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Author |
Petia Radeva |
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Title |
Uncertainty Modeling within an End-to-end Framework for Food Image Analysis |
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Conference Article |
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2020 |
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1st DELTA |
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MILAB |
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no |
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Admin @ si @ Rad2020 |
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3527 |
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Author |
Raul Gomez; Jaume Gibert; Lluis Gomez; Dimosthenis Karatzas |
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Title |
Location Sensitive Image Retrieval and Tagging |
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Conference Article |
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Year |
2020 |
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16th European Conference on Computer Vision |
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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. |
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Virtual; August 2020 |
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ECCV |
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DAG; 600.121; 600.129 |
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no |
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Call Number |
Admin @ si @ GGG2020b |
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3420 |
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Author |
Lei Kang; Pau Riba; Yaxing Wang; Marçal Rusiñol; Alicia Fornes; Mauricio Villegas |
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Title |
GANwriting: Content-Conditioned Generation of Styled Handwritten Word Images |
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Conference Article |
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2020 |
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16th European Conference on Computer Vision |
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Although current image generation methods have reached impressive quality levels, they are still unable to produce plausible yet diverse images of handwritten words. On the contrary, when writing by hand, a great variability is observed across different writers, and even when analyzing words scribbled by the same individual, involuntary variations are conspicuous. In this work, we take a step closer to producing realistic and varied artificially rendered handwritten words. We propose a novel method that is able to produce credible handwritten word images by conditioning the generative process with both calligraphic style features and textual content. Our generator is guided by three complementary learning objectives: to produce realistic images, to imitate a certain handwriting style and to convey a specific textual content. Our model is unconstrained to any predefined vocabulary, being able to render whatever input word. Given a sample writer, it is also able to mimic its calligraphic features in a few-shot setup. We significantly advance over prior art and demonstrate with qualitative, quantitative and human-based evaluations the realistic aspect of our synthetically produced images. |
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Virtual; August 2020 |
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ECCV |
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DAG; 600.140; 600.121; 600.129 |
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no |
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Call Number |
Admin @ si @ KPW2020 |
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3426 |
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Author |
Hugo Bertiche; Meysam Madadi; Sergio Escalera |
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Title |
CLOTH3D: Clothed 3D Humans |
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Conference Article |
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2020 |
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16th European Conference on Computer Vision |
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This work presents CLOTH3D, the first big scale synthetic dataset of 3D clothed human sequences. CLOTH3D contains a large variability on garment type, topology, shape, size, tightness and fabric. Clothes are simulated on top of thousands of different pose sequences and body shapes, generating realistic cloth dynamics. We provide the dataset with a generative model for cloth generation. We propose a Conditional Variational Auto-Encoder (CVAE) based on graph convolutions (GCVAE) to learn garment latent spaces. This allows for realistic generation of 3D garments on top of SMPL model for any pose and shape. |
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Virtual; August 2020 |
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ECCV |
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HUPBA |
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no |
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Admin @ si @ BME2020 |
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3519 |
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Author |
Kai Wang; Luis Herranz; Anjan Dutta; Joost Van de Weijer |
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Title |
Bookworm continual learning: beyond zero-shot learning and continual learning |
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Conference Article |
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2020 |
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Workshop TASK-CV 2020 |
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We propose bookworm continual learning(BCL), a flexible setting where unseen classes can be inferred via a semantic model, and the visual model can be updated continually. Thus BCL generalizes both continual learning (CL) and zero-shot learning (ZSL). We also propose the bidirectional imagination (BImag) framework to address BCL where features of both past and future classes are generated. We observe that conditioning the feature generator on attributes can actually harm the continual learning ability, and propose two variants (joint class-attribute conditioning and asymmetric generation) to alleviate this problem. |
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Virtual; August 2020 |
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ECCVW |
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LAMP; 600.141; 600.120 |
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no |
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Call Number |
Admin @ si @ WHD2020 |
Serial |
3466 |
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Author |
Tomas Sixta; Julio C. S. Jacques Junior; Pau Buch Cardona; Eduard Vazquez; Sergio Escalera |
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Title |
FairFace Challenge at ECCV 2020: Analyzing Bias in Face Recognition |
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Conference Article |
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Year |
2020 |
Publication |
ECCV Workshops |
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Volume |
12540 |
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463-481 |
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This work summarizes the 2020 ChaLearn Looking at People Fair Face Recognition and Analysis Challenge and provides a description of the top-winning solutions and analysis of the results. The aim of the challenge was to evaluate accuracy and bias in gender and skin colour of submitted algorithms on the task of 1:1 face verification in the presence of other confounding attributes. Participants were evaluated using an in-the-wild dataset based on reannotated IJB-C, further enriched 12.5K new images and additional labels. The dataset is not balanced, which simulates a real world scenario where AI-based models supposed to present fair outcomes are trained and evaluated on imbalanced data. The challenge attracted 151 participants, who made more 1.8K submissions in total. The final phase of the challenge attracted 36 active teams out of which 10 exceeded 0.999 AUC-ROC while achieving very low scores in the proposed bias metrics. Common strategies by the participants were face pre-processing, homogenization of data distributions, the use of bias aware loss functions and ensemble models. The analysis of top-10 teams shows higher false positive rates (and lower false negative rates) for females with dark skin tone as well as the potential of eyeglasses and young age to increase the false positive rates too. |
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Virtual; August 2020 |
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ECCVW |
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HUPBA |
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no |
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Call Number |
Admin @ si @ SJB2020 |
Serial |
3499 |
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Author |
Reza Azad; Maryam Asadi-Aghbolaghi; Mahmood Fathy; Sergio Escalera |
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Title |
Attention Deeplabv3+: Multi-level Context Attention Mechanism for Skin Lesion Segmentation |
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Conference Article |
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2020 |
Publication |
Bioimage computation workshop |
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Virtual; August 2020 |
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ECCVW |
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HUPBA |
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no |
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Call Number |
Admin @ si @ AAF2020 |
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3520 |
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Author |
Martin Menchon; Estefania Talavera; Jose M. Massa; Petia Radeva |
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Title |
Behavioural Pattern Discovery from Collections of Egocentric Photo-Streams |
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Conference Article |
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2020 |
Publication |
ECCV Workshops |
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12538 |
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469-484 |
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The automatic discovery of behaviour is of high importance when aiming to assess and improve the quality of life of people. Egocentric images offer a rich and objective description of the daily life of the camera wearer. This work proposes a new method to identify a person’s patterns of behaviour from collected egocentric photo-streams. Our model characterizes time-frames based on the context (place, activities and environment objects) that define the images composition. Based on the similarity among the time-frames that describe the collected days for a user, we propose a new unsupervised greedy method to discover the behavioural pattern set based on a novel semantic clustering approach. Moreover, we present a new score metric to evaluate the performance of the proposed algorithm. We validate our method on 104 days and more than 100k images extracted from 7 users. Results show that behavioural patterns can be discovered to characterize the routine of individuals and consequently their lifestyle. |
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Virtual; August 2020 |
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ECCVW |
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MILAB; no proj |
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no |
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Call Number |
Admin @ si @ MTM2020 |
Serial |
3528 |
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Author |
Albert Clapes; Julio C. S. Jacques Junior; Carla Morral; Sergio Escalera |
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Title |
ChaLearn LAP 2020 Challenge on Identity-preserved Human Detection: Dataset and Results |
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Conference Article |
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Year |
2020 |
Publication |
15th IEEE International Conference on Automatic Face and Gesture Recognition |
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801-808 |
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This paper summarizes the ChaLearn Looking at People 2020 Challenge on Identity-preserved Human Detection (IPHD). For the purpose, we released a large novel dataset containing more than 112K pairs of spatiotemporally aligned depth and thermal frames (and 175K instances of humans) sampled from 780 sequences. The sequences contain hundreds of non-identifiable people appearing in a mix of in-the-wild and scripted scenarios recorded in public and private places. The competition was divided into three tracks depending on the modalities exploited for the detection: (1) depth, (2) thermal, and (3) depth-thermal fusion. Color was also captured but only used to facilitate the groundtruth annotation. Still the temporal synchronization of three sensory devices is challenging, so bad temporal matches across modalities can occur. Hence, the labels provided should considered “weak”, although test frames were carefully selected to minimize this effect and ensure the fairest comparison of the participants’ results. Despite this added difficulty, the results got by the participants demonstrate current fully-supervised methods can deal with that and achieve outstanding detection performance when measured in terms of AP@0.50. |
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Virtual; November 2020 |
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HUPBA |
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no |
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Call Number |
Admin @ si @ CJM2020 |
Serial |
3501 |
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Author |
Josep Famadas; Meysam Madadi; Cristina Palmero; Sergio Escalera |
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Title |
Generative Video Face Reenactment by AUs and Gaze Regularization |
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Conference Article |
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Year |
2020 |
Publication |
15th IEEE International Conference on Automatic Face and Gesture Recognition |
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444-451 |
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In this work, we propose an encoder-decoder-like architecture to perform face reenactment in image sequences. Our goal is to transfer the training subject identity to a given test subject. We regularize face reenactment by facial action unit intensity and 3D gaze vector regression. This way, we enforce the network to transfer subtle facial expressions and eye dynamics, providing a more lifelike result. The proposed encoder-decoder receives as input the previous sequence frame stacked to the current frame image of facial landmarks. Thus, the generated frames benefit from appearance and geometry, while keeping temporal coherence for the generated sequence. At test stage, a new target subject with the facial performance of the source subject and the appearance of the training subject is reenacted. Principal component analysis is applied to project the test subject geometry to the closest training subject geometry before reenactment. Evaluation of our proposal shows faster convergence, and more accurate and realistic results in comparison to other architectures without action units and gaze regularization. |
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Virtual; November 2020 |
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HUPBA |
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no |
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Admin @ si @ FMP2020 |
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3517 |
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Author |
Ciprian Corneanu; Meysam Madadi; Sergio Escalera; Aleix Martinez |
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Title |
Explainable Early Stopping for Action Unit Recognition |
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Conference Article |
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Year |
2020 |
Publication |
Faces and Gestures in E-health and welfare workshop |
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693-699 |
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A common technique to avoid overfitting when training deep neural networks (DNN) is to monitor the performance in a dedicated validation data partition and to stop
training as soon as it saturates. This only focuses on what the model does, while completely ignoring what happens inside it.
In this work, we open the “black-box” of DNN in order to perform early stopping. We propose to use a novel theoretical framework that analyses meso-scale patterns in the topology of the functional graph of a network while it trains. Based on it,
we decide when it transitions from learning towards overfitting in a more explainable way. We exemplify the benefits of this approach on a state-of-the art custom DNN that jointly learns local representations and label structure employing an ensemble of dedicated subnetworks. We show that it is practically equivalent in performance to early stopping with patience, the standard early stopping algorithm in the literature. This proves beneficial for AU recognition performance and provides new insights into how learning of AUs occurs in DNNs. |
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Virtual; November 2020 |
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HUPBA; |
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Admin @ si @ CME2020 |
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3514 |
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Author |
Anna Esposito; Terry Amorese; Nelson Maldonato; Alessandro Vinciarelli; Maria Ines Torres; Sergio Escalera; Gennaro Cordasco |
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Title |
Seniors’ ability to decode differently aged facial emotional expressions |
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Conference Article |
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2020 |
Publication |
Faces and Gestures in E-health and welfare workshop |
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716-722 |
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Virtual; November 2020 |
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HUPBA |
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Admin @ si @ EAM2020 |
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3515 |
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Author |
Anna Esposito; Italia Cirillo; Antonietta Esposito; Leopoldina Fortunati; Gian Luca Foresti; Sergio Escalera; Nikolaos Bourbakis |
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Impairments in decoding facial and vocal emotional expressions in high functioning autistic adults and adolescents |
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Conference Article |
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2020 |
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Faces and Gestures in E-health and welfare workshop |
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667-674 |
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Virtual; November 2020 |
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HUPBA |
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no |
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Admin @ si @ ECE2020 |
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3516 |
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Author |
Jialuo Chen; M.A.Souibgui; Alicia Fornes; Beata Megyesi |
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Title |
A Web-based Interactive Transcription Tool for Encrypted Manuscripts |
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Conference Article |
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2020 |
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3rd International Conference on Historical Cryptology |
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52-59 |
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Manual transcription of handwritten text is a time consuming task. In the case of encrypted manuscripts, the recognition is even more complex due to the huge variety of alphabets and symbol sets. To speed up and ease this process, we present a web-based tool aimed to (semi)-automatically transcribe the encrypted sources. The user uploads one or several images of the desired encrypted document(s) as input, and the system returns the transcription(s). This process is carried out in an interactive fashion with
the user to obtain more accurate results. For discovering and testing, the developed web tool is freely available. |
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Virtual; June 2020 |
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HistoCrypt |
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DAG; 600.140; 602.230; 600.121 |
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Admin @ si @ CSF2020 |
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3447 |
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Arnau Baro; Alicia Fornes; Carles Badal |
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Handwritten Historical Music Recognition by Sequence-to-Sequence with Attention Mechanism |
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2020 |
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17th International Conference on Frontiers in Handwriting Recognition |
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Despite decades of research in Optical Music Recognition (OMR), the recognition of old handwritten music scores remains a challenge because of the variabilities in the handwriting styles, paper degradation, lack of standard notation, etc. Therefore, the research in OMR systems adapted to the particularities of old manuscripts is crucial to accelerate the conversion of music scores existing in archives into digital libraries, fostering the dissemination and preservation of our music heritage. In this paper we explore the adaptation of sequence-to-sequence models with attention mechanism (used in translation and handwritten text recognition) and the generation of specific synthetic data for recognizing old music scores. The experimental validation demonstrates that our approach is promising, especially when compared with long short-term memory neural networks. |
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Virtual ICFHR; September 2020 |
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ICFHR |
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DAG; 600.140; 600.121 |
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Admin @ si @ BFB2020 |
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3448 |
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