Methods › Computer Vision › Video Instance Segmentation Models › VisTR

VisTR

3 papers tagged archive 2025-07-28

Introduced by Yuqing Wang et al. in End-to-End Video Instance Segmentation with Transformers

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

VisTR is a Transformer based video instance segmentation model. It views video instance segmentation as a direct end-to-end parallel sequence decoding/prediction problem. Given a video clip consisting of multiple image frames as input, VisTR outputs the sequence of masks for each instance in the video in order directly. At the core is a new, effective instance sequence matching and segmentation strategy, which supervises and segments instances at the sequence level as a whole. VisTR frames the instance segmentation and tracking in the same perspective of similarity learning, thus considerably simplifying the overall pipeline and is significantly different from existing approaches.

PaperSource

Papers archive 2025-07-28

3 shown of 3, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

6 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Instance Segmentation3
Semantic Segmentation3
Video Instance Segmentation3
Segmentation2
GPU1
Video Understanding1

Usage over time archive 2025-07-28

Papers per year tagged with VisTR: 2020 to 2022, peak 2 2 0 2020: 1 paper 2020 2021: 0 papers 2021 2022: 2 papers 2022
Papers per year the archive tags with this method, by the paper's archive date (3 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Video Instance Segmentation ModelsInstance Segmentation Models

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections