Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Momeni, Liliane"'
Autor:
Raude, Charles, Prajwal, K R, Momeni, Liliane, Bull, Hannah, Albanie, Samuel, Zisserman, Andrew, Varol, Gül
In this work, our goals are two fold: large-vocabulary continuous sign language recognition (CSLR), and sign language retrieval. To this end, we introduce a multi-task Transformer model, CSLR2, that is able to ingest a signing sequence and output in
Externí odkaz:
http://arxiv.org/abs/2405.10266
Understanding verbs is crucial to modelling how people and objects interact with each other and the environment through space and time. Recently, state-of-the-art video-language models based on CLIP have been shown to have limited verb understanding
Externí odkaz:
http://arxiv.org/abs/2304.06708
Driven by recent advances AI, we passengers are entering a golden age of scientific discovery. But golden for whom? Confronting our insecurity that others may beat us to the most acclaimed breakthroughs of the era, we propose a novel solution to the
Externí odkaz:
http://arxiv.org/abs/2304.00521
The goal of this work is to detect and recognize sequences of letters signed using fingerspelling in British Sign Language (BSL). Previous fingerspelling recognition methods have not focused on BSL, which has a very different signing alphabet (e.g.,
Externí odkaz:
http://arxiv.org/abs/2211.08954
Recently, sign language researchers have turned to sign language interpreted TV broadcasts, comprising (i) a video of continuous signing and (ii) subtitles corresponding to the audio content, as a readily available and large-scale source of training
Externí odkaz:
http://arxiv.org/abs/2208.02802
Publikováno v:
International Journal of Computer Vision (2022)
The focus of this work is $\textit{sign spotting}$ - given a video of an isolated sign, our task is to identify $\textit{whether}$ and $\textit{where}$ it has been signed in a continuous, co-articulated sign language video. To achieve this sign spott
Externí odkaz:
http://arxiv.org/abs/2205.04152
Autor:
Albanie, Samuel, Varol, Gül, Momeni, Liliane, Bull, Hannah, Afouras, Triantafyllos, Chowdhury, Himel, Fox, Neil, Woll, Bencie, Cooper, Rob, McParland, Andrew, Zisserman, Andrew
In this work, we introduce the BBC-Oxford British Sign Language (BOBSL) dataset, a large-scale video collection of British Sign Language (BSL). BOBSL is an extended and publicly released dataset based on the BSL-1K dataset introduced in previous work
Externí odkaz:
http://arxiv.org/abs/2111.03635
In this paper, we consider the task of spotting spoken keywords in silent video sequences -- also known as visual keyword spotting. To this end, we investigate Transformer-based models that ingest two streams, a visual encoding of the video and a pho
Externí odkaz:
http://arxiv.org/abs/2110.15957
Autor:
Bull, Hannah, Afouras, Triantafyllos, Varol, Gül, Albanie, Samuel, Momeni, Liliane, Zisserman, Andrew
The goal of this work is to temporally align asynchronous subtitles in sign language videos. In particular, we focus on sign-language interpreted TV broadcast data comprising (i) a video of continuous signing, and (ii) subtitles corresponding to the
Externí odkaz:
http://arxiv.org/abs/2105.02877
The objective of this work is to annotate sign instances across a broad vocabulary in continuous sign language. We train a Transformer model to ingest a continuous signing stream and output a sequence of written tokens on a large-scale collection of
Externí odkaz:
http://arxiv.org/abs/2103.16481