| Records |
| Author |
Carola Figueroa Flores; Abel Gonzalez-Garcia; Joost Van de Weijer; Bogdan Raducanu |
| Title |
Saliency for fine-grained object recognition in domains with scarce training data |
Type |
Journal Article |
| Year |
2019 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
94 |
Issue  |
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Pages |
62-73 |
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| Abstract |
This paper investigates the role of saliency to improve the classification accuracy of a Convolutional Neural Network (CNN) for the case when scarce training data is available. Our approach consists in adding a saliency branch to an existing CNN architecture which is used to modulate the standard bottom-up visual features from the original image input, acting as an attentional mechanism that guides the feature extraction process. The main aim of the proposed approach is to enable the effective training of a fine-grained recognition model with limited training samples and to improve the performance on the task, thereby alleviating the need to annotate a large dataset. The vast majority of saliency methods are evaluated on their ability to generate saliency maps, and not on their functionality in a complete vision pipeline. Our proposed pipeline allows to evaluate saliency methods for the high-level task of object recognition. We perform extensive experiments on various fine-grained datasets (Flowers, Birds, Cars, and Dogs) under different conditions and show that saliency can considerably improve the network’s performance, especially for the case of scarce training data. Furthermore, our experiments show that saliency methods that obtain improved saliency maps (as measured by traditional saliency benchmarks) also translate to saliency methods that yield improved performance gains when applied in an object recognition pipeline. |
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| Notes |
LAMP; 600.109; 600.141; 600.120 |
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no |
| Call Number |
Admin @ si @ FGW2019 |
Serial |
3264 |
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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 |
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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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NEUROBIT; 600.128; 600.120 |
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no |
| Call Number |
Admin @ si @ BFO2019a |
Serial |
3274 |
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| Author |
Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes |
| Title |
From Optical Music Recognition to Handwritten Music Recognition: a Baseline |
Type |
Journal Article |
| Year |
2019 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
123 |
Issue |
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Pages |
1-8 |
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| Abstract |
Optical Music Recognition (OMR) is the branch of document image analysis that aims to convert images of musical scores into a computer-readable format. Despite decades of research, the recognition of handwritten music scores, concretely the Western notation, is still an open problem, and the few existing works only focus on a specific stage of OMR. In this work, we propose a full Handwritten Music Recognition (HMR) system based on Convolutional Recurrent Neural Networks, data augmentation and transfer learning, that can serve as a baseline for the research community. |
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DAG; 600.097; 601.302; 601.330; 600.140; 600.121 |
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no |
| Call Number |
Admin @ si @ BRC2019 |
Serial |
3275 |
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| Author |
Md. Mostafa Kamal Sarker; Hatem A. Rashwan; Farhan Akram; Estefania Talavera; Syeda Furruka Banu; Petia Radeva; Domenec Puig |
| Title |
Recognizing Food Places in Egocentric Photo-Streams Using Multi-Scale Atrous Convolutional Networks and Self-Attention Mechanism |
Type |
Journal Article |
| Year |
2019 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
| Volume |
7 |
Issue |
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Pages |
39069-39082 |
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| Abstract |
Wearable sensors (e.g., lifelogging cameras) represent very useful tools to monitor people's daily habits and lifestyle. Wearable cameras are able to continuously capture different moments of the day of their wearers, their environment, and interactions with objects, people, and places reflecting their personal lifestyle. The food places where people eat, drink, and buy food, such as restaurants, bars, and supermarkets, can directly affect their daily dietary intake and behavior. Consequently, developing an automated monitoring system based on analyzing a person's food habits from daily recorded egocentric photo-streams of the food places can provide valuable means for people to improve their eating habits. This can be done by generating a detailed report of the time spent in specific food places by classifying the captured food place images to different groups. In this paper, we propose a self-attention mechanism with multi-scale atrous convolutional networks to generate discriminative features from image streams to recognize a predetermined set of food place categories. We apply our model on an egocentric food place dataset called “EgoFoodPlaces” that comprises of 43 392 images captured by 16 individuals using a lifelogging camera. The proposed model achieved an overall classification accuracy of 80% on the “EgoFoodPlaces” dataset, respectively, outperforming the baseline methods, such as VGG16, ResNet50, and InceptionV3. |
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MILAB; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ SRA2019 |
Serial |
3296 |
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| Author |
Stefan Lonn; Petia Radeva; Mariella Dimiccoli |
| Title |
Smartphone picture organization: A hierarchical approach |
Type |
Journal Article |
| Year |
2019 |
Publication |
Computer Vision and Image Understanding |
Abbreviated Journal |
CVIU |
| Volume |
187 |
Issue |
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Pages |
102789 |
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| Abstract |
We live in a society where the large majority of the population has a camera-equipped smartphone. In addition, hard drives and cloud storage are getting cheaper and cheaper, leading to a tremendous growth in stored personal photos. Unlike photo collections captured by a digital camera, which typically are pre-processed by the user who organizes them into event-related folders, smartphone pictures are automatically stored in the cloud. As a consequence, photo collections captured by a smartphone are highly unstructured and because smartphones are ubiquitous, they present a larger variability compared to pictures captured by a digital camera. To solve the need of organizing large smartphone photo collections automatically, we propose here a new methodology for hierarchical photo organization into topics and topic-related categories. Our approach successfully estimates latent topics in the pictures by applying probabilistic Latent Semantic Analysis, and automatically assigns a name to each topic by relying on a lexical database. Topic-related categories are then estimated by using a set of topic-specific Convolutional Neuronal Networks. To validate our approach, we ensemble and make public a large dataset of more than 8,000 smartphone pictures from 40 persons. Experimental results demonstrate major user satisfaction with respect to state of the art solutions in terms of organization. |
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MILAB; no proj |
Approved |
no |
| Call Number |
Admin @ si @ LRD2019 |
Serial |
3297 |
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| Author |
Eduardo Aguilar; Marc Bolaños; Petia Radeva |
| Title |
Regularized uncertainty-based multi-task learning model for food analysis |
Type |
Journal Article |
| Year |
2019 |
Publication |
Journal of Visual Communication and Image Representation |
Abbreviated Journal |
JVCIR |
| Volume |
60 |
Issue |
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Pages |
360-370 |
| Keywords |
Multi-task models; Uncertainty modeling; Convolutional neural networks; Food image analysis; Food recognition; Food group recognition; Ingredients recognition; Cuisine recognition |
| Abstract |
Food plays an important role in several aspects of our daily life. Several computer vision approaches have been proposed for tackling food analysis problems, but very little effort has been done in developing methodologies that could take profit of the existent correlation between tasks. In this paper, we propose a new multi-task model that is able to simultaneously predict different food-related tasks, e.g. dish, cuisine and food categories. Here, we extend the homoscedastic uncertainty modeling to allow single-label and multi-label classification and propose a regularization term, which jointly weighs the tasks as well as their correlations. Furthermore, we propose a new Multi-Attribute Food dataset and a new metric, Multi-Task Accuracy. We prove that using both our uncertainty-based loss and the class regularization term, we are able to improve the coherence of outputs between different tasks. Moreover, we outperform the use of task-specific models on classical measures like accuracy or . |
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MILAB; no proj |
Approved |
no |
| Call Number |
Admin @ si @ ABR2019 |
Serial |
3298 |
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| Author |
Aitor Alvarez-Gila; Adrian Galdran; Estibaliz Garrote; Joost Van de Weijer |
| Title |
Self-supervised blur detection from synthetically blurred scenes |
Type |
Journal Article |
| Year |
2019 |
Publication |
Image and Vision Computing |
Abbreviated Journal |
IMAVIS |
| Volume |
92 |
Issue |
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Pages |
103804 |
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| Abstract |
Blur detection aims at segmenting the blurred areas of a given image. Recent deep learning-based methods approach this problem by learning an end-to-end mapping between the blurred input and a binary mask representing the localization of its blurred areas. Nevertheless, the effectiveness of such deep models is limited due to the scarcity of datasets annotated in terms of blur segmentation, as blur annotation is labor intensive. In this work, we bypass the need for such annotated datasets for end-to-end learning, and instead rely on object proposals and a model for blur generation in order to produce a dataset of synthetically blurred images. This allows us to perform self-supervised learning over the generated image and ground truth blur mask pairs using CNNs, defining a framework that can be employed in purely self-supervised, weakly supervised or semi-supervised configurations. Interestingly, experimental results of such setups over the largest blur segmentation datasets available show that this approach achieves state of the art results in blur segmentation, even without ever observing any real blurred image. |
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LAMP; 600.109; 600.120 |
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no |
| Call Number |
Admin @ si @ AGG2019 |
Serial |
3301 |
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| Author |
Jiaolong Xu; Liang Xiao; Antonio Lopez |
| Title |
Self-supervised Domain Adaptation for Computer Vision Tasks |
Type |
Journal Article |
| Year |
2019 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
| Volume |
7 |
Issue |
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Pages |
156694 - 156706 |
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Recent progress of self-supervised visual representation learning has achieved remarkable success on many challenging computer vision benchmarks. However, whether these techniques can be used for domain adaptation has not been explored. In this work, we propose a generic method for self-supervised domain adaptation, using object recognition and semantic segmentation of urban scenes as use cases. Focusing on simple pretext/auxiliary tasks (e.g. image rotation prediction), we assess different learning strategies to improve domain adaptation effectiveness by self-supervision. Additionally, we propose two complementary strategies to further boost the domain adaptation accuracy on semantic segmentation within our method, consisting of prediction layer alignment and batch normalization calibration. The experimental results show adaptation levels comparable to most studied domain adaptation methods, thus, bringing self-supervision as a new alternative for reaching domain adaptation. The code is available at this link. https://github.com/Jiaolong/self-supervised-da. |
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ADAS; 600.118 |
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no |
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Admin @ si @ XXL2019 |
Serial |
3302 |
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| Author |
Cesar de Souza; Adrien Gaidon; Yohann Cabon; Naila Murray; Antonio Lopez |
| Title |
Generating Human Action Videos by Coupling 3D Game Engines and Probabilistic Graphical Models |
Type |
Journal Article |
| Year |
2020 |
Publication |
International Journal of Computer Vision |
Abbreviated Journal |
IJCV |
| Volume |
128 |
Issue |
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Pages |
1505–1536 |
| Keywords |
Procedural generation; Human action recognition; Synthetic data; Physics |
| Abstract |
Deep video action recognition models have been highly successful in recent years but require large quantities of manually-annotated data, which are expensive and laborious to obtain. In this work, we investigate the generation of synthetic training data for video action recognition, as synthetic data have been successfully used to supervise models for a variety of other computer vision tasks. We propose an interpretable parametric generative model of human action videos that relies on procedural generation, physics models and other components of modern game engines. With this model we generate a diverse, realistic, and physically plausible dataset of human action videos, called PHAV for “Procedural Human Action Videos”. PHAV contains a total of 39,982 videos, with more than 1000 examples for each of 35 action categories. Our video generation approach is not limited to existing motion capture sequences: 14 of these 35 categories are procedurally-defined synthetic actions. In addition, each video is represented with 6 different data modalities, including RGB, optical flow and pixel-level semantic labels. These modalities are generated almost simultaneously using the Multiple Render Targets feature of modern GPUs. In order to leverage PHAV, we introduce a deep multi-task (i.e. that considers action classes from multiple datasets) representation learning architecture that is able to simultaneously learn from synthetic and real video datasets, even when their action categories differ. Our experiments on the UCF-101 and HMDB-51 benchmarks suggest that combining our large set of synthetic videos with small real-world datasets can boost recognition performance. Our approach also significantly outperforms video representations produced by fine-tuning state-of-the-art unsupervised generative models of videos. |
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| Notes |
ADAS; 600.124; 600.118 |
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no |
| Call Number |
Admin @ si @ SGC2019 |
Serial |
3303 |
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| Author |
Daniel Hernandez; Lukas Schneider; P. Cebrian; A. Espinosa; David Vazquez; Antonio Lopez; Uwe Franke; Marc Pollefeys; Juan Carlos Moure |
| Title |
Slanted Stixels: A way to represent steep streets |
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Journal Article |
| Year |
2019 |
Publication |
International Journal of Computer Vision |
Abbreviated Journal |
IJCV |
| Volume |
127 |
Issue |
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Pages |
1643–1658 |
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This work presents and evaluates a novel compact scene representation based on Stixels that infers geometric and semantic information. Our approach overcomes the previous rather restrictive geometric assumptions for Stixels by introducing a novel depth model to account for non-flat roads and slanted objects. Both semantic and depth cues are used jointly to infer the scene representation in a sound global energy minimization formulation. Furthermore, a novel approximation scheme is introduced in order to significantly reduce the computational complexity of the Stixel algorithm, and then achieve real-time computation capabilities. The idea is to first perform an over-segmentation of the image, discarding the unlikely Stixel cuts, and apply the algorithm only on the remaining Stixel cuts. This work presents a novel over-segmentation strategy based on a fully convolutional network, which outperforms an approach based on using local extrema of the disparity map. We evaluate the proposed methods in terms of semantic and geometric accuracy as well as run-time on four publicly available benchmark datasets. Our approach maintains accuracy on flat road scene datasets while improving substantially on a novel non-flat road dataset. |
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| Notes |
ADAS; 600.118; 600.124 |
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no |
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Admin @ si @ HSC2019 |
Serial |
3304 |
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| Author |
David Berga; Xavier Otazu; Xose R. Fernandez-Vidal; Victor Leboran; Xose M. Pardo |
| Title |
Generating Synthetic Images for Visual Attention Modeling |
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Journal Article |
| Year |
2019 |
Publication |
Perception |
Abbreviated Journal |
PER |
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48 |
Issue |
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99 |
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NEUROBIT; no menciona |
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no |
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Admin @ si @ BOF2019 |
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3309 |
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| Author |
Ivet Rafegas; Maria Vanrell; Luis A Alexandre; G. Arias |
| Title |
Understanding trained CNNs by indexing neuron selectivity |
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Journal Article |
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2020 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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136 |
Issue |
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318-325 |
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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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CIC; 600.087; 600.140; 600.118 |
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no |
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Admin @ si @ RVL2019 |
Serial |
3310 |
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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 |
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Journal Article |
| Year |
2020 |
Publication |
Automation in Construction |
Abbreviated Journal |
AC |
| Volume |
110 |
Issue |
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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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HuPBA; no proj |
Approved |
no |
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Admin @ si @ DMK2020 |
Serial |
3314 |
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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 |
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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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HuPBA; no proj |
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no |
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Admin @ si @ RKL2019 |
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3315 |
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| Author |
Dena Bazazian; Raul Gomez; Anguelos Nicolaou; Lluis Gomez; Dimosthenis Karatzas; Andrew Bagdanov |
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Fast: Facilitated and accurate scene text proposals through fcn guided pruning |
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Journal Article |
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2019 |
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Pattern Recognition Letters |
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PRL |
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119 |
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112-120 |
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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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DAG; 600.084; 600.121; 600.129 |
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no |
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Admin @ si @ BGN2019 |
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3342 |
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