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We introduce VISOR, a new dataset of pixel annotations and a benchmark suitefor segmenting hands and active objects in egocentric video. VISOR annotatesvideos from EPIC-KITCHENS, which comes with a new set of challenges notencountered in current video segmentation datasets. Specifically, we need toensure both short- and long-term consistency of pixel-level annotations asobjects undergo transformative interactions, e.g. an onion is peeled, diced andcooked - where we aim to obtain accurate pixel-level annotations of the peel,onion pieces, chopping board, knife, pan, as well as the acting hands. VISORintroduces an annotation pipeline, AI-powered in parts, for scalability andquality. In total, we publicly release 272K manual semantic masks of 257 objectclasses, 9.9M interpolated dense masks, 67K hand-object relations, covering 36hours of 179 untrimmed videos. Along with the annotations, we introduce threechallenges in video object segmentation, interaction understanding andlong-term reasoning.  For data, code and leaderboards: http://epic-kitchens.github.io/VISOR

EPIC-KITCHENS VISOR Benchmark: VIdeo Segmentations and Object Relations