Search Results for author:
Found 12 papers, 6 papers with code
Verbs in Action: Improving verb understanding in video-language models
Understanding verbs is crucial to modelling how people and objects interact with each other and the environment through space and time.
Ranked #11 on
Zero-Shot Video Question Answer
on NExT-QA
Large Language Models are Few-shot Publication Scoopers
Driven by recent advances AI, we passengers are entering a golden age of scientific discovery.
Weakly-supervised Fingerspelling Recognition in British Sign Language Videos
Through extensive evaluations, we verify our method for automatic annotation and our model architecture.
Automatic dense annotation of large-vocabulary sign language videos
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 data.
Scaling up sign spotting through sign language dictionaries
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.
BBC-Oxford British Sign Language Dataset
In this work, we introduce the BBC-Oxford British Sign Language (BOBSL) dataset, a large-scale video collection of British Sign Language (BSL).
Visual Keyword Spotting with Attention
In this paper, we consider the task of spotting spoken keywords in silent video sequences -- also known as visual keyword spotting.
Ranked #1 on
Visual Keyword Spotting
on LRS2
Aligning Subtitles in Sign Language Videos
The goal of this work is to temporally align asynchronous subtitles in sign language videos.
Read and Attend: Temporal Localisation in Sign Language Videos
Our contributions are as follows: (1) we demonstrate the ability to leverage large quantities of continuous signing videos with weakly-aligned subtitles to localise signs in continuous sign language; (2) we employ the learned attention to automatically generate hundreds of thousands of annotations for a large sign vocabulary; (3) we collect a set of 37K manually verified sign instances across a vocabulary of 950 sign classes to support our study of sign language recognition; (4) by training on the newly annotated data from our method, we outperform the prior state of the art on the BSL-1K sign language recognition benchmark.
Watch, read and lookup: learning to spot signs from multiple supervisors
The focus of this work is sign spotting - given a video of an isolated sign, our task is to identify whether and where it has been signed in a continuous, co-articulated sign language video.
Seeing wake words: Audio-visual Keyword Spotting
The goal of this work is to automatically determine whether and when a word of interest is spoken by a talking face, with or without the audio.
BSL-1K: Scaling up co-articulated sign language recognition using mouthing cues
Recent progress in fine-grained gesture and action classification, and machine translation, point to the possibility of automated sign language recognition becoming a reality.
Ranked #4 on
Sign Language Recognition
on WLASL-2000
