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| Author | Mariona Caros; Maite Garolera; Petia Radeva; Xavier Giro | ||||
| Title | Automatic Reminiscence Therapy for Dementia | Type | Conference Article | ||
| Year | 2020 | Publication | 10th ACM International Conference on Multimedia Retrieval | Abbreviated Journal | |
| Volume | Issue | Pages | 383-387 | ||
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| Abstract | With people living longer than ever, the number of cases with dementia such as Alzheimer's disease increases steadily. It affects more than 46 million people worldwide, and it is estimated that in 2050 more than 100 million will be affected. While there are not effective treatments for these terminal diseases, therapies such as reminiscence, that stimulate memories from the past are recommended. Currently, reminiscence therapy takes place in care homes and is guided by a therapist or a carer. In this work, we present an AI-based solution to automatize the reminiscence therapy, which consists in a dialogue system that uses photos as input to generate questions. We run a usability case study with patients diagnosed of mild cognitive impairment that shows they found the system very entertaining and challenging. Overall, this paper presents how reminiscence therapy can be automatized by using machine learning, and deployed to smartphones and laptops, making the therapy more accessible to every person affected by dementia. | ||||
| Address | Virtual; October 2020 | ||||
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| Area | Expedition | Conference | ICRM | ||
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Approved | no | |||
| Call Number | Admin @ si @ CGR2020 | Serial | 3529 | ||
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| Author | Yi Xiao; Felipe Codevilla; Akhil Gurram; Onay Urfalioglu; Antonio Lopez | ||||
| Title | Multimodal end-to-end autonomous driving | Type | Journal Article | ||
| Year | 2020 | Publication | IEEE Transactions on Intelligent Transportation Systems | Abbreviated Journal | TITS |
| Volume | Issue | Pages | 1-11 | ||
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| Abstract | A crucial component of an autonomous vehicle (AV) is the artificial intelligence (AI) is able to drive towards a desired destination. Today, there are different paradigms addressing the development of AI drivers. On the one hand, we find modular pipelines, which divide the driving task into sub-tasks such as perception and maneuver planning and control. On the other hand, we find end-to-end driving approaches that try to learn a direct mapping from input raw sensor data to vehicle control signals. The later are relatively less studied, but are gaining popularity since they are less demanding in terms of sensor data annotation. This paper focuses on end-to-end autonomous driving. So far, most proposals relying on this paradigm assume RGB images as input sensor data. However, AVs will not be equipped only with cameras, but also with active sensors providing accurate depth information (e.g., LiDARs). Accordingly, this paper analyses whether combining RGB and depth modalities, i.e. using RGBD data, produces better end-to-end AI drivers than relying on a single modality. We consider multimodality based on early, mid and late fusion schemes, both in multisensory and single-sensor (monocular depth estimation) settings. Using the CARLA simulator and conditional imitation learning (CIL), we show how, indeed, early fusion multimodality outperforms single-modality. | ||||
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ADAS | Approved | no | ||
| Call Number | Admin @ si @ XCG2020 | Serial | 3490 | ||
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| Author | Yi Xiao; Felipe Codevilla; Christopher Pal; Antonio Lopez | ||||
| Title | Action-Based Representation Learning for Autonomous Driving | Type | Conference Article | ||
| Year | 2020 | Publication | Conference on Robot Learning | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | Human drivers produce a vast amount of data which could, in principle, be used to improve autonomous driving systems. Unfortunately, seemingly straightforward approaches for creating end-to-end driving models that map sensor data directly into driving actions are problematic in terms of interpretability, and typically have significant difficulty dealing with spurious correlations. Alternatively, we propose to use this kind of action-based driving data for learning representations. Our experiments show that an affordance-based driving model pre-trained with this approach can leverage a relatively small amount of weakly annotated imagery and outperform pure end-to-end driving models, while being more interpretable. Further, we demonstrate how this strategy outperforms previous methods based on learning inverse dynamics models as well as other methods based on heavy human supervision (ImageNet). | ||||
| Address | virtual; November 2020 | ||||
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| Area | Expedition | Conference | CORL | ||
| Notes |
ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ XCP2020 | Serial | 3487 | ||
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| Author | Gabriel Villalonga; Antonio Lopez | ||||
| Title | Co-Training for On-Board Deep Object Detection | Type | Journal Article | ||
| Year | 2020 | Publication | IEEE Access | Abbreviated Journal | ACCESS |
| Volume | Issue | Pages | 194441 - 194456 | ||
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| Abstract | Providing ground truth supervision to train visual models has been a bottleneck over the years, exacerbated by domain shifts which degenerate the performance of such models. This was the case when visual tasks relied on handcrafted features and shallow machine learning and, despite its unprecedented performance gains, the problem remains open within the deep learning paradigm due to its data-hungry nature. Best performing deep vision-based object detectors are trained in a supervised manner by relying on human-labeled bounding boxes which localize class instances (i.e. objects) within the training images. Thus, object detection is one of such tasks for which human labeling is a major bottleneck. In this article, we assess co-training as a semi-supervised learning method for self-labeling objects in unlabeled images, so reducing the human-labeling effort for developing deep object detectors. Our study pays special attention to a scenario involving domain shift; in particular, when we have automatically generated virtual-world images with object bounding boxes and we have real-world images which are unlabeled. Moreover, we are particularly interested in using co-training for deep object detection in the context of driver assistance systems and/or self-driving vehicles. Thus, using well-established datasets and protocols for object detection in these application contexts, we will show how co-training is a paradigm worth to pursue for alleviating object labeling, working both alone and together with task-agnostic domain adaptation. | ||||
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| Notes |
ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ ViL2020 | Serial | 3488 | ||
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| Author | Hannes Mueller; Andre Groger; Jonathan Hersh; Andrea Matranga; Joan Serrat | ||||
| Title | Monitoring War Destruction from Space: A Machine Learning Approach | Type | Miscellaneous | ||
| Year | 2020 | Publication | Arxiv | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | Existing data on building destruction in conflict zones rely on eyewitness reports or manual detection, which makes it generally scarce, incomplete and potentially biased. This lack of reliable data imposes severe limitations for media reporting, humanitarian relief efforts, human rights monitoring, reconstruction initiatives, and academic studies of violent conflict. This article introduces an automated method of measuring destruction in high-resolution satellite images using deep learning techniques combined with data augmentation to expand training samples. We apply this method to the Syrian civil war and reconstruct the evolution of damage in major cities across the country. The approach allows generating destruction data with unprecedented scope, resolution, and frequency – only limited by the available satellite imagery – which can alleviate data limitations decisively. | ||||
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| Notes |
ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ MGH2020 | Serial | 3489 | ||
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| Author | Idoia Ruiz; Joan Serrat | ||||
| Title | Rank-based ordinal classification | Type | Conference Article | ||
| Year | 2020 | Publication | 25th International Conference on Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 8069-8076 | ||
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| Abstract | Differently from the regular classification task, in ordinal classification there is an order in the classes. As a consequence not all classification errors matter the same: a predicted class close to the groundtruth one is better than predicting a farther away class. To account for this, most previous works employ loss functions based on the absolute difference between the predicted and groundtruth class labels. We argue that there are many cases in ordinal classification where label values are arbitrary (for instance 1. . . C, being C the number of classes) and thus such loss functions may not be the best choice. We instead propose a network architecture that produces not a single class prediction but an ordered vector, or ranking, of all the possible classes from most to least likely. This is thanks to a loss function that compares groundtruth and predicted rankings of these class labels, not the labels themselves. Another advantage of this new formulation is that we can enforce consistency in the predictions, namely, predicted rankings come from some unimodal vector of scores with mode at the groundtruth class. We compare with the state of the art ordinal classification methods, showing
that ours attains equal or better performance, as measured by common ordinal classification metrics, on three benchmark datasets. Furthermore, it is also suitable for a new task on image aesthetics assessment, i.e. most voted score prediction. Finally, we also apply it to building damage assessment from satellite images, providing an analysis of its performance depending on the degree of imbalance of the dataset. |
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| Address | Virtual; January 2021 | ||||
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| Area | Expedition | Conference | ICPR | ||
| Notes |
ADAS; 600.118; 600.124 | Approved | no | ||
| Call Number | Admin @ si @ RuS2020 | Serial | 3549 | ||
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| Author | Cesar de Souza; Adrien Gaidon; Yohann Cabon; Naila Murray; Antonio Lopez | ||||
| Title | Generating Human Action Videos by Coupling 3D Game Engines and Probabilistic Graphical Models | Type | Journal Article | ||
| Year | 2020 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
| Volume | 128 | Issue | Pages | 1505–1536 | |
| Keywords | Procedural generation; Human action recognition; Synthetic data; Physics | ||||
| Abstract | Deep video action recognition models have been highly successful in recent years but require large quantities of manually-annotated data, which are expensive and laborious to obtain. In this work, we investigate the generation of synthetic training data for video action recognition, as synthetic data have been successfully used to supervise models for a variety of other computer vision tasks. We propose an interpretable parametric generative model of human action videos that relies on procedural generation, physics models and other components of modern game engines. With this model we generate a diverse, realistic, and physically plausible dataset of human action videos, called PHAV for “Procedural Human Action Videos”. PHAV contains a total of 39,982 videos, with more than 1000 examples for each of 35 action categories. Our video generation approach is not limited to existing motion capture sequences: 14 of these 35 categories are procedurally-defined synthetic actions. In addition, each video is represented with 6 different data modalities, including RGB, optical flow and pixel-level semantic labels. These modalities are generated almost simultaneously using the Multiple Render Targets feature of modern GPUs. In order to leverage PHAV, we introduce a deep multi-task (i.e. that considers action classes from multiple datasets) representation learning architecture that is able to simultaneously learn from synthetic and real video datasets, even when their action categories differ. Our experiments on the UCF-101 and HMDB-51 benchmarks suggest that combining our large set of synthetic videos with small real-world datasets can boost recognition performance. Our approach also significantly outperforms video representations produced by fine-tuning state-of-the-art unsupervised generative models of videos. | ||||
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| Notes |
ADAS; 600.124; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ SGC2019 | Serial | 3303 | ||
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| Author | Akhil Gurram; Onay Urfalioglu; Ibrahim Halfaoui; Fahd Bouzaraa; Antonio Lopez | ||||
| Title | Semantic Monocular Depth Estimation Based on Artificial Intelligence | Type | Journal Article | ||
| Year | 2020 | Publication | IEEE Intelligent Transportation Systems Magazine | Abbreviated Journal | ITSM |
| Volume | 13 | Issue | 4 | Pages | 99-103 |
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| Abstract | Depth estimation provides essential information to perform autonomous driving and driver assistance. A promising line of work consists of introducing additional semantic information about the traffic scene when training CNNs for depth estimation. In practice, this means that the depth data used for CNN training is complemented with images having pixel-wise semantic labels where the same raw training data is associated with both types of ground truth, i.e., depth and semantic labels. The main contribution of this paper is to show that this hard constraint can be circumvented, i.e., that we can train CNNs for depth estimation by leveraging the depth and semantic information coming from heterogeneous datasets. In order to illustrate the benefits of our approach, we combine KITTI depth and Cityscapes semantic segmentation datasets, outperforming state-of-the-art results on monocular depth estimation. | ||||
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ADAS; 600.124; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ GUH2019 | Serial | 3306 | ||
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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 | Pages | 6845-6854 | ||
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| 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 | ||||
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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 | Ivet Rafegas; Maria Vanrell; Luis A Alexandre; G. Arias | ||||
| Title | Understanding trained CNNs by indexing neuron selectivity | Type | Journal Article | ||
| Year | 2020 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 136 | Issue | Pages | 318-325 | |
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| Abstract | The impressive performance of Convolutional Neural Networks (CNNs) when solving different vision problems is shadowed by their black-box nature and our consequent lack of understanding of the representations they build and how these representations are organized. To help understanding these issues, we propose to describe the activity of individual neurons by their Neuron Feature visualization and quantify their inherent selectivity with two specific properties. We explore selectivity indexes for: an image feature (color); and an image label (class membership). Our contribution is a framework to seek or classify neurons by indexing on these selectivity properties. It helps to find color selective neurons, such as a red-mushroom neuron in layer Conv4 or class selective neurons such as dog-face neurons in layer Conv5 in VGG-M, and establishes a methodology to derive other selectivity properties. Indexing on neuron selectivity can statistically draw how features and classes are represented through layers in a moment when the size of trained nets is growing and automatic tools to index neurons can be helpful. | ||||
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CIC; 600.087; 600.140; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ RVL2019 | Serial | 3310 | ||
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| Author | Sagnik Das; Hassan Ahmed Sial; Ke Ma; Ramon Baldrich; Maria Vanrell; Dimitris Samaras | ||||
| Title | Intrinsic Decomposition of Document Images In-the-Wild | Type | Conference Article | ||
| Year | 2020 | Publication | 31st British Machine Vision Conference | Abbreviated Journal | |
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| Abstract | Automatic document content processing is affected by artifacts caused by the shape
of the paper, non-uniform and diverse color of lighting conditions. Fully-supervised methods on real data are impossible due to the large amount of data needed. Hence, the current state of the art deep learning models are trained on fully or partially synthetic images. However, document shadow or shading removal results still suffer because: (a) prior methods rely on uniformity of local color statistics, which limit their application on real-scenarios with complex document shapes and textures and; (b) synthetic or hybrid datasets with non-realistic, simulated lighting conditions are used to train the models. In this paper we tackle these problems with our two main contributions. First, a physically constrained learning-based method that directly estimates document reflectance based on intrinsic image formation which generalizes to challenging illumination conditions. Second, a new dataset that clearly improves previous synthetic ones, by adding a large range of realistic shading and diverse multi-illuminant conditions, uniquely customized to deal with documents in-the-wild. The proposed architecture works in two steps. First, a white balancing module neutralizes the color of the illumination on the input image. Based on the proposed multi-illuminant dataset we achieve a good white-balancing in really difficult conditions. Second, the shading separation module accurately disentangles the shading and paper material in a self-supervised manner where only the synthetic texture is used as a weak training signal (obviating the need for very costly ground truth with disentangled versions of shading and reflectance). The proposed approach leads to significant generalization of document reflectance estimation in real scenes with challenging illumination. We extensively evaluate on the real benchmark datasets available for intrinsic image decomposition and document shadow removal tasks. Our reflectance estimation scheme, when used as a pre-processing step of an OCR pipeline, shows a 21% improvement of character error rate (CER), thus, proving the practical applicability. The data and code will be available at: https://github.com/cvlab-stonybrook/DocIIW. |
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| Address | Virtual; September 2020 | ||||
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| Area | Expedition | Conference | BMVC | ||
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CIC; 600.087; 600.140; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ DSM2020 | Serial | 3461 | ||
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| Author | Hassan Ahmed Sial; Ramon Baldrich; Maria Vanrell; Dimitris Samaras | ||||
| Title | Light Direction and Color Estimation from Single Image with Deep Regression | Type | Conference Article | ||
| Year | 2020 | Publication | London Imaging Conference | Abbreviated Journal | |
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| Abstract | We present a method to estimate the direction and color of the scene light source from a single image. Our method is based on two main ideas: (a) we use a new synthetic dataset with strong shadow effects with similar constraints to the SID dataset; (b) we define a deep architecture trained on the mentioned dataset to estimate the direction and color of the scene light source. Apart from showing good performance on synthetic images, we additionally propose a preliminary procedure to obtain light positions of the Multi-Illumination dataset, and, in this way, we also prove that our trained model achieves good performance when it is applied to real scenes. | ||||
| Address | Virtual; September 2020 | ||||
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| Area | Expedition | Conference | LIM | ||
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CIC; 600.118; 600.140; | Approved | no | ||
| Call Number | Admin @ si @ SBV2020 | Serial | 3460 | ||
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| Author | Hassan Ahmed Sial; Ramon Baldrich; Maria Vanrell | ||||
| Title | Deep intrinsic decomposition trained on surreal scenes yet with realistic light effects | Type | Journal Article | ||
| Year | 2020 | Publication | Journal of the Optical Society of America A | Abbreviated Journal | JOSA A |
| Volume | 37 | Issue | 1 | Pages | 1-15 |
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| Abstract | Estimation of intrinsic images still remains a challenging task due to weaknesses of ground-truth datasets, which either are too small or present non-realistic issues. On the other hand, end-to-end deep learning architectures start to achieve interesting results that we believe could be improved if important physical hints were not ignored. In this work, we present a twofold framework: (a) a flexible generation of images overcoming some classical dataset problems such as larger size jointly with coherent lighting appearance; and (b) a flexible architecture tying physical properties through intrinsic losses. Our proposal is versatile, presents low computation time, and achieves state-of-the-art results. | ||||
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CIC; 600.140; 600.12; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ SBV2019 | Serial | 3311 | ||
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| Author | Pau Riba; Josep Llados; Alicia Fornes | ||||
| Title | Hierarchical graphs for coarse-to-fine error tolerant matching | Type | Journal Article | ||
| Year | 2020 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 134 | Issue | Pages | 116-124 | |
| Keywords | Hierarchical graph representation; Coarse-to-fine graph matching; Graph-based retrieval | ||||
| Abstract | During the last years, graph-based representations are experiencing a growing usage in visual recognition and retrieval due to their ability to capture both structural and appearance-based information. Thus, they provide a greater representational power than classical statistical frameworks. However, graph-based representations leads to high computational complexities usually dealt by graph embeddings or approximated matching techniques. Despite their representational power, they are very sensitive to noise and small variations of the input image. With the aim to cope with the time complexity and the variability present in the generated graphs, in this paper we propose to construct a novel hierarchical graph representation. Graph clustering techniques adapted from social media analysis have been used in order to contract a graph at different abstraction levels while keeping information about the topology. Abstract nodes attributes summarise information about the contracted graph partition. For the proposed representations, a coarse-to-fine matching technique is defined. Hence, small graphs are used as a filtering before more accurate matching methods are applied. This approach has been validated in real scenarios such as classification of colour images or retrieval of handwritten words (i.e. word spotting). | ||||
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DAG; 600.097; 601.302; 603.057; 600.140; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ RLF2020 | Serial | 3349 | ||
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| Author | Mohamed Ali Souibgui; Y.Kessentini; Alicia Fornes | ||||
| Title | A conditional GAN based approach for distorted camera captured documents recovery | Type | Conference Article | ||
| Year | 2020 | Publication | 4th Mediterranean Conference on Pattern Recognition and Artificial Intelligence | Abbreviated Journal | |
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| Address | Virtual; December 2020 | ||||
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| Area | Expedition | Conference | MedPRAI | ||
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DAG; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ SKF2020 | Serial | 3450 | ||
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