283 citations · 299 across the 10 of their papers we have counts for
12 papers
HD-EPIC: A Highly-Detailed Egocentric Video Dataset
Toby Perrett, Ahmad Darkhalil, Saptarshi Sinha +16
We present a validation dataset of newly-collected kitchen-based egocentric videos, manually annotated with highly detailed and interconnected ground-truth labels covering: recipe…
Learning Action Changes by Measuring Verb-Adverb Textual Relationships
Davide Moltisanti, Frank Keller, Hakan Bilen +1
The goal of this work is to understand the way actions are performed in videos. That is, given a video, we aim to predict an adverb indicating a modification applied to the action…
An Action Is Worth Multiple Words: Handling Ambiguity in Action Recognition
Kiyoon Kim, Davide Moltisanti, Oisin Mac Aodha +1
Precisely naming the action depicted in a video can be a challenging and oftentimes ambiguous task. In contrast to object instances represented as nouns (e.g. dog, cat, chair, etc.…
BRACE: The Breakdancing Competition Dataset for Dance Motion Synthesis
Davide Moltisanti, Jinyi Wu, Bo Dai +1
Generative models for audio-conditioned dance motion synthesis map music features to dance movements. Models are trained to associate motion patterns to audio patterns, usually wit…
Rescaling Egocentric Vision
Dima Damen, Hazel Doughty, Giovanni Maria Farinella +8
This paper introduces the pipeline to extend the largest dataset in egocentric vision, EPIC-KITCHENS. The effort culminates in EPIC-KITCHENS-100, a collection of 100 hours, 20M fra…
The EPIC-KITCHENS Dataset: Collection, Challenges and Baselines
Dima Damen, Hazel Doughty, Giovanni Maria Farinella +8
Since its introduction in 2018, EPIC-KITCHENS has attracted attention as the largest egocentric video benchmark, offering a unique viewpoint on people's interaction with objects, t…
Action Recognition from Single Timestamp Supervision in Untrimmed Videos
Davide Moltisanti, Sanja Fidler, Dima Damen
Recognising actions in videos relies on labelled supervision during training, typically the start and end times of each action instance. This supervision is not only subjective, bu…
Towards an Unequivocal Representation of Actions
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 differin…
Scaling Egocentric Vision: The EPIC-KITCHENS Dataset
Dima Damen, Hazel Doughty, Giovanni Maria Farinella +8
First-person vision is gaining interest as it offers a unique viewpoint on people's interaction with objects, their attention, and even intention. However, progress in this challen…
Improving Classification by Improving Labelling: Introducing Probabilistic Multi-Label Object Interaction Recognition
Michael Wray, Davide Moltisanti, Walterio Mayol-Cuevas +1
This work deviates from easy-to-define class boundaries for object interactions. For the task of object interaction recognition, often captured using an egocentric view, we show th…
Trespassing the Boundaries: Labeling Temporal Bounds for Object Interactions in Egocentric Video
Davide Moltisanti, Michael Wray, Walterio Mayol-Cuevas +1
Manual annotations of temporal bounds for object interactions (i.e. start and end times) are typical training input to recognition, localization and detection algorithms. For three…
SEMBED: Semantic Embedding of Egocentric Action Videos
Michael Wray, Davide Moltisanti, Walterio Mayol-Cuevas +1
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…