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Home
Lomm: Latest Object Memory Management For Temporally Consistent Video Instance Segmentation.

Home
Lomm: Latest Object Memory Management For Temporally Consistent Video Instance Segmentation.

Related Experiment Video

A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures

A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures

Published on: July 2, 2014

LOMM: Latest Object Memory Management for Temporally Consistent Video Instance Segmentation.

Seunghun Lee¹, Jiwan Seo¹, Minwoo Choi¹

¹DGIST, Daegu, Republic of Korea.

... IEEE International Conference on Computer Vision Workshops. IEEE International Conference on Computer Vision

|May 6, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces Latest Object Memory (LOM) and Latest Object Memory Management (LOMM) for robust video instance segmentation. These methods enhance object tracking and identity consistency, achieving state-of-the-art results on challenging datasets.

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Area of Science:

Computer Vision
Artificial Intelligence

Background:

Video instance segmentation requires robust object tracking and identity management across frames.
Existing methods struggle with long-term tracking and dynamic scenes where objects frequently appear and disappear.

Purpose of the Study:

To introduce a novel system for robustly tracking and continuously updating object states in videos.
To improve temporally consistent video instance segmentation and long-term instance tracking.
To enhance object index assignment accuracy and identity consistency in dynamic scenes.

Main Methods:

Developed Latest Object Memory (LOM) to model object presence across video frames for consistent tracking.
Introduced Latest Object Memory Management (LOMM) for temporally consistent video instance segmentation.

Proposed Decoupled Object Association (DOA) to separately handle newly appearing and existing objects for improved matching accuracy.

Main Results:

The proposed LOMM system achieves state-of-the-art performance in video instance segmentation.
Achieved a 54.0 AP score on the challenging YouTube-VIS 2022 dataset.
Demonstrated superior performance over traditional approaches in extensive experiments and ablation studies.

Conclusions:

The LOM and LOMM systems provide robust and reliable object tracking and identity management in videos.
The DOA strategy effectively handles dynamic scenes, improving matching accuracy and identity consistency.
The method sets a new state-of-the-art for video instance segmentation, particularly for long videos.