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arXiv · 2209.13064

EPIC-KITCHENS VISOR Benchmark: VIdeo Segmentations and Object Relations

Ahmad Darkhalil·Dandan Shan·Bin Zhu·Jian Ma·Amlan Kar·Richard Higgins·Sanja Fidler·David Fouhey·Dima Damen
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Abstract

We introduce VISOR, a new dataset of pixel annotations and a benchmark suite for segmenting hands and active objects in egocentric video. VISOR annotates videos from EPIC-KITCHENS, which comes with a new set of challenges not encountered in current video segmentation datasets. Specifically, we need to ensure both short- and long-term consistency of pixel-level annotations as objects undergo transformative interactions, e.g. an onion is peeled, diced and cooked - where we aim to obtain accurate pixel-level annotations of the peel, onion pieces, chopping board, knife, pan, as well as the acting hands. VISOR introduces an annotation pipeline, AI-powered in parts, for scalability and quality. In total, we publicly release 272K manual semantic masks of 257 object classes, 9.9M interpolated dense masks, 67K hand-object relations, covering 36 hours of 179 untrimmed videos. Along with the annotations, we introduce three challenges in video object segmentation, interaction understanding and long-term reasoning. For data, code and leaderboards: http://epic-kitchens.github.io/VISOR

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Ahmad Darkhalil, Dandan Shan, Bin Zhu, Jian Ma, Amlan Kar, Richard Higgins, Sanja Fidler, David Fouhey, Dima Damen. 2022-09-26. EPIC-KITCHENS VISOR Benchmark: VIdeo Segmentations and Object Relations. https://arxiv.org/abs/2209.13064

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