| Records |
| Author |
Baiyu Chen; Sergio Escalera; Isabelle Guyon; Victor Ponce; N. Shah; Marc Oliu |
| Title |
Overcoming Calibration Problems in Pattern Labeling with Pairwise Ratings: Application to Personality Traits |
Type |
Conference Article |
| Year |
2016 |
Publication |
14th European Conference on Computer Vision Workshops |
Abbreviated Journal |
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Issue |
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| Keywords |
Calibration of labels; Label bias; Ordinal labeling; Variance Models; Bradley-Terry-Luce model; Continuous labels; Regression; Personality traits; Crowd-sourced labels |
| Abstract |
We address the problem of calibration of workers whose task is to label patterns with continuous variables, which arises for instance in labeling images of videos of humans with continuous traits. Worker bias is particularly dicult to evaluate and correct when many workers contribute just a few labels, a situation arising typically when labeling is crowd-sourced. In the scenario of labeling short videos of people facing a camera with personality traits, we evaluate the feasibility of the pairwise ranking method to alleviate bias problems. Workers are exposed to pairs of videos at a time and must order by preference. The variable levels are reconstructed by fitting a Bradley-Terry-Luce model with maximum likelihood. This method may at first sight, seem prohibitively expensive because for N videos, p = N (N-1)/2 pairs must be potentially processed by workers rather that N videos. However, by performing extensive simulations, we determine an empirical law for the scaling of the number of pairs needed as a function of the number of videos in order to achieve a given accuracy of score reconstruction and show that the pairwise method is a ordable. We apply the method to the labeling of a large scale dataset of 10,000 videos used in the ChaLearn Apparent Personality Trait challenge. |
| Address |
Amsterdam; The Netherlands; October 2016 |
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ECCVW |
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HuPBA;MILAB; |
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no |
| Call Number |
Admin @ si @ CEG2016 |
Serial |
2829 |
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| Author |
Iiris Lusi; Sergio Escalera; Gholamreza Anbarjafari |
| Title |
SASE: RGB-Depth Database for Human Head Pose Estimation |
Type |
Conference Article |
| Year |
2016 |
Publication |
14th European Conference on Computer Vision Workshops |
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| Address |
Amsterdam; The Netherlands; October 2016 |
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ECCVW |
| Notes |
HuPBA;MILAB; |
Approved |
no |
| Call Number |
Admin @ si @ LEA2016a |
Serial |
2840 |
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| Author |
Saad Minhas; Aura Hernandez-Sabate; Shoaib Ehsan; Katerine Diaz; Ales Leonardis; Antonio Lopez; Klaus McDonald Maier |
| Title |
LEE: A photorealistic Virtual Environment for Assessing Driver-Vehicle Interactions in Self-Driving Mode |
Type |
Conference Article |
| Year |
2016 |
Publication |
14th European Conference on Computer Vision Workshops |
Abbreviated Journal |
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| Volume |
9915 |
Issue |
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Pages |
894-900 |
| Keywords |
Simulation environment; Automated Driving; Driver-Vehicle interaction |
| Abstract |
Photorealistic virtual environments are crucial for developing and testing automated driving systems in a safe way during trials. As commercially available simulators are expensive and bulky, this paper presents a low-cost, extendable, and easy-to-use (LEE) virtual environment with the aim to highlight its utility for level 3 driving automation. In particular, an experiment is performed using the presented simulator to explore the influence of different variables regarding control transfer of the car after the system was driving autonomously in a highway scenario. The results show that the speed of the car at the time when the system needs to transfer the control to the human driver is critical. |
| Address |
Amsterdam; The Netherlands; October 2016 |
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LNCS |
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Conference |
ECCVW |
| Notes |
ADAS;IAM; 600.085; 600.076 |
Approved |
no |
| Call Number |
MHE2016 |
Serial |
2865 |
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| Author |
Raul Gomez; Lluis Gomez; Jaume Gibert; Dimosthenis Karatzas |
| Title |
Learning to Learn from Web Data through Deep Semantic Embeddings |
Type |
Conference Article |
| Year |
2018 |
Publication |
15th European Conference on Computer Vision Workshops |
Abbreviated Journal |
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| Volume |
11134 |
Issue |
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Pages |
514-529 |
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| Abstract |
In this paper we propose to learn a multimodal image and text embedding from Web and Social Media data, aiming to leverage the semantic knowledge learnt in the text domain and transfer it to a visual model for semantic image retrieval. We demonstrate that the pipeline can learn from images with associated text without supervision and perform a thourough analysis of five different text embeddings in three different benchmarks. We show that the embeddings learnt with Web and Social Media data have competitive performances over supervised methods in the text based image retrieval task, and we clearly outperform state of the art in the MIRFlickr dataset when training in the target data. Further we demonstrate how semantic multimodal image retrieval can be performed using the learnt embeddings, going beyond classical instance-level retrieval problems. Finally, we present a new dataset, InstaCities1M, composed by Instagram images and their associated texts that can be used for fair comparison of image-text embeddings. |
| Address |
Munich; Alemanya; September 2018 |
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LNCS |
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Conference |
ECCVW |
| Notes |
DAG; 600.129; 601.338; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ GGG2018a |
Serial |
3175 |
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| Author |
Dena Bazazian; Dimosthenis Karatzas; Andrew Bagdanov |
| Title |
Soft-PHOC Descriptor for End-to-End Word Spotting in Egocentric Scene Images |
Type |
Conference Article |
| Year |
2018 |
Publication |
International Workshop on Egocentric Perception, Interaction and Computing at ECCV |
Abbreviated Journal |
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Issue |
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Pages |
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Word spotting in natural scene images has many applications in scene understanding and visual assistance. We propose Soft-PHOC, an intermediate representation of images based on character probability maps. Our representation extends the concept of the Pyramidal Histogram Of Characters (PHOC) by exploiting Fully Convolutional Networks to derive a pixel-wise mapping of the character distribution within candidate word regions. We show how to use our descriptors for word spotting tasks in egocentric camera streams through an efficient text line proposal algorithm. This is based on the Hough Transform over character attribute maps followed by scoring using Dynamic Time Warping (DTW). We evaluate our results on ICDAR 2015 Challenge 4 dataset of incidental scene text captured by an egocentric camera. |
| Address |
Munich; Alemanya; September 2018 |
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ECCVW |
| Notes |
DAG; 600.129; 600.121; |
Approved |
no |
| Call Number |
Admin @ si @ BKB2018b |
Serial |
3174 |
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| Author |
Raul Gomez; Lluis Gomez; Jaume Gibert; Dimosthenis Karatzas |
| Title |
Learning from# Barcelona Instagram data what Locals and Tourists post about its Neighbourhoods |
Type |
Conference Article |
| Year |
2018 |
Publication |
15th European Conference on Computer Vision Workshops |
Abbreviated Journal |
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| Volume |
11134 |
Issue |
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Pages |
530-544 |
| Keywords |
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| Abstract |
Massive tourism is becoming a big problem for some cities, such as Barcelona, due to its concentration in some neighborhoods. In this work we gather Instagram data related to Barcelona consisting on images-captions pairs and, using the text as a supervisory signal, we learn relations between images, words and neighborhoods. Our goal is to learn which visual elements appear in photos when people is posting about each neighborhood. We perform a language separate treatment of the data and show that it can be extrapolated to a tourists and locals separate analysis, and that tourism is reflected in Social Media at a neighborhood level. The presented pipeline allows analyzing the differences between the images that tourists and locals associate to the different neighborhoods. The proposed method, which can be extended to other cities or subjects, proves that Instagram data can be used to train multi-modal (image and text) machine learning models that are useful to analyze publications about a city at a neighborhood level. We publish the collected dataset, InstaBarcelona and the code used in the analysis. |
| Address |
Munich; Alemanya; September 2018 |
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LNCS |
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ECCVW |
| Notes |
DAG; 600.129; 601.338; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ GGG2018b |
Serial |
3176 |
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| Author |
Md. Mostafa Kamal Sarker; Hatem A. Rashwan; Hatem A. Rashwan; Estefania Talavera; Syeda Furruka Banu; Petia Radeva; Domenec Puig |
| Title |
MACNet: Multi-scale Atrous Convolution Networks for Food Places Classification in Egocentric Photo-streams |
Type |
Conference Article |
| Year |
2018 |
Publication |
European Conference on Computer Vision workshops |
Abbreviated Journal |
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Issue |
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Pages |
423-433 |
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| Abstract |
First-person (wearable) camera continually captures unscripted interactions of the camera user with objects, people, and scenes reflecting his personal and relational tendencies. One of the preferences of people is their interaction with food events. The regulation of food intake and its duration has a great importance to protect against diseases. Consequently, this work aims to develop a smart model that is able to determine the recurrences of a person on food places during a day. This model is based on a deep end-to-end model for automatic food places recognition by analyzing egocentric photo-streams. In this paper, we apply multi-scale Atrous convolution networks to extract the key features related to food places of the input images. The proposed model is evaluated on an in-house private dataset called “EgoFoodPlaces”. Experimental results shows promising results of food places classification recognition in egocentric photo-streams. |
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LCNS |
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Conference |
ECCVW |
| Notes |
MILAB; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ SRR2018b |
Serial |
3185 |
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| Author |
Mohamed Ilyes Lakhal; Albert Clapes; Sergio Escalera; Oswald Lanz; Andrea Cavallaro |
| Title |
Residual Stacked RNNs for Action Recognition |
Type |
Conference Article |
| Year |
2018 |
Publication |
9th International Workshop on Human Behavior Understanding |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
534-548 |
| Keywords |
Action recognition; Deep residual learning; Two-stream RNN |
| Abstract |
Action recognition pipelines that use Recurrent Neural Networks (RNN) are currently 5–10% less accurate than Convolutional Neural Networks (CNN). While most works that use RNNs employ a 2D CNN on each frame to extract descriptors for action recognition, we extract spatiotemporal features from a 3D CNN and then learn the temporal relationship of these descriptors through a stacked residual recurrent neural network (Res-RNN). We introduce for the first time residual learning to counter the degradation problem in multi-layer RNNs, which have been successful for temporal aggregation in two-stream action recognition pipelines. Finally, we use a late fusion strategy to combine RGB and optical flow data of the two-stream Res-RNN. Experimental results show that the proposed pipeline achieves competitive results on UCF-101 and state of-the-art results for RNN-like architectures on the challenging HMDB-51 dataset. |
| Address |
Munich; September 2018 |
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ECCVW |
| Notes |
HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ LCE2018b |
Serial |
3206 |
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| 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 |
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We propose bookworm continual learning(BCL), a flexible setting where unseen classes can be inferred via a semantic model, and the visual model can be updated continually. Thus BCL generalizes both continual learning (CL) and zero-shot learning (ZSL). We also propose the bidirectional imagination (BImag) framework to address BCL where features of both past and future classes are generated. We observe that conditioning the feature generator on attributes can actually harm the continual learning ability, and propose two variants (joint class-attribute conditioning and asymmetric generation) to alleviate this problem. |
| Address |
Virtual; August 2020 |
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Conference |
ECCVW |
| Notes |
LAMP; 600.141; 600.120 |
Approved |
no |
| Call Number |
Admin @ si @ WHD2020 |
Serial |
3466 |
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| 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 |
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| Volume |
12540 |
Issue |
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Pages |
463-481 |
| Keywords |
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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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LNCS |
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ECCVW |
| Notes |
HUPBA |
Approved |
no |
| Call Number |
Admin @ si @ SJB2020 |
Serial |
3499 |
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| 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 |
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| Address |
Virtual; August 2020 |
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ECCVW |
| Notes |
HUPBA |
Approved |
no |
| Call Number |
Admin @ si @ AAF2020 |
Serial |
3520 |
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| 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 |
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| Volume |
12538 |
Issue |
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Pages |
469-484 |
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| 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 |
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LNCS |
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Conference |
ECCVW |
| Notes |
MILAB; no proj |
Approved |
no |
| Call Number |
Admin @ si @ MTM2020 |
Serial |
3528 |
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| Author |
Emanuele Vivoli; Ali Furkan Biten; Andres Mafla; Dimosthenis Karatzas; Lluis Gomez |
| Title |
MUST-VQA: MUltilingual Scene-text VQA |
Type |
Conference Article |
| Year |
2022 |
Publication |
Proceedings European Conference on Computer Vision Workshops |
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13804 |
Issue |
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Pages |
345–358 |
| Keywords |
Visual question answering; Scene text; Translation robustness; Multilingual models; Zero-shot transfer; Power of language models |
| Abstract |
In this paper, we present a framework for Multilingual Scene Text Visual Question Answering that deals with new languages in a zero-shot fashion. Specifically, we consider the task of Scene Text Visual Question Answering (STVQA) in which the question can be asked in different languages and it is not necessarily aligned to the scene text language. Thus, we first introduce a natural step towards a more generalized version of STVQA: MUST-VQA. Accounting for this, we discuss two evaluation scenarios in the constrained setting, namely IID and zero-shot and we demonstrate that the models can perform on a par on a zero-shot setting. We further provide extensive experimentation and show the effectiveness of adapting multilingual language models into STVQA tasks. |
| Address |
Tel-Aviv; Israel; October 2022 |
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LNCS |
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ECCVW |
| Notes |
DAG; 302.105; 600.155; 611.002 |
Approved |
no |
| Call Number |
Admin @ si @ VBM2022 |
Serial |
3770 |
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| Author |
Sergi Garcia Bordils; Andres Mafla; Ali Furkan Biten; Oren Nuriel; Aviad Aberdam; Shai Mazor; Ron Litman; Dimosthenis Karatzas |
| Title |
Out-of-Vocabulary Challenge Report |
Type |
Conference Article |
| Year |
2022 |
Publication |
Proceedings European Conference on Computer Vision Workshops |
Abbreviated Journal |
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| Volume |
13804 |
Issue |
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Pages |
359–375 |
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| Abstract |
This paper presents final results of the Out-Of-Vocabulary 2022 (OOV) challenge. The OOV contest introduces an important aspect that is not commonly studied by Optical Character Recognition (OCR) models, namely, the recognition of unseen scene text instances at training time. The competition compiles a collection of public scene text datasets comprising of 326,385 images with 4,864,405 scene text instances, thus covering a wide range of data distributions. A new and independent validation and test set is formed with scene text instances that are out of vocabulary at training time. The competition was structured in two tasks, end-to-end and cropped scene text recognition respectively. A thorough analysis of results from baselines and different participants is presented. Interestingly, current state-of-the-art models show a significant performance gap under the newly studied setting. We conclude that the OOV dataset proposed in this challenge will be an essential area to be explored in order to develop scene text models that achieve more robust and generalized predictions. |
| Address |
Tel-Aviv; Israel; October 2022 |
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LNCS |
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ECCVW |
| Notes |
DAG; 600.155; 302.105; 611.002 |
Approved |
no |
| Call Number |
Admin @ si @ GMB2022 |
Serial |
3771 |
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| Author |
Onur Ferhat; Fernando Vilariño |
| Title |
A Cheap Portable Eye-Tracker Solution for Common Setups |
Type |
Conference Article |
| Year |
2013 |
Publication |
17th European Conference on Eye Movements |
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Low cost; eye-tracker; software; webcam; Raspberry Pi |
| Abstract |
We analyze the feasibility of a cheap eye-tracker where the hardware consists of a single webcam and a Raspberry Pi device. Our aim is to discover the limits of such a system and to see whether it provides an acceptable performance. We base our work on the open source Opengazer (Zielinski, 2013) and we propose several improvements to create a robust, real-time system. After assessing the accuracy of our eye-tracker in elaborated experiments involving 18 subjects under 4 different system setups, we developed a simple game to see how it performs in practice and we also installed it on a Raspberry Pi to create a portable stand-alone eye-tracker which achieves 1.62° horizontal accuracy with 3 fps refresh rate for a building cost of 70 Euros. |
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Lund; Sweden; August 2013 |
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ECEM |
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MV;SIAI |
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no |
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Admin @ si @ FeV2013 |
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2374 |
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