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Author |
Swathikiran Sudhakaran; Sergio Escalera; Oswald Lanz |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Gate-Shift Networks for Video Action Recognition |
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Conference Article |
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Year |
2020 |
Publication |
33rd IEEE Conference on Computer Vision and Pattern Recognition |
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Deep 3D CNNs for video action recognition are designed to learn powerful representations in the joint spatio-temporal feature space. In practice however, because of the large number of parameters and computations involved, they may under-perform in the lack of sufficiently large datasets for training them at scale. In this paper we introduce spatial gating in spatial-temporal decomposition of 3D kernels. We implement this concept with Gate-Shift Module (GSM). GSM is lightweight and turns a 2D-CNN into a highly efficient spatio-temporal feature extractor. With GSM plugged in, a 2D-CNN learns to adaptively route features through time and combine them, at almost no additional parameters and computational overhead. We perform an extensive evaluation of the proposed module to study its effectiveness in video action recognition, achieving state-of-the-art results on Something Something-V1 and Diving48 datasets, and obtaining competitive results on EPIC-Kitchens with far less model complexity. |
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Virtual CVPR |
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HuPBA; no proj |
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no |
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Admin @ si @ SEL2020 |
Serial |
3438 |
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Author |
Jun Wan; Guodong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z. Li |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Multi-modal Face Presentation Attach Detection |
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Book Whole |
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2020 |
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Synthesis Lectures on Computer Vision |
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13 |
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HuPBA |
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Admin @ si @ WGE2020 |
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3440 |
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Author |
Mohamed Ali Souibgui; Y.Kessentini; Alicia Fornes |
![download PDF file pdf](img/file_PDF.gif)
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Title |
A conditional GAN based approach for distorted camera captured documents recovery |
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Conference Article |
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2020 |
Publication |
4th Mediterranean Conference on Pattern Recognition and Artificial Intelligence |
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Virtual; December 2020 |
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MedPRAI |
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DAG; 600.121 |
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Admin @ si @ SKF2020 |
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3450 |
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Author |
Fernando Vilariño |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Library Living Lab, Numérisation 3D des chapiteaux du cloître de Saint-Cugat : des citoyens co- créant le nouveau patrimoine culturel numérique |
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Conference Article |
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Year |
2019 |
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Intersectorialité et approche Living Labs. Entretiens Jacques-Cartier |
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Montreal; Canada; December 2019 |
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MV; DAG; 600.140; 600.121;SIAI |
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Call Number |
Admin @ si @ Vil2019a |
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3457 |
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Author |
Fernando Vilariño |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Public Libraries Exploring how technology transforms the cultural experience of people |
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Conference Article |
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2019 |
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Workshop on Social Impact of AI. Open Living Lab Days Conference. |
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Thessaloniki; Grecia; September 2019 |
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MV; DAG; 600.140; 600.121;SIAI |
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Call Number |
Admin @ si @ Vil2019b |
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3458 |
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Author |
Fernando Vilariño |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Unveiling the Social Impact of AI |
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Conference Article |
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2020 |
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Workshop at Digital Living Lab Days Conference |
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September 2020 |
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MV; DAG; 600.121; 600.140;SIAI |
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Admin @ si @ Vil2020 |
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3459 |
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Author |
Hassan Ahmed Sial; Ramon Baldrich; Maria Vanrell; Dimitris Samaras |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Light Direction and Color Estimation from Single Image with Deep Regression |
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Conference Article |
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2020 |
Publication |
London Imaging Conference |
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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. |
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Virtual; September 2020 |
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LIM |
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CIC; 600.118; 600.140; |
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Call Number |
Admin @ si @ SBV2020 |
Serial |
3460 |
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Author |
Sagnik Das; Hassan Ahmed Sial; Ke Ma; Ramon Baldrich; Maria Vanrell; Dimitris Samaras |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Intrinsic Decomposition of Document Images In-the-Wild |
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Conference Article |
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Year |
2020 |
Publication |
31st British Machine Vision Conference |
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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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Virtual; September 2020 |
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BMVC |
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CIC; 600.087; 600.140; 600.118 |
Approved |
no |
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Call Number |
Admin @ si @ DSM2020 |
Serial |
3461 |
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Permanent link to this record |
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Author |
Fernando Vilariño |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
3D Scanning of Capitals at Library Living Lab |
Type |
Book Whole |
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Year |
2019 |
Publication |
“Living Lab Projects 2019”. ENoLL. |
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MV; DAG; 600.140; 600.121;SIAI |
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Call Number |
Admin @ si @ Vil2019c |
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3463 |
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Author |
Kai Wang; Luis Herranz; Anjan Dutta; Joost Van de Weijer |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Bookworm continual learning: beyond zero-shot learning and continual learning |
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Conference Article |
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Year |
2020 |
Publication |
Workshop TASK-CV 2020 |
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We propose bookworm continual learning(BCL), a flexible setting where unseen classes can be inferred via a semantic model, and the visual model can be updated continually. Thus BCL generalizes both continual learning (CL) and zero-shot learning (ZSL). We also propose the bidirectional imagination (BImag) framework to address BCL where features of both past and future classes are generated. We observe that conditioning the feature generator on attributes can actually harm the continual learning ability, and propose two variants (joint class-attribute conditioning and asymmetric generation) to alleviate this problem. |
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Virtual; August 2020 |
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ECCVW |
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LAMP; 600.141; 600.120 |
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Admin @ si @ WHD2020 |
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3466 |
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Author |
Carola Figueroa Flores |
![find book details (via ISBN) isbn](img/isbn.gif)
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Title |
Visual Saliency for Object Recognition, and Object Recognition for Visual Saliency |
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2021 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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computer vision; visual saliency; fine-grained object recognition; convolutional neural networks; images classification |
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Abstract |
For humans, the recognition of objects is an almost instantaneous, precise and
extremely adaptable process. Furthermore, we have the innate capability to learn
new object classes from only few examples. The human brain lowers the complexity
of the incoming data by filtering out part of the information and only processing
those things that capture our attention. This, mixed with our biological predisposition to respond to certain shapes or colors, allows us to recognize in a simple
glance the most important or salient regions from an image. This mechanism can
be observed by analyzing on which parts of images subjects place attention; where
they fix their eyes when an image is shown to them. The most accurate way to
record this behavior is to track eye movements while displaying images.
Computational saliency estimation aims to identify to what extent regions or
objects stand out with respect to their surroundings to human observers. Saliency
maps can be used in a wide range of applications including object detection, image
and video compression, and visual tracking. The majority of research in the field has
focused on automatically estimating saliency maps given an input image. Instead, in
this thesis, we set out to incorporate saliency maps in an object recognition pipeline:
we want to investigate whether saliency maps can improve object recognition
results.
In this thesis, we identify several problems related to visual saliency estimation.
First, to what extent the estimation of saliency can be exploited to improve the
training of an object recognition model when scarce training data is available. To
solve this problem, we design an image classification network that incorporates
saliency information as input. This network processes the saliency map through a
dedicated network branch and uses the resulting characteristics to modulate the
standard bottom-up visual characteristics of the original image input. We will refer to this technique as saliency-modulated image classification (SMIC). In extensive
experiments on standard benchmark datasets for fine-grained object recognition,
we show that our proposed architecture can significantly improve performance,
especially on dataset with scarce training data.
Next, we address the main drawback of the above pipeline: SMIC requires an
explicit saliency algorithm that must be trained on a saliency dataset. To solve this,
we implement a hallucination mechanism that allows us to incorporate the saliency
estimation branch in an end-to-end trained neural network architecture that only
needs the RGB image as an input. A side-effect of this architecture is the estimation
of saliency maps. In experiments, we show that this architecture can obtain similar
results on object recognition as SMIC but without the requirement of ground truth
saliency maps to train the system.
Finally, we evaluated the accuracy of the saliency maps that occur as a sideeffect of object recognition. For this purpose, we use a set of benchmark datasets
for saliency evaluation based on eye-tracking experiments. Surprisingly, the estimated saliency maps are very similar to the maps that are computed from human
eye-tracking experiments. Our results show that these saliency maps can obtain
competitive results on benchmark saliency maps. On one synthetic saliency dataset
this method even obtains the state-of-the-art without the need of ever having seen
an actual saliency image for training. |
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March 2021 |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Joost Van de Weijer;Bogdan Raducanu |
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978-84-122714-4-7 |
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LAMP; 600.120 |
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Admin @ si @ Fig2021 |
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3600 |
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Author |
Debora Gil; Guillermo Torres |
![download PDF file pdf](img/file_PDF.gif)
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Title |
A multi-shape loss function with adaptive class balancing for the segmentation of lung structures |
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Conference Article |
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2020 |
Publication |
34th International Congress and Exhibition on Computer Assisted Radiology & Surgery |
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Virtual; June 2020 |
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CARS |
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IAM; 600.139; 600.145 |
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Admin @ si @ GiT2020 |
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3472 |
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Author |
Debora Gil; Oriol Ramos Terrades; Raquel Perez |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Topological Radiomics (TOPiomics): Early Detection of Genetic Abnormalities in Cancer Treatment Evolution |
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Conference Article |
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2020 |
Publication |
Women in Geometry and Topology |
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Barcelona; September 2019 |
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IAM; DAG; 600.139; 600.145; 600.121 |
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Admin @ si @ GRP2020 |
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3473 |
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Author |
Gabriel Villalonga |
![find book details (via ISBN) isbn](img/isbn.gif)
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Title |
Leveraging Synthetic Data to Create Autonomous Driving Perception Systems |
Type |
Book Whole |
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2021 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Manually annotating images to develop vision models has been a major bottleneck
since computer vision and machine learning started to walk together. This has
been more evident since computer vision falls on the shoulders of data-hungry
deep learning techniques. When addressing on-board perception for autonomous
driving, the curse of data annotation is exacerbated due to the use of additional
sensors such as LiDAR. Therefore, any approach aiming at reducing such a timeconsuming and costly work is of high interest for addressing autonomous driving
and, in fact, for any application requiring some sort of artificial perception. In the
last decade, it has been shown that leveraging from synthetic data is a paradigm
worth to pursue in order to minimizing manual data annotation. The reason is
that the automatic process of generating synthetic data can also produce different
types of associated annotations (e.g. object bounding boxes for synthetic images
and LiDAR pointclouds, pixel/point-wise semantic information, etc.). Directly
using synthetic data for training deep perception models may not be the definitive
solution in all circumstances since it can appear a synth-to-real domain shift. In
this context, this work focuses on leveraging synthetic data to alleviate manual
annotation for three perception tasks related to driving assistance and autonomous
driving. In all cases, we assume the use of deep convolutional neural networks
(CNNs) to develop our perception models.
The first task addresses traffic sign recognition (TSR), a kind of multi-class
classification problem. We assume that the number of sign classes to be recognized
must be suddenly increased without having annotated samples to perform the
corresponding TSR CNN re-training. We show that leveraging synthetic samples of
such new classes and transforming them by a generative adversarial network (GAN)
trained on the known classes (i.e. without using samples from the new classes), it is
possible to re-train the TSR CNN to properly classify all the signs for a ∼ 1/4 ratio of
new/known sign classes. The second task addresses on-board 2D object detection,
focusing on vehicles and pedestrians. In this case, we assume that we receive a set
of images without the annotations required to train an object detector, i.e. without
object bounding boxes. Therefore, our goal is to self-annotate these images so
that they can later be used to train the desired object detector. In order to reach
this goal, we leverage from synthetic data and propose a semi-supervised learning
approach based on the co-training idea. In fact, we use a GAN to reduce the synthto-real domain shift before applying co-training. Our quantitative results show
that co-training and GAN-based image-to-image translation complement each
other up to allow the training of object detectors without manual annotation, and still almost reaching the upper-bound performances of the detectors trained from
human annotations. While in previous tasks we focus on vision-based perception,
the third task we address focuses on LiDAR pointclouds. Our initial goal was to
develop a 3D object detector trained on synthetic LiDAR-style pointclouds. While
for images we may expect synth/real-to-real domain shift due to differences in
their appearance (e.g. when source and target images come from different camera
sensors), we did not expect so for LiDAR pointclouds since these active sensors
factor out appearance and provide sampled shapes. However, in practice, we have
seen that it can be domain shift even among real-world LiDAR pointclouds. Factors
such as the sampling parameters of the LiDARs, the sensor suite configuration onboard the ego-vehicle, and the human annotation of 3D bounding boxes, do induce
a domain shift. We show it through comprehensive experiments with different
publicly available datasets and 3D detectors. This redirected our goal towards the
design of a GAN for pointcloud-to-pointcloud translation, a relatively unexplored
topic.
Finally, it is worth to mention that all the synthetic datasets used for these three
tasks, have been designed and generated in the context of this PhD work and will
be publicly released. Overall, we think this PhD presents several steps forward to
encourage leveraging synthetic data for developing deep perception models in the
field of driving assistance and autonomous driving. |
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Address |
February 2021 |
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Corporate Author |
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Thesis |
Ph.D. thesis |
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Publisher |
Ediciones Graficas Rey |
Place of Publication |
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Editor |
Antonio Lopez;German Ros |
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Summary Language |
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Original Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
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ISBN |
978-84-122714-2-3 |
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Expedition |
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Conference |
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Notes |
ADAS; 600.118 |
Approved |
no |
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Call Number |
Admin @ si @ Vil2021 |
Serial |
3599 |
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Permanent link to this record |
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Author |
Pau Riba |
![find book details (via ISBN) isbn](img/isbn.gif)
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Title |
Distilling Structure from Imagery: Graph-based Models for the Interpretation of Document Images |
Type |
Book Whole |
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Year |
2020 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
Abbreviated Journal |
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Abstract |
From its early stages, the community of Pattern Recognition and Computer Vision has considered the importance of leveraging the structural information when understanding images. Usually, graphs have been proposed as a suitable model to represent this kind of information due to their flexibility and representational power able to codify both, the components, objects, or entities and their pairwise relationship. Even though graphs have been successfully applied to a huge variety of tasks, as a result of their symbolic and relational nature, graphs have always suffered from some limitations compared to statistical approaches. Indeed, some trivial mathematical operations do not have an equivalence in the graph domain. For instance, in the core of many pattern recognition applications, there is a need to compare two objects. This operation, which is trivial when considering feature vectors defined in \(\mathbb{R}^n\), is not properly defined for graphs.
In this thesis, we have investigated the importance of the structural information from two perspectives, the traditional graph-based methods and the new advances on Geometric Deep Learning. On the one hand, we explore the problem of defining a graph representation and how to deal with it on a large scale and noisy scenario. On the other hand, Graph Neural Networks are proposed to first redefine a Graph Edit Distance methodologies as a metric learning problem, and second, to apply them in a real use case scenario for the detection of repetitive patterns which define tables in invoice documents. As experimental framework, we have validated the different methodological contributions in the domain of Document Image Analysis and Recognition. |
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Corporate Author |
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Thesis |
Ph.D. thesis |
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Publisher |
Ediciones Graficas Rey |
Place of Publication |
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Editor |
Josep Llados;Alicia Fornes |
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Summary Language |
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Original Title |
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Series Editor |
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Abbreviated Series Title |
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Edition |
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ISSN |
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ISBN |
978-84-121011-6-4 |
Medium |
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Area |
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Expedition |
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Conference |
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Notes |
DAG; 600.121 |
Approved |
no |
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Call Number |
Admin @ si @ Rib20 |
Serial |
3478 |
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Permanent link to this record |