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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 (down) IEEE Transactions on Image Processing Abbreviated Journal TIP
Volume 30 Issue Pages 3069 - 3083
Keywords
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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Notes LAMP; ADAS; 600.120; 600.118 Approved no
Call Number Admin @ si @ KEH2021 Serial 3543
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Author Ricardo Dario Perez Principi; Cristina Palmero; Julio C. S. Jacques Junior; Sergio Escalera
Title On the Effect of Observed Subject Biases in Apparent Personality Analysis from Audio-visual Signals Type Journal Article
Year 2021 Publication (down) IEEE Transactions on Affective Computing Abbreviated Journal TAC
Volume 12 Issue 3 Pages 607-621
Keywords
Abstract Personality perception is implicitly biased due to many subjective factors, such as cultural, social, contextual, gender and appearance. Approaches developed for automatic personality perception are not expected to predict the real personality of the target, but the personality external observers attributed to it. Hence, they have to deal with human bias, inherently transferred to the training data. However, bias analysis in personality computing is an almost unexplored area. In this work, we study different possible sources of bias affecting personality perception, including emotions from facial expressions, attractiveness, age, gender, and ethnicity, as well as their influence on prediction ability for apparent personality estimation. To this end, we propose a multi-modal deep neural network that combines raw audio and visual information alongside predictions of attribute-specific models to regress apparent personality. We also analyse spatio-temporal aggregation schemes and the effect of different time intervals on first impressions. We base our study on the ChaLearn First Impressions dataset, consisting of one-person conversational videos. Our model shows state-of-the-art results regressing apparent personality based on the Big-Five model. Furthermore, given the interpretability nature of our network design, we provide an incremental analysis on the impact of each possible source of bias on final network predictions.
Address 1 July-Sept. 2021
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Notes HuPBA; no proj Approved no
Call Number Admin @ si @ PPJ2019 Serial 3312
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Author Fatemeh Noroozi; Ciprian Corneanu; Dorota Kamińska; Tomasz Sapiński; Sergio Escalera; Gholamreza Anbarjafari
Title Survey on Emotional Body Gesture Recognition Type Journal Article
Year 2021 Publication (down) IEEE Transactions on Affective Computing Abbreviated Journal TAC
Volume 12 Issue 2 Pages 505 - 523
Keywords
Abstract Automatic emotion recognition has become a trending research topic in the past decade. While works based on facial expressions or speech abound, recognizing affect from body gestures remains a less explored topic. We present a new comprehensive survey hoping to boost research in the field. We first introduce emotional body gestures as a component of what is commonly known as “body language” and comment general aspects as gender differences and culture dependence. We then define a complete framework for automatic emotional body gesture recognition. We introduce person detection and comment static and dynamic body pose estimation methods both in RGB and 3D. We then comment the recent literature related to representation learning and emotion recognition from images of emotionally expressive gestures. We also discuss multi-modal approaches that combine speech or face with body gestures for improved emotion recognition. While pre-processing methodologies (e.g. human detection and pose estimation) are nowadays mature technologies fully developed for robust large scale analysis, we show that for emotion recognition the quantity of labelled data is scarce, there is no agreement on clearly defined output spaces and the representations are shallow and largely based on naive geometrical representations.
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Notes HUPBA; no proj Approved no
Call Number Admin @ si @ NCK2021 Serial 3657
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Author Kaustubh Kulkarni; Ciprian Corneanu; Ikechukwu Ofodile; Sergio Escalera; Xavier Baro; Sylwia Hyniewska; Juri Allik; Gholamreza Anbarjafari
Title Automatic Recognition of Facial Displays of Unfelt Emotions Type Journal Article
Year 2021 Publication (down) IEEE Transactions on Affective Computing Abbreviated Journal TAC
Volume 12 Issue 2 Pages 377 - 390
Keywords
Abstract Humans modify their facial expressions in order to communicate their internal states and sometimes to mislead observers regarding their true emotional states. Evidence in experimental psychology shows that discriminative facial responses are short and subtle. This suggests that such behavior would be easier to distinguish when captured in high resolution at an increased frame rate. We are proposing SASE-FE, the first dataset of facial expressions that are either congruent or incongruent with underlying emotion states. We show that overall the problem of recognizing whether facial movements are expressions of authentic emotions or not can be successfully addressed by learning spatio-temporal representations of the data. For this purpose, we propose a method that aggregates features along fiducial trajectories in a deeply learnt space. Performance of the proposed model shows that on average, it is easier to distinguish among genuine facial expressions of emotion than among unfelt facial expressions of emotion and that certain emotion pairs such as contempt and disgust are more difficult to distinguish than the rest. Furthermore, the proposed methodology improves state of the art results on CK+ and OULU-CASIA datasets for video emotion recognition, and achieves competitive results when classifying facial action units on BP4D datase.
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Notes HUPBA; no proj Approved no
Call Number Admin @ si @ KCO2021 Serial 3658
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Author Shiqi Yang; Kai Wang; Luis Herranz; Joost Van de Weijer
Title On Implicit Attribute Localization for Generalized Zero-Shot Learning Type Journal Article
Year 2021 Publication (down) IEEE Signal Processing Letters Abbreviated Journal
Volume 28 Issue Pages 872 - 876
Keywords
Abstract Zero-shot learning (ZSL) aims to discriminate images from unseen classes by exploiting relations to seen classes via their attribute-based descriptions. Since attributes are often related to specific parts of objects, many recent works focus on discovering discriminative regions. However, these methods usually require additional complex part detection modules or attention mechanisms. In this paper, 1) we show that common ZSL backbones (without explicit attention nor part detection) can implicitly localize attributes, yet this property is not exploited. 2) Exploiting it, we then propose SELAR, a simple method that further encourages attribute localization, surprisingly achieving very competitive generalized ZSL (GZSL) performance when compared with more complex state-of-the-art methods. Our findings provide useful insight for designing future GZSL methods, and SELAR provides an easy to implement yet strong baseline.
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Notes LAMP; 600.120 Approved no
Call Number YWH2021 Serial 3563
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Author Alina Matei; Andreea Glavan; Petia Radeva; Estefania Talavera
Title Towards Eating Habits Discovery in Egocentric Photo-Streams Type Journal Article
Year 2021 Publication (down) IEEE Access Abbreviated Journal ACCESS
Volume 9 Issue Pages 17495-17506
Keywords
Abstract Eating habits are learned throughout the early stages of our lives. However, it is not easy to be aware of how our food-related routine affects our healthy living. In this work, we address the unsupervised discovery of nutritional habits from egocentric photo-streams. We build a food-related behavioral pattern discovery model, which discloses nutritional routines from the activities performed throughout the days. To do so, we rely on Dynamic-Time-Warping for the evaluation of similarity among the collected days. Within this framework, we present a simple, but robust and fast novel classification pipeline that outperforms the state-of-the-art on food-related image classification with a weighted accuracy and F-score of 70% and 63%, respectively. Later, we identify days composed of nutritional activities that do not describe the habits of the person as anomalies in the daily life of the user with the Isolation Forest method. Furthermore, we show an application for the identification of food-related scenes when the camera wearer eats in isolation. Results have shown the good performance of the proposed model and its relevance to visualize the nutritional habits of individuals.
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Notes MILAB; no proj Approved no
Call Number Admin @ si @ MGR2021 Serial 3637
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Author Diego Velazquez; Josep M. Gonfaus; Pau Rodriguez; Xavier Roca; Seiichi Ozawa; Jordi Gonzalez
Title Logo Detection With No Priors Type Journal Article
Year 2021 Publication (down) IEEE Access Abbreviated Journal ACCESS
Volume 9 Issue Pages 106998-107011
Keywords
Abstract In recent years, top referred methods on object detection like R-CNN have implemented this task as a combination of proposal region generation and supervised classification on the proposed bounding boxes. Although this pipeline has achieved state-of-the-art results in multiple datasets, it has inherent limitations that make object detection a very complex and inefficient task in computational terms. Instead of considering this standard strategy, in this paper we enhance Detection Transformers (DETR) which tackles object detection as a set-prediction problem directly in an end-to-end fully differentiable pipeline without requiring priors. In particular, we incorporate Feature Pyramids (FP) to the DETR architecture and demonstrate the effectiveness of the resulting DETR-FP approach on improving logo detection results thanks to the improved detection of small logos. So, without requiring any domain specific prior to be fed to the model, DETR-FP obtains competitive results on the OpenLogo and MS-COCO datasets offering a relative improvement of up to 30%, when compared to a Faster R-CNN baseline which strongly depends on hand-designed priors.
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Notes ISE Approved no
Call Number Admin @ si @ VGR2021 Serial 3664
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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 (down) Generative Adversarial Networks for Image-to-Image Translation Abbreviated Journal
Volume Issue Pages 205-234
Keywords
Abstract Chapter 9
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Publisher Elsevier Place of Publication Editor A.Solanki; A.Nayyar; M.Naved
Language Summary Language Original Title
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Notes MSIAU; 600.122 Approved no
Call Number Admin @ si @ SSV2021a Serial 3578
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Author Debora Gil; Oriol Ramos Terrades; Raquel Perez
Title Topological Radiomics (TOPiomics): Early Detection of Genetic Abnormalities in Cancer Treatment Evolution Type Book Chapter
Year 2021 Publication (down) Extended Abstracts GEOMVAP 2019, Trends in Mathematics 15 Abbreviated Journal
Volume 15 Issue Pages 89–93
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Abstract Abnormalities in radiomic measures correlate to genomic alterations prone to alter the outcome of personalized anti-cancer treatments. TOPiomics is a new method for the early detection of variations in tumor imaging phenotype from a topological structure in multi-view radiomic spaces.
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Publisher Springer Nature Place of Publication Editor
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Notes IAM; DAG; 600.120; 600.145; 600.139 Approved no
Call Number Admin @ si @ GRP2021 Serial 3594
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Author Md. Mostafa Kamal Sarker; Hatem A. Rashwan; Farhan Akram; Vivek Kumar Singh; Syeda Furruka Banu; Forhad U H Chowdhury; Kabir Ahmed Choudhury; Sylvie Chambon; Petia Radeva; Domenec Puig; Mohamed Abdel-Nasser
Title SLSNet: Skin lesion segmentation using a lightweight generative adversarial network Type Journal Article
Year 2021 Publication (down) Expert Systems With Applications Abbreviated Journal ESWA
Volume 183 Issue Pages 115433
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Abstract The determination of precise skin lesion boundaries in dermoscopic images using automated methods faces many challenges, most importantly, the presence of hair, inconspicuous lesion edges and low contrast in dermoscopic images, and variability in the color, texture and shapes of skin lesions. Existing deep learning-based skin lesion segmentation algorithms are expensive in terms of computational time and memory. Consequently, running such segmentation algorithms requires a powerful GPU and high bandwidth memory, which are not available in dermoscopy devices. Thus, this article aims to achieve precise skin lesion segmentation with minimum resources: a lightweight, efficient generative adversarial network (GAN) model called SLSNet, which combines 1-D kernel factorized networks, position and channel attention, and multiscale aggregation mechanisms with a GAN model. The 1-D kernel factorized network reduces the computational cost of 2D filtering. The position and channel attention modules enhance the discriminative ability between the lesion and non-lesion feature representations in spatial and channel dimensions, respectively. A multiscale block is also used to aggregate the coarse-to-fine features of input skin images and reduce the effect of the artifacts. SLSNet is evaluated on two publicly available datasets: ISBI 2017 and the ISIC 2018. Although SLSNet has only 2.35 million parameters, the experimental results demonstrate that it achieves segmentation results on a par with the state-of-the-art skin lesion segmentation methods with an accuracy of 97.61%, and Dice and Jaccard similarity coefficients of 90.63% and 81.98%, respectively. SLSNet can run at more than 110 frames per second (FPS) in a single GTX1080Ti GPU, which is faster than well-known deep learning-based image segmentation models, such as FCN. Therefore, SLSNet can be used for practical dermoscopic applications.
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Notes MILAB; no proj Approved no
Call Number Admin @ si @ SRA2021 Serial 3633
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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 (down) Expert Systems with Applications Abbreviated Journal ESWA
Volume 171 Issue Pages 114506
Keywords
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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Publisher Place of Publication Editor
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Notes MILAB; no proj Approved no
Call Number Admin @ si @ GMR2021 Serial 3634
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Author Razieh Rastgoo; Kourosh Kiani; Sergio Escalera
Title Sign Language Recognition: A Deep Survey Type Journal Article
Year 2021 Publication (down) Expert Systems With Applications Abbreviated Journal ESWA
Volume 164 Issue Pages 113794
Keywords
Abstract Sign language, as a different form of the communication language, is important to large groups of people in society. There are different signs in each sign language with variability in hand shape, motion profile, and position of the hand, face, and body parts contributing to each sign. So, visual sign language recognition is a complex research area in computer vision. Many models have been proposed by different researchers with significant improvement by deep learning approaches in recent years. In this survey, we review the vision-based proposed models of sign language recognition using deep learning approaches from the last five years. While the overall trend of the proposed models indicates a significant improvement in recognition accuracy in sign language recognition, there are some challenges yet that need to be solved. We present a taxonomy to categorize the proposed models for isolated and continuous sign language recognition, discussing applications, datasets, hybrid models, complexity, and future lines of research in the field.
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Notes HUPBA; no proj Approved no
Call Number Admin @ si @ RKE2021a Serial 3521
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Author O.F.Ahmad; Y.Mori; M.Misawa; S.Kudo; J.T.Anderson; Jorge Bernal
Title Establishing key research questions for the implementation of artificial intelligence in colonoscopy: a modified Delphi method Type Journal Article
Year 2021 Publication (down) Endoscopy Abbreviated Journal END
Volume 53 Issue 9 Pages 893-901
Keywords
Abstract BACKGROUND : Artificial intelligence (AI) research in colonoscopy is progressing rapidly but widespread clinical implementation is not yet a reality. We aimed to identify the top implementation research priorities. METHODS : An established modified Delphi approach for research priority setting was used. Fifteen international experts, including endoscopists and translational computer scientists/engineers, from nine countries participated in an online survey over 9 months. Questions related to AI implementation in colonoscopy were generated as a long-list in the first round, and then scored in two subsequent rounds to identify the top 10 research questions. RESULTS : The top 10 ranked questions were categorized into five themes. Theme 1: clinical trial design/end points (4 questions), related to optimum trial designs for polyp detection and characterization, determining the optimal end points for evaluation of AI, and demonstrating impact on interval cancer rates. Theme 2: technological developments (3 questions), including improving detection of more challenging and advanced lesions, reduction of false-positive rates, and minimizing latency. Theme 3: clinical adoption/integration (1 question), concerning the effective combination of detection and characterization into one workflow. Theme 4: data access/annotation (1 question), concerning more efficient or automated data annotation methods to reduce the burden on human experts. Theme 5: regulatory approval (1 question), related to making regulatory approval processes more efficient. CONCLUSIONS : This is the first reported international research priority setting exercise for AI in colonoscopy. The study findings should be used as a framework to guide future research with key stakeholders to accelerate the clinical implementation of AI in endoscopy.
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Publisher Place of Publication Editor
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Notes ISE Approved no
Call Number Admin @ si @ AMM2021 Serial 3670
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Author Dorota Kaminska; Kadir Aktas; Davit Rizhinashvili; Danila Kuklyanov; Abdallah Hussein Sham; Sergio Escalera; Kamal Nasrollahi; Thomas B. Moeslund; Gholamreza Anbarjafari
Title Two-stage Recognition and Beyond for Compound Facial Emotion Recognition Type Journal Article
Year 2021 Publication (down) Electronics Abbreviated Journal ELEC
Volume 10 Issue 22 Pages 2847
Keywords compound emotion recognition; facial expression recognition; dominant and complementary emotion recognition; deep learning
Abstract Facial emotion recognition is an inherently complex problem due to individual diversity in facial features and racial and cultural differences. Moreover, facial expressions typically reflect the mixture of people’s emotional statuses, which can be expressed using compound emotions. Compound facial emotion recognition makes the problem even more difficult because the discrimination between dominant and complementary emotions is usually weak. We have created a database that includes 31,250 facial images with different emotions of 115 subjects whose gender distribution is almost uniform to address compound emotion recognition. In addition, we have organized a competition based on the proposed dataset, held at FG workshop 2020. This paper analyzes the winner’s approach—a two-stage recognition method (1st stage, coarse recognition; 2nd stage, fine recognition), which enhances the classification of symmetrical emotion labels.
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Publisher Place of Publication Editor
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Notes HUPBA; no proj Approved no
Call Number Admin @ si @ KAR2021 Serial 3642
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Author Josep Llados; Daniel Lopresti; Seiichi Uchida (eds)
Title 16th International Conference, 2021, Proceedings, Part III Type Book Whole
Year 2021 Publication (down) Document Analysis and Recognition – ICDAR 2021 Abbreviated Journal
Volume 12823 Issue Pages
Keywords
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.
Address Lausanne, Switzerland, September 5-10, 2021
Corporate Author Thesis
Publisher Springer Cham Place of Publication Editor Josep Llados; Daniel Lopresti; Seiichi Uchida
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN 978-3-030-86333-3 Medium
Area Expedition Conference ICDAR
Notes DAG Approved no
Call Number Admin @ si @ Serial 3727
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