Home | [211–220] << 221 222 223 224 225 226 227 228 >> |
![]() |
Records | |||||
---|---|---|---|---|---|
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 | ||
Permanent link to this record | |||||
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 | |||
Keywords | |||||
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 | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | SEPAR | ||
Notes | IAM | Approved | no | ||
Call Number | Admin @ si @ BGS2023 | Serial | 3951 | ||
Permanent link to this record | |||||
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 | |||
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 @ GTS2023 | Serial | 3952 | ||
Permanent link to this record | |||||
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 | |||
Keywords | |||||
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 | ||
Permanent link to this record | |||||
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 | ||
Permanent link to this record | |||||
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 | |
Volume | Issue | Pages | |||
Keywords | |||||
Abstract | |||||
Address | |||||
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 | |||
Notes | HUPBA | Approved | no | ||
Call Number | Admin @ si @ WGE2023a | Serial | 3955 | ||
Permanent link to this record | |||||
Author | Jun Wan; Guodong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z Li | ||||
Title | Face Presentation Attack Detection (PAD) Challenges | Type | Book Chapter | ||
Year | 2023 | Publication | Advances in Face Presentation Attack Detection | Abbreviated Journal | |
Volume | Issue | Pages | 17–35 | ||
Keywords | |||||
Abstract | In recent years, the security of face recognition systems has been increasingly threatened. Face Anti-spoofing (FAS) is essential to secure face recognition systems primarily from various attacks. In order to attract researchers and push forward the state of the art in Face Presentation Attack Detection (PAD), we organized three editions of Face Anti-spoofing Workshop and Competition at CVPR 2019, CVPR 2020, and ICCV 2021, which have attracted more than 800 teams from academia and industry, and greatly promoted the algorithms to overcome many challenging problems. In this chapter, we introduce the detailed competition process, including the challenge phases, timeline and evaluation metrics. Along with the workshop, we will introduce the corresponding dataset for each competition including data acquisition details, data processing, statistics, and evaluation protocol. Finally, we provide the available link to download the datasets used in the challenges. | ||||
Address | |||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language ![]() |
Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | SLCV | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | HUPBA | Approved | no | ||
Call Number | Admin @ si @ WGE2023b | Serial | 3956 | ||
Permanent link to this record | |||||
Author | Jun Wan; Guodong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z Li | ||||
Title | Best Solutions Proposed in the Context of the Face Anti-spoofing Challenge Series | Type | Book Chapter | ||
Year | 2023 | Publication | Advances in Face Presentation Attack Detection | Abbreviated Journal | |
Volume | Issue | Pages | 37–78 | ||
Keywords | |||||
Abstract | The PAD competitions we organized attracted more than 835 teams from home and abroad, most of them from the industry, which shows that the topic of face anti-spoofing is closely related to daily life, and there is an urgent need for advanced algorithms to solve its application needs. Specifically, the Chalearn LAP multi-modal face anti-spoofing attack detection challenge attracted more than 300 teams for the development phase with a total of 13 teams qualifying for the final round; the Chalearn Face Anti-spoofing Attack Detection Challenge attracted 340 teams in the development stage, and finally, 11 and 8 teams have submitted their codes in the single-modal and multi-modal face anti-spoofing recognition challenges, respectively; the 3D High-Fidelity Mask Face Presentation Attack Detection Challenge attracted 195 teams for the development phase with a total of 18 teams qualifying for the final round. All the results were verified and re-run by the organizing team, and the results were used for the final ranking. In this chapter, we briefly the methods developed by the teams participating in each competition, and introduce the algorithm details of the top-three ranked teams in detail. | ||||
Address | |||||
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 | |||
Notes | HUPBA | Approved | no | ||
Call Number | Admin @ si @ WGE2023d | Serial | 3958 | ||
Permanent link to this record | |||||
Author | Jun Wan; Guodong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z Li | ||||
Title | Face Anti-spoofing Progress Driven by Academic Challenges | Type | Book Chapter | ||
Year | 2023 | Publication | Advances in Face Presentation Attack Detection | Abbreviated Journal | |
Volume | Issue | Pages | 1–15 | ||
Keywords | |||||
Abstract | With the ubiquity of facial authentication systems and the prevalence of security cameras around the world, the impact that facial presentation attack techniques may have is huge. However, research progress in this field has been slowed by a number of factors, including the lack of appropriate and realistic datasets, ethical and privacy issues that prevent the recording and distribution of facial images, the little attention that the community has given to potential ethnic biases among others. This chapter provides an overview of contributions derived from the organization of academic challenges in the context of face anti-spoofing detection. Specifically, we discuss the limitations of benchmarks and summarize our efforts in trying to boost research by the community via the participation in academic challenges | ||||
Address | |||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language ![]() |
Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | SLCV | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | HUPBA | Approved | no | ||
Call Number | Admin @ si @ WGE2023c | Serial | 3957 | ||
Permanent link to this record | |||||
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 | ||
Permanent link to this record | |||||
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 | |||||
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 | |
Volume | Issue | Pages | |||
Keywords | |||||
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 | |
Language | Summary Language ![]() |
Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | ADAS | Approved | no | ||
Call Number | Admin @ si @ Gom2023 | Serial | 3961 | ||
Permanent link to this record | |||||
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 | |
Volume | Issue | Pages | |||
Keywords | |||||
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 | |
Language | Summary Language ![]() |
Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | IAM | Approved | no | ||
Call Number | Admin @ si @ Yau2023 | Serial | 3962 | ||
Permanent link to this record | |||||
Author | Shiqi Yang | ||||
Title | Towards Source-Free Domain Adaption of Neural Networks in an Open World | Type | Book Whole | ||
Year | 2023 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | |||||
Abstract | Though they achieve great success, deep neural networks typically require a huge
amount of labeled data for training. However, collecting labeled data is often laborious and expensive. It would, therefore, be ideal if the knowledge obtained from label-rich datasets could be transferred to unlabeled data. However, deep networks are weak at generalizing to unseen domains, even when the differences are only subtle between the datasets. In real-world situations, a typical factor impairing the model generalization ability is the distribution shift between data from different domains, which is a long-standing problem usually termed as (unsupervised) domain adaptation. A crucial requirement in the methodology of these domain adaptation methods is that they require access to source domain data during the adaptation process to the target domain. Accessibility to the source data of a trained source model is often impossible in real-world applications, for example, when deploying domain adaptation algorithms on mobile devices where the computational capacity is limited or in situations where data privacy rules limit access to the source domain data. Without access to the source domain data, existing methods suffer from inferior performance. Thus, in this thesis, we investigate domain adaptation without source data (termed as source-free domain adaptation) in multiple different scenarios that focus on image classification tasks. We first study the source-free domain adaptation problem in a closed-set setting, where the label space of different domains is identical. Only accessing the pretrained source model, we propose to address source-free domain adaptation from the perspective of unsupervised clustering. We achieve this based on nearest neighborhood clustering. In this way, we can transfer the challenging source-free domain adaptation task to a type of clustering problem. The final optimization objective is an upper bound containing only two simple terms, which can be explained as discriminability and diversity. We show that this allows us to relate several other methods in domain adaptation, unsupervised clustering and contrastive learning via the perspective of discriminability and diversity. |
||||
Address | |||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | IMPRIMA | Place of Publication | Editor | Joost | |
Language | Summary Language ![]() |
Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-84-126409-3-9 | Medium | ||
Area | Expedition | Conference | |||
Notes | LAMP | Approved | no | ||
Call Number | Admin @ si @ Yan2023 | Serial | 3963 | ||
Permanent link to this record | |||||
Author | Yi Xiao | ||||
Title | Advancing Vision-based End-to-End Autonomous Driving | Type | Book Whole | ||
Year | 2023 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | |||||
Abstract | In autonomous driving, artificial intelligence (AI) processes the traffic environment to drive the vehicle to a desired destination. Currently, there are different paradigms that address the development of AI-enabled drivers. On the one hand, we find modular pipelines, which divide the driving task into sub-tasks such as perception, maneuver planning, and control. On the other hand, we find end-to-end driving approaches that attempt to learn the direct mapping of raw data from input sensors to vehicle control signals. The latter are relatively less studied but are gaining popularity as they are less demanding in terms of data labeling. Therefore, in this thesis, our goal is to investigate end-to-end autonomous driving.
We propose to evaluate three approaches to tackle the challenge of end-to-end autonomous driving. First, we focus on the input, considering adding depth information as complementary to RGB data, in order to mimic the human being’s ability to estimate the distance to obstacles. Notice that, in the real world, these depth maps can be obtained either from a LiDAR sensor, or a trained monocular depth estimation module, where human labeling is not needed. Then, based on the intuition that the latent space of end-to-end driving models encodes relevant information for driving, we use it as prior knowledge for training an affordancebased driving model. In this case, the trained affordance-based model can achieve good performance while requiring less human-labeled data, and it can provide interpretability regarding driving actions. Finally, we present a new pure vision-based end-to-end driving model termed CIL++, which is trained by imitation learning. CIL++ leverages modern best practices, such as a large horizontal field of view and a self-attention mechanism, which are contributing to the agent’s understanding of the driving scene and bringing a better imitation of human drivers. Using training data without any human labeling, our model yields almost expert performance in the CARLA NoCrash benchmark and could rival SOTA models that require large amounts of human-labeled data. |
||||
Address | |||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | IMPRIMA | Place of Publication | Editor | Antonio Lopez | |
Language | Summary Language ![]() |
Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-84-126409-4-6 | Medium | ||
Area | Expedition | Conference | |||
Notes | ADAS | Approved | no | ||
Call Number | Admin @ si @ Xia2023 | Serial | 3964 | ||
Permanent link to this record |