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Author |
B. Moghaddam; David Guillamet; Jordi Vitria |
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Title |
, Local Appearance-Based Models using High-Order Statistics of Image Features |
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Miscellaneous |
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2003 |
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IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) |
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OR;MV |
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no |
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BCNPCL @ bcnpcl @ MGV2003 |
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395 |
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Author |
Ajian Liu; Jun Wan; Sergio Escalera; Hugo Jair Escalante; Zichang Tan; Qi Yuan; Kai Wang; Chi Lin; Guodong Guo; Isabelle Guyon; Stan Z. Li |
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Title |
Multi-Modal Face Anti-Spoofing Attack Detection Challenge at CVPR2019 |
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Conference Article |
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Year |
2019 |
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IEEE International Conference on Computer Vision and Pattern Recognition-Workshop |
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Anti-spoofing attack detection is critical to guarantee the security of face-based authentication and facial analysis systems. Recently, a multi-modal face anti-spoofing dataset, CASIA-SURF, has been released with the goal of boosting research in this important topic. CASIA-SURF is the largest public data set for facial anti-spoofing attack detection in terms of both, diversity and modalities: it comprises 1,000 subjects and 21,000 video samples. We organized a challenge around this novel resource to boost research in the subject. The Chalearn LAP multi-modal face anti-spoofing attack detection challenge attracted more than 300 teams for the development phase with a total of 13 teams qualifying for the final round. 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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California; June 2019 |
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CVPRW |
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HuPBA; no proj |
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no |
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Admin @ si @ LWE2019 |
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3329 |
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Author |
Armin Mehri; Angel Sappa |
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Title |
Colorizing Near Infrared Images through a Cyclic Adversarial Approach of Unpaired Samples |
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Conference Article |
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2019 |
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IEEE International Conference on Computer Vision and Pattern Recognition-Workshops |
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This paper presents a novel approach for colorizing near infrared (NIR) images. The approach is based on image-to-image translation using a Cycle-Consistent adversarial network for learning the color channels on unpaired dataset. This architecture is able to handle unpaired datasets. The approach uses as generators tailored networks that require less computation times, converge faster and generate high quality samples. The obtained results have been quantitatively—using standard evaluation metrics—and qualitatively evaluated showing considerable improvements with respect to the state of the art |
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Long beach; California; USA; June 2019 |
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CVPRW |
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MSIAU; 600.130; 601.349; 600.122 |
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no |
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Admin @ si @ MeS2019 |
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3271 |
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Author |
Patricia Suarez; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud |
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Title |
Image Vegetation Index through a Cycle Generative Adversarial Network |
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Conference Article |
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Year |
2019 |
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IEEE International Conference on Computer Vision and Pattern Recognition-Workshops |
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This paper proposes a novel approach to estimate the Normalized Difference Vegetation Index (NDVI) just from an RGB image. The NDVI values are obtained by using images from the visible spectral band together with a synthetic near infrared image obtained by a cycled GAN. The cycled GAN network is able to obtain a NIR image from a given gray scale image. It is trained by using unpaired set of gray scale and NIR images by using a U-net architecture and a multiple loss function (gray scale images are obtained from the provided RGB images). Then, the NIR image estimated with the proposed cycle generative adversarial network is used to compute the NDVI index. Experimental results are provided showing the validity of the proposed approach. Additionally, comparisons with previous approaches are also provided. |
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Long beach; California; USA; June 2019 |
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CVPRW |
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MSIAU; 600.130; 601.349; 600.122 |
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no |
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Call Number |
Admin @ si @ SSV2019 |
Serial |
3272 |
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Author |
Lichao Zhang; Martin Danelljan; Abel Gonzalez-Garcia; Joost Van de Weijer; Fahad Shahbaz Khan |
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Title |
Multi-Modal Fusion for End-to-End RGB-T Tracking |
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Conference Article |
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Year |
2019 |
Publication |
IEEE International Conference on Computer Vision Workshops |
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2252-2261 |
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We propose an end-to-end tracking framework for fusing the RGB and TIR modalities in RGB-T tracking. Our baseline tracker is DiMP (Discriminative Model Prediction), which employs a carefully designed target prediction network trained end-to-end using a discriminative loss. We analyze the effectiveness of modality fusion in each of the main components in DiMP, i.e. feature extractor, target estimation network, and classifier. We consider several fusion mechanisms acting at different levels of the framework, including pixel-level, feature-level and response-level. Our tracker is trained in an end-to-end manner, enabling the components to learn how to fuse the information from both modalities. As data to train our model, we generate a large-scale RGB-T dataset by considering an annotated RGB tracking dataset (GOT-10k) and synthesizing paired TIR images using an image-to-image translation approach. We perform extensive experiments on VOT-RGBT2019 dataset and RGBT210 dataset, evaluating each type of modality fusing on each model component. The results show that the proposed fusion mechanisms improve the performance of the single modality counterparts. We obtain our best results when fusing at the feature-level on both the IoU-Net and the model predictor, obtaining an EAO score of 0.391 on VOT-RGBT2019 dataset. With this fusion mechanism we achieve the state-of-the-art performance on RGBT210 dataset. |
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Seul; Corea; October 2019 |
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ICCVW |
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LAMP; 600.109; 600.141; 600.120 |
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no |
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Call Number |
Admin @ si @ ZDG2019 |
Serial |
3279 |
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Author |
Javad Zolfaghari Bengar; Abel Gonzalez-Garcia; Gabriel Villalonga; Bogdan Raducanu; Hamed H. Aghdam; Mikhail Mozerov; Antonio Lopez; Joost Van de Weijer |
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Title |
Temporal Coherence for Active Learning in Videos |
Type |
Conference Article |
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Year |
2019 |
Publication |
IEEE International Conference on Computer Vision Workshops |
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Pages |
914-923 |
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Abstract |
Autonomous driving systems require huge amounts of data to train. Manual annotation of this data is time-consuming and prohibitively expensive since it involves human resources. Therefore, active learning emerged as an alternative to ease this effort and to make data annotation more manageable. In this paper, we introduce a novel active learning approach for object detection in videos by exploiting temporal coherence. Our active learning criterion is based on the estimated number of errors in terms of false positives and false negatives. The detections obtained by the object detector are used to define the nodes of a graph and tracked forward and backward to temporally link the nodes. Minimizing an energy function defined on this graphical model provides estimates of both false positives and false negatives. Additionally, we introduce a synthetic video dataset, called SYNTHIA-AL, specially designed to evaluate active learning for video object detection in road scenes. Finally, we show that our approach outperforms active learning baselines tested on two datasets. |
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Seul; Corea; October 2019 |
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ICCVW |
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LAMP; ADAS; 600.124; 602.200; 600.118; 600.120; 600.141 |
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no |
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Call Number |
Admin @ si @ ZGV2019 |
Serial |
3294 |
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Author |
Mohammed Al Rawi; Ernest Valveny |
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Title |
Compact and Efficient Multitask Learning in Vision, Language and Speech |
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Conference Article |
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Year |
2019 |
Publication |
IEEE International Conference on Computer Vision Workshops |
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2933-2942 |
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Across-domain multitask learning is a challenging area of computer vision and machine learning due to the intra-similarities among class distributions. Addressing this problem to cope with the human cognition system by considering inter and intra-class categorization and recognition complicates the problem even further. We propose in this work an effective holistic and hierarchical learning by using a text embedding layer on top of a deep learning model. We also propose a novel sensory discriminator approach to resolve the collisions between different tasks and domains. We then train the model concurrently on textual sentiment analysis, speech recognition, image classification, action recognition from video, and handwriting word spotting of two different scripts (Arabic and English). The model we propose successfully learned different tasks across multiple domains. |
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Seul; Korea; October 2019 |
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ICCVW |
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DAG; 600.121; 600.129 |
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no |
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Call Number |
Admin @ si @ RaV2019 |
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3365 |
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Author |
Alejandro Cartas; Jordi Luque; Petia Radeva; Carlos Segura; Mariella Dimiccoli |
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Title |
Seeing and Hearing Egocentric Actions: How Much Can We Learn? |
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Conference Article |
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2019 |
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IEEE International Conference on Computer Vision Workshops |
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4470-4480 |
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Our interaction with the world is an inherently multimodal experience. However, the understanding of human-to-object interactions has historically been addressed focusing on a single modality. In particular, a limited number of works have considered to integrate the visual and audio modalities for this purpose. In this work, we propose a multimodal approach for egocentric action recognition in a kitchen environment that relies on audio and visual information. Our model combines a sparse temporal sampling strategy with a late fusion of audio, spatial, and temporal streams. Experimental results on the EPIC-Kitchens dataset show that multimodal integration leads to better performance than unimodal approaches. In particular, we achieved a 5.18% improvement over the state of the art on verb classification. |
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Seul; Korea; October 2019 |
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ICCVW |
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MILAB; no proj |
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no |
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Call Number |
Admin @ si @ CLR2019b |
Serial |
3385 |
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Author |
Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez |
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Title |
An Adapted Alternation Approach for Recommender Systems |
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Conference Article |
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Year |
2008 |
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IEEE International Conference on e–Business Engineering, |
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128–135 |
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This paper presents an adaptation of the alternation technique to tackle the prediction task in recommender systems. These systems are widely considered in electronic commerce to help customers to find products they will probably like or dislike. As the SVD-based approaches, the proposed adapted alternation technique uses all the information stored in the system to find the predictions. The main advantage of this technique with respect to the SVD-based ones is that it can deal with missing data. Furthermore, it has a smaller computational cost. Experimental results with public data sets are provided in order to show the viability of the proposed adapted alternation approach. |
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Xi’an (Xina) |
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ADAS |
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ADAS @ adas @ JSL2008e |
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1044 |
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Author |
Laura Lopez-Fuentes; Sebastia Massanet; Manuel Gonzalez-Hidalgo |
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Title |
Image vignetting reduction via a maximization of fuzzy entropy |
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2017 |
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IEEE International Conference on Fuzzy Systems |
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In many computer vision applications, vignetting is an undesirable effect which must be removed in a pre-processing step. Recently, an algorithm for image vignetting correction has been presented by means of a minimization of log-intensity entropy. This method relies on an increase of the entropy of the image when it is affected with vignetting. In this paper, we propose a novel algorithm to reduce image vignetting via a maximization of the fuzzy entropy of the image. Fuzzy entropy quantifies the fuzziness degree of a fuzzy set and its value is also modified by the presence of vignetting. The experimental results show that this novel algorithm outperforms in most cases the algorithm based on the minimization of log-intensity entropy both from the qualitative and the quantitative point of view. |
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Napoles; Italia; July 2017 |
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FUZZ-IEEE |
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LAMP; 600.120 |
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Admin @ si @ LMG2017 |
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2972 |
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Author |
Angel Sappa; Niki Aifanti; Sotiris Malassiotis; Michael G. Strintzis |
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Title |
3D Gait Estimation from Monoscopic Video |
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Miscellaneous |
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2004 |
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IEEE International Conference on Image Processing |
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Singapore |
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ADAS |
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ADAS @ adas @ SAM2004c |
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495 |
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A. Pujol; Juan J. Villanueva; Jose Luis Alba |
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Efficient Computation of Face Shape Similarity Using Distance Transform Eigendecomposition and Valleys. |
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2001 |
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IEEE International Conference on Image Processing (ICIP 2001), 1:1030–1033 |
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Grecia |
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ISE @ ise @ PVA2001 |
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203 |
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Author |
David Guillamet; B. Moghaddam; Jordi Vitria |
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Title |
Higher-Order Dependencies in Local Appearance Models |
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Miscellaneous |
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2003 |
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IEEE International Conference on Image Processing (ICIP) |
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OR;MV |
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no |
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BCNPCL @ bcnpcl @ GMV2003b |
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377 |
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Author |
Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat |
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Title |
Photometric Stereo through and Adapted Alternation Approach |
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2008 |
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IEEE International Conference on Image Processing, |
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1500–1503 |
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San Diego; CA; USA |
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ADAS @ adas @ JSL2008d |
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Author |
Angel Sappa; Niki Aifanti; Sotiris Malassiotis; Michael G. Strintzis |
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Monocular 3D Human Body Reconstruction Towards Depth Augmentation of Television Sequences |
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2003 |
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IEEE International Conference on Image Processing, Barcelona, Spain, September 2003 |
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325-328 |
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Barcelona |
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ADAS @ adas @ SAM2003 |
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418 |
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