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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
Keywords
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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Notes HUPBA; no menciona Approved no
Call Number Admin @ si @ FPS2022 Serial 3822
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Author Razieh Rastgoo; Kourosh Kiani; Sergio Escalera
Title Word separation in continuous sign language using isolated signs and post-processing Type Miscellaneous
Year 2022 Publication Arxiv Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Continuous Sign Language Recognition (CSLR) is a long challenging task in Computer Vision due to the difficulties in detecting the explicit boundaries between the words in a sign sentence. To deal with this challenge, we propose a two-stage model. In the first stage, the predictor model, which includes a combination of CNN, SVD, and LSTM, is trained with the isolated signs. In the second stage, we apply a post-processing algorithm to the Softmax outputs obtained from the first part of the model in order to separate the isolated signs in the continuous signs. Due to the lack of a large dataset, including both the sign sequences and the corresponding isolated signs, two public datasets in Isolated Sign Language Recognition (ISLR), RKS-PERSIANSIGN and ASLVID, are used for evaluation. Results of the continuous sign videos confirm the efficiency of the proposed model to deal with isolated sign boundaries detection.
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference
Notes HUPBA; no menciona Approved no
Call Number Admin @ si @ RKE2022b Serial 3824
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Author Marco Cotogni; Fei Yang; Claudio Cusano; Andrew Bagdanov; Joost Van de Weijer
Title Gated Class-Attention with Cascaded Feature Drift Compensation for Exemplar-free Continual Learning of Vision Transformers Type Miscellaneous
Year 2022 Publication Arxiv Abbreviated Journal
Volume Issue Pages
Keywords Marco Cotogni, Fei Yang, Claudio Cusano, Andrew D. Bagdanov, Joost van de Weijer
Abstract We propose a new method for exemplar-free class incremental training of ViTs. The main challenge of exemplar-free continual learning is maintaining plasticity of the learner without causing catastrophic forgetting of previously learned tasks. This is often achieved via exemplar replay which can help recalibrate previous task classifiers to the feature drift which occurs when learning new tasks. Exemplar replay, however, comes at the cost of retaining samples from previous tasks which for many applications may not be possible. To address the problem of continual ViT training, we first propose gated class-attention to minimize the drift in the final ViT transformer block. This mask-based gating is applied to class-attention mechanism of the last transformer block and strongly regulates the weights crucial for previous tasks. Importantly, gated class-attention does not require the task-ID during inference, which distinguishes it from other parameter isolation methods. Secondly, we propose a new method of feature drift compensation that accommodates feature drift in the backbone when learning new tasks. The combination of gated class-attention and cascaded feature drift compensation allows for plasticity towards new tasks while limiting forgetting of previous ones. Extensive experiments performed on CIFAR-100, Tiny-ImageNet and ImageNet100 demonstrate that our exemplar-free method obtains competitive results when compared to rehearsal based ViT methods.
Address
Corporate Author Thesis
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Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference
Notes LAMP; no proj Approved no
Call Number Admin @ si @ CYC2022 Serial 3827
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Author Razieh Rastgoo; Kourosh Kiani; Sergio Escalera
Title A Non-Anatomical Graph Structure for isolated hand gesture separation in continuous gesture sequences Type Miscellaneous
Year 2022 Publication Arxiv Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Continuous Hand Gesture Recognition (CHGR) has been extensively studied by researchers in the last few decades. Recently, one model has been presented to deal with the challenge of the boundary detection of isolated gestures in a continuous gesture video [17]. To enhance the model performance and also replace the handcrafted feature extractor in the presented model in [17], we propose a GCN model and combine it with the stacked Bi-LSTM and Attention modules to push the temporal information in the video stream. Considering the breakthroughs of GCN models for skeleton modality, we propose a two-layer GCN model to empower the 3D hand skeleton features. Finally, the class probabilities of each isolated gesture are fed to the post-processing module, borrowed from [17]. Furthermore, we replace the anatomical graph structure with some non-anatomical graph structures. Due to the lack of a large dataset, including both the continuous gesture sequences and the corresponding isolated gestures, three public datasets in Dynamic Hand Gesture Recognition (DHGR), RKS-PERSIANSIGN, and ASLVID, are used for evaluation. Experimental results show the superiority of the proposed model in dealing with isolated gesture boundaries detection in continuous gesture sequences
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Notes HuPBA; no menciona Approved no
Call Number Admin @ si @ RKE2022d Serial 3828
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Author Juan Borrego-Carazo; Carles Sanchez; David Castells; Jordi Carrabina; Debora Gil
Title A benchmark for the evaluation of computational methods for bronchoscopic navigation Type Journal Article
Year 2022 Publication International Journal of Computer Assisted Radiology and Surgery Abbreviated Journal IJCARS
Volume 17 Issue 1 Pages
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Address
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Notes IAM Approved no
Call Number Admin @ si @ BSC2022 Serial 3832
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Author Antoni Rosell; Sonia Baeza; S. Garcia-Reina; JL. Mate; Ignasi Guasch; I. Nogueira; I. Garcia-Olive; Guillermo Torres; Carles Sanchez; Debora Gil
Title Radiomics to increase the effectiveness of lung cancer screening programs. Radiolung preliminary results. Type Journal Article
Year 2022 Publication European Respiratory Journal Abbreviated Journal ERJ
Volume 60 Issue 66 Pages
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Abstract
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference
Notes IAM Approved no
Call Number Admin @ si @ RBG2022c Serial 3835
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Author Patricia Suarez; Angel Sappa
Title Toward a Thermal Image-Like Representation Type Conference Article
Year 2023 Publication Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications Abbreviated Journal
Volume Issue Pages 133-140
Keywords
Abstract This paper proposes a novel model to obtain thermal image-like representations to be used as an input in any thermal image compressive sensing approach (e.g., thermal image: filtering, enhancing, super-resolution). Thermal images offer interesting information about the objects in the scene, in addition to their temperature. Unfortunately, in most of the cases thermal cameras acquire low resolution/quality images. Hence, in order to improve these images, there are several state-of-the-art approaches that exploit complementary information from a low-cost channel (visible image) to increase the image quality of an expensive channel (infrared image). In these SOTA approaches visible images are fused at different levels without paying attention the images acquire information at different bands of the spectral. In this paper a novel approach is proposed to generate thermal image-like representations from a low cost visible images, by means of a contrastive cycled GAN network. Obtained representations (synthetic thermal image) can be later on used to improve the low quality thermal image of the same scene. Experimental results on different datasets are presented.
Address Lisboa; Portugal; February 2023
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference VISIGRAPP
Notes MSIAU Approved no
Call Number Admin @ si @ SuS2023b Serial 3927
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Author David Dueñas; Mostafa Kamal; Petia Radeva
Title Efficient Deep Learning Ensemble for Skin Lesion Classification Type Conference Article
Year 2023 Publication Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications Abbreviated Journal
Volume Issue Pages 303-314
Keywords
Abstract Vision Transformers (ViTs) are deep learning techniques that have been gaining in popularity in recent years.
In this work, we study the performance of ViTs and Convolutional Neural Networks (CNNs) on skin lesions classification tasks, specifically melanoma diagnosis. We show that regardless of the performance of both architectures, an ensemble of them can improve their generalization. We also present an adaptation to the Gram-OOD* method (detecting Out-of-distribution (OOD) using Gram matrices) for skin lesion images. Moreover, the integration of super-convergence was critical to success in building models with strict computing and training time constraints. We evaluated our ensemble of ViTs and CNNs, demonstrating that generalization is enhanced by placing first in the 2019 and third in the 2020 ISIC Challenge Live Leaderboards
(available at https://challenge.isic-archive.com/leaderboards/live/).
Address Lisboa; Portugal; February 2023
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference VISIGRAPP
Notes MILAB Approved no
Call Number Admin @ si @ DKR2023 Serial 3928
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Author Guillermo Torres; Debora Gil; Antoni Rosell; S. Mena; Carles Sanchez
Title Virtual Radiomics Biopsy for the Histological Diagnosis of Pulmonary Nodules Type Conference Article
Year 2023 Publication 37th International Congress and Exhibition is organized by Computer Assisted Radiology and Surgery Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Pòster
Address Munich; Germany; June 2023
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CARS
Notes IAM Approved no
Call Number Admin @ si @ TGR2023a Serial 3950
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Author Sonia Baeza; Debora Gil; Carles Sanchez; Guillermo Torres; Ignasi Garcia Olive; Ignasi Guasch; Samuel Garcia Reina; Felipe Andreo; Jose Luis Mate; Jose Luis Vercher; Antonio Rosell
Title Biopsia virtual radiomica para el diagnóstico histológico de nódulos pulmonares – Resultados intermedios del proyecto Radiolung Type Conference Article
Year 2023 Publication SEPAR Abbreviated Journal
Volume Issue Pages
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Abstract Pòster
Address Granada; Spain; June 2023
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference SEPAR
Notes IAM Approved no
Call Number Admin @ si @ BGS2023 Serial 3951
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Author Debora Gil; Guillermo Torres; Carles Sanchez
Title Transforming radiomic features into radiological words Type Conference Article
Year 2023 Publication IEEE International Symposium on Biomedical Imaging Abbreviated Journal
Volume Issue Pages
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Abstract Pòster
Address Cartagena de Indias; Colombia; April 2023
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ISBI
Notes IAM Approved no
Call Number Admin @ si @ GTS2023 Serial 3952
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Author Pau Cano; Debora Gil; Eva Musulen
Title Towards automatic detection of helicobacter pylori in histological samples of gastric tissue Type Conference Article
Year 2023 Publication IEEE International Symposium on Biomedical Imaging Abbreviated Journal
Volume Issue Pages
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Abstract
Address Cartagena de Indias; Colombia; April 2023
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ISBI
Notes IAM Approved no
Call Number Admin @ si @ CGM2023 Serial 3953
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Author Guillermo Torres; Debora Gil; Antonio Rosell; Sonia Baeza; Carles Sanchez
Title A radiomic biopsy for virtual histology of pulmonary nodules Type Conference Article
Year 2023 Publication IEEE International Symposium on Biomedical Imaging Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Pòster
Address Cartagena de Indias; Colombia; April 2023
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ISBI
Notes IAM Approved no
Call Number Admin @ si @ TGR2023b Serial 3954
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Author Armin Mehri
Title Deep learning based architectures for cross-domain image processing Type Book Whole
Year 2023 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Human vision is restricted to the visual-optical spectrum. Machine vision is not.
Cameras sensitive to diverse infrared spectral bands can improve the capacities of
autonomous systems and provide a comprehensive view. Relevant scene content
can be made visible, particularly in situations when sensors of other modalities,
such as a visual-optical camera, require a source of illumination. As a result, increasing the level of automation not only avoids human errors but also reduces
machine-induced errors. Furthermore, multi-spectral sensor systems with infrared
imagery as one modality are a rich source of information and can conceivably
increase the robustness of many autonomous systems. Robotics, automobiles,
biometrics, security, surveillance, and the military are some examples of fields
that can profit from the use of infrared imagery in their respective applications.
Although multimodal spectral sensors have come a long way, there are still several
bottlenecks that prevent us from combining their output information and using
them as comprehensive images. The primary issue with infrared imaging is the lack
of potential benefits due to their cost influence on sensor resolution, which grows
exponentially with greater resolution. Due to the more costly sensor technology
required for their development, their resolutions are substantially lower than thoseof regular digital cameras.
This thesis aims to improve beyond-visible-spectrum machine vision by integrating multi-modal spectral sensors. The emphasis is on transforming the produced images to enhance their resolution to match expected human perception, bring the color representation close to human understanding of natural color, and improve machine vision application performance. This research focuses mainly on two tasks, image Colorization and Image Super resolution for both single- and cross-domain problems. We first start with an extensive review of the state of the art in both tasks, point out the shortcomings of existing approaches, and then present our solutions to address their limitations. Our solutions demonstrate that low-cost channel information (i.e., visible image) can be used to improve expensive channel
information (i.e., infrared image), resulting in images with higher quality and closer to human perception at a lower cost than a high-cost infrared camera.
Address
Corporate Author Thesis Ph.D. thesis
Publisher IMPRIMA Place of Publication Editor Angel Sappa
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-126409-1-5 Medium
Area Expedition Conference
Notes MSIAU Approved no
Call Number Admin @ si @ Meh2023 Serial 3959
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Author Chenshen Wu
Title Going beyond Classification Problems for the Continual Learning of Deep Neural Networks Type Book Whole
Year 2023 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Deep learning has made tremendous progress in the last decade due to the explosion of training data and computational power. Through end-to-end training on a
large dataset, image representations are more discriminative than the previously
used hand-crafted features. However, for many real-world applications, training
and testing on a single dataset is not realistic, as the test distribution may change over time. Continuous learning takes this situation into account, where the learner must adapt to a sequence of tasks, each with a different distribution. If you would naively continue training the model with a new task, the performance of the model would drop dramatically for the previously learned data. This phenomenon is known as catastrophic forgetting.
Many approaches have been proposed to address this problem, which can be divided into three main categories: regularization-based approaches, rehearsal-based
approaches, and parameter isolation-based approaches. However, most of the existing works focus on image classification tasks and many other computer vision tasks
have not been well-explored in the continual learning setting. Therefore, in this
thesis, we study continual learning for image generation, object re-identification,
and object counting.
For the image generation problem, since the model can generate images from the previously learned task, it is free to apply rehearsal without any limitation. We developed two methods based on generative replay. The first one uses the generated image for joint training together with the new data. The second one is based on
output pixel-wise alignment. We extensively evaluate these methods on several
benchmarks.
Next, we study continual learning for object Re-Identification (ReID). Although
most state-of-the-art methods of ReID and continual ReID use softmax-triplet loss,
we found that it is better to solve the ReID problem from a meta-learning perspective because continual learning of reID can benefit a lot from the generalization of metalearning. We also propose a distillation loss and found that the removal of the positive pairs before the distillation loss is critical.
Finally, we study continual learning for the counting problem. We study the mainstream method based on density maps and propose a new approach for density
map distillation. We found that fixing the counter head is crucial for the continual learning of object counting. To further improve results, we propose an adaptor to adapt the changing feature extractor for the fixed counter head. Extensive evaluation shows that this results in improved continual learning performance.
Address
Corporate Author Thesis Ph.D. thesis
Publisher IMPRIMA Place of Publication Editor Joost Van de Weijer;Bogdan Raducanu
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-126409-0-8 Medium
Area Expedition Conference
Notes LAMP Approved no
Call Number Admin @ si @ Wu2023 Serial 3960
Permanent link to this record