Diego Porres. (2021). Discriminator Synthesis: On reusing the other half of Generative Adversarial Networks. In Machine Learning for Creativity and Design, Neurips Workshop.
Abstract: Generative Adversarial Networks have long since revolutionized the world of computer vision and, tied to it, the world of art. Arduous efforts have gone into fully utilizing and stabilizing training so that outputs of the Generator network have the highest possible fidelity, but little has gone into using the Discriminator after training is complete. In this work, we propose to use the latter and show a way to use the features it has learned from the training dataset to both alter an image and generate one from scratch. We name this method Discriminator Dreaming, and the full code can be found at this https URL.
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Joan Codina-Filba, Sergio Escalera, Joan Escudero, Coen Antens, Pau Buch-Cardona, & Mireia Farrus. (2021). Mobile eHealth Platform for Home Monitoring of Bipolar Disorder. In 27th ACM International Conference on Multimedia Modeling (Vol. 12573, pp. 330–341). LNCS.
Abstract: People suffering Bipolar Disorder (BD) experiment changes in mood status having depressive or manic episodes with normal periods in the middle. BD is a chronic disease with a high level of non-adherence to medication that needs a continuous monitoring of patients to detect when they relapse in an episode, so that physicians can take care of them. Here we present MoodRecord, an easy-to-use, non-intrusive, multilingual, robust and scalable platform suitable for home monitoring patients with BD, that allows physicians and relatives to track the patient state and get alarms when abnormalities occur.
MoodRecord takes advantage of the capabilities of smartphones as a communication and recording device to do a continuous monitoring of patients. It automatically records user activity, and asks the user to answer some questions or to record himself in video, according to a predefined plan designed by physicians. The video is analysed, recognising the mood status from images and bipolar assessment scores are extracted from speech parameters. The data obtained from the different sources are merged periodically to observe if a relapse may start and if so, raise the corresponding alarm. The application got a positive evaluation in a pilot with users from three different countries. During the pilot, the predictions of the voice and image modules showed a coherent correlation with the diagnosis performed by clinicians.
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Henry Velesaca, Patricia Suarez, Dario Carpio, & Angel Sappa. (2021). Synthesized Image Datasets: Towards an Annotation-Free Instance Segmentation Strategy. In 16th International Symposium on Visual Computing (Vol. 13017, 131–143). LNCS.
Abstract: This paper presents a complete pipeline to perform deep learning-based instance segmentation of different types of grains (e.g., corn, sunflower, soybeans, lentils, chickpeas, mote, and beans). The proposed approach consists of using synthesized image datasets for the training process, which are easily generated according to the category of the instance to be segmented. The synthesized imaging process allows generating a large set of well-annotated grain samples with high variability—as large and high as the user requires. Instance segmentation is performed through a popular deep learning based approach, the Mask R-CNN architecture, but any learning-based instance segmentation approach can be considered. Results obtained by the proposed pipeline show that the strategy of using synthesized image datasets for training instance segmentation helps to avoid the time-consuming image annotation stage, as well as to achieve higher intersection over union and average precision performances. Results obtained with different varieties of grains are shown, as well as comparisons with manually annotated images, showing both the simplicity of the process and the improvements in the performance.
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Patricia Suarez, Dario Carpio, & Angel Sappa. (2021). Non-homogeneous Haze Removal Through a Multiple Attention Module Architecture. In 16th International Symposium on Visual Computing (Vol. 13018, 178–190). LNCS.
Abstract: This paper presents a novel attention based architecture to remove non-homogeneous haze. The proposed model is focused on obtaining the most representative characteristics of the image, at each learning cycle, by means of adaptive attention modules coupled with a residual learning convolutional network. The latter is based on the Res2Net model. The proposed architecture is trained with just a few set of images. Its performance is evaluated on a public benchmark—images from the non-homogeneous haze NTIRE 2021 challenge—and compared with state of the art approaches reaching the best result.
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Pau Torras, Arnau Baro, Lei Kang, & Alicia Fornes. (2021). On the Integration of Language Models into Sequence to Sequence Architectures for Handwritten Music Recognition. In International Society for Music Information Retrieval Conference (pp. 690–696).
Abstract: Despite the latest advances in Deep Learning, the recognition of handwritten music scores is still a challenging endeavour. Even though the recent Sequence to Sequence(Seq2Seq) architectures have demonstrated its capacity to reliably recognise handwritten text, their performance is still far from satisfactory when applied to historical handwritten scores. Indeed, the ambiguous nature of handwriting, the non-standard musical notation employed by composers of the time and the decaying state of old paper make these scores remarkably difficult to read, sometimes even by trained humans. Thus, in this work we explore the incorporation of language models into a Seq2Seq-based architecture to try to improve transcriptions where the aforementioned unclear writing produces statistically unsound mistakes, which as far as we know, has never been attempted for this field of research on this architecture. After studying various Language Model integration techniques, the experimental evaluation on historical handwritten music scores shows a significant improvement over the state of the art, showing that this is a promising research direction for dealing with such difficult manuscripts.
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Mohamed Ali Souibgui, Alicia Fornes, Y.Kessentini, & C.Tudor. (2021). A Few-shot Learning Approach for Historical Encoded Manuscript Recognition. In 25th International Conference on Pattern Recognition (pp. 5413–5420).
Abstract: Encoded (or ciphered) manuscripts are a special type of historical documents that contain encrypted text. The automatic recognition of this kind of documents is challenging because: 1) the cipher alphabet changes from one document to another, 2) there is a lack of annotated corpus for training and 3) touching symbols make the symbol segmentation difficult and complex. To overcome these difficulties, we propose a novel method for handwritten ciphers recognition based on few-shot object detection. Our method first detects all symbols of a given alphabet in a line image, and then a decoding step maps the symbol similarity scores to the final sequence of transcribed symbols. By training on synthetic data, we show that the proposed architecture is able to recognize handwritten ciphers with unseen alphabets. In addition, if few labeled pages with the same alphabet are used for fine tuning, our method surpasses existing unsupervised and supervised HTR methods for ciphers recognition.
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Alejandro Cartas, Petia Radeva, & Mariella Dimiccoli. (2021). Modeling long-term interactions to enhance action recognition. In 25th International Conference on Pattern Recognition (pp. 10351–10358).
Abstract: In this paper, we propose a new approach to under-stand actions in egocentric videos that exploits the semantics of object interactions at both frame and temporal levels. At the frame level, we use a region-based approach that takes as input a primary region roughly corresponding to the user hands and a set of secondary regions potentially corresponding to the interacting objects and calculates the action score through a CNN formulation. This information is then fed to a Hierarchical LongShort-Term Memory Network (HLSTM) that captures temporal dependencies between actions within and across shots. Ablation studies thoroughly validate the proposed approach, showing in particular that both levels of the HLSTM architecture contribute to performance improvement. Furthermore, quantitative comparisons show that the proposed approach outperforms the state-of-the-art in terms of action recognition on standard benchmarks,without relying on motion information
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Albin Soutif, Marc Masana, Joost Van de Weijer, & Bartlomiej Twardowski. (2021). On the importance of cross-task features for class-incremental learning. In Theory and Foundation of continual learning workshop of ICML.
Abstract: In class-incremental learning, an agent with limited resources needs to learn a sequence of classification tasks, forming an ever growing classification problem, with the constraint of not being able to access data from previous tasks. The main difference with task-incremental learning, where a task-ID is available at inference time, is that the learner also needs to perform crosstask discrimination, i.e. distinguish between classes that have not been seen together. Approaches to tackle this problem are numerous and mostly make use of an external memory (buffer) of non-negligible size. In this paper, we ablate the learning of crosstask features and study its influence on the performance of basic replay strategies used for class-IL. We also define a new forgetting measure for class-incremental learning, and see that forgetting is not the principal cause of low performance. Our experimental results show that future algorithms for class-incremental learning should not only prevent forgetting, but also aim to improve the quality of the cross-task features. This is especially important when the number of classes per task is small.
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Javier M. Olaso, Alain Vazquez, Leila Ben Letaifa, Mikel de Velasco, Aymen Mtibaa, Mohamed Amine Hmani, et al. (2021). The EMPATHIC Virtual Coach: a demo. In 23rd ACM International Conference on Multimodal Interaction (pp. 848–851).
Abstract: The main objective of the EMPATHIC project has been the design and development of a virtual coach to engage the healthy-senior user and to enhance well-being through awareness of personal status. The EMPATHIC approach addresses this objective through multimodal interactions supported by the GROW coaching model. The paper summarizes the main components of the EMPATHIC Virtual Coach (EMPATHIC-VC) and introduces a demonstration of the coaching sessions in selected scenarios.
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Patricia Suarez, Angel Sappa, Boris X. Vintimilla, & Riad I. Hammoud. (2021). Cycle Generative Adversarial Network: Towards A Low-Cost Vegetation Index Estimation. In 28th IEEE International Conference on Image Processing (pp. 19–22).
Abstract: This paper presents a novel unsupervised approach to estimate the Normalized Difference Vegetation Index (NDVI). The NDVI is obtained as the ratio between information from the visible and near infrared spectral bands; in the current work, the NDVI is estimated just from an image of the visible spectrum through a Cyclic Generative Adversarial Network (CyclicGAN). This unsupervised architecture learns to estimate the NDVI index by means of an image translation between the red channel of a given RGB image and the NDVI unpaired index’s image. The translation is obtained by means of a ResNET architecture and a multiple loss function. Experimental results obtained with this unsupervised scheme show the validity of the implemented model. Additionally, comparisons with the state of the art approaches are provided showing improvements with the proposed approach.
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Josep Llados, Daniel Lopresti, & Seiichi Uchida (Eds.). (2021). 16th International Conference, 2021, Proceedings, Part III (Vol. 12823). LNCS. Springer Cham.
Abstract: This four-volume set of LNCS 12821, LNCS 12822, LNCS 12823 and LNCS 12824, constitutes the refereed proceedings of the 16th International Conference on Document Analysis and Recognition, ICDAR 2021, held in Lausanne, Switzerland in September 2021. The 182 full papers were carefully reviewed and selected from 340 submissions, and are presented with 13 competition reports.
The papers are organized into the following topical sections: document analysis for literature search, document summarization and translation, multimedia document analysis, mobile text recognition, document analysis for social good, indexing and retrieval of documents, physical and logical layout analysis, recognition of tables and formulas, and natural language processing (NLP) for document understanding.
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Josep Llados, Daniel Lopresti, & Seiichi Uchida (Eds.). (2021). 16th International Conference, 2021, Proceedings, Part IV (Vol. 12824). LNCS. Springer Cham.
Abstract: This four-volume set of LNCS 12821, LNCS 12822, LNCS 12823 and LNCS 12824, constitutes the refereed proceedings of the 16th International Conference on Document Analysis and Recognition, ICDAR 2021, held in Lausanne, Switzerland in September 2021. The 182 full papers were carefully reviewed and selected from 340 submissions, and are presented with 13 competition reports.
The papers are organized into the following topical sections: document analysis for literature search, document summarization and translation, multimedia document analysis, mobile text recognition, document analysis for social good, indexing and retrieval of documents, physical and logical layout analysis, recognition of tables and formulas, and natural language processing (NLP) for document understanding.
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Adria Molina, Pau Riba, Lluis Gomez, Oriol Ramos Terrades, & Josep Llados. (2021). Date Estimation in the Wild of Scanned Historical Photos: An Image Retrieval Approach. In 16th International Conference on Document Analysis and Recognition (Vol. 12822, pp. 306–320). LNCS.
Abstract: This paper presents a novel method for date estimation of historical photographs from archival sources. The main contribution is to formulate the date estimation as a retrieval task, where given a query, the retrieved images are ranked in terms of the estimated date similarity. The closer are their embedded representations the closer are their dates. Contrary to the traditional models that design a neural network that learns a classifier or a regressor, we propose a learning objective based on the nDCG ranking metric. We have experimentally evaluated the performance of the method in two different tasks: date estimation and date-sensitive image retrieval, using the DEW public database, overcoming the baseline methods.
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Pau Riba, Adria Molina, Lluis Gomez, Oriol Ramos Terrades, & Josep Llados. (2021). Learning to Rank Words: Optimizing Ranking Metrics for Word Spotting. In 16th International Conference on Document Analysis and Recognition (Vol. 12822, 381–395).
Abstract: In this paper, we explore and evaluate the use of ranking-based objective functions for learning simultaneously a word string and a word image encoder. We consider retrieval frameworks in which the user expects a retrieval list ranked according to a defined relevance score. In the context of a word spotting problem, the relevance score has been set according to the string edit distance from the query string. We experimentally demonstrate the competitive performance of the proposed model on query-by-string word spotting for both, handwritten and real scene word images. We also provide the results for query-by-example word spotting, although it is not the main focus of this work.
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Josep Llados, Daniel Lopresti, & Seiichi Uchida (Eds.). (2021). 16th International Conference, 2021, Proceedings, Part I (Vol. 12821). LNCS. Springer Cham.
Abstract: This four-volume set of LNCS 12821, LNCS 12822, LNCS 12823 and LNCS 12824, constitutes the refereed proceedings of the 16th International Conference on Document Analysis and Recognition, ICDAR 2021, held in Lausanne, Switzerland in September 2021. The 182 full papers were carefully reviewed and selected from 340 submissions, and are presented with 13 competition reports.
The papers are organized into the following topical sections: historical document analysis, document analysis systems, handwriting recognition, scene text detection and recognition, document image processing, natural language processing (NLP) for document understanding, and graphics, diagram and math recognition.
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