| |
Records |
Links |
|
Author |
Arnau Baro; Pau Riba; Alicia Fornes |
|
| |
Title |
A Starting Point for Handwritten Music Recognition |
Type |
Conference Article |
| |
Year |
2018 |
Publication |
1st International Workshop on Reading Music Systems |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
5-6 |
|
| |
Keywords |
Optical Music Recognition; Long Short-Term Memory; Convolutional Neural Networks; MUSCIMA++; CVCMUSCIMA |
|
| |
Abstract |
In the last years, the interest in Optical Music Recognition (OMR) has reawakened, especially since the appearance of deep learning. However, there are very few works addressing handwritten scores. In this work we describe a full OMR pipeline for handwritten music scores by using Convolutional and Recurrent Neural Networks that could serve as a baseline for the research community. |
|
| |
Address |
Paris; France; September 2018 |
|
| |
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  |
WORMS |
|
| |
Notes |
DAG; 600.097; 601.302; 601.330; 600.121 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ BRF2018 |
Serial |
3223 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Arnau Baro; Carles Badal; Pau Torras; Alicia Fornes |
|
| |
Title |
Handwritten Historical Music Recognition through Sequence-to-Sequence with Attention Mechanism |
Type |
Conference Article |
| |
Year |
2022 |
Publication |
3rd International Workshop on Reading Music Systems (WoRMS2021) |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
55-59 |
|
| |
Keywords |
Optical Music Recognition; Digits; Image Classification |
|
| |
Abstract |
Despite decades of research in Optical Music Recognition (OMR), the recognition of old handwritten music scores remains a challenge because of the variabilities in the handwriting styles, paper degradation, lack of standard notation, etc. Therefore, the research in OMR systems adapted to the particularities of old manuscripts is crucial to accelerate the conversion of music scores existing in archives into digital libraries, fostering the dissemination and preservation of our music heritage. In this paper we explore the adaptation of sequence-to-sequence models with attention mechanism (used in translation and handwritten text recognition) and the generation of specific synthetic data for recognizing old music scores. The experimental validation demonstrates that our approach is promising, especially when compared with long short-term memory neural networks. |
|
| |
Address |
July 23, 2021, Alicante (Spain) |
|
| |
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 |
WoRMS |
|
| |
Notes |
DAG; 600.121; 600.162; 602.230; 600.140 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ BBT2022 |
Serial |
3734 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Pau Torras; Arnau Baro; Alicia Fornes; Lei Kang |
|
| |
Title |
Improving Handwritten Music Recognition through Language Model Integration |
Type |
Conference Article |
| |
Year |
2022 |
Publication |
4th International Workshop on Reading Music Systems (WoRMS2022) |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
42-46 |
|
| |
Keywords |
optical music recognition; historical sources; diversity; music theory; digital humanities |
|
| |
Abstract |
Handwritten Music Recognition, especially in the historical domain, is an inherently challenging endeavour; paper degradation artefacts and the ambiguous nature of handwriting make recognising such scores an error-prone process, even for the current state-of-the-art Sequence to Sequence models. In this work we propose a way of reducing the production of statistically implausible output sequences by fusing a Language Model into a recognition Sequence to Sequence model. The idea is leveraging visually-conditioned and context-conditioned output distributions in order to automatically find and correct any mistakes that would otherwise break context significantly. We have found this approach to improve recognition results to 25.15 SER (%) from a previous best of 31.79 SER (%) in the literature. |
|
| |
Address |
November 18, 2022 |
|
| |
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 |
WoRMS |
|
| |
Notes |
DAG; 600.121; 600.162; 602.230 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ TBF2022 |
Serial |
3735 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Ilke Demir; Dena Bazazian; Adriana Romero; Viktoriia Sharmanska; Lyne P. Tchapmi |
|
| |
Title |
WiCV 2018: The Fourth Women In Computer Vision Workshop |
Type |
Conference Article |
| |
Year |
2018 |
Publication |
4th Women in Computer Vision Workshop |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
1941-19412 |
|
| |
Keywords |
Conferences; Computer vision; Industries; Object recognition; Engineering profession; Collaboration; Machine learning |
|
| |
Abstract |
We present WiCV 2018 – Women in Computer Vision Workshop to increase the visibility and inclusion of women researchers in computer vision field, organized in conjunction with CVPR 2018. Computer vision and machine learning have made incredible progress over the past years, yet the number of female researchers is still low both in academia and industry. WiCV is organized to raise visibility of female researchers, to increase the collaboration,
and to provide mentorship and give opportunities to femaleidentifying junior researchers in the field. In its fourth year, we are proud to present the changes and improvements over the past years, summary of statistics for presenters and attendees, followed by expectations from future generations. |
|
| |
Address |
Salt Lake City; USA; June 2018 |
|
| |
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 |
WiCV |
|
| |
Notes |
DAG; 600.121; 600.129 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ DBR2018 |
Serial |
3222 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Lei Kang; Marçal Rusiñol; Alicia Fornes; Pau Riba; Mauricio Villegas |
|
| |
Title |
Unsupervised Adaptation for Synthetic-to-Real Handwritten Word Recognition |
Type |
Conference Article |
| |
Year |
2020 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
Handwritten Text Recognition (HTR) is still a challenging problem because it must deal with two important difficulties: the variability among writing styles, and the scarcity of labelled data. To alleviate such problems, synthetic data generation and data augmentation are typically used to train HTR systems. However, training with such data produces encouraging but still inaccurate transcriptions in real words. In this paper, we propose an unsupervised writer adaptation approach that is able to automatically adjust a generic handwritten word recognizer, fully trained with synthetic fonts, towards a new incoming writer. We have experimentally validated our proposal using five different datasets, covering several challenges (i) the document source: modern and historic samples, which may involve paper degradation problems; (ii) different handwriting styles: single and multiple writer collections; and (iii) language, which involves different character combinations. Across these challenging collections, we show that our system is able to maintain its performance, thus, it provides a practical and generic approach to deal with new document collections without requiring any expensive and tedious manual annotation step. |
|
| |
Address |
Aspen; Colorado; USA; March 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 |
WACV |
|
| |
Notes |
DAG; 600.129; 600.140; 601.302; 601.312; 600.121 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ KRF2020 |
Serial |
3446 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Raul Gomez; Jaume Gibert; Lluis Gomez; Dimosthenis Karatzas |
|
| |
Title |
Exploring Hate Speech Detection in Multimodal Publications |
Type |
Conference Article |
| |
Year |
2020 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
In this work we target the problem of hate speech detection in multimodal publications formed by a text and an image. We gather and annotate a large scale dataset from Twitter, MMHS150K, and propose different models that jointly analyze textual and visual information for hate speech detection, comparing them with unimodal detection. We provide quantitative and qualitative results and analyze the challenges of the proposed task. We find that, even though images are useful for the hate speech detection task, current multimodal models cannot outperform models analyzing only text. We discuss why and open the field and the dataset for further research. |
|
| |
Address |
Aspen; March 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 |
WACV |
|
| |
Notes |
DAG; 600.121; 600.129 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ GGG2020a |
Serial |
3280 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Andres Mafla; Sounak Dey; Ali Furkan Biten; Lluis Gomez; Dimosthenis Karatzas |
|
| |
Title |
Fine-grained Image Classification and Retrieval by Combining Visual and Locally Pooled Textual Features |
Type |
Conference Article |
| |
Year |
2020 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
Text contained in an image carries high-level semantics that can be exploited to achieve richer image understanding. In particular, the mere presence of text provides strong guiding content that should be employed to tackle a diversity of computer vision tasks such as image retrieval, fine-grained classification, and visual question answering. In this paper, we address the problem of fine-grained classification and image retrieval by leveraging textual information along with visual cues to comprehend the existing intrinsic relation between the two modalities. The novelty of the proposed model consists of the usage of a PHOC descriptor to construct a bag of textual words along with a Fisher Vector Encoding that captures the morphology of text. This approach provides a stronger multimodal representation for this task and as our experiments demonstrate, it achieves state-of-the-art results on two different tasks, fine-grained classification and image retrieval. |
|
| |
Address |
Aspen; Colorado; USA; March 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 |
WACV |
|
| |
Notes |
DAG; 600.121; 600.129 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ MDB2020 |
Serial |
3334 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Andres Mafla; Sounak Dey; Ali Furkan Biten; Lluis Gomez; Dimosthenis Karatzas |
|
| |
Title |
Multi-modal reasoning graph for scene-text based fine-grained image classification and retrieval |
Type |
Conference Article |
| |
Year |
2021 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
4022-4032 |
|
| |
Keywords |
|
|
| |
Abstract |
|
|
| |
Address |
Virtual; January 2021 |
|
| |
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 |
WACV |
|
| |
Notes |
DAG; 600.121 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ MDB2021 |
Serial |
3491 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Andres Mafla; Rafael S. Rezende; Lluis Gomez; Diana Larlus; Dimosthenis Karatzas |
|
| |
Title |
StacMR: Scene-Text Aware Cross-Modal Retrieval |
Type |
Conference Article |
| |
Year |
2021 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
2219-2229 |
|
| |
Keywords |
|
|
| |
Abstract |
|
|
| |
Address |
Virtual; January 2021 |
|
| |
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 |
WACV |
|
| |
Notes |
DAG; 600.121 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ MRG2021a |
Serial |
3492 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Minesh Mathew; Dimosthenis Karatzas; C.V. Jawahar |
|
| |
Title |
DocVQA: A Dataset for VQA on Document Images |
Type |
Conference Article |
| |
Year |
2021 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
2200-2209 |
|
| |
Keywords |
|
|
| |
Abstract |
We present a new dataset for Visual Question Answering (VQA) on document images called DocVQA. The dataset consists of 50,000 questions defined on 12,000+ document images. Detailed analysis of the dataset in comparison with similar datasets for VQA and reading comprehension is presented. We report several baseline results by adopting existing VQA and reading comprehension models. Although the existing models perform reasonably well on certain types of questions, there is large performance gap compared to human performance (94.36% accuracy). The models need to improve specifically on questions where understanding structure of the document is crucial. The dataset, code and leaderboard are available at docvqa. org |
|
| |
Address |
Virtual; January 2021 |
|
| |
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 |
WACV |
|
| |
Notes |
DAG; 600.121 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ MKJ2021 |
Serial |
3498 |
|
| Permanent link to this record |
