| Records |
| Author |
David Curto; Albert Clapes; Javier Selva; Sorina Smeureanu; Julio C. S. Jacques Junior; David Gallardo-Pujol; Georgina Guilera; David Leiva; Thomas B. Moeslund; Sergio Escalera; Cristina Palmero |
| Title |
Dyadformer: A Multi-Modal Transformer for Long-Range Modeling of Dyadic Interactions |
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  |
2177-2188 |
| Keywords |
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| Abstract |
Personality computing has become an emerging topic in computer vision, due to the wide range of applications it can be used for. However, most works on the topic have focused on analyzing the individual, even when applied to interaction scenarios, and for short periods of time. To address these limitations, we present the Dyadformer, a novel multi-modal multi-subject Transformer architecture to model individual and interpersonal features in dyadic interactions using variable time windows, thus allowing the capture of long-term interdependencies. Our proposed cross-subject layer allows the network to explicitly model interactions among subjects through attentional operations. This proof-of-concept approach shows how multi-modality and joint modeling of both interactants for longer periods of time helps to predict individual attributes. With Dyadformer, we improve state-of-the-art self-reported personality inference results on individual subjects on the UDIVA v0.5 dataset. |
| Address |
Virtual; October 2021 |
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Conference |
ICCVW |
| Notes |
HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ CCS2021 |
Serial |
3648 |
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| Author |
Manisha Das; Deep Gupta; Petia Radeva; Ashwini M. Bakde |
| Title |
Multi-scale decomposition-based CT-MR neurological image fusion using optimized bio-inspired spiking neural model with meta-heuristic optimization |
Type |
Journal Article |
| Year |
2021 |
Publication |
International Journal of Imaging Systems and Technology |
Abbreviated Journal |
IMA |
| Volume |
31 |
Issue |
4 |
Pages |
2170-2188 |
| Keywords |
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| Abstract |
Multi-modal medical image fusion plays an important role in clinical diagnosis and works as an assistance model for clinicians. In this paper, a computed tomography-magnetic resonance (CT-MR) image fusion model is proposed using an optimized bio-inspired spiking feedforward neural network in different decomposition domains. First, source images are decomposed into base (low-frequency) and detail (high-frequency) layer components. Low-frequency subbands are fused using texture energy measures to capture the local energy, contrast, and small edges in the fused image. High-frequency coefficients are fused using firing maps obtained by pixel-activated neural model with the optimized parameters using three different optimization techniques such as differential evolution, cuckoo search, and gray wolf optimization, individually. In the optimization model, a fitness function is computed based on the edge index of resultant fused images, which helps to extract and preserve sharp edges available in the source CT and MR images. To validate the fusion performance, a detailed comparative analysis is presented among the proposed and state-of-the-art methods in terms of quantitative and qualitative measures along with computational complexity. Experimental results show that the proposed method produces a significantly better visual quality of fused images meanwhile outperforms the existing methods. |
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| Notes |
MILAB; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ DGR2021a |
Serial |
3630 |
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| Author |
Claudia Greco; Carmela Buono; Pau Buch-Cardona; Gennaro Cordasco; Sergio Escalera; Anna Esposito; Anais Fernandez; Daria Kyslitska; Maria Stylianou Kornes; Cristina Palmero; Jofre Tenorio Laranga; Anna Torp Johansen; Maria Ines Torres |
| Title |
Emotional Features of Interactions With Empathic Agents |
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 |
2168-2176 |
| Keywords |
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| Abstract |
The current study is part of the EMPATHIC project, whose aim is to develop an Empathic Virtual Coach (VC) capable of promoting healthy and independent aging. To this end, the VC needs to be capable of perceiving the emotional states of users and adjusting its behaviour during the interactions according to what the users are experiencing in terms of emotions and comfort. Thus, the present work focuses on some sessions where elderly users of three different countries interact with a simulated system. Audio and video information extracted from these sessions were examined by external observers to assess participants' emotional experience with the EMPATHIC-VC in terms of categorical and dimensional assessment of emotions. Analyses were conducted on the emotional labels assigned by the external observers while participants were engaged in two different scenarios: a generic one, where the interaction was carried out with no intention to discuss a specific topic, and a nutrition one, aimed to accomplish a conversation on users' nutritional habits. Results of analyses performed on both audio and video data revealed that the EMPATHIC coach did not elicit negative feelings in the users. Indeed, users from all countries have shown relaxed and positive behavior when interacting with the simulated VC during both scenarios. Overall, the EMPATHIC-VC was capable to offer an enjoyable experience without eliciting negative feelings in the users. This supports the hypothesis that an Empathic Virtual Coach capable of considering users' expectations and emotional states could support elderly people in daily life activities and help them to remain independent. |
| Address |
VIRTUAL; October 2021 |
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Conference |
ICCVW |
| Notes |
HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ GBB2021 |
Serial |
3647 |
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| Author |
Neelu Madan; Arya Farkhondeh; Kamal Nasrollahi; Sergio Escalera; Thomas B. Moeslund |
| Title |
Temporal Cues From Socially Unacceptable Trajectories for Anomaly Detection |
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 |
2150-2158 |
| Keywords |
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| Abstract |
State-of-the-Art (SoTA) deep learning-based approaches to detect anomalies in surveillance videos utilize limited temporal information, including basic information from motion, e.g., optical flow computed between consecutive frames. In this paper, we compliment the SoTA methods by including long-range dependencies from trajectories for anomaly detection. To achieve that, we first created trajectories by running a tracker on two SoTA datasets, namely Avenue and Shanghai-Tech. We propose a prediction-based anomaly detection method using trajectories based on Social GANs, also called in this paper as temporal-based anomaly detection. Then, we hypothesize that late fusion of the result of this temporal-based anomaly detection system with spatial-based anomaly detection systems produces SoTA results. We verify this hypothesis on two spatial-based anomaly detection systems. We show that both cases produce results better than baseline spatial-based systems, indicating the usefulness of the temporal information coming from the trajectories for anomaly detection. We observe that the proposed approach depicts the maximum improvement in micro-level Area-Under-the-Curve (AUC) by 4.1% on CUHK Avenue and 3.4% on Shanghai-Tech over one of the baseline method. We also show a high performance on cross-data evaluation, where we learn the weights to combine spatial and temporal information on Shanghai-Tech and perform evaluation on CUHK Avenue and vice-versa. |
| Address |
Virtual; October 2021 |
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Conference |
ICCVW |
| Notes |
HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ MFN2021 |
Serial |
3649 |
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| Author |
Shun Yao; Fei Yang; Yongmei Cheng; Mikhail Mozerov |
| Title |
3D Shapes Local Geometry Codes Learning with SDF |
Type |
Conference Article |
| Year |
2021 |
Publication |
International Conference on Computer Vision Workshops |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
2110-2117 |
| Keywords |
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| Abstract |
A signed distance function (SDF) as the 3D shape description is one of the most effective approaches to represent 3D geometry for rendering and reconstruction. Our work is inspired by the state-of-the-art method DeepSDF [17] that learns and analyzes the 3D shape as the iso-surface of its shell and this method has shown promising results especially in the 3D shape reconstruction and compression domain. In this paper, we consider the degeneration problem of reconstruction coming from the capacity decrease of the DeepSDF model, which approximates the SDF with a neural network and a single latent code. We propose Local Geometry Code Learning (LGCL), a model that improves the original DeepSDF results by learning from a local shape geometry of the full 3D shape. We add an extra graph neural network to split the single transmittable latent code into a set of local latent codes distributed on the 3D shape. Mentioned latent codes are used to approximate the SDF in their local regions, which will alleviate the complexity of the approximation compared to the original DeepSDF. Furthermore, we introduce a new geometric loss function to facilitate the training of these local latent codes. Note that other local shape adjusting methods use the 3D voxel representation, which in turn is a problem highly difficult to solve or even is insolvable. In contrast, our architecture is based on graph processing implicitly and performs the learning regression process directly in the latent code space, thus make the proposed architecture more flexible and also simple for realization. Our experiments on 3D shape reconstruction demonstrate that our LGCL method can keep more details with a significantly smaller size of the SDF decoder and outperforms considerably the original DeepSDF method under the most important quantitative metrics. |
| Address |
VIRTUAL; October 2021 |
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Conference |
ICCVW |
| Notes |
LAMP |
Approved |
no |
| Call Number |
Admin @ si @ YYC2021 |
Serial |
3681 |
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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 |
Javad Zolfaghari Bengar; Joost Van de Weijer; Bartlomiej Twardowski; Bogdan Raducanu |
| Title |
Reducing Label Effort: Self- Supervised Meets Active Learning |
Type |
Conference Article |
| Year |
2021 |
Publication |
International Conference on Computer Vision Workshops |
Abbreviated Journal |
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Issue |
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Pages |
1631-1639 |
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| Abstract |
Active learning is a paradigm aimed at reducing the annotation effort by training the model on actively selected informative and/or representative samples. Another paradigm to reduce the annotation effort is self-training that learns from a large amount of unlabeled data in an unsupervised way and fine-tunes on few labeled samples. Recent developments in self-training have achieved very impressive results rivaling supervised learning on some datasets. The current work focuses on whether the two paradigms can benefit from each other. We studied object recognition datasets including CIFAR10, CIFAR100 and Tiny ImageNet with several labeling budgets for the evaluations. Our experiments reveal that self-training is remarkably more efficient than active learning at reducing the labeling effort, that for a low labeling budget, active learning offers no benefit to self-training, and finally that the combination of active learning and self-training is fruitful when the labeling budget is high. The performance gap between active learning trained either with self-training or from scratch diminishes as we approach to the point where almost half of the dataset is labeled. |
| Address |
October 2021 |
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ICCVW |
| Notes |
LAMP; |
Approved |
no |
| Call Number |
Admin @ si @ ZVT2021 |
Serial |
3672 |
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| Author |
Ajian Liu; Zichang Tan; Jun Wan; Sergio Escalera; Guodong Guo; Stan Z. Li |
| Title |
CASIA-SURF CeFA: A Benchmark for Multi-modal Cross-Ethnicity Face Anti-Spoofing |
Type |
Conference Article |
| Year |
2021 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
Abbreviated Journal |
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Issue |
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Pages |
1178-1186 |
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The issue of ethnic bias has proven to affect the performance of face recognition in previous works, while it still remains to be vacant in face anti-spoofing. Therefore, in order to study the ethnic bias for face anti-spoofing, we introduce the largest CASIA-SURF Cross-ethnicity Face Anti-spoofing (CeFA) dataset, covering 3 ethnicities, 3 modalities, 1,607 subjects, and 2D plus 3D attack types. Five protocols are introduced to measure the affect under varied evaluation conditions, such as cross-ethnicity, unknown spoofs or both of them. As our knowledge, CASIA-SURF CeFA is the first dataset including explicit ethnic labels in current released datasets. Then, we propose a novel multi-modal fusion method as a strong baseline to alleviate the ethnic bias, which employs a partially shared fusion strategy to learn complementary information from multiple modalities. Extensive experiments have been conducted on the proposed dataset to verify its significance and generalization capability for other existing datasets, i.e., CASIA-SURF, OULU-NPU and SiW datasets. The dataset is available at https://sites.google.com/qq.com/face-anti-spoofing/welcome/challengecvpr2020?authuser=0. |
| Address |
Virtual; January 2021 |
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Conference |
WACV |
| Notes |
HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ LTW2021 |
Serial |
3661 |
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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 |
Endoscopy |
Abbreviated Journal |
END |
| Volume |
53 |
Issue |
9 |
Pages |
893-901 |
| Keywords |
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| 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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ISE |
Approved |
no |
| Call Number |
Admin @ si @ AMM2021 |
Serial |
3670 |
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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 |
IEEE Signal Processing Letters |
Abbreviated Journal |
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| Volume |
28 |
Issue |
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Pages |
872 - 876 |
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| 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 |
Javier M. Olaso; Alain Vazquez; Leila Ben Letaifa; Mikel de Velasco; Aymen Mtibaa; Mohamed Amine Hmani; Dijana Petrovska-Delacretaz; Gerard Chollet; Cesar Montenegro; Asier Lopez-Zorrilla; Raquel Justo; Roberto Santana; Jofre Tenorio-Laranga; Eduardo Gonzalez-Fraile; Begoña Fernandez-Ruanova; Gennaro Cordasco; Anna Esposito; Kristin Beck Gjellesvik; Anna Torp Johansen; Maria Stylianou Kornes; Colin Pickard; Cornelius Glackin; Gary Cahalane; Pau Buch; Cristina Palmero; Sergio Escalera; Olga Gordeeva; Olivier Deroo; Anaïs Fernandez; Daria Kyslitska; Jose Antonio Lozano; Maria Ines Torres; Stephan Schlogl |
| Title |
The EMPATHIC Virtual Coach: a demo |
Type |
Conference Article |
| Year |
2021 |
Publication |
23rd ACM International Conference on Multimodal Interaction |
Abbreviated Journal |
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Issue |
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Pages |
848-851 |
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| 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. |
| Address |
Virtual; October 2021 |
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Conference |
ICMI |
| Notes |
HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ OVB2021 |
Serial |
3644 |
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| Author |
Ajian Liu; Chenxu Zhao; Zitong Yu; Anyang Su; Xing Liu; Zijian Kong; Jun Wan; Sergio Escalera; Hugo Jair Escalante; Zhen Lei; Guodong Guo |
| Title |
3D High-Fidelity Mask Face Presentation Attack Detection Challenge |
Type |
Conference Article |
| Year |
2021 |
Publication |
IEEE/CVF International Conference on Computer Vision Workshops |
Abbreviated Journal |
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Issue |
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Pages |
814-823 |
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| Abstract |
The threat of 3D mask to face recognition systems is increasing serious, and has been widely concerned by researchers. To facilitate the study of the algorithms, a large-scale High-Fidelity Mask dataset, namely CASIA-SURF HiFiMask (briefly HiFiMask) has been collected. Specifically, it consists of total amount of 54,600 videos which are recorded from 75 subjects with 225 realistic masks under 7 new kinds of sensors. Based on this dataset and Protocol 3 which evaluates both the discrimination and generalization ability of the algorithm under the open set scenarios, we organized a 3D High-Fidelity Mask Face Presentation Attack Detection Challenge to boost the research of 3D mask based attack detection. It attracted more than 200 teams for the development phase with a total of 18 teams qualifying for the final round. All the results were verified and re-ran by the organizing team, and the results were used for the final ranking. This paper presents an overview of the challenge, including the introduction of the dataset used, the definition of the protocol, the calculation of the evaluation criteria, and the summary and publication of the competition results. Finally, we focus on introducing and analyzing the top ranked algorithms, the conclusion summary, and the research ideas for mask attack detection provided by this competition. |
| Address |
Virtual; October 2021 |
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ICCVW |
| Notes |
HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ LZY2021 |
Serial |
3646 |
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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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DAG; 600.121 |
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Admin @ si @ TKV2021 |
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3622 |
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AN Ruchai; VI Kober; KA Dorofeev; VN Karnaukhov; Mikhail Mozerov |
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Classification of breast abnormalities using a deep convolutional neural network and transfer learning |
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Journal Article |
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2021 |
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Journal of Communications Technology and Electronics |
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66 |
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6 |
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778–783 |
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A new algorithm for classification of breast pathologies in digital mammography using a convolutional neural network and transfer learning is proposed. The following pretrained neural networks were chosen: MobileNetV2, InceptionResNetV2, Xception, and ResNetV2. All mammographic images were pre-processed to improve classification reliability. Transfer training was carried out using additional data augmentation and fine-tuning. The performance of the proposed algorithm for classification of breast pathologies in terms of accuracy on real data is discussed and compared with that of state-of-the-art algorithms on the available MIAS database. |
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LAMP; |
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Admin @ si @ RKD2022 |
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3680 |
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Pau Torras; Arnau Baro; Lei Kang; Alicia Fornes |
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On the Integration of Language Models into Sequence to Sequence Architectures for Handwritten Music Recognition |
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2021 |
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International Society for Music Information Retrieval Conference |
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690-696 |
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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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Virtual; November 2021 |
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ISMIR |
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DAG; 600.140; 600.121 |
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Admin @ si @ TBK2021 |
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3616 |
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