@conference {cTarres22, title = {Tackling Low-Resourced Sign Language Translation: UPC at WMT-SLT 22}, booktitle = {EMNLP 2022 Seventh Conference on Machine Translation (WMT22)}, year = {2022}, month = {10/2022}, abstract = {

This paper describes the system developed at the Universitat Polit{\`e}cnica de Catalunya for the Workshop on Machine Translation 2022 Sign Language Translation Task, in particular, for the sign-to-text direction. We use a Transformer model implemented with the Fairseq modeling toolkit. We have experimented with the vocabulary size, data augmentation tech- niques and pretraining the model with the PHOENIX-14T dataset. Our system obtains 0.50 BLEU score for the test set, improving the organizers{\textquoteright} baseline by 0.38 BLEU. We remark the poor results for both the baseline and our system, and thus, the unreliability of our findings.

Poster view

}, author = {Laia Tarr{\'e}s and Gerard I. G{\'a}llego and Xavier Gir{\'o}-i-Nieto and Jordi Torres} } @conference {cBudria, title = {Topic Detection in Continuous Sign Language Videos}, booktitle = {Accessibility, Vision, and Autonomy Meet (AVA) CVPR Workshop}, year = {2022}, month = {06/2022}, abstract = {

Significant progress has been made recently on challenging tasks in automatic sign language understanding, such as sign language recognition, translation and production. However, these works have focused on datasets with relatively few samples, short recordings and limited vocabulary and signing space.\ In this work, we introduce the novel task of sign language topic detection. We base our experiments on How2Sign, a large-scale video dataset spanning multiple semantic domains. We provide strong baselines for the task of topic detection,\ \ and present a comparison between different visual features commonly used in the domain of sign language.

}, author = {{\'A}lvaro Budria and Laia Tarr{\'e}s and Gerard I. G{\'a}llego and Moreno, Francesc and Jordi Torres and Xavier Gir{\'o}-i-Nieto} } @conference {cDuarte, title = {Towards Speech to Sign Language Translation}, booktitle = {ECCV 2018 Workshop on Shortcomings in Vision and Language}, year = {2018}, month = {08/2018}, abstract = {

Sign Language (SL) is the primary means of communication for a majority of the hearing-impaired community. Current computational approaches in this research area have focused specifically on Sign Language Recognition (SLR) and Sign Language Translation (from SL to text) (SLT). However, the reverse problem of translating from spoken language to sign language has so far been unexplored. The goal of our ongoing project is to provide to people with hearing disabilities the audio tracks from online videos, by automatically generating a video-based speech to sign language translation. In this paper, we will point out the shortcomings that limit the advances of this research area and propose first steps towards this end.

}, author = {Amanda Duarte and Camli, Gorkem and Jordi Torres and Xavier Gir{\'o}-i-Nieto} } @conference {xDuarte, title = {Temporal-aware Cross-modal Embeddings for Video and Audio Retrieval}, booktitle = {NIPS 2017 Women in Machine Learning Workshop (WiML)}, year = {2017}, month = {12/2017}, publisher = {NIPS 2017 Women in Machine Learning Workshop}, organization = {NIPS 2017 Women in Machine Learning Workshop}, address = {Long Beach, CA, USA}, abstract = {

The increasing amount of videos online brings several opportunities for training self-supervised neural networks. In this work, we explore cross-modal embeddings between audio and vision by exploiting their alignment on YouTube videos.

Joint audio-visual embeddings allow creating links between audio and visual documents by projecting them to a common region of the feature space. They can be applied to enriching radio broadcasts with images, finding soundtracks for user-generated videos or simply enriching a topic search with both audio and video documents.

The idea of creating a joint embedding space across modalities has being exploited by other areas [3, 4]. However, joint representation between the video frames and its audio have yet to be fully exploited. A similar approach to the proposed one was [2], where a soundtrack was retrieved to match a music video. However, this work did not target a synchronization between both modalities.

We aim at training a temporal-aware embedding which can align both audio and visual tracks. We use the visual and audio features provided in the YouTube-8M dataset [1]. The dataset includes features at both the clip and frame (temporal window) level. 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.

We aim at not only finding related documents, but synchronize both sequences. The alignment between the two sequences will rely on computing temporal-aware features with recurrent neural networks at different scales. At retrieval time, different scales will be assessed and results evaluated both with ranking metrics and Amazon Mechanical Turk.

References

[1] Sami Abu-El-Haija, Nisarg Kothari, Joonseok Lee, Paul Natsev, George Toderici, Balakrishnan Varadarajan, and Sudheendra Vijayanarasimhan. Youtube-8m: A large-scale video classification benchmark. arXiv preprint arXiv:1609.08675, 2016.

[2] Sungeun Hong, Woobin Im, and Hyun S Yang. Deep learning for content-based, cross-modal retrieval of videos and music. arXiv preprint arXiv:1704.06761, 2017.

[3] Amaia Salvador, Nicholas Hynes, Yusuf Aytar, Javier Marin, Ferda Ofli, Ingmar Weber, and Antonio Torralba. Learning cross-modal embeddings for cooking recipes and food images. In CVPR, 2017.

[4] Liwei Wang, Yin Li, and Svetlana Lazebnik. Learning deep structure-preserving image-text embeddings. In CVPR, 2016.

}, author = {Amanda Duarte and Sur{\'\i}s, D{\'\i}dac and Amaia Salvador and Jordi Torres and Xavier Gir{\'o}-i-Nieto} }