no code implementations • 9 Jan 2024 • Yunhua Zhang, Hazel Doughty, Cees G. M. Snoek
Low-resource settings are well-established in natural language processing, where many languages lack sufficient data for deep learning at scale.
1 code implementation • NeurIPS 2023 • Sarah Rastegar, Hazel Doughty, Cees G. M. Snoek
In the quest for unveiling novel categories at test time, we confront the inherent limitations of traditional supervised recognition models that are restricted by a predefined category set.
2 code implementations • ICCV 2023 • Fida Mohammad Thoker, Hazel Doughty, Cees Snoek
By simulating different tubelet motions and applying transformations, such as scaling and rotation, we introduce motion patterns beyond what is present in the pretraining data.
no code implementations • 5 Dec 2022 • Yunhua Zhang, Hazel Doughty, Cees G. M. Snoek
The main causes are the limited availability of labeled dark videos to learn from, as well as the distribution shift towards the lower color contrast at test-time.
1 code implementation • CVPR 2022 • Yunhua Zhang, Hazel Doughty, Ling Shao, Cees G. M. Snoek
This paper strives for activity recognition under domain shift, for example caused by change of scenery or camera viewpoint.
1 code implementation • 27 Mar 2022 • Fida Mohammad Thoker, Hazel Doughty, Piyush Bagad, Cees Snoek
Despite the recent success of video self-supervised learning models, there is much still to be understood about their generalization capability.
1 code implementation • CVPR 2022 • Hazel Doughty, Cees G. M. Snoek
We aim to understand how actions are performed and identify subtle differences, such as 'fold firmly' vs. 'fold gently'.
1 code implementation • 8 Aug 2021 • Fida Mohammad Thoker, Hazel Doughty, Cees G. M. Snoek
In particular, we propose inter-skeleton contrastive learning, which learns from multiple different input skeleton representations in a cross-contrastive manner.
3 code implementations • CVPR 2021 • Michael Wray, Hazel Doughty, Dima Damen
Current video retrieval efforts all found their evaluation on an instance-based assumption, that only a single caption is relevant to a query video and vice versa.
no code implementations • 11 Jan 2021 • Hazel Doughty, Nour Karessli, Kathryn Leonard, Boyi Li, Carianne Martinez, Azadeh Mobasher, Arsha Nagrani, Srishti Yadav
It provides a voice to a minority (female) group in computer vision community and focuses on increasingly the visibility of these researchers, both in academia and industry.
7 code implementations • 23 Jun 2020 • Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Antonino Furnari, Evangelos Kazakos, Jian Ma, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, Michael Wray
This paper introduces the pipeline to extend the largest dataset in egocentric vision, EPIC-KITCHENS.
Ranked #6 on Action Anticipation on EPIC-KITCHENS-100
2 code implementations • 29 Apr 2020 • Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Sanja Fidler, Antonino Furnari, Evangelos Kazakos, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, Michael Wray
Our dataset features 55 hours of video consisting of 11. 5M frames, which we densely labelled for a total of 39. 6K action segments and 454. 2K object bounding boxes.
1 code implementation • CVPR 2020 • Hazel Doughty, Ivan Laptev, Walterio Mayol-Cuevas, Dima Damen
We present a method to learn a representation for adverbs from instructional videos using weak supervision from the accompanying narrations.
1 code implementation • CVPR 2019 • Hazel Doughty, Walterio Mayol-Cuevas, Dima Damen
In addition to attending to task relevant video parts, our proposed loss jointly trains two attention modules to separately attend to video parts which are indicative of higher (pros) and lower (cons) skill.
2 code implementations • ECCV 2018 • Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Sanja Fidler, Antonino Furnari, Evangelos Kazakos, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, Michael Wray
First-person vision is gaining interest as it offers a unique viewpoint on people's interaction with objects, their attention, and even intention.
no code implementations • CVPR 2018 • Hazel Doughty, Dima Damen, Walterio Mayol-Cuevas
We present a method for assessing skill from video, applicable to a variety of tasks, ranging from surgery to drawing and rolling pizza dough.