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Author Yaxing Wang; Joost Van de Weijer; Luis Herranz
Title Mix and match networks: encoder-decoder alignment for zero-pair image translation Type Conference Article
Year 2018 Publication (up) 31st IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue Pages 5467 - 5476
Keywords
Abstract We address the problem of image translation between domains or modalities for which no direct paired data is available (i.e. zero-pair translation). We propose mix and match networks, based on multiple encoders and decoders aligned in such a way that other encoder-decoder pairs can be composed at test time to perform unseen image translation tasks between domains or modalities for which explicit paired samples were not seen during training. We study the impact of autoencoders, side information and losses in improving the alignment and transferability of trained pairwise translation models to unseen translations. We show our approach is scalable and can perform colorization and style transfer between unseen combinations of domains. We evaluate our system in a challenging cross-modal setting where semantic segmentation is estimated from depth images, without explicit access to any depth-semantic segmentation training pairs. Our model outperforms baselines based on pix2pix and CycleGAN models.
Address Salt Lake City; USA; June 2018
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CVPR
Notes LAMP; 600.109; 600.106; 600.120 Approved no
Call Number Admin @ si @ WWH2018b Serial 3131
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Author Adrian Galdran; Aitor Alvarez-Gila; Alessandro Bria; Javier Vazquez; Marcelo Bertalmio
Title On the Duality Between Retinex and Image Dehazing Type Conference Article
Year 2018 Publication (up) 31st IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue Pages 8212–8221
Keywords Image color analysis; Task analysis; Atmospheric modeling; Computer vision; Computational modeling; Lighting
Abstract Image dehazing deals with the removal of undesired loss of visibility in outdoor images due to the presence of fog. Retinex is a color vision model mimicking the ability of the Human Visual System to robustly discount varying illuminations when observing a scene under different spectral lighting conditions. Retinex has been widely explored in the computer vision literature for image enhancement and other related tasks. While these two problems are apparently unrelated, the goal of this work is to show that they can be connected by a simple linear relationship. Specifically, most Retinex-based algorithms have the characteristic feature of always increasing image brightness, which turns them into ideal candidates for effective image dehazing by directly applying Retinex to a hazy image whose intensities have been inverted. In this paper, we give theoretical proof that Retinex on inverted intensities is a solution to the image dehazing problem. Comprehensive qualitative and quantitative results indicate that several classical and modern implementations of Retinex can be transformed into competing image dehazing algorithms performing on pair with more complex fog removal methods, and can overcome some of the main challenges associated with this problem.
Address Salt Lake City; USA; June 2018
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CVPR
Notes LAMP; 600.120 Approved no
Call Number Admin @ si @ GAB2018 Serial 3146
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Author Xialei Liu; Joost Van de Weijer; Andrew Bagdanov
Title Leveraging Unlabeled Data for Crowd Counting by Learning to Rank Type Conference Article
Year 2018 Publication (up) 31st IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue Pages 7661 - 7669
Keywords Task analysis; Training; Computer vision; Visualization; Estimation; Head; Context modeling
Abstract We propose a novel crowd counting approach that leverages abundantly available unlabeled crowd imagery in a learning-to-rank framework. To induce a ranking of
cropped images , we use the observation that any sub-image of a crowded scene image is guaranteed to contain the same number or fewer persons than the super-image. This allows us to address the problem of limited size of existing
datasets for crowd counting. We collect two crowd scene datasets from Google using keyword searches and queryby-example image retrieval, respectively. We demonstrate how to efficiently learn from these unlabeled datasets by incorporating learning-to-rank in a multi-task network which simultaneously ranks images and estimates crowd density maps. Experiments on two of the most challenging crowd counting datasets show that our approach obtains state-ofthe-art results.
Address Salt Lake City; USA; June 2018
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CVPR
Notes LAMP; 600.109; 600.106; 600.120 Approved no
Call Number Admin @ si @ LWB2018 Serial 3159
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Author Abel Gonzalez-Garcia; Davide Modolo; Vittorio Ferrari
Title Objects as context for detecting their semantic parts Type Conference Article
Year 2018 Publication (up) 31st IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue Pages 6907 - 6916
Keywords Proposals; Semantics; Wheels; Automobiles; Context modeling; Task analysis; Object detection
Abstract We present a semantic part detection approach that effectively leverages object information. We use the object appearance and its class as indicators of what parts to expect. We also model the expected relative location of parts inside the objects based on their appearance. We achieve this with a new network module, called OffsetNet, that efficiently predicts a variable number of part locations within a given object. Our model incorporates all these cues to
detect parts in the context of their objects. This leads to considerably higher performance for the challenging task of part detection compared to using part appearance alone (+5 mAP on the PASCAL-Part dataset). We also compare
to other part detection methods on both PASCAL-Part and CUB200-2011 datasets.
Address Salt Lake City; USA; June 2018
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CVPR
Notes LAMP; 600.109; 600.120 Approved no
Call Number Admin @ si @ GMF2018 Serial 3229
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Author Patricia Suarez; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud
Title Deep Learning based Single Image Dehazing Type Conference Article
Year 2018 Publication (up) 31st IEEE Conference on Computer Vision and Pattern Recognition Workhsop Abbreviated Journal
Volume Issue Pages 1250 - 12507
Keywords Gallium nitride; Atmospheric modeling; Generators; Generative adversarial networks; Convergence; Image color analysis
Abstract This paper proposes a novel approach to remove haze degradations in RGB images using a stacked conditional Generative Adversarial Network (GAN). It employs a triplet of GAN to remove the haze on each color channel independently.
A multiple loss functions scheme, applied over a conditional probabilistic model, is proposed. The proposed GAN architecture learns to remove the haze, using as conditioned entrance, the images with haze from which the clear
images will be obtained. Such formulation ensures a fast model training convergence and a homogeneous model generalization. Experiments showed that the proposed method generates high-quality clear images.
Address Salt Lake City; USA; June 2018
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CVPRW
Notes MSIAU; 600.086; 600.130; 600.122 Approved no
Call Number Admin @ si @ SSV2018d Serial 3197
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Author David Vazquez; Jorge Bernal; F. Javier Sanchez; Gloria Fernandez Esparrach; Antonio Lopez; Adriana Romero; Michal Drozdzal; Aaron Courville
Title A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images Type Conference Article
Year 2017 Publication (up) 31st International Congress and Exhibition on Computer Assisted Radiology and Surgery Abbreviated Journal
Volume Issue Pages
Keywords Deep Learning; Medical Imaging
Abstract Colorectal cancer (CRC) is the third cause of cancer death worldwide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss-rate and inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing Decision Support Systems (DSS) aiming to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extended benchmark of colonoscopy image, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. We provide new baselines on this dataset by training standard fully convolutional networks (FCN) for semantic segmentation and significantly outperforming, without any further post-processing, prior results in endoluminal scene segmentation.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CARS
Notes ADAS; MV; 600.075; 600.085; 600.076; 601.281; 600.118 Approved no
Call Number ADAS @ adas @ VBS2017a Serial 2880
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Author Quentin Angermann; Jorge Bernal; Cristina Sanchez Montes; Maroua Hammami; Gloria Fernandez Esparrach; Xavier Dray; Olivier Romain; F. Javier Sanchez; Aymeric Histace
Title Real-Time Polyp Detection in Colonoscopy Videos: A Preliminary Study For Adapting Still Frame-based Methodology To Video Sequences Analysis Type Conference Article
Year 2017 Publication (up) 31st International Congress and Exhibition on Computer Assisted Radiology and Surgery Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Barcelona; Spain; June 2017
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CARS
Notes MV; no menciona Approved no
Call Number Admin @ si @ ABS2017 Serial 2947
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Author Boris N. Oreshkin; Pau Rodriguez; Alexandre Lacoste
Title TADAM: Task dependent adaptive metric for improved few-shot learning Type Conference Article
Year 2018 Publication (up) 32nd Annual Conference on Neural Information Processing Systems Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Few-shot learning has become essential for producing models that generalize from few examples. In this work, we identify that metric scaling and metric task conditioning are important to improve the performance of few-shot algorithms. Our analysis reveals that simple metric scaling completely changes the nature of few-shot algorithm parameter updates. Metric scaling provides improvements up to 14% in accuracy for certain metrics on the mini-Imagenet 5-way 5-shot classification task. We further propose a simple and effective way of conditioning a learner on the task sample set, resulting in learning a task-dependent metric space. Moreover, we propose and empirically test a practical end-to-end optimization procedure based on auxiliary task co-training to learn a task-dependent metric space. The resulting few-shot learning model based on the task-dependent scaled metric achieves state of the art on mini-Imagenet. We confirm these results on another few-shot dataset that we introduce in this paper based on CIFAR100.
Address Montreal; Canada; December 2018
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference NIPS
Notes ISE; 600.098; 600.119 Approved no
Call Number Admin @ si @ ORL2018 Serial 3140
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Author Abel Gonzalez-Garcia; Joost Van de Weijer; Yoshua Bengio
Title Image-to-image translation for cross-domain disentanglement Type Conference Article
Year 2018 Publication (up) 32nd Annual Conference on Neural Information Processing Systems Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Montreal; Canada; December 2018
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference NIPS
Notes LAMP; 600.120 Approved no
Call Number Admin @ si @ GWB2018 Serial 3155
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Author Chenshen Wu; Luis Herranz; Xialei Liu; Joost Van de Weijer; Bogdan Raducanu
Title Memory Replay GANs: Learning to Generate New Categories without Forgetting Type Conference Article
Year 2018 Publication (up) 32nd Annual Conference on Neural Information Processing Systems Abbreviated Journal
Volume Issue Pages 5966-5976
Keywords
Abstract Previous works on sequential learning address the problem of forgetting in discriminative models. In this paper we consider the case of generative models. In particular, we investigate generative adversarial networks (GANs) in the task of learning new categories in a sequential fashion. We first show that sequential fine tuning renders the network unable to properly generate images from previous categories (ie forgetting). Addressing this problem, we propose Memory Replay GANs (MeRGANs), a conditional GAN framework that integrates a memory replay generator. We study two methods to prevent forgetting by leveraging these replays, namely joint training with replay and replay alignment. Qualitative and quantitative experimental results in MNIST, SVHN and LSUN datasets show that our memory replay approach can generate competitive images while significantly mitigating the forgetting of previous categories.
Address Montreal; Canada; December 2018
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference NIPS
Notes LAMP; 600.106; 600.109; 602.200; 600.120 Approved no
Call Number Admin @ si @ WHL2018 Serial 3249
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Author Jaume Garcia; Albert Andaluz; Debora Gil; Francesc Carreras
Title Decoupled External Forces in a Predictor-Corrector Segmentation Scheme for LV Contours in Tagged MR Images Type Conference Article
Year 2010 Publication (up) 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society Abbreviated Journal
Volume Issue Pages 4805-4808
Keywords
Abstract Computation of functional regional scores requires proper identification of LV contours. On one hand, manual segmentation is robust, but it is time consuming and requires high expertise. On the other hand, the tag pattern in TMR sequences is a problem for automatic segmentation of LV boundaries. We propose a segmentation method based on a predictorcorrector (Active Contours – Shape Models) scheme. Special stress is put in the definition of the AC external forces. First, we introduce a semantic description of the LV that discriminates myocardial tissue by using texture and motion descriptors. Second, in order to ensure convergence regardless of the initial contour, the external energy is decoupled according to the orientation of the edges in the image potential. We have validated the model in terms of error in segmented contours and accuracy of regional clinical scores.
Address Buenos Aires (Argentina)
Corporate Author IEEE EMB Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1557-170X ISBN 978-1-4244-4123-5 Medium
Area Expedition Conference EMBC
Notes IAM Approved no
Call Number IAM @ iam @ GAG2010 Serial 1514
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Author Anjan Dutta; Zeynep Akata
Title Semantically Tied Paired Cycle Consistency for Zero-Shot Sketch-based Image Retrieval Type Conference Article
Year 2019 Publication (up) 32nd IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue Pages 5089-5098
Keywords
Abstract Zero-shot sketch-based image retrieval (SBIR) is an emerging task in computer vision, allowing to retrieve natural images relevant to sketch queries that might not been seen in the training phase. Existing works either require aligned sketch-image pairs or inefficient memory fusion layer for mapping the visual information to a semantic space. In this work, we propose a semantically aligned paired cycle-consistent generative (SEM-PCYC) model for zero-shot SBIR, where each branch maps the visual information to a common semantic space via an adversarial training. Each of these branches maintains a cycle consistency that only requires supervision at category levels, and avoids the need of highly-priced aligned sketch-image pairs. A classification criteria on the generators' outputs ensures the visual to semantic space mapping to be discriminating. Furthermore, we propose to combine textual and hierarchical side information via a feature selection auto-encoder that selects discriminating side information within a same end-to-end model. Our results demonstrate a significant boost in zero-shot SBIR performance over the state-of-the-art on the challenging Sketchy and TU-Berlin datasets.
Address Long beach; California; USA; June 2019
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CVPR
Notes DAG; 600.141; 600.121 Approved no
Call Number Admin @ si @ DuA2019 Serial 3268
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Author Lu Yu; Vacit Oguz Yazici; Xialei Liu; Joost Van de Weijer; Yongmei Cheng; Arnau Ramisa
Title Learning Metrics from Teachers: Compact Networks for Image Embedding Type Conference Article
Year 2019 Publication (up) 32nd IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue Pages 2907-2916
Keywords
Abstract Metric learning networks are used to compute image embeddings, which are widely used in many applications such as image retrieval and face recognition. In this paper, we propose to use network distillation to efficiently compute image embeddings with small networks. Network distillation has been successfully applied to improve image classification, but has hardly been explored for metric learning. To do so, we propose two new loss functions that model the
communication of a deep teacher network to a small student network. We evaluate our system in several datasets, including CUB-200-2011, Cars-196, Stanford Online Products and show that embeddings computed using small student networks perform significantly better than those computed using standard networks of similar size. Results on a very compact network (MobileNet-0.25), which can be
used on mobile devices, show that the proposed method can greatly improve Recall@1 results from 27.5% to 44.6%. Furthermore, we investigate various aspects of distillation for embeddings, including hint and attention layers, semisupervised learning and cross quality distillation.
Address Long beach; California; june 2019
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CVPR
Notes LAMP; 600.109; 600.120 Approved no
Call Number Admin @ si @ YYL2019 Serial 3281
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Author Ali Furkan Biten; Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas
Title Good News, Everyone! Context driven entity-aware captioning for news images Type Conference Article
Year 2019 Publication (up) 32nd IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue Pages 12458-12467
Keywords
Abstract Current image captioning systems perform at a merely descriptive level, essentially enumerating the objects in the scene and their relations. Humans, on the contrary, interpret images by integrating several sources of prior knowledge of the world. In this work, we aim to take a step closer to producing captions that offer a plausible interpretation of the scene, by integrating such contextual information into the captioning pipeline. For this we focus on the captioning of images used to illustrate news articles. We propose a novel captioning method that is able to leverage contextual information provided by the text of news articles associated with an image. Our model is able to selectively draw information from the article guided by visual cues, and to dynamically extend the output dictionary to out-of-vocabulary named entities that appear in the context source. Furthermore we introduce“ GoodNews”, the largest news image captioning dataset in the literature and demonstrate state-of-the-art results.
Address Long beach; California; USA; june 2019
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CVPR
Notes DAG; 600.129; 600.135; 601.338; 600.121 Approved no
Call Number Admin @ si @ BGR2019 Serial 3289
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Author Shifeng Zhang; Xiaobo Wang; Ajian Liu; Chenxu Zhao; Jun Wan; Sergio Escalera; Hailin Shi; Zezheng Wang; Stan Z. Li
Title A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing Type Conference Article
Year 2019 Publication (up) 32nd IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue Pages 919-928
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 visual 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 a measurement set, evaluation protocol and training/validation/testing subsets, developing a new benchmark for face anti-spoofing. Moreover, we present a new multi-modal fusion method as baseline, which performs feature re-weighting to select the more informative channel features while suppressing the less useful ones for each modal. 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/chalearnfacespoofingattackdete/.
Address California; June 2019
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference CVPR
Notes HuPBA; no proj Approved no
Call Number Admin @ si @ ZWL2019 Serial 3331
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