|
Records |
Links |
|
Author |
Carola Figueroa Flores; Bogdan Raducanu; David Berga; Joost Van de Weijer |
![download PDF file pdf](img/file_PDF.gif)
|
|
Title |
Hallucinating Saliency Maps for Fine-Grained Image Classification for Limited Data Domains |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
Conference Article |
|
Year |
2021 |
Publication |
16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications |
Abbreviated Journal |
|
|
|
Volume |
4 |
Issue |
|
Pages |
163-171 |
|
|
Keywords |
|
|
|
Abstract |
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). |
|
|
Address |
Virtual; February 2021 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
VISAPP |
|
|
Notes |
LAMP |
Approved |
no |
|
|
Call Number |
Admin @ si @ FRB2021c |
Serial |
3540 |
|
Permanent link to this record |
|
|
|
|
Author |
Kai Wang; Luis Herranz; Joost Van de Weijer |
![download PDF file pdf](img/file_PDF.gif)
![goto web page (via DOI) doi](img/doi.gif)
|
|
Title |
Continual learning in cross-modal retrieval |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
Conference Article |
|
Year |
2021 |
Publication |
2nd CLVISION workshop |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
3628-3638 |
|
|
Keywords |
|
|
|
Abstract |
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. |
|
|
Address |
Virtual; June 2021 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
CVPRW |
|
|
Notes |
LAMP; 600.120; 600.141; 600.147; 601.379 |
Approved |
no |
|
|
Call Number |
Admin @ si @ WHW2021 |
Serial |
3566 |
|
Permanent link to this record |
|
|
|
|
Author |
Vincenzo Lomonaco; Lorenzo Pellegrini; Andrea Cossu; Antonio Carta; Gabriele Graffieti; Tyler L. Hayes; Matthias De Lange; Marc Masana; Jary Pomponi; Gido van de Ven; Martin Mundt; Qi She; Keiland Cooper; Jeremy Forest; Eden Belouadah; Simone Calderara; German I. Parisi; Fabio Cuzzolin; Andreas Tolias; Simone Scardapane; Luca Antiga; Subutai Amhad; Adrian Popescu; Christopher Kanan; Joost Van de Weijer; Tinne Tuytelaars; Davide Bacciu; Davide Maltoni |
![download PDF file pdf](img/file_PDF.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title |
Avalanche: an End-to-End Library for Continual Learning |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
Conference Article |
|
Year |
2021 |
Publication |
34th IEEE Conference on Computer Vision and Pattern Recognition Workshops |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
3595-3605 |
|
|
Keywords |
|
|
|
Abstract |
Learning continually from non-stationary data streams is a long-standing goal and a challenging problem in machine learning. Recently, we have witnessed a renewed and fast-growing interest in continual learning, especially within the deep learning community. However, algorithmic solutions are often difficult to re-implement, evaluate and port across different settings, where even results on standard benchmarks are hard to reproduce. In this work, we propose Avalanche, an open-source end-to-end library for continual learning research based on PyTorch. Avalanche is designed to provide a shared and collaborative codebase for fast prototyping, training, and reproducible evaluation of continual learning algorithms. |
|
|
Address |
Virtual; June 2021 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
CVPRW |
|
|
Notes |
LAMP; 600.120 |
Approved |
no |
|
|
Call Number |
Admin @ si @ LPC2021 |
Serial |
3567 |
|
Permanent link to this record |
|
|
|
|
Author |
Idoia Ruiz; Lorenzo Porzi; Samuel Rota Bulo; Peter Kontschieder; Joan Serrat |
![download PDF file pdf](img/file_PDF.gif)
|
|
Title |
Weakly Supervised Multi-Object Tracking and Segmentation |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
Conference Article |
|
Year |
2021 |
Publication |
IEEE Winter Conference on Applications of Computer Vision Workshops |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
125-133 |
|
|
Keywords |
|
|
|
Abstract |
We introduce the problem of weakly supervised MultiObject Tracking and Segmentation, i.e. joint weakly supervised instance segmentation and multi-object tracking, in which we do not provide any kind of mask annotation.
To address it, we design a novel synergistic training strategy by taking advantage of multi-task learning, i.e. classification and tracking tasks guide the training of the unsupervised instance segmentation. For that purpose, we extract weak foreground localization information, provided by
Grad-CAM heatmaps, to generate a partial ground truth to learn from. Additionally, RGB image level information is employed to refine the mask prediction at the edges of the
objects. We evaluate our method on KITTI MOTS, the most representative benchmark for this task, reducing the performance gap on the MOTSP metric between the fully supervised and weakly supervised approach to just 12% and 12.7 % for cars and pedestrians, respectively. |
|
|
Address |
Virtual; January 2021 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
WACVW |
|
|
Notes |
ADAS; 600.118; 600.124 |
Approved |
no |
|
|
Call Number |
Admin @ si @ RPR2021 |
Serial |
3548 |
|
Permanent link to this record |
|
|
|
|
Author |
Idoia Ruiz; Joan Serrat |
![download PDF file pdf](img/file_PDF.gif)
![goto web page (via DOI) doi](img/doi.gif)
|
|
Title |
Rank-based ordinal classification |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
Conference Article |
|
Year |
2020 |
Publication |
25th International Conference on Pattern Recognition |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
8069-8076 |
|
|
Keywords |
|
|
|
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. |
|
|
Address |
Virtual; January 2021 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
ICPR |
|
|
Notes |
ADAS; 600.118; 600.124 |
Approved |
no |
|
|
Call Number |
Admin @ si @ RuS2020 |
Serial |
3549 |
|
Permanent link to this record |
|
|
|
|
Author |
Pau Rodriguez; Jordi Gonzalez; Josep M. Gonfaus; Xavier Roca |
![download PDF file pdf](img/file_PDF.gif)
|
|
Title |
Towards Visual Personality Questionnaires based on Deep Learning and Social Media |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
Conference Article |
|
Year |
2019 |
Publication |
21st International Conference on Social Influence and Social Psychology |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
April 2019; Tokio; Japan |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
ICSISP |
|
|
Notes |
ISE; 600.119 |
Approved |
no |
|
|
Call Number |
Admin @ si @ RGG2020 |
Serial |
3554 |
|
Permanent link to this record |
|
|
|
|
Author |
Klara Janousckova; Jiri Matas; Lluis Gomez; Dimosthenis Karatzas |
![download PDF file pdf](img/file_PDF.gif)
![goto web page (via DOI) doi](img/doi.gif)
|
|
Title |
Text Recognition – Real World Data and Where to Find Them |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
Conference Article |
|
Year |
2020 |
Publication |
25th International Conference on Pattern Recognition |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
4489-4496 |
|
|
Keywords |
|
|
|
Abstract |
We present a method for exploiting weakly annotated images to improve text extraction pipelines. The approach uses an arbitrary end-to-end text recognition system to obtain text region proposals and their, possibly erroneous, transcriptions. The method includes matching of imprecise transcriptions to weak annotations and an edit distance guided neighbourhood search. It produces nearly error-free, localised instances of scene text, which we treat as “pseudo ground truth” (PGT). The method is applied to two weakly-annotated datasets. Training with the extracted PGT consistently improves the accuracy of a state of the art recognition model, by 3.7% on average, across different benchmark datasets (image domains) and 24.5% on one of the weakly annotated datasets 1 1 Acknowledgements. The authors were supported by Czech Technical University student grant SGS20/171/0HK3/3TJ13, the MEYS VVV project CZ.02.1.01/0.010.0J16 019/0000765 Research Center for Informatics, the Spanish Research project TIN2017-89779-P and the CERCA Programme / Generalitat de Catalunya. |
|
|
Address |
Virtual; January 2021 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
ICPR |
|
|
Notes |
DAG; 600.121; 600.129 |
Approved |
no |
|
|
Call Number |
Admin @ si @ JMG2020 |
Serial |
3557 |
|
Permanent link to this record |
|
|
|
|
Author |
Minesh Mathew; Ruben Tito; Dimosthenis Karatzas; R.Manmatha; C.V. Jawahar |
![download PDF file pdf](img/file_PDF.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title |
Document Visual Question Answering Challenge 2020 |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
Conference Article |
|
Year |
2020 |
Publication |
33rd IEEE Conference on Computer Vision and Pattern Recognition – Short paper |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
This paper presents results of Document Visual Question Answering Challenge organized as part of “Text and Documents in the Deep Learning Era” workshop, in CVPR 2020. The challenge introduces a new problem – Visual Question Answering on document images. The challenge comprised two tasks. The first task concerns with asking questions on a single document image. On the other hand, the second task is set as a retrieval task where the question is posed over a collection of images. For the task 1 a new dataset is introduced comprising 50,000 questions-answer(s) pairs defined over 12,767 document images. For task 2 another dataset has been created comprising 20 questions over 14,362 document images which share the same document template. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
CVPR |
|
|
Notes |
DAG; 600.121 |
Approved |
no |
|
|
Call Number |
Admin @ si @ MTK2020 |
Serial |
3558 |
|
Permanent link to this record |
|
|
|
|
Author |
Ozge Mercanoglu Sincan; Julio C. S. Jacques Junior; Sergio Escalera; Hacer Yalim Keles |
![download PDF file pdf](img/file_PDF.gif)
|
|
Title |
ChaLearn LAP Large Scale Signer Independent Isolated Sign Language Recognition Challenge: Design, Results and Future Research |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
Conference Article |
|
Year |
2021 |
Publication |
Conference on Computer Vision and Pattern Recognition Workshops |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
3467-3476 |
|
|
Keywords |
|
|
|
Abstract |
The performances of Sign Language Recognition (SLR) systems have improved considerably in recent years. However, several open challenges still need to be solved to allow SLR to be useful in practice. The research in the field is in its infancy in regards to the robustness of the models to a large diversity of signs and signers, and to fairness of the models to performers from different demographics. This work summarises the ChaLearn LAP Large Scale Signer Independent Isolated SLR Challenge, organised at CVPR 2021 with the goal of overcoming some of the aforementioned challenges. We analyse and discuss the challenge design, top winning solutions and suggestions for future research. The challenge attracted 132 participants in the RGB track and 59 in the RGB+Depth track, receiving more than 1.5K submissions in total. Participants were evaluated using a new large-scale multi-modal Turkish Sign Language (AUTSL) dataset, consisting of 226 sign labels and 36,302 isolated sign video samples performed by 43 different signers. Winning teams achieved more than 96% recognition rate, and their approaches benefited from pose/hand/face estimation, transfer learning, external data, fusion/ensemble of modalities and different strategies to model spatio-temporal information. However, methods still fail to distinguish among very similar signs, in particular those sharing similar hand trajectories. |
|
|
Address |
Virtual; June 2021 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
CVPRW |
|
|
Notes |
HuPBA; no proj |
Approved |
no |
|
|
Call Number |
Admin @ si @ MJE2021 |
Serial |
3560 |
|
Permanent link to this record |
|
|
|
|
Author |
Marc Masana; Tinne Tuytelaars; Joost Van de Weijer |
![download PDF file pdf](img/file_PDF.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title |
Ternary Feature Masks: zero-forgetting for task-incremental learning |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
Conference Article |
|
Year |
2021 |
Publication |
34th IEEE Conference on Computer Vision and Pattern Recognition Workshops |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
3565-3574 |
|
|
Keywords |
|
|
|
Abstract |
We propose an approach without any forgetting to continual learning for the task-aware regime, where at inference the task-label is known. By using ternary masks we can upgrade a model to new tasks, reusing knowledge from previous tasks while not forgetting anything about them. Using masks prevents both catastrophic forgetting and backward transfer. We argue -- and show experimentally -- that avoiding the former largely compensates for the lack of the latter, which is rarely observed in practice. In contrast to earlier works, our masks are applied to the features (activations) of each layer instead of the weights. This considerably reduces the number of mask parameters for each new task; with more than three orders of magnitude for most networks. The encoding of the ternary masks into two bits per feature creates very little overhead to the network, avoiding scalability issues. To allow already learned features to adapt to the current task without changing the behavior of these features for previous tasks, we introduce task-specific feature normalization. Extensive experiments on several finegrained datasets and ImageNet show that our method outperforms current state-of-the-art while reducing memory overhead in comparison to weight-based approaches. |
|
|
Address |
Virtual; June 2021 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
CVPRW |
|
|
Notes |
LAMP; 600.120 |
Approved |
no |
|
|
Call Number |
Admin @ si @ MTW2021 |
Serial |
3565 |
|
Permanent link to this record |
|
|
|
|
Author |
Sudeep Katakol; Luis Herranz; Fei Yang; Marta Mrak |
![download PDF file pdf](img/file_PDF.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title |
DANICE: Domain adaptation without forgetting in neural image compression |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
Conference Article |
|
Year |
2021 |
Publication |
Conference on Computer Vision and Pattern Recognition Workshops |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
1921-1925 |
|
|
Keywords |
|
|
|
Abstract |
Neural image compression (NIC) is a new coding paradigm where coding capabilities are captured by deep models learned from data. This data-driven nature enables new potential functionalities. In this paper, we study the adaptability of codecs to custom domains of interest. We show that NIC codecs are transferable and that they can be adapted with relatively few target domain images. However, naive adaptation interferes with the solution optimized for the original source domain, resulting in forgetting the original coding capabilities in that domain, and may even break the compatibility with previously encoded bitstreams. Addressing these problems, we propose Codec Adaptation without Forgetting (CAwF), a framework that can avoid these problems by adding a small amount of custom parameters, where the source codec remains embedded and unchanged during the adaptation process. Experiments demonstrate its effectiveness and provide useful insights on the characteristics of catastrophic interference in NIC. |
|
|
Address |
Virtual; June 2021 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
CVPRW |
|
|
Notes |
LAMP; 600.120; 600.141; 601.379 |
Approved |
no |
|
|
Call Number |
Admin @ si @ KHY2021 |
Serial |
3568 |
|
Permanent link to this record |
|
|
|
|
Author |
Fei Yang; Luis Herranz; Yongmei Cheng; Mikhail Mozerov |
![download PDF file pdf](img/file_PDF.gif)
![goto web page (via DOI) doi](img/doi.gif)
|
|
Title |
Slimmable compressive autoencoders for practical neural image compression |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
Conference Article |
|
Year |
2021 |
Publication |
34th IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
4996-5005 |
|
|
Keywords |
|
|
|
Abstract |
Neural image compression leverages deep neural networks to outperform traditional image codecs in rate-distortion performance. However, the resulting models are also heavy, computationally demanding and generally optimized for a single rate, limiting their practical use. Focusing on practical image compression, we propose slimmable compressive autoencoders (SlimCAEs), where rate (R) and distortion (D) are jointly optimized for different capacities. Once trained, encoders and decoders can be executed at different capacities, leading to different rates and complexities. We show that a successful implementation of SlimCAEs requires suitable capacity-specific RD tradeoffs. Our experiments show that SlimCAEs are highly flexible models that provide excellent rate-distortion performance, variable rate, and dynamic adjustment of memory, computational cost and latency, thus addressing the main requirements of practical image compression. |
|
|
Address |
Virtual; June 2021 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
CVPR |
|
|
Notes |
LAMP; 600.120 |
Approved |
no |
|
|
Call Number |
Admin @ si @ YHC2021 |
Serial |
3569 |
|
Permanent link to this record |
|
|
|
|
Author |
Arturo Fuentes; F. Javier Sanchez; Thomas Voncina; Jorge Bernal |
![goto web page (via DOI) doi](img/doi.gif)
|
|
Title |
LAMV: Learning to Predict Where Spectators Look in Live Music Performances |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
Conference Article |
|
Year |
2021 |
Publication |
16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications |
Abbreviated Journal |
|
|
|
Volume |
5 |
Issue |
|
Pages |
500-507 |
|
|
Keywords |
|
|
|
Abstract |
The advent of artificial intelligence has supposed an evolution on how different daily work tasks are performed. The analysis of cultural content has seen a huge boost by the development of computer-assisted methods that allows easy and transparent data access. In our case, we deal with the automation of the production of live shows, like music concerts, aiming to develop a system that can indicate the producer which camera to show based on what each of them is showing. In this context, we consider that is essential to understand where spectators look and what they are interested in so the computational method can learn from this information. The work that we present here shows the results of a first preliminary study in which we compare areas of interest defined by human beings and those indicated by an automatic system. Our system is based on the extraction of motion textures from dynamic Spatio-Temporal Volumes (STV) and then analyzing the patterns by means of texture analysis techniques. We validate our approach over several video sequences that have been labeled by 16 different experts. Our method is able to match those relevant areas identified by the experts, achieving recall scores higher than 80% when a distance of 80 pixels between method and ground truth is considered. Current performance shows promise when detecting abnormal peaks and movement trends. |
|
|
Address |
Virtual; February 2021 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
VISIGRAPP |
|
|
Notes |
MV; ISE; 600.119; |
Approved |
no |
|
|
Call Number |
Admin @ si @ FSV2021 |
Serial |
3570 |
|
Permanent link to this record |
|
|
|
|
Author |
Adria Molina; Pau Riba; Lluis Gomez; Oriol Ramos Terrades; Josep Llados |
![download PDF file pdf](img/file_PDF.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title |
Date Estimation in the Wild of Scanned Historical Photos: An Image Retrieval Approach |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
Conference Article |
|
Year |
2021 |
Publication |
16th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
|
|
|
Volume |
12822 |
Issue |
|
Pages |
306-320 |
|
|
Keywords |
|
|
|
Abstract |
This paper presents a novel method for date estimation of historical photographs from archival sources. The main contribution is to formulate the date estimation as a retrieval task, where given a query, the retrieved images are ranked in terms of the estimated date similarity. The closer are their embedded representations the closer are their dates. Contrary to the traditional models that design a neural network that learns a classifier or a regressor, we propose a learning objective based on the nDCG ranking metric. We have experimentally evaluated the performance of the method in two different tasks: date estimation and date-sensitive image retrieval, using the DEW public database, overcoming the baseline methods. |
|
|
Address |
Lausanne; Suissa; September 2021 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
LNCS |
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
ICDAR |
|
|
Notes |
DAG; 600.121; 600.140; 110.312 |
Approved |
no |
|
|
Call Number |
Admin @ si @ MRG2021b |
Serial |
3571 |
|
Permanent link to this record |
|
|
|
|
Author |
Pau Riba; Adria Molina; Lluis Gomez; Oriol Ramos Terrades; Josep Llados |
![download PDF file pdf](img/file_PDF.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title |
Learning to Rank Words: Optimizing Ranking Metrics for Word Spotting |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
Conference Article |
|
Year |
2021 |
Publication |
16th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
|
|
|
Volume |
12822 |
Issue |
|
Pages |
381–395 |
|
|
Keywords |
|
|
|
Abstract |
In this paper, we explore and evaluate the use of ranking-based objective functions for learning simultaneously a word string and a word image encoder. We consider retrieval frameworks in which the user expects a retrieval list ranked according to a defined relevance score. In the context of a word spotting problem, the relevance score has been set according to the string edit distance from the query string. We experimentally demonstrate the competitive performance of the proposed model on query-by-string word spotting for both, handwritten and real scene word images. We also provide the results for query-by-example word spotting, although it is not the main focus of this work. |
|
|
Address |
Lausanne; Suissa; September 2021 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
ICDAR |
|
|
Notes |
DAG; 600.121; 600.140; 110.312 |
Approved |
no |
|
|
Call Number |
Admin @ si @ RMG2021 |
Serial |
3572 |
|
Permanent link to this record |