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Found 15 papers, 9 papers with code
HD-EPIC: A Highly-Detailed Egocentric Video Dataset
We present a validation dataset of newly-collected kitchen-based egocentric videos, manually annotated with highly detailed and interconnected ground-truth labels covering: recipe steps, fine-grained actions, ingredients with nutritional values, moving objects, and audio annotations.
Continual Learning Improves Zero-Shot Action Recognition
In this paper, we propose a novel method based on continual learning to address zero-shot action recognition.
Coarse or Fine? Recognising Action End States without Labels
We use our synthetic data to train a model based on UNet and test it on real images showing coarsely/finely cut objects.
Efficient Pre-training for Localized Instruction Generation of Videos
To mitigate these issues, we propose a novel technique, Sieve-&-Swap, to automatically generate high-quality training data for the recipe domain: (i) Sieve: filters irrelevant transcripts and (ii) Swap: acquires high-quality text by replacing transcripts with human-written instruction from a text-only recipe dataset.
Learning Action Changes by Measuring Verb-Adverb Textual Relationships
The goal of this work is to understand the way actions are performed in videos.
Ranked #2 on
Video-Adverb Retrieval
on HowTo100M Adverbs
An Action Is Worth Multiple Words: Handling Ambiguity in Action Recognition
In practice, a given video can contain multiple valid positive annotations for the same action.
BRACE: The Breakdancing Competition Dataset for Dance Motion Synthesis
Estimating human keypoints from these videos is difficult due to the complexity of the dance, as well as the multiple moving cameras recording setup.
Rescaling Egocentric Vision
This paper introduces the pipeline to extend the largest dataset in egocentric vision, EPIC-KITCHENS.
Ranked #9 on
Action Anticipation
on EPIC-KITCHENS-100
The EPIC-KITCHENS Dataset: Collection, Challenges and Baselines
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.
Action Recognition from Single Timestamp Supervision in Untrimmed Videos
We propose a method that is supervised by single timestamps located around each action instance, in untrimmed videos.
Towards an Unequivocal Representation of Actions
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.
Scaling Egocentric Vision: The EPIC-KITCHENS Dataset
First-person vision is gaining interest as it offers a unique viewpoint on people's interaction with objects, their attention, and even intention.
Trespassing the Boundaries: Labeling Temporal Bounds for Object Interactions in Egocentric Video
Manual annotations of temporal bounds for object interactions (i. e. start and end times) are typical training input to recognition, localization and detection algorithms.
Improving Classification by Improving Labelling: Introducing Probabilistic Multi-Label Object Interaction Recognition
This work deviates from easy-to-define class boundaries for object interactions.
SEMBED: Semantic Embedding of Egocentric Action Videos
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.
