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Author David Berga; Xavier Otazu
Title Computations of top-down attention by modulating V1 dynamics Type Conference Article
Year 2020 Publication Computational and Mathematical Models in Vision Abbreviated Journal
Volume Issue (up) Pages
Keywords
Abstract
Address St. Pete Beach; Florida; May 2020
Corporate Author Thesis
Publisher Place of Publication Editor
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Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference MODVIS
Notes NEUROBIT Approved no
Call Number Admin @ si @ BeO2020a Serial 3376
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Author Yaxing Wang
Title Transferring and Learning Representations for Image Generation and Translation Type Book Whole
Year 2020 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue (up) Pages
Keywords
Abstract Image generation is arguably one of the most attractive, compelling, and challenging tasks in computer vision. Among the methods which perform image generation, generative adversarial networks (GANs) play a key role. The most common image generation models based on GANs can be divided into two main approaches. The first one, called simply image generation takes random noise as an input and synthesizes an image which follows the same distribution as the images in the training set. The second class, which is called image-to-image translation, aims to map an image from a source domain to one that is indistinguishable from those in the target domain. Image-to-image translation methods can further be divided into paired and unpaired image-to-image translation based on whether they require paired data or not. In this thesis, we aim to address some challenges of both image generation and image-to-image generation.GANs highly rely upon having access to vast quantities of data, and fail to generate realistic images from random noise when applied to domains with few images. To address this problem, we aim to transfer knowledge from a model trained on a large dataset (source domain) to the one learned on limited data (target domain). We find that both GANs andconditional GANs can benefit from models trained on large datasets. Our experiments show that transferring the discriminator is more important than the generator. Using both the generator and discriminator results in the best performance. We found, however, that this method suffers from overfitting, since we update all parameters to adapt to the target data. We propose a novel architecture, which is tailored to address knowledge transfer to very small target domains. Our approach effectively exploreswhich part of the latent space is more related to the target domain. Additionally, the proposed method is able to transfer knowledge from multiple pretrained GANs. Although image-to-image translation has achieved outstanding performance, it still facesseveral problems. First, for translation between complex domains (such as translations between different modalities) image-to-image translation methods require paired data. We show that when only some of the pairwise translations have been seen (i.e. during training), we can infer the remaining unseen translations (where training pairs are not available). We propose a new approach where we align multiple encoders and decoders in such a way that the desired translation can be obtained by simply cascadingthe source encoder and the target decoder, even when they have not interacted during the training stage (i.e. unseen). Second, we address the issue of bias in image-to-image translation. Biased datasets unavoidably contain undesired changes, which are dueto the fact that the target dataset has a particular underlying visual distribution. We use carefully designed semantic constraints to reduce the effects of the bias. The semantic constraint aims to enforce the preservation of desired image properties. Finally, current approaches fail to generate diverse outputs or perform scalable image transfer in a single model. To alleviate this problem, we propose a scalable and diverse image-to-image translation. We employ random noise to control the diversity. The scalabitlity is determined by conditioning the domain label.computer vision, deep learning, imitation learning, adversarial generative networks, image generation, image-to-image translation.
Address January 2020
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Joost Van de Weijer;Abel Gonzalez;Luis Herranz
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-121011-5-7 Medium
Area Expedition Conference
Notes LAMP; 600.141; 600.120 Approved no
Call Number Admin @ si @ Wan2020 Serial 3397
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Author Sangeeth Reddy; Minesh Mathew; Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas; C.V. Jawahar
Title RoadText-1K: Text Detection and Recognition Dataset for Driving Videos Type Conference Article
Year 2020 Publication IEEE International Conference on Robotics and Automation Abbreviated Journal
Volume Issue (up) Pages
Keywords
Abstract Perceiving text is crucial to understand semantics of outdoor scenes and hence is a critical requirement to build intelligent systems for driver assistance and self-driving. Most of the existing datasets for text detection and recognition comprise still images and are mostly compiled keeping text in mind. This paper introduces a new ”RoadText-1K” dataset for text in driving videos. The dataset is 20 times larger than the existing largest dataset for text in videos. Our dataset comprises 1000 video clips of driving without any bias towards text and with annotations for text bounding boxes and transcriptions in every frame. State of the art methods for text detection,
recognition and tracking are evaluated on the new dataset and the results signify the challenges in unconstrained driving videos compared to existing datasets. This suggests that RoadText-1K is suited for research and development of reading systems, robust enough to be incorporated into more complex downstream tasks like driver assistance and self-driving. The dataset can be found at http://cvit.iiit.ac.in/research/
projects/cvit-projects/roadtext-1k
Address Paris; Francia; ???
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 ICRA
Notes DAG; 600.121; 600.129 Approved no
Call Number Admin @ si @ RMG2020 Serial 3400
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Author Idoia Ruiz; Bogdan Raducanu; Rakesh Mehta; Jaume Amores
Title Optimizing speed/accuracy trade-off for person re-identification via knowledge distillation Type Journal Article
Year 2020 Publication Engineering Applications of Artificial Intelligence Abbreviated Journal EAAI
Volume 87 Issue (up) Pages 103309
Keywords Person re-identification; Network distillation; Image retrieval; Model compression; Surveillance
Abstract Finding a person across a camera network plays an important role in video surveillance. For a real-world person re-identification application, in order to guarantee an optimal time response, it is crucial to find the balance between accuracy and speed. We analyse this trade-off, comparing a classical method, that comprises hand-crafted feature description and metric learning, in particular, LOMO and XQDA, to deep learning based techniques, using image classification networks, ResNet and MobileNets. Additionally, we propose and analyse network distillation as a learning strategy to reduce the computational cost of the deep learning approach at test time. We evaluate both methods on the Market-1501 and DukeMTMC-reID large-scale datasets, showing that distillation helps reducing the computational cost at inference time while even increasing the accuracy performance.
Address
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Notes LAMP; 600.109; 600.120 Approved no
Call Number Admin @ si @ RRM2020 Serial 3401
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Author Lorenzo Porzi; Markus Hofinger; Idoia Ruiz; Joan Serrat; Samuel Rota Bulo; Peter Kontschieder
Title Learning Multi-Object Tracking and Segmentation from Automatic Annotations Type Conference Article
Year 2020 Publication 33rd IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue (up) Pages 6845-6854
Keywords
Abstract In this work we contribute a novel pipeline to automatically generate training data, and to improve over state-of-the-art multi-object tracking and segmentation (MOTS) methods. Our proposed track mining algorithm turns raw street-level videos into high-fidelity MOTS training data, is scalable and overcomes the need of expensive and time-consuming manual annotation approaches. We leverage state-of-the-art instance segmentation results in combination with optical flow predictions, also trained on automatically harvested training data. Our second major contribution is MOTSNet – a deep learning, tracking-by-detection architecture for MOTS – deploying a novel mask-pooling layer for improved object association over time. Training MOTSNet with our automatically extracted data leads to significantly improved sMOTSA scores on the novel KITTI MOTS dataset (+1.9%/+7.5% on cars/pedestrians), and MOTSNet improves by +4.1% over previously best methods on the MOTSChallenge dataset. Our most impressive finding is that we can improve over previous best-performing works, even in complete absence of manually annotated MOTS training data.
Address virtual; June 2020
Corporate Author Thesis
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Area Expedition Conference CVPR
Notes ADAS; 600.124; 600.118 Approved no
Call Number Admin @ si @ PHR2020 Serial 3402
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Author Vacit Oguz Yazici; Abel Gonzalez-Garcia; Arnau Ramisa; Bartlomiej Twardowski; Joost Van de Weijer
Title Orderless Recurrent Models for Multi-label Classification Type Conference Article
Year 2020 Publication 33rd IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue (up) Pages
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Abstract Recurrent neural networks (RNN) are popular for many computer vision tasks, including multi-label classification. Since RNNs produce sequential outputs, labels need to be ordered for the multi-label classification task. Current approaches sort labels according to their frequency, typically ordering them in either rare-first or frequent-first. These imposed orderings do not take into account that the natural order to generate the labels can change for each image, e.g.\ first the dominant object before summing up the smaller objects in the image. Therefore, in this paper, we propose ways to dynamically order the ground truth labels with the predicted label sequence. This allows for the faster training of more optimal LSTM models for multi-label classification. Analysis evidences that our method does not suffer from duplicate generation, something which is common for other models. Furthermore, it outperforms other CNN-RNN models, and we show that a standard architecture of an image encoder and language decoder trained with our proposed loss obtains the state-of-the-art results on the challenging MS-COCO, WIDER Attribute and PA-100K and competitive results on NUS-WIDE.
Address
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Publisher Place of Publication Editor
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Area Expedition Conference CVPR
Notes LAMP; 600.109; 601.309; 600.141; 600.120 Approved no
Call Number Admin @ si @ YGR2020 Serial 3408
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Author Khalid El Asnaoui; Petia Radeva
Title Automatically Assess Day Similarity Using Visual Lifelogs Type Journal Article
Year 2020 Publication International Journal of Intelligent Systems Abbreviated Journal IJIS
Volume 29 Issue (up) Pages 298–310
Keywords
Abstract Today, we witness the appearance of many lifelogging cameras that are able to capture the life of a person wearing the camera and which produce a large number of images everyday. Automatically characterizing the experience and extracting patterns of behavior of individuals from this huge collection of unlabeled and unstructured egocentric data present major challenges and require novel and efficient algorithmic solutions. The main goal of this work is to propose a new method to automatically assess day similarity from the lifelogging images of a person. We propose a technique to measure the similarity between images based on the Swain’s distance and generalize it to detect the similarity between daily visual data. To this purpose, we apply the dynamic time warping (DTW) combined with the Swain’s distance for final day similarity estimation. For validation, we apply our technique on the Egocentric Dataset of University of Barcelona (EDUB) of 4912 daily images acquired by four persons with preliminary encouraging results.
Address
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Notes MILAB; no proj Approved no
Call Number AsR2020 Serial 3409
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Author Margarita Torre; Beatriz Remeseiro; Petia Radeva; Fernando Martinez
Title 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 (up) 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.
Address
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Notes MILAB Approved no
Call Number Admin @ si @ TRR2020 Serial 3410
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Author Razieh Rastgoo; Kourosh Kiani; Sergio Escalera
Title Hand sign language recognition using multi-view hand skeleton Type Journal Article
Year 2020 Publication Expert Systems With Applications Abbreviated Journal ESWA
Volume 150 Issue (up) Pages 113336
Keywords Multi-view hand skeleton; Hand sign language recognition; 3DCNN; Hand pose estimation; RGB video; Hand action recognition
Abstract Hand sign language recognition from video is a challenging research area in computer vision, which performance is affected by hand occlusion, fast hand movement, illumination changes, or background complexity, just to mention a few. In recent years, deep learning approaches have achieved state-of-the-art results in the field, though previous challenges are not completely solved. In this work, we propose a novel deep learning-based pipeline architecture for efficient automatic hand sign language recognition using Single Shot Detector (SSD), 2D Convolutional Neural Network (2DCNN), 3D Convolutional Neural Network (3DCNN), and Long Short-Term Memory (LSTM) from RGB input videos. We use a CNN-based model which estimates the 3D hand keypoints from 2D input frames. After that, we connect these estimated keypoints to build the hand skeleton by using midpoint algorithm. In order to obtain a more discriminative representation of hands, we project 3D hand skeleton into three views surface images. We further employ the heatmap image of detected keypoints as input for refinement in a stacked fashion. We apply 3DCNNs on the stacked features of hand, including pixel level, multi-view hand skeleton, and heatmap features, to extract discriminant local spatio-temporal features from these stacked inputs. The outputs of the 3DCNNs are fused and fed to a LSTM to model long-term dynamics of hand sign gestures. Analyzing 2DCNN vs. 3DCNN using different number of stacked inputs into the network, we demonstrate that 3DCNN better capture spatio-temporal dynamics of hands. To the best of our knowledge, this is the first time that this multi-modal and multi-view set of hand skeleton features are applied for hand sign language recognition. Furthermore, we present a new large-scale hand sign language dataset, namely RKS-PERSIANSIGN, including 10′000 RGB videos of 100 Persian sign words. Evaluation results of the proposed model on three datasets, NYU, First-Person, and RKS-PERSIANSIGN, indicate that our model outperforms state-of-the-art models in hand sign language recognition, hand pose estimation, and hand action recognition.
Address
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Notes HuPBA; no proj Approved no
Call Number Admin @ si @ RKE2020a Serial 3411
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Author Yunan Li; Jun Wan; Qiguang Miao; Sergio Escalera; Huijuan Fang; Huizhou Chen; Xiangda Qi; Guodong Guo
Title 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 (up) 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.
Address
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Notes HuPBA; no menciona Approved no
Call Number Admin @ si @ LWM2020 Serial 3413
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Author Xialei Liu; Chenshen Wu; Mikel Menta; Luis Herranz; Bogdan Raducanu; Andrew Bagdanov; Shangling Jui; Joost Van de Weijer
Title Generative Feature Replay for Class-Incremental Learning Type Conference Article
Year 2020 Publication CLVISION – Workshop on Continual Learning in Computer Vision Abbreviated Journal
Volume Issue (up) Pages
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Abstract Humans are capable of learning new tasks without forgetting previous ones, while neural networks fail due to catastrophic forgetting between new and previously-learned tasks. We consider a class-incremental setting which means that the task-ID is unknown at inference time. The imbalance between old and new classes typically results in a bias of the network towards the newest ones. This imbalance problem can either be addressed by storing exemplars from previous tasks, or by using image replay methods. However, the latter can only be applied to toy datasets since image generation for complex datasets is a hard problem.
We propose a solution to the imbalance problem based on generative feature replay which does not require any exemplars. To do this, we split the network into two parts: a feature extractor and a classifier. To prevent forgetting, we combine generative feature replay in the classifier with feature distillation in the feature extractor. Through feature generation, our method reduces the complexity of generative replay and prevents the imbalance problem. Our approach is computationally efficient and scalable to large datasets. Experiments confirm that our approach achieves state-of-the-art results on CIFAR-100 and ImageNet, while requiring only a fraction of the storage needed for exemplar-based continual learning
Address Virtual CVPR
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
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Area Expedition Conference CVPRW
Notes LAMP; 601.309; 602.200; 600.141; 600.120 Approved no
Call Number Admin @ si @ LWM2020 Serial 3419
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Author Raul Gomez; Jaume Gibert; Lluis Gomez; Dimosthenis Karatzas
Title Location Sensitive Image Retrieval and Tagging Type Conference Article
Year 2020 Publication 16th European Conference on Computer Vision Abbreviated Journal
Volume Issue (up) Pages
Keywords
Abstract People from different parts of the globe describe objects and concepts in distinct manners. Visual appearance can thus vary across different geographic locations, which makes location a relevant contextual information when analysing visual data. In this work, we address the task of image retrieval related to a given tag conditioned on a certain location on Earth. We present LocSens, a model that learns to rank triplets of images, tags and coordinates by plausibility, and two training strategies to balance the location influence in the final ranking. LocSens learns to fuse textual and location information of multimodal queries to retrieve related images at different levels of location granularity, and successfully utilizes location information to improve image tagging.
Address Virtual; August 2020
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 ECCV
Notes DAG; 600.121; 600.129 Approved no
Call Number Admin @ si @ GGG2020b Serial 3420
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Author Yaxing Wang; Abel Gonzalez-Garcia; David Berga; Luis Herranz; Fahad Shahbaz Khan; Joost Van de Weijer
Title MineGAN: effective knowledge transfer from GANs to target domains with few images Type Conference Article
Year 2020 Publication 33rd IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue (up) Pages
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Abstract One of the attractive characteristics of deep neural networks is their ability to transfer knowledge obtained in one domain to other related domains. As a result, high-quality networks can be trained in domains with relatively little training data. This property has been extensively studied for discriminative networks but has received significantly less attention for generative models. Given the often enormous effort required to train GANs, both computationally as well as in the dataset collection, the re-use of pretrained GANs is a desirable objective. We propose a novel knowledge transfer method for generative models based on mining the knowledge that is most beneficial to a specific target domain, either from a single or multiple pretrained GANs. This is done using a miner network that identifies which part of the generative distribution of each pretrained GAN outputs samples closest to the target domain. Mining effectively steers GAN sampling towards suitable regions of the latent space, which facilitates the posterior finetuning and avoids pathologies of other methods such as mode collapse and lack of flexibility. We perform experiments on several complex datasets using various GAN architectures (BigGAN, Progressive GAN) and show that the proposed method, called MineGAN, effectively transfers knowledge to domains with few target images, outperforming existing methods. In addition, MineGAN can successfully transfer knowledge from multiple pretrained GANs.
Address Virtual CVPR
Corporate Author Thesis
Publisher Place of Publication Editor
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Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CVPR
Notes LAMP; 600.109; 600.141; 600.120 Approved no
Call Number Admin @ si @ WGB2020 Serial 3421
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Author Lu Yu; Bartlomiej Twardowski; Xialei Liu; Luis Herranz; Kai Wang; Yongmai Cheng; Shangling Jui; Joost Van de Weijer
Title Semantic Drift Compensation for Class-Incremental Learning of Embeddings Type Conference Article
Year 2020 Publication 33rd IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue (up) Pages
Keywords
Abstract Class-incremental learning of deep networks sequentially increases the number of classes to be classified. During training, the network has only access to data of one task at a time, where each task contains several classes. In this setting, networks suffer from catastrophic forgetting which refers to the drastic drop in performance on previous tasks. The vast majority of methods have studied this scenario for classification networks, where for each new task the classification layer of the network must be augmented with additional weights to make room for the newly added classes. Embedding networks have the advantage that new classes can be naturally included into the network without adding new weights. Therefore, we study incremental learning for embedding networks. In addition, we propose a new method to estimate the drift, called semantic drift, of features and compensate for it without the need of any exemplars. We approximate the drift of previous tasks based on the drift that is experienced by current task data. We perform experiments on fine-grained datasets, CIFAR100 and ImageNet-Subset. We demonstrate that embedding networks suffer significantly less from catastrophic forgetting. We outperform existing methods which do not require exemplars and obtain competitive results compared to methods which store exemplars. Furthermore, we show that our proposed SDC when combined with existing methods to prevent forgetting consistently improves results.
Address Virtual CVPR
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Publisher Place of Publication Editor
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Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
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Area Expedition Conference CVPR
Notes LAMP; 600.141; 601.309; 602.200; 600.120 Approved no
Call Number Admin @ si @ YTL2020 Serial 3422
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Author Xiangyang Li; Luis Herranz; Shuqiang Jiang
Title Multifaceted Analysis of Fine-Tuning in Deep Model for Visual Recognition Type Journal
Year 2020 Publication ACM Transactions on Data Science Abbreviated Journal ACM
Volume Issue (up) Pages
Keywords
Abstract In recent years, convolutional neural networks (CNNs) have achieved impressive performance for various visual recognition scenarios. CNNs trained on large labeled datasets can not only obtain significant performance on most challenging benchmarks but also provide powerful representations, which can be used to a wide range of other tasks. However, the requirement of massive amounts of data to train deep neural networks is a major drawback of these models, as the data available is usually limited or imbalanced. Fine-tuning (FT) is an effective way to transfer knowledge learned in a source dataset to a target task. In this paper, we introduce and systematically investigate several factors that influence the performance of fine-tuning for visual recognition. These factors include parameters for the retraining procedure (e.g., the initial learning rate of fine-tuning), the distribution of the source and target data (e.g., the number of categories in the source dataset, the distance between the source and target datasets) and so on. We quantitatively and qualitatively analyze these factors, evaluate their influence, and present many empirical observations. The results reveal insights into what fine-tuning changes CNN parameters and provide useful and evidence-backed intuitions about how to implement fine-tuning for computer vision tasks.
Address
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Notes LAMP; 600.141; 600.120 Approved no
Call Number Admin @ si @ LHJ2020 Serial 3423
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