|
Records |
Links |
|
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. |
|
|
Address |
Virtual IWSSIP |
|
|
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 |
IWSSIP |
|
|
Notes |
MSIAU; 600.130; 601.349; 600.122 |
Approved |
no |
|
|
Call Number |
Admin @ si @ VAS2020 |
Serial |
3429 |
|
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 |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
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 |
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 |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
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. |
|
|
Address |
Virtual ICFHR; September 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 |
ICFHR |
|
|
Notes |
DAG; 600.129; 600.140; 600.121 |
Approved |
no |
|
|
Call Number |
Admin @ si @ KRR2020 |
Serial |
3425 |
|
Permanent link to this record |
|
|
|
|
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 |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
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 |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
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 |
|
Permanent link to this record |
|
|
|
|
Author |
Yaxing Wang; Abel Gonzalez-Garcia; David Berga; Luis Herranz; Fahad Shahbaz Khan; Joost Van de Weijer |
|
|
Title |
MineGAN: effective knowledge transfer from GANs to target domains with few images |
Type |
Conference Article |
|
Year |
2020 |
Publication |
33rd IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
One of the attractive characteristics of deep neural networks is their ability to transfer knowledge obtained in one domain to other related domains. As a result, high-quality networks can be trained in domains with relatively little training data. This property has been extensively studied for discriminative networks but has received significantly less attention for generative models. Given the often enormous effort required to train GANs, both computationally as well as in the dataset collection, the re-use of pretrained GANs is a desirable objective. We propose a novel knowledge transfer method for generative models based on mining the knowledge that is most beneficial to a specific target domain, either from a single or multiple pretrained GANs. This is done using a miner network that identifies which part of the generative distribution of each pretrained GAN outputs samples closest to the target domain. Mining effectively steers GAN sampling towards suitable regions of the latent space, which facilitates the posterior finetuning and avoids pathologies of other methods such as mode collapse and lack of flexibility. We perform experiments on several complex datasets using various GAN architectures (BigGAN, Progressive GAN) and show that the proposed method, called MineGAN, effectively transfers knowledge to domains with few target images, outperforming existing methods. In addition, MineGAN can successfully transfer knowledge from multiple pretrained GANs. |
|
|
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 |
LAMP; 600.109; 600.141; 600.120 |
Approved |
no |
|
|
Call Number |
Admin @ si @ WGB2020 |
Serial |
3421 |
|
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 |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
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 |
|
|
|
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; 600.121; 600.129 |
Approved |
no |
|
|
Call Number |
Admin @ si @ GGG2020b |
Serial |
3420 |
|
Permanent link to this record |
|
|
|
|
Author |
Xialei Liu; Chenshen Wu; Mikel Menta; Luis Herranz; Bogdan Raducanu; Andrew Bagdanov; Shangling Jui; Joost Van de Weijer |
|
|
Title |
Generative Feature Replay for Class-Incremental Learning |
Type |
Conference Article |
|
Year |
2020 |
Publication |
CLVISION – Workshop on Continual Learning in Computer Vision |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
Humans are capable of learning new tasks without forgetting previous ones, while neural networks fail due to catastrophic forgetting between new and previously-learned tasks. We consider a class-incremental setting which means that the task-ID is unknown at inference time. The imbalance between old and new classes typically results in a bias of the network towards the newest ones. This imbalance problem can either be addressed by storing exemplars from previous tasks, or by using image replay methods. However, the latter can only be applied to toy datasets since image generation for complex datasets is a hard problem.
We propose a solution to the imbalance problem based on generative feature replay which does not require any exemplars. To do this, we split the network into two parts: a feature extractor and a classifier. To prevent forgetting, we combine generative feature replay in the classifier with feature distillation in the feature extractor. Through feature generation, our method reduces the complexity of generative replay and prevents the imbalance problem. Our approach is computationally efficient and scalable to large datasets. Experiments confirm that our approach achieves state-of-the-art results on CIFAR-100 and ImageNet, while requiring only a fraction of the storage needed for exemplar-based continual learning |
|
|
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 |
LAMP; 601.309; 602.200; 600.141; 600.120 |
Approved |
no |
|
|
Call Number |
Admin @ si @ LWM2020 |
Serial |
3419 |
|
Permanent link to this record |
|
|
|
|
Author |
Parichehr Behjati Ardakani; Diego Velazquez; Josep M. Gonfaus; Pau Rodriguez; Xavier Roca; Jordi Gonzalez |
|
|
Title |
Catastrophic interference in Disguised Face Recognition |
Type |
Conference Article |
|
Year |
2019 |
Publication |
9th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
|
|
|
Volume |
11868 |
Issue |
|
Pages |
64-75 |
|
|
Keywords |
Neural network forgetness; Face recognition; Disguised Faces |
|
|
Abstract |
It is commonly known the natural tendency of artificial neural networks to completely and abruptly forget previously known information when learning new information. We explore this behaviour in the context of Face Verification on the recently proposed Disguised Faces in the Wild dataset (DFW). We empirically evaluate several commonly used DCNN architectures on Face Recognition and distill some insights about the effect of sequential learning on distinct identities from different datasets, showing that the catastrophic forgetness phenomenon is present even in feature embeddings fine-tuned on different tasks from the original domain. |
|
|
Address |
|
|
|
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 |
IbPRIA |
|
|
Notes |
ISE; 600.098; 600.119 |
Approved |
no |
|
|
Call Number |
Admin @ si @ AVG2019 |
Serial |
3416 |
|
Permanent link to this record |
|
|
|
|
Author |
M. Ivasic-Kos; M. Pobar; Jordi Gonzalez |
|
|
Title |
Active Player Detection in Handball Videos Using Optical Flow and STIPs Based Measures |
Type |
Conference Article |
|
Year |
2019 |
Publication |
13th International Conference on Signal Processing and Communication Systems |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
In handball videos recorded during the training, multiple players are present in the scene at the same time. Although they all might move and interact, not all players contribute to the currently relevant exercise nor practice the given handball techniques. The goal of this experiment is to automatically determine players on training footage that perform given handball techniques and are therefore considered active. It is a very challenging task for which a precise object detector is needed that can handle cluttered scenes with poor illumination, with many players present in different sizes and distances from the camera, partially occluded, moving fast. To determine which of the detected players are active, additional information is needed about the level of player activity. Since many handball actions are characterized by considerable changes in speed, position, and variations in the player's appearance, we propose using spatio-temporal interest points (STIPs) and optical flow (OF). Therefore, we propose an active player detection method combining the YOLO object detector and two activity measures based on STIPs and OF. The performance of the proposed method and activity measures are evaluated on a custom handball video dataset acquired during handball training lessons. |
|
|
Address |
Gold Coast; Australia; December 2019 |
|
|
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 |
ICSPCS2 |
|
|
Notes |
ISE; 600.098; 600.119 |
Approved |
no |
|
|
Call Number |
Admin @ si @ IPG2019 |
Serial |
3415 |
|
Permanent link to this record |
|
|
|
|
Author |
Vacit Oguz Yazici; Abel Gonzalez-Garcia; Arnau Ramisa; Bartlomiej Twardowski; Joost Van de Weijer |
|
|
Title |
Orderless Recurrent Models for Multi-label Classification |
Type |
Conference Article |
|
Year |
2020 |
Publication |
33rd IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
Recurrent neural networks (RNN) are popular for many computer vision tasks, including multi-label classification. Since RNNs produce sequential outputs, labels need to be ordered for the multi-label classification task. Current approaches sort labels according to their frequency, typically ordering them in either rare-first or frequent-first. These imposed orderings do not take into account that the natural order to generate the labels can change for each image, e.g.\ first the dominant object before summing up the smaller objects in the image. Therefore, in this paper, we propose ways to dynamically order the ground truth labels with the predicted label sequence. This allows for the faster training of more optimal LSTM models for multi-label classification. Analysis evidences that our method does not suffer from duplicate generation, something which is common for other models. Furthermore, it outperforms other CNN-RNN models, and we show that a standard architecture of an image encoder and language decoder trained with our proposed loss obtains the state-of-the-art results on the challenging MS-COCO, WIDER Attribute and PA-100K and competitive results on NUS-WIDE. |
|
|
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 |
LAMP; 600.109; 601.309; 600.141; 600.120 |
Approved |
no |
|
|
Call Number |
Admin @ si @ YGR2020 |
Serial |
3408 |
|
Permanent link to this record |
|
|
|
|
Author |
Lorenzo Porzi; Markus Hofinger; Idoia Ruiz; Joan Serrat; Samuel Rota Bulo; Peter Kontschieder |
|
|
Title |
Learning Multi-Object Tracking and Segmentation from Automatic Annotations |
Type |
Conference Article |
|
Year |
2020 |
Publication |
33rd IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
6845-6854 |
|
|
Keywords |
|
|
|
Abstract |
In this work we contribute a novel pipeline to automatically generate training data, and to improve over state-of-the-art multi-object tracking and segmentation (MOTS) methods. Our proposed track mining algorithm turns raw street-level videos into high-fidelity MOTS training data, is scalable and overcomes the need of expensive and time-consuming manual annotation approaches. We leverage state-of-the-art instance segmentation results in combination with optical flow predictions, also trained on automatically harvested training data. Our second major contribution is MOTSNet – a deep learning, tracking-by-detection architecture for MOTS – deploying a novel mask-pooling layer for improved object association over time. Training MOTSNet with our automatically extracted data leads to significantly improved sMOTSA scores on the novel KITTI MOTS dataset (+1.9%/+7.5% on cars/pedestrians), and MOTSNet improves by +4.1% over previously best methods on the MOTSChallenge dataset. Our most impressive finding is that we can improve over previous best-performing works, even in complete absence of manually annotated MOTS training data. |
|
|
Address |
virtual; June 2020 |
|
|
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 |
ADAS; 600.124; 600.118 |
Approved |
no |
|
|
Call Number |
Admin @ si @ PHR2020 |
Serial |
3402 |
|
Permanent link to this record |
|
|
|
|
Author |
Sangeeth Reddy; Minesh Mathew; Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas; C.V. Jawahar |
|
|
Title |
RoadText-1K: Text Detection and Recognition Dataset for Driving Videos |
Type |
Conference Article |
|
Year |
2020 |
Publication |
IEEE International Conference on Robotics and Automation |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
Perceiving text is crucial to understand semantics of outdoor scenes and hence is a critical requirement to build intelligent systems for driver assistance and self-driving. Most of the existing datasets for text detection and recognition comprise still images and are mostly compiled keeping text in mind. This paper introduces a new ”RoadText-1K” dataset for text in driving videos. The dataset is 20 times larger than the existing largest dataset for text in videos. Our dataset comprises 1000 video clips of driving without any bias towards text and with annotations for text bounding boxes and transcriptions in every frame. State of the art methods for text detection,
recognition and tracking are evaluated on the new dataset and the results signify the challenges in unconstrained driving videos compared to existing datasets. This suggests that RoadText-1K is suited for research and development of reading systems, robust enough to be incorporated into more complex downstream tasks like driver assistance and self-driving. The dataset can be found at http://cvit.iiit.ac.in/research/
projects/cvit-projects/roadtext-1k |
|
|
Address |
Paris; Francia; ??? |
|
|
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 |
ICRA |
|
|
Notes |
DAG; 600.121; 600.129 |
Approved |
no |
|
|
Call Number |
Admin @ si @ RMG2020 |
Serial |
3400 |
|
Permanent link to this record |
|
|
|
|
Author |
Alejandro Cartas; Jordi Luque; Petia Radeva; Carlos Segura; Mariella Dimiccoli |
|
|
Title |
Seeing and Hearing Egocentric Actions: How Much Can We Learn? |
Type |
Conference Article |
|
Year |
2019 |
Publication |
IEEE International Conference on Computer Vision Workshops |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
4470-4480 |
|
|
Keywords |
|
|
|
Abstract |
Our interaction with the world is an inherently multimodal experience. However, the understanding of human-to-object interactions has historically been addressed focusing on a single modality. In particular, a limited number of works have considered to integrate the visual and audio modalities for this purpose. In this work, we propose a multimodal approach for egocentric action recognition in a kitchen environment that relies on audio and visual information. Our model combines a sparse temporal sampling strategy with a late fusion of audio, spatial, and temporal streams. Experimental results on the EPIC-Kitchens dataset show that multimodal integration leads to better performance than unimodal approaches. In particular, we achieved a 5.18% improvement over the state of the art on verb classification. |
|
|
Address |
Seul; Korea; October 2019 |
|
|
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 |
ICCVW |
|
|
Notes |
MILAB; no proj |
Approved |
no |
|
|
Call Number |
Admin @ si @ CLR2019b |
Serial |
3385 |
|
Permanent link to this record |
|
|
|
|
Author |
David Berga; Xavier Otazu |
|
|
Title |
Computations of top-down attention by modulating V1 dynamics |
Type |
Conference Article |
|
Year |
2020 |
Publication |
Computational and Mathematical Models in Vision |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
St. Pete Beach; Florida; May 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 |
MODVIS |
|
|
Notes |
NEUROBIT |
Approved |
no |
|
|
Call Number |
Admin @ si @ BeO2020a |
Serial |
3376 |
|
Permanent link to this record |
|
|
|
|
Author |
David Berga; Xose R. Fernandez-Vidal; Xavier Otazu; Victor Leboran; Xose M. Pardo |
|
|
Title |
Measuring bottom-up visual attention in eye tracking experimentation with synthetic images |
Type |
Conference Article |
|
Year |
2019 |
Publication |
8th Iberian Conference on Perception |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
A benchmark of saliency models performance with a synthetic image dataset is provided. Model performance is evaluated through saliency metrics as well as the influence of model inspiration and consistency with human psychophysics. SID4VAM is composed of 230 synthetic images, with known salient regions. Images were generated with 15 distinct types of low-level features (e.g. orientation, brightness, color, size...) with a target-distractor pop-out type of synthetic patterns. We have used Free-Viewing and Visual Search task instructions and 7 feature contrasts for each feature category. Our study reveals that state-of-the-art Deep Learning saliency models do not perform well with synthetic pattern images, instead, models with Spectral/Fourier inspiration outperform others in saliency metrics and are more consistent with human psychophysical experimentation. This study proposes a new way to evaluate saliency models in the forthcoming literature, accounting for synthetic images with uniquely low-level feature contexts, distinct from previous eye tracking image datasets. |
|
|
Address |
San Lorenzo El Escorial; July 2019 |
|
|
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 |
CIP |
|
|
Notes |
NEUROBIT; 600.128 |
Approved |
no |
|
|
Call Number |
Admin @ si @ BFO2019c |
Serial |
3375 |
|
Permanent link to this record |