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| Author | Arya Farkhondeh; Cristina Palmero; Simone Scardapane; Sergio Escalera | ||||
Title  |
Towards Self-Supervised Gaze Estimation | Type | Miscellaneous | ||
| Year | 2022 | Publication | Arxiv | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | Recent joint embedding-based self-supervised methods have surpassed standard supervised approaches on various image recognition tasks such as image classification. These self-supervised methods aim at maximizing agreement between features extracted from two differently transformed views of the same image, which results in learning an invariant representation with respect to appearance and geometric image transformations. However, the effectiveness of these approaches remains unclear in the context of gaze estimation, a structured regression task that requires equivariance under geometric transformations (e.g., rotations, horizontal flip). In this work, we propose SwAT, an equivariant version of the online clustering-based self-supervised approach SwAV, to learn more informative representations for gaze estimation. We demonstrate that SwAT, with ResNet-50 and supported with uncurated unlabeled face images, outperforms state-of-the-art gaze estimation methods and supervised baselines in various experiments. In particular, we achieve up to 57% and 25% improvements in cross-dataset and within-dataset evaluation tasks on existing benchmarks (ETH-XGaze, Gaze360, and MPIIFaceGaze). | ||||
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| Area | Expedition | Conference | |||
| Notes | HUPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ FPS2022 | Serial | 3822 | ||
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| Author | Diego Velazquez | ||||
| Title |
Towards Robustness in Computer-based Image Understanding | Type | Book Whole | ||
| Year | 2023 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | This thesis embarks on an exploratory journey into robustness in deep learning,
with a keen focus on the intertwining facets of generalization, explainability, and edge cases within the realm of computer vision. In deep learning, robustness epitomizes a model’s resilience and flexibility, grounded on its capacity to generalize across diverse data distributions, explain its predictions transparently, and navigate the intricacies of edge cases effectively. The challenges associated with robust generalization are multifaceted, encompassing the model’s performance on unseen data and its defense against out-of-distribution data and adversarial attacks. Bridging this gap, the potential of Embedding Propagation (EP) for improving out-of-distribution generalization is explored. EP is depicted as a powerful tool facilitating manifold smoothing, which in turn fortifies the model’s robustness against adversarial onslaughts and bolsters performance in few-shot and self-/semi-supervised learning scenarios. In the labyrinth of deep learning models, the path to robustness often intersects with explainability. As model complexity increases, so does the urgency to decipher their decision-making processes. Acknowledging this, the thesis introduces a robust framework for evaluating and comparing various counterfactual explanation methods, echoing the imperative of explanation quality over quantity and spotlighting the intricacies of diversifying explanations. Simultaneously, the deep learning landscape is fraught with edge cases – anomalies in the form of small objects or rare instances in object detection tasks that defy the norm. Confronting this, the thesis presents an extension of the DETR (DEtection TRansformer) model to enhance small object detection. The devised DETR-FP, embedding the Feature Pyramid technique, demonstrating improvement in small objects detection accuracy, albeit facing challenges like high computational costs. With emergence of foundation models in mind, the thesis unveils EarthView, the largest scale remote sensing dataset to date, built for the self-supervised learning of a robust foundational model for remote sensing. Collectively, these studies contribute to the grand narrative of robustness in deep learning, weaving together the strands of generalization, explainability, and edge case performance. Through these methodological advancements and novel datasets, the thesis calls for continued exploration, innovation, and refinement to fortify the bastion of robust computer vision. |
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| Address | |||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | IMPRIMA | Place of Publication | Editor | Jordi Gonzalez;Josep M. Gonfaus;Pau Rodriguez | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-81-126409-5-3 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ISE | Approved | no | ||
| Call Number | Admin @ si @ Vel2023 | Serial | 3965 | ||
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| Author | Pau Rodriguez | ||||
| Title |
Towards Robust Neural Models for Fine-Grained Image Recognition | Type | Book Whole | ||
| Year | 2019 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | Fine-grained recognition, i.e. identifying similar subcategories of the same superclass, is central to human activity. Recognizing a friend, finding bacteria in microscopic imagery, or discovering a new kind of galaxy, are just but few examples. However, fine-grained image recognition is still a challenging computer vision task since the differences between two images of the same category can overwhelm the differences between two images of different fine-grained categories. In this regime, where the difference between two categories resides on subtle input changes, excessively invariant CNNs discard those details that help to discriminate between categories and focus on more obvious changes, yielding poor classification performance.
On the other hand, CNNs with too much capacity tend to memorize instance-specific details, thus causing overfitting. In this thesis,motivated by the potential impact of automatic fine-grained image recognition, we tackle the previous challenges and demonstrate that proper alignment of the inputs, multiple levels of attention, regularization, and explicitmodeling of the output space, results inmore accurate fine-grained recognitionmodels, that generalize better, and are more robust to intra-class variation. Concretely, we study the different stages of the neural network pipeline: input pre-processing, attention to regions, feature activations, and the label space. In each stage, we address different issues that hinder the recognition performance on various fine-grained tasks, and devise solutions in each chapter: i)We deal with the sensitivity to input alignment on fine-grained human facial motion such as pain. ii) We introduce an attention mechanism to allow CNNs to choose and process in detail the most discriminate regions of the image. iii)We further extend attention mechanisms to act on the network activations, thus allowing them to correct their predictions by looking back at certain regions, at different levels of abstraction. iv) We propose a regularization loss to prevent high-capacity neural networks to memorize instance details by means of almost-identical feature detectors. v)We finally study the advantages of explicitly modeling the output space within the error-correcting framework. As a result, in this thesis we demonstrate that attention and regularization seem promising directions to overcome the problems of fine-grained image recognition, as well as proper treatment of the input and the output space. |
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| Address | March 2019 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Jordi Gonzalez;Josep M. Gonfaus;Xavier Roca | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-84-948531-3-5 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ISE; 600.119 | Approved | no | ||
| Call Number | Admin @ si @ Rod2019 | Serial | 3258 | ||
| Permanent link to this record | |||||
| Author | Dani Rowe | ||||
| Title |
Towards Robust Multiple-Target Tracking in Unconstrained Human-Populated Environments | Type | Miscellaneous | ||
| Year | 2008 | Publication | CVC–UAB | Abbreviated Journal | |
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| ISSN | ISBN | 978–84–935251–5–6 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | Approved | no | |||
| Call Number | Admin @ si @ Row2008 | Serial | 1103 | ||
| Permanent link to this record | |||||
| Author | Dani Rowe | ||||
| Title |
Towards Robust Multiple-People Tracking in Unconstrained Environments | Type | Report | ||
| Year | 2007 | Publication | CVC Technical Report #100 | Abbreviated Journal | |
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| Address | CVC (UAB) | ||||
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| Area | Expedition | Conference | |||
| Notes | Approved | no | |||
| Call Number | Admin @ si @ Row2007 | Serial | 765 | ||
| Permanent link to this record | |||||
| Author | Quentin Angermann; Jorge Bernal; Cristina Sanchez Montes; Gloria Fernandez Esparrach; Xavier Gray; Olivier Romain; F. Javier Sanchez; Aymeric Histace | ||||
| Title |
Towards Real-Time Polyp Detection in Colonoscopy Videos: Adapting Still Frame-Based Methodologies for Video Sequences Analysis | Type | Conference Article | ||
| Year | 2017 | Publication | 4th International Workshop on Computer Assisted and Robotic Endoscopy | Abbreviated Journal | |
| Volume | Issue | Pages | 29-41 | ||
| Keywords | Polyp detection; colonoscopy; real time; spatio temporal coherence | ||||
| Abstract | Colorectal cancer is the second cause of cancer death in United States: precursor lesions (polyps) detection is key for patient survival. Though colonoscopy is the gold standard screening tool, some polyps are still missed. Several computational systems have been proposed but none of them are used in the clinical room mainly due to computational constraints. Besides, most of them are built over still frame databases, decreasing their performance on video analysis due to the lack of output stability and not coping with associated variability on image quality and polyp appearance. We propose a strategy to adapt these methods to video analysis by adding a spatio-temporal stability module and studying a combination of features to capture polyp appearance variability. We validate our strategy, incorporated on a real-time detection method, on a public video database. Resulting method detects all
polyps under real time constraints, increasing its performance due to our adaptation strategy. |
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| Address | Quebec; Canada; September 2017 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CARE | ||
| Notes | MV; 600.096; 600.075 | Approved | no | ||
| Call Number | Admin @ si @ ABS2017b | Serial | 2977 | ||
| Permanent link to this record | |||||
| Author | Bhaskar Chakraborty; Andrew Bagdanov; Jordi Gonzalez | ||||
| Title |
Towards Real-Time Human Action Recognition | Type | Conference Article | ||
| Year | 2009 | Publication | 4th Iberian Conference on Pattern Recognition and Image Analysis | Abbreviated Journal | |
| Volume | 5524 | Issue | Pages | ||
| Keywords | |||||
| Abstract | This work presents a novel approach to human detection based action-recognition in real-time. To realize this goal our method first detects humans in different poses using a correlation-based approach. Recognition of actions is done afterward based on the change of the angular values subtended by various body parts. Real-time human detection and action recognition are very challenging, and most state-of-the-art approaches employ complex feature extraction and classification techniques, which ultimately becomes a handicap for real-time recognition. Our correlation-based method, on the other hand, is computationally efficient and uses very simple gradient-based features. For action recognition angular features of body parts are extracted using a skeleton technique. Results for action recognition are comparable with the present state-of-the-art. | ||||
| Address | Póvoa de Varzim, Portugal | ||||
| Corporate Author | Thesis | ||||
| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-642-02171-8 | Medium | |
| Area | Expedition | Conference | IbPRIA | ||
| Notes | ISE | Approved | no | ||
| Call Number | DAG @ dag @ CBG2009 | Serial | 1215 | ||
| Permanent link to this record | |||||
| Author | Roger Max Calle Quispe; Maya Aghaei Gavari; Eduardo Aguilar Torres | ||||
| Title |
Towards real-time accurate safety helmets detection through a deep learning-based method | Type | Journal | ||
| Year | 2023 | Publication | Ingeniare. Revista chilena de ingenieria | Abbreviated Journal | |
| Volume | 31 | Issue | 12 | Pages | |
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| Abstract | Occupational safety is a fundamental activity in industries and revolves around the management of the necessary controls that must be present to mitigate occupational risks. These controls include verifying the use of Personal Protection Equipment (PPE). Within PPE, safety helmets are vital to reducing severe or fatal consequences caused by head injuries. This problem has been addressed recently by various research based on deep learning to detect the usage of safety helmets by the present people in the industrial field.
These works have achieved promising results for safety helmet detection using object detection methods from the YOLO family. In this work, we propose to analyze the performance of Scaled-YOLOv4, a novel model of the YOLO family that has yet to be previously studied for this problem. The performance of the Scaled-YOLOv4 is evaluated on two public databases, carefully selected among the previously proposed datasets for the occupational safety framework. We demonstrate the superiority of Scaled-YOLOv4 in terms of mAP and Fl-score concerning the previous works for both databases. Further, we summarize the currently available datasets for safety helmet detection purposes and discuss their suitability. |
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| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ CAA2023 | Serial | 3846 | ||
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| Author | Marçal Rusiñol; David Aldavert; Ricardo Toledo; Josep Llados | ||||
| Title |
Towards Query-by-Speech Handwritten Keyword Spotting | Type | Conference Article | ||
| Year | 2015 | Publication | 13th International Conference on Document Analysis and Recognition ICDAR2015 | Abbreviated Journal | |
| Volume | Issue | Pages | 501-505 | ||
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| Abstract | In this paper, we present a new querying paradigm for handwritten keyword spotting. We propose to represent handwritten word images both by visual and audio representations, enabling a query-by-speech keyword spotting system. The two representations are merged together and projected to a common sub-space in the training phase. This transform allows to, given a spoken query, retrieve word instances that were only represented by the visual modality. In addition, the same method can be used backwards at no additional cost to produce a handwritten text-tospeech system. We present our first results on this new querying mechanism using synthetic voices over the George Washington
dataset. |
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| Address | Nancy; France; August 2015 | ||||
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| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.084; 600.061; 601.223; 600.077;ADAS | Approved | no | ||
| Call Number | Admin @ si @ RAT2015b | Serial | 2682 | ||
| Permanent link to this record | |||||
| Author | Fei Yang | ||||
| Title |
Towards Practical Neural Image Compression | Type | Book Whole | ||
| Year | 2021 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | Images and videos are pervasive in our life and communication. With advances in smart and portable devices, high capacity communication networks and high definition cinema, image and video compression are more relevant than ever. Traditional block-based linear transform codecs such as JPEG, H.264/AVC or the recent H.266/VVC are carefully designed to meet not only the rate-distortion criteria, but also the practical requirements of applications.
Recently, a new paradigm based on deep neural networks (i.e., neural image/video compression) has become increasingly popular due to its ability to learn powerful nonlinear transforms and other coding tools directly from data instead of being crafted by humans, as was usual in previous coding formats. While achieving excellent rate-distortion performance, these approaches are still limited mostly to research environments due to heavy models and other practical limitations, such as being limited to function on a particular rate and due to high memory and computational cost. In this thesis, we study these practical limitations, and designing more practical neural image compression approaches. After analyzing the differences between traditional and neural image compression, our first contribution is the modulated autoencoder (MAE), a framework that includes a mechanism to provide multiple rate-distortion options within a single model with comparable performance to independent models. In a second contribution, we propose the slimmable compressive autoencoder (SlimCAE), which in addition to variable rate, can optimize the complexity of the model and thus reduce significantly the memory and computational burden. Modern generative models can learn custom image transformation directly from suitable datasets following encoder-decoder architectures, task known as image-to-image (I2I) translation. Building on our previous work, we study the problem of distributed I2I translation, where the latent representation is transmitted through a binary channel and decoded in a remote receiving side. We also propose a variant that can perform both translation and the usual autoencoding functionality. Finally, we also consider neural video compression, where the autoencoder is typically augmented with temporal prediction via motion compensation. One of the main bottlenecks of that framework is the optical flow module that estimates the displacement to predict the next frame. Focusing on this module, we propose a method that improves the accuracy of the optical flow estimation and a simplified variant that reduces the computational cost. Key words: neural image compression, neural video compression, optical flow, practical neural image compression, compressive autoencoders, image-to-image translation, deep learning. |
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| Address | December 2021 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | IMPRIMA | Place of Publication | Editor | Luis Herranz;Mikhail Mozerov;Yongmei Cheng | |
| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-84-122714-7-8 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | LAMP | Approved | no | ||
| Call Number | Admin @ si @ Yan2021 | Serial | 3608 | ||
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| Author | Carles Fernandez; Jordi Gonzalez; Joao Manuel R. S. Taveres; Xavier Roca | ||||
| Title |
Towards Ontological Cognitive System | Type | Book Chapter | ||
| Year | 2013 | Publication | Topics in Medical Image Processing and Computational Vision | Abbreviated Journal | |
| Volume | 8 | Issue | Pages | 87-99 | |
| Keywords | |||||
| Abstract | The increasing ubiquitousness of digital information in our daily lives has positioned video as a favored information vehicle, and given rise to an astonishing generation of social media and surveillance footage. This raises a series of technological demands for automatic video understanding and management, which together with the compromising attentional limitations of human operators, have motivated the research community to guide its steps towards a better attainment of such capabilities. As a result, current trends on cognitive vision promise to recognize complex events and self-adapt to different environments, while managing and integrating several types of knowledge. Future directions suggest to reinforce the multi-modal fusion of information sources and the communication with end-users. | ||||
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| Publisher | Springer Netherlands | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 2212-9391 | ISBN | 978-94-007-0725-2 | Medium | |
| Area | Expedition | Conference | |||
| Notes | ISE; 605.203; 302.018; 600.049 | Approved | no | ||
| Call Number | Admin @ si @ FGT2013 | Serial | 2287 | ||
| Permanent link to this record | |||||
| Author | Carles Sanchez; Jorge Bernal; F. Javier Sanchez; Antoni Rosell; Marta Diez-Ferrer; Debora Gil | ||||
| Title |
Towards On-line Quantification of Tracheal Stenosis from Videobronchoscopy | Type | Journal Article | ||
| Year | 2015 | Publication | International Journal of Computer Assisted Radiology and Surgery | Abbreviated Journal | IJCAR |
| Volume | 10 | Issue | 6 | Pages | 935-945 |
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| Notes | IAM; MV; 600.075 | Approved | no | ||
| Call Number | Admin @ si @ SBS2015a | Serial | 2611 | ||
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| Author | Carles Sanchez; Jorge Bernal; F. Javier Sanchez; Marta Diez-Ferrer; Antoni Rosell; Debora Gil | ||||
| Title |
Towards On-line Quantification of Tracheal Stenosis from Videobronchoscopy | Type | Conference Article | ||
| Year | 2015 | Publication | 6th International Conference on Information Processing in Computer-Assisted Interventions IPCAI2015 | Abbreviated Journal | |
| Volume | 10 | Issue | 6 | Pages | 935-945 |
| Keywords | |||||
| Abstract | PURPOSE:
Lack of objective measurement of tracheal obstruction degree has a negative impact on the chosen treatment prone to lead to unnecessary repeated explorations and other scanners. Accurate computation of tracheal stenosis in videobronchoscopy would constitute a breakthrough for this noninvasive technique and a reduction in operation cost for the public health service. METHODS: Stenosis calculation is based on the comparison of the region delimited by the lumen in an obstructed frame and the region delimited by the first visible ring in a healthy frame. We propose a parametric strategy for the extraction of lumen and tracheal ring regions based on models of their geometry and appearance that guide a deformable model. To ensure a systematic applicability, we present a statistical framework to choose optimal parametric values and a strategy to choose the frames that minimize the impact of scope optical distortion. RESULTS: Our method has been tested in 40 cases covering different stenosed tracheas. Experiments report a non- clinically relevant [Formula: see text] of discrepancy in the calculated stenotic area and a computational time allowing online implementation in the operating room. CONCLUSIONS: Our methodology allows reliable measurements of airway narrowing in the operating room. To fully assess its clinical impact, a prospective clinical trial should be done. |
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| Address | Barcelona; Spain; June 2015 | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | IPCAI | ||
| Notes | IAM; MV; 600.075 | Approved | no | ||
| Call Number | Admin @ si @ SBS2015b | Serial | 2613 | ||
| Permanent link to this record | |||||
| Author | Rahat Khan; Joost Van de Weijer; Dimosthenis Karatzas; Damien Muselet | ||||
| Title |
Towards multispectral data acquisition with hand-held devices | Type | Conference Article | ||
| Year | 2013 | Publication | 20th IEEE International Conference on Image Processing | Abbreviated Journal | |
| Volume | Issue | Pages | 2053 - 2057 | ||
| Keywords | Multispectral; mobile devices; color measurements | ||||
| Abstract | We propose a method to acquire multispectral data with handheld devices with front-mounted RGB cameras. We propose to use the display of the device as an illuminant while the camera captures images illuminated by the red, green and
blue primaries of the display. Three illuminants and three response functions of the camera lead to nine response values which are used for reflectance estimation. Results are promising and show that the accuracy of the spectral reconstruction improves in the range from 30-40% over the spectral reconstruction based on a single illuminant. Furthermore, we propose to compute sensor-illuminant aware linear basis by discarding the part of the reflectances that falls in the sensorilluminant null-space. We show experimentally that optimizing reflectance estimation on these new basis functions decreases the RMSE significantly over basis functions that are independent to sensor-illuminant. We conclude that, multispectral data acquisition is potentially possible with consumer hand-held devices such as tablets, mobiles, and laptops, opening up applications which are currently considered to be unrealistic. |
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| Address | Melbourne; Australia; September 2013 | ||||
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| Area | Expedition | Conference | ICIP | ||
| Notes | CIC; DAG; 600.048 | Approved | no | ||
| Call Number | Admin @ si @ KWK2013b | Serial | 2265 | ||
| Permanent link to this record | |||||
| Author | Antonio Clavelli; Dimosthenis Karatzas; Josep Llados; Mario Ferraro; Giuseppe Boccignone | ||||
| Title |
Towards Modelling an Attention-Based Text Localization Process | Type | Conference Article | ||
| Year | 2013 | Publication | 6th Iberian Conference on Pattern Recognition and Image Analysis | Abbreviated Journal | |
| Volume | 7887 | Issue | Pages | 296-303 | |
| Keywords | text localization; visual attention; eye guidance | ||||
| Abstract | This note introduces a visual attention model of text localization in real-world scenes. The core of the model built upon the proto-object concept is discussed. It is shown how such dynamic mid-level representation of the scene can be derived in the framework of an action-perception loop engaging salience, text information value computation, and eye guidance mechanisms.
Preliminary results that compare model generated scanpaths with those eye-tracked from human subjects are presented. |
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| Address | Madeira; Portugal; June 2013 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-642-38627-5 | Medium | |
| Area | Expedition | Conference | IbPRIA | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ CKL2013 | Serial | 2291 | ||
| Permanent link to this record | |||||