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Author |
Felipe Codevilla; Antonio Lopez; Vladlen Koltun; Alexey Dosovitskiy |
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Title |
On Offline Evaluation of Vision-based Driving Models |
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Conference Article |
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Year |
2018 |
Publication |
15th European Conference on Computer Vision |
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11219 |
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Pages |
246-262 |
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Keywords |
Autonomous driving; deep learning |
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Abstract |
Autonomous driving models should ideally be evaluated by deploying
them on a fleet of physical vehicles in the real world. Unfortunately, this approach is not practical for the vast majority of researchers. An attractive alternative is to evaluate models offline, on a pre-collected validation dataset with ground truth annotation. In this paper, we investigate the relation between various online and offline metrics for evaluation of autonomous driving models. We find that offline prediction error is not necessarily correlated with driving quality, and two models with identical prediction error can differ dramatically in their driving performance. We show that the correlation of offline evaluation with driving quality can be significantly improved by selecting an appropriate validation dataset and
suitable offline metrics. |
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Munich; September 2018 |
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ECCV |
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ADAS; 600.124; 600.118 |
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no |
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Call Number |
Admin @ si @ CLK2018 |
Serial |
3162 |
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Author |
F. Javier Sanchez; Jorge Bernal |
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Title |
Use of Software Tools for Real-time Monitoring of Learning Processes: Application to Compilers subject |
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Conference Article |
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Year |
2018 |
Publication |
4th International Conference of Higher Education Advances |
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1359-1366 |
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Keywords |
Monitoring; Evaluation tool; Gamification; Student motivation |
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Abstract |
The effective implementation of the Higher European Education Area has meant a change regarding the focus of the learning process, being now the student at its very center. This shift of focus requires a strong involvement and fluent communication between teachers and students to succeed. Considering the difficulties associated to motivate students to take a more active role in the learning process, we explore how the use of a software tool can help both actors to improve the learning experience. We present a tool that can help students to obtain instantaneous feedback with respect to their progress in the subject as well as providing teachers with useful information about the evolution of knowledge acquisition with respect to each of the subject areas. We compare the performance achieved by students in two academic years: results show an improvement in overall performance which, after observing graphs provided by our tool, can be associated to an increase in students interest in the subject. |
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Valencia; June 2018 |
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HEAD |
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MV; no proj |
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no |
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Admin @ si @ SaB2018 |
Serial |
3165 |
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Author |
Lei Kang; Juan Ignacio Toledo; Pau Riba; Mauricio Villegas; Alicia Fornes; Marçal Rusiñol |
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Title |
Convolve, Attend and Spell: An Attention-based Sequence-to-Sequence Model for Handwritten Word Recognition |
Type |
Conference Article |
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Year |
2018 |
Publication |
40th German Conference on Pattern Recognition |
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459-472 |
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This paper proposes Convolve, Attend and Spell, an attention based sequence-to-sequence model for handwritten word recognition. The proposed architecture has three main parts: an encoder, consisting of a CNN and a bi-directional GRU, an attention mechanism devoted to focus on the pertinent features and a decoder formed by a one-directional GRU, able to spell the corresponding word, character by character. Compared with the recent state-of-the-art, our model achieves competitive results on the IAM dataset without needing any pre-processing step, predefined lexicon nor language model. Code and additional results are available in https://github.com/omni-us/research-seq2seq-HTR. |
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Stuttgart; Germany; October 2018 |
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GCPR |
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Notes |
DAG; 600.097; 603.057; 302.065; 601.302; 600.084; 600.121; 600.129 |
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no |
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Call Number |
Admin @ si @ KTR2018 |
Serial |
3167 |
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Author |
Pau Riba; Andreas Fischer; Josep Llados; Alicia Fornes |
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Title |
Learning Graph Distances with Message Passing Neural Networks |
Type |
Conference Article |
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Year |
2018 |
Publication |
24th International Conference on Pattern Recognition |
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2239-2244 |
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Keywords |
★Best Paper Award★ |
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Abstract |
Graph representations have been widely used in pattern recognition thanks to their powerful representation formalism and rich theoretical background. A number of error-tolerant graph matching algorithms such as graph edit distance have been proposed for computing a distance between two labelled graphs. However, they typically suffer from a high
computational complexity, which makes it difficult to apply
these matching algorithms in a real scenario. In this paper, we propose an efficient graph distance based on the emerging field of geometric deep learning. Our method employs a message passing neural network to capture the graph structure and learns a metric with a siamese network approach. The performance of the proposed graph distance is validated in two application cases, graph classification and graph retrieval of handwritten words, and shows a promising performance when compared with
(approximate) graph edit distance benchmarks. |
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Beijing; China; August 2018 |
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ICPR |
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Notes |
DAG; 600.097; 603.057; 601.302; 600.121 |
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no |
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Admin @ si @ RFL2018 |
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3168 |
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Author |
Jialuo Chen; Pau Riba; Alicia Fornes; Juan Mas; Josep Llados; Joana Maria Pujadas-Mora |
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Title |
Word-Hunter: A Gamesourcing Experience to Validate the Transcription of Historical Manuscripts |
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Conference Article |
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Year |
2018 |
Publication |
16th International Conference on Frontiers in Handwriting Recognition |
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Pages |
528-533 |
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Keywords |
Crowdsourcing; Gamification; Handwritten documents; Performance evaluation |
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Abstract |
Nowadays, there are still many handwritten historical documents in archives waiting to be transcribed and indexed. Since manual transcription is tedious and time consuming, the automatic transcription seems the path to follow. However, the performance of current handwriting recognition techniques is not perfect, so a manual validation is mandatory. Crowdsourcing is a good strategy for manual validation, however it is a tedious task. In this paper we analyze experiences based in gamification
in order to propose and design a gamesourcing framework that increases the interest of users. Then, we describe and analyze our experience when validating the automatic transcription using the gamesourcing application. Moreover, thanks to the combination of clustering and handwriting recognition techniques, we can speed up the validation while maintaining the performance. |
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Niagara Falls, USA; August 2018 |
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ICFHR |
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DAG; 600.097; 603.057; 600.121 |
Approved |
no |
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Call Number |
Admin @ si @ CRF2018 |
Serial |
3169 |
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Author |
Manuel Carbonell; Mauricio Villegas; Alicia Fornes; Josep Llados |
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Title |
Joint Recognition of Handwritten Text and Named Entities with a Neural End-to-end Model |
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Conference Article |
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Year |
2018 |
Publication |
13th IAPR International Workshop on Document Analysis Systems |
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399-404 |
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Named entity recognition; Handwritten Text Recognition; neural networks |
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When extracting information from handwritten documents, text transcription and named entity recognition are usually faced as separate subsequent tasks. This has the disadvantage that errors in the first module affect heavily the
performance of the second module. In this work we propose to do both tasks jointly, using a single neural network with a common architecture used for plain text recognition. Experimentally, the work has been tested on a collection of historical marriage records. Results of experiments are presented to show the effect on the performance for different
configurations: different ways of encoding the information, doing or not transfer learning and processing at text line or multi-line region level. The results are comparable to state of the art reported in the ICDAR 2017 Information Extraction competition, even though the proposed technique does not use any dictionaries, language modeling or post processing. |
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Vienna; Austria; April 2018 |
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DAS |
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DAG; 600.097; 603.057; 601.311; 600.121 |
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no |
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Call Number |
Admin @ si @ CVF2018 |
Serial |
3170 |
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Author |
Raul Gomez; Lluis Gomez; Jaume Gibert; Dimosthenis Karatzas |
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Title |
Learning from# Barcelona Instagram data what Locals and Tourists post about its Neighbourhoods |
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Conference Article |
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Year |
2018 |
Publication |
15th European Conference on Computer Vision Workshops |
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Volume |
11134 |
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530-544 |
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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. |
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Munich; Alemanya; September 2018 |
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ECCVW |
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DAG; 600.129; 601.338; 600.121 |
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no |
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Admin @ si @ GGG2018b |
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3176 |
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Author |
Anguelos Nicolaou; Sounak Dey; V.Christlein; A.Maier; Dimosthenis Karatzas |
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Title |
Non-deterministic Behavior of Ranking-based Metrics when Evaluating Embeddings |
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Conference Article |
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2018 |
Publication |
International Workshop on Reproducible Research in Pattern Recognition |
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11455 |
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71-82 |
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Embedding data into vector spaces is a very popular strategy of pattern recognition methods. When distances between embeddings are quantized, performance metrics become ambiguous. In this paper, we present an analysis of the ambiguity quantized distances introduce and provide bounds on the effect. We demonstrate that it can have a measurable effect in empirical data in state-of-the-art systems. We also approach the phenomenon from a computer security perspective and demonstrate how someone being evaluated by a third party can exploit this ambiguity and greatly outperform a random predictor without even access to the input data. We also suggest a simple solution making the performance metrics, which rely on ranking, totally deterministic and impervious to such exploits. |
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DAG; 600.121; 600.129 |
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no |
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Admin @ si @ NDC2018 |
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3178 |
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Author |
Dena Bazazian; Dimosthenis Karatzas; Andrew Bagdanov |
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Title |
Word Spotting in Scene Images based on Character Recognition |
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Conference Article |
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2018 |
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IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops |
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1872-1874 |
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In this paper we address the problem of unconstrained Word Spotting in scene images. We train a Fully Convolutional Network to produce heatmaps of all the character classes. Then, we employ the Text Proposals approach and, via a rectangle classifier, detect the most likely rectangle for each query word based on the character attribute maps. We evaluate the proposed method on ICDAR2015 and show that it is capable of identifying and recognizing query words in natural scene images. |
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Salt Lake City; USA; June 2018 |
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CVPRW |
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DAG; 600.129; 600.121 |
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no |
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BKB2018a |
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3179 |
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Author |
Zhijie Fang; Antonio Lopez |
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Title |
Is the Pedestrian going to Cross? Answering by 2D Pose Estimation |
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Conference Article |
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2018 |
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IEEE Intelligent Vehicles Symposium |
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1271 - 1276 |
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Our recent work suggests that, thanks to nowadays powerful CNNs, image-based 2D pose estimation is a promising cue for determining pedestrian intentions such as crossing the road in the path of the ego-vehicle, stopping before entering the road, and starting to walk or bending towards the road. This statement is based on the results obtained on non-naturalistic sequences (Daimler dataset), i.e. in sequences choreographed specifically for performing the study. Fortunately, a new publicly available dataset (JAAD) has appeared recently to allow developing methods for detecting pedestrian intentions in naturalistic driving conditions; more specifically, for addressing the relevant question is the pedestrian going to cross? Accordingly, in this paper we use JAAD to assess the usefulness of 2D pose estimation for answering such a question. We combine CNN-based pedestrian detection, tracking and pose estimation to predict the crossing action from monocular images. Overall, the proposed pipeline provides new state-ofthe-art results. |
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IV |
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ADAS; 600.124; 600.116; 600.118 |
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no |
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Admin @ si @ FaL2018 |
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3181 |
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Author |
Akhil Gurram; Onay Urfalioglu; Ibrahim Halfaoui; Fahd Bouzaraa; Antonio Lopez |
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Title |
Monocular Depth Estimation by Learning from Heterogeneous Datasets |
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Conference Article |
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2018 |
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IEEE Intelligent Vehicles Symposium |
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2176 - 2181 |
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Depth estimation provides essential information to perform autonomous driving and driver assistance. Especially, Monocular Depth Estimation is interesting from a practical point of view, since using a single camera is cheaper than many other options and avoids the need for continuous calibration strategies as required by stereo-vision approaches. State-of-the-art methods for Monocular Depth Estimation are based on Convolutional Neural Networks (CNNs). A promising line of work consists of introducing additional semantic information about the traffic scene when training CNNs for depth estimation. In practice, this means that the depth data used for CNN training is complemented with images having pixel-wise semantic labels, which usually are difficult to annotate (eg crowded urban images). Moreover, so far it is common practice to assume that the same raw training data is associated with both types of ground truth, ie, depth and semantic labels. The main contribution of this paper is to show that this hard constraint can be circumvented, ie, that we can train CNNs for depth estimation by leveraging the depth and semantic information coming from heterogeneous datasets. In order to illustrate the benefits of our approach, we combine KITTI depth and Cityscapes semantic segmentation datasets, outperforming state-of-the-art results on Monocular Depth Estimation. |
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IV |
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ADAS; 600.124; 600.116; 600.118 |
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Admin @ si @ GUH2018 |
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3183 |
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Md. Mostafa Kamal Sarker; Hatem A. Rashwan; Hatem A. Rashwan; Estefania Talavera; Syeda Furruka Banu; Petia Radeva; Domenec Puig |
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MACNet: Multi-scale Atrous Convolution Networks for Food Places Classification in Egocentric Photo-streams |
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Conference Article |
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2018 |
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European Conference on Computer Vision workshops |
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423-433 |
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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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ECCVW |
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MILAB; no menciona |
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no |
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Admin @ si @ SRR2018b |
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3185 |
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Author |
Xavier Soria; Angel Sappa |
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Title |
Improving Edge Detection in RGB Images by Adding NIR Channel |
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Conference Article |
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2018 |
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14th IEEE International Conference on Signal Image Technology & Internet Based System |
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Edge detection; Contour detection; VGG; CNN; RGB-NIR; Near infrared images |
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The edge detection is yet a critical problem in many computer vision and image processing tasks. The manuscript presents an Holistically-Nested Edge Detection based approach to study the inclusion of Near-Infrared in the Visible spectrum
images. To do so, a Single Sensor based dataset has been acquired in the range of 400nm to 1100nm wavelength spectral band. Prominent results have been obtained even when the ground truth (annotated edge-map) is based in the visible wavelength spectrum. |
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Las Palmas de Gran Canaria; November 2018 |
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MSIAU; 600.122 |
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Admin @ si @ SoS2018 |
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3192 |
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Patricia Suarez; Angel Sappa; Boris X. Vintimilla |
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Cross-spectral image dehaze through a dense stacked conditional GAN based approach |
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2018 |
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14th IEEE International Conference on Signal Image Technology & Internet Based System |
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Infrared imaging; Dense; Stacked CGAN; Crossspectral; Convolutional networks |
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This paper proposes a novel approach to remove haze from RGB images using a near infrared images based on a dense stacked conditional Generative Adversarial Network (CGAN). The architecture of the deep network implemented
receives, besides the images with haze, its corresponding image in the near infrared spectrum, which serve to accelerate the learning process of the details of the characteristics of the images. The model uses a triplet layer that allows the independence learning of each channel of the visible spectrum image to remove the haze on each color channel separately. A multiple loss function scheme is proposed, which ensures balanced learning between the colors
and the structure of the images. Experimental results have shown that the proposed method effectively removes the haze from the images. Additionally, the proposed approach is compared with a state of the art approach showing better results. |
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Las Palmas de Gran Canaria; November 2018 |
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978-1-5386-9385-8 |
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MSIAU; 600.086; 600.130; 600.122 |
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Admin @ si @ SSV2018a |
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3193 |
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Jorge Charco; Boris X. Vintimilla; Angel Sappa |
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Deep learning based camera pose estimation in multi-view environment |
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2018 |
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14th IEEE International Conference on Signal Image Technology & Internet Based System |
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Deep learning; Camera pose estimation; Multiview environment; Siamese architecture |
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This paper proposes to use a deep learning network architecture for relative camera pose estimation on a multi-view environment. The proposed network is a variant architecture of AlexNet to use as regressor for prediction the relative translation and rotation as output. The proposed approach is trained from
scratch on a large data set that takes as input a pair of imagesfrom the same scene. This new architecture is compared with a previous approach using standard metrics, obtaining better results on the relative camera pose. |
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Las Palmas de Gran Canaria; November 2018 |
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MSIAU; 600.086; 600.130; 600.122 |
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Admin @ si @ CVS2018 |
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3194 |
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