| Records |
| Author |
Andreea Glavan; Alina Matei; Petia Radeva; Estefania Talavera |
Title  |
Does our social life influence our nutritional behaviour? Understanding nutritional habits from egocentric photo-streams |
Type |
Journal Article |
| Year |
2021 |
Publication |
Expert Systems with Applications |
Abbreviated Journal |
ESWA |
| Volume |
171 |
Issue |
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Pages |
114506 |
| Keywords |
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| Abstract |
Nutrition and social interactions are both key aspects of the daily lives of humans. In this work, we propose a system to evaluate the influence of social interaction in the nutritional habits of a person from a first-person perspective. In order to detect the routine of an individual, we construct a nutritional behaviour pattern discovery model, which outputs routines over a number of days. Our method evaluates similarity of routines with respect to visited food-related scenes over the collected days, making use of Dynamic Time Warping, as well as considering social engagement and its correlation with food-related activities. The nutritional and social descriptors of the collected days are evaluated and encoded using an LSTM Autoencoder. Later, the obtained latent space is clustered to find similar days unaffected by outliers using the Isolation Forest method. Moreover, we introduce a new score metric to evaluate the performance of the proposed algorithm. We validate our method on 104 days and more than 100 k egocentric images gathered by 7 users. Several different visualizations are evaluated for the understanding of the findings. Our results demonstrate good performance and applicability of our proposed model for social-related nutritional behaviour understanding. At the end, relevant applications of the model are discussed by analysing the discovered routine of particular individuals. |
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| Notes |
MILAB; no proj |
Approved |
no |
| Call Number |
Admin @ si @ GMR2021 |
Serial |
3634 |
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| Author |
Minesh Mathew; Dimosthenis Karatzas; C.V. Jawahar |
| Title |
DocVQA: A Dataset for VQA on Document Images |
Type |
Conference Article |
| Year |
2021 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
2200-2209 |
| Keywords |
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| Abstract |
We present a new dataset for Visual Question Answering (VQA) on document images called DocVQA. The dataset consists of 50,000 questions defined on 12,000+ document images. Detailed analysis of the dataset in comparison with similar datasets for VQA and reading comprehension is presented. We report several baseline results by adopting existing VQA and reading comprehension models. Although the existing models perform reasonably well on certain types of questions, there is large performance gap compared to human performance (94.36% accuracy). The models need to improve specifically on questions where understanding structure of the document is crucial. The dataset, code and leaderboard are available at docvqa. org |
| Address |
Virtual; January 2021 |
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Conference |
WACV |
| Notes |
DAG; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ MKJ2021 |
Serial |
3498 |
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| Author |
Ruben Tito; Dimosthenis Karatzas; Ernest Valveny |
| Title |
Document Collection Visual Question Answering |
Type |
Conference Article |
| Year |
2021 |
Publication |
16th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
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| Volume |
12822 |
Issue |
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Pages |
778-792 |
| Keywords |
Document collection; Visual Question Answering |
| Abstract |
Current tasks and methods in Document Understanding aims to process documents as single elements. However, documents are usually organized in collections (historical records, purchase invoices), that provide context useful for their interpretation. To address this problem, we introduce Document Collection Visual Question Answering (DocCVQA) a new dataset and related task, where questions are posed over a whole collection of document images and the goal is not only to provide the answer to the given question, but also to retrieve the set of documents that contain the information needed to infer the answer. Along with the dataset we propose a new evaluation metric and baselines which provide further insights to the new dataset and task. |
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LNCS |
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Conference |
ICDAR |
| Notes |
DAG; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ TKV2021 |
Serial |
3622 |
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| Author |
Sanket Biswas; Pau Riba; Josep Llados; Umapada Pal |
| Title |
DocSynth: A Layout Guided Approach for Controllable Document Image Synthesis |
Type |
Conference Article |
| Year |
2021 |
Publication |
16th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
|
| Volume |
12823 |
Issue |
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Pages |
555–568 |
| Keywords |
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| Abstract |
Despite significant progress on current state-of-the-art image generation models, synthesis of document images containing multiple and complex object layouts is a challenging task. This paper presents a novel approach, called DocSynth, to automatically synthesize document images based on a given layout. In this work, given a spatial layout (bounding boxes with object categories) as a reference by the user, our proposed DocSynth model learns to generate a set of realistic document images consistent with the defined layout. Also, this framework has been adapted to this work as a superior baseline model for creating synthetic document image datasets for augmenting real data during training for document layout analysis tasks. Different sets of learning objectives have been also used to improve the model performance. Quantitatively, we also compare the generated results of our model with real data using standard evaluation metrics. The results highlight that our model can successfully generate realistic and diverse document images with multiple objects. We also present a comprehensive qualitative analysis summary of the different scopes of synthetic image generation tasks. Lastly, to our knowledge this is the first work of its kind. |
| Address |
Lausanne; Suissa; September 2021 |
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LNCS |
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Expedition |
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Conference |
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| Notes |
DAG; 600.121; 600.140; 110.312 |
Approved |
no |
| Call Number |
Admin @ si @ BRL2021a |
Serial |
3573 |
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| Author |
Sudeep Katakol; Basem Elbarashy; Luis Herranz; Joost Van de Weijer; Antonio Lopez |
| Title |
Distributed Learning and Inference with Compressed Images |
Type |
Journal Article |
| Year |
2021 |
Publication |
IEEE Transactions on Image Processing |
Abbreviated Journal |
TIP |
| Volume |
30 |
Issue |
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Pages |
3069 - 3083 |
| Keywords |
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| Abstract |
Modern computer vision requires processing large amounts of data, both while training the model and/or during inference, once the model is deployed. Scenarios where images are captured and processed in physically separated locations are increasingly common (e.g. autonomous vehicles, cloud computing). In addition, many devices suffer from limited resources to store or transmit data (e.g. storage space, channel capacity). In these scenarios, lossy image compression plays a crucial role to effectively increase the number of images collected under such constraints. However, lossy compression entails some undesired degradation of the data that may harm the performance of the downstream analysis task at hand, since important semantic information may be lost in the process. Moreover, we may only have compressed images at training time but are able to use original images at inference time, or vice versa, and in such a case, the downstream model suffers from covariate shift. In this paper, we analyze this phenomenon, with a special focus on vision-based perception for autonomous driving as a paradigmatic scenario. We see that loss of semantic information and covariate shift do indeed exist, resulting in a drop in performance that depends on the compression rate. In order to address the problem, we propose dataset restoration, based on image restoration with generative adversarial networks (GANs). Our method is agnostic to both the particular image compression method and the downstream task; and has the advantage of not adding additional cost to the deployed models, which is particularly important in resource-limited devices. The presented experiments focus on semantic segmentation as a challenging use case, cover a broad range of compression rates and diverse datasets, and show how our method is able to significantly alleviate the negative effects of compression on the downstream visual task. |
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Conference |
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| Notes |
LAMP; ADAS; 600.120; 600.118 |
Approved |
no |
| Call Number |
Admin @ si @ KEH2021 |
Serial |
3543 |
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| Author |
Diego Porres |
| Title |
Discriminator Synthesis: On reusing the other half of Generative Adversarial Networks |
Type |
Conference Article |
| Year |
2021 |
Publication |
Machine Learning for Creativity and Design, Neurips Workshop |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
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| 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. |
| Address |
Virtual; December 2021 |
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Conference |
NEURIPSW |
| Notes |
ADAS; 601.365 |
Approved |
no |
| Call Number |
Admin @ si @ Por2021 |
Serial |
3597 |
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| Author |
Hugo Bertiche; Meysam Madadi; Emilio Tylson; Sergio Escalera |
| Title |
DeePSD: Automatic Deep Skinning And Pose Space Deformation For 3D Garment Animation |
Type |
Conference Article |
| Year |
2021 |
Publication |
19th IEEE International Conference on Computer Vision |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
5471-5480 |
| Keywords |
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| Abstract |
We present a novel solution to the garment animation problem through deep learning. Our contribution allows animating any template outfit with arbitrary topology and geometric complexity. Recent works develop models for garment edition, resizing and animation at the same time by leveraging the support body model (encoding garments as body homotopies). This leads to complex engineering solutions that suffer from scalability, applicability and compatibility. By limiting our scope to garment animation only, we are able to propose a simple model that can animate any outfit, independently of its topology, vertex order or connectivity. Our proposed architecture maps outfits to animated 3D models into the standard format for 3D animation (blend weights and blend shapes matrices), automatically providing of compatibility with any graphics engine. We also propose a methodology to complement supervised learning with an unsupervised physically based learning that implicitly solves collisions and enhances cloth quality. |
| Address |
Virtual; October 2021 |
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Conference |
ICCV |
| Notes |
HUPBA; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ BMT2021 |
Serial |
3606 |
| Permanent link to this record |
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| Author |
Meysam Madadi; Hugo Bertiche; Sergio Escalera |
| Title |
Deep unsupervised 3D human body reconstruction from a sparse set of landmarks |
Type |
Journal Article |
| Year |
2021 |
Publication |
International Journal of Computer Vision |
Abbreviated Journal |
IJCV |
| Volume |
129 |
Issue |
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Pages |
2499–2512 |
| Keywords |
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| Abstract |
In this paper we propose the first deep unsupervised approach in human body reconstruction to estimate body surface from a sparse set of landmarks, so called DeepMurf. We apply a denoising autoencoder to estimate missing landmarks. Then we apply an attention model to estimate body joints from landmarks. Finally, a cascading network is applied to regress parameters of a statistical generative model that reconstructs body. Our set of proposed loss functions allows us to train the network in an unsupervised way. Results on four public datasets show that our approach accurately reconstructs the human body from real world mocap data. |
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| Notes |
HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ MBE2021 |
Serial |
3654 |
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| Author |
Hugo Bertiche; Meysam Madadi; Sergio Escalera |
| Title |
Deep Parametric Surfaces for 3D Outfit Reconstruction from Single View Image |
Type |
Conference Article |
| Year |
2021 |
Publication |
16th IEEE International Conference on Automatic Face and Gesture Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1-8 |
| Keywords |
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| Abstract |
We present a methodology to retrieve analytical surfaces parametrized as a neural network. Previous works on 3D reconstruction yield point clouds, voxelized objects or meshes. Instead, our approach yields 2-manifolds in the euclidean space through deep learning. To this end, we implement a novel formulation for fully connected layers as parametrized manifolds that allows continuous predictions with differential geometry. Based on this property we propose a novel smoothness loss. Results on CLOTH3D++ dataset show the possibility to infer different topologies and the benefits of the smoothness term based on differential geometry. |
| Address |
Virtual; December 2021 |
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Conference |
FG |
| Notes |
HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ BME2021 |
Serial |
3640 |
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| Author |
Patricia Suarez; Angel Sappa; Boris X. Vintimilla |
| Title |
Deep learning-based vegetation index estimation |
Type |
Book Chapter |
| Year |
2021 |
Publication |
Generative Adversarial Networks for Image-to-Image Translation |
Abbreviated Journal |
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Issue |
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Pages |
205-234 |
| Keywords |
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| Abstract |
Chapter 9 |
| Address |
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Thesis |
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| Publisher |
Elsevier |
Place of Publication |
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Editor |
A.Solanki; A.Nayyar; M.Naved |
| Language |
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Conference |
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| Notes |
MSIAU; 600.122 |
Approved |
no |
| Call Number |
Admin @ si @ SSV2021a |
Serial |
3578 |
| Permanent link to this record |
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| Author |
Reza Azad; Afshin Bozorgpour; Maryam Asadi-Aghbolaghi; Dorit Merhof; Sergio Escalera |
| Title |
Deep Frequency Re-Calibration U-Net for Medical Image Segmentation |
Type |
Conference Article |
| Year |
2021 |
Publication |
IEEE/CVF International Conference on Computer Vision Workshops |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
3274-3283 |
| Keywords |
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| Abstract |
We present a novel solution to the garment animation problem through deep learning. Our contribution allows animating any template outfit with arbitrary topology and geometric complexity. Recent works develop models for garment edition, resizing and animation at the same time by leveraging the support body model (encoding garments as body homotopies). This leads to complex engineering solutions that suffer from scalability, applicability and compatibility. By limiting our scope to garment animation only, we are able to propose a simple model that can animate any outfit, independently of its topology, vertex order or connectivity. Our proposed architecture maps outfits to animated 3D models into the standard format for 3D animation (blend weights and blend shapes matrices), automatically providing of compatibility with any graphics engine. We also propose a methodology to complement supervised learning with an unsupervised physically based learning that implicitly solves collisions and enhances cloth quality. |
| Address |
VIRTUAL; October 2021 |
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Edition |
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Expedition |
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Conference |
ICCVW |
| Notes |
HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ ABA2021 |
Serial |
3645 |
| Permanent link to this record |
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| Author |
Clementine Decamps; Alexis Arnaud; Florent Petitprez; Mira Ayadi; Aurelia Baures; Lucile Armenoult; Sergio Escalera; Isabelle Guyon; Remy Nicolle; Richard Tomasini; Aurelien de Reynies; Jerome Cros; Yuna Blum; Magali Richard |
| Title |
DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification |
Type |
Journal Article |
| Year |
2021 |
Publication |
BMC Bioinformatics |
Abbreviated Journal |
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| Volume |
22 |
Issue |
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Pages |
473 |
| Keywords |
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| Abstract |
Quantification of tumor heterogeneity is essential to better understand cancer progression and to adapt therapeutic treatments to patient specificities. Bioinformatic tools to assess the different cell populations from single-omic datasets as bulk transcriptome or methylome samples have been recently developed, including reference-based and reference-free methods. Improved methods using multi-omic datasets are yet to be developed in the future and the community would need systematic tools to perform a comparative evaluation of these algorithms on controlled data. |
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| Notes |
HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ DAP2021 |
Serial |
3650 |
| Permanent link to this record |
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| Author |
Adria Molina; Pau Riba; Lluis Gomez; Oriol Ramos Terrades; Josep Llados |
| Title |
Date Estimation in the Wild of Scanned Historical Photos: An Image Retrieval Approach |
Type |
Conference Article |
| Year |
2021 |
Publication |
16th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
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| Volume |
12822 |
Issue |
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Pages |
306-320 |
| Keywords |
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| 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. |
| Address |
Lausanne; Suissa; September 2021 |
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LNCS |
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Conference |
ICDAR |
| Notes |
DAG; 600.121; 600.140; 110.312 |
Approved |
no |
| Call Number |
Admin @ si @ MRG2021b |
Serial |
3571 |
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| Author |
Sudeep Katakol; Luis Herranz; Fei Yang; Marta Mrak |
| Title |
DANICE: Domain adaptation without forgetting in neural image compression |
Type |
Conference Article |
| Year |
2021 |
Publication |
Conference on Computer Vision and Pattern Recognition Workshops |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1921-1925 |
| Keywords |
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| Abstract |
Neural image compression (NIC) is a new coding paradigm where coding capabilities are captured by deep models learned from data. This data-driven nature enables new potential functionalities. In this paper, we study the adaptability of codecs to custom domains of interest. We show that NIC codecs are transferable and that they can be adapted with relatively few target domain images. However, naive adaptation interferes with the solution optimized for the original source domain, resulting in forgetting the original coding capabilities in that domain, and may even break the compatibility with previously encoded bitstreams. Addressing these problems, we propose Codec Adaptation without Forgetting (CAwF), a framework that can avoid these problems by adding a small amount of custom parameters, where the source codec remains embedded and unchanged during the adaptation process. Experiments demonstrate its effectiveness and provide useful insights on the characteristics of catastrophic interference in NIC. |
| Address |
Virtual; June 2021 |
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Conference |
CVPRW |
| Notes |
LAMP; 600.120; 600.141; 601.379 |
Approved |
no |
| Call Number |
Admin @ si @ KHY2021 |
Serial |
3568 |
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| Author |
Patricia Suarez; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud |
| Title |
Cycle Generative Adversarial Network: Towards A Low-Cost Vegetation Index Estimation |
Type |
Conference Article |
| Year |
2021 |
Publication |
28th IEEE International Conference on Image Processing |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
19-22 |
| Keywords |
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| 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. |
| Address |
Anchorage-Alaska; USA; September 2021 |
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Summary Language |
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Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
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| Series Volume |
|
Series Issue |
|
Edition |
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| ISSN |
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ISBN |
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Medium |
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| Area |
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Expedition |
|
Conference |
ICIP |
| Notes |
MSIAU; 600.130; 600.122; 601.349 |
Approved |
no |
| Call Number |
Admin @ si @ SSV2021b |
Serial |
3579 |
| Permanent link to this record |
