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Author Zahra Raisi-Estabragh; Carlos Martin-Isla; Louise Nissen; Liliana Szabo; Victor M. Campello; Sergio Escalera; Simon Winther; Morten Bottcher; Karim Lekadir; and Steffen E. Petersen
Title Radiomics analysis enhances the diagnostic performance of CMR stress perfusion: a proof-of-concept study using the Dan-NICAD dataset Type Journal Article
Year 2023 Publication Frontiers in Cardiovascular Medicine Abbreviated Journal FCM
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Notes HUPBA Approved no
Call Number Admin @ si @ RMN2023 Serial 3937
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Author Albin Soutif; Antonio Carta; Andrea Cossu; Julio Hurtado; Hamed Hemati; Vincenzo Lomonaco; Joost Van de Weijer
Title A Comprehensive Empirical Evaluation on Online Continual Learning Type Conference Article
Year 2023 Publication Visual Continual Learning (ICCV-W) Abbreviated Journal
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Abstract Online continual learning aims to get closer to a live learning experience by learning directly on a stream of data with temporally shifting distribution and by storing a minimum amount of data from that stream. In this empirical evaluation, we evaluate various methods from the literature that tackle online continual learning. More specifically, we focus on the class-incremental setting in the context of image classification, where the learner must learn new classes incrementally from a stream of data. We compare these methods on the Split-CIFAR100 and Split-TinyImagenet benchmarks, and measure their average accuracy, forgetting, stability, and quality of the representations, to evaluate various aspects of the algorithm at the end but also during the whole training period. We find that most methods suffer from stability and underfitting issues. However, the learned representations are comparable to i.i.d. training under the same computational budget. No clear winner emerges from the results and basic experience replay, when properly tuned and implemented, is a very strong baseline. We release our modular and extensible codebase at this https URL based on the avalanche framework to reproduce our results and encourage future research.
Address Paris; France; October 2023
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Area Expedition Conference ICCVW
Notes LAMP Approved no
Call Number Admin @ si @ SCC2023 Serial 3938
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Author Joakim Bruslund Haurum; Sergio Escalera; Graham W. Taylor; Thomas B.
Title Which Tokens to Use? Investigating Token Reduction in Vision Transformers Type Conference Article
Year 2023 Publication Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops Abbreviated Journal
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Abstract Since the introduction of the Vision Transformer (ViT), researchers have sought to make ViTs more efficient by removing redundant information in the processed tokens. While different methods have been explored to achieve this goal, we still lack understanding of the resulting reduction patterns and how those patterns differ across token reduction methods and datasets. To close this gap, we set out to understand the reduction patterns of 10 different token reduction methods using four image classification datasets. By systematically comparing these methods on the different classification tasks, we find that the Top-K pruning method is a surprisingly strong baseline. Through in-depth analysis of the different methods, we determine that: the reduction patterns are generally not consistent when varying the capacity of the backbone model, the reduction patterns of pruning-based methods significantly differ from fixed radial patterns, and the reduction patterns of pruning-based methods are correlated across classification datasets. Finally we report that the similarity of reduction patterns is a moderate-to-strong proxy for model performance. Project page at https://vap.aau.dk/tokens.
Address Paris; France; October 2023
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Notes HUPBA Approved no
Call Number Admin @ si @ BET2023 Serial 3940
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Author Xavier Soria; Yachuan Li; Mohammad Rouhani; Angel Sappa
Title Tiny and Efficient Model for the Edge Detection Generalization Type Conference Article
Year 2023 Publication Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops Abbreviated Journal
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Abstract Most high-level computer vision tasks rely on low-level image operations as their initial processes. Operations such as edge detection, image enhancement, and super-resolution, provide the foundations for higher level image analysis. In this work we address the edge detection considering three main objectives: simplicity, efficiency, and generalization since current state-of-the-art (SOTA) edge detection models are increased in complexity for better accuracy. To achieve this, we present Tiny and Efficient Edge Detector (TEED), a light convolutional neural network with only 58K parameters, less than 0:2% of the state-of-the-art models. Training on the BIPED dataset takes less than 30 minutes, with each epoch requiring less than 5 minutes. Our proposed model is easy to train and it quickly converges within very first few epochs, while the predicted edge-maps are crisp and of high quality. Additionally, we propose a new dataset to test the generalization of edge detection, which comprises samples from popular images used in edge detection and image segmentation. The source code is available in https://github.com/xavysp/TEED.
Address Paris; France; October 2023
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Notes MSIAU Approved no
Call Number Admin @ si @ SLR2023 Serial 3941
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Author Soumya Jahagirdar; Minesh Mathew; Dimosthenis Karatzas; CV Jawahar
Title Understanding Video Scenes Through Text: Insights from Text-Based Video Question Answering Type Conference Article
Year 2023 Publication Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops Abbreviated Journal
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Abstract Researchers have extensively studied the field of vision and language, discovering that both visual and textual content is crucial for understanding scenes effectively. Particularly, comprehending text in videos holds great significance, requiring both scene text understanding and temporal reasoning. This paper focuses on exploring two recently introduced datasets, NewsVideoQA and M4-ViteVQA, which aim to address video question answering based on textual content. The NewsVideoQA dataset contains question-answer pairs related to the text in news videos, while M4- ViteVQA comprises question-answer pairs from diverse categories like vlogging, traveling, and shopping. We provide an analysis of the formulation of these datasets on various levels, exploring the degree of visual understanding and multi-frame comprehension required for answering the questions. Additionally, the study includes experimentation with BERT-QA, a text-only model, which demonstrates comparable performance to the original methods on both datasets, indicating the shortcomings in the formulation of these datasets. Furthermore, we also look into the domain adaptation aspect by examining the effectiveness of training on M4-ViteVQA and evaluating on NewsVideoQA and vice-versa, thereby shedding light on the challenges and potential benefits of out-of-domain training.
Address Paris; France; October 2023
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Notes DAG Approved no
Call Number Admin @ si @ JMK2023 Serial 3946
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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
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Address Munich; Germany; June 2023
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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
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Address Granada; Spain; June 2023
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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
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Address Cartagena de Indias; Colombia; April 2023
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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
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Address Cartagena de Indias; Colombia; April 2023
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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
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Address Cartagena de Indias; Colombia; April 2023
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Notes IAM Approved no
Call Number Admin @ si @ TGR2023b Serial 3954
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Author Jun Wan; Guodong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z Li
Title Advances in Face Presentation Attack Detection Type Book Whole
Year 2023 Publication Advances in Face Presentation Attack Detection Abbreviated Journal
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Call Number Admin @ si @ WGE2023a Serial 3955
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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
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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
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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
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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
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ISSN ISBN 978-84-126409-0-8 Medium
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Notes LAMP Approved no
Call Number Admin @ si @ Wu2023 Serial 3960
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Author Jose Luis Gomez
Title Synth-to-real semi-supervised learning for visual tasks Type Book Whole
Year 2023 Publication Going beyond Classification Problems for the Continual Learning of Deep Neural Networks Abbreviated Journal
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Abstract The curse of data labeling is a costly bottleneck in supervised deep learning, where large amounts of labeled data are needed to train intelligent systems. In onboard perception for autonomous driving, this cost corresponds to the labeling of raw data from sensors such as cameras, LiDARs, RADARs, etc. Therefore, synthetic data with automatically generated ground truth (labels) has aroused as a reliable alternative for training onboard perception models.
However, synthetic data commonly suffers from synth-to-real domain shift, i.e., models trained on the synthetic domain do not show their achievable accuracy when performing in the real world. This shift needs to be addressed by techniques falling in the realm of domain adaptation (DA).
The semi-supervised learning (SSL) paradigm can be followed to address DA. In this case, a model is trained using source data with labels (here synthetic) and leverages minimal knowledge from target data (here the real world) to generate pseudo-labels. These pseudo-labels help the training process to reduce the gap between the source and the target domains. In general, we can assume accessing both, pseudo-labels and a few amounts of human-provided labels for the target-domain data. However, the most interesting and challenging setting consists in assuming that we do not have human-provided labels at all. This setting is known as unsupervised domain adaptation (UDA). This PhD focuses on applying SSL to the UDA setting, for onboard visual tasks related to autonomous driving. We start by addressing the synth-to-real UDA problem on onboard vision-based object detection (pedestrians and cars), a critical task for autonomous driving and driving assistance. In particular, we propose to apply an SSL technique known as co-training, which we adapt to work with deep models that process a multi-modal input. The multi-modality consists of the visual appearance of the images (RGB) and their monocular depth estimation. The synthetic data we use as the source domain contains both, object bounding boxes and depth information. This prior knowledge is the
starting point for the co-training technique, which iteratively labels unlabeled real-world data and uses such pseudolabels (here bounding boxes with an assigned object class) to progressively improve the labeling results. Along this
process, two models collaborate to automatically label the images, in a way that one model compensates for the errors of the other, so avoiding error drift. While this automatic labeling process is done offline, the resulting pseudolabels can be used to train object detection models that must perform in real-time onboard a vehicle. We show that multi-modal co-training improves the labeling results compared to single-modal co-training, remaining competitive compared to human labeling.
Given the success of co-training in the context of object detection, we have also adapted this technique to a more crucial and challenging visual task, namely, onboard semantic segmentation. In fact, providing labels for a single image
can take from 30 to 90 minutes for a human labeler, depending on the content of the image. Thus, developing automatic labeling techniques for this visual task is of great interest to the automotive industry. In particular, the new co-training framework addresses synth-to-real UDA by an initial stage of self-training. Intermediate models arising from this stage are used to start the co-training procedure, for which we have elaborated an accurate collaboration policy between the two models performing the automatic labeling. Moreover, our co-training seamlessly leverages datasets from different synthetic domains. In addition, the co-training procedure is agnostic to the loss function used to train the semantic segmentation models which perform the automatic labeling. We achieve state-of-the-art results on publicly available benchmark datasets, again, remaining competitive compared to human labeling.
Finally, on the ground of our previous experience, we have designed and implemented a new SSL technique for UDA in the context of visual semantic segmentation. In this case, we mimic the labeling methodology followed by human labelers. In particular, rather than labeling full images at a time, categories of semantic classes are defined and only those are labeled in a labeling pass. In fact, different human labelers can become specialists in labeling different categories. Afterward, these per-category-labeled layers are combined to provide fully labeled images. Our technique is inspired by this methodology since we perform synth-to-real UDA per category, using the self-training stage previously developed as part of our co-training framework. The pseudo-labels obtained for each category are finally
fused to obtain fully automatically labeled images. In this context, we have also contributed to the development of a new photo-realistic synthetic dataset based on path-tracing rendering. Our new SSL technique seamlessly leverages publicly available synthetic datasets as well as this new one to obtain state-of-the-art results on synth-to-real UDA for semantic segmentation. We show that the new dataset allows us to reach better labeling accuracy than previously existing datasets, at the same time that it complements well them when combined. Moreover, we also show that the new human-inspired SSL technique outperforms co-training.
Address
Corporate Author Thesis Ph.D. thesis
Publisher IMPRIMA Place of Publication Editor Antonio Lopez
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Notes ADAS Approved no
Call Number Admin @ si @ Gom2023 Serial 3961
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Author Jose Elias Yauri
Title Deep Learning Based Data Fusion Approaches for the Assessment of Cognitive States on EEG Signals Type Book Whole
Year 2023 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
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Abstract For millennia, the study of the couple brain-mind has fascinated the humanity in order to understand the complex nature of cognitive states. A cognitive state is the state of the mind at a specific time and involves cognition activities to acquire and process information for making a decision, solving a problem, or achieving a goal.
While normal cognitive states assist in the successful accomplishment of tasks; on the contrary, abnormal states of the mind can lead to task failures due to a reduced cognition capability. In this thesis, we focus on the assessment of cognitive states by means of the analysis of ElectroEncephaloGrams (EEG) signals using deep learning methods. EEG records the electrical activity of the brain using a set of electrodes placed on the scalp that output a set of spatiotemporal signals that are expected to be correlated to a specific mental process.
From the point of view of artificial intelligence, any method for the assessment of cognitive states using EEG signals as input should face several challenges. On the one hand, one should determine which is the most suitable approach for the optimal combination of the multiple signals recorded by EEG electrodes. On the other hand, one should have a protocol for the collection of good quality unambiguous annotated data, and an experimental design for the assessment of the generalization and transfer of models. In order to tackle them, first, we propose several convolutional neural architectures to perform data fusion of the signals recorded by EEG electrodes, at raw signal and feature levels. Four channel fusion methods, easy to incorporate into any neural network architecture, are proposed and assessed. Second, we present a method to create an unambiguous dataset for the prediction of cognitive mental workload using serious games and an Airbus-320 flight simulator. Third, we present a validation protocol that takes into account the levels of generalization of models based on the source and amount of test data.
Finally, the approaches for the assessment of cognitive states are applied to two use cases of high social impact: the assessment of mental workload for personalized support systems in the cockpit and the detection of epileptic seizures. The results obtained from the first use case show the feasibility of task transfer of models trained to detect workload in serious games to real flight scenarios. The results from the second use case show the generalization capability of our EEG channel fusion methods at k-fold cross-validation, patient-specific, and population levels.
Address
Corporate Author Thesis Ph.D. thesis
Publisher IMPRIMA Place of Publication Editor Aura Hernandez;Debora Gil
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Notes IAM Approved no
Call Number Admin @ si @ Yau2023 Serial 3962
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