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| Author | Lu Yu; Bartlomiej Twardowski; Xialei Liu; Luis Herranz; Kai Wang; Yongmai Cheng; Shangling Jui; Joost Van de Weijer | ||||
| Title | Semantic Drift Compensation for Class-Incremental Learning of Embeddings | Type | Conference Article | ||
| Year | 2020 | Publication | 33rd IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
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| Abstract | Class-incremental learning of deep networks sequentially increases the number of classes to be classified. During training, the network has only access to data of one task at a time, where each task contains several classes. In this setting, networks suffer from catastrophic forgetting which refers to the drastic drop in performance on previous tasks. The vast majority of methods have studied this scenario for classification networks, where for each new task the classification layer of the network must be augmented with additional weights to make room for the newly added classes. Embedding networks have the advantage that new classes can be naturally included into the network without adding new weights. Therefore, we study incremental learning for embedding networks. In addition, we propose a new method to estimate the drift, called semantic drift, of features and compensate for it without the need of any exemplars. We approximate the drift of previous tasks based on the drift that is experienced by current task data. We perform experiments on fine-grained datasets, CIFAR100 and ImageNet-Subset. We demonstrate that embedding networks suffer significantly less from catastrophic forgetting. We outperform existing methods which do not require exemplars and obtain competitive results compared to methods which store exemplars. Furthermore, we show that our proposed SDC when combined with existing methods to prevent forgetting consistently improves results. | ||||
Address  |
Virtual CVPR | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CVPR | ||
| Notes | LAMP; 600.141; 601.309; 602.200; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ YTL2020 | Serial | 3422 | ||
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| Author | Henry Velesaca; Raul Mira; Patricia Suarez; Christian X. Larrea; Angel Sappa | ||||
| Title | Deep Learning Based Corn Kernel Classification | Type | Conference Article | ||
| Year | 2020 | Publication | 1st International Workshop and Prize Challenge on Agriculture-Vision: Challenges & Opportunities for Computer Vision in Agriculture | Abbreviated Journal | |
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| Abstract | This paper presents a full pipeline to classify sample sets of corn kernels. The proposed approach follows a segmentation-classification scheme. The image segmentation is performed through a well known deep learningbased approach, the Mask R-CNN architecture, while the classification is performed hrough a novel-lightweight network specially designed for this task—good corn kernel, defective corn kernel and impurity categories are considered. As a second contribution, a carefully annotated multitouching corn kernel dataset has been generated. This dataset has been used for training the segmentation and the classification modules. Quantitative evaluations have been
performed and comparisons with other approaches are provided showing improvements with the proposed pipeline. |
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| Address |
Virtual CVPR | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CVPRW | ||
| Notes | MSIAU; 600.130; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ VMS2020 | Serial | 3430 | ||
| Permanent link to this record | |||||
| Author | Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla; Lin Guo; Jiankun Hou; Armin Mehri; Parichehr Behjati Ardakani; Heena Patel; Vishal Chudasama; Kalpesh Prajapati; Kishor P. Upla; Raghavendra Ramachandra; Kiran Raja; Christoph Busch; Feras Almasri; Olivier Debeir; Sabari Nathan; Priya Kansal; Nolan Gutierrez; Bardia Mojra; William J. Beksi | ||||
| Title | Thermal Image Super-Resolution Challenge – PBVS 2020 | Type | Conference Article | ||
| Year | 2020 | Publication | 16h IEEE Workshop on Perception Beyond the Visible Spectrum | Abbreviated Journal | |
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| Abstract | This paper summarizes the top contributions to the first challenge on thermal image super-resolution (TISR), which was organized as part of the Perception Beyond the Visible Spectrum (PBVS) 2020 workshop. In this challenge, a novel thermal image dataset is considered together with state-of-the-art approaches evaluated under a common framework. The dataset used in the challenge consists of 1021 thermal images, obtained from three distinct thermal cameras at different resolutions (low-resolution, mid-resolution, and high-resolution), resulting in a total of 3063 thermal images. From each resolution, 951 images are used for training and 50 for testing while the 20 remaining images are used for two proposed evaluations. The first evaluation consists of downsampling the low-resolution, mid-resolution, and high-resolution thermal images by x2, x3 and x4 respectively, and comparing their super-resolution results with the corresponding ground truth images. The second evaluation is comprised of obtaining the x2 super-resolution from a given mid-resolution thermal image and comparing it with the corresponding semi-registered high-resolution thermal image. Out of 51 registered participants, 6 teams reached the final validation phase. | ||||
| Address |
Virtual CVPR | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CVPRW | ||
| Notes | MSIAU; ISE; 600.119; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ RSV2020 | Serial | 3431 | ||
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| Author | Ciprian Corneanu; Sergio Escalera; Aleix M. Martinez | ||||
| Title | Computing the Testing Error Without a Testing Set | Type | Conference Article | ||
| Year | 2020 | Publication | 33rd IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
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| Abstract | Oral. Paper award nominee.
Deep Neural Networks (DNNs) have revolutionized computer vision. We now have DNNs that achieve top (performance) results in many problems, including object recognition, facial expression analysis, and semantic segmentation, to name but a few. The design of the DNNs that achieve top results is, however, non-trivial and mostly done by trailand-error. That is, typically, researchers will derive many DNN architectures (i.e., topologies) and then test them on multiple datasets. However, there are no guarantees that the selected DNN will perform well in the real world. One can use a testing set to estimate the performance gap between the training and testing sets, but avoiding overfitting-to-thetesting-data is almost impossible. Using a sequestered testing dataset may address this problem, but this requires a constant update of the dataset, a very expensive venture. Here, we derive an algorithm to estimate the performance gap between training and testing that does not require any testing dataset. Specifically, we derive a number of persistent topology measures that identify when a DNN is learning to generalize to unseen samples. This allows us to compute the DNN’s testing error on unseen samples, even when we do not have access to them. We provide extensive experimental validation on multiple networks and datasets to demonstrate the feasibility of the proposed approach. |
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| Address |
Virtual CVPR | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CVPR | ||
| Notes | HuPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ CEM2020 | Serial | 3437 | ||
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| Author | Swathikiran Sudhakaran; Sergio Escalera; Oswald Lanz | ||||
| Title | Gate-Shift Networks for Video Action Recognition | Type | Conference Article | ||
| Year | 2020 | Publication | 33rd IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
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| Abstract | 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. | ||||
| Address |
Virtual CVPR | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CVPR | ||
| Notes | HuPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ SEL2020 | Serial | 3438 | ||
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| Author | Arnau Baro; Alicia Fornes; Carles Badal | ||||
| Title | Handwritten Historical Music Recognition by Sequence-to-Sequence with Attention Mechanism | Type | Conference Article | ||
| Year | 2020 | Publication | 17th International Conference on Frontiers in Handwriting Recognition | Abbreviated Journal | |
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| Abstract | Despite decades of research in Optical Music Recognition (OMR), the recognition of old handwritten music scores remains a challenge because of the variabilities in the handwriting styles, paper degradation, lack of standard notation, etc. Therefore, the research in OMR systems adapted to the particularities of old manuscripts is crucial to accelerate the conversion of music scores existing in archives into digital libraries, fostering the dissemination and preservation of our music heritage. In this paper we explore the adaptation of sequence-to-sequence models with attention mechanism (used in translation and handwritten text recognition) and the generation of specific synthetic data for recognizing old music scores. The experimental validation demonstrates that our approach is promising, especially when compared with long short-term memory neural networks. | ||||
| Address |
Virtual ICFHR; September 2020 | ||||
| Corporate Author | Thesis | ||||
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| Language | Summary Language | Original Title | |||
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| Area | Expedition | Conference | ICFHR | ||
| Notes | DAG; 600.140; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ BFB2020 | Serial | 3448 | ||
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| Author | Lei Kang; Pau Riba; Marçal Rusiñol; Alicia Fornes; Mauricio Villegas | ||||
| Title | Distilling Content from Style for Handwritten Word Recognition | Type | Conference Article | ||
| Year | 2020 | Publication | 17th International Conference on Frontiers in Handwriting Recognition | Abbreviated Journal | |
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| Abstract | Despite the latest transcription accuracies reached using deep neural network architectures, handwritten text recognition still remains a challenging problem, mainly because of the large inter-writer style variability. Both augmenting the training set with artificial samples using synthetic fonts, and writer adaptation techniques have been proposed to yield more generic approaches aimed at dodging style unevenness. In this work, we take a step closer to learn style independent features from handwritten word images. We propose a novel method that is able to disentangle the content and style aspects of input images by jointly optimizing a generative process and a handwritten
word recognizer. The generator is aimed at transferring writing style features from one sample to another in an image-to-image translation approach, thus leading to a learned content-centric features that shall be independent to writing style attributes. Our proposed recognition model is able then to leverage such writer-agnostic features to reach better recognition performances. We advance over prior training strategies and demonstrate with qualitative and quantitative evaluations the performance of both the generative process and the recognition efficiency in the IAM dataset. |
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| Address |
Virtual ICFHR; September 2020 | ||||
| Corporate Author | Thesis | ||||
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| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICFHR | ||
| Notes | DAG; 600.129; 600.140; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ KRR2020 | Serial | 3425 | ||
| Permanent link to this record | |||||
| Author | Henry Velesaca; Steven Araujo; Patricia Suarez; Angel Sanchez; Angel Sappa | ||||
| Title | Off-the-Shelf Based System for Urban Environment Video Analytics | Type | Conference Article | ||
| Year | 2020 | Publication | 27th International Conference on Systems, Signals and Image Processing | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | greenhouse gases; carbon footprint; object detection; object tracking; website framework; off-the-shelf video analytics | ||||
| Abstract | This paper presents the design and implementation details of a system build-up by using off-the-shelf algorithms for urban video analytics. The system allows the connection to
public video surveillance camera networks to obtain the necessary information to generate statistics from urban scenarios (e.g., amount of vehicles, type of cars, direction, numbers of persons, etc.). The obtained information could be used not only for traffic management but also to estimate the carbon footprint of urban scenarios. As a case study, a university campus is selected to evaluate the performance of the proposed system. The system is implemented in a modular way so that it is being used as a testbed to evaluate different algorithms. Implementation results are provided showing the validity and utility of the proposed approach. |
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| Address |
Virtual IWSSIP | ||||
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| Language | Summary Language | Original Title | |||
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| Area | Expedition | Conference | IWSSIP | ||
| Notes | MSIAU; 600.130; 601.349; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ VAS2020 | Serial | 3429 | ||
| Permanent link to this record | |||||
| Author | Raul Gomez; Jaume Gibert; Lluis Gomez; Dimosthenis Karatzas | ||||
| Title | Location Sensitive Image Retrieval and Tagging | Type | Conference Article | ||
| Year | 2020 | Publication | 16th European Conference on Computer Vision | Abbreviated Journal | |
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| Abstract | People from different parts of the globe describe objects and concepts in distinct manners. Visual appearance can thus vary across different geographic locations, which makes location a relevant contextual information when analysing visual data. In this work, we address the task of image retrieval related to a given tag conditioned on a certain location on Earth. We present LocSens, a model that learns to rank triplets of images, tags and coordinates by plausibility, and two training strategies to balance the location influence in the final ranking. LocSens learns to fuse textual and location information of multimodal queries to retrieve related images at different levels of location granularity, and successfully utilizes location information to improve image tagging. | ||||
| Address |
Virtual; August 2020 | ||||
| Corporate Author | Thesis | ||||
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| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ECCV | ||
| Notes | DAG; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ GGG2020b | Serial | 3420 | ||
| Permanent link to this record | |||||
| Author | Lei Kang; Pau Riba; Yaxing Wang; Marçal Rusiñol; Alicia Fornes; Mauricio Villegas | ||||
| Title | GANwriting: Content-Conditioned Generation of Styled Handwritten Word Images | Type | Conference Article | ||
| Year | 2020 | Publication | 16th European Conference on Computer Vision | Abbreviated Journal | |
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| Abstract | Although current image generation methods have reached impressive quality levels, they are still unable to produce plausible yet diverse images of handwritten words. On the contrary, when writing by hand, a great variability is observed across different writers, and even when analyzing words scribbled by the same individual, involuntary variations are conspicuous. In this work, we take a step closer to producing realistic and varied artificially rendered handwritten words. We propose a novel method that is able to produce credible handwritten word images by conditioning the generative process with both calligraphic style features and textual content. Our generator is guided by three complementary learning objectives: to produce realistic images, to imitate a certain handwriting style and to convey a specific textual content. Our model is unconstrained to any predefined vocabulary, being able to render whatever input word. Given a sample writer, it is also able to mimic its calligraphic features in a few-shot setup. We significantly advance over prior art and demonstrate with qualitative, quantitative and human-based evaluations the realistic aspect of our synthetically produced images. | ||||
| Address |
Virtual; August 2020 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ECCV | ||
| Notes | DAG; 600.140; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ KPW2020 | Serial | 3426 | ||
| Permanent link to this record | |||||
| Author | Kai Wang; Luis Herranz; Anjan Dutta; Joost Van de Weijer | ||||
| Title | Bookworm continual learning: beyond zero-shot learning and continual learning | Type | Conference Article | ||
| Year | 2020 | Publication | Workshop TASK-CV 2020 | Abbreviated Journal | |
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| Abstract | 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. | ||||
| Address |
Virtual; August 2020 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ECCVW | ||
| Notes | LAMP; 600.141; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ WHD2020 | Serial | 3466 | ||
| Permanent link to this record | |||||
| Author | Tomas Sixta; Julio C. S. Jacques Junior; Pau Buch Cardona; Eduard Vazquez; Sergio Escalera | ||||
| Title | FairFace Challenge at ECCV 2020: Analyzing Bias in Face Recognition | Type | Conference Article | ||
| Year | 2020 | Publication | ECCV Workshops | Abbreviated Journal | |
| Volume | 12540 | Issue | Pages | 463-481 | |
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| Abstract | This work summarizes the 2020 ChaLearn Looking at People Fair Face Recognition and Analysis Challenge and provides a description of the top-winning solutions and analysis of the results. The aim of the challenge was to evaluate accuracy and bias in gender and skin colour of submitted algorithms on the task of 1:1 face verification in the presence of other confounding attributes. Participants were evaluated using an in-the-wild dataset based on reannotated IJB-C, further enriched 12.5K new images and additional labels. The dataset is not balanced, which simulates a real world scenario where AI-based models supposed to present fair outcomes are trained and evaluated on imbalanced data. The challenge attracted 151 participants, who made more 1.8K submissions in total. The final phase of the challenge attracted 36 active teams out of which 10 exceeded 0.999 AUC-ROC while achieving very low scores in the proposed bias metrics. Common strategies by the participants were face pre-processing, homogenization of data distributions, the use of bias aware loss functions and ensemble models. The analysis of top-10 teams shows higher false positive rates (and lower false negative rates) for females with dark skin tone as well as the potential of eyeglasses and young age to increase the false positive rates too. | ||||
| Address |
Virtual; August 2020 | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ECCVW | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ SJB2020 | Serial | 3499 | ||
| Permanent link to this record | |||||
| Author | Hugo Bertiche; Meysam Madadi; Sergio Escalera | ||||
| Title | CLOTH3D: Clothed 3D Humans | Type | Conference Article | ||
| Year | 2020 | Publication | 16th European Conference on Computer Vision | Abbreviated Journal | |
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| Abstract | This work presents CLOTH3D, the first big scale synthetic dataset of 3D clothed human sequences. CLOTH3D contains a large variability on garment type, topology, shape, size, tightness and fabric. Clothes are simulated on top of thousands of different pose sequences and body shapes, generating realistic cloth dynamics. We provide the dataset with a generative model for cloth generation. We propose a Conditional Variational Auto-Encoder (CVAE) based on graph convolutions (GCVAE) to learn garment latent spaces. This allows for realistic generation of 3D garments on top of SMPL model for any pose and shape. | ||||
| Address |
Virtual; August 2020 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ECCV | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ BME2020 | Serial | 3519 | ||
| Permanent link to this record | |||||
| Author | Reza Azad; Maryam Asadi-Aghbolaghi; Mahmood Fathy; Sergio Escalera | ||||
| Title | Attention Deeplabv3+: Multi-level Context Attention Mechanism for Skin Lesion Segmentation | Type | Conference Article | ||
| Year | 2020 | Publication | Bioimage computation workshop | Abbreviated Journal | |
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| Abstract | |||||
| Address |
Virtual; August 2020 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ECCVW | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ AAF2020 | Serial | 3520 | ||
| Permanent link to this record | |||||
| Author | Martin Menchon; Estefania Talavera; Jose M. Massa; Petia Radeva | ||||
| Title | Behavioural Pattern Discovery from Collections of Egocentric Photo-Streams | Type | Conference Article | ||
| Year | 2020 | Publication | ECCV Workshops | Abbreviated Journal | |
| Volume | 12538 | Issue | Pages | 469-484 | |
| Keywords | |||||
| Abstract | The automatic discovery of behaviour is of high importance when aiming to assess and improve the quality of life of people. Egocentric images offer a rich and objective description of the daily life of the camera wearer. This work proposes a new method to identify a person’s patterns of behaviour from collected egocentric photo-streams. Our model characterizes time-frames based on the context (place, activities and environment objects) that define the images composition. Based on the similarity among the time-frames that describe the collected days for a user, we propose a new unsupervised greedy method to discover the behavioural pattern set based on a novel semantic clustering approach. Moreover, we present a new score metric to evaluate the performance of the proposed algorithm. We validate our method on 104 days and more than 100k images extracted from 7 users. Results show that behavioural patterns can be discovered to characterize the routine of individuals and consequently their lifestyle. | ||||
| Address |
Virtual; August 2020 | ||||
| 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 | ECCVW | ||
| Notes | MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ MTM2020 | Serial | 3528 | ||
| Permanent link to this record | |||||