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Author | Sounak Dey; Pau Riba; Anjan Dutta; Josep Llados; Yi-Zhe Song | ||||
Title | Doodle to Search: Practical Zero-Shot Sketch-Based Image Retrieval | Type | Conference Article | ||
Year | 2019 | Publication | IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages ![]() |
2179-2188 | ||
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Abstract | In this paper, we investigate the problem of zero-shot sketch-based image retrieval (ZS-SBIR), where human sketches are used as queries to conduct retrieval of photos from unseen categories. We importantly advance prior arts by proposing a novel ZS-SBIR scenario that represents a firm step forward in its practical application. The new setting uniquely recognizes two important yet often neglected challenges of practical ZS-SBIR, (i) the large domain gap between amateur sketch and photo, and (ii) the necessity for moving towards large-scale retrieval. We first contribute to the community a novel ZS-SBIR dataset, QuickDraw-Extended, that consists of 330,000 sketches and 204,000 photos spanning across 110 categories. Highly abstract amateur human sketches are purposefully sourced to maximize the domain gap, instead of ones included in existing datasets that can often be semi-photorealistic. We then formulate a ZS-SBIR framework to jointly model sketches and photos into a common embedding space. A novel strategy to mine the mutual information among domains is specifically engineered to alleviate the domain gap. External semantic knowledge is further embedded to aid semantic transfer. We show that, rather surprisingly, retrieval performance significantly outperforms that of state-of-the-art on existing datasets that can already be achieved using a reduced version of our model. We further demonstrate the superior performance of our full model by comparing with a number of alternatives on the newly proposed dataset. The new dataset, plus all training and testing code of our model, will be publicly released to facilitate future research. | ||||
Address | Long beach; CA; USA; June 2019 | ||||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | CVPR | ||
Notes | DAG; 600.140; 600.121; 600.097 | Approved | no | ||
Call Number | Admin @ si @ DRD2019 | Serial | 3462 | ||
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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 | |
Volume | Issue | Pages ![]() |
2177-2188 | ||
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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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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ICCVW | ||
Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ CCS2021 | Serial | 3648 | ||
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Author | Akhil Gurram; Onay Urfalioglu; Ibrahim Halfaoui; Fahd Bouzaraa; Antonio Lopez | ||||
Title | Monocular Depth Estimation by Learning from Heterogeneous Datasets | Type | Conference Article | ||
Year | 2018 | Publication | IEEE Intelligent Vehicles Symposium | Abbreviated Journal | |
Volume | Issue | Pages ![]() |
2176 - 2181 | ||
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Abstract | Depth estimation provides essential information to perform autonomous driving and driver assistance. Especially, Monocular Depth Estimation is interesting from a practical point of view, since using a single camera is cheaper than many other options and avoids the need for continuous calibration strategies as required by stereo-vision approaches. State-of-the-art methods for Monocular Depth Estimation are based on Convolutional Neural Networks (CNNs). A promising line of work consists of introducing additional semantic information about the traffic scene when training CNNs for depth estimation. In practice, this means that the depth data used for CNN training is complemented with images having pixel-wise semantic labels, which usually are difficult to annotate (eg crowded urban images). Moreover, so far it is common practice to assume that the same raw training data is associated with both types of ground truth, ie, depth and semantic labels. The main contribution of this paper is to show that this hard constraint can be circumvented, ie, that we can train CNNs for depth estimation by leveraging the depth and semantic information coming from heterogeneous datasets. In order to illustrate the benefits of our approach, we combine KITTI depth and Cityscapes semantic segmentation datasets, outperforming state-of-the-art results on Monocular Depth Estimation. | ||||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | IV | ||
Notes | ADAS; 600.124; 600.116; 600.118 | Approved | no | ||
Call Number | Admin @ si @ GUH2018 | Serial | 3183 | ||
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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 |
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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 | |
Volume | Issue | Pages ![]() |
2168-2176 | ||
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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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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ICCVW | ||
Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ GBB2021 | Serial | 3647 | ||
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Author | Karla Lizbeth Caballero; Joel Barajas; Petia Radeva | ||||
Title | Using Reconstructed IVUS Images for Coronary Plaque Classification | Type | Conference Article | ||
Year | 2007 | Publication | Engineering in Medicine and Biology Society, 29th Annual International Conference of the IEEE | Abbreviated Journal | |
Volume | Issue | Pages ![]() |
2167–2170 | ||
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Address | Lyon (France) | ||||
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Notes | MILAB | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ CBR2007 | Serial | 925 | ||
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Author | Mohammad Rouhani; Angel Sappa | ||||
Title | Correspondence Free Registration through a Point-to-Model Distance Minimization | Type | Conference Article | ||
Year | 2011 | Publication | 13th IEEE International Conference on Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages ![]() |
2150-2157 | ||
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Abstract | This paper presents a novel formulation, which derives in a smooth minimization problem, to tackle the rigid registration between a given point set and a model set. Unlike most of the existing works, which are based on minimizing a point-wise correspondence term, we propose to describe the model set by means of an implicit representation. It allows a new definition of the registration error, which works beyond the point level representation. Moreover, it could be used in a gradient-based optimization framework. The proposed approach consists of two stages. Firstly, a novel formulation is proposed that relates the registration parameters with the distance between the model and data set. Secondly, the registration parameters are obtained by means of the Levengberg-Marquardt algorithm. Experimental results and comparisons with state of the art show the validity of the proposed framework. | ||||
Address | Barcelona | ||||
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Series Volume | Series Issue | Edition | |||
ISSN | 1550-5499 | ISBN | 978-1-4577-1101-5 | Medium | |
Area | Expedition | Conference | ICCV | ||
Notes | ADAS | Approved | no | ||
Call Number | Admin @ si @ RoS2011b; ADAS @ adas @ | Serial | 1832 | ||
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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 | |
Volume | Issue | Pages ![]() |
2150-2158 | ||
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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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Language | Summary Language | Original Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ICCVW | ||
Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ MFN2021 | Serial | 3649 | ||
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Author | Jiaolong Xu; David Vazquez; Antonio Lopez; Javier Marin; Daniel Ponsa | ||||
Title | Learning a Part-based Pedestrian Detector in Virtual World | Type | Journal Article | ||
Year | 2014 | Publication | IEEE Transactions on Intelligent Transportation Systems | Abbreviated Journal | TITS |
Volume | 15 | Issue | 5 | Pages ![]() |
2121-2131 |
Keywords | Domain Adaptation; Pedestrian Detection; Virtual Worlds | ||||
Abstract | Detecting pedestrians with on-board vision systems is of paramount interest for assisting drivers to prevent vehicle-to-pedestrian accidents. The core of a pedestrian detector is its classification module, which aims at deciding if a given image window contains a pedestrian. Given the difficulty of this task, many classifiers have been proposed during the last fifteen years. Among them, the so-called (deformable) part-based classifiers including multi-view modeling are usually top ranked in accuracy. Training such classifiers is not trivial since a proper aspect clustering and spatial part alignment of the pedestrian training samples are crucial for obtaining an accurate classifier. In this paper, first we perform automatic aspect clustering and part alignment by using virtual-world pedestrians, i.e., human annotations are not required. Second, we use a mixture-of-parts approach that allows part sharing among different aspects. Third, these proposals are integrated in a learning framework which also allows to incorporate real-world training data to perform domain adaptation between virtual- and real-world cameras. Overall, the obtained results on four popular on-board datasets show that our proposal clearly outperforms the state-of-the-art deformable part-based detector known as latent SVM. | ||||
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Series Volume | Series Issue | Edition | |||
ISSN | 1931-0587 | ISBN | 978-1-4673-2754-1 | Medium | |
Area | Expedition | Conference | |||
Notes | ADAS; 600.076 | Approved | no | ||
Call Number | ADAS @ adas @ XVL2014 | Serial | 2433 | ||
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Author | Fadi Dornaika; Angel Sappa | ||||
Title | Rigid and Non-rigid Face Motion Tracking by Aligning Texture Maps and Stereo 3D Models | Type | Journal Article | ||
Year | 2007 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 28 | Issue | 15 | Pages ![]() |
2116-2126 |
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Notes | ADAS | Approved | no | ||
Call Number | ADAS @ adas @ DoS2007c | Serial | 877 | ||
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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 | |
Volume | Issue | Pages ![]() |
2110-2117 | ||
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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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Language | Summary Language | Original Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ICCVW | ||
Notes | LAMP | Approved | no | ||
Call Number | Admin @ si @ YYC2021 | Serial | 3681 | ||
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Author | Sergio Escalera; Ana Puig; Oscar Amoros; Maria Salamo | ||||
Title | Intelligent GPGPU Classification in Volume Visualization: a framework based on Error-Correcting Output Codes | Type | Journal Article | ||
Year | 2011 | Publication | Computer Graphics Forum | Abbreviated Journal | CGF |
Volume | 30 | Issue | 7 | Pages ![]() |
2107-2115 |
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Abstract | IF JCR 1.455 2010 25/99
In volume visualization, the definition of the regions of interest is inherently an iterative trial-and-error process finding out the best parameters to classify and render the final image. Generally, the user requires a lot of expertise to analyze and edit these parameters through multi-dimensional transfer functions. In this paper, we present a framework of intelligent methods to label on-demand multiple regions of interest. These methods can be split into a two-level GPU-based labelling algorithm that computes in time of rendering a set of labelled structures using the Machine Learning Error-Correcting Output Codes (ECOC) framework. In a pre-processing step, ECOC trains a set of Adaboost binary classifiers from a reduced pre-labelled data set. Then, at the testing stage, each classifier is independently applied on the features of a set of unlabelled samples and combined to perform multi-class labelling. We also propose an alternative representation of these classifiers that allows to highly parallelize the testing stage. To exploit that parallelism we implemented the testing stage in GPU-OpenCL. The empirical results on different data sets for several volume structures shows high computational performance and classification accuracy. |
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Notes | MILAB; HuPBA | Approved | no | ||
Call Number | Admin @ si @ EPA2011 | Serial | 1881 | ||
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Author | Jose Antonio Rodriguez; Florent Perronnin | ||||
Title | Handwritten word-spotting using hidden Markov models and universal vocabularies | Type | Journal Article | ||
Year | 2009 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 42 | Issue | 9 | Pages ![]() |
2103-2116 |
Keywords | Word-spotting; Hidden Markov model; Score normalization; Universal vocabulary; Handwriting recognition | ||||
Abstract | Handwritten word-spotting is traditionally viewed as an image matching task between one or multiple query word-images and a set of candidate word-images in a database. This is a typical instance of the query-by-example paradigm. In this article, we introduce a statistical framework for the word-spotting problem which employs hidden Markov models (HMMs) to model keywords and a Gaussian mixture model (GMM) for score normalization. We explore the use of two types of HMMs for the word modeling part: continuous HMMs (C-HMMs) and semi-continuous HMMs (SC-HMMs), i.e. HMMs with a shared set of Gaussians. We show on a challenging multi-writer corpus that the proposed statistical framework is always superior to a traditional matching system which uses dynamic time warping (DTW) for word-image distance computation. A very important finding is that the SC-HMM is superior when labeled training data is scarce—as low as one sample per keyword—thanks to the prior information which can be incorporated in the shared set of Gaussians. | ||||
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Publisher | Elsevier | Place of Publication | Editor | ||
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Series Volume | Series Issue | Edition | |||
ISSN | 0031-3203 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | Approved | no | |||
Call Number | Admin @ si @ RoP2009 | Serial | 1053 | ||
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Author | Mohammad Rouhani; Angel Sappa | ||||
Title | Implicit Polynomial Representation through a Fast Fitting Error Estimation | Type | Journal Article | ||
Year | 2012 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
Volume | 21 | Issue | 4 | Pages ![]() |
2089-2098 |
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Abstract | Impact Factor
This paper presents a simple distance estimation for implicit polynomial fitting. It is computed as the height of a simplex built between the point and the surface (i.e., a triangle in 2-D or a tetrahedron in 3-D), which is used as a coarse but reliable estimation of the orthogonal distance. The proposed distance can be described as a function of the coefficients of the implicit polynomial. Moreover, it is differentiable and has a smooth behavior . Hence, it can be used in any gradient-based optimization. In this paper, its use in a Levenberg-Marquardt framework is shown, which is particularly devoted for nonlinear least squares problems. The proposed estimation is a generalization of the gradient-based distance estimation, which is widely used in the literature. Experimental results, both in 2-D and 3-D data sets, are provided. Comparisons with state-of-the-art techniques are presented, showing the advantages of the proposed approach. |
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ISSN | 1057-7149 | ISBN | Medium | ||
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Notes | ADAS | Approved | no | ||
Call Number | Admin @ si @ RoS2012b; ADAS @ adas @ | Serial | 1937 | ||
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Author | Arash Akbarinia; C. Alejandro Parraga | ||||
Title | Colour Constancy Beyond the Classical Receptive Field | Type | Journal Article | ||
Year | 2018 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
Volume | 40 | Issue | 9 | Pages ![]() |
2081 - 2094 |
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Abstract | The problem of removing illuminant variations to preserve the colours of objects (colour constancy) has already been solved by the human brain using mechanisms that rely largely on centre-surround computations of local contrast. In this paper we adopt some of these biological solutions described by long known physiological findings into a simple, fully automatic, functional model (termed Adaptive Surround Modulation or ASM). In ASM, the size of a visual neuron's receptive field (RF) as well as the relationship with its surround varies according to the local contrast within the stimulus, which in turn determines the nature of the centre-surround normalisation of cortical neurons higher up in the processing chain. We modelled colour constancy by means of two overlapping asymmetric Gaussian kernels whose sizes are adapted based on the contrast of the surround pixels, resembling the change of RF size. We simulated the contrast-dependent surround modulation by weighting the contribution of each Gaussian according to the centre-surround contrast. In the end, we obtained an estimation of the illuminant from the set of the most activated RFs' outputs. Our results on three single-illuminant and one multi-illuminant benchmark datasets show that ASM is highly competitive against the state-of-the-art and it even outperforms learning-based algorithms in one case. Moreover, the robustness of our model is more tangible if we consider that our results were obtained using the same parameters for all datasets, that is, mimicking how the human visual system operates. These results might provide an insight on how dynamical adaptation mechanisms contribute to make object's colours appear constant to us. | ||||
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Notes | NEUROBIT; 600.068; 600.072 | Approved | no | ||
Call Number | Admin @ si @ AkP2018a | Serial | 2990 | ||
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