Automatic Dense Annotation of Large-Vocabulary Sign Language Videos

Autor: Momeni, L, Bull, H, Prajwal, KR, Albanie, S, Varol, G, Zisserman, A

Publikováno v: European Conference on Computer Vision (ECCV) 2022
European Conference on Computer Vision (ECCV) 2022, Oct 2022, Tel Aviv, Israel. pp.671-690, ⟨10.1007/978-3-031-19833-5_39⟩
Lecture Notes in Computer Science ISBN: 9783031198328

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: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d922b8ca5d034d81326aa4516cf7b228
https://hal.science/hal-03981733

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TeachText: CrossModal Generalized Distillation for Text-Video Retrieval

Autor: Croitoru, I, Bogolin, SV, Leordeanu, M, Jin, H, Zisserman, A, Albanie, S, Liu, Y

Publikováno v: 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

In recent years, considerable progress on the task of text-video retrieval has been achieved by leveraging large-scale pretraining on visual and audio datasets to construct powerful video encoders. By contrast, despite the natural symmetry, the desig

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::42e5f57b3f1fd7851791d214a4157b83
https://doi.org/10.1109/iccv48922.2021.01138

Signer diarisation in the wild

Autor: Albanie, S, Varol, G, Momeni, L, Afouras, T, Brown, A, Zhang, C, Coto, E, Camgoz, NC, Saunders, B, Dutta, A, Fox, N, Bowden, R, Woll, B, Zisserman, A

In this work, we propose a framework that enables collection of large-scale, diverse sign language datasets that can be used to train automatic sign language recognition models. The first contribution of this work is SDTRACK, a generic method for sig

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______1064::2327eb624f05c24b0891ec2b99101f74
https://ora.ox.ac.uk/objects/uuid:ffd79f6f-5ff4-4e9d-b635-0e29493d2030

Use what you have: Video retrieval using representations from collaborative experts

Autor: Liu, Y, Albanie, S, Nagrani, A, Zisserman, A

The rapid growth of video on the internet has made searching for video content using natural language queries a significant challenge. Human-generated queries for video datasets `in the wild' vary a lot in terms of degree of specificity, with some qu

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0510df38d04449f1ac1dc413c671af5a
https://ora.ox.ac.uk/objects/uuid:502da19a-2a9c-45f4-95f0-ee09ecf77340