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Author | Razieh Rastgoo; Kourosh Kiani; Sergio Escalera | ||||
Title | Hand sign language recognition using multi-view hand skeleton | Type | Journal Article | ||
Year | 2020 | Publication | Expert Systems With Applications | Abbreviated Journal | ESWA |
Volume | 150 | Issue | Pages | 113336 | |
Keywords | Multi-view hand skeleton; Hand sign language recognition; 3DCNN; Hand pose estimation; RGB video; Hand action recognition | ||||
Abstract | Hand sign language recognition from video is a challenging research area in computer vision, which performance is affected by hand occlusion, fast hand movement, illumination changes, or background complexity, just to mention a few. In recent years, deep learning approaches have achieved state-of-the-art results in the field, though previous challenges are not completely solved. In this work, we propose a novel deep learning-based pipeline architecture for efficient automatic hand sign language recognition using Single Shot Detector (SSD), 2D Convolutional Neural Network (2DCNN), 3D Convolutional Neural Network (3DCNN), and Long Short-Term Memory (LSTM) from RGB input videos. We use a CNN-based model which estimates the 3D hand keypoints from 2D input frames. After that, we connect these estimated keypoints to build the hand skeleton by using midpoint algorithm. In order to obtain a more discriminative representation of hands, we project 3D hand skeleton into three views surface images. We further employ the heatmap image of detected keypoints as input for refinement in a stacked fashion. We apply 3DCNNs on the stacked features of hand, including pixel level, multi-view hand skeleton, and heatmap features, to extract discriminant local spatio-temporal features from these stacked inputs. The outputs of the 3DCNNs are fused and fed to a LSTM to model long-term dynamics of hand sign gestures. Analyzing 2DCNN vs. 3DCNN using different number of stacked inputs into the network, we demonstrate that 3DCNN better capture spatio-temporal dynamics of hands. To the best of our knowledge, this is the first time that this multi-modal and multi-view set of hand skeleton features are applied for hand sign language recognition. Furthermore, we present a new large-scale hand sign language dataset, namely RKS-PERSIANSIGN, including 10′000 RGB videos of 100 Persian sign words. Evaluation results of the proposed model on three datasets, NYU, First-Person, and RKS-PERSIANSIGN, indicate that our model outperforms state-of-the-art models in hand sign language recognition, hand pose estimation, and hand action recognition. | ||||
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Notes | HuPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ RKE2020a | Serial | 3411 | ||
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Author | Razieh Rastgoo; Kourosh Kiani; Sergio Escalera | ||||
Title | Video-based Isolated Hand Sign Language Recognition Using a Deep Cascaded Model | Type | Journal Article | ||
Year | 2020 | Publication | Multimedia Tools and Applications | Abbreviated Journal | MTAP |
Volume | 79 | Issue | Pages | 22965–22987 | |
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Abstract | In this paper, we propose an efficient cascaded model for sign language recognition taking benefit from spatio-temporal hand-based information using deep learning approaches, especially Single Shot Detector (SSD), Convolutional Neural Network (CNN), and Long Short Term Memory (LSTM), from videos. Our simple yet efficient and accurate model includes two main parts: hand detection and sign recognition. Three types of spatial features, including hand features, Extra Spatial Hand Relation (ESHR) features, and Hand Pose (HP) features, have been fused in the model to feed to LSTM for temporal features extraction. We train SSD model for hand detection using some videos collected from five online sign dictionaries. Our model is evaluated on our proposed dataset (Rastgoo et al., Expert Syst Appl 150: 113336, 2020), including 10’000 sign videos for 100 Persian sign using 10 contributors in 10 different backgrounds, and isoGD dataset. Using the 5-fold cross-validation method, our model outperforms state-of-the-art alternatives in sign language recognition | ||||
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Notes | HuPBA; no menciona | Approved | no | ||
Call Number | Admin @ si @ RKE2020b | Serial | 3442 | ||
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Author | Razieh Rastgoo; Kourosh Kiani; Sergio Escalera | ||||
Title | Hand pose aware multimodal isolated sign language recognition | Type | Journal Article | ||
Year | 2020 | Publication | Multimedia Tools and Applications | Abbreviated Journal | MTAP |
Volume | 80 | Issue | Pages | 127–163 | |
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Abstract | Isolated hand sign language recognition from video is a challenging research area in computer vision. Some of the most important challenges in this area include dealing with hand occlusion, fast hand movement, illumination changes, or background complexity. While most of the state-of-the-art results in the field have been achieved using deep learning-based models, the previous challenges are not completely solved. In this paper, we propose a hand pose aware model for isolated hand sign language recognition using deep learning approaches from two input modalities, RGB and depth videos. Four spatial feature types: pixel-level, flow, deep hand, and hand pose features, fused from both visual modalities, are input to LSTM for temporal sign recognition. While we use Optical Flow (OF) for flow information in RGB video inputs, Scene Flow (SF) is used for depth video inputs. By including hand pose features, we show a consistent performance improvement of the sign language recognition model. To the best of our knowledge, this is the first time that this discriminant spatiotemporal features, benefiting from the hand pose estimation features and multi-modal inputs, are fused for isolated hand sign language recognition. We perform a step-by-step analysis of the impact in terms of recognition performance of the hand pose features, different combinations of the spatial features, and different recurrent models, especially LSTM and GRU. Results on four public datasets confirm that the proposed model outperforms the current state-of-the-art models on Montalbano II, MSR Daily Activity 3D, and CAD-60 datasets with a relative accuracy improvement of 1.64%, 6.5%, and 7.6%. Furthermore, our model obtains a competitive results on isoGD dataset with only 0.22% margin lower than the current state-of-the-art model. | ||||
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Notes | HUPBA; no menciona | Approved | no | ||
Call Number | Admin @ si @ RKE2020 | Serial | 3524 | ||
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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 | |
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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 | Raul Gomez; Jaume Gibert; Lluis Gomez; Dimosthenis Karatzas | ||||
Title | Exploring Hate Speech Detection in Multimodal Publications | Type | Conference Article | ||
Year | 2020 | Publication | IEEE Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
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Abstract | In this work we target the problem of hate speech detection in multimodal publications formed by a text and an image. We gather and annotate a large scale dataset from Twitter, MMHS150K, and propose different models that jointly analyze textual and visual information for hate speech detection, comparing them with unimodal detection. We provide quantitative and qualitative results and analyze the challenges of the proposed task. We find that, even though images are useful for the hate speech detection task, current multimodal models cannot outperform models analyzing only text. We discuss why and open the field and the dataset for further research. | ||||
Address | Aspen; March 2020 | ||||
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Area | Expedition | Conference | WACV | ||
Notes | DAG; 600.121; 600.129 | Approved | no | ||
Call Number | Admin @ si @ GGG2020a | Serial | 3280 | ||
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Author | Raul Gomez; Jaume Gibert; Lluis Gomez; Dimosthenis Karatzas | ||||
Title | Location Sensitive Image Retrieval and Tagging | Type | Conference Article | ||
Year | 2020 | Publication | 16th European Conference on Computer Vision | Abbreviated Journal | |
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Abstract | People from different parts of the globe describe objects and concepts in distinct manners. Visual appearance can thus vary across different geographic locations, which makes location a relevant contextual information when analysing visual data. In this work, we address the task of image retrieval related to a given tag conditioned on a certain location on Earth. We present LocSens, a model that learns to rank triplets of images, tags and coordinates by plausibility, and two training strategies to balance the location influence in the final ranking. LocSens learns to fuse textual and location information of multimodal queries to retrieve related images at different levels of location granularity, and successfully utilizes location information to improve image tagging. | ||||
Address | Virtual; August 2020 | ||||
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Area | Expedition | Conference | ECCV | ||
Notes | DAG; 600.121; 600.129 | Approved | no | ||
Call Number | Admin @ si @ GGG2020b | Serial | 3420 | ||
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Author | Raul Gomez | ||||
Title | Exploiting the Interplay between Visual and Textual Data for Scene Interpretation | Type | Book Whole | ||
Year | 2020 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Abstract | Machine learning experimentation under controlled scenarios and standard datasets is necessary to compare algorithms performance by evaluating all of them in the same setup. However, experimentation on how those algorithms perform on unconstrained data and applied tasks to solve real world problems is also a must to ascertain how that research can contribute to our society.
In this dissertation we experiment with the latest computer vision and natural language processing algorithms applying them to multimodal scene interpretation. Particularly, we research on how image and text understanding can be jointly exploited to address real world problems, focusing on learning from Social Media data. We address several tasks that involve image and textual information, discuss their characteristics and offer our experimentation conclusions. First, we work on detection of scene text in images. Then, we work with Social Media posts, exploiting the captions associated to images as supervision to learn visual features, which we apply to multimodal semantic image retrieval. Subsequently, we work with geolocated Social Media images with associated tags, experimenting on how to use the tags as supervision, on location sensitive image retrieval and on exploiting location information for image tagging. Finally, we work on a specific classification problem of Social Media publications consisting on an image and a text: Multimodal hate speech classification. |
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Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Dimosthenis Karatzas;Lluis Gomez;Jaume Gibert | |
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ISSN | ISBN | 978-84-121011-7-1 | Medium | ||
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Notes | DAG; 600.121 | Approved | no | ||
Call Number | Admin @ si @ Gom20 | Serial | 3479 | ||
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Author | Raquel Justo; Leila Ben Letaifa; Cristina Palmero; Eduardo Gonzalez-Fraile; Anna Torp Johansen; Alain Vazquez; Gennaro Cordasco; Stephan Schlogl; Begoña Fernandez-Ruanova; Micaela Silva; Sergio Escalera; Mikel de Velasco; Joffre Tenorio-Laranga; Anna Esposito; Maria Korsnes; M. Ines Torres | ||||
Title | Analysis of the Interaction between Elderly People and a Simulated Virtual Coach, Journal of Ambient Intelligence and Humanized Computing | Type | Journal Article | ||
Year | 2020 | Publication | Journal of Ambient Intelligence and Humanized Computing | Abbreviated Journal | AIHC |
Volume | 11 | Issue | 12 | Pages | 6125-6140 |
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Abstract | The EMPATHIC project develops and validates new interaction paradigms for personalized virtual coaches (VC) to promote healthy and independent aging. To this end, the work presented in this paper is aimed to analyze the interaction between the EMPATHIC-VC and the users. One of the goals of the project is to ensure an end-user driven design, involving senior users from the beginning and during each phase of the project. Thus, the paper focuses on some sessions where the seniors carried out interactions with a Wizard of Oz driven, simulated system. A coaching strategy based on the GROW model was used throughout these sessions so as to guide interactions and engage the elderly with the goals of the project. In this interaction framework, both the human and the system behavior were analyzed. The way the wizard implements the GROW coaching strategy is a key aspect of the system behavior during the interaction. The language used by the virtual agent as well as his or her physical aspect are also important cues that were analyzed. Regarding the user behavior, the vocal communication provides information about the speaker’s emotional status, that is closely related to human behavior and which can be extracted from the speech and language analysis. In the same way, the analysis of the facial expression, gazes and gestures can provide information on the non verbal human communication even when the user is not talking. In addition, in order to engage senior users, their preferences and likes had to be considered. To this end, the effect of the VC on the users was gathered by means of direct questionnaires. These analyses have shown a positive and calm behavior of users when interacting with the simulated virtual coach as well as some difficulties of the system to develop the proposed coaching strategy. | ||||
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Notes | HuPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ JLP2020 | Serial | 3443 | ||
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Author | Rahma Kalboussi; Aymen Azaza; Joost Van de Weijer; Mehrez Abdellaoui; Ali Douik | ||||
Title | Object proposals for salient object segmentation in videos | Type | Journal Article | ||
Year | 2020 | Publication | Multimedia Tools and Applications | Abbreviated Journal | MTAP |
Volume | 79 | Issue | 13 | Pages | 8677-8693 |
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Abstract | Salient object segmentation in videos is generally broken up in a video segmentation part and a saliency assignment part. Recently, object proposals, which are used to segment the image, have had significant impact on many computer vision applications, including image segmentation, object detection, and recently saliency detection in still images. However, their usage has not yet been evaluated for salient object segmentation in videos. Therefore, in this paper, we investigate the application of object proposals to salient object segmentation in videos. In addition, we propose a new motion feature derived from the optical flow structure tensor for video saliency detection. Experiments on two standard benchmark datasets for video saliency show that the proposed motion feature improves saliency estimation results, and that object proposals are an efficient method for salient object segmentation. Results on the challenging SegTrack v2 and Fukuchi benchmark data sets show that we significantly outperform the state-of-the-art. | ||||
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Notes | LAMP; 600.120 | Approved | no | ||
Call Number | KAW2020 | Serial | 3504 | ||
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Author | Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla; Lin Guo; Jiankun Hou; Armin Mehri; Parichehr Behjati Ardakani; Heena Patel; Vishal Chudasama; Kalpesh Prajapati; Kishor P. Upla; Raghavendra Ramachandra; Kiran Raja; Christoph Busch; Feras Almasri; Olivier Debeir; Sabari Nathan; Priya Kansal; Nolan Gutierrez; Bardia Mojra; William J. Beksi | ||||
Title | Thermal Image Super-Resolution Challenge – PBVS 2020 | Type | Conference Article | ||
Year | 2020 | Publication | 16h IEEE Workshop on Perception Beyond the Visible Spectrum | Abbreviated Journal | |
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Abstract | This paper summarizes the top contributions to the first challenge on thermal image super-resolution (TISR), which was organized as part of the Perception Beyond the Visible Spectrum (PBVS) 2020 workshop. In this challenge, a novel thermal image dataset is considered together with state-of-the-art approaches evaluated under a common framework. The dataset used in the challenge consists of 1021 thermal images, obtained from three distinct thermal cameras at different resolutions (low-resolution, mid-resolution, and high-resolution), resulting in a total of 3063 thermal images. From each resolution, 951 images are used for training and 50 for testing while the 20 remaining images are used for two proposed evaluations. The first evaluation consists of downsampling the low-resolution, mid-resolution, and high-resolution thermal images by x2, x3 and x4 respectively, and comparing their super-resolution results with the corresponding ground truth images. The second evaluation is comprised of obtaining the x2 super-resolution from a given mid-resolution thermal image and comparing it with the corresponding semi-registered high-resolution thermal image. Out of 51 registered participants, 6 teams reached the final validation phase. | ||||
Address | Virtual CVPR | ||||
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Area | Expedition | Conference | CVPRW | ||
Notes | MSIAU; ISE; 600.119; 600.122 | Approved | no | ||
Call Number | Admin @ si @ RSV2020 | Serial | 3431 | ||
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Author | Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla | ||||
Title | Thermal Image Super-resolution: A Novel Architecture and Dataset | Type | Conference Article | ||
Year | 2020 | Publication | 15th International Conference on Computer Vision Theory and Applications | Abbreviated Journal | |
Volume | Issue | Pages | 111-119 | ||
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Abstract | This paper proposes a novel CycleGAN architecture for thermal image super-resolution, together with a large dataset consisting of thermal images at different resolutions. The dataset has been acquired using three thermal cameras at different resolutions, which acquire images from the same scenario at the same time. The thermal cameras are mounted in rig trying to minimize the baseline distance to make easier the registration problem.
The proposed architecture is based on ResNet6 as a Generator and PatchGAN as Discriminator. The novelty on the proposed unsupervised super-resolution training (CycleGAN) is possible due to the existence of aforementioned thermal images—images of the same scenario with different resolutions. The proposed approach is evaluated in the dataset and compared with classical bicubic interpolation. The dataset and the network are available. |
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Address | Valletta; Malta; February 2020 | ||||
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Area | Expedition | Conference | VISAPP | ||
Notes | MSIAU; 600.130; 600.122 | Approved | no | ||
Call Number | Admin @ si @ RSV2020 | Serial | 3432 | ||
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Author | Petia Radeva | ||||
Title | Uncertainty Modeling within an End-to-end Framework for Food Image Analysis | Type | Conference Article | ||
Year | 2020 | Publication | 1st DELTA | Abbreviated Journal | |
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Area | Expedition | Conference | DELTA | ||
Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @ Rad2020 | Serial | 3527 | ||
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Author | Pau Rodriguez; Diego Velazquez; Guillem Cucurull; Josep M. Gonfaus; Xavier Roca; Seiichi Ozawa; Jordi Gonzalez | ||||
Title | Personality Trait Analysis in Social Networks Based on Weakly Supervised Learning of Shared Images | Type | Journal Article | ||
Year | 2020 | Publication | Applied Sciences | Abbreviated Journal | APPLSCI |
Volume | 10 | Issue | 22 | Pages | 8170 |
Keywords | sentiment analysis, personality trait analysis; weakly-supervised learning; visual classification; OCEAN model; social networks | ||||
Abstract | Social networks have attracted the attention of psychologists, as the behavior of users can be used to assess personality traits, and to detect sentiments and critical mental situations such as depression or suicidal tendencies. Recently, the increasing amount of image uploads to social networks has shifted the focus from text to image-based personality assessment. However, obtaining the ground-truth requires giving personality questionnaires to the users, making the process very costly and slow, and hindering research on large populations. In this paper, we demonstrate that it is possible to predict which images are most associated with each personality trait of the OCEAN personality model, without requiring ground-truth personality labels. Namely, we present a weakly supervised framework which shows that the personality scores obtained using specific images textually associated with particular personality traits are highly correlated with scores obtained using standard text-based personality questionnaires. We trained an OCEAN trait model based on Convolutional Neural Networks (CNNs), learned from 120K pictures posted with specific textual hashtags, to infer whether the personality scores from the images uploaded by users are consistent with those scores obtained from text. In order to validate our claims, we performed a personality test on a heterogeneous group of 280 human subjects, showing that our model successfully predicts which kind of image will match a person with a given level of a trait. Looking at the results, we obtained evidence that personality is not only correlated with text, but with image content too. Interestingly, different visual patterns emerged from those images most liked by persons with a particular personality trait: for instance, pictures most associated with high conscientiousness usually contained healthy food, while low conscientiousness pictures contained injuries, guns, and alcohol. These findings could pave the way to complement text-based personality questionnaires with image-based questions. | ||||
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Notes | ISE; 600.119 | Approved | no | ||
Call Number | Admin @ si @ RVC2020b | Serial | 3553 | ||
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Author | Pau Rodriguez; Diego Velazquez; Guillem Cucurull; Josep M. Gonfaus; Xavier Roca; Jordi Gonzalez | ||||
Title | Pay attention to the activations: a modular attention mechanism for fine-grained image recognition | Type | Journal Article | ||
Year | 2020 | Publication | IEEE Transactions on Multimedia | Abbreviated Journal | TMM |
Volume | 22 | Issue | 2 | Pages | 502-514 |
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Abstract | Fine-grained image recognition is central to many multimedia tasks such as search, retrieval, and captioning. Unfortunately, these tasks are still challenging since the appearance of samples of the same class can be more different than those from different classes. This issue is mainly due to changes in deformation, pose, and the presence of clutter. In the literature, attention has been one of the most successful strategies to handle the aforementioned problems. Attention has been typically implemented in neural networks by selecting the most informative regions of the image that improve classification. In contrast, in this paper, attention is not applied at the image level but to the convolutional feature activations. In essence, with our approach, the neural model learns to attend to lower-level feature activations without requiring part annotations and uses those activations to update and rectify the output likelihood distribution. The proposed mechanism is modular, architecture-independent, and efficient in terms of both parameters and computation required. Experiments demonstrate that well-known networks such as wide residual networks and ResNeXt, when augmented with our approach, systematically improve their classification accuracy and become more robust to changes in deformation and pose and to the presence of clutter. As a result, our proposal reaches state-of-the-art classification accuracies in CIFAR-10, the Adience gender recognition task, Stanford Dogs, and UEC-Food100 while obtaining competitive performance in ImageNet, CIFAR-100, CUB200 Birds, and Stanford Cars. In addition, we analyze the different components of our model, showing that the proposed attention modules succeed in finding the most discriminative regions of the image. Finally, as a proof of concept, we demonstrate that with only local predictions, an augmented neural network can successfully classify an image before reaching any fully connected layer, thus reducing the computational amount up to 10%. | ||||
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Notes | ISE; 600.119; 600.098 | Approved | no | ||
Call Number | Admin @ si @ RVC2020a | Serial | 3417 | ||
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Author | Pau Riba; Josep Llados; Alicia Fornes | ||||
Title | Hierarchical graphs for coarse-to-fine error tolerant matching | Type | Journal Article | ||
Year | 2020 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 134 | Issue | Pages | 116-124 | |
Keywords | Hierarchical graph representation; Coarse-to-fine graph matching; Graph-based retrieval | ||||
Abstract | During the last years, graph-based representations are experiencing a growing usage in visual recognition and retrieval due to their ability to capture both structural and appearance-based information. Thus, they provide a greater representational power than classical statistical frameworks. However, graph-based representations leads to high computational complexities usually dealt by graph embeddings or approximated matching techniques. Despite their representational power, they are very sensitive to noise and small variations of the input image. With the aim to cope with the time complexity and the variability present in the generated graphs, in this paper we propose to construct a novel hierarchical graph representation. Graph clustering techniques adapted from social media analysis have been used in order to contract a graph at different abstraction levels while keeping information about the topology. Abstract nodes attributes summarise information about the contracted graph partition. For the proposed representations, a coarse-to-fine matching technique is defined. Hence, small graphs are used as a filtering before more accurate matching methods are applied. This approach has been validated in real scenarios such as classification of colour images or retrieval of handwritten words (i.e. word spotting). | ||||
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Notes | DAG; 600.097; 601.302; 603.057; 600.140; 600.121 | Approved | no | ||
Call Number | Admin @ si @ RLF2020 | Serial | 3349 | ||
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