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| Author | Ivet Rafegas; Maria Vanrell; Luis A Alexandre; G. Arias | ||||
| Title | Understanding trained CNNs by indexing neuron selectivity | Type  |
Journal Article | ||
| Year | 2020 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 136 | Issue | Pages | 318-325 | |
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| Abstract | The impressive performance of Convolutional Neural Networks (CNNs) when solving different vision problems is shadowed by their black-box nature and our consequent lack of understanding of the representations they build and how these representations are organized. To help understanding these issues, we propose to describe the activity of individual neurons by their Neuron Feature visualization and quantify their inherent selectivity with two specific properties. We explore selectivity indexes for: an image feature (color); and an image label (class membership). Our contribution is a framework to seek or classify neurons by indexing on these selectivity properties. It helps to find color selective neurons, such as a red-mushroom neuron in layer Conv4 or class selective neurons such as dog-face neurons in layer Conv5 in VGG-M, and establishes a methodology to derive other selectivity properties. Indexing on neuron selectivity can statistically draw how features and classes are represented through layers in a moment when the size of trained nets is growing and automatic tools to index neurons can be helpful. | ||||
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| Notes | CIC; 600.087; 600.140; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ RVL2019 | Serial | 3310 | ||
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| Author | Hassan Ahmed Sial; Ramon Baldrich; Maria Vanrell | ||||
| Title | Deep intrinsic decomposition trained on surreal scenes yet with realistic light effects | Type |
Journal Article | ||
| Year | 2020 | Publication | Journal of the Optical Society of America A | Abbreviated Journal | JOSA A |
| Volume | 37 | Issue | 1 | Pages | 1-15 |
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| Abstract | Estimation of intrinsic images still remains a challenging task due to weaknesses of ground-truth datasets, which either are too small or present non-realistic issues. On the other hand, end-to-end deep learning architectures start to achieve interesting results that we believe could be improved if important physical hints were not ignored. In this work, we present a twofold framework: (a) a flexible generation of images overcoming some classical dataset problems such as larger size jointly with coherent lighting appearance; and (b) a flexible architecture tying physical properties through intrinsic losses. Our proposal is versatile, presents low computation time, and achieves state-of-the-art results. | ||||
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| Notes | CIC; 600.140; 600.12; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ SBV2019 | Serial | 3311 | ||
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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 | IEEE Transactions on Affective Computing | Abbreviated Journal | TAC |
| Volume | 12 | Issue | 3 | Pages | 607-621 |
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| 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 | Mohammad Naser Sabet; Pau Buch Cardona; Egils Avots; Kamal Nasrollahi; Sergio Escalera; Thomas B. Moeslund; Gholamreza Anbarjafari | ||||
| Title | Privacy-Constrained Biometric System for Non-cooperative Users | Type |
Journal Article | ||
| Year | 2019 | Publication | Entropy | Abbreviated Journal | ENTROPY |
| Volume | 21 | Issue | 11 | Pages | 1033 |
| Keywords | biometric recognition; multimodal-based human identification; privacy; deep learning | ||||
| Abstract | With the consolidation of the new data protection regulation paradigm for each individual within the European Union (EU), major biometric technologies are now confronted with many concerns related to user privacy in biometric deployments. When individual biometrics are disclosed, the sensitive information about his/her personal data such as financial or health are at high risk of being misused or compromised. This issue can be escalated considerably over scenarios of non-cooperative users, such as elderly people residing in care homes, with their inability to interact conveniently and securely with the biometric system. The primary goal of this study is to design a novel database to investigate the problem of automatic people recognition under privacy constraints. To do so, the collected data-set contains the subject’s hand and foot traits and excludes the face biometrics of individuals in order to protect their privacy. We carried out extensive simulations using different baseline methods, including deep learning. Simulation results show that, with the spatial features extracted from the subject sequence in both individual hand or foot videos, state-of-the-art deep models provide promising recognition performance. | ||||
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| Notes | HuPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ NBA2019 | Serial | 3313 | ||
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| Author | Wenlong Deng; Yongli Mou; Takahiro Kashiwa; Sergio Escalera; Kohei Nagai; Kotaro Nakayama; Yutaka Matsuo; Helmut Prendinger | ||||
| Title | Vision based Pixel-level Bridge Structural Damage Detection Using a Link ASPP Network | Type |
Journal Article | ||
| Year | 2020 | Publication | Automation in Construction | Abbreviated Journal | AC |
| Volume | 110 | Issue | Pages | 102973 | |
| Keywords | Semantic image segmentation; Deep learning | ||||
| Abstract | Structural Health Monitoring (SHM) has greatly benefited from computer vision. Recently, deep learning approaches are widely used to accurately estimate the state of deterioration of infrastructure. In this work, we focus on the problem of bridge surface structural damage detection, such as delamination and rebar exposure. It is well known that the quality of a deep learning model is highly dependent on the quality of the training dataset. Bridge damage detection, our application domain, has the following main challenges: (i) labeling the damages requires knowledgeable civil engineering professionals, which makes it difficult to collect a large annotated dataset; (ii) the damage area could be very small, whereas the background area is large, which creates an unbalanced training environment; (iii) due to the difficulty to exactly determine the extension of the damage, there is often a variation among different labelers who perform pixel-wise labeling. In this paper, we propose a novel model for bridge structural damage detection to address the first two challenges. This paper follows the idea of an atrous spatial pyramid pooling (ASPP) module that is designed as a novel network for bridge damage detection. Further, we introduce the weight balanced Intersection over Union (IoU) loss function to achieve accurate segmentation on a highly unbalanced small dataset. The experimental results show that (i) the IoU loss function improves the overall performance of damage detection, as compared to cross entropy loss or focal loss, and (ii) the proposed model has a better ability to detect a minority class than other light segmentation networks. | ||||
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| Notes | HuPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ DMK2020 | Serial | 3314 | ||
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| Author | Juan Jose Rubio; Takahiro Kashiwa; Teera Laiteerapong; Wenlong Deng; Kohei Nagai; Sergio Escalera; Kotaro Nakayama; Yutaka Matsuo; Helmut Prendinger | ||||
| Title | Multi-class structural damage segmentation using fully convolutional networks | Type |
Journal Article | ||
| Year | 2019 | Publication | Computers in Industry | Abbreviated Journal | COMPUTIND |
| Volume | 112 | Issue | Pages | 103121 | |
| Keywords | Bridge damage detection; Deep learning; Semantic segmentation | ||||
| Abstract | Structural Health Monitoring (SHM) has benefited from computer vision and more recently, Deep Learning approaches, to accurately estimate the state of deterioration of infrastructure. In our work, we test Fully Convolutional Networks (FCNs) with a dataset of deck areas of bridges for damage segmentation. We create a dataset for delamination and rebar exposure that has been collected from inspection records of bridges in Niigata Prefecture, Japan. The dataset consists of 734 images with three labels per image, which makes it the largest dataset of images of bridge deck damage. This data allows us to estimate the performance of our method based on regions of agreement, which emulates the uncertainty of in-field inspections. We demonstrate the practicality of FCNs to perform automated semantic segmentation of surface damages. Our model achieves a mean accuracy of 89.7% for delamination and 78.4% for rebar exposure, and a weighted F1 score of 81.9%. | ||||
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| Notes | HuPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ RKL2019 | Serial | 3315 | ||
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| Author | Egils Avots; Meysam Madadi; Sergio Escalera; Jordi Gonzalez; Xavier Baro; Paul Pallin; Gholamreza Anbarjafari | ||||
| Title | From 2D to 3D geodesic-based garment matching | Type |
Journal Article | ||
| Year | 2019 | Publication | Multimedia Tools and Applications | Abbreviated Journal | MTAP |
| Volume | 78 | Issue | 18 | Pages | 25829–25853 |
| Keywords | Shape matching; Geodesic distance; Texture mapping; RGBD image processing; Gaussian mixture model | ||||
| Abstract | A new approach for 2D to 3D garment retexturing is proposed based on Gaussian mixture models and thin plate splines (TPS). An automatically segmented garment of an individual is matched to a new source garment and rendered, resulting in augmented images in which the target garment has been retextured using the texture of the source garment. We divide the problem into garment boundary matching based on Gaussian mixture models and then interpolate inner points using surface topology extracted through geodesic paths, which leads to a more realistic result than standard approaches. We evaluated and compared our system quantitatively by root mean square error (RMS) and qualitatively using the mean opinion score (MOS), showing the benefits of the proposed methodology on our gathered dataset. | ||||
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| Notes | HuPBA; ISE; 600.098; 600.119; 602.133 | Approved | no | ||
| Call Number | Admin @ si @ AME2019 | Serial | 3317 | ||
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| Author | Andre Litvin; Kamal Nasrollahi; Sergio Escalera; Cagri Ozcinar; Thomas B. Moeslund; Gholamreza Anbarjafari | ||||
| Title | A Novel Deep Network Architecture for Reconstructing RGB Facial Images from Thermal for Face Recognition | Type |
Journal Article | ||
| Year | 2019 | Publication | Multimedia Tools and Applications | Abbreviated Journal | MTAP |
| Volume | 78 | Issue | 18 | Pages | 25259–25271 |
| Keywords | Fully convolutional networks; FusionNet; Thermal imaging; Face recognition | ||||
| Abstract | This work proposes a fully convolutional network architecture for RGB face image generation from a given input thermal face image to be applied in face recognition scenarios. The proposed method is based on the FusionNet architecture and increases robustness against overfitting using dropout after bridge connections, randomised leaky ReLUs (RReLUs), and orthogonal regularization. Furthermore, we propose to use a decoding block with resize convolution instead of transposed convolution to improve final RGB face image generation. To validate our proposed network architecture, we train a face classifier and compare its face recognition rate on the reconstructed RGB images from the proposed architecture, to those when reconstructing images with the original FusionNet, as well as when using the original RGB images. As a result, we are introducing a new architecture which leads to a more accurate network. | ||||
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| Notes | HuPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ LNE2019 | Serial | 3318 | ||
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| Author | Ikechukwu Ofodile; Ahmed Helmi; Albert Clapes; Egils Avots; Kerttu Maria Peensoo; Sandhra Mirella Valdma; Andreas Valdmann; Heli Valtna Lukner; Sergey Omelkov; Sergio Escalera; Cagri Ozcinar; Gholamreza Anbarjafari | ||||
| Title | Action recognition using single-pixel time-of-flight detection | Type |
Journal Article | ||
| Year | 2019 | Publication | Entropy | Abbreviated Journal | ENTROPY |
| Volume | 21 | Issue | 4 | Pages | 414 |
| Keywords | single pixel single photon image acquisition; time-of-flight; action recognition | ||||
| Abstract | Action recognition is a challenging task that plays an important role in many robotic systems, which highly depend on visual input feeds. However, due to privacy concerns, it is important to find a method which can recognise actions without using visual feed. In this paper, we propose a concept for detecting actions while preserving the test subject’s privacy. Our proposed method relies only on recording the temporal evolution of light pulses scattered back from the scene.
Such data trace to record one action contains a sequence of one-dimensional arrays of voltage values acquired by a single-pixel detector at 1 GHz repetition rate. Information about both the distance to the object and its shape are embedded in the traces. We apply machine learning in the form of recurrent neural networks for data analysis and demonstrate successful action recognition. The experimental results show that our proposed method could achieve on average 96.47% accuracy on the actions walking forward, walking backwards, sitting down, standing up and waving hand, using recurrent neural network. |
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| Notes | HuPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ OHC2019 | Serial | 3319 | ||
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| Author | Dena Bazazian; Raul Gomez; Anguelos Nicolaou; Lluis Gomez; Dimosthenis Karatzas; Andrew Bagdanov | ||||
| Title | Fast: Facilitated and accurate scene text proposals through fcn guided pruning | Type |
Journal Article | ||
| Year | 2019 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 119 | Issue | Pages | 112-120 | |
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| Abstract | Class-specific text proposal algorithms can efficiently reduce the search space for possible text object locations in an image. In this paper we combine the Text Proposals algorithm with Fully Convolutional Networks to efficiently reduce the number of proposals while maintaining the same recall level and thus gaining a significant speed up. Our experiments demonstrate that such text proposal approaches yield significantly higher recall rates than state-of-the-art text localization techniques, while also producing better-quality localizations. Our results on the ICDAR 2015 Robust Reading Competition (Challenge 4) and the COCO-text datasets show that, when combined with strong word classifiers, this recall margin leads to state-of-the-art results in end-to-end scene text recognition. | ||||
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| Notes | DAG; 600.084; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ BGN2019 | Serial | 3342 | ||
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| Author | Lei Kang; Pau Riba; Mauricio Villegas; Alicia Fornes; Marçal Rusiñol | ||||
| Title | Candidate Fusion: Integrating Language Modelling into a Sequence-to-Sequence Handwritten Word Recognition Architecture | Type |
Journal Article | ||
| Year | 2021 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 112 | Issue | Pages | 107790 | |
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| Abstract | Sequence-to-sequence models have recently become very popular for tackling
handwritten word recognition problems. However, how to effectively integrate an external language model into such recognizer is still a challenging problem. The main challenge faced when training a language model is to deal with the language model corpus which is usually different to the one used for training the handwritten word recognition system. Thus, the bias between both word corpora leads to incorrectness on the transcriptions, providing similar or even worse performances on the recognition task. In this work, we introduce Candidate Fusion, a novel way to integrate an external language model to a sequence-to-sequence architecture. Moreover, it provides suggestions from an external language knowledge, as a new input to the sequence-to-sequence recognizer. Hence, Candidate Fusion provides two improvements. On the one hand, the sequence-to-sequence recognizer has the flexibility not only to combine the information from itself and the language model, but also to choose the importance of the information provided by the language model. On the other hand, the external language model has the ability to adapt itself to the training corpus and even learn the most commonly errors produced from the recognizer. Finally, by conducting comprehensive experiments, the Candidate Fusion proves to outperform the state-of-the-art language models for handwritten word recognition tasks. |
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| Notes | DAG; 600.140; 601.302; 601.312; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ KRV2021 | Serial | 3343 | ||
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| Author | Fei Yang; Yongmei Cheng; Joost Van de Weijer; Mikhail Mozerov | ||||
| Title | Improved Discrete Optical Flow Estimation With Triple Image Matching Cost | Type |
Journal Article | ||
| Year | 2020 | Publication | IEEE Access | Abbreviated Journal | ACCESS |
| Volume | 8 | Issue | Pages | 17093 - 17102 | |
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| Abstract | Approaches that use more than two consecutive video frames in the optical flow estimation have a long research history. However, almost all such methods utilize extra information for a pre-processing flow prediction or for a post-processing flow correction and filtering. In contrast, this paper differs from previously developed techniques. We propose a new algorithm for the likelihood function calculation (alternatively the matching cost volume) that is used in the maximum a posteriori estimation. We exploit the fact that in general, optical flow is locally constant in the sense of time and the likelihood function depends on both the previous and the future frame. Implementation of our idea increases the robustness of optical flow estimation. As a result, our method outperforms 9% over the DCFlow technique, which we use as prototype for our CNN based computation architecture, on the most challenging MPI-Sintel dataset for the non-occluded mask metric. Furthermore, our approach considerably increases the accuracy of the flow estimation for the matching cost processing, consequently outperforming the original DCFlow algorithm results up to 50% in occluded regions and up to 9% in non-occluded regions on the MPI-Sintel dataset. The experimental section shows that the proposed method achieves state-of-the-arts results especially on the MPI-Sintel dataset. | ||||
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| Notes | LAMP; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ YCW2020 | Serial | 3345 | ||
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| Author | Fei Yang; Luis Herranz; Joost Van de Weijer; Jose Antonio Iglesias; Antonio Lopez; Mikhail Mozerov | ||||
| Title | Variable Rate Deep Image Compression with Modulated Autoencoder | Type |
Journal Article | ||
| Year | 2020 | Publication | IEEE Signal Processing Letters | Abbreviated Journal | SPL |
| Volume | 27 | Issue | Pages | 331-335 | |
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| Abstract | Variable rate is a requirement for flexible and adaptable image and video compression. However, deep image compression methods (DIC) are optimized for a single fixed rate-distortion (R-D) tradeoff. While this can be addressed by training multiple models for different tradeoffs, the memory requirements increase proportionally to the number of models. Scaling the bottleneck representation of a shared autoencoder can provide variable rate compression with a single shared autoencoder. However, the R-D performance using this simple mechanism degrades in low bitrates, and also shrinks the effective range of bitrates. To address these limitations, we formulate the problem of variable R-D optimization for DIC, and propose modulated autoencoders (MAEs), where the representations of a shared autoencoder are adapted to the specific R-D tradeoff via a modulation network. Jointly training this modulated autoencoder and the modulation network provides an effective way to navigate the R-D operational curve. Our experiments show that the proposed method can achieve almost the same R-D performance of independent models with significantly fewer parameters. | ||||
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| Notes | LAMP; ADAS; 600.141; 600.120; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ YHW2020 | Serial | 3346 | ||
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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 | Anjan Dutta; Pau Riba; Josep Llados; Alicia Fornes | ||||
| Title | Hierarchical Stochastic Graphlet Embedding for Graph-based Pattern Recognition | Type |
Journal Article | ||
| Year | 2020 | Publication | Neural Computing and Applications | Abbreviated Journal | NEUCOMA |
| Volume | 32 | Issue | Pages | 11579–11596 | |
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| Abstract | Despite being very successful within the pattern recognition and machine learning community, graph-based methods are often unusable because of the lack of mathematical operations defined in graph domain. Graph embedding, which maps graphs to a vectorial space, has been proposed as a way to tackle these difficulties enabling the use of standard machine learning techniques. However, it is well known that graph embedding functions usually suffer from the loss of structural information. In this paper, we consider the hierarchical structure of a graph as a way to mitigate this loss of information. The hierarchical structure is constructed by topologically clustering the graph nodes and considering each cluster as a node in the upper hierarchical level. Once this hierarchical structure is constructed, we consider several configurations to define the mapping into a vector space given a classical graph embedding, in particular, we propose to make use of the stochastic graphlet embedding (SGE). Broadly speaking, SGE produces a distribution of uniformly sampled low-to-high-order graphlets as a way to embed graphs into the vector space. In what follows, the coarse-to-fine structure of a graph hierarchy and the statistics fetched by the SGE complements each other and includes important structural information with varied contexts. Altogether, these two techniques substantially cope with the usual information loss involved in graph embedding techniques, obtaining a more robust graph representation. This fact has been corroborated through a detailed experimental evaluation on various benchmark graph datasets, where we outperform the state-of-the-art methods. | ||||
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| Notes | DAG; 600.140; 600.121; 600.141 | Approved | no | ||
| Call Number | Admin @ si @ DRL2020 | Serial | 3348 | ||
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