@conference {cDuarteb, title = {Cross-modal Neural Sign Language Translation}, booktitle = {Proceedings of the 27th ACM International Conference on Multimedia - Doctoral Symposium}, year = {2019}, month = {10/2019}, publisher = {ACM}, organization = {ACM}, address = {Nice, France}, abstract = {

Sign Language is the primary means of communication for the majority of the Deaf and hard-of-hearing communities. Current computational approaches in this general research area have focused specifically on sign language recognition and the translation of sign language to text. However, the reverse problem of translating from spoken to sign language has so far not been widely explored.

The goal of this doctoral research is to explore sign language translation in this generalized setting, i.e. translating from spoken language to sign language and vice versa. Towards that end, we propose a concrete methodology for tackling the problem of speech to sign language translation and introduce How2Sign, the first public, continuous American Sign Language dataset that enables such research. With a parallel corpus of almost 60 hours of sign language videos (collected with both RGB and depth sensor data) and the corresponding speech transcripts for over 2500 instructional videos, How2Sign is a public dataset of unprecedented scale that can be used to advance not only sign language translation, but also a wide range of sign language understanding tasks.

Poster at ACM Multimedia 2019

Xavier Giro and Amanda Duarte in ACM Multimedia 2019.

}, doi = {10.1145/3343031.3352587}, url = {https://dl.acm.org/citation.cfm?id=3352587}, author = {Amanda Duarte}, editor = {Jordi Torres and Xavier Gir{\'o}-i-Nieto} } @conference {cSuris, title = {Cross-modal Embeddings for Video and Audio Retrieval}, booktitle = {ECCV 2018 Women in Computer Vision Workshop}, year = {2018}, month = {09/2018}, publisher = {Springer}, organization = {Springer}, address = {Munich, Germany}, abstract = {

The increasing amount of online videos brings several opportunities for training self-supervised neural networks. The creation of large scale datasets of videos such as the YouTube-8M allows us to deal with this large amount of data in manageable way. In this work, we find new ways of exploiting this dataset by taking advantage of the multi-modal information it provides. By means of a neural network, we are able to create links between audio and visual documents, by projecting them into a common region of the feature space, obtaining joint audio-visual embeddings. These links are used to retrieve audio samples that fit well to a given silent video, and also to retrieve images that match a given a query audio. The results in terms of Recall@K obtained over a subset of YouTube-8M videos show the potential of this unsupervised approach for cross-modal feature learning. We train embeddings for both scales and assess their quality in a retrieval problem, formulated as using the feature extracted from one modality to retrieve the most similar videos based on the features computed in the other modality.

Cross Modal Embeddings for Video and Audio Retrieval $\#$WiCV18 from Universitat Polit{\`e}cnica de Catalunya
}, isbn = {978-3-030-11018-5}, doi = {10.1007/978-3-030-11018-5_62}, url = {https://doi.org/10.1007/978-3-030-11018-5_62}, author = {Sur{\'\i}s, D{\'\i}dac and Amanda Duarte and Amaia Salvador and Jordi Torres and Xavier Gir{\'o}-i-Nieto} } @conference {cAnton05, title = {Collaborative Network Space: Infrastructure and Learning Application}, booktitle = {IEEE Region 8 EUROCON 2005 Conference: Computer as a tool.}, year = {2005}, pages = {803{\textendash}806}, isbn = {1-4244-0050-3}, url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=01630054}, author = {Anton, M. and Vall-Llosera, M. and Jordi Torres and Romeu, J. and Jofre, L. and Sole, F. and Marqu{\'e}s, F. and Sabate, F. and Berenguer, J.} }