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Author | Xinhang Song; Haitao Zeng; Sixian Zhang; Luis Herranz; Shuqiang Jiang | ||||
Title | Generalized Zero-shot Learning with Multi-source Semantic Embeddings for Scene Recognition | Type | Conference Article | ||
Year | 2020 | Publication ![]() |
28th ACM International Conference on Multimedia | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | Recognizing visual categories from semantic descriptions is a promising way to extend the capability of a visual classifier beyond the concepts represented in the training data (i.e. seen categories). This problem is addressed by (generalized) zero-shot learning methods (GZSL), which leverage semantic descriptions that connect them to seen categories (e.g. label embedding, attributes). Conventional GZSL are designed mostly for object recognition. In this paper we focus on zero-shot scene recognition, a more challenging setting with hundreds of categories where their differences can be subtle and often localized in certain objects or regions. Conventional GZSL representations are not rich enough to capture these local discriminative differences. Addressing these limitations, we propose a feature generation framework with two novel components: 1) multiple sources of semantic information (i.e. attributes, word embeddings and descriptions), 2) region descriptions that can enhance scene discrimination. To generate synthetic visual features we propose a two-step generative approach, where local descriptions are sampled and used as conditions to generate visual features. The generated features are then aggregated and used together with real features to train a joint classifier. In order to evaluate the proposed method, we introduce a new dataset for zero-shot scene recognition with multi-semantic annotations. Experimental results on the proposed dataset and SUN Attribute dataset illustrate the effectiveness of the proposed method. | ||||
Address | Virtual; October 2020 | ||||
Corporate Author | Thesis | ||||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ACM | ||
Notes | LAMP; 600.141; 600.120 | Approved | no | ||
Call Number | Admin @ si @ SZZ2020 | Serial | 3465 | ||
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Author | Raul Gomez; Yahui Liu; Marco de Nadai; Dimosthenis Karatzas; Bruno Lepri; Nicu Sebe | ||||
Title | Retrieval Guided Unsupervised Multi-domain Image to Image Translation | Type | Conference Article | ||
Year | 2020 | Publication ![]() |
28th ACM International Conference on Multimedia | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | Image to image translation aims to learn a mapping that transforms an image from one visual domain to another. Recent works assume that images descriptors can be disentangled into a domain-invariant content representation and a domain-specific style representation. Thus, translation models seek to preserve the content of source images while changing the style to a target visual domain. However, synthesizing new images is extremely challenging especially in multi-domain translations, as the network has to compose content and style to generate reliable and diverse images in multiple domains. In this paper we propose the use of an image retrieval system to assist the image-to-image translation task. First, we train an image-to-image translation model to map images to multiple domains. Then, we train an image retrieval model using real and generated images to find images similar to a query one in content but in a different domain. Finally, we exploit the image retrieval system to fine-tune the image-to-image translation model and generate higher quality images. Our experiments show the effectiveness of the proposed solution and highlight the contribution of the retrieval network, which can benefit from additional unlabeled data and help image-to-image translation models in the presence of scarce data. | ||||
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Area | Expedition | Conference | ACM | ||
Notes | DAG; 600.121 | Approved | no | ||
Call Number | Admin @ si @ GLN2020 | Serial | 3497 | ||
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Author | Henry Velesaca; Steven Araujo; Patricia Suarez; Angel Sanchez; Angel Sappa | ||||
Title | Off-the-Shelf Based System for Urban Environment Video Analytics | Type | Conference Article | ||
Year | 2020 | Publication ![]() |
27th International Conference on Systems, Signals and Image Processing | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | greenhouse gases; carbon footprint; object detection; object tracking; website framework; off-the-shelf video analytics | ||||
Abstract | This paper presents the design and implementation details of a system build-up by using off-the-shelf algorithms for urban video analytics. The system allows the connection to
public video surveillance camera networks to obtain the necessary information to generate statistics from urban scenarios (e.g., amount of vehicles, type of cars, direction, numbers of persons, etc.). The obtained information could be used not only for traffic management but also to estimate the carbon footprint of urban scenarios. As a case study, a university campus is selected to evaluate the performance of the proposed system. The system is implemented in a modular way so that it is being used as a testbed to evaluate different algorithms. Implementation results are provided showing the validity and utility of the proposed approach. |
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Address | Virtual IWSSIP | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | IWSSIP | ||
Notes | MSIAU; 600.130; 601.349; 600.122 | Approved | no | ||
Call Number | Admin @ si @ VAS2020 | Serial | 3429 | ||
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Author | Marc Serra; Olivier Penacchio; Robert Benavente; Maria Vanrell; Dimitris Samaras | ||||
Title | The Photometry of Intrinsic Images | Type | Conference Article | ||
Year | 2014 | Publication ![]() |
27th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 1494-1501 | ||
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Abstract | Intrinsic characterization of scenes is often the best way to overcome the illumination variability artifacts that complicate most computer vision problems, from 3D reconstruction to object or material recognition. This paper examines the deficiency of existing intrinsic image models to accurately account for the effects of illuminant color and sensor characteristics in the estimation of intrinsic images and presents a generic framework which incorporates insights from color constancy research to the intrinsic image decomposition problem. The proposed mathematical formulation includes information about the color of the illuminant and the effects of the camera sensors, both of which modify the observed color of the reflectance of the objects in the scene during the acquisition process. By modeling these effects, we get a “truly intrinsic” reflectance image, which we call absolute reflectance, which is invariant to changes of illuminant or camera sensors. This model allows us to represent a wide range of intrinsic image decompositions depending on the specific assumptions on the geometric properties of the scene configuration and the spectral properties of the light source and the acquisition system, thus unifying previous models in a single general framework. We demonstrate that even partial information about sensors improves significantly the estimated reflectance images, thus making our method applicable for a wide range of sensors. We validate our general intrinsic image framework experimentally with both synthetic data and natural images. | ||||
Address | Columbus; Ohio; USA; June 2014 | ||||
Corporate Author | Thesis | ||||
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Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | CVPR | ||
Notes | CIC; 600.052; 600.051; 600.074 | Approved | no | ||
Call Number | Admin @ si @ SPB2014 | Serial | 2506 | ||
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Author | M. Danelljan; Fahad Shahbaz Khan; Michael Felsberg; Joost Van de Weijer | ||||
Title | Adaptive color attributes for real-time visual tracking | Type | Conference Article | ||
Year | 2014 | Publication ![]() |
27th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 1090 - 1097 | ||
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Abstract | Visual tracking is a challenging problem in computer vision. Most state-of-the-art visual trackers either rely on luminance information or use simple color representations for image description. Contrary to visual tracking, for object
recognition and detection, sophisticated color features when combined with luminance have shown to provide excellent performance. Due to the complexity of the tracking problem, the desired color feature should be computationally efficient, and possess a certain amount of photometric invariance while maintaining high discriminative power. This paper investigates the contribution of color in a tracking-by-detection framework. Our results suggest that color attributes provides superior performance for visual tracking. We further propose an adaptive low-dimensional variant of color attributes. Both quantitative and attributebased evaluations are performed on 41 challenging benchmark color sequences. The proposed approach improves the baseline intensity-based tracker by 24% in median distance precision. Furthermore, we show that our approach outperforms state-of-the-art tracking methods while running at more than 100 frames per second. |
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Address | Nottingham; UK; September 2014 | ||||
Corporate Author | Thesis | ||||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | CVPR | ||
Notes | CIC; LAMP; 600.074; 600.079 | Approved | no | ||
Call Number | Admin @ si @ DKF2014 | Serial | 2509 | ||
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Author | Vassileios Balntas; Edgar Riba; Daniel Ponsa; Krystian Mikolajczyk | ||||
Title | Learning local feature descriptors with triplets and shallow convolutional neural networks | Type | Conference Article | ||
Year | 2016 | Publication ![]() |
27th British Machine Vision Conference | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | It has recently been demonstrated that local feature descriptors based on convolutional neural networks (CNN) can significantly improve the matching performance. Previous work on learning such descriptors has focused on exploiting pairs of positive and negative patches to learn discriminative CNN representations. In this work, we propose to utilize triplets of training samples, together with in-triplet mining of hard negatives.
We show that our method achieves state of the art results, without the computational overhead typically associated with mining of negatives and with lower complexity of the network architecture. We compare our approach to recently introduced convolutional local feature descriptors, and demonstrate the advantages of the proposed methods in terms of performance and speed. We also examine different loss functions associated with triplets. |
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Address | York; UK; September 2016 | ||||
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Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | BMVC | ||
Notes | ADAS; 600.086 | Approved | no | ||
Call Number | Admin @ si @ BRP2016 | Serial | 2818 | ||
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Author | Arash Akbarinia; C. Alejandro Parraga | ||||
Title | Biologically plausible boundary detection | Type | Conference Article | ||
Year | 2016 | Publication ![]() |
27th British Machine Vision Conference | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | Edges are key components of any visual scene to the extent that we can recognise objects merely by their silhouettes. The human visual system captures edge information through neurons in the visual cortex that are sensitive to both intensity discontinuities and particular orientations. The “classical approach” assumes that these cells are only responsive to the stimulus present within their receptive fields, however, recent studies demonstrate that surrounding regions and inter-areal feedback connections influence their responses significantly. In this work we propose a biologically-inspired edge detection model in which orientation selective neurons are represented through the first derivative of a Gaussian function resembling double-opponent cells in the primary visual cortex (V1). In our model we account for four kinds of surround, i.e. full, far, iso- and orthogonal-orientation, whose contributions are contrast-dependant. The output signal from V1 is pooled in its perpendicular direction by larger V2 neurons employing a contrast-variant centre-surround kernel. We further introduce a feedback connection from higher-level visual areas to the lower ones. The results of our model on two benchmark datasets show a big improvement compared to the current non-learning and biologically-inspired state-of-the-art algorithms while being competitive to the learning-based methods. | ||||
Address | York; UK; September 2016 | ||||
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Language | Summary Language | Original Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | BMVC | ||
Notes | NEUROBIT; 600.068; 600.072 | Approved | no | ||
Call Number | Admin @ si @ AkP2016a | Serial | 2867 | ||
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Author | Corina Krauter; Ursula Reiter; Albrecht Schmidt; Marc Masana; Rudolf Stollberger; Michael Fuchsjager; Gert Reiter | ||||
Title | Objective extraction of the temporal evolution of the mitral valve vortex ring from 4D flow MRI | Type | Conference Article | ||
Year | 2019 | Publication ![]() |
27th Annual Meeting & Exhibition of the International Society for Magnetic Resonance in Medicine | Abbreviated Journal | |
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Abstract | The mitral valve vortex ring is a promising flow structure for analysis of diastolic function, however, methods for objective extraction of its formation to dissolution are lacking. We present a novel algorithm for objective extraction of the temporal evolution of the mitral valve vortex ring from magnetic resonance 4D flow data and validated the method against visual analysis. The algorithm successfully extracted mitral valve vortex rings during both early- and late-diastolic filling and agreed substantially with visual assessment. Early-diastolic mitral valve vortex ring properties differed between healthy subjects and patients with ischemic heart disease. | ||||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ISMRM | ||
Notes | LAMP; 600.120 | Approved | no | ||
Call Number | Admin @ si @ KRS2019 | Serial | 3300 | ||
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Author | Joan Codina-Filba; Sergio Escalera; Joan Escudero; Coen Antens; Pau Buch-Cardona; Mireia Farrus | ||||
Title | Mobile eHealth Platform for Home Monitoring of Bipolar Disorder | Type | Conference Article | ||
Year | 2021 | Publication ![]() |
27th ACM International Conference on Multimedia Modeling | Abbreviated Journal | |
Volume | 12573 | Issue | Pages | 330-341 | |
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Abstract | People suffering Bipolar Disorder (BD) experiment changes in mood status having depressive or manic episodes with normal periods in the middle. BD is a chronic disease with a high level of non-adherence to medication that needs a continuous monitoring of patients to detect when they relapse in an episode, so that physicians can take care of them. Here we present MoodRecord, an easy-to-use, non-intrusive, multilingual, robust and scalable platform suitable for home monitoring patients with BD, that allows physicians and relatives to track the patient state and get alarms when abnormalities occur.
MoodRecord takes advantage of the capabilities of smartphones as a communication and recording device to do a continuous monitoring of patients. It automatically records user activity, and asks the user to answer some questions or to record himself in video, according to a predefined plan designed by physicians. The video is analysed, recognising the mood status from images and bipolar assessment scores are extracted from speech parameters. The data obtained from the different sources are merged periodically to observe if a relapse may start and if so, raise the corresponding alarm. The application got a positive evaluation in a pilot with users from three different countries. During the pilot, the predictions of the voice and image modules showed a coherent correlation with the diagnosis performed by clinicians. |
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Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | MMM | ||
Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ CEE2021 | Serial | 3659 | ||
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Author | Yaxing Wang; Abel Gonzalez-Garcia; Joost Van de Weijer; Luis Herranz | ||||
Title | SDIT: Scalable and Diverse Cross-domain Image Translation | Type | Conference Article | ||
Year | 2019 | Publication ![]() |
27th ACM International Conference on Multimedia | Abbreviated Journal | |
Volume | Issue | Pages | 1267–1276 | ||
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Abstract | Recently, image-to-image translation research has witnessed remarkable progress. Although current approaches successfully generate diverse outputs or perform scalable image transfer, these properties have not been combined into a single method. To address this limitation, we propose SDIT: Scalable and Diverse image-to-image translation. These properties are combined into a single generator. The diversity is determined by a latent variable which is randomly sampled from a normal distribution. The scalability is obtained by conditioning the network on the domain attributes. Additionally, we also exploit an attention mechanism that permits the generator to focus on the domain-specific attribute. We empirically demonstrate the performance of the proposed method on face mapping and other datasets beyond faces. | ||||
Address | Nice; Francia; October 2019 | ||||
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Area | Expedition | Conference | ACM-MM | ||
Notes | LAMP; 600.106; 600.109; 600.141; 600.120 | Approved | no | ||
Call Number | Admin @ si @ WGW2019 | Serial | 3363 | ||
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Author | Mohamed Ali Souibgui; Sanket Biswas; Sana Khamekhem Jemni; Yousri Kessentini; Alicia Fornes; Josep Llados; Umapada Pal | ||||
Title | DocEnTr: An End-to-End Document Image Enhancement Transformer | Type | Conference Article | ||
Year | 2022 | Publication ![]() |
26th International Conference on Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 1699-1705 | ||
Keywords | Degradation; Head; Optical character recognition; Self-supervised learning; Benchmark testing; Transformers; Magnetic heads | ||||
Abstract | Document images can be affected by many degradation scenarios, which cause recognition and processing difficulties. In this age of digitization, it is important to denoise them for proper usage. To address this challenge, we present a new encoder-decoder architecture based on vision transformers to enhance both machine-printed and handwritten document images, in an end-to-end fashion. The encoder operates directly on the pixel patches with their positional information without the use of any convolutional layers, while the decoder reconstructs a clean image from the encoded patches. Conducted experiments show a superiority of the proposed model compared to the state-of the-art methods on several DIBCO benchmarks. Code and models will be publicly available at: https://github.com/dali92002/DocEnTR | ||||
Address | August 21-25, 2022 , Montréal Québec | ||||
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Area | Expedition | Conference | ICPR | ||
Notes | DAG; 600.121; 600.162; 602.230; 600.140 | Approved | no | ||
Call Number | Admin @ si @ SBJ2022 | Serial | 3730 | ||
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Author | Carlos Boned Riera; Oriol Ramos Terrades | ||||
Title | Discriminative Neural Variational Model for Unbalanced Classification Tasks in Knowledge Graph | Type | Conference Article | ||
Year | 2022 | Publication ![]() |
26th International Conference on Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 2186-2191 | ||
Keywords | Measurement; Couplings; Semantics; Ear; Benchmark testing; Data models; Pattern recognition | ||||
Abstract | Nowadays the paradigm of link discovery problems has shown significant improvements on Knowledge Graphs. However, method performances are harmed by the unbalanced nature of this classification problem, since many methods are easily biased to not find proper links. In this paper we present a discriminative neural variational auto-encoder model, called DNVAE from now on, in which we have introduced latent variables to serve as embedding vectors. As a result, the learnt generative model approximate better the underlying distribution and, at the same time, it better differentiate the type of relations in the knowledge graph. We have evaluated this approach on benchmark knowledge graph and Census records. Results in this last data set are quite impressive since we reach the highest possible score in the evaluation metrics. However, further experiments are still needed to deeper evaluate the performance of the method in more challenging tasks. | ||||
Address | Montreal; Quebec; Canada; August 2022 | ||||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ICPR | ||
Notes | DAG; 600.121; 600.162 | Approved | no | ||
Call Number | Admin @ si @ BoR2022 | Serial | 3741 | ||
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Author | Vacit Oguz Yazici; Joost Van de Weijer; Longlong Yu | ||||
Title | Visual Transformers with Primal Object Queries for Multi-Label Image Classification | Type | Conference Article | ||
Year | 2022 | Publication ![]() |
26th International Conference on Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | Multi-label image classification is about predicting a set of class labels that can be considered as orderless sequential data. Transformers process the sequential data as a whole, therefore they are inherently good at set prediction. The first vision-based transformer model, which was proposed for the object detection task introduced the concept of object queries. Object queries are learnable positional encodings that are used by attention modules in decoder layers to decode the object classes or bounding boxes using the region of interests in an image. However, inputting the same set of object queries to different decoder layers hinders the training: it results in lower performance and delays convergence. In this paper, we propose the usage of primal object queries that are only provided at the start of the transformer decoder stack. In addition, we improve the mixup technique proposed for multi-label classification. The proposed transformer model with primal object queries improves the state-of-the-art class wise F1 metric by 2.1% and 1.8%; and speeds up the convergence by 79.0% and 38.6% on MS-COCO and NUS-WIDE datasets respectively. | ||||
Address | Montreal; Quebec; Canada; August 2022 | ||||
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Area | Expedition | Conference | ICPR | ||
Notes | LAMP; 600.147; 601.309 | Approved | no | ||
Call Number | Admin @ si @ YWY2022 | Serial | 3786 | ||
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Author | Ayan Banerjee; Palaiahnakote Shivakumara; Parikshit Acharya; Umapada Pal; Josep Llados | ||||
Title | TWD: A New Deep E2E Model for Text Watermark Detection in Video Images | Type | Conference Article | ||
Year | 2022 | Publication ![]() |
26th International Conference on Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Deep learning; U-Net; FCENet; Scene text detection; Video text detection; Watermark text detection | ||||
Abstract | Text watermark detection in video images is challenging because text watermark characteristics are different from caption and scene texts in the video images. Developing a successful model for detecting text watermark, caption, and scene texts is an open challenge. This study aims at developing a new Deep End-to-End model for Text Watermark Detection (TWD), caption and scene text in video images. To standardize non-uniform contrast, quality, and resolution, we explore the U-Net3+ model for enhancing poor quality text without affecting high-quality text. Similarly, to address the challenges of arbitrary orientation, text shapes and complex background, we explore Stacked Hourglass Encoded Fourier Contour Embedding Network (SFCENet) by feeding the output of the U-Net3+ model as input. Furthermore, the proposed work integrates enhancement and detection models as an end-to-end model for detecting multi-type text in video images. To validate the proposed model, we create our own dataset (named TW-866), which provides video images containing text watermark, caption (subtitles), as well as scene text. The proposed model is also evaluated on standard natural scene text detection datasets, namely, ICDAR 2019 MLT, CTW1500, Total-Text, and DAST1500. The results show that the proposed method outperforms the existing methods. This is the first work on text watermark detection in video images to the best of our knowledge | ||||
Address | Montreal; Quebec; Canada; August 2022 | ||||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ICPR | ||
Notes | DAG; | Approved | no | ||
Call Number | Admin @ si @ BSA2022 | Serial | 3788 | ||
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Author | Emanuel Sanchez Aimar; Petia Radeva; Mariella Dimiccoli | ||||
Title | Social Relation Recognition in Egocentric Photostreams | Type | Conference Article | ||
Year | 2019 | Publication ![]() |
26th International Conference on Image Processing | Abbreviated Journal | |
Volume | Issue | Pages | 3227-3231 | ||
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Abstract | This paper proposes an approach to automatically categorize the social interactions of a user wearing a photo-camera (2fpm), by relying solely on what the camera is seeing. The problem is challenging due to the overwhelming complexity of social life and the extreme intra-class variability of social interactions captured under unconstrained conditions. We adopt the formalization proposed in Bugental's social theory, that groups human relations into five social domains with related categories. Our method is a new deep learning architecture that exploits the hierarchical structure of the label space and relies on a set of social attributes estimated at frame level to provide a semantic representation of social interactions. Experimental results on the new EgoSocialRelation dataset demonstrate the effectiveness of our proposal. | ||||
Address | Taipei; Taiwan; September 2019 | ||||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ICIP | ||
Notes | MILAB; no menciona | Approved | no | ||
Call Number | Admin @ si @ SRD2019 | Serial | 3370 | ||
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