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Author |
Dena Bazazian; Raul Gomez; Anguelos Nicolaou; Lluis Gomez; Dimosthenis Karatzas; Andrew Bagdanov |
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Title |
Fast: Facilitated and accurate scene text proposals through fcn guided pruning |
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Journal Article |
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2019 |
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Pattern Recognition Letters |
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PRL |
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119 |
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112-120 |
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Class-specific text proposal algorithms can efficiently reduce the search space for possible text object locations in an image. In this paper we combine the Text Proposals algorithm with Fully Convolutional Networks to efficiently reduce the number of proposals while maintaining the same recall level and thus gaining a significant speed up. Our experiments demonstrate that such text proposal approaches yield significantly higher recall rates than state-of-the-art text localization techniques, while also producing better-quality localizations. Our results on the ICDAR 2015 Robust Reading Competition (Challenge 4) and the COCO-text datasets show that, when combined with strong word classifiers, this recall margin leads to state-of-the-art results in end-to-end scene text recognition. |
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DAG; 600.084; 600.121; 600.129 |
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Admin @ si @ BGN2019 |
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3342 |
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Author |
R. Bertrand; P. Gomez-Krämer; Oriol Ramos Terrades; P. Franco; Jean-Marc Ogier |
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Title |
A System Based On Intrinsic Features for Fraudulent Document Detection |
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2013 |
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12th International Conference on Document Analysis and Recognition |
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106-110 |
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paper document; document analysis; fraudulent document; forgery; fake |
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Paper documents still represent a large amount of information supports used nowadays and may contain critical data. Even though official documents are secured with techniques such as printed patterns or artwork, paper documents suffer froma lack of security.
However, the high availability of cheap scanning and printing hardware allows non-experts to easily create fake documents. As the use of a watermarking system added during the document production step is hardly possible, solutions have to be proposed to distinguish a genuine document from a forged one.
In this paper, we present an automatic forgery detection method based on document’s intrinsic features at character level. This method is based on the one hand on outlier character detection in a discriminant feature space and on the other hand on the detection of strictly similar characters. Therefore, a feature set iscomputed for all characters. Then, based on a distance between characters of the same class. |
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Washington; USA; August 2013 |
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1520-5363 |
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ICDAR |
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DAG; 600.061 |
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Admin @ si @ BGR2013a |
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2332 |
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Ali Furkan Biten; Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas |
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Title |
Good News, Everyone! Context driven entity-aware captioning for news images |
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2019 |
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32nd IEEE Conference on Computer Vision and Pattern Recognition |
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12458-12467 |
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Current image captioning systems perform at a merely descriptive level, essentially enumerating the objects in the scene and their relations. Humans, on the contrary, interpret images by integrating several sources of prior knowledge of the world. In this work, we aim to take a step closer to producing captions that offer a plausible interpretation of the scene, by integrating such contextual information into the captioning pipeline. For this we focus on the captioning of images used to illustrate news articles. We propose a novel captioning method that is able to leverage contextual information provided by the text of news articles associated with an image. Our model is able to selectively draw information from the article guided by visual cues, and to dynamically extend the output dictionary to out-of-vocabulary named entities that appear in the context source. Furthermore we introduce“ GoodNews”, the largest news image captioning dataset in the literature and demonstrate state-of-the-art results. |
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Long beach; California; USA; june 2019 |
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CVPR |
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DAG; 600.129; 600.135; 601.338; 600.121 |
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Admin @ si @ BGR2019 |
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3289 |
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Author |
Ali Furkan Biten |
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Title |
A Bitter-Sweet Symphony on Vision and Language: Bias and World Knowledge |
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2022 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Vision and Language are broadly regarded as cornerstones of intelligence. Even though language and vision have different aims – language having the purpose of communication, transmission of information and vision having the purpose of constructing mental representations around us to navigate and interact with objects – they cooperate and depend on one another in many tasks we perform effortlessly. This reliance is actively being studied in various Computer Vision tasks, e.g. image captioning, visual question answering, image-sentence retrieval, phrase grounding, just to name a few. All of these tasks share the inherent difficulty of the aligning the two modalities, while being robust to language
priors and various biases existing in the datasets. One of the ultimate goal for vision and language research is to be able to inject world knowledge while getting rid of the biases that come with the datasets. In this thesis, we mainly focus on two vision and language tasks, namely Image Captioning and Scene-Text Visual Question Answering (STVQA).
In both domains, we start by defining a new task that requires the utilization of world knowledge and in both tasks, we find that the models commonly employed are prone to biases that exist in the data. Concretely, we introduce new tasks and discover several problems that impede performance at each level and provide remedies or possible solutions in each chapter: i) We define a new task to move beyond Image Captioning to Image Interpretation that can utilize Named Entities in the form of world knowledge. ii) We study the object hallucination problem in classic Image Captioning systems and develop an architecture-agnostic solution. iii) We define a sub-task of Visual Question Answering that requires reading the text in the image (STVQA), where we highlight the limitations of current models. iv) We propose an architecture for the STVQA task that can point to the answer in the image and show how to combine it with classic VQA models. v) We show how far language can get us in STVQA and discover yet another bias which causes the models to disregard the image while doing Visual Question Answering. |
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Ph.D. thesis |
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IMPRIMA |
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Dimosthenis Karatzas;Lluis Gomez |
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978-84-124793-5-5 |
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DAG |
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no |
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Admin @ si @ Bit2022 |
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3755 |
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Author |
Dena Bazazian; Dimosthenis Karatzas; Andrew Bagdanov |
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Title |
Soft-PHOC Descriptor for End-to-End Word Spotting in Egocentric Scene Images |
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Conference Article |
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2018 |
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International Workshop on Egocentric Perception, Interaction and Computing at ECCV |
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Word spotting in natural scene images has many applications in scene understanding and visual assistance. We propose Soft-PHOC, an intermediate representation of images based on character probability maps. Our representation extends the concept of the Pyramidal Histogram Of Characters (PHOC) by exploiting Fully Convolutional Networks to derive a pixel-wise mapping of the character distribution within candidate word regions. We show how to use our descriptors for word spotting tasks in egocentric camera streams through an efficient text line proposal algorithm. This is based on the Hough Transform over character attribute maps followed by scoring using Dynamic Time Warping (DTW). We evaluate our results on ICDAR 2015 Challenge 4 dataset of incidental scene text captured by an egocentric camera. |
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Munich; Alemanya; September 2018 |
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ECCVW |
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DAG; 600.129; 600.121; |
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no |
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Admin @ si @ BKB2018b |
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3174 |
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Author |
Souhail Bakkali; Zuheng Ming; Mickael Coustaty; Marçal Rusiñol; Oriol Ramos Terrades |
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Title |
VLCDoC: Vision-Language Contrastive Pre-Training Model for Cross-Modal Document Classification |
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Journal Article |
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Year |
2023 |
Publication |
Pattern Recognition |
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PR |
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139 |
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109419 |
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Multimodal learning from document data has achieved great success lately as it allows to pre-train semantically meaningful features as a prior into a learnable downstream approach. In this paper, we approach the document classification problem by learning cross-modal representations through language and vision cues, considering intra- and inter-modality relationships. Instead of merging features from different modalities into a common representation space, the proposed method exploits high-level interactions and learns relevant semantic information from effective attention flows within and across modalities. The proposed learning objective is devised between intra- and inter-modality alignment tasks, where the similarity distribution per task is computed by contracting positive sample pairs while simultaneously contrasting negative ones in the common feature representation space}. Extensive experiments on public document classification datasets demonstrate the effectiveness and the generalization capacity of our model on both low-scale and large-scale datasets. |
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ISSN 0031-3203 |
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DAG; 600.140; 600.121 |
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no |
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Admin @ si @ BMC2023 |
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3826 |
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Author |
Ali Furkan Biten; Andres Mafla; Lluis Gomez; Dimosthenis Karatzas |
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Title |
Is An Image Worth Five Sentences? A New Look into Semantics for Image-Text Matching |
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Conference Article |
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2022 |
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Winter Conference on Applications of Computer Vision |
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1391-1400 |
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Measurement; Training; Integrated circuits; Annotations; Semantics; Training data; Semisupervised learning |
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The task of image-text matching aims to map representations from different modalities into a common joint visual-textual embedding. However, the most widely used datasets for this task, MSCOCO and Flickr30K, are actually image captioning datasets that offer a very limited set of relationships between images and sentences in their ground-truth annotations. This limited ground truth information forces us to use evaluation metrics based on binary relevance: given a sentence query we consider only one image as relevant. However, many other relevant images or captions may be present in the dataset. In this work, we propose two metrics that evaluate the degree of semantic relevance of retrieved items, independently of their annotated binary relevance. Additionally, we incorporate a novel strategy that uses an image captioning metric, CIDEr, to define a Semantic Adaptive Margin (SAM) to be optimized in a standard triplet loss. By incorporating our formulation to existing models, a large improvement is obtained in scenarios where available training data is limited. We also demonstrate that the performance on the annotated image-caption pairs is maintained while improving on other non-annotated relevant items when employing the full training set. The code for our new metric can be found at github. com/furkanbiten/ncsmetric and the model implementation at github. com/andrespmd/semanticadaptive_margin. |
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Virtual; Waikoloa; Hawai; USA; January 2022 |
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WACV |
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DAG; 600.155; 302.105; |
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Admin @ si @ BMG2022 |
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3663 |
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Author |
Carlos Boned Riera; Oriol Ramos Terrades |
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Title |
Discriminative Neural Variational Model for Unbalanced Classification Tasks in Knowledge Graph |
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2022 |
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26th International Conference on Pattern Recognition |
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2186-2191 |
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Measurement; Couplings; Semantics; Ear; Benchmark testing; Data models; Pattern recognition |
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Nowadays the paradigm of link discovery problems has shown significant improvements on Knowledge Graphs. However, method performances are harmed by the unbalanced nature of this classification problem, since many methods are easily biased to not find proper links. In this paper we present a discriminative neural variational auto-encoder model, called DNVAE from now on, in which we have introduced latent variables to serve as embedding vectors. As a result, the learnt generative model approximate better the underlying distribution and, at the same time, it better differentiate the type of relations in the knowledge graph. We have evaluated this approach on benchmark knowledge graph and Census records. Results in this last data set are quite impressive since we reach the highest possible score in the evaluation metrics. However, further experiments are still needed to deeper evaluate the performance of the method in more challenging tasks. |
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Montreal; Quebec; Canada; August 2022 |
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ICPR |
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DAG; 600.121; 600.162 |
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Admin @ si @ BoR2022 |
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3741 |
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Asma Bensalah; Antonio Parziale; Giuseppe De Gregorio; Angelo Marcelli; Alicia Fornes; Josep Llados |
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Title |
I Can’t Believe It’s Not Better: In-air Movement for Alzheimer Handwriting Synthetic Generation |
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Conference Article |
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2023 |
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21st International Graphonomics Conference |
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136–148 |
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During recent years, there here has been a boom in terms of deep learning use for handwriting analysis and recognition. One main application for handwriting analysis is early detection and diagnosis in the health field. Unfortunately, most real case problems still suffer a scarcity of data, which makes difficult the use of deep learning-based models. To alleviate this problem, some works resort to synthetic data generation. Lately, more works are directed towards guided data synthetic generation, a generation that uses the domain and data knowledge to generate realistic data that can be useful to train deep learning models. In this work, we combine the domain knowledge about the Alzheimer’s disease for handwriting and use it for a more guided data generation. Concretely, we have explored the use of in-air movements for synthetic data generation. |
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Evora; Portugal; October 2023 |
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IGS |
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DAG |
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Admin @ si @ BPG2023 |
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3838 |
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Q. Bao; Marçal Rusiñol; M.Coustaty; Muhammad Muzzamil Luqman; C.D. Tran; Jean-Marc Ogier |
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Delaunay triangulation-based features for Camera-based document image retrieval system |
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2016 |
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12th IAPR Workshop on Document Analysis Systems |
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1-6 |
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Camera-based Document Image Retrieval; Delaunay Triangulation; Feature descriptors; Indexing |
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In this paper, we propose a new feature vector, named DElaunay TRIangulation-based Features (DETRIF), for real-time camera-based document image retrieval. DETRIF is computed based on the geometrical constraints from each pair of adjacency triangles in delaunay triangulation which is constructed from centroids of connected components. Besides, we employ a hashing-based indexing system in order to evaluate the performance of DETRIF and to compare it with other systems such as LLAH and SRIF. The experimentation is carried out on two datasets comprising of 400 heterogeneous-content complex linguistic map images (huge size, 9800 X 11768 pixels resolution)and 700 textual document images. |
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Santorini; Greece; April 2016 |
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DAS |
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DAG; 600.061; 600.084; 600.077 |
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Admin @ si @ BRC2016 |
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2757 |
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