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| Author | Wenjuan Gong; Yue Zhang; Wei Wang; Peng Cheng; Jordi Gonzalez | ||||
| Title | Meta-MMFNet: Meta-learning-based Multi-model Fusion Network for Micro-expression Recognition | Type | Journal Article | ||
| Year | 2023 | Publication | ACM Transactions on Multimedia Computing, Communications, and Applications | Abbreviated Journal | TMCCA |
| Volume | 20 | Issue | 2 | Pages | 1–20 |
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| Abstract | Despite its wide applications in criminal investigations and clinical communications with patients suffering from autism, automatic micro-expression recognition remains a challenging problem because of the lack of training data and imbalanced classes problems. In this study, we proposed a meta-learning-based multi-model fusion network (Meta-MMFNet) to solve the existing problems. The proposed method is based on the metric-based meta-learning pipeline, which is specifically designed for few-shot learning and is suitable for model-level fusion. The frame difference and optical flow features were fused, deep features were extracted from the fused feature, and finally in the meta-learning-based framework, weighted sum model fusion method was applied for micro-expression classification. Meta-MMFNet achieved better results than state-of-the-art methods on four datasets. The code is available at https://github.com/wenjgong/meta-fusion-based-method. | ||||
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| Notes | ISE | Approved | no | ||
| Call Number | Admin @ si @ GZW2023 | Serial | 3862 | ||
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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 | |
| Volume | Issue | 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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| Area | Expedition | Conference | ICMI | ||
| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ OVB2021 | Serial | 3644 | ||
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| Author | Raul Gomez; Yahui Liu; Marco de Nadai; Dimosthenis Karatzas; Bruno Lepri; Nicu Sebe | ||||
| Title | Retrieval Guided Unsupervised Multi-domain Image to Image Translation | Type | Conference Article | ||
| Year | 2020 | Publication | 28th ACM International Conference on Multimedia | Abbreviated Journal | |
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| Abstract | Image to image translation aims to learn a mapping that transforms an image from one visual domain to another. Recent works assume that images descriptors can be disentangled into a domain-invariant content representation and a domain-specific style representation. Thus, translation models seek to preserve the content of source images while changing the style to a target visual domain. However, synthesizing new images is extremely challenging especially in multi-domain translations, as the network has to compose content and style to generate reliable and diverse images in multiple domains. In this paper we propose the use of an image retrieval system to assist the image-to-image translation task. First, we train an image-to-image translation model to map images to multiple domains. Then, we train an image retrieval model using real and generated images to find images similar to a query one in content but in a different domain. Finally, we exploit the image retrieval system to fine-tune the image-to-image translation model and generate higher quality images. Our experiments show the effectiveness of the proposed solution and highlight the contribution of the retrieval network, which can benefit from additional unlabeled data and help image-to-image translation models in the presence of scarce data. | ||||
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| Area | Expedition | Conference | ACM | ||
| Notes | DAG; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ GLN2020 | Serial | 3497 | ||
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| Author | Yaxing Wang; Abel Gonzalez-Garcia; Joost Van de Weijer; Luis Herranz | ||||
| Title | SDIT: Scalable and Diverse Cross-domain Image Translation | Type | Conference Article | ||
| Year | 2019 | Publication | 27th ACM International Conference on Multimedia | Abbreviated Journal | |
| Volume | Issue | Pages | 1267–1276 | ||
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| Abstract | Recently, image-to-image translation research has witnessed remarkable progress. Although current approaches successfully generate diverse outputs or perform scalable image transfer, these properties have not been combined into a single method. To address this limitation, we propose SDIT: Scalable and Diverse image-to-image translation. These properties are combined into a single generator. The diversity is determined by a latent variable which is randomly sampled from a normal distribution. The scalability is obtained by conditioning the network on the domain attributes. Additionally, we also exploit an attention mechanism that permits the generator to focus on the domain-specific attribute. We empirically demonstrate the performance of the proposed method on face mapping and other datasets beyond faces. | ||||
| Address | Nice; Francia; October 2019 | ||||
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| Area | Expedition | Conference | ACM-MM | ||
| Notes | LAMP; 600.106; 600.109; 600.141; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ WGW2019 | Serial | 3363 | ||
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| Author | Xiangyang Li; Luis Herranz; Shuqiang Jiang | ||||
| Title | Multifaceted Analysis of Fine-Tuning in Deep Model for Visual Recognition | Type | Journal | ||
| Year | 2020 | Publication | ACM Transactions on Data Science | Abbreviated Journal | ACM |
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| Abstract | In recent years, convolutional neural networks (CNNs) have achieved impressive performance for various visual recognition scenarios. CNNs trained on large labeled datasets can not only obtain significant performance on most challenging benchmarks but also provide powerful representations, which can be used to a wide range of other tasks. However, the requirement of massive amounts of data to train deep neural networks is a major drawback of these models, as the data available is usually limited or imbalanced. Fine-tuning (FT) is an effective way to transfer knowledge learned in a source dataset to a target task. In this paper, we introduce and systematically investigate several factors that influence the performance of fine-tuning for visual recognition. These factors include parameters for the retraining procedure (e.g., the initial learning rate of fine-tuning), the distribution of the source and target data (e.g., the number of categories in the source dataset, the distance between the source and target datasets) and so on. We quantitatively and qualitatively analyze these factors, evaluate their influence, and present many empirical observations. The results reveal insights into what fine-tuning changes CNN parameters and provide useful and evidence-backed intuitions about how to implement fine-tuning for computer vision tasks. | ||||
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| Notes | LAMP; 600.141; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ LHJ2020 | Serial | 3423 | ||
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| Author | Patrick Brandao; O. Zisimopoulos; E. Mazomenos; G. Ciutib; Jorge Bernal; M. Visentini-Scarzanell; A. Menciassi; P. Dario; A. Koulaouzidis; A. Arezzo; D.J. Hawkes; D. Stoyanov | ||||
| Title | Towards a computed-aided diagnosis system in colonoscopy: Automatic polyp segmentation using convolution neural networks | Type | Journal | ||
| Year | 2018 | Publication | Journal of Medical Robotics Research | Abbreviated Journal | JMRR |
| Volume | 3 | Issue | 2 | Pages | |
| Keywords | convolutional neural networks; colonoscopy; computer aided diagnosis | ||||
| Abstract | Early diagnosis is essential for the successful treatment of bowel cancers including colorectal cancer (CRC) and capsule endoscopic imaging with robotic actuation can be a valuable diagnostic tool when combined with automated image analysis. We present a deep learning rooted detection and segmentation framework for recognizing lesions in colonoscopy and capsule endoscopy images. We restructure established convolution architectures, such as VGG and ResNets, by converting them into fully-connected convolution networks (FCNs), ne-tune them and study their capabilities for polyp segmentation and detection. We additionally use Shape-from-Shading (SfS) to recover depth and provide a richer representation of the tissue's structure in colonoscopy images. Depth is
incorporated into our network models as an additional input channel to the RGB information and we demonstrate that the resulting network yields improved performance. Our networks are tested on publicly available datasets and the most accurate segmentation model achieved a mean segmentation IU of 47.78% and 56.95% on the ETIS-Larib and CVC-Colon datasets, respectively. For polyp detection, the top performing models we propose surpass the current state of the art with detection recalls superior to 90% for all datasets tested. To our knowledge, we present the rst work to use FCNs for polyp segmentation in addition to proposing a novel combination of SfS and RGB that boosts performance. |
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| Notes | MV; no menciona | Approved | no | ||
| Call Number | BZM2018 | Serial | 2976 | ||
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| Author | Alicia Fornes; Josep Llados; Joana Maria Pujadas-Mora | ||||
| Title | Browsing of the Social Network of the Past: Information Extraction from Population Manuscript Images | Type | Book Chapter | ||
| Year | 2020 | Publication | Handwritten Historical Document Analysis, Recognition, and Retrieval – State of the Art and Future Trends | Abbreviated Journal | |
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| Publisher | World Scientific | Place of Publication | Editor | ||
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| ISSN | ISBN | 978-981-120-323-7 | Medium | ||
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| Notes | DAG; 600.140; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ FLP2020 | Serial | 3350 | ||
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| Author | Danna Xue; Javier Vazquez; Luis Herranz; Yang Zhang; Michael S Brown | ||||
| Title | Integrating High-Level Features for Consistent Palette-based Multi-image Recoloring | Type | Journal Article | ||
| Year | 2023 | Publication | Computer Graphics Forum | Abbreviated Journal | CGF |
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| Abstract | Achieving visually consistent colors across multiple images is important when images are used in photo albums, websites, and brochures. Unfortunately, only a handful of methods address multi-image color consistency compared to one-to-one color transfer techniques. Furthermore, existing methods do not incorporate high-level features that can assist graphic designers in their work. To address these limitations, we introduce a framework that builds upon a previous palette-based color consistency method and incorporates three high-level features: white balance, saliency, and color naming. We show how these features overcome the limitations of the prior multi-consistency workflow and showcase the user-friendly nature of our framework. | ||||
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| Notes | CIC; MACO | Approved | no | ||
| Call Number | Admin @ si @ XVH2023 | Serial | 3883 | ||
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| Author | Debora Gil; Antonio Esteban Lansaque; Agnes Borras; Carles Sanchez | ||||
| Title | Enhancing virtual bronchoscopy with intra-operative data using a multi-objective GAN | Type | Journal Article | ||
| Year | 2019 | Publication | International Journal of Computer Assisted Radiology and Surgery | Abbreviated Journal | IJCAR |
| Volume | 7 | Issue | 1 | Pages | |
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| Abstract | This manuscript has been withdrawn by bioRxiv due to upload of an incorrect version of the manuscript by the authors. Therefore, this manuscript should not be cited as reference for this project. | ||||
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| Notes | IAM; 600.139; 600.145 | Approved | no | ||
| Call Number | Admin @ si @ GEB2019 | Serial | 3307 | ||
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| Author | David Berga; Xavier Otazu | ||||
| Title | Modeling Bottom-Up and Top-Down Attention with a Neurodynamic Model of V1 | Type | Journal Article | ||
| Year | 2020 | Publication | Neurocomputing | Abbreviated Journal | NEUCOM |
| Volume | 417 | Issue | Pages | 270-289 | |
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| Abstract | Previous studies suggested that lateral interactions of V1 cells are responsible, among other visual effects, of bottom-up visual attention (alternatively named visual salience or saliency). Our objective is to mimic these connections with a neurodynamic network of firing-rate neurons in order to predict visual attention. Early visual subcortical processes (i.e. retinal and thalamic) are functionally simulated. An implementation of the cortical magnification function is included to define the retinotopical projections towards V1, processing neuronal activity for each distinct view during scene observation. Novel computational definitions of top-down inhibition (in terms of inhibition of return, oculomotor and selection mechanisms), are also proposed to predict attention in Free-Viewing and Visual Search tasks. Results show that our model outpeforms other biologically inspired models of saliency prediction while predicting visual saccade sequences with the same model. We also show how temporal and spatial characteristics of saccade amplitude and inhibition of return can improve prediction of saccades, as well as how distinct search strategies (in terms of feature-selective or category-specific inhibition) can predict attention at distinct image contexts. | ||||
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| Notes | NEUROBIT | Approved | no | ||
| Call Number | Admin @ si @ BeO2020c | Serial | 3444 | ||
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| Author | Beata Megyesi; Bernhard Esslinger; Alicia Fornes; Nils Kopal; Benedek Lang; George Lasry; Karl de Leeuw; Eva Pettersson; Arno Wacker; Michelle Waldispuhl | ||||
| Title | Decryption of historical manuscripts: the DECRYPT project | Type | Journal Article | ||
| Year | 2020 | Publication | Cryptologia | Abbreviated Journal | CRYPT |
| Volume | 44 | Issue | 6 | Pages | 545-559 |
| Keywords | automatic decryption; cipher collection; historical cryptology; image transcription | ||||
| Abstract | Many historians and linguists are working individually and in an uncoordinated fashion on the identification and decryption of historical ciphers. This is a time-consuming process as they often work without access to automatic methods and processes that can accelerate the decipherment. At the same time, computer scientists and cryptologists are developing algorithms to decrypt various cipher types without having access to a large number of original ciphertexts. In this paper, we describe the DECRYPT project aiming at the creation of resources and tools for historical cryptology by bringing the expertise of various disciplines together for collecting data, exchanging methods for faster progress to transcribe, decrypt and contextualize historical encrypted manuscripts. We present our goals and work-in progress of a general approach for analyzing historical encrypted manuscripts using standardized methods and a new set of state-of-the-art tools. We release the data and tools as open-source hoping that all mentioned disciplines would benefit and contribute to the research infrastructure of historical cryptology. | ||||
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| Notes | DAG; 600.140; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ MEF2020 | Serial | 3347 | ||
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| Author | Hannes Mueller; Andre Groeger; Jonathan Hersh; Andrea Matranga; Joan Serrat | ||||
| Title | Monitoring war destruction from space using machine learning | Type | Journal Article | ||
| Year | 2021 | Publication | Proceedings of the National Academy of Sciences of the United States of America | Abbreviated Journal | PNAS |
| Volume | 118 | Issue | 23 | Pages | e2025400118 |
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| Abstract | Existing data on building destruction in conflict zones rely on eyewitness reports or manual detection, which makes it generally scarce, incomplete, and potentially biased. This lack of reliable data imposes severe limitations for media reporting, humanitarian relief efforts, human-rights monitoring, reconstruction initiatives, and academic studies of violent conflict. This article introduces an automated method of measuring destruction in high-resolution satellite images using deep-learning techniques combined with label augmentation and spatial and temporal smoothing, which exploit the underlying spatial and temporal structure of destruction. As a proof of concept, we apply this method to the Syrian civil war and reconstruct the evolution of damage in major cities across the country. Our approach allows generating destruction data with unprecedented scope, resolution, and frequency—and makes use of the ever-higher frequency at which satellite imagery becomes available. | ||||
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| Notes | ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ MGH2021 | Serial | 3584 | ||
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| Author | Ajian Liu; Xuan Li; Jun Wan; Yanyan Liang; Sergio Escalera; Hugo Jair Escalante; Meysam Madadi; Yi Jin; Zhuoyuan Wu; Xiaogang Yu; Zichang Tan; Qi Yuan; Ruikun Yang; Benjia Zhou; Guodong Guo; Stan Z. Li | ||||
| Title | Cross-ethnicity Face Anti-spoofing Recognition Challenge: A Review | Type | Journal Article | ||
| Year | 2020 | Publication | IET Biometrics | Abbreviated Journal | BIO |
| Volume | 10 | Issue | 1 | Pages | 24-43 |
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| Abstract | Face anti-spoofing is critical to prevent face recognition systems from a security breach. The biometrics community has %possessed achieved impressive progress recently due the excellent performance of deep neural networks and the availability of large datasets. Although ethnic bias has been verified to severely affect the performance of face recognition systems, it still remains an open research problem in face anti-spoofing. Recently, a multi-ethnic face anti-spoofing dataset, CASIA-SURF CeFA, has been released with the goal of measuring the ethnic bias. It is the largest up to date cross-ethnicity face anti-spoofing dataset covering 3 ethnicities, 3 modalities, 1,607 subjects, 2D plus 3D attack types, and the first dataset including explicit ethnic labels among the recently released datasets for face anti-spoofing. We organized the Chalearn Face Anti-spoofing Attack Detection Challenge which consists of single-modal (e.g., RGB) and multi-modal (e.g., RGB, Depth, Infrared (IR)) tracks around this novel resource to boost research aiming to alleviate the ethnic bias. Both tracks have attracted 340 teams in the development stage, and finally 11 and 8 teams have submitted their codes in the single-modal and multi-modal face anti-spoofing recognition challenges, respectively. 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 its design, evaluation protocol and a summary of results. We analyze the top ranked solutions and draw conclusions derived from the competition. In addition we outline future work directions. | ||||
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| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ LLW2020b | Serial | 3523 | ||
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| Author | I. Sorodoc; S. Pezzelle; A. Herbelot; Mariella Dimiccoli; R. Bernardi | ||||
| Title | Learning quantification from images: A structured neural architecture | Type | Journal Article | ||
| Year | 2018 | Publication | Natural Language Engineering | Abbreviated Journal | NLE |
| Volume | 24 | Issue | 3 | Pages | 363-392 |
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| Abstract | Major advances have recently been made in merging language and vision representations. Most tasks considered so far have confined themselves to the processing of objects and lexicalised relations amongst objects (content words). We know, however, that humans (even pre-school children) can abstract over raw multimodal data to perform certain types of higher level reasoning, expressed in natural language by function words. A case in point is given by their ability to learn quantifiers, i.e. expressions like few, some and all. From formal semantics and cognitive linguistics, we know that quantifiers are relations over sets which, as a simplification, we can see as proportions. For instance, in most fish are red, most encodes the proportion of fish which are red fish. In this paper, we study how well current neural network strategies model such relations. We propose a task where, given an image and a query expressed by an object–property pair, the system must return a quantifier expressing which proportions of the queried object have the queried property. Our contributions are twofold. First, we show that the best performance on this task involves coupling state-of-the-art attention mechanisms with a network architecture mirroring the logical structure assigned to quantifiers by classic linguistic formalisation. Second, we introduce a new balanced dataset of image scenarios associated with quantification queries, which we hope will foster further research in this area. | ||||
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| Notes | MILAB; no menciona | Approved | no | ||
| Call Number | Admin @ si @ SPH2018 | Serial | 3021 | ||
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| Author | David Berga; Xose R. Fernandez-Vidal; Xavier Otazu; V. Leboran; Xose M. Pardo | ||||
| Title | Psychophysical evaluation of individual low-level feature influences on visual attention | Type | Journal Article | ||
| Year | 2019 | Publication | Vision Research | Abbreviated Journal | VR |
| Volume | 154 | Issue | Pages | 60-79 | |
| Keywords | Visual attention; Psychophysics; Saliency; Task; Context; Contrast; Center bias; Low-level; Synthetic; Dataset | ||||
| Abstract | In this study we provide the analysis of eye movement behavior elicited by low-level feature distinctiveness with a dataset of synthetically-generated image patterns. Design of visual stimuli was inspired by the ones used in previous psychophysical experiments, namely in free-viewing and visual searching tasks, to provide a total of 15 types of stimuli, divided according to the task and feature to be analyzed. Our interest is to analyze the influences of low-level feature contrast between a salient region and the rest of distractors, providing fixation localization characteristics and reaction time of landing inside the salient region. Eye-tracking data was collected from 34 participants during the viewing of a 230 images dataset. Results show that saliency is predominantly and distinctively influenced by: 1. feature type, 2. feature contrast, 3. temporality of fixations, 4. task difficulty and 5. center bias. This experimentation proposes a new psychophysical basis for saliency model evaluation using synthetic images. | ||||
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| Notes | NEUROBIT; 600.128; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ BFO2019a | Serial | 3274 | ||
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