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| Author | Julio C. S. Jacques Junior; Cagri Ozcinar; Marina Marjanovic; Xavier Baro; Gholamreza Anbarjafari; Sergio Escalera | ||||
| Title | On the effect of age perception biases for real age regression | Type | Conference Article | ||
| Year | 2019 | Publication | 14th IEEE International Conference on Automatic Face and Gesture Recognition | Abbreviated Journal | |
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| Abstract | Automatic age estimation from facial images represents an important task in computer vision. This paper analyses the effect of gender, age, ethnic, makeup and expression attributes of faces as sources of bias to improve deep apparent age prediction. Following recent works where it is shown that apparent age labels benefit real age estimation, rather than direct real to real age regression, our main contribution is the integration, in an end-to-end architecture, of face attributes for apparent age prediction with an additional loss for real age regression. Experimental results on the APPA-REAL dataset indicate the proposed network successfully take advantage of the adopted attributes to improve both apparent and real age estimation. Our model outperformed a state-of-the-art architecture proposed to separately address apparent and real age regression. Finally, we present preliminary results and discussion of a proof of concept application using the proposed model to regress the apparent age of an individual based on the gender of an external observer. | ||||
| Address | Lille; France; May 2019 | ||||
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| Area | Expedition | Conference | FG | ||
| Notes | HuPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ JOM2019 | Serial  |
3262 | ||
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| Author | Bojana Gajic; Ariel Amato; Ramon Baldrich; Carlo Gatta | ||||
| Title | Bag of Negatives for Siamese Architectures | Type | Conference Article | ||
| Year | 2019 | Publication | 30th British Machine Vision Conference | Abbreviated Journal | |
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| Abstract | Training a Siamese architecture for re-identification with a large number of identities is a challenging task due to the difficulty of finding relevant negative samples efficiently. In this work we present Bag of Negatives (BoN), a method for accelerated and improved training of Siamese networks that scales well on datasets with a very large number of identities. BoN is an efficient and loss-independent method, able to select a bag of high quality negatives, based on a novel online hashing strategy. | ||||
| Address | Cardiff; United Kingdom; September 2019 | ||||
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| Area | Expedition | Conference | BMVC | ||
| Notes | CIC; 600.140; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ GAB2019b | Serial |
3263 | ||
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| 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 | 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; OR; 600.109; 600.141; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ FGW2019 | Serial |
3264 | ||
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| Author | Raul Gomez; Ali Furkan Biten; Lluis Gomez; Jaume Gibert; Marçal Rusiñol; Dimosthenis Karatzas | ||||
| Title | Selective Style Transfer for Text | Type | Conference Article | ||
| Year | 2019 | Publication | 15th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 805-812 | ||
| Keywords | transfer; text style transfer; data augmentation; scene text detection | ||||
| Abstract | This paper explores the possibilities of image style transfer applied to text maintaining the original transcriptions. Results on different text domains (scene text, machine printed text and handwritten text) and cross-modal results demonstrate that this is feasible, and open different research lines. Furthermore, two architectures for selective style transfer, which means
transferring style to only desired image pixels, are proposed. Finally, scene text selective style transfer is evaluated as a data augmentation technique to expand scene text detection datasets, resulting in a boost of text detectors performance. Our implementation of the described models is publicly available. |
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| Address | Sydney; Australia; September 2019 | ||||
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| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.129; 600.135; 601.338; 601.310; 600.121 | Approved | no | ||
| Call Number | GBG2019 | Serial |
3265 | ||
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| Author | Raul Gomez; Lluis Gomez; Jaume Gibert; Dimosthenis Karatzas | ||||
| Title | Self-Supervised Learning from Web Data for Multimodal Retrieval | Type | Book Chapter | ||
| Year | 2019 | Publication | Multi-Modal Scene Understanding Book | Abbreviated Journal | |
| Volume | Issue | Pages | 279-306 | ||
| Keywords | self-supervised learning; webly supervised learning; text embeddings; multimodal retrieval; multimodal embedding | ||||
| Abstract | Self-Supervised learning from multimodal image and text data allows deep neural networks to learn powerful features with no need of human annotated data. Web and Social Media platforms provide a virtually unlimited amount of this multimodal data. In this work we propose to exploit this free available data to learn a multimodal image and text embedding, aiming to leverage the semantic knowledge learnt in the text domain and transfer it to a visual model for semantic image retrieval. We demonstrate that the proposed pipeline can learn from images with associated text without supervision and analyze the semantic structure of the learnt joint image and text embeddingspace. Weperformathoroughanalysisandperformancecomparisonoffivedifferentstateof the art text embeddings in three different benchmarks. We show that the embeddings learnt with Web and Social Media data have competitive performances over supervised methods in the text basedimageretrievaltask,andweclearlyoutperformstateoftheartintheMIRFlickrdatasetwhen training in the target data. Further, we demonstrate how semantic multimodal image retrieval can be performed using the learnt embeddings, going beyond classical instance-level retrieval problems. Finally, we present a new dataset, InstaCities1M, composed by Instagram images and their associated texts that can be used for fair comparison of image-text embeddings. | ||||
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| Notes | DAG; 600.129; 601.338; 601.310 | Approved | no | ||
| Call Number | Admin @ si @ GGG2019 | Serial |
3266 | ||
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| Author | Xialei Liu; Joost Van de Weijer; Andrew Bagdanov | ||||
| Title | Exploiting Unlabeled Data in CNNs by Self-Supervised Learning to Rank | Type | Journal Article | ||
| Year | 2019 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
| Volume | 41 | Issue | 8 | Pages | 1862-1878 |
| Keywords | Task analysis;Training;Image quality;Visualization;Uncertainty;Labeling;Neural networks;Learning from rankings;image quality assessment;crowd counting;active learning | ||||
| Abstract | For many applications the collection of labeled data is expensive laborious. Exploitation of unlabeled data during training is thus a long pursued objective of machine learning. Self-supervised learning addresses this by positing an auxiliary task (different, but related to the supervised task) for which data is abundantly available. In this paper, we show how ranking can be used as a proxy task for some regression problems. As another contribution, we propose an efficient backpropagation technique for Siamese networks which prevents the redundant computation introduced by the multi-branch network architecture. We apply our framework to two regression problems: Image Quality Assessment (IQA) and Crowd Counting. For both we show how to automatically generate ranked image sets from unlabeled data. Our results show that networks trained to regress to the ground truth targets for labeled data and to simultaneously learn to rank unlabeled data obtain significantly better, state-of-the-art results for both IQA and crowd counting. In addition, we show that measuring network uncertainty on the self-supervised proxy task is a good measure of informativeness of unlabeled data. This can be used to drive an algorithm for active learning and we show that this reduces labeling effort by up to 50 percent. | ||||
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| Notes | LAMP; 600.109; 600.106; 600.120 | Approved | no | ||
| Call Number | LWB2019 | Serial |
3267 | ||
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| Author | Anjan Dutta; Zeynep Akata | ||||
| Title | Semantically Tied Paired Cycle Consistency for Zero-Shot Sketch-based Image Retrieval | Type | Conference Article | ||
| Year | 2019 | Publication | 32nd IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 5089-5098 | ||
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| Abstract | Zero-shot sketch-based image retrieval (SBIR) is an emerging task in computer vision, allowing to retrieve natural images relevant to sketch queries that might not been seen in the training phase. Existing works either require aligned sketch-image pairs or inefficient memory fusion layer for mapping the visual information to a semantic space. In this work, we propose a semantically aligned paired cycle-consistent generative (SEM-PCYC) model for zero-shot SBIR, where each branch maps the visual information to a common semantic space via an adversarial training. Each of these branches maintains a cycle consistency that only requires supervision at category levels, and avoids the need of highly-priced aligned sketch-image pairs. A classification criteria on the generators' outputs ensures the visual to semantic space mapping to be discriminating. Furthermore, we propose to combine textual and hierarchical side information via a feature selection auto-encoder that selects discriminating side information within a same end-to-end model. Our results demonstrate a significant boost in zero-shot SBIR performance over the state-of-the-art on the challenging Sketchy and TU-Berlin datasets. | ||||
| Address | Long beach; California; USA; June 2019 | ||||
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| Area | Expedition | Conference | CVPR | ||
| Notes | DAG; 600.141; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ DuA2019 | Serial |
3268 | ||
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| Author | Rafael E. Rivadeneira; Patricia Suarez; Angel Sappa; Boris X. Vintimilla | ||||
| Title | Thermal Image SuperResolution Through Deep Convolutional Neural Network | Type | Conference Article | ||
| Year | 2019 | Publication | 16th International Conference on Images Analysis and Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 417-426 | ||
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| Abstract | Due to the lack of thermal image datasets, a new dataset has been acquired for proposed a super-resolution approach using a Deep Convolution Neural Network schema. In order to achieve this image enhancement process, a new thermal images dataset is used. Different experiments have been carried out, firstly, the proposed architecture has been trained using only images of the visible spectrum, and later it has been trained with images of the thermal spectrum, the results showed that with the network trained with thermal images, better results are obtained in the process of enhancing the images, maintaining the image details and perspective. The thermal dataset is available at http://www.
cidis.espol.edu.ec/es/dataset. |
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| Address | Waterloo; Canada; August 2019 | ||||
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| Area | Expedition | Conference | ICIAR | ||
| Notes | MSIAU; 600.130; 601.349; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ RSS2019 | Serial |
3269 | ||
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| Author | Angel Morera; Angel Sanchez; Angel Sappa; Jose F. Velez | ||||
| Title | Robust Detection of Outdoor Urban Advertising Panels in Static Images | Type | Conference Article | ||
| Year | 2019 | Publication | 18th International Conference on Practical Applications of Agents and Multi-Agent Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 246-256 | ||
| Keywords | Object detection; Urban ads panels; Deep learning; Single Shot Detector (SSD) architecture; Intersection over Union (IoU) metric; Augmented Reality | ||||
| Abstract | One interesting publicity application for Smart City environments is recognizing brand information contained in urban advertising panels. For such a purpose, a previous stage is to accurately detect and locate the position of these panels in images. This work presents an effective solution to this problem using a Single Shot Detector (SSD) based on a deep neural network architecture that minimizes the number of false detections under multiple variable conditions regarding the panels and the scene. Achieved experimental results using the Intersection over Union (IoU) accuracy metric make this proposal applicable in real complex urban images. | ||||
| Address | Aquila; Italia; June 2019 | ||||
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| Area | Expedition | Conference | PAAMS | ||
| Notes | MSIAU; 600.130; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ MSS2019 | Serial |
3270 | ||
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| Author | Armin Mehri; Angel Sappa | ||||
| Title | Colorizing Near Infrared Images through a Cyclic Adversarial Approach of Unpaired Samples | Type | Conference Article | ||
| Year | 2019 | Publication | IEEE International Conference on Computer Vision and Pattern Recognition-Workshops | Abbreviated Journal | |
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| Abstract | This paper presents a novel approach for colorizing near infrared (NIR) images. The approach is based on image-to-image translation using a Cycle-Consistent adversarial network for learning the color channels on unpaired dataset. This architecture is able to handle unpaired datasets. The approach uses as generators tailored networks that require less computation times, converge faster and generate high quality samples. The obtained results have been quantitatively—using standard evaluation metrics—and qualitatively evaluated showing considerable improvements with respect to the state of the art | ||||
| Address | Long beach; California; USA; June 2019 | ||||
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| Area | Expedition | Conference | CVPRW | ||
| Notes | MSIAU; 600.130; 601.349; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ MeS2019 | Serial |
3271 | ||
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| Author | Patricia Suarez; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud | ||||
| Title | Image Vegetation Index through a Cycle Generative Adversarial Network | Type | Conference Article | ||
| Year | 2019 | Publication | IEEE International Conference on Computer Vision and Pattern Recognition-Workshops | Abbreviated Journal | |
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| Abstract | This paper proposes a novel approach to estimate the Normalized Difference Vegetation Index (NDVI) just from an RGB image. The NDVI values are obtained by using images from the visible spectral band together with a synthetic near infrared image obtained by a cycled GAN. The cycled GAN network is able to obtain a NIR image from a given gray scale image. It is trained by using unpaired set of gray scale and NIR images by using a U-net architecture and a multiple loss function (gray scale images are obtained from the provided RGB images). Then, the NIR image estimated with the proposed cycle generative adversarial network is used to compute the NDVI index. Experimental results are provided showing the validity of the proposed approach. Additionally, comparisons with previous approaches are also provided. | ||||
| Address | Long beach; California; USA; June 2019 | ||||
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| Area | Expedition | Conference | CVPRW | ||
| Notes | MSIAU; 600.130; 601.349; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ SSV2019 | Serial |
3272 | ||
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| Author | Victoria Ruiz; Angel Sanchez; Jose F. Velez; Bogdan Raducanu | ||||
| Title | Automatic Image-Based Waste Classification | Type | Conference Article | ||
| Year | 2019 | Publication | International Work-Conference on the Interplay Between Natural and Artificial Computation. From Bioinspired Systems and Biomedical Applications to Machine Learning | Abbreviated Journal | |
| Volume | 11487 | Issue | Pages | 422–431 | |
| Keywords | Computer Vision; Deep learning; Convolutional neural networks; Waste classification | ||||
| Abstract | The management of solid waste in large urban environments has become a complex problem due to increasing amount of waste generated every day by citizens and companies. Current Computer Vision and Deep Learning techniques can help in the automatic detection and classification of waste types for further recycling tasks. In this work, we use the TrashNet dataset to train and compare different deep learning architectures for automatic classification of garbage types. In particular, several Convolutional Neural Networks (CNN) architectures were compared: VGG, Inception and ResNet. The best classification results were obtained using a combined Inception-ResNet model that achieved 88.6% of accuracy. These are the best results obtained with the considered dataset. | ||||
| Address | Almeria; June 2019 | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | IWINAC | ||
| Notes | LAMP; 600.120 | Approved | no | ||
| Call Number | RSV2019 | Serial |
3273 | ||
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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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| 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 | 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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| Notes | DAG; 600.097; 601.302; 601.330; 600.140; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ BRC2019 | Serial |
3275 | ||
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| Author | Arnau Baro; Jialuo Chen; Alicia Fornes; Beata Megyesi | ||||
| Title | Towards a generic unsupervised method for transcription of encoded manuscripts | Type | Conference Article | ||
| Year | 2019 | Publication | 3rd International Conference on Digital Access to Textual Cultural Heritage | Abbreviated Journal | |
| Volume | Issue | Pages | 73-78 | ||
| Keywords | A. Baró, J. Chen, A. Fornés, B. Megyesi. | ||||
| Abstract | Historical ciphers, a special type of manuscripts, contain encrypted information, important for the interpretation of our history. The first step towards decipherment is to transcribe the images, either manually or by automatic image processing techniques. Despite the improvements in handwritten text recognition (HTR) thanks to deep learning methodologies, the need of labelled data to train is an important limitation. Given that ciphers often use symbol sets across various alphabets and unique symbols without any transcription scheme available, these supervised HTR techniques are not suitable to transcribe ciphers. In this paper we propose an un-supervised method for transcribing encrypted manuscripts based on clustering and label propagation, which has been successfully applied to community detection in networks. We analyze the performance on ciphers with various symbol sets, and discuss the advantages and drawbacks compared to supervised HTR methods. | ||||
| Address | Brussels; May 2019 | ||||
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| Area | Expedition | Conference | DATeCH | ||
| Notes | DAG; 600.097; 600.140; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ BCF2019 | Serial |
3276 | ||
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