EPIC-KITCHENS VISOR Benchmark: VIdeo Segmentations and Object RelationsDownload PDF

Published: 17 Sept 2022, Last Modified: 12 Mar 2024NeurIPS 2022 Datasets and Benchmarks Readers: Everyone
Keywords: Egocentric Vision, Pixel Segmentations, Hands, Active Objects, Action, Long-Term Understanding
TL;DR: New dataset and benchmark suite for long-term pixel-level segmentations of hand-object interactions in egocentric video
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
Author Statement: Yes
URL: https://epic-kitchens.github.io/VISOR/
Dataset Url: https://epic-kitchens.github.io/VISOR/
License: CC BY-NC 4.0 license
Supplementary Material: pdf
Contribution Process Agreement: Yes
In Person Attendance: Yes
Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 7 code implementations](https://www.catalyzex.com/paper/arxiv:2209.13064/code)
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