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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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Year |
2021 |
Publication |
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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Publisher |
Ediciones Graficas Rey |
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Editor |
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 |
Diego Porres |
![download PDF file pdf](img/file_PDF.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Discriminator Synthesis: On reusing the other half of Generative Adversarial Networks |
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Conference Article |
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Year |
2021 |
Publication |
Machine Learning for Creativity and Design, Neurips Workshop |
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Generative Adversarial Networks have long since revolutionized the world of computer vision and, tied to it, the world of art. Arduous efforts have gone into fully utilizing and stabilizing training so that outputs of the Generator network have the highest possible fidelity, but little has gone into using the Discriminator after training is complete. In this work, we propose to use the latter and show a way to use the features it has learned from the training dataset to both alter an image and generate one from scratch. We name this method Discriminator Dreaming, and the full code can be found at this https URL. |
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Virtual; December 2021 |
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NEURIPSW |
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ADAS; 601.365 |
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Admin @ si @ Por2021 |
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3597 |
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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 |
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Year |
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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February 2021 |
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Ph.D. thesis |
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Publisher |
Ediciones Graficas Rey |
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Editor |
Antonio Lopez;German Ros |
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978-84-122714-2-3 |
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ADAS; 600.118 |
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Call Number |
Admin @ si @ Vil2021 |
Serial |
3599 |
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Author |
Andres Mafla; Sounak Dey; Ali Furkan Biten; Lluis Gomez; Dimosthenis Karatzas |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Multi-modal reasoning graph for scene-text based fine-grained image classification and retrieval |
Type |
Conference Article |
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Year |
2021 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
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4022-4032 |
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Virtual; January 2021 |
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WACV |
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DAG; 600.121 |
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Admin @ si @ MDB2021 |
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3491 |
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Author |
Andres Mafla; Rafael S. Rezende; Lluis Gomez; Diana Larlus; Dimosthenis Karatzas |
![download PDF file pdf](img/file_PDF.gif)
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Title |
StacMR: Scene-Text Aware Cross-Modal Retrieval |
Type |
Conference Article |
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Year |
2021 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
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2219-2229 |
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Virtual; January 2021 |
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WACV |
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DAG; 600.121 |
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Admin @ si @ MRG2021a |
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3492 |
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Author |
Andres Mafla; Ruben Tito; Sounak Dey; Lluis Gomez; Marçal Rusiñol; Ernest Valveny; Dimosthenis Karatzas |
![goto web page url](img/www.gif)
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Title |
Real-time Lexicon-free Scene Text Retrieval |
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Journal Article |
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2021 |
Publication |
Pattern Recognition |
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PR |
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110 |
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107656 |
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In this work, we address the task of scene text retrieval: given a text query, the system returns all images containing the queried text. The proposed model uses a single shot CNN architecture that predicts bounding boxes and builds a compact representation of spotted words. In this way, this problem can be modeled as a nearest neighbor search of the textual representation of a query over the outputs of the CNN collected from the totality of an image database. Our experiments demonstrate that the proposed model outperforms previous state-of-the-art, while offering a significant increase in processing speed and unmatched expressiveness with samples never seen at training time. Several experiments to assess the generalization capability of the model are conducted in a multilingual dataset, as well as an application of real-time text spotting in videos. |
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DAG; 600.121; 600.129; 601.338 |
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Admin @ si @ MTD2021 |
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3493 |
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Author |
Minesh Mathew; Dimosthenis Karatzas; C.V. Jawahar |
![download PDF file pdf](img/file_PDF.gif)
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Title |
DocVQA: A Dataset for VQA on Document Images |
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Conference Article |
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Year |
2021 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
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2200-2209 |
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We present a new dataset for Visual Question Answering (VQA) on document images called DocVQA. The dataset consists of 50,000 questions defined on 12,000+ document images. Detailed analysis of the dataset in comparison with similar datasets for VQA and reading comprehension is presented. We report several baseline results by adopting existing VQA and reading comprehension models. Although the existing models perform reasonably well on certain types of questions, there is large performance gap compared to human performance (94.36% accuracy). The models need to improve specifically on questions where understanding structure of the document is crucial. The dataset, code and leaderboard are available at docvqa. org |
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Virtual; January 2021 |
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WACV |
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DAG; 600.121 |
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Admin @ si @ MKJ2021 |
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3498 |
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Author |
Gemma Rotger |
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Title |
Lifelike Humans: Detailed Reconstruction of Expressive Human Faces |
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2021 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Developing human-like digital characters is a challenging task since humans are used to recognizing our fellows, and find the computed generated characters inadequately humanized. To fulfill the standards of the videogame and digital film productions it is necessary to model and animate these characters the most closely to human beings. However, it is an arduous and expensive task, since many artists and specialists are required to work on a single character. Therefore, to fulfill these requirements we found an interesting option to study the automatic creation of detailed characters through inexpensive setups. In this work, we develop novel techniques to bring detailed characters by combining different aspects that stand out when developing realistic characters, skin detail, facial hairs, expressions, and microexpressions. We examine each of the mentioned areas with the aim of automatically recover each of the parts without user interaction nor training data. We study the problems for their robustness but also for the simplicity of the setup, preferring single-image with uncontrolled illumination and methods that can be easily computed with the commodity of a standard laptop. A detailed face with wrinkles and skin details is vital to develop a realistic character. In this work, we introduce our method to automatically describe facial wrinkles on the image and transfer to the recovered base face. Then we advance to facial hair recovery by resolving a fitting problem with a novel parametrization model. As of last, we develop a mapping function that allows transfer expressions and microexpressions between different meshes, which provides realistic animations to our detailed mesh. We cover all the mentioned points with the focus on key aspects as (i) how to describe skin wrinkles in a simple and straightforward manner, (ii) how to recover 3D from 2D detections, (iii) how to recover and model facial hair from 2D to 3D, (iv) how to transfer expressions between models holding both skin detail and facial hair, (v) how to perform all the described actions without training data nor user interaction. In this work, we present our proposals to solve these aspects with an efficient and simple setup. We validate our work with several datasets both synthetic and real data, prooving remarkable results even in challenging cases as occlusions as glasses, thick beards, and indeed working with different face topologies like single-eyed cyclops. |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Felipe Lumbreras;Antonio Agudo |
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978-84-122714-3-0 |
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ADAS |
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Admin @ si @ Rot2021 |
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3513 |
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Author |
Razieh Rastgoo; Kourosh Kiani; Sergio Escalera |
![goto web page url](img/www.gif)
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Title |
Sign Language Recognition: A Deep Survey |
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Journal Article |
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2021 |
Publication |
Expert Systems With Applications |
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ESWA |
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164 |
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113794 |
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Sign language, as a different form of the communication language, is important to large groups of people in society. There are different signs in each sign language with variability in hand shape, motion profile, and position of the hand, face, and body parts contributing to each sign. So, visual sign language recognition is a complex research area in computer vision. Many models have been proposed by different researchers with significant improvement by deep learning approaches in recent years. In this survey, we review the vision-based proposed models of sign language recognition using deep learning approaches from the last five years. While the overall trend of the proposed models indicates a significant improvement in recognition accuracy in sign language recognition, there are some challenges yet that need to be solved. We present a taxonomy to categorize the proposed models for isolated and continuous sign language recognition, discussing applications, datasets, hybrid models, complexity, and future lines of research in the field. |
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HUPBA; no proj |
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Admin @ si @ RKE2021a |
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3521 |
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Author |
Giuseppe Pezzano; Vicent Ribas Ripoll; Petia Radeva |
![goto web page url](img/www.gif)
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CoLe-CNN: Context-learning convolutional neural network with adaptive loss function for lung nodule segmentation |
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Journal Article |
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Year |
2021 |
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Computer Methods and Programs in Biomedicine |
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CMPB |
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198 |
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105792 |
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Background and objective:An accurate segmentation of lung nodules in computed tomography images is a crucial step for the physical characterization of the tumour. Being often completely manually accomplished, nodule segmentation turns to be a tedious and time-consuming procedure and this represents a high obstacle in clinical practice. In this paper, we propose a novel Convolutional Neural Network for nodule segmentation that combines a light and efficient architecture with innovative loss function and segmentation strategy. Methods:In contrast to most of the standard end-to-end architectures for nodule segmentation, our network learns the context of the nodules by producing two masks representing all the background and secondary-important elements in the Computed Tomography scan. The nodule is detected by subtracting the context from the original scan image. Additionally, we introduce an asymmetric loss function that automatically compensates for potential errors in the nodule annotations. We trained and tested our Neural Network on the public LIDC-IDRI database, compared it with the state of the art and run a pseudo-Turing test between four radiologists and the network. Results:The results proved that the behaviour of the algorithm is very near to the human performance and its segmentation masks are almost indistinguishable from the ones made by the radiologists. Our method clearly outperforms the state of the art on CT nodule segmentation in terms of F1 score and IoU of and respectively. Conclusions: The main structure of the network ensures all the properties of the UNet architecture, while the Multi Convolutional Layers give a more accurate pattern recognition. The newly adopted solutions also increase the details on the border of the nodule, even under the noisiest conditions. This method can be applied now for single CT slice nodule segmentation and it represents a starting point for the future development of a fully automatic 3D segmentation software. |
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MILAB; no proj |
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Admin @ si @ PRR2021 |
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3530 |
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Author |
Cristina Palmero; Javier Selva; Sorina Smeureanu; Julio C. S. Jacques Junior; Albert Clapes; Alexa Mosegui; Zejian Zhang; David Gallardo; Georgina Guilera; David Leiva; Sergio Escalera |
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Title |
Context-Aware Personality Inference in Dyadic Scenarios: Introducing the UDIVA Dataset |
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Conference Article |
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Year |
2021 |
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IEEE Winter Conference on Applications of Computer Vision |
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1-12 |
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This paper introduces UDIVA, a new non-acted dataset of face-to-face dyadic interactions, where interlocutors perform competitive and collaborative tasks with different behavior elicitation and cognitive workload. The dataset consists of 90.5 hours of dyadic interactions among 147 participants distributed in 188 sessions, recorded using multiple audiovisual and physiological sensors. Currently, it includes sociodemographic, self- and peer-reported personality, internal state, and relationship profiling from participants. As an initial analysis on UDIVA, we propose a
transformer-based method for self-reported personality inference in dyadic scenarios, which uses audiovisual data and different sources of context from both interlocutors to
regress a target person’s personality traits. Preliminary results from an incremental study show consistent improvements when using all available context information. |
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Virtual; January 2021 |
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WACV |
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HUPBA |
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Admin @ si @ PSS2021 |
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3532 |
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Author |
Julio C. S. Jacques Junior; Agata Lapedriza; Cristina Palmero; Xavier Baro; Sergio Escalera |
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Title |
Person Perception Biases Exposed: Revisiting the First Impressions Dataset |
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Conference Article |
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Year |
2021 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
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13-21 |
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This work revisits the ChaLearn First Impressions database, annotated for personality perception using pairwise comparisons via crowdsourcing. We analyse for the first time the original pairwise annotations, and reveal existing person perception biases associated to perceived attributes like gender, ethnicity, age and face attractiveness.
We show how person perception bias can influence data labelling of a subjective task, which has received little attention from the computer vision and machine learning communities by now. We further show that the mechanism used to convert pairwise annotations to continuous values may magnify the biases if no special treatment is considered. The findings of this study are relevant for the computer vision community that is still creating new datasets on subjective tasks, and using them for practical applications, ignoring these perceptual biases. |
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Virtual; January 2021 |
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HUPBA |
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no |
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Admin @ si @ JLP2021 |
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3533 |
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Author |
Carola Figueroa Flores; Bogdan Raducanu; David Berga; Joost Van de Weijer |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Hallucinating Saliency Maps for Fine-Grained Image Classification for Limited Data Domains |
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Conference Article |
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2021 |
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16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications |
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4 |
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163-171 |
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arXiv:2007.12562
Most of the saliency methods are evaluated on their ability to generate saliency maps, and not on their functionality in a complete vision pipeline, like for instance, image classification. In the current paper, we propose an approach which does not require explicit saliency maps to improve image classification, but they are learned implicitely, during the training of an end-to-end image classification task. We show that our approach obtains similar results as the case when the saliency maps are provided explicitely. Combining RGB data with saliency maps represents a significant advantage for object recognition, especially for the case when training data is limited. We validate our method on several datasets for fine-grained classification tasks (Flowers, Birds and Cars). In addition, we show that our saliency estimation method, which is trained without any saliency groundtruth data, obtains competitive results on real image saliency benchmark (Toronto), and outperforms deep saliency models with synthetic images (SID4VAM). |
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Virtual; February 2021 |
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VISAPP |
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LAMP |
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no |
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Admin @ si @ FRB2021c |
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3540 |
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Author |
Sudeep Katakol; Basem Elbarashy; Luis Herranz; Joost Van de Weijer; Antonio Lopez |
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Title |
Distributed Learning and Inference with Compressed Images |
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Journal Article |
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2021 |
Publication |
IEEE Transactions on Image Processing |
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TIP |
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30 |
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3069 - 3083 |
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Modern computer vision requires processing large amounts of data, both while training the model and/or during inference, once the model is deployed. Scenarios where images are captured and processed in physically separated locations are increasingly common (e.g. autonomous vehicles, cloud computing). In addition, many devices suffer from limited resources to store or transmit data (e.g. storage space, channel capacity). In these scenarios, lossy image compression plays a crucial role to effectively increase the number of images collected under such constraints. However, lossy compression entails some undesired degradation of the data that may harm the performance of the downstream analysis task at hand, since important semantic information may be lost in the process. Moreover, we may only have compressed images at training time but are able to use original images at inference time, or vice versa, and in such a case, the downstream model suffers from covariate shift. In this paper, we analyze this phenomenon, with a special focus on vision-based perception for autonomous driving as a paradigmatic scenario. We see that loss of semantic information and covariate shift do indeed exist, resulting in a drop in performance that depends on the compression rate. In order to address the problem, we propose dataset restoration, based on image restoration with generative adversarial networks (GANs). Our method is agnostic to both the particular image compression method and the downstream task; and has the advantage of not adding additional cost to the deployed models, which is particularly important in resource-limited devices. The presented experiments focus on semantic segmentation as a challenging use case, cover a broad range of compression rates and diverse datasets, and show how our method is able to significantly alleviate the negative effects of compression on the downstream visual task. |
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LAMP; ADAS; 600.120; 600.118 |
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Admin @ si @ KEH2021 |
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3543 |
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Author |
Kai Wang; Luis Herranz; Joost Van de Weijer |
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Title |
Continual learning in cross-modal retrieval |
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Conference Article |
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Year |
2021 |
Publication |
2nd CLVISION workshop |
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3628-3638 |
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Multimodal representations and continual learning are two areas closely related to human intelligence. The former considers the learning of shared representation spaces where information from different modalities can be compared and integrated (we focus on cross-modal retrieval between language and visual representations). The latter studies how to prevent forgetting a previously learned task when learning a new one. While humans excel in these two aspects, deep neural networks are still quite limited. In this paper, we propose a combination of both problems into a continual cross-modal retrieval setting, where we study how the catastrophic interference caused by new tasks impacts the embedding spaces and their cross-modal alignment required for effective retrieval. We propose a general framework that decouples the training, indexing and querying stages. We also identify and study different factors that may lead to forgetting, and propose tools to alleviate it. We found that the indexing stage pays an important role and that simply avoiding reindexing the database with updated embedding networks can lead to significant gains. We evaluated our methods in two image-text retrieval datasets, obtaining significant gains with respect to the fine tuning baseline. |
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Virtual; June 2021 |
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CVPRW |
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LAMP; 600.120; 600.141; 600.147; 601.379 |
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Call Number |
Admin @ si @ WHW2021 |
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3566 |
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