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Author Alicia Fornes; Josep Llados; Joana Maria Pujadas-Mora
Title (up) Browsing of the Social Network of the Past: Information Extraction from Population Manuscript Images Type Book Chapter
Year 2020 Publication Handwritten Historical Document Analysis, Recognition, and Retrieval – State of the Art and Future Trends Abbreviated Journal
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Publisher World Scientific Place of Publication Editor
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ISSN ISBN 978-981-120-323-7 Medium
Area Expedition Conference
Notes DAG; 600.140; 600.121 Approved no
Call Number Admin @ si @ FLP2020 Serial 3350
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Author Shifeng Zhang; Ajian Liu; Jun Wan; Yanyan Liang; Guogong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z. Li
Title (up) CASIA-SURF: A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing Type Journal
Year 2020 Publication IEEE Transactions on Biometrics, Behavior, and Identity Science Abbreviated Journal TTBIS
Volume 2 Issue 2 Pages 182 - 193
Keywords
Abstract Face anti-spoofing is essential to prevent face recognition systems from a security breach. Much of the progresses have been made by the availability of face anti-spoofing benchmark datasets in recent years. However, existing face anti-spoofing benchmarks have limited number of subjects (≤170) and modalities (≤2), which hinder the further development of the academic community. To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest publicly available dataset for face anti-spoofing in terms of both subjects and modalities. Specifically, it consists of 1,000 subjects with 21,000 videos and each sample has 3 modalities ( i.e. , RGB, Depth and IR). We also provide comprehensive evaluation metrics, diverse evaluation protocols, training/validation/testing subsets and a measurement tool, developing a new benchmark for face anti-spoofing. Moreover, we present a novel multi-modal multi-scale fusion method as a strong baseline, which performs feature re-weighting to select the more informative channel features while suppressing the less useful ones for each modality across different scales. Extensive experiments have been conducted on the proposed dataset to verify its significance and generalization capability. The dataset is available at https://sites.google.com/qq.com/face-anti-spoofing/welcome/challengecvpr2019?authuser=0
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Notes HuPBA; no proj Approved no
Call Number Admin @ si @ ZLW2020 Serial 3412
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Author Albert Clapes; Julio C. S. Jacques Junior; Carla Morral; Sergio Escalera
Title (up) ChaLearn LAP 2020 Challenge on Identity-preserved Human Detection: Dataset and Results Type Conference Article
Year 2020 Publication 15th IEEE International Conference on Automatic Face and Gesture Recognition Abbreviated Journal
Volume Issue Pages 801-808
Keywords
Abstract 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.
Address Virtual; November 2020
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Area Expedition Conference FG
Notes HUPBA Approved no
Call Number Admin @ si @ CJM2020 Serial 3501
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Author Hugo Bertiche; Meysam Madadi; Sergio Escalera
Title (up) CLOTH3D: Clothed 3D Humans Type Conference Article
Year 2020 Publication 16th European Conference on Computer Vision Abbreviated Journal
Volume Issue Pages
Keywords
Abstract 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.
Address Virtual; August 2020
Corporate Author Thesis
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Area Expedition Conference ECCV
Notes HUPBA Approved no
Call Number Admin @ si @ BME2020 Serial 3519
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Author Gabriel Villalonga; Antonio Lopez
Title (up) Co-Training for On-Board Deep Object Detection Type Journal Article
Year 2020 Publication IEEE Access Abbreviated Journal ACCESS
Volume Issue Pages 194441 - 194456
Keywords
Abstract Providing ground truth supervision to train visual models has been a bottleneck over the years, exacerbated by domain shifts which degenerate the performance of such models. This was the case when visual tasks relied on handcrafted features and shallow machine learning and, despite its unprecedented performance gains, the problem remains open within the deep learning paradigm due to its data-hungry nature. Best performing deep vision-based object detectors are trained in a supervised manner by relying on human-labeled bounding boxes which localize class instances (i.e. objects) within the training images. Thus, object detection is one of such tasks for which human labeling is a major bottleneck. In this article, we assess co-training as a semi-supervised learning method for self-labeling objects in unlabeled images, so reducing the human-labeling effort for developing deep object detectors. Our study pays special attention to a scenario involving domain shift; in particular, when we have automatically generated virtual-world images with object bounding boxes and we have real-world images which are unlabeled. Moreover, we are particularly interested in using co-training for deep object detection in the context of driver assistance systems and/or self-driving vehicles. Thus, using well-established datasets and protocols for object detection in these application contexts, we will show how co-training is a paradigm worth to pursue for alleviating object labeling, working both alone and together with task-agnostic domain adaptation.
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Notes ADAS; 600.118 Approved no
Call Number Admin @ si @ ViL2020 Serial 3488
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Author David Berga; Xavier Otazu
Title (up) Computations of top-down attention by modulating V1 dynamics Type Conference Article
Year 2020 Publication Computational and Mathematical Models in Vision Abbreviated Journal
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Address St. Pete Beach; Florida; May 2020
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Area Expedition Conference MODVIS
Notes NEUROBIT Approved no
Call Number Admin @ si @ BeO2020a Serial 3376
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Author Ciprian Corneanu; Sergio Escalera; Aleix M. Martinez
Title (up) Computing the Testing Error Without a Testing Set Type Conference Article
Year 2020 Publication 33rd IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue Pages
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Abstract Oral. Paper award nominee.
Deep Neural Networks (DNNs) have revolutionized computer vision. We now have DNNs that achieve top (performance) results in many problems, including object recognition, facial expression analysis, and semantic segmentation, to name but a few. The design of the DNNs that achieve top results is, however, non-trivial and mostly done by trailand-error. That is, typically, researchers will derive many DNN architectures (i.e., topologies) and then test them on multiple datasets. However, there are no guarantees that the selected DNN will perform well in the real world. One can use a testing set to estimate the performance gap between the training and testing sets, but avoiding overfitting-to-thetesting-data is almost impossible. Using a sequestered testing dataset may address this problem, but this requires a constant update of the dataset, a very expensive venture. Here, we derive an algorithm to estimate the performance gap between training and testing that does not require any testing dataset. Specifically, we derive a number of persistent topology measures that identify when a DNN is learning to generalize to unseen samples. This allows us to compute the DNN’s testing error on unseen samples, even when we do not have access to them. We provide extensive experimental validation on multiple networks and datasets to demonstrate the feasibility of the proposed approach.
Address Virtual CVPR
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Area Expedition Conference CVPR
Notes HuPBA; no proj Approved no
Call Number Admin @ si @ CEM2020 Serial 3437
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Author Yunan Li; Jun Wan; Qiguang Miao; Sergio Escalera; Huijuan Fang; Huizhou Chen; Xiangda Qi; Guodong Guo
Title (up) CR-Net: A Deep Classification-Regression Network for Multimodal Apparent Personality Analysis Type Journal Article
Year 2020 Publication International Journal of Computer Vision Abbreviated Journal IJCV
Volume 128 Issue Pages 2763–2780
Keywords
Abstract First impressions strongly influence social interactions, having a high impact in the personal and professional life. In this paper, we present a deep Classification-Regression Network (CR-Net) for analyzing the Big Five personality problem and further assisting on job interview recommendation in a first impressions setup. The setup is based on the ChaLearn First Impressions dataset, including multimodal data with video, audio, and text converted from the corresponding audio data, where each person is talking in front of a camera. In order to give a comprehensive prediction, we analyze the videos from both the entire scene (including the person’s motions and background) and the face of the person. Our CR-Net first performs personality trait classification and applies a regression later, which can obtain accurate predictions for both personality traits and interview recommendation. Furthermore, we present a new loss function called Bell Loss to address inaccurate predictions caused by the regression-to-the-mean problem. Extensive experiments on the First Impressions dataset show the effectiveness of our proposed network, outperforming the state-of-the-art.
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Notes HuPBA; no menciona Approved no
Call Number Admin @ si @ LWM2020 Serial 3413
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Author Ajian Liu; Xuan Li; Jun Wan; Yanyan Liang; Sergio Escalera; Hugo Jair Escalante; Meysam Madadi; Yi Jin; Zhuoyuan Wu; Xiaogang Yu; Zichang Tan; Qi Yuan; Ruikun Yang; Benjia Zhou; Guodong Guo; Stan Z. Li
Title (up) Cross-ethnicity Face Anti-spoofing Recognition Challenge: A Review Type Journal Article
Year 2020 Publication IET Biometrics Abbreviated Journal BIO
Volume 10 Issue 1 Pages 24-43
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Abstract Face anti-spoofing is critical to prevent face recognition systems from a security breach. The biometrics community has %possessed achieved impressive progress recently due the excellent performance of deep neural networks and the availability of large datasets. Although ethnic bias has been verified to severely affect the performance of face recognition systems, it still remains an open research problem in face anti-spoofing. Recently, a multi-ethnic face anti-spoofing dataset, CASIA-SURF CeFA, has been released with the goal of measuring the ethnic bias. It is the largest up to date cross-ethnicity face anti-spoofing dataset covering 3 ethnicities, 3 modalities, 1,607 subjects, 2D plus 3D attack types, and the first dataset including explicit ethnic labels among the recently released datasets for face anti-spoofing. We organized the Chalearn Face Anti-spoofing Attack Detection Challenge which consists of single-modal (e.g., RGB) and multi-modal (e.g., RGB, Depth, Infrared (IR)) tracks around this novel resource to boost research aiming to alleviate the ethnic bias. Both tracks have attracted 340 teams in the development stage, and finally 11 and 8 teams have submitted their codes in the single-modal and multi-modal face anti-spoofing recognition challenges, respectively. All the results were verified and re-ran by the organizing team, and the results were used for the final ranking. This paper presents an overview of the challenge, including its design, evaluation protocol and a summary of results. We analyze the top ranked solutions and draw conclusions derived from the competition. In addition we outline future work directions.
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Notes HUPBA; no proj Approved no
Call Number Admin @ si @ LLW2020b Serial 3523
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Author Beata Megyesi; Bernhard Esslinger; Alicia Fornes; Nils Kopal; Benedek Lang; George Lasry; Karl de Leeuw; Eva Pettersson; Arno Wacker; Michelle Waldispuhl
Title (up) Decryption of historical manuscripts: the DECRYPT project Type Journal Article
Year 2020 Publication Cryptologia Abbreviated Journal CRYPT
Volume 44 Issue 6 Pages 545-559
Keywords automatic decryption; cipher collection; historical cryptology; image transcription
Abstract Many historians and linguists are working individually and in an uncoordinated fashion on the identification and decryption of historical ciphers. This is a time-consuming process as they often work without access to automatic methods and processes that can accelerate the decipherment. At the same time, computer scientists and cryptologists are developing algorithms to decrypt various cipher types without having access to a large number of original ciphertexts. In this paper, we describe the DECRYPT project aiming at the creation of resources and tools for historical cryptology by bringing the expertise of various disciplines together for collecting data, exchanging methods for faster progress to transcribe, decrypt and contextualize historical encrypted manuscripts. We present our goals and work-in progress of a general approach for analyzing historical encrypted manuscripts using standardized methods and a new set of state-of-the-art tools. We release the data and tools as open-source hoping that all mentioned disciplines would benefit and contribute to the research infrastructure of historical cryptology.
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Notes DAG; 600.140; 600.121 Approved no
Call Number Admin @ si @ MEF2020 Serial 3347
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Author Hassan Ahmed Sial; Ramon Baldrich; Maria Vanrell
Title (up) Deep intrinsic decomposition trained on surreal scenes yet with realistic light effects Type Journal Article
Year 2020 Publication Journal of the Optical Society of America A Abbreviated Journal JOSA A
Volume 37 Issue 1 Pages 1-15
Keywords
Abstract Estimation of intrinsic images still remains a challenging task due to weaknesses of ground-truth datasets, which either are too small or present non-realistic issues. On the other hand, end-to-end deep learning architectures start to achieve interesting results that we believe could be improved if important physical hints were not ignored. In this work, we present a twofold framework: (a) a flexible generation of images overcoming some classical dataset problems such as larger size jointly with coherent lighting appearance; and (b) a flexible architecture tying physical properties through intrinsic losses. Our proposal is versatile, presents low computation time, and achieves state-of-the-art results.
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Notes CIC; 600.140; 600.12; 600.118 Approved no
Call Number Admin @ si @ SBV2019 Serial 3311
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Author Henry Velesaca; Raul Mira; Patricia Suarez; Christian X. Larrea; Angel Sappa
Title (up) Deep Learning Based Corn Kernel Classification Type Conference Article
Year 2020 Publication 1st International Workshop and Prize Challenge on Agriculture-Vision: Challenges & Opportunities for Computer Vision in Agriculture Abbreviated Journal
Volume Issue Pages
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Abstract This paper presents a full pipeline to classify sample sets of corn kernels. The proposed approach follows a segmentation-classification scheme. The image segmentation is performed through a well known deep learningbased approach, the Mask R-CNN architecture, while the classification is performed hrough a novel-lightweight network specially designed for this task—good corn kernel, defective corn kernel and impurity categories are considered. As a second contribution, a carefully annotated multitouching corn kernel dataset has been generated. This dataset has been used for training the segmentation and the classification modules. Quantitative evaluations have been
performed and comparisons with other approaches are provided showing improvements with the proposed pipeline.
Address Virtual CVPR
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Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference CVPRW
Notes MSIAU; 600.130; 600.122 Approved no
Call Number Admin @ si @ VMS2020 Serial 3430
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Author Yaxing Wang; Lu Yu; Joost Van de Weijer
Title (up) DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs Type Conference Article
Year 2020 Publication 34th Conference on Neural Information Processing Systems Abbreviated Journal
Volume Issue Pages
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Abstract Image-to-image translation has recently achieved remarkable results. But despite current success, it suffers from inferior performance when translations between classes require large shape changes. We attribute this to the high-resolution bottlenecks which are used by current state-of-the-art image-to-image methods. Therefore, in this work, we propose a novel deep hierarchical Image-to-Image Translation method, called DeepI2I. We learn a model by leveraging hierarchical features: (a) structural information contained in the shallow layers and (b) semantic information extracted from the deep layers. To enable the training of deep I2I models on small datasets, we propose a novel transfer learning method, that transfers knowledge from pre-trained GANs. Specifically, we leverage the discriminator of a pre-trained GANs (i.e. BigGAN or StyleGAN) to initialize both the encoder and the discriminator and the pre-trained generator to initialize the generator of our model. Applying knowledge transfer leads to an alignment problem between the encoder and generator. We introduce an adaptor network to address this. On many-class image-to-image translation on three datasets (Animal faces, Birds, and Foods) we decrease mFID by at least 35% when compared to the state-of-the-art. Furthermore, we qualitatively and quantitatively demonstrate that transfer learning significantly improves the performance of I2I systems, especially for small datasets. Finally, we are the first to perform I2I translations for domains with over 100 classes.
Address virtual; December 2020
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Area Expedition Conference NEURIPS
Notes LAMP; 600.120 Approved no
Call Number Admin @ si @ WYW2020 Serial 3485
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Author Margarita Torre; Beatriz Remeseiro; Petia Radeva; Fernando Martinez
Title (up) DeepNEM: Deep Network Energy-Minimization for Agricultural Field Segmentation Type Journal Article
Year 2020 Publication IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Abbreviated Journal JSTAEOR
Volume 13 Issue Pages 726-737
Keywords
Abstract One of the main characteristics of agricultural fields is that the appearance of different crops and their growth status, in an aerial image, is varied, and has a wide range of radiometric values and high level of variability. The extraction of these fields and their monitoring are activities that require a high level of human intervention. In this article, we propose a novel automatic algorithm, named deep network energy-minimization (DeepNEM), to extract agricultural fields in aerial images. The model-guided process selects the most relevant image clues extracted by a deep network, completes them and finally generates regions that represent the agricultural fields under a minimization scheme. DeepNEM has been tested over a broad range of fields in terms of size, shape, and content. Different measures were used to compare the DeepNEM with other methods, and to prove that it represents an improved approach to achieve a high-quality segmentation of agricultural fields. Furthermore, this article also presents a new public dataset composed of 1200 images with their parcels boundaries annotations.
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Notes MILAB Approved no
Call Number Admin @ si @ TRR2020 Serial 3410
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Author Xavier Soria; Edgar Riba; Angel Sappa
Title (up) Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection Type Conference Article
Year 2020 Publication IEEE Winter Conference on Applications of Computer Vision Abbreviated Journal
Volume Issue Pages
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Abstract This paper proposes a Deep Learning based edge detector, which is inspired on both HED (Holistically-Nested Edge Detection) and Xception networks. The proposed approach generates thin edge-maps that are plausible for human eyes; it can be used in any edge detection task without previous training or fine tuning process. As a second contribution, a large dataset with carefully annotated edges has been generated. This dataset has been used for training the proposed approach as well the state-of-the-art algorithms for comparisons. Quantitative and qualitative evaluations have been performed on different benchmarks showing improvements with the proposed method when F-measure of ODS and OIS are considered.
Address Aspen; USA; March 2020
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Area Expedition Conference WACV
Notes MSIAU; 600.130; 601.349; 600.122 Approved no
Call Number Admin @ si @ SRS2020 Serial 3434
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