marin; Sergio Escalera edit   pdf
url  openurl
  Title SSSGAN: Satellite Style and Structure Generative Adversarial Networks Type Journal Article
  Year (down) 2021 Publication Remote Sensing Abbreviated Journal  
  Volume 13 Issue 19 Pages 3984  
  Keywords  
  Abstract This work presents Satellite Style and Structure Generative Adversarial Network (SSGAN), a generative model of high resolution satellite imagery to support image segmentation. Based on spatially adaptive denormalization modules (SPADE) that modulate the activations with respect to segmentation map structure, in addition to global descriptor vectors that capture the semantic information in a vector with respect to Open Street Maps (OSM) classes, this model is able to produce
consistent aerial imagery. By decoupling the generation of aerial images into a structure map and a carefully defined style vector, we were able to improve the realism and geodiversity of the synthesis with respect to the state-of-the-art baseline. Therefore, the proposed model allows us to control the generation not only with respect to the desired structure, but also with respect to a geographic area.		  
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  Notes HUPBA; no proj;MILAB;ADAS Approved no  
  Call Number Admin @ si @ MaE2021 Serial 3651  
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Author Cesar de Souza; Adrien Gaidon; Yohann Cabon; Naila Murray; Antonio Lopez edit   pdf
doi  openurl
  Title Generating Human Action Videos by Coupling 3D Game Engines and Probabilistic Graphical Models Type Journal Article
  Year (down) 2020 Publication International Journal of Computer Vision Abbreviated Journal IJCV  
  Volume 128 Issue Pages 1505–1536  
  Keywords Procedural generation; Human action recognition; Synthetic data; Physics  
  Abstract Deep video action recognition models have been highly successful in recent years but require large quantities of manually-annotated data, which are expensive and laborious to obtain. In this work, we investigate the generation of synthetic training data for video action recognition, as synthetic data have been successfully used to supervise models for a variety of other computer vision tasks. We propose an interpretable parametric generative model of human action videos that relies on procedural generation, physics models and other components of modern game engines. With this model we generate a diverse, realistic, and physically plausible dataset of human action videos, called PHAV for “Procedural Human Action Videos”. PHAV contains a total of 39,982 videos, with more than 1000 examples for each of 35 action categories. Our video generation approach is not limited to existing motion capture sequences: 14 of these 35 categories are procedurally-defined synthetic actions. In addition, each video is represented with 6 different data modalities, including RGB, optical flow and pixel-level semantic labels. These modalities are generated almost simultaneously using the Multiple Render Targets feature of modern GPUs. In order to leverage PHAV, we introduce a deep multi-task (i.e. that considers action classes from multiple datasets) representation learning architecture that is able to simultaneously learn from synthetic and real video datasets, even when their action categories differ. Our experiments on the UCF-101 and HMDB-51 benchmarks suggest that combining our large set of synthetic videos with small real-world datasets can boost recognition performance. Our approach also significantly outperforms video representations produced by fine-tuning state-of-the-art unsupervised generative models of videos.  
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  Notes ADAS; 600.124; 600.118;CIC Approved no  
  Call Number Admin @ si @ SGC2019 Serial 3303  
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Author Akhil Gurram; Onay Urfalioglu; Ibrahim Halfaoui; Fahd Bouzaraa; Antonio Lopez edit  url
doi  openurl
  Title Semantic Monocular Depth Estimation Based on Artificial Intelligence Type Journal Article
  Year (down) 2020 Publication IEEE Intelligent Transportation Systems Magazine Abbreviated Journal ITSM  
  Volume 13 Issue 4 Pages 99-103  
  Keywords  
  Abstract Depth estimation provides essential information to perform autonomous driving and driver assistance. A promising line of work consists of introducing additional semantic information about the traffic scene when training CNNs for depth estimation. In practice, this means that the depth data used for CNN training is complemented with images having pixel-wise semantic labels where the same raw training data is associated with both types of ground truth, i.e., depth and semantic labels. The main contribution of this paper is to show that this hard constraint can be circumvented, i.e., that we can train CNNs for depth estimation by leveraging the depth and semantic information coming from heterogeneous datasets. In order to illustrate the benefits of our approach, we combine KITTI depth and Cityscapes semantic segmentation datasets, outperforming state-of-the-art results on monocular depth estimation.  
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  Notes ADAS; 600.124; 600.118 Approved no  
  Call Number Admin @ si @ GUH2019 Serial 3306  
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Author Fei Yang; Luis Herranz; Joost Van de Weijer; Jose Antonio Iglesias; Antonio Lopez; Mikhail Mozerov edit   pdf
url  doi
openurl 
  Title Variable Rate Deep Image Compression with Modulated Autoencoder Type Journal Article
  Year (down) 2020 Publication IEEE Signal Processing Letters Abbreviated Journal SPL  
  Volume 27 Issue Pages 331-335  
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  Abstract 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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  Notes LAMP; ADAS; 600.141; 600.120; 600.118;ISE;CIC Approved no  
  Call Number Admin @ si @ YHW2020 Serial 3346  
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Author Gabriel Villalonga; Joost Van de Weijer; Antonio Lopez edit  url
doi  openurl
  Title Recognizing new classes with synthetic data in the loop: application to traffic sign recognition Type Journal Article
  Year (down) 2020 Publication Sensors Abbreviated Journal SENS  
  Volume 20 Issue 3 Pages 583  
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  Abstract 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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  Notes LAMP; ADAS; 600.118; 600.120;CIC Approved no  
  Call Number Admin @ si @ VWL2020 Serial 3405  
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Author Gabriel Villalonga; Antonio Lopez edit   pdf
doi  openurl
  Title Co-Training for On-Board Deep Object Detection Type Journal Article
  Year (down) 2020 Publication IEEE Access Abbreviated Journal ACCESS  
  Volume Issue Pages 194441 - 194456  
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  Abstract 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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  Notes ADAS; 600.118 Approved no  
  Call Number Admin @ si @ ViL2020 Serial 3488  
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Author Yi Xiao; Felipe Codevilla; Akhil Gurram; Onay Urfalioglu; Antonio Lopez edit   pdf
url  doi
openurl 
  Title Multimodal end-to-end autonomous driving Type Journal Article
  Year (down) 2020 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS  
  Volume Issue Pages 1-11  
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  Abstract 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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  Notes ADAS Approved no  
  Call Number Admin @ si @ XCG2020 Serial 3490  
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Author Katerine Diaz; Jesus Martinez del Rincon; Marçal Rusiñol; Aura Hernandez-Sabate edit   pdf
doi  openurl
  Title Feature Extraction by Using Dual-Generalized Discriminative Common Vectors Type Journal Article
  Year (down) 2019 Publication Journal of Mathematical Imaging and Vision Abbreviated Journal JMIV  
  Volume 61 Issue 3 Pages 331-351  
  Keywords Online feature extraction; Generalized discriminative common vectors; Dual learning; Incremental learning; Decremental learning  
  Abstract In this paper, a dual online subspace-based learning method called dual-generalized discriminative common vectors (Dual-GDCV) is presented. The method extends incremental GDCV by exploiting simultaneously both the concepts of incremental and decremental learning for supervised feature extraction and classification. Our methodology is able to update the feature representation space without recalculating the full projection or accessing the previously processed training data. It allows both adding information and removing unnecessary data from a knowledge base in an efficient way, while retaining the previously acquired knowledge. The proposed method has been theoretically proved and empirically validated in six standard face recognition and classification datasets, under two scenarios: (1) removing and adding samples of existent classes, and (2) removing and adding new classes to a classification problem. Results show a considerable computational gain without compromising the accuracy of the model in comparison with both batch methodologies and other state-of-art adaptive methods.  
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  Notes DAG; ADAS; 600.084; 600.118; 600.121; 600.129;IAM Approved no  
  Call Number Admin @ si @ DRR2019 Serial 3172  
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