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Author | Alloy Das; Sanket Biswas; Ayan Banerjee; Josep Llados; Umapada Pal; Saumik Bhattacharya | ||||
Title | Harnessing the Power of Multi-Lingual Datasets for Pre-training: Towards Enhancing Text Spotting Performance | Type | Conference Article | ||
Year | 2024 | Publication | Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages | 718-728 | ||
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Abstract | The adaptation capability to a wide range of domains is crucial for scene text spotting models when deployed to real-world conditions. However, existing state-of-the-art (SOTA) approaches usually incorporate scene text detection and recognition simply by pretraining on natural scene text datasets, which do not directly exploit the intermediate feature representations between multiple domains. Here, we investigate the problem of domain-adaptive scene text spotting, i.e., training a model on multi-domain source data such that it can directly adapt to target domains rather than being specialized for a specific domain or scenario. Further, we investigate a transformer baseline called Swin-TESTR to focus on solving scene-text spotting for both regular and arbitrary-shaped scene text along with an exhaustive evaluation. The results clearly demonstrate the potential of intermediate representations to achieve significant performance on text spotting benchmarks across multiple domains (e.g. language, synth-to-real, and documents). both in terms of accuracy and efficiency. | ||||
Address | Waikoloa; Hawai; USA; January 2024 | ||||
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Area | Expedition | Conference ![]() |
WACV | ||
Notes | DAG | Approved | no | ||
Call Number | Admin @ si @ DBB2024 | Serial | 3986 | ||
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Author | Alex Gomez-Villa; Bartlomiej Twardowski; Kai Wang; Joost van de Weijer | ||||
Title | Plasticity-Optimized Complementary Networks for Unsupervised Continual Learning | Type | Conference Article | ||
Year | 2024 | Publication | Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages | 1690-1700 | ||
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Abstract | Continuous unsupervised representation learning (CURL) research has greatly benefited from improvements in self-supervised learning (SSL) techniques. As a result, existing CURL methods using SSL can learn high-quality representations without any labels, but with a notable performance drop when learning on a many-tasks data stream. We hypothesize that this is caused by the regularization losses that are imposed to prevent forgetting, leading to a suboptimal plasticity-stability trade-off: they either do not adapt fully to the incoming data (low plasticity), or incur significant forgetting when allowed to fully adapt to a new SSL pretext-task (low stability). In this work, we propose to train an expert network that is relieved of the duty of keeping the previous knowledge and can focus on performing optimally on the new tasks (optimizing plasticity). In the second phase, we combine this new knowledge with the previous network in an adaptation-retrospection phase to avoid forgetting and initialize a new expert with the knowledge of the old network. We perform several experiments showing that our proposed approach outperforms other CURL exemplar-free methods in few- and many-task split settings. Furthermore, we show how to adapt our approach to semi-supervised continual learning (Semi-SCL) and show that we surpass the accuracy of other exemplar-free Semi-SCL methods and reach the results of some others that use exemplars. | ||||
Address | Waikoloa; Hawai; USA; January 2024 | ||||
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WACV | ||
Notes | LAMP | Approved | no | ||
Call Number | Admin @ si @ GTW2024 | Serial | 3989 | ||
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Author | Sergi Garcia Bordils; Dimosthenis Karatzas; Marçal Rusiñol | ||||
Title | STEP – Towards Structured Scene-Text Spotting | Type | Conference Article | ||
Year | 2024 | Publication | Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages | 883-892 | ||
Keywords | |||||
Abstract | We introduce the structured scene-text spotting task, which requires a scene-text OCR system to spot text in the wild according to a query regular expression. Contrary to generic scene text OCR, structured scene-text spotting seeks to dynamically condition both scene text detection and recognition on user-provided regular expressions. To tackle this task, we propose the Structured TExt sPotter (STEP), a model that exploits the provided text structure to guide the OCR process. STEP is able to deal with regular expressions that contain spaces and it is not bound to detection at the word-level granularity. Our approach enables accurate zero-shot structured text spotting in a wide variety of real-world reading scenarios and is solely trained on publicly available data. To demonstrate the effectiveness of our approach, we introduce a new challenging test dataset that contains several types of out-of-vocabulary structured text, reflecting important reading applications of fields such as prices, dates, serial numbers, license plates etc. We demonstrate that STEP can provide specialised OCR performance on demand in all tested scenarios. | ||||
Address | Waikoloa; Hawai; USA; January 2024 | ||||
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 ![]() |
WACV | ||
Notes | DAG | Approved | no | ||
Call Number | Admin @ si @ GKR2024 | Serial | 3992 | ||
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Author | Hunor Laczko; Meysam Madadi; Sergio Escalera; Jordi Gonzalez | ||||
Title | A Generative Multi-Resolution Pyramid and Normal-Conditioning 3D Cloth Draping | Type | Conference Article | ||
Year | 2024 | Publication | Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages | 8709-8718 | ||
Keywords | |||||
Abstract | RGB cloth generation has been deeply studied in the related literature, however, 3D garment generation remains an open problem. In this paper, we build a conditional variational autoencoder for 3D garment generation and draping. We propose a pyramid network to add garment details progressively in a canonical space, i.e. unposing and unshaping the garments w.r.t. the body. We study conditioning the network on surface normal UV maps, as an intermediate representation, which is an easier problem to optimize than 3D coordinates. Our results on two public datasets, CLOTH3D and CAPE, show that our model is robust, controllable in terms of detail generation by the use of multi-resolution pyramids, and achieves state-of-the-art results that can highly generalize to unseen garments, poses, and shapes even when training with small amounts of data. | ||||
Address | Waikoloa; Hawai; USA; January 2024 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference ![]() |
WACV | ||
Notes | ISE; HUPBA | Approved | no | ||
Call Number | Admin @ si @ LME2024 | Serial | 3996 | ||
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Author | Mohammad N. S. Jahromi; Morten Bojesen Bonderup; Maryam Asadi-Aghbolaghi; Egils Avots; Kamal Nasrollahi; Sergio Escalera; Shohreh Kasaei; Thomas B. Moeslund; Gholamreza Anbarjafari | ||||
Title | Automatic Access Control Based on Face and Hand Biometrics in a Non-cooperative Context | Type | Conference Article | ||
Year | 2018 | Publication | IEEE Winter Applications of Computer Vision Workshops | Abbreviated Journal | |
Volume | Issue | Pages | 28-36 | ||
Keywords | IEEE Winter Applications of Computer Vision Workshops | ||||
Abstract | Automatic access control systems (ACS) based on the human biometrics or physical tokens are widely employed in public and private areas. Yet these systems, in their conventional forms, are restricted to active interaction from the users. In scenarios where users are not cooperating with the system, these systems are challenged. Failure in cooperation with the biometric systems might be intentional or because the users are incapable of handling the interaction procedure with the biometric system or simply forget to cooperate with it, due to for example, illness like dementia. This work introduces a challenging bimodal database, including face and hand information of the users when they approach a door to open it by its handle in a noncooperative context. We have defined two (an easy and a challenging) protocols on how to use the database. We have reported results on many baseline methods, including deep learning techniques as well as conventional methods on the database. The obtained results show the merit of the proposed database and the challenging nature of access control with non-cooperative users. | ||||
Address | Lake Tahoe; USA; March 2018 | ||||
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WACVW | ||
Notes | HUPBA; 602.133 | Approved | no | ||
Call Number | Admin @ si @ JBA2018 | Serial | 3121 | ||
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Author | Idoia Ruiz; Lorenzo Porzi; Samuel Rota Bulo; Peter Kontschieder; Joan Serrat | ||||
Title | Weakly Supervised Multi-Object Tracking and Segmentation | Type | Conference Article | ||
Year | 2021 | Publication | IEEE Winter Conference on Applications of Computer Vision Workshops | Abbreviated Journal | |
Volume | Issue | Pages | 125-133 | ||
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Abstract | We introduce the problem of weakly supervised MultiObject Tracking and Segmentation, i.e. joint weakly supervised instance segmentation and multi-object tracking, in which we do not provide any kind of mask annotation.
To address it, we design a novel synergistic training strategy by taking advantage of multi-task learning, i.e. classification and tracking tasks guide the training of the unsupervised instance segmentation. For that purpose, we extract weak foreground localization information, provided by Grad-CAM heatmaps, to generate a partial ground truth to learn from. Additionally, RGB image level information is employed to refine the mask prediction at the edges of the objects. We evaluate our method on KITTI MOTS, the most representative benchmark for this task, reducing the performance gap on the MOTSP metric between the fully supervised and weakly supervised approach to just 12% and 12.7 % for cars and pedestrians, respectively. |
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Address | Virtual; January 2021 | ||||
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WACVW | ||
Notes | ADAS; 600.118; 600.124 | Approved | no | ||
Call Number | Admin @ si @ RPR2021 | Serial | 3548 | ||
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Author | Mickael Cormier; Andreas Specker; Julio C. S. Jacques; Lucas Florin; Jurgen Metzler; Thomas B. Moeslund; Kamal Nasrollahi; Sergio Escalera; Jurgen Beyerer | ||||
Title | UPAR Challenge: Pedestrian Attribute Recognition and Attribute-based Person Retrieval – Dataset, Design, and Results | Type | Conference Article | ||
Year | 2023 | Publication | 2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops | Abbreviated Journal | |
Volume | Issue | Pages | 166-175 | ||
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Abstract | In civilian video security monitoring, retrieving and tracking a person of interest often rely on witness testimony and their appearance description. Deployed systems rely on a large amount of annotated training data and are expected to show consistent performance in diverse areas and gen-eralize well between diverse settings w.r.t. different view-points, illumination, resolution, occlusions, and poses for indoor and outdoor scenes. However, for such generalization, the system would require a large amount of various an-notated data for training and evaluation. The WACV 2023 Pedestrian Attribute Recognition and Attributed-based Per-son Retrieval Challenge (UPAR-Challenge) aimed to spot-light the problem of domain gaps in a real-world surveil-lance context and highlight the challenges and limitations of existing methods. The UPAR dataset, composed of 40 important binary attributes over 12 attribute categories across four datasets, was extended with data captured from a low-flying UAV from the P-DESTRE dataset. To this aim, 0.6M additional annotations were manually labeled and vali-dated. Each track evaluated the robustness of the competing methods to domain shifts by training on limited data from a specific domain and evaluating using data from unseen do-mains. The challenge attracted 41 registered participants, but only one team managed to outperform the baseline on one track, emphasizing the task's difficulty. This work de-scribes the challenge design, the adopted dataset, obtained results, as well as future directions on the topic. | ||||
Address | Waikoloa; Hawai; USA; January 2023 | ||||
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Area | Expedition | Conference ![]() |
WACVW | ||
Notes | HUPBA | Approved | no | ||
Call Number | Admin @ si @ CSJ2023 | Serial | 3902 | ||
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Author | Henry Velesaca; Patricia Suarez; Angel Sappa; Dario Carpio; Rafael E. Rivadeneira; Angel Sanchez | ||||
Title | Review on Common Techniques for Urban Environment Video Analytics | Type | Conference Article | ||
Year | 2022 | Publication | Anais do III Workshop Brasileiro de Cidades Inteligentes | Abbreviated Journal | |
Volume | Issue | Pages | 107-118 | ||
Keywords | Video Analytics; Review; Urban Environments; Smart Cities | ||||
Abstract | This work compiles the different computer vision-based approaches
from the state-of-the-art intended for video analytics in urban environments. The manuscript groups the different approaches according to the typical modules present in video analysis, including image preprocessing, object detection, classification, and tracking. This proposed pipeline serves as a basic guide to representing these most representative approaches in this topic of video analysis that will be addressed in this work. Furthermore, the manuscript is not intended to be an exhaustive review of the most advanced approaches, but only a list of common techniques proposed to address recurring problems in this field. |
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WBCI | ||
Notes | MSIAU; 601.349 | Approved | no | ||
Call Number | Admin @ si @ VSS2022 | Serial | 3773 | ||
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Author | Jose Ramirez Moreno; Juan R Revilla; Miguel Reyes; Sergio Escalera | ||||
Title | Validación del Software ADIBAS asociado al sensor Kinect de Microsoft para la evaluación de la posición corporal | Type | Conference Article | ||
Year | 2016 | Publication | 4th Congreso WCPT-SAR | Abbreviated Journal | |
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Abstract | |||||
Address | Buenos Aires; Argentina; June 2016 | ||||
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Area | Expedition | Conference ![]() |
WCPT-SAR | ||
Notes | HuPBA;MILAB | Approved | no | ||
Call Number | Admin @ si @ RRR2016 | Serial | 2853 | ||
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Author | Miguel Angel Bautista; Antonio Hernandez; Victor Ponce; Xavier Perez Sala; Xavier Baro; Oriol Pujol; Cecilio Angulo; Sergio Escalera | ||||
Title | Probability-based Dynamic TimeWarping for Gesture Recognition on RGB-D data | Type | Conference Article | ||
Year | 2012 | Publication | 21st International Conference on Pattern Recognition International Workshop on Depth Image Analysis | Abbreviated Journal | |
Volume | 7854 | Issue | Pages | 126-135 | |
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Abstract | Dynamic Time Warping (DTW) is commonly used in gesture recognition tasks in order to tackle the temporal length variability of gestures. In the DTW framework, a set of gesture patterns are compared one by one to a maybe infinite test sequence, and a query gesture category is recognized if a warping cost below a certain threshold is found within the test sequence. Nevertheless, either taking one single sample per gesture category or a set of isolated samples may not encode the variability of such gesture category. In this paper, a probability-based DTW for gesture recognition is proposed. Different samples of the same gesture pattern obtained from RGB-Depth data are used to build a Gaussian-based probabilistic model of the gesture. Finally, the cost of DTW has been adapted accordingly to the new model. The proposed approach is tested in a challenging scenario, showing better performance of the probability-based DTW in comparison to state-of-the-art approaches for gesture recognition on RGB-D data. | ||||
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Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
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ISSN | 0302-9743 | ISBN | 978-3-642-40302-6 | Medium | |
Area | Expedition | Conference ![]() |
WDIA | ||
Notes | MILAB; OR;HuPBA;MV | Approved | no | ||
Call Number | Admin @ si @ BHP2012 | Serial | 2120 | ||
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Author | Miguel Reyes; Albert Clapes; Luis Felipe Mejia; Jose Ramirez; Juan R Revilla; Sergio Escalera | ||||
Title | Posture Analysis and Range of Movement Estimation using Depth Maps | Type | Conference Article | ||
Year | 2012 | Publication | 21st International Conference on Pattern Recognition International Workshop on Depth Image Analysis | Abbreviated Journal | |
Volume | 7854 | Issue | Pages | 97-105 | |
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Abstract | World Health Organization estimates that 80% of the world population is affected of back pain during his life. Current practices to analyze back problems are expensive, subjective, and invasive. In this work, we propose a novel tool for posture and range of movement estimation based on the analysis of 3D information from depth maps. Given a set of keypoints defined by the user, RGB and depth data are aligned, depth surface is reconstructed, keypoints are matching using a novel point-to-point fitting procedure, and accurate measurements about posture, spinal curvature, and range of movement are computed. The system shows high precision and reliable measurements, being useful for posture reeducation purposes to prevent musculoskeletal disorders, such as back pain, as well as tracking the posture evolution of patients in rehabilitation treatments. | ||||
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Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
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ISSN | 0302-9743 | ISBN | 978-3-642-40302-6 | Medium | |
Area | Expedition | Conference ![]() |
WDIA | ||
Notes | HuPBA;MILAB | Approved | no | ||
Call Number | Admin @ si @ RCM2012 | Serial | 2121 | ||
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Author | Adriana Romero; Carlo Gatta; Gustavo Camps-Valls | ||||
Title | Unsupervised Deep Feature Extraction Of Hyperspectral Images | Type | Conference Article | ||
Year | 2014 | Publication | 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Convolutional networks; deep learning; sparse learning; feature extraction; hyperspectral image classification | ||||
Abstract | This paper presents an effective unsupervised sparse feature learning algorithm to train deep convolutional networks on hyperspectral images. Deep convolutional hierarchical representations are learned and then used for pixel classification. Features in lower layers present less abstract representations of data, while higher layers represent more abstract and complex characteristics. We successfully illustrate the performance of the extracted representations in a challenging AVIRIS hyperspectral image classification problem, compared to standard dimensionality reduction methods like principal component analysis (PCA) and its kernel counterpart (kPCA). The proposed method largely outperforms the previous state-ofthe-art results on the same experimental setting. Results show that single layer networks can extract powerful discriminative features only when the receptive field accounts for neighboring pixels. Regarding the deep architecture, we can conclude that: (1) additional layers in a deep architecture significantly improve the performance w.r.t. single layer variants; (2) the max-pooling step in each layer is mandatory to achieve satisfactory results; and (3) the performance gain w.r.t. the number of layers is upper bounded, since the spatial resolution is reduced at each pooling, resulting in too spatially coarse output features. | ||||
Address | Lausanne; Switzerland; June 2014 | ||||
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WHISPERS | ||
Notes | MILAB; LAMP; 600.079 | Approved | no | ||
Call Number | Admin @ si @ RGC2014 | Serial | 2513 | ||
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Author | Ilke Demir; Dena Bazazian; Adriana Romero; Viktoriia Sharmanska; Lyne P. Tchapmi | ||||
Title | WiCV 2018: The Fourth Women In Computer Vision Workshop | Type | Conference Article | ||
Year | 2018 | Publication | 4th Women in Computer Vision Workshop | Abbreviated Journal | |
Volume | Issue | Pages | 1941-19412 | ||
Keywords | Conferences; Computer vision; Industries; Object recognition; Engineering profession; Collaboration; Machine learning | ||||
Abstract | We present WiCV 2018 – Women in Computer Vision Workshop to increase the visibility and inclusion of women researchers in computer vision field, organized in conjunction with CVPR 2018. Computer vision and machine learning have made incredible progress over the past years, yet the number of female researchers is still low both in academia and industry. WiCV is organized to raise visibility of female researchers, to increase the collaboration,
and to provide mentorship and give opportunities to femaleidentifying junior researchers in the field. In its fourth year, we are proud to present the changes and improvements over the past years, summary of statistics for presenters and attendees, followed by expectations from future generations. |
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Address | Salt Lake City; USA; June 2018 | ||||
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Area | Expedition | Conference ![]() |
WiCV | ||
Notes | DAG; 600.121; 600.129 | Approved | no | ||
Call Number | Admin @ si @ DBR2018 | Serial | 3222 | ||
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Author | Ozan Caglayan; Walid Aransa; Yaxing Wang; Marc Masana; Mercedes Garcıa-Martinez; Fethi Bougares; Loic Barrault; Joost Van de Weijer | ||||
Title | Does Multimodality Help Human and Machine for Translation and Image Captioning? | Type | Conference Article | ||
Year | 2016 | Publication | 1st conference on machine translation | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | This paper presents the systems developed by LIUM and CVC for the WMT16 Multimodal Machine Translation challenge. We explored various comparative methods, namely phrase-based systems and attentional recurrent neural networks models trained using monomodal or multimodal data. We also performed a human evaluation in order to estimate theusefulness of multimodal data for human machine translation and image description generation. Our systems obtained the best results for both tasks according to the automatic evaluation metrics BLEU and METEOR. | ||||
Address | Berlin; Germany; August 2016 | ||||
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WMT | ||
Notes | LAMP; 600.106 ; 600.068 | Approved | no | ||
Call Number | Admin @ si @ CAW2016 | Serial | 2761 | ||
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Author | Ozan Caglayan; Walid Aransa; Adrien Bardet; Mercedes Garcia-Martinez; Fethi Bougares; Loic Barrault; Marc Masana; Luis Herranz; Joost Van de Weijer | ||||
Title | LIUM-CVC Submissions for WMT17 Multimodal Translation Task | Type | Conference Article | ||
Year | 2017 | Publication | 2nd Conference on Machine Translation | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | This paper describes the monomodal and multimodal Neural Machine Translation systems developed by LIUM and CVC for WMT17 Shared Task on Multimodal Translation. We mainly explored two multimodal architectures where either global visual features or convolutional feature maps are integrated in order to benefit from visual context. Our final systems ranked first for both En-De and En-Fr language pairs according to the automatic evaluation metrics METEOR and BLEU. | ||||
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Area | Expedition | Conference ![]() |
WMT | ||
Notes | LAMP; 600.106; 600.120 | Approved | no | ||
Call Number | Admin @ si @ CAB2017 | Serial | 3035 | ||
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