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Author |
Mohammed Al Rawi; Ernest Valveny; Dimosthenis Karatzas |
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Title |
Can One Deep Learning Model Learn Script-Independent Multilingual Word-Spotting? |
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Conference Article |
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Year |
2019 |
Publication |
15th International Conference on Document Analysis and Recognition |
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260-267 |
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Abstract |
Word spotting has gained increased attention lately as it can be used to extract textual information from handwritten documents and scene-text images. Current word spotting approaches are designed to work on a single language and/or script. Building intelligent models that learn script-independent multilingual word-spotting is challenging due to the large variability of multilingual alphabets and symbols. We used ResNet-152 and the Pyramidal Histogram of Characters (PHOC) embedding to build a one-model script-independent multilingual word-spotting and we tested it on Latin, Arabic, and Bangla (Indian) languages. The one-model we propose performs on par with the multi-model language-specific word-spotting system, and thus, reduces the number of models needed for each script and/or language. |
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Sydney; Australia; September 2019 |
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ICDAR |
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DAG; 600.129; 600.121 |
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no |
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Admin @ si @ RVK2019 |
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3337 |
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Author |
Zheng Huang; Kai Chen; Jianhua He; Xiang Bai; Dimosthenis Karatzas; Shijian Lu; CV Jawahar |
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Title |
ICDAR2019 Competition on Scanned Receipt OCR and Information Extraction |
Type |
Conference Article |
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Year |
2019 |
Publication |
15th International Conference on Document Analysis and Recognition |
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1516-1520 |
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The ICDAR 2019 Challenge on “Scanned receipts OCR and key information extraction” (SROIE) covers important aspects related to the automated analysis of scanned receipts. The SROIE tasks play a key role in many document analysis systems and hold significant commercial potential. Although a lot of work has been published over the years on administrative document analysis, the community has advanced relatively slowly, as most datasets have been kept private. One of the key contributions of SROIE to the document analysis community is to offer a first, standardized dataset of 1000 whole scanned receipt images and annotations, as well as an evaluation procedure for such tasks. The Challenge is structured around three tasks, namely Scanned Receipt Text Localization (Task 1), Scanned Receipt OCR (Task 2) and Key Information Extraction from Scanned Receipts (Task 3). The competition opened on 10th February, 2019 and closed on 5th May, 2019. We received 29, 24 and 18 valid submissions received for the three competition tasks, respectively. This report presents the competition datasets, define the tasks and the evaluation protocols, offer detailed submission statistics, as well as an analysis of the submitted performance. While the tasks of text localization and recognition seem to be relatively easy to tackle, it is interesting to observe the variety of ideas and approaches proposed for the information extraction task. According to the submissions' performance we believe there is still margin for improving information extraction performance, although the current dataset would have to grow substantially in following editions. Given the success of the SROIE competition evidenced by the wide interest generated and the healthy number of submissions from academic, research institutes and industry over different countries, we consider that the SROIE competition can evolve into a useful resource for the community, drawing further attention and promoting research and development efforts in this field. |
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Sydney; Australia; September 2019 |
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ICDAR |
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DAG; 600.129 |
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no |
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Admin @ si @ HCH2019 |
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3338 |
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Author |
Raul Gomez; Ali Furkan Biten; Lluis Gomez; Jaume Gibert; Marçal Rusiñol; Dimosthenis Karatzas |
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Title |
Selective Style Transfer for Text |
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Conference Article |
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Year |
2019 |
Publication |
15th International Conference on Document Analysis and Recognition |
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805-812 |
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Keywords |
transfer; text style transfer; data augmentation; scene text detection |
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This paper explores the possibilities of image style transfer applied to text maintaining the original transcriptions. Results on different text domains (scene text, machine printed text and handwritten text) and cross-modal results demonstrate that this is feasible, and open different research lines. Furthermore, two architectures for selective style transfer, which means
transferring style to only desired image pixels, are proposed. Finally, scene text selective style transfer is evaluated as a data augmentation technique to expand scene text detection datasets, resulting in a boost of text detectors performance. Our implementation of the described models is publicly available. |
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Sydney; Australia; September 2019 |
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ICDAR |
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DAG; 600.129; 600.135; 601.338; 601.310; 600.121 |
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no |
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Call Number |
GBG2019 |
Serial |
3265 |
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Author |
Shifeng Zhang; Xiaobo Wang; Ajian Liu; Chenxu Zhao; Jun Wan; Sergio Escalera; Hailin Shi; Zezheng Wang; Stan Z. Li |
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Title |
A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing |
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Conference Article |
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Year |
2019 |
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32nd IEEE Conference on Computer Vision and Pattern Recognition |
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919-928 |
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Face anti-spoofing is essential to prevent face recognition systems from a security breach. Much of the progresses have been made by the availability of face anti-spoofing benchmark datasets in recent years. However, existing face anti-spoofing benchmarks have limited number of subjects (≤170) and modalities (≤2), which hinder the further development of the academic community. To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest publicly available dataset for face anti-spoofing in terms of both subjects and visual modalities. Specifically, it consists of 1,000 subjects with 21,000 videos and each sample has 3 modalities (i.e., RGB, Depth and IR). We also provide a measurement set, evaluation protocol and training/validation/testing subsets, developing a new benchmark for face anti-spoofing. Moreover, we present a new multi-modal fusion method as baseline, which performs feature re-weighting to select the more informative channel features while suppressing the less useful ones for each modal. Extensive experiments have been conducted on the proposed dataset to verify its significance and generalization capability. The dataset is available at https://sites.google.com/qq.com/chalearnfacespoofingattackdete/. |
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California; June 2019 |
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CVPR |
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Notes |
HuPBA; no proj |
Approved |
no |
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Call Number |
Admin @ si @ ZWL2019 |
Serial |
3331 |
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Author |
Helena Muñoz; Fernando Vilariño; Dimosthenis Karatzas |
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Title |
Eye-Movements During Information Extraction from Administrative Documents |
Type |
Conference Article |
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Year |
2019 |
Publication |
International Conference on Document Analysis and Recognition Workshops |
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6-9 |
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A key aspect of digital mailroom processes is the extraction of relevant information from administrative documents. More often than not, the extraction process cannot be fully automated, and there is instead an important amount of manual intervention. In this work we study the human process of information extraction from invoice document images. We explore whether the gaze of human annotators during an manual information extraction process could be exploited towards reducing the manual effort and automating the process. To this end, we perform an eye-tracking experiment replicating real-life interfaces for information extraction. Through this pilot study we demonstrate that relevant areas in the document can be identified reliably through automatic fixation classification, and the obtained models generalize well to new subjects. Our findings indicate that it is in principle possible to integrate the human in the document image analysis loop, making use of the scanpath to automate the extraction process or verify extracted information. |
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Sydney; Australia; September 2019 |
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ICDARW |
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Notes |
DAG; 600.140; 600.121; 600.129;SIAI |
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no |
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Call Number |
Admin @ si @ MVK2019 |
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3336 |
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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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Year |
2018 |
Publication |
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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Admin @ si @ FaL2018 |
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3181 |
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Author |
Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas |
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Title |
Cutting Sayre's Knot: Reading Scene Text without Segmentation. Application to Utility Meters |
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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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97-102 |
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Robust Reading; End-to-end Systems; CNN; Utility Meters |
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In this paper we present a segmentation-free system for reading text in natural scenes. A CNN architecture is trained in an end-to-end manner, and is able to directly output readings without any explicit text localization step. In order to validate our proposal, we focus on the specific case of reading utility meters. We present our results in a large dataset of images acquired by different users and devices, so text appears in any location, with different sizes, fonts and lengths, and the images present several distortions such as
dirt, illumination highlights or blur. |
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Viena; Austria; April 2018 |
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DAS |
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DAG; 600.084; 600.121; 600.129 |
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no |
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Admin @ si @ GRK2018 |
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3102 |
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Author |
Dimosthenis Karatzas; Lluis Gomez; Marçal Rusiñol; Anguelos Nicolaou |
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Title |
The Robust Reading Competition Annotation and Evaluation Platform |
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Conference Article |
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2018 |
Publication |
13th IAPR International Workshop on Document Analysis Systems |
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61-66 |
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The ICDAR Robust Reading Competition (RRC), initiated in 2003 and reestablished in 2011, has become the defacto evaluation standard for the international community. Concurrent with its second incarnation in 2011, a continuous
effort started to develop an online framework to facilitate the hosting and management of competitions. This short paper briefly outlines the Robust Reading Competition Annotation and Evaluation Platform, the backbone of the
Robust Reading Competition, comprising a collection of tools and processes that aim to simplify the management and annotation of data, and to provide online and offline performance evaluation and analysis services. |
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Viena; Austria; April 2018 |
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DAS |
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DAG; 600.084; 600.121 |
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no |
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KGR2018 |
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3103 |
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Author |
Roberto Morales; Juan Quispe; Eduardo Aguilar |
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Title |
Exploring multi-food detection using deep learning-based algorithms |
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Conference Article |
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2023 |
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13th International Conference on Pattern Recognition Systems |
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1-7 |
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People are becoming increasingly concerned about their diet, whether for disease prevention, medical treatment or other purposes. In meals served in restaurants, schools or public canteens, it is not easy to identify the ingredients and/or the nutritional information they contain. Currently, technological solutions based on deep learning models have facilitated the recording and tracking of food consumed based on the recognition of the main dish present in an image. Considering that sometimes there may be multiple foods served on the same plate, food analysis should be treated as a multi-class object detection problem. EfficientDet and YOLOv5 are object detection algorithms that have demonstrated high mAP and real-time performance on general domain data. However, these models have not been evaluated and compared on public food datasets. Unlike general domain objects, foods have more challenging features inherent in their nature that increase the complexity of detection. In this work, we performed a performance evaluation of Efficient-Det and YOLOv5 on three public food datasets: UNIMIB2016, UECFood256 and ChileanFood64. From the results obtained, it can be seen that YOLOv5 provides a significant difference in terms of both mAP and response time compared to EfficientDet in all datasets. Furthermore, YOLOv5 outperforms the state-of-the-art on UECFood256, achieving an improvement of more than 4% in terms of mAP@.50 over the best reported. |
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Guayaquil; Ecuador; July 2023 |
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ICPRS |
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MILAB |
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no |
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Admin @ si @ MQA2023 |
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3843 |
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Author |
Lei Kang; Lichao Zhang; Dazhi Jiang |
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Title |
Learning Robust Self-Attention Features for Speech Emotion Recognition with Label-Adaptive Mixup |
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Conference Article |
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2023 |
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IEEE International Conference on Acoustics, Speech and Signal Processing |
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Speech Emotion Recognition (SER) is to recognize human emotions in a natural verbal interaction scenario with machines, which is considered as a challenging problem due to the ambiguous human emotions. Despite the recent progress in SER, state-of-the-art models struggle to achieve a satisfactory performance. We propose a self-attention based method with combined use of label-adaptive mixup and center loss. By adapting label probabilities in mixup and fitting center loss to the mixup training scheme, our proposed method achieves a superior performance to the state-of-the-art methods. |
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Rodhes Islands; Greece; June 2023 |
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ICASSP |
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LAMP |
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no |
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Admin @ si @ KZJ2023 |
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3984 |
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Author |
Simone Zini; Alex Gomez-Villa; Marco Buzzelli; Bartlomiej Twardowski; Andrew D. Bagdanov; Joost Van de Weijer |
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Title |
Planckian Jitter: countering the color-crippling effects of color jitter on self-supervised training |
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Conference Article |
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2023 |
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11th International Conference on Learning Representations |
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Several recent works on self-supervised learning are trained by mapping different augmentations of the same image to the same feature representation. The data augmentations used are of crucial importance to the quality of learned feature representations. In this paper, we analyze how the color jitter traditionally used in data augmentation negatively impacts the quality of the color features in learned feature representations. To address this problem, we propose a more realistic, physics-based color data augmentation – which we call Planckian Jitter – that creates realistic variations in chromaticity and produces a model robust to illumination changes that can be commonly observed in real life, while maintaining the ability to discriminate image content based on color information. Experiments confirm that such a representation is complementary to the representations learned with the currently-used color jitter augmentation and that a simple concatenation leads to significant performance gains on a wide range of downstream datasets. In addition, we present a color sensitivity analysis that documents the impact of different training methods on model neurons and shows that the performance of the learned features is robust with respect to illuminant variations. |
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1 -5 May 2023, Kigali, Ruanda |
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ICLR |
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LAMP; 600.147; 611.008; 5300006 |
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Admin @ si @ ZGB2023 |
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3820 |
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Author |
Youssef El Rhabi; Simon Loic; Brun Luc |
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Title |
Estimation de la pose d’une caméra à partir d’un flux vidéo en s’approchant du temps réel |
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Conference Article |
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2015 |
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15ème édition d'ORASIS, journées francophones des jeunes chercheurs en vision par ordinateur ORASIS2015 |
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Augmented Reality; SFM; SLAM; real time pose computation; 2D/3D registration |
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Finding a way to estimate quickly and robustly the pose of an image is essential in augmented reality. Here we will discuss the approach we chose in order to get closer to real time by using SIFT points [4]. We propose a method based on filtering both SIFT points and images on which to focus on. Hence we will focus on relevant data. |
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Amiens; France; June 2015 |
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ORASIS |
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DAG; 600.077 |
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Admin @ si @ RLL2015 |
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2626 |
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Author |
Zhengying Liu; Isabelle Guyon; Julio C. S. Jacques Junior; Meysam Madadi; Sergio Escalera; Adrien Pavao; Hugo Jair Escalante; Wei-Wei Tu; Zhen Xu; Sebastien Treguer |
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Title |
AutoCV Challenge Design and Baseline Results |
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Conference Article |
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2019 |
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La Conference sur l’Apprentissage Automatique |
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We present the design and beta tests of a new machine learning challenge called AutoCV (for Automated Computer Vision), which is the first event in a series of challenges we are planning on the theme of Automated Deep Learning. We target applications for which Deep Learning methods have had great success in the past few years, with the aim of pushing the state of the art in fully automated methods to design the architecture of neural networks and train them without any human intervention. The tasks are restricted to multi-label image classification problems, from domains including medical, areal, people, object, and handwriting imaging. Thus the type of images will vary a lot in scales, textures, and structure. Raw data are provided (no features extracted), but all datasets are formatted in a uniform tensor manner (although images may have fixed or variable sizes within a dataset). The participants's code will be blind tested on a challenge platform in a controlled manner, with restrictions on training and test time and memory limitations. The challenge is part of the official selection of IJCNN 2019. |
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Toulouse; Francia; July 2019 |
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HUPBA; no proj |
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Admin @ si @ LGJ2019 |
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3323 |
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Olivier Lefebvre; Pau Riba; Charles Fournier; Alicia Fornes; Josep Llados; Rejean Plamondon; Jules Gagnon-Marchand |
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Monitoring neuromotricity on-line: a cloud computing approach |
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2015 |
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17th Conference of the International Graphonomics Society IGS2015 |
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The goal of our experiment is to develop a useful and accessible tool that can be used to evaluate a patient's health by analyzing handwritten strokes. We use a cloud computing approach to analyze stroke data sampled on a commercial tablet working on the Android platform and a distant server to perform complex calculations using the Delta and Sigma lognormal algorithms. A Google Drive account is used to store the data and to ease the development of the project. The communication between the tablet, the cloud and the server is encrypted to ensure biomedical information confidentiality. Highly parameterized biomedical tests are implemented on the tablet as well as a free drawing test to evaluate the validity of the data acquired by the first test compared to the second one. A blurred shape model descriptor pattern recognition algorithm is used to classify the data obtained by the free drawing test. The functions presented in this paper are still currently under development and other improvements are needed before launching the application in the public domain. |
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Pointe-à-Pitre; Guadeloupe; June 2015 |
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IGS |
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DAG; 600.077 |
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Admin @ si @ LRF2015 |
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2617 |
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Corina Krauter; Ursula Reiter; Albrecht Schmidt; Marc Masana; Rudolf Stollberger; Michael Fuchsjager; Gert Reiter |
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Objective extraction of the temporal evolution of the mitral valve vortex ring from 4D flow MRI |
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2019 |
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27th Annual Meeting & Exhibition of the International Society for Magnetic Resonance in Medicine |
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The mitral valve vortex ring is a promising flow structure for analysis of diastolic function, however, methods for objective extraction of its formation to dissolution are lacking. We present a novel algorithm for objective extraction of the temporal evolution of the mitral valve vortex ring from magnetic resonance 4D flow data and validated the method against visual analysis. The algorithm successfully extracted mitral valve vortex rings during both early- and late-diastolic filling and agreed substantially with visual assessment. Early-diastolic mitral valve vortex ring properties differed between healthy subjects and patients with ischemic heart disease. |
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ISMRM |
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LAMP; 600.120 |
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Admin @ si @ KRS2019 |
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3300 |
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