# EPIC-KITCHENS VISOR Benchmark: VIdeo Segmentations and Object Relations

Source: [EPIC-KITCHENS VISOR Benchmark: VIdeo Segmentations and Object Relations](<https://www.sota2.com/research/paper/68fa6d801e209131cea3a19e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-04T05:22:33.796Z

Authors: Ahmad Darkhalil, Dandan Shan, Bin Zhu, Jian Ma, Amlan Kar, Richard Higgins, Sanja Fidler, David Fouhey, Dima Damen

Published: 2022-09-26T23:03:26.000Z

## 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

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Spatial contact segmentation on EPIC-VISOR (val)](<https://www.sota2.com/research/sota/spatial-contact-segmentation-on-epic-visor-val.md>): Spatial contact segmentation; EPIC-VISOR (val); IOU: 40.2.

- [Temporal contact detection on VISOR (val)](<https://www.sota2.com/research/sota/temporal-contact-detection-on-visor-val.md>): Temporal contact detection; VISOR (val); BC: 66.3.

- [Spatial contact segmentation on HOI4D (val)](<https://www.sota2.com/research/sota/spatial-contact-segmentation-on-hoi4d-val.md>): Spatial contact segmentation; HOI4D (val); IoU: 50.2.

- [Spatial contact segmentation on FEEL (val)](<https://www.sota2.com/research/sota/spatial-contact-segmentation-on-feel-val.md>): Spatial contact segmentation; FEEL (val); IoU: 11.1.

- [Temporal contact detection on HOI4D (val)](<https://www.sota2.com/research/sota/temporal-contact-detection-on-hoi4d-val.md>): Temporal contact detection; HOI4D (val); BC: 55.3.

- [Temporal contact detection on FEEL (val)](<https://www.sota2.com/research/sota/temporal-contact-detection-on-feel-val.md>): Temporal contact detection; FEEL (val); BC: 45.2.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2209.13064>)

[PDF](<https://arxiv.org/pdf/2209.13064.pdf>)

[Implementation](<https://github.com/epic-kitchens/VISOR-VOS>)
