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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 |
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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Year |
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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Ali Furkan Biten; Lluis Gomez; Dimosthenis Karatzas |
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Title |
Let there be a clock on the beach: Reducing Object Hallucination in Image Captioning |
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Conference Article |
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Year |
2022 |
Publication |
Winter Conference on Applications of Computer Vision |
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1381-1390 |
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Measurement; Training; Visualization; Analytical models; Computer vision; Computational modeling; Training data |
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Explaining an image with missing or non-existent objects is known as object bias (hallucination) in image captioning. This behaviour is quite common in the state-of-the-art captioning models which is not desirable by humans. To decrease the object hallucination in captioning, we propose three simple yet efficient training augmentation method for sentences which requires no new training data or increase
in the model size. By extensive analysis, we show that the proposed methods can significantly diminish our models’ object bias on hallucination metrics. Moreover, we experimentally demonstrate that our methods decrease the dependency on the visual features. All of our code, configuration files and model weights are available online. |
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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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no |
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Admin @ si @ BGK2022 |
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3662 |
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Author |
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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Conference Article |
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Year |
2019 |
Publication |
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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Call Number |
Admin @ si @ BGR2019 |
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3289 |
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Author |
Ali Furkan Biten; R. Tito; Andres Mafla; Lluis Gomez; Marçal Rusiñol; C.V. Jawahar; Ernest Valveny; Dimosthenis Karatzas |
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Title |
Scene Text Visual Question Answering |
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Conference Article |
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Year |
2019 |
Publication |
18th IEEE International Conference on Computer Vision |
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4291-4301 |
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Current visual question answering datasets do not consider the rich semantic information conveyed by text within an image. In this work, we present a new dataset, ST-VQA, that aims to highlight the importance of exploiting highlevel semantic information present in images as textual cues in the Visual Question Answering process. We use this dataset to define a series of tasks of increasing difficulty for which reading the scene text in the context provided by the visual information is necessary to reason and generate an appropriate answer. We propose a new evaluation metric for these tasks to account both for reasoning errors as well as shortcomings of the text recognition module. In addition we put forward a series of baseline methods, which provide further insight to the newly released dataset, and set the scene for further research. |
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Seul; Corea; October 2019 |
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ICCV |
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DAG; 600.129; 600.135; 601.338; 600.121 |
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Call Number |
Admin @ si @ BTM2019b |
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3285 |
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Ali Furkan Biten; R. Tito; Andres Mafla; Lluis Gomez; Marçal Rusiñol; M. Mathew; C.V. Jawahar; Ernest Valveny; Dimosthenis Karatzas |
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Title |
ICDAR 2019 Competition on Scene Text Visual Question Answering |
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Conference Article |
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2019 |
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15th International Conference on Document Analysis and Recognition |
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1563-1570 |
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This paper presents final results of ICDAR 2019 Scene Text Visual Question Answering competition (ST-VQA). ST-VQA introduces an important aspect that is not addressed by any Visual Question Answering system up to date, namely the incorporation of scene text to answer questions asked about an image. The competition introduces a new dataset comprising 23,038 images annotated with 31,791 question / answer pairs where the answer is always grounded on text instances present in the image. The images are taken from 7 different public computer vision datasets, covering a wide range of scenarios. The competition was structured in three tasks of increasing difficulty, that require reading the text in a scene and understanding it in the context of the scene, to correctly answer a given question. A novel evaluation metric is presented, which elegantly assesses both key capabilities expected from an optimal model: text recognition and image understanding. A detailed analysis of results from different participants is showcased, which provides insight into the current capabilities of VQA systems that can read. We firmly believe the dataset proposed in this challenge will be an important milestone to consider towards a path of more robust and general models that can exploit scene text to achieve holistic image understanding. |
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Sydney; Australia; September 2019 |
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ICDAR |
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DAG; 600.129; 601.338; 600.121 |
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Admin @ si @ BTM2019c |
Serial |
3286 |
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Ali Furkan Biten; R. Tito; Andres Mafla; Lluis Gomez; Marçal Rusiñol; M. Mathew; C.V. Jawahar; Ernest Valveny; Dimosthenis Karatzas |
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Title |
ICDAR 2019 Competition on Scene Text Visual Question Answering |
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Conference Article |
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2019 |
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3rd Workshop on Closing the Loop Between Vision and Language, in conjunction with ICCV2019 |
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This paper presents final results of ICDAR 2019 Scene Text Visual Question Answering competition (ST-VQA). ST-VQA introduces an important aspect that is not addressed
by any Visual Question Answering system up to date, namely the incorporation of scene text to answer questions asked about an image. The competition introduces a new dataset comprising 23, 038 images annotated with 31, 791 question / answer pairs where the answer is always grounded on text instances present in the image. The images are taken from 7 different public computer vision datasets, covering a wide range of scenarios.
The competition was structured in three tasks of increasing difficulty, that require reading the text in a scene and understanding it in the context of the scene, to correctly answer a given question. A novel evaluation metric is presented, which elegantly assesses both key capabilities expected from an optimal model: text recognition and image understanding. A detailed analysis of results from different participants is showcased, which provides insight into the current capabilities of VQA systems that can read. We firmly believe the dataset proposed in this challenge will be an important milestone to consider towards a path of more robust and general models that
can exploit scene text to achieve holistic image understanding. |
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Sydney; Australia; September 2019 |
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CLVL |
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Notes |
DAG; 600.129; 601.338; 600.135; 600.121 |
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Admin @ si @ BTM2019a |
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3284 |
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Author |
Ali Furkan Biten; Ruben Tito; Lluis Gomez; Ernest Valveny; Dimosthenis Karatzas |
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Title |
OCR-IDL: OCR Annotations for Industry Document Library Dataset |
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Conference Article |
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2022 |
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ECCV Workshop on Text in Everything |
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Pretraining has proven successful in Document Intelligence tasks where deluge of documents are used to pretrain the models only later to be finetuned on downstream tasks. One of the problems of the pretraining approaches is the inconsistent usage of pretraining data with different OCR engines leading to incomparable results between models. In other words, it is not obvious whether the performance gain is coming from diverse usage of amount of data and distinct OCR engines or from the proposed models. To remedy the problem, we make public the OCR annotations for IDL documents using commercial OCR engine given their superior performance over open source OCR models. The contributed dataset (OCR-IDL) has an estimated monetary value over 20K US$. It is our hope that OCR-IDL can be a starting point for future works on Document Intelligence. All of our data and its collection process with the annotations can be found in this https URL. |
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ECCV |
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DAG; no proj |
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Admin @ si @ BTG2022 |
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3817 |
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Author |
Alicia Fornes |
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Title |
Writer Identification by a Combination of Graphical Features in the Framework of Old Handwritten Music Scores |
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2009 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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The analysis and recognition of historical document images has attracted growing interest in the last years. Mass digitization and document image understanding allows the preservation, access and indexation of this artistic, cultural and technical heritage. The analysis of handwritten documents is an outstanding subfield. The main interest is not only the transcription of the document to a standard format, but also, the identification of the author of a document from a set of writers (namely writer identification).
Writer identification in handwritten text documents is an active area of study, however, the identification of the writer of graphical documents is still a challenge. The main objective of this thesis is the identification of the writer in old music scores, as an example of graphic documents. Concerning old music scores, many historical archives contain a huge number of sheets of musical compositions without information about the composer, and the research on this field could be helpful for musicologists.
The writer identification framework proposed in this thesis combines three different writer identification approaches, which are the main scientific contributions. The first one is based on symbol recognition methods. For this purpose, two novel symbol recognition methods are proposed for coping with the typical distortions in hand-drawn symbols. The second approach preprocesses the music score for obtaining music lines, and extracts information about the slant, width of the writing, connected components, contours and fractals. Finally, the third approach extracts global information by generating texture images from the music scores and extracting textural features (such as Gabor filters and co-occurence matrices).
The high identification rates obtained in the experimental results demonstrate the suitability of the proposed ensemble architecture. To the best of our knowledge, this work is the first contribution on writer identification from images containing graphical languages. |
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Barcelona (Spain) |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Josep Llados;Gemma Sanchez |
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DAG @ dag @ For2009 |
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1265 |
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Alicia Fornes |
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Analysis of Old Handwritten Musical Scores |
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2005 |
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CVC Technical Report #88 |
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CVC (UAB) |
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DAG @ dag @ For2005 |
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575 |
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Alicia Fornes; Anjan Dutta; Albert Gordo; Josep Llados |
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CVC-MUSCIMA: A Ground-Truth of Handwritten Music Score Images for Writer Identification and Staff Removal |
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2012 |
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International Journal on Document Analysis and Recognition |
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IJDAR |
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15 |
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3 |
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243-251 |
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Music scores; Handwritten documents; Writer identification; Staff removal; Performance evaluation; Graphics recognition; Ground truths |
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0,405JCR
The analysis of music scores has been an active research field in the last decades. However, there are no publicly available databases of handwritten music scores for the research community. In this paper we present the CVC-MUSCIMA database and ground-truth of handwritten music score images. The dataset consists of 1,000 music sheets written by 50 different musicians. It has been especially designed for writer identification and staff removal tasks. In addition to the description of the dataset, ground-truth, partitioning and evaluation metrics, we also provide some base-line results for easing the comparison between different approaches. |
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1433-2833 |
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DAG |
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Admin @ si @ FDG2012 |
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2129 |
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Alicia Fornes; Anjan Dutta; Albert Gordo; Josep Llados |
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The ICDAR 2011 Music Scores Competition: Staff Removal and Writer Identification |
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2011 |
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11th International Conference on Document Analysis and Recognition |
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1511-1515 |
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In the last years, there has been a growing interest in the analysis of handwritten music scores. In this sense, our goal has been to foster the interest in the analysis of handwritten music scores by the proposal of two different competitions: Staff removal and Writer Identification. Both competitions have been tested on the CVC-MUSCIMA database: a ground-truth of handwritten music score images. This paper describes the competition details, including the dataset and ground-truth, the evaluation metrics, and a short description of the participants, their methods, and the obtained results. |
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Beijing, China |
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978-0-7695-4520-2 |
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ICDAR |
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DAG |
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Admin @ si @ FDG2011b |
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1794 |
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Author |
Alicia Fornes; Asma Bensalah; Cristina Carmona_Duarte; Jialuo Chen; Miguel A. Ferrer; Andreas Fischer; Josep Llados; Cristina Martin; Eloy Opisso; Rejean Plamondon; Anna Scius-Bertrand; Josep Maria Tormos |
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Title |
The RPM3D Project: 3D Kinematics for Remote Patient Monitoring |
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Conference Article |
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2022 |
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Intertwining Graphonomics with Human Movements. 20th International Conference of the International Graphonomics Society, IGS 2022 |
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13424 |
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217-226 |
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Healthcare applications; Kinematic; Theory of Rapid Human Movements; Human activity recognition; Stroke rehabilitation; 3D kinematics |
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This project explores the feasibility of remote patient monitoring based on the analysis of 3D movements captured with smartwatches. We base our analysis on the Kinematic Theory of Rapid Human Movement. We have validated our research in a real case scenario for stroke rehabilitation at the Guttmann Institute (https://www.guttmann.com/en/) (neurorehabilitation hospital), showing promising results. Our work could have a great impact in remote healthcare applications, improving the medical efficiency and reducing the healthcare costs. Future steps include more clinical validation, developing multi-modal analysis architectures (analysing data from sensors, images, audio, etc.), and exploring the application of our technology to monitor other neurodegenerative diseases. |
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June 7-9, 2022, Las Palmas de Gran Canaria, Spain |
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DAG; 600.121; 600.162; 602.230; 600.140 |
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Admin @ si @ FBC2022 |
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3739 |
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Author |
Alicia Fornes; Bart Lamiroy |
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Title |
Graphics Recognition, Current Trends and Evolutions |
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2018 |
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Graphics Recognition, Current Trends and Evolutions |
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11009 |
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This book constitutes the thoroughly refereed post-conference proceedings of the 12th International Workshop on Graphics Recognition, GREC 2017, held in Kyoto, Japan, in November 2017.
The 10 revised full papers presented were carefully reviewed and selected from 14 initial submissions. They contain both classical and emerging topics of graphics rcognition, namely analysis and detection of diagrams, search and classification, optical music recognition, interpretation of engineering drawings and maps. |
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Springer International Publishing |
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978-3-030-02283-9 |
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DAG; 600.121 |
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Admin @ si @ FoL2018 |
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3171 |
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Author |
Alicia Fornes; Beata Megyesi; Joan Mas |
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Title |
Transcription of Encoded Manuscripts with Image Processing Techniques |
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Conference Article |
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2017 |
Publication |
Digital Humanities Conference |
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441-443 |
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DAG; 600.097; 600.121 |
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Admin @ si @ FMM2017 |
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3061 |
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