Search Results for author: Davide Moltisanti

Found 12 papers, 7 papers with code

Efficient Pre-training for Localized Instruction Generation of Videos

no code implementations27 Nov 2023 Anil Batra, Davide Moltisanti, Laura Sevilla-Lara, Marcus Rohrbach, Frank Keller

Understanding such videos is challenging, involving the precise localization of steps and the generation of textual instructions.

BRACE: The Breakdancing Competition Dataset for Dance Motion Synthesis

1 code implementation20 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.

Motion Synthesis Pose Estimation

The EPIC-KITCHENS Dataset: Collection, Challenges and Baselines

2 code implementations29 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.


Towards an Unequivocal Representation of Actions

no code implementations10 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.

Action Recognition Retrieval +1

Trespassing the Boundaries: Labeling Temporal Bounds for Object Interactions in Egocentric Video

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.


SEMBED: Semantic Embedding of Egocentric Action Videos

no code implementations28 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.

General Classification Object

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