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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 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
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Area Expedition Conference CVPR
Notes (down) LAMP; 600.109; 600.106; 600.120 Approved no
Call Number Admin @ si @ WWH2018b Serial 3131
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Author Muhammad Anwer Rao; Fahad Shahbaz Khan; Joost Van de Weijer; Matthieu Molinier; Jorma Laaksonen
Title Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification Type Journal Article
Year 2018 Publication ISPRS Journal of Photogrammetry and Remote Sensing Abbreviated Journal ISPRS J
Volume 138 Issue Pages 74-85
Keywords Remote sensing; Deep learning; Scene classification; Local Binary Patterns; Texture analysis
Abstract Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless statistical distribution of local features. However, most recent approaches to texture recognition and remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show that Local Binary Patterns (LBP) encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit LBP based texture information provide complementary information to the standard RGB deep models. Additionally, two deep architectures, namely early and late fusion, are investigated to combine the texture and color information. To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification. We perform comprehensive experiments on four texture recognition datasets and four remote sensing scene classification benchmarks: UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with 7 categories and the recently introduced large scale aerial image dataset (AID) with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary information to standard RGB deep model of the same network architecture. Our late fusion TEX-Net architecture always improves the overall performance compared to the standard RGB network on both recognition problems. Furthermore, our final combination leads to consistent improvement over the state-of-the-art for remote sensing scene
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Notes (down) LAMP; 600.109; 600.106; 600.120 Approved no
Call Number Admin @ si @ RKW2018 Serial 3158
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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 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
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
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Area Expedition Conference CVPR
Notes (down) LAMP; 600.109; 600.106; 600.120 Approved no
Call Number Admin @ si @ LWB2018 Serial 3159
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Author Xialei Liu; Joost Van de Weijer; Andrew Bagdanov
Title Exploiting Unlabeled Data in CNNs by Self-Supervised Learning to Rank Type Journal Article
Year 2019 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI
Volume 41 Issue 8 Pages 1862-1878
Keywords Task analysis;Training;Image quality;Visualization;Uncertainty;Labeling;Neural networks;Learning from rankings;image quality assessment;crowd counting;active learning
Abstract For many applications the collection of labeled data is expensive laborious. Exploitation of unlabeled data during training is thus a long pursued objective of machine learning. Self-supervised learning addresses this by positing an auxiliary task (different, but related to the supervised task) for which data is abundantly available. In this paper, we show how ranking can be used as a proxy task for some regression problems. As another contribution, we propose an efficient backpropagation technique for Siamese networks which prevents the redundant computation introduced by the multi-branch network architecture. We apply our framework to two regression problems: Image Quality Assessment (IQA) and Crowd Counting. For both we show how to automatically generate ranked image sets from unlabeled data. Our results show that networks trained to regress to the ground truth targets for labeled data and to simultaneously learn to rank unlabeled data obtain significantly better, state-of-the-art results for both IQA and crowd counting. In addition, we show that measuring network uncertainty on the self-supervised proxy task is a good measure of informativeness of unlabeled data. This can be used to drive an algorithm for active learning and we show that this reduces labeling effort by up to 50 percent.
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Notes (down) LAMP; 600.109; 600.106; 600.120 Approved no
Call Number LWB2019 Serial 3267
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Author Muhammad Anwer Rao; Fahad Shahbaz Khan; Joost Van de Weijer; Jorma Laaksonen
Title Tex-Nets: Binary Patterns Encoded Convolutional Neural Networks for Texture Recognition Type Conference Article
Year 2017 Publication 19th International Conference on Multimodal Interaction Abbreviated Journal
Volume Issue Pages
Keywords Convolutional Neural Networks; Texture Recognition; Local Binary Paterns
Abstract Recognizing materials and textures in realistic imaging conditions is a challenging computer vision problem. For many years, local features based orderless representations were a dominant approach for texture recognition. Recently deep local features, extracted from the intermediate layers of a Convolutional Neural Network (CNN), are used as filter banks. These dense local descriptors from a deep model, when encoded with Fisher Vectors, have shown to provide excellent results for texture recognition. The CNN models, employed in such approaches, take RGB patches as input and train on a large amount of labeled images. We show that CNN models, which we call TEX-Nets, trained using mapped coded images with explicit texture information provide complementary information to the standard deep models trained on RGB patches. We further investigate two deep architectures, namely early and late fusion, to combine the texture and color information. Experiments on benchmark texture datasets clearly demonstrate that TEX-Nets provide complementary information to standard RGB deep network. Our approach provides a large gain of 4.8%, 3.5%, 2.6% and 4.1% respectively in accuracy on the DTD, KTH-TIPS-2a, KTH-TIPS-2b and Texture-10 datasets, compared to the standard RGB network of the same architecture. Further, our final combination leads to consistent improvements over the state-of-the-art on all four datasets.
Address Glasgow; Scothland; November 2017
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Area Expedition Conference ACM
Notes (down) LAMP; 600.109; 600.068; 600.120 Approved no
Call Number Admin @ si @ RKW2017 Serial 3038
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Author Muhammad Anwer Rao; Fahad Shahbaz Khan; Joost Van de Weijer; Jorma Laaksonen
Title Top-Down Deep Appearance Attention for Action Recognition Type Conference Article
Year 2017 Publication 20th Scandinavian Conference on Image Analysis Abbreviated Journal
Volume 10269 Issue Pages 297-309
Keywords Action recognition; CNNs; Feature fusion
Abstract Recognizing human actions in videos is a challenging problem in computer vision. Recently, convolutional neural network based deep features have shown promising results for action recognition. In this paper, we investigate the problem of fusing deep appearance and motion cues for action recognition. We propose a video representation which combines deep appearance and motion based local convolutional features within the bag-of-deep-features framework. Firstly, dense deep appearance and motion based local convolutional features are extracted from spatial (RGB) and temporal (flow) networks, respectively. Both visual cues are processed in parallel by constructing separate visual vocabularies for appearance and motion. A category-specific appearance map is then learned to modulate the weights of the deep motion features. The proposed representation is discriminative and binds the deep local convolutional features to their spatial locations. Experiments are performed on two challenging datasets: JHMDB dataset with 21 action classes and ACT dataset with 43 categories. The results clearly demonstrate that our approach outperforms both standard approaches of early and late feature fusion. Further, our approach is only employing action labels and without exploiting body part information, but achieves competitive performance compared to the state-of-the-art deep features based approaches.
Address Tromso; June 2017
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference SCIA
Notes (down) LAMP; 600.109; 600.068; 600.120 Approved no
Call Number Admin @ si @ RKW2017b Serial 3039
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Author Ozan Caglayan; Walid Aransa; Adrien Bardet; Mercedes Garcia-Martinez; Fethi Bougares; Loic Barrault; Marc Masana; Luis Herranz; Joost Van de Weijer
Title LIUM-CVC Submissions for WMT17 Multimodal Translation Task Type Conference Article
Year 2017 Publication 2nd Conference on Machine Translation Abbreviated Journal
Volume Issue Pages
Keywords
Abstract This paper describes the monomodal and multimodal Neural Machine Translation systems developed by LIUM and CVC for WMT17 Shared Task on Multimodal Translation. We mainly explored two multimodal architectures where either global visual features or convolutional feature maps are integrated in order to benefit from visual context. Our final systems ranked first for both En-De and En-Fr language pairs according to the automatic evaluation metrics METEOR and BLEU.
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Area Expedition Conference WMT
Notes (down) LAMP; 600.106; 600.120 Approved no
Call Number Admin @ si @ CAB2017 Serial 3035
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Author Ozan Caglayan; Adrien Bardet; Fethi Bougares; Loic Barrault; Kai Wang; Marc Masana; Luis Herranz; Joost Van de Weijer
Title LIUM-CVC Submissions for WMT18 Multimodal Translation Task Type Conference Article
Year 2018 Publication 3rd Conference on Machine Translation Abbreviated Journal
Volume Issue Pages
Keywords
Abstract This paper describes the multimodal Neural Machine Translation systems developed by LIUM and CVC for WMT18 Shared Task on Multimodal Translation. This year we propose several modifications to our previou multimodal attention architecture in order to better integrate convolutional features and refine them using encoder-side information. Our final constrained submissions
ranked first for English→French and second for English→German language pairs among the constrained submissions according to the automatic evaluation metric METEOR.
Address Brussels; Belgium; October 2018
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Area Expedition Conference WMT
Notes (down) LAMP; 600.106; 600.120 Approved no
Call Number Admin @ si @ CBB2018 Serial 3240
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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 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
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Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference NIPS
Notes (down) LAMP; 600.106; 600.109; 602.200; 600.120 Approved no
Call Number Admin @ si @ WHL2018 Serial 3249
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Author Yaxing Wang; Abel Gonzalez-Garcia; Joost Van de Weijer; Luis Herranz
Title SDIT: Scalable and Diverse Cross-domain Image Translation Type Conference Article
Year 2019 Publication 27th ACM International Conference on Multimedia Abbreviated Journal
Volume Issue Pages 1267–1276
Keywords
Abstract Recently, image-to-image translation research has witnessed remarkable progress. Although current approaches successfully generate diverse outputs or perform scalable image transfer, these properties have not been combined into a single method. To address this limitation, we propose SDIT: Scalable and Diverse image-to-image translation. These properties are combined into a single generator. The diversity is determined by a latent variable which is randomly sampled from a normal distribution. The scalability is obtained by conditioning the network on the domain attributes. Additionally, we also exploit an attention mechanism that permits the generator to focus on the domain-specific attribute. We empirically demonstrate the performance of the proposed method on face mapping and other datasets beyond faces.
Address Nice; Francia; October 2019
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Area Expedition Conference ACM-MM
Notes (down) LAMP; 600.106; 600.109; 600.141; 600.120 Approved no
Call Number Admin @ si @ WGW2019 Serial 3363
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Author Xialei Liu; Joost Van de Weijer; Andrew Bagdanov
Title RankIQA: Learning from Rankings for No-reference Image Quality Assessment Type Conference Article
Year 2017 Publication 17th IEEE International Conference on Computer Vision Abbreviated Journal
Volume Issue Pages
Keywords
Abstract We propose a no-reference image quality assessment (NR-IQA) approach that learns from rankings (RankIQA). To address the problem of limited IQA dataset size, we train a Siamese Network to rank images in terms of image quality by using synthetically generated distortions for which relative image quality is known. These ranked image sets can be automatically generated without laborious human labeling. We then use fine-tuning to transfer the knowledge represented in the trained Siamese Network to a traditional CNN that estimates absolute image quality from single images. We demonstrate how our approach can be made significantly more efficient than traditional Siamese Networks by forward propagating a batch of images through a single network and backpropagating gradients derived from all pairs of images in the batch. Experiments on the TID2013 benchmark show that we improve the state-of-the-art by over 5%. Furthermore, on the LIVE benchmark we show that our approach is superior to existing NR-IQA techniques and that we even outperform the state-of-the-art in full-reference IQA (FR-IQA) methods without having to resort to high-quality reference images to infer IQA.
Address Venice; Italy; October 2017
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Area Expedition Conference ICCV
Notes (down) LAMP; 600.106; 600.109; 600.120 Approved no
Call Number Admin @ si @ LWB2017b Serial 3036
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Author Ozan Caglayan; Walid Aransa; Yaxing Wang; Marc Masana; Mercedes Garcıa-Martinez; Fethi Bougares; Loic Barrault; Joost Van de Weijer
Title Does Multimodality Help Human and Machine for Translation and Image Captioning? Type Conference Article
Year 2016 Publication 1st conference on machine translation Abbreviated Journal
Volume Issue Pages
Keywords
Abstract This paper presents the systems developed by LIUM and CVC for the WMT16 Multimodal Machine Translation challenge. We explored various comparative methods, namely phrase-based systems and attentional recurrent neural networks models trained using monomodal or multimodal data. We also performed a human evaluation in order to estimate theusefulness of multimodal data for human machine translation and image description generation. Our systems obtained the best results for both tasks according to the automatic evaluation metrics BLEU and METEOR.
Address Berlin; Germany; August 2016
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Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
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Area Expedition Conference WMT
Notes (down) LAMP; 600.106 ; 600.068 Approved no
Call Number Admin @ si @ CAW2016 Serial 2761
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Author Esteve Cervantes; Long Long Yu; Andrew Bagdanov; Marc Masana; Joost Van de Weijer
Title Hierarchical Part Detection with Deep Neural Networks Type Conference Article
Year 2016 Publication 23rd IEEE International Conference on Image Processing Abbreviated Journal
Volume Issue Pages
Keywords Object Recognition; Part Detection; Convolutional Neural Networks
Abstract Part detection is an important aspect of object recognition. Most approaches apply object proposals to generate hundreds of possible part bounding box candidates which are then evaluated by part classifiers. Recently several methods have investigated directly regressing to a limited set of bounding boxes from deep neural network representation. However, for object parts such methods may be unfeasible due to their relatively small size with respect to the image. We propose a hierarchical method for object and part detection. In a single network we first detect the object and then regress to part location proposals based only on the feature representation inside the object. Experiments show that our hierarchical approach outperforms a network which directly regresses the part locations. We also show that our approach obtains part detection accuracy comparable or better than state-of-the-art on the CUB-200 bird and Fashionista clothing item datasets with only a fraction of the number of part proposals.
Address Phoenix; Arizona; USA; September 2016
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Area Expedition Conference ICIP
Notes (down) LAMP; 600.106 Approved no
Call Number Admin @ si @ CLB2016 Serial 2762
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Author Mikhail Mozerov; Joost Van de Weijer
Title One-view occlusion detection for stereo matching with a fully connected CRF model Type Journal Article
Year 2019 Publication IEEE Transactions on Image Processing Abbreviated Journal TIP
Volume 28 Issue 6 Pages 2936-2947
Keywords Stereo matching; energy minimization; fully connected MRF model; geodesic distance filter
Abstract In this paper, we extend the standard belief propagation (BP) sequential technique proposed in the tree-reweighted sequential method [15] to the fully connected CRF models with the geodesic distance affinity. The proposed method has been applied to the stereo matching problem. Also a new approach to the BP marginal solution is proposed that we call one-view occlusion detection (OVOD). In contrast to the standard winner takes all (WTA) estimation, the proposed OVOD solution allows to find occluded regions in the disparity map and simultaneously improve the matching result. As a result we can perform only
one energy minimization process and avoid the cost calculation for the second view and the left-right check procedure. We show that the OVOD approach considerably improves results for cost augmentation and energy minimization techniques in comparison with the standard one-view affinity space implementation. We apply our method to the Middlebury data set and reach state-ofthe-art especially for median, average and mean squared error metrics.
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Notes (down) LAMP; 600.098; 600.109; 602.133; 600.120 Approved no
Call Number Admin @ si @ MoW2019 Serial 3221
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Author Laura Lopez-Fuentes; Claudio Rossi; Harald Skinnemoen
Title River segmentation for flood monitoring Type Conference Article
Year 2017 Publication Data Science for Emergency Management at Big Data 2017 Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Floods are major natural disasters which cause deaths and material damages every year. Monitoring these events is crucial in order to reduce both the affected people and the economic losses. In this work we train and test three different Deep Learning segmentation algorithms to estimate the water area from river images, and compare their performances. We discuss the implementation of a novel data chain aimed to monitor river water levels by automatically process data collected from surveillance cameras, and to give alerts in case of high increases of the water level or flooding. We also create and openly publish the first image dataset for river water segmentation.
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Notes (down) LAMP; 600.084; 600.120 Approved no
Call Number Admin @ si @ LRS2017 Serial 3078
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