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Author | Marc Masana | ||||
Title | Lifelong Learning of Neural Networks: Detecting Novelty and Adapting to New Domains without Forgetting | Type | Book Whole | ||
Year | 2020 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Abstract | Computer vision has gone through considerable changes in the last decade as neural networks have come into common use. As available computational capabilities have grown, neural networks have achieved breakthroughs in many computer vision tasks, and have even surpassed human performance in others. With accuracy being so high, focus has shifted to other issues and challenges. One research direction that saw a notable increase in interest is on lifelong learning systems. Such systems should be capable of efficiently performing tasks, identifying and learning new ones, and should moreover be able to deploy smaller versions of themselves which are experts on specific tasks. In this thesis, we contribute to research on lifelong learning and address the compression and adaptation of networks to small target domains, the incremental learning of networks faced with a variety of tasks, and finally the detection of out-of-distribution samples at inference time.
We explore how knowledge can be transferred from large pretrained models to more task-specific networks capable of running on smaller devices by extracting the most relevant information. Using a pretrained model provides more robust representations and a more stable initialization when learning a smaller task, which leads to higher performance and is known as domain adaptation. However, those models are too large for certain applications that need to be deployed on devices with limited memory and computational capacity. In this thesis we show that, after performing domain adaptation, some learned activations barely contribute to the predictions of the model. Therefore, we propose to apply network compression based on low-rank matrix decomposition using the activation statistics. This results in a significant reduction of the model size and the computational cost. Like human intelligence, machine intelligence aims to have the ability to learn and remember knowledge. However, when a trained neural network is presented with learning a new task, it ends up forgetting previous ones. This is known as catastrophic forgetting and its avoidance is studied in continual learning. The work presented in this thesis extensively surveys continual learning techniques and presents an approach to avoid catastrophic forgetting in sequential task learning scenarios. Our technique is based on using ternary masks in order to update a network to new tasks, reusing the knowledge of previous ones while not forgetting anything about them. In contrast to earlier work, our masks are applied to the activations of each layer instead of the weights. This considerably reduces the number of parameters to be added for each new task. Furthermore, the analysis on a wide range of work on incremental learning without access to the task-ID, provides insight on current state-of-the-art approaches that focus on avoiding catastrophic forgetting by using regularization, rehearsal of previous tasks from a small memory, or compensating the task-recency bias. Neural networks trained with a cross-entropy loss force the outputs of the model to tend toward a one-hot encoded vector. This leads to models being too overly confident when presented with images or classes that were not present in the training distribution. The capacity of a system to be aware of the boundaries of the learned tasks and identify anomalies or classes which have not been learned yet is key to lifelong learning and autonomous systems. In this thesis, we present a metric learning approach to out-of-distribution detection that learns the task at hand on an embedding space. |
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Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Joost Van de Weijer;Andrew Bagdanov | |
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ISSN | ISBN | 978-84-121011-9-5 | Medium | ||
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Notes | LAMP; 600.120 | Approved | no | ||
Call Number | Admin @ si @ Mas20 | Serial | 3481 | ||
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Author | Lei Kang | ||||
Title | Robust Handwritten Text Recognition in Scarce Labeling Scenarios: Disentanglement, Adaptation and Generation | Type | Book Whole | ||
Year | 2020 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Abstract | Handwritten documents are not only preserved in historical archives but also widely used in administrative documents such as cheques and claims. With the rise of the deep learning era, many state-of-the-art approaches have achieved good performance on specific datasets for Handwritten Text Recognition (HTR). However, it is still challenging to solve real use cases because of the varied handwriting styles across different writers and the limited labeled data. Thus, both explorin a more robust handwriting recognition architectures and proposing methods to diminish the gap between the source and target data in an unsupervised way are
demanded. In this thesis, firstly, we explore novel architectures for HTR, from Sequence-to-Sequence (Seq2Seq) method with attention mechanism to non-recurrent Transformer-based method. Secondly, we focus on diminishing the performance gap between source and target data in an unsupervised way. Finally, we propose a group of generative methods for handwritten text images, which could be utilized to increase the training set to obtain a more robust recognizer. In addition, by simply modifying the generative method and joining it with a recognizer, we end up with an effective disentanglement method to distill textual content from handwriting styles so as to achieve a generalized recognition performance. We outperform state-of-the-art HTR performances in the experimental results among different scientific and industrial datasets, which prove the effectiveness of the proposed methods. To the best of our knowledge, the non-recurrent recognizer and the disentanglement method are the first contributions in the handwriting recognition field. Furthermore, we have outlined the potential research lines, which would be interesting to explore in the future. |
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Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Alicia Fornes;Marçal Rusiñol;Mauricio Villegas | |
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ISSN | ISBN | 978-84-122714-0-9 | Medium | ||
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Notes | DAG; 600.121 | Approved | no | ||
Call Number | Admin @ si @ Kan20 | Serial | 3482 | ||
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Author | Manuel Carbonell | ||||
Title | Neural Information Extraction from Semi-structured Documents A | Type | Book Whole | ||
Year | 2020 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Abstract | Sectors as fintech, legaltech or insurance process an inflow of millions of forms, invoices, id documents, claims or similar every day. Together with these, historical archives provide gigantic amounts of digitized documents containing useful information that needs to be stored in machine encoded text with a meaningful structure. This procedure, known as information extraction (IE) comprises the steps of localizing and recognizing text, identifying named entities contained in it and optionally finding relationships among its elements. In this work we explore multi-task neural models at image and graph level to solve all steps in a unified way. While doing so we find benefits and limitations of these end-to-end approaches in comparison with sequential separate methods. More specifically, we first propose a method to produce textual as well as semantic labels with a unified model from handwritten text line images. We do so with the use of a convolutional recurrent neural model trained with connectionist temporal classification to predict the textual as well as semantic information encoded in the images. Secondly, motivated by the success of this approach we investigate the unification of the localization and recognition tasks of handwritten text in full pages with an end-to-end model, observing benefits in doing so. Having two models that tackle information extraction subsequent task pairs in an end-to-end to end manner, we lastly contribute with a method to put them all together in a single neural network to solve the whole information extraction pipeline in a unified way. Doing so we observe some benefits and some limitations in the approach, suggesting that in certain cases it is beneficial to train specialized models that excel at a single challenging task of the information extraction process, as it can be the recognition of named entities or the extraction of relationships between them. For this reason we lastly study the use of the recently arrived graph neural network architectures for the semantic tasks of the information extraction process, which are recognition of named entities and relation extraction, achieving promising results on the relation extraction part. | ||||
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Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Alicia Fornes;Mauricio Villegas;Josep Llados | |
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ISSN | ISBN | 978-84-122714-1-6 | Medium | ||
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Notes | DAG; 600.121 | Approved | no | ||
Call Number | Admin @ si @ Car20 | Serial | 3483 | ||
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Author | Riccardo Del Chiaro; Bartlomiej Twardowski; Andrew Bagdanov; Joost Van de Weijer | ||||
Title | Recurrent attention to transient tasks for continual image captioning | Type | Conference Article | ||
Year | 2020 | Publication | 34th Conference on Neural Information Processing Systems | Abbreviated Journal | |
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Abstract | Research on continual learning has led to a variety of approaches to mitigating catastrophic forgetting in feed-forward classification networks. Until now surprisingly little attention has been focused on continual learning of recurrent models applied to problems like image captioning. In this paper we take a systematic look at continual learning of LSTM-based models for image captioning. We propose an attention-based approach that explicitly accommodates the transient nature of vocabularies in continual image captioning tasks -- i.e. that task vocabularies are not disjoint. We call our method Recurrent Attention to Transient Tasks (RATT), and also show how to adapt continual learning approaches based on weight egularization and knowledge distillation to recurrent continual learning problems. We apply our approaches to incremental image captioning problem on two new continual learning benchmarks we define using the MS-COCO and Flickr30 datasets. Our results demonstrate that RATT is able to sequentially learn five captioning tasks while incurring no forgetting of previously learned ones. | ||||
Address | virtual; December 2020 | ||||
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Area | Expedition | Conference | NEURIPS | ||
Notes | LAMP; 600.120 | Approved | no | ||
Call Number | Admin @ si @ CTB2020 | Serial | 3484 | ||
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Author | Yaxing Wang; Lu Yu; Joost Van de Weijer | ||||
Title | DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs | Type | Conference Article | ||
Year | 2020 | Publication | 34th Conference on Neural Information Processing Systems | Abbreviated Journal | |
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Abstract | Image-to-image translation has recently achieved remarkable results. But despite current success, it suffers from inferior performance when translations between classes require large shape changes. We attribute this to the high-resolution bottlenecks which are used by current state-of-the-art image-to-image methods. Therefore, in this work, we propose a novel deep hierarchical Image-to-Image Translation method, called DeepI2I. We learn a model by leveraging hierarchical features: (a) structural information contained in the shallow layers and (b) semantic information extracted from the deep layers. To enable the training of deep I2I models on small datasets, we propose a novel transfer learning method, that transfers knowledge from pre-trained GANs. Specifically, we leverage the discriminator of a pre-trained GANs (i.e. BigGAN or StyleGAN) to initialize both the encoder and the discriminator and the pre-trained generator to initialize the generator of our model. Applying knowledge transfer leads to an alignment problem between the encoder and generator. We introduce an adaptor network to address this. On many-class image-to-image translation on three datasets (Animal faces, Birds, and Foods) we decrease mFID by at least 35% when compared to the state-of-the-art. Furthermore, we qualitatively and quantitatively demonstrate that transfer learning significantly improves the performance of I2I systems, especially for small datasets. Finally, we are the first to perform I2I translations for domains with over 100 classes. | ||||
Address | virtual; December 2020 | ||||
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Area | Expedition | Conference | NEURIPS | ||
Notes | LAMP; 600.120 | Approved | no | ||
Call Number | Admin @ si @ WYW2020 | Serial | 3485 | ||
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Author | Yaxing Wang; Salman Khan; Abel Gonzalez-Garcia; Joost Van de Weijer; Fahad Shahbaz Khan | ||||
Title | Semi-supervised Learning for Few-shot Image-to-Image Translation | Type | Conference Article | ||
Year | 2020 | Publication | 33rd IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
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Abstract | In the last few years, unpaired image-to-image translation has witnessed remarkable progress. Although the latest methods are able to generate realistic images, they crucially rely on a large number of labeled images. Recently, some methods have tackled the challenging setting of few-shot image-to-image translation, reducing the labeled data requirements for the target domain during inference. In this work, we go one step further and reduce the amount of required labeled data also from the source domain during training. To do so, we propose applying semi-supervised learning via a noise-tolerant pseudo-labeling procedure. We also apply a cycle consistency constraint to further exploit the information from unlabeled images, either from the same dataset or external. Additionally, we propose several structural modifications to facilitate the image translation task under these circumstances. Our semi-supervised method for few-shot image translation, called SEMIT, achieves excellent results on four different datasets using as little as 10% of the source labels, and matches the performance of the main fully-supervised competitor using only 20% labeled data. Our code and models are made public at: this https URL. | ||||
Address | Virtual; June 2020 | ||||
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Area | Expedition | Conference | CVPR | ||
Notes | LAMP; 600.120 | Approved | no | ||
Call Number | Admin @ si @ WKG2020 | Serial | 3486 | ||
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Author | Yi Xiao; Felipe Codevilla; Christopher Pal; Antonio Lopez | ||||
Title | Action-Based Representation Learning for Autonomous Driving | Type | Conference Article | ||
Year | 2020 | Publication | Conference on Robot Learning | Abbreviated Journal | |
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Abstract | Human drivers produce a vast amount of data which could, in principle, be used to improve autonomous driving systems. Unfortunately, seemingly straightforward approaches for creating end-to-end driving models that map sensor data directly into driving actions are problematic in terms of interpretability, and typically have significant difficulty dealing with spurious correlations. Alternatively, we propose to use this kind of action-based driving data for learning representations. Our experiments show that an affordance-based driving model pre-trained with this approach can leverage a relatively small amount of weakly annotated imagery and outperform pure end-to-end driving models, while being more interpretable. Further, we demonstrate how this strategy outperforms previous methods based on learning inverse dynamics models as well as other methods based on heavy human supervision (ImageNet). | ||||
Address | virtual; November 2020 | ||||
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Area | Expedition | Conference | CORL | ||
Notes | ADAS; 600.118 | Approved | no | ||
Call Number | Admin @ si @ XCP2020 | Serial | 3487 | ||
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Author | Gabriel Villalonga; Antonio Lopez | ||||
Title | Co-Training for On-Board Deep Object Detection | Type | Journal Article | ||
Year | 2020 | Publication | IEEE Access | Abbreviated Journal | ACCESS |
Volume | Issue | Pages | 194441 - 194456 | ||
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Abstract | Providing ground truth supervision to train visual models has been a bottleneck over the years, exacerbated by domain shifts which degenerate the performance of such models. This was the case when visual tasks relied on handcrafted features and shallow machine learning and, despite its unprecedented performance gains, the problem remains open within the deep learning paradigm due to its data-hungry nature. Best performing deep vision-based object detectors are trained in a supervised manner by relying on human-labeled bounding boxes which localize class instances (i.e. objects) within the training images. Thus, object detection is one of such tasks for which human labeling is a major bottleneck. In this article, we assess co-training as a semi-supervised learning method for self-labeling objects in unlabeled images, so reducing the human-labeling effort for developing deep object detectors. Our study pays special attention to a scenario involving domain shift; in particular, when we have automatically generated virtual-world images with object bounding boxes and we have real-world images which are unlabeled. Moreover, we are particularly interested in using co-training for deep object detection in the context of driver assistance systems and/or self-driving vehicles. Thus, using well-established datasets and protocols for object detection in these application contexts, we will show how co-training is a paradigm worth to pursue for alleviating object labeling, working both alone and together with task-agnostic domain adaptation. | ||||
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Notes | ADAS; 600.118 | Approved | no | ||
Call Number | Admin @ si @ ViL2020 | Serial | 3488 | ||
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Author | Hannes Mueller; Andre Groger; Jonathan Hersh; Andrea Matranga; Joan Serrat | ||||
Title | Monitoring War Destruction from Space: A Machine Learning Approach | Type | Miscellaneous | ||
Year | 2020 | Publication | Arxiv | Abbreviated Journal | |
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Abstract | Existing data on building destruction in conflict zones rely on eyewitness reports or manual detection, which makes it generally scarce, incomplete and potentially biased. This lack of reliable data imposes severe limitations for media reporting, humanitarian relief efforts, human rights monitoring, reconstruction initiatives, and academic studies of violent conflict. This article introduces an automated method of measuring destruction in high-resolution satellite images using deep learning techniques combined with data augmentation to expand training samples. We apply this method to the Syrian civil war and reconstruct the evolution of damage in major cities across the country. The approach allows generating destruction data with unprecedented scope, resolution, and frequency – only limited by the available satellite imagery – which can alleviate data limitations decisively. | ||||
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Notes | ADAS; 600.118 | Approved | no | ||
Call Number | Admin @ si @ MGH2020 | Serial | 3489 | ||
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Author | Yi Xiao; Felipe Codevilla; Akhil Gurram; Onay Urfalioglu; Antonio Lopez | ||||
Title | Multimodal end-to-end autonomous driving | Type | Journal Article | ||
Year | 2020 | Publication | IEEE Transactions on Intelligent Transportation Systems | Abbreviated Journal | TITS |
Volume | Issue | Pages | 1-11 | ||
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Abstract | A crucial component of an autonomous vehicle (AV) is the artificial intelligence (AI) is able to drive towards a desired destination. Today, there are different paradigms addressing the development of AI drivers. On the one hand, we find modular pipelines, which divide the driving task into sub-tasks such as perception and maneuver planning and control. On the other hand, we find end-to-end driving approaches that try to learn a direct mapping from input raw sensor data to vehicle control signals. The later are relatively less studied, but are gaining popularity since they are less demanding in terms of sensor data annotation. This paper focuses on end-to-end autonomous driving. So far, most proposals relying on this paradigm assume RGB images as input sensor data. However, AVs will not be equipped only with cameras, but also with active sensors providing accurate depth information (e.g., LiDARs). Accordingly, this paper analyses whether combining RGB and depth modalities, i.e. using RGBD data, produces better end-to-end AI drivers than relying on a single modality. We consider multimodality based on early, mid and late fusion schemes, both in multisensory and single-sensor (monocular depth estimation) settings. Using the CARLA simulator and conditional imitation learning (CIL), we show how, indeed, early fusion multimodality outperforms single-modality. | ||||
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Notes | ADAS | Approved | no | ||
Call Number | Admin @ si @ XCG2020 | Serial | 3490 | ||
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Author | Andres Mafla; Sounak Dey; Ali Furkan Biten; Lluis Gomez; Dimosthenis Karatzas | ||||
Title | Multi-modal reasoning graph for scene-text based fine-grained image classification and retrieval | Type | Conference Article | ||
Year | 2021 | Publication | IEEE Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages | 4022-4032 | ||
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Address | Virtual; January 2021 | ||||
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Area | Expedition | Conference | WACV | ||
Notes | DAG; 600.121 | Approved | no | ||
Call Number | Admin @ si @ MDB2021 | Serial | 3491 | ||
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Author | Andres Mafla; Rafael S. Rezende; Lluis Gomez; Diana Larlus; Dimosthenis Karatzas | ||||
Title | StacMR: Scene-Text Aware Cross-Modal Retrieval | Type | Conference Article | ||
Year | 2021 | Publication | IEEE Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages | 2219-2229 | ||
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Address | Virtual; January 2021 | ||||
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Area | Expedition | Conference | WACV | ||
Notes | DAG; 600.121 | Approved | no | ||
Call Number | Admin @ si @ MRG2021a | Serial | 3492 | ||
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Author | Andres Mafla; Ruben Tito; Sounak Dey; Lluis Gomez; Marçal Rusiñol; Ernest Valveny; Dimosthenis Karatzas | ||||
Title | Real-time Lexicon-free Scene Text Retrieval | Type | Journal Article | ||
Year | 2021 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 110 | Issue | Pages | 107656 | |
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Abstract | In this work, we address the task of scene text retrieval: given a text query, the system returns all images containing the queried text. The proposed model uses a single shot CNN architecture that predicts bounding boxes and builds a compact representation of spotted words. In this way, this problem can be modeled as a nearest neighbor search of the textual representation of a query over the outputs of the CNN collected from the totality of an image database. Our experiments demonstrate that the proposed model outperforms previous state-of-the-art, while offering a significant increase in processing speed and unmatched expressiveness with samples never seen at training time. Several experiments to assess the generalization capability of the model are conducted in a multilingual dataset, as well as an application of real-time text spotting in videos. | ||||
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Notes | DAG; 600.121; 600.129; 601.338 | Approved | no | ||
Call Number | Admin @ si @ MTD2021 | Serial | 3493 | ||
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Author | Lluis Gomez; Anguelos Nicolaou; Marçal Rusiñol; Dimosthenis Karatzas | ||||
Title | 12 years of ICDAR Robust Reading Competitions: The evolution of reading systems for unconstrained text understanding | Type | Book Chapter | ||
Year | 2020 | Publication | Visual Text Interpretation – Algorithms and Applications in Scene Understanding and Document Analysis | Abbreviated Journal | |
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Publisher | Springer | Place of Publication | Editor | K. Alahari; C.V. Jawahar | |
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Series Editor | Series Title | Series on Advances in Computer Vision and Pattern Recognition | Abbreviated Series Title | ||
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Notes | DAG; 600.121 | Approved | no | ||
Call Number | GNR2020 | Serial | 3494 | ||
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Author | Lluis Gomez; Dena Bazazian; Dimosthenis Karatzas | ||||
Title | Historical review of scene text detection research | Type | Book Chapter | ||
Year | 2020 | Publication | Visual Text Interpretation – Algorithms and Applications in Scene Understanding and Document Analysis | Abbreviated Journal | |
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Publisher | Springer | Place of Publication | Editor | K. Alahari; C.V. Jawahar | |
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Series Editor | Series Title | Series on Advances in Computer Vision and Pattern Recognition | Abbreviated Series Title | ||
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Notes | DAG; 600.121 | Approved | no | ||
Call Number | Admin @ si @ GBK2020 | Serial | 3495 | ||
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