1 code implementation • 13 May 2024 • Davide Moltisanti, Hakan Bilen, Laura Sevilla-Lara, Frank Keller
We use our synthetic data to train a model based on UNet and test it on real images showing coarsely/finely cut objects.
no code implementations • 27 Nov 2023 • Anil Batra, Davide Moltisanti, Laura Sevilla-Lara, Marcus Rohrbach, Frank Keller
The resulting dataset is three orders of magnitude smaller than current web-scale datasets but enables efficient training of large-scale models.
1 code implementation • CVPR 2023 • Davide Moltisanti, Frank Keller, Hakan Bilen, Laura Sevilla-Lara
The goal of this work is to understand the way actions are performed in videos.
Ranked #2 on Video-Adverb Retrieval on HowTo100M Adverbs
2 code implementations • 10 Oct 2022 • Kiyoon Kim, Davide Moltisanti, Oisin Mac Aodha, Laura Sevilla-Lara
In practice, a given video can contain multiple valid positive annotations for the same action.
1 code implementation • 20 Jul 2022 • Davide Moltisanti, Jinyi Wu, Bo Dai, Chen Change Loy
Estimating human keypoints from these videos is difficult due to the complexity of the dance, as well as the multiple moving cameras recording setup.
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 #7 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 2019 • Davide Moltisanti, Sanja Fidler, Dima Damen
We propose a method that is supervised by single timestamps located around each action instance, in untrimmed videos.
no code implementations • 10 May 2018 • Michael Wray, Davide Moltisanti, Dima Damen
This work introduces verb-only representations for actions and interactions; the problem of describing similar motions (e. g. 'open door', 'open cupboard'), and distinguish differing ones (e. g. 'open door' vs 'open bottle') using verb-only labels.
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 • ICCV 2017 • Davide Moltisanti, Michael Wray, Walterio Mayol-Cuevas, Dima Damen
Manual annotations of temporal bounds for object interactions (i. e. start and end times) are typical training input to recognition, localization and detection algorithms.
no code implementations • 24 Mar 2017 • Michael Wray, Davide Moltisanti, Walterio Mayol-Cuevas, Dima Damen
This work deviates from easy-to-define class boundaries for object interactions.
no code implementations • 28 Jul 2016 • Michael Wray, Davide Moltisanti, Walterio Mayol-Cuevas, Dima Damen
We present SEMBED, an approach for embedding an egocentric object interaction video in a semantic-visual graph to estimate the probability distribution over its potential semantic labels.