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| Author | Anna Esposito; Italia Cirillo; Antonietta Esposito; Leopoldina Fortunati; Gian Luca Foresti; Sergio Escalera; Nikolaos Bourbakis | ||||
| Title | Impairments in decoding facial and vocal emotional expressions in high functioning autistic adults and adolescents | Type | Conference Article | ||
| Year | 2020 | Publication  |
Faces and Gestures in E-health and welfare workshop | Abbreviated Journal | |
| Volume | Issue | Pages | 667-674 | ||
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| Address | Virtual; November 2020 | ||||
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| Area | Expedition | Conference | FGW | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ ECE2020 | Serial | 3516 | ||
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| Author | Cristina Sanchez Montes; Jorge Bernal; Ana Garcia Rodriguez; Henry Cordova; Gloria Fernandez Esparrach | ||||
| Title | Revisión de métodos computacionales de detección y clasificación de pólipos en imagen de colonoscopia | Type | Journal Article | ||
| Year | 2020 | Publication |
Gastroenterología y Hepatología | Abbreviated Journal | GH |
| Volume | 43 | Issue | 4 | Pages | 222-232 |
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| Abstract | Computer-aided diagnosis (CAD) is a tool with great potential to help endoscopists in the tasks of detecting and histologically classifying colorectal polyps. In recent years, different technologies have been described and their potential utility has been increasingly evidenced, which has generated great expectations among scientific societies. However, most of these works are retrospective and use images of different quality and characteristics which are analysed off line. This review aims to familiarise gastroenterologists with computational methods and the particularities of endoscopic imaging, which have an impact on image processing analysis. Finally, the publicly available image databases, needed to compare and confirm the results obtained with different methods, are presented. | ||||
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| Notes | MV; | Approved | no | ||
| Call Number | Admin @ si @ SBG2020 | Serial | 3404 | ||
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| Author | Alicia Fornes; Josep Llados; Joana Maria Pujadas-Mora | ||||
| Title | Browsing of the Social Network of the Past: Information Extraction from Population Manuscript Images | Type | Book Chapter | ||
| Year | 2020 | Publication |
Handwritten Historical Document Analysis, Recognition, and Retrieval – State of the Art and Future Trends | Abbreviated Journal | |
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| Publisher | World Scientific | Place of Publication | Editor | ||
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| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-981-120-323-7 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | DAG; 600.140; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ FLP2020 | Serial | 3350 | ||
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| Author | Marc Masana; Bartlomiej Twardowski; Joost Van de Weijer | ||||
| Title | On Class Orderings for Incremental Learning | Type | Conference Article | ||
| Year | 2020 | Publication |
ICML Workshop on Continual Learning | Abbreviated Journal | |
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| Abstract | The influence of class orderings in the evaluation of incremental learning has received very little attention. In this paper, we investigate the impact of class orderings for incrementally learned classifiers. We propose a method to compute various orderings for a dataset. The orderings are derived by simulated annealing optimization from the confusion matrix and reflect different incremental learning scenarios, including maximally and minimally confusing tasks. We evaluate a wide range of state-of-the-art incremental learning methods on the proposed orderings. Results show that orderings can have a significant impact on performance and the ranking of the methods. | ||||
| Address | Virtual; July 2020 | ||||
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| Area | Expedition | Conference | ICMLW | ||
| Notes | LAMP; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ MTW2020 | Serial | 3505 | ||
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| Author | David Berga; Marc Masana; Joost Van de Weijer | ||||
| Title | Disentanglement of Color and Shape Representations for Continual Learning | Type | Conference Article | ||
| Year | 2020 | Publication |
ICML Workshop on Continual Learning | Abbreviated Journal | |
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| Abstract | We hypothesize that disentangled feature representations suffer less from catastrophic forgetting. As a case study we perform explicit disentanglement of color and shape, by adjusting the network architecture. We tested classification accuracy and forgetting in a task-incremental setting with Oxford-102 Flowers dataset. We combine our method with Elastic Weight Consolidation, Learning without Forgetting, Synaptic Intelligence and Memory Aware Synapses, and show that feature disentanglement positively impacts continual learning performance. | ||||
| Address | Virtual; July 2020 | ||||
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| Area | Expedition | Conference | ICMLW | ||
| Notes | LAMP; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ BMW2020 | Serial | 3506 | ||
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| Author | Fei Yang; Yongmei Cheng; Joost Van de Weijer; Mikhail Mozerov | ||||
| Title | Improved Discrete Optical Flow Estimation With Triple Image Matching Cost | Type | Journal Article | ||
| Year | 2020 | Publication |
IEEE Access | Abbreviated Journal | ACCESS |
| Volume | 8 | Issue | Pages | 17093 - 17102 | |
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| Abstract | Approaches that use more than two consecutive video frames in the optical flow estimation have a long research history. However, almost all such methods utilize extra information for a pre-processing flow prediction or for a post-processing flow correction and filtering. In contrast, this paper differs from previously developed techniques. We propose a new algorithm for the likelihood function calculation (alternatively the matching cost volume) that is used in the maximum a posteriori estimation. We exploit the fact that in general, optical flow is locally constant in the sense of time and the likelihood function depends on both the previous and the future frame. Implementation of our idea increases the robustness of optical flow estimation. As a result, our method outperforms 9% over the DCFlow technique, which we use as prototype for our CNN based computation architecture, on the most challenging MPI-Sintel dataset for the non-occluded mask metric. Furthermore, our approach considerably increases the accuracy of the flow estimation for the matching cost processing, consequently outperforming the original DCFlow algorithm results up to 50% in occluded regions and up to 9% in non-occluded regions on the MPI-Sintel dataset. The experimental section shows that the proposed method achieves state-of-the-arts results especially on the MPI-Sintel dataset. | ||||
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| Notes | LAMP; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ YCW2020 | Serial | 3345 | ||
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| Author | Alejandro Cartas; Petia Radeva; Mariella Dimiccoli | ||||
| Title | Activities of Daily Living Monitoring via a Wearable Camera: Toward Real-World Applications | Type | Journal Article | ||
| Year | 2020 | Publication |
IEEE Access | Abbreviated Journal | ACCESS |
| Volume | 8 | Issue | Pages | 77344 - 77363 | |
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| Abstract | Activity recognition from wearable photo-cameras is crucial for lifestyle characterization and health monitoring. However, to enable its wide-spreading use in real-world applications, a high level of generalization needs to be ensured on unseen users. Currently, state-of-the-art methods have been tested only on relatively small datasets consisting of data collected by a few users that are partially seen during training. In this paper, we built a new egocentric dataset acquired by 15 people through a wearable photo-camera and used it to test the generalization capabilities of several state-of-the-art methods for egocentric activity recognition on unseen users and daily image sequences. In addition, we propose several variants to state-of-the-art deep learning architectures, and we show that it is possible to achieve 79.87% accuracy on users unseen during training. Furthermore, to show that the proposed dataset and approach can be useful in real-world applications, where data can be acquired by different wearable cameras and labeled data are scarcely available, we employed a domain adaptation strategy on two egocentric activity recognition benchmark datasets. These experiments show that the model learned with our dataset, can easily be transferred to other domains with a very small amount of labeled data. Taken together, those results show that activity recognition from wearable photo-cameras is mature enough to be tested in real-world applications. | ||||
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| Notes | MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ CRD2020 | Serial | 3436 | ||
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| Author | Debora Gil; Antonio Esteban Lansaque; Agnes Borras; Esmitt Ramirez; Carles Sanchez | ||||
| Title | Intraoperative Extraction of Airways Anatomy in VideoBronchoscopy | Type | Journal Article | ||
| Year | 2020 | Publication |
IEEE Access | Abbreviated Journal | ACCESS |
| Volume | 8 | Issue | Pages | 159696 - 159704 | |
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| Abstract | A main bottleneck in bronchoscopic biopsy sampling is to efficiently reach the lesion navigating across bronchial levels. Any guidance system should be able to localize the scope position during the intervention with minimal costs and alteration of clinical protocols. With the final goal of an affordable image-based guidance, this work presents a novel strategy to extract and codify the anatomical structure of bronchi, as well as, the scope navigation path from videobronchoscopy. Experiments using interventional data show that our method accurately identifies the bronchial structure. Meanwhile, experiments using simulated data verify that the extracted navigation path matches the 3D route. | ||||
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| Notes | IAM; 600.139; 600.145 | Approved | no | ||
| Call Number | Admin @ si @ GEB2020 | Serial | 3467 | ||
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| Author | Gabriel Villalonga; Antonio Lopez | ||||
| Title | Co-Training for On-Board Deep Object Detection | Type | Journal Article | ||
| Year | 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 | Akhil Gurram; Onay Urfalioglu; Ibrahim Halfaoui; Fahd Bouzaraa; Antonio Lopez | ||||
| Title | Semantic Monocular Depth Estimation Based on Artificial Intelligence | Type | Journal Article | ||
| Year | 2020 | Publication |
IEEE Intelligent Transportation Systems Magazine | Abbreviated Journal | ITSM |
| Volume | 13 | Issue | 4 | Pages | 99-103 |
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| 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 | Sangeeth Reddy; Minesh Mathew; Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas; C.V. Jawahar | ||||
| Title | RoadText-1K: Text Detection and Recognition Dataset for Driving Videos | Type | Conference Article | ||
| Year | 2020 | Publication |
IEEE International Conference on Robotics and Automation | Abbreviated Journal | |
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| Abstract | Perceiving text is crucial to understand semantics of outdoor scenes and hence is a critical requirement to build intelligent systems for driver assistance and self-driving. Most of the existing datasets for text detection and recognition comprise still images and are mostly compiled keeping text in mind. This paper introduces a new ”RoadText-1K” dataset for text in driving videos. The dataset is 20 times larger than the existing largest dataset for text in videos. Our dataset comprises 1000 video clips of driving without any bias towards text and with annotations for text bounding boxes and transcriptions in every frame. State of the art methods for text detection,
recognition and tracking are evaluated on the new dataset and the results signify the challenges in unconstrained driving videos compared to existing datasets. This suggests that RoadText-1K is suited for research and development of reading systems, robust enough to be incorporated into more complex downstream tasks like driver assistance and self-driving. The dataset can be found at http://cvit.iiit.ac.in/research/ projects/cvit-projects/roadtext-1k |
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| Address | Paris; Francia; ??? | ||||
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| Area | Expedition | Conference | ICRA | ||
| Notes | DAG; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ RMG2020 | Serial | 3400 | ||
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| Author | Estefania Talavera; Maria Leyva-Vallina; Md. Mostafa Kamal Sarker; Domenec Puig; Nicolai Petkov; Petia Radeva | ||||
| Title | Hierarchical approach to classify food scenes in egocentric photo-streams | Type | Journal Article | ||
| Year | 2020 | Publication |
IEEE Journal of Biomedical and Health Informatics | Abbreviated Journal | J-BHI |
| Volume | 24 | Issue | 3 | Pages | 866 - 877 |
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| Abstract | Recent studies have shown that the environment where people eat can affect their nutritional behaviour. In this work, we provide automatic tools for a personalised analysis of a person's health habits by the examination of daily recorded egocentric photo-streams. Specifically, we propose a new automatic approach for the classification of food-related environments, that is able to classify up to 15 such scenes. In this way, people can monitor the context around their food intake in order to get an objective insight into their daily eating routine. We propose a model that classifies food-related scenes organized in a semantic hierarchy. Additionally, we present and make available a new egocentric dataset composed of more than 33000 images recorded by a wearable camera, over which our proposed model has been tested. Our approach obtains an accuracy and F-score of 56\% and 65\%, respectively, clearly outperforming the baseline methods. | ||||
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| Notes | MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ TLM2020 | Serial | 3380 | ||
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| Author | Margarita Torre; Beatriz Remeseiro; Petia Radeva; Fernando Martinez | ||||
| Title | DeepNEM: Deep Network Energy-Minimization for Agricultural Field Segmentation | Type | Journal Article | ||
| Year | 2020 | Publication |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | Abbreviated Journal | JSTAEOR |
| Volume | 13 | Issue | Pages | 726-737 | |
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| Abstract | One of the main characteristics of agricultural fields is that the appearance of different crops and their growth status, in an aerial image, is varied, and has a wide range of radiometric values and high level of variability. The extraction of these fields and their monitoring are activities that require a high level of human intervention. In this article, we propose a novel automatic algorithm, named deep network energy-minimization (DeepNEM), to extract agricultural fields in aerial images. The model-guided process selects the most relevant image clues extracted by a deep network, completes them and finally generates regions that represent the agricultural fields under a minimization scheme. DeepNEM has been tested over a broad range of fields in terms of size, shape, and content. Different measures were used to compare the DeepNEM with other methods, and to prove that it represents an improved approach to achieve a high-quality segmentation of agricultural fields. Furthermore, this article also presents a new public dataset composed of 1200 images with their parcels boundaries annotations. | ||||
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| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ TRR2020 | Serial | 3410 | ||
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| Author | Fei Yang; Luis Herranz; Joost Van de Weijer; Jose Antonio Iglesias; Antonio Lopez; Mikhail Mozerov | ||||
| Title | Variable Rate Deep Image Compression with Modulated Autoencoder | Type | Journal Article | ||
| Year | 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 | Approved | no | ||
| Call Number | Admin @ si @ YHW2020 | Serial | 3346 | ||
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| Author | Shifeng Zhang; Ajian Liu; Jun Wan; Yanyan Liang; Guogong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z. Li | ||||
| Title | CASIA-SURF: A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing | Type | Journal | ||
| Year | 2020 | Publication |
IEEE Transactions on Biometrics, Behavior, and Identity Science | Abbreviated Journal | TTBIS |
| Volume | 2 | Issue | 2 | Pages | 182 - 193 |
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| Abstract | Face anti-spoofing is essential to prevent face recognition systems from a security breach. Much of the progresses have been made by the availability of face anti-spoofing benchmark datasets in recent years. However, existing face anti-spoofing benchmarks have limited number of subjects (≤170) and modalities (≤2), which hinder the further development of the academic community. To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest publicly available dataset for face anti-spoofing in terms of both subjects and modalities. Specifically, it consists of 1,000 subjects with 21,000 videos and each sample has 3 modalities ( i.e. , RGB, Depth and IR). We also provide comprehensive evaluation metrics, diverse evaluation protocols, training/validation/testing subsets and a measurement tool, developing a new benchmark for face anti-spoofing. Moreover, we present a novel multi-modal multi-scale fusion method as a strong baseline, which performs feature re-weighting to select the more informative channel features while suppressing the less useful ones for each modality across different scales. Extensive experiments have been conducted on the proposed dataset to verify its significance and generalization capability. The dataset is available at https://sites.google.com/qq.com/face-anti-spoofing/welcome/challengecvpr2019?authuser=0 | ||||
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| Notes | HuPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ ZLW2020 | Serial | 3412 | ||
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