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Author |
Raul Gomez |
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Title |
Exploiting the Interplay between Visual and Textual Data for Scene Interpretation |
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Year |
2020 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Machine learning experimentation under controlled scenarios and standard datasets is necessary to compare algorithms performance by evaluating all of them in the same setup. However, experimentation on how those algorithms perform on unconstrained data and applied tasks to solve real world problems is also a must to ascertain how that research can contribute to our society.
In this dissertation we experiment with the latest computer vision and natural language processing algorithms applying them to multimodal scene interpretation. Particularly, we research on how image and text understanding can be jointly exploited to address real world problems, focusing on learning from Social Media data.
We address several tasks that involve image and textual information, discuss their characteristics and offer our experimentation conclusions. First, we work on detection of scene text in images. Then, we work with Social Media posts, exploiting the captions associated to images as supervision to learn visual features, which we apply to multimodal semantic image retrieval. Subsequently, we work with geolocated Social Media images with associated tags, experimenting on how to use the tags as supervision, on location sensitive image retrieval and on exploiting location information for image tagging. Finally, we work on a specific classification problem of Social Media publications consisting on an image and a text: Multimodal hate speech classification. |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Dimosthenis Karatzas;Lluis Gomez;Jaume Gibert |
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978-84-121011-7-1 |
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DAG; 600.121 |
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no |
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Call Number |
Admin @ si @ Gom20 |
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3479 |
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Author |
Sounak Dey |
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Title |
Mapping between Images and Conceptual Spaces: Sketch-based Image Retrieval |
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2020 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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This thesis presents several contributions to the literature of sketch based image retrieval (SBIR). In SBIR the first challenge we face is how to map two different domains to common space for effective retrieval of images, while tackling the different levels of abstraction people use to express their notion of objects around while sketching. To this extent we first propose a cross-modal learning framework that maps both sketches and text into a joint embedding space invariant to depictive style, while preserving semantics. Then we have also investigated different query types possible to encompass people's dilema in sketching certain world objects. For this we propose an approach for multi-modal image retrieval in multi-labelled images. A multi-modal deep network architecture is formulated to jointly model sketches and text as input query modalities into a common embedding space, which is then further aligned with the image feature space. This permits encoding the object-based features and its alignment with the query irrespective of the availability of the co-occurrence of different objects in the training set.
Finally, we explore the problem of zero-shot sketch-based image retrieval (ZS-SBIR), where human sketches are used as queries to conduct retrieval of photos from unseen categories. We importantly advance prior arts by proposing a novel ZS-SBIR scenario that represents a firm step forward in its practical application. The new setting uniquely recognises two important yet often neglected challenges of practical ZS-SBIR, (i) the large domain gap between amateur sketch and photo, and (ii) the necessity for moving towards large-scale retrieval. We first contribute to the community a novel ZS-SBIR dataset, QuickDraw-Extended. We also in this dissertation pave the path to the future direction of research in this domain. |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Editor |
Josep Llados;Umapada Pal |
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978-84-121011-8-8 |
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DAG; 600.121 |
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Admin @ si @ Dey20 |
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3480 |
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Author |
Lei Kang |
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Title |
Robust Handwritten Text Recognition in Scarce Labeling Scenarios: Disentanglement, Adaptation and Generation |
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Year |
2020 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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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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Ph.D. thesis |
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Publisher |
Ediciones Graficas Rey |
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Editor |
Alicia Fornes;Marçal Rusiñol;Mauricio Villegas |
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978-84-122714-0-9 |
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DAG; 600.121 |
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Call Number |
Admin @ si @ Kan20 |
Serial |
3482 |
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Author |
Manuel Carbonell |
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Title |
Neural Information Extraction from Semi-structured Documents A |
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Year |
2020 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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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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Thesis |
Ph.D. thesis |
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Publisher |
Ediciones Graficas Rey |
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Editor |
Alicia Fornes;Mauricio Villegas;Josep Llados |
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978-84-122714-1-6 |
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DAG; 600.121 |
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Call Number |
Admin @ si @ Car20 |
Serial |
3483 |
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Author |
Andres Mafla; Sounak Dey; Ali Furkan Biten; Lluis Gomez; Dimosthenis Karatzas |
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Title |
Multi-modal reasoning graph for scene-text based fine-grained image classification and retrieval |
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Conference Article |
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Year |
2021 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
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4022-4032 |
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Virtual; January 2021 |
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WACV |
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DAG; 600.121 |
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Admin @ si @ MDB2021 |
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3491 |
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Author |
Andres Mafla; Rafael S. Rezende; Lluis Gomez; Diana Larlus; Dimosthenis Karatzas |
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Title |
StacMR: Scene-Text Aware Cross-Modal Retrieval |
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Conference Article |
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Year |
2021 |
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IEEE Winter Conference on Applications of Computer Vision |
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2219-2229 |
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Virtual; January 2021 |
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WACV |
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DAG; 600.121 |
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Admin @ si @ MRG2021a |
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3492 |
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Author |
Lluis Gomez; Anguelos Nicolaou; Marçal Rusiñol; Dimosthenis Karatzas |
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Title |
12 years of ICDAR Robust Reading Competitions: The evolution of reading systems for unconstrained text understanding |
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2020 |
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Visual Text Interpretation – Algorithms and Applications in Scene Understanding and Document Analysis |
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Springer |
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K. Alahari; C.V. Jawahar |
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Series on Advances in Computer Vision and Pattern Recognition |
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DAG; 600.121 |
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Call Number |
GNR2020 |
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3494 |
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Author |
Lluis Gomez; Dena Bazazian; Dimosthenis Karatzas |
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Title |
Historical review of scene text detection research |
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2020 |
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Visual Text Interpretation – Algorithms and Applications in Scene Understanding and Document Analysis |
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Springer |
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K. Alahari; C.V. Jawahar |
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Series on Advances in Computer Vision and Pattern Recognition |
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DAG; 600.121 |
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Call Number |
Admin @ si @ GBK2020 |
Serial |
3495 |
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Author |
Jon Almazan; Lluis Gomez; Suman Ghosh; Ernest Valveny; Dimosthenis Karatzas |
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Title |
WATTS: A common representation of word images and strings using embedded attributes for text recognition and retrieval |
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2020 |
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Visual Text Interpretation – Algorithms and Applications in Scene Understanding and Document Analysis |
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Springer |
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Analysis”, K. Alahari; C.V. Jawahar |
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Series on Advances in Computer Vision and Pattern Recognition |
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DAG; 600.121 |
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Admin @ si @ AGG2020 |
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3496 |
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Author |
Raul Gomez; Yahui Liu; Marco de Nadai; Dimosthenis Karatzas; Bruno Lepri; Nicu Sebe |
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Title |
Retrieval Guided Unsupervised Multi-domain Image to Image Translation |
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Conference Article |
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2020 |
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28th ACM International Conference on Multimedia |
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Image to image translation aims to learn a mapping that transforms an image from one visual domain to another. Recent works assume that images descriptors can be disentangled into a domain-invariant content representation and a domain-specific style representation. Thus, translation models seek to preserve the content of source images while changing the style to a target visual domain. However, synthesizing new images is extremely challenging especially in multi-domain translations, as the network has to compose content and style to generate reliable and diverse images in multiple domains. In this paper we propose the use of an image retrieval system to assist the image-to-image translation task. First, we train an image-to-image translation model to map images to multiple domains. Then, we train an image retrieval model using real and generated images to find images similar to a query one in content but in a different domain. Finally, we exploit the image retrieval system to fine-tune the image-to-image translation model and generate higher quality images. Our experiments show the effectiveness of the proposed solution and highlight the contribution of the retrieval network, which can benefit from additional unlabeled data and help image-to-image translation models in the presence of scarce data. |
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ACM |
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DAG; 600.121 |
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Call Number |
Admin @ si @ GLN2020 |
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3497 |
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