# PM-VIS+: High-Performance Video Instance Segmentation without Video Annotation

Source: [PM-VIS+: High-Performance Video Instance Segmentation without Video Annotation](<https://www.sota2.com/research/paper/68fa73da1e209131cea5e842>)

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

Data updated: 2025-10-26T02:57:11.769Z

Authors: Zhangjing Yang, Dun Liu, Xin Wang, Zhe Li, Barathwaj Anandan, Yi Wu

Published: 2024-06-28T05:22:39.000Z

## Abstract

Video instance segmentation requires detecting, segmenting, and tracking objects in videos, typically relying on costly video annotations. This paper introduces a method that eliminates video annotations by utilizing image datasets. The PM-VIS algorithm is adapted to handle both bounding box and instance-level pixel annotations dynamically. We introduce ImageNet-bbox to supplement missing categories in video datasets and propose the PM-VIS+ algorithm to adjust supervision based on annotation types. To enhance accuracy, we use pseudo masks and semi-supervised optimization techniques on unannotated video data. This method achieves high video instance segmentation performance without manual video annotations, offering a cost-effective solution and new perspectives for video instance segmentation applications. The code will be available in https://github.com/ldknight/PM-VIS-plus

## 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.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2406.19665>)

[PDF](<https://arxiv.org/pdf/2406.19665.pdf>)