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Related Experiment Video

Updated: Sep 19, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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MTCM: Multi-context temporal consistent modeling for referring video object segmentation.

Sun-Hyuk Choi1, Hayoung Jo1, Seong-Whan Lee1

  • 1Department of Artificial Intelligence, Korea University, Anam-dong, Seongbuk-gu, Seoul, 02841, Republic of Korea.

Neural Networks : the Official Journal of the International Neural Network Society
|June 18, 2025
PubMed
Summary

This study introduces a novel module to improve referring video object segmentation (RVOS) by enhancing temporal consistency and context awareness. The proposed method significantly boosts performance in segmenting objects based on text descriptions.

Keywords:
Multi-contextReferring video object segmentationTemporal consistency

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Referring Video Object Segmentation (RVOS) methods leverage transformers for improved text-video modality interaction.
  • Existing transformer-based RVOS approaches face challenges in temporal modeling, specifically query inconsistency and limited context awareness.
  • These limitations lead to unstable object masks and inaccurate segmentation due to poor text-video alignment.

Purpose of the Study:

  • To address the limitations in temporal modeling for RVOS.
  • To enhance query consistency and context awareness in transformer-based RVOS models.
  • To improve the accuracy and stability of object segmentation based on textual descriptions.

Main Methods:

  • Proposes the Multi-context Temporal Consistency Module (MTCM) integrating an Aligner and a Multi-Context Enhancer (MCE).
  • The Aligner component focuses on enhancing query consistency by filtering noise and aligning queries.
  • The MCE component improves text-relevance by selecting queries through comprehensive context analysis.

Main Results:

  • The MTCM was applied to four different RVOS models, demonstrating performance improvements across all.
  • Achieved a J&F score of 47.6 on the MeViS dataset, indicating enhanced segmentation accuracy.
  • The module effectively improved temporal consistency and context awareness in the tested models.

Conclusions:

  • The proposed MTCM effectively overcomes challenges in temporal modeling for RVOS.
  • The module enhances query consistency and context awareness, leading to more accurate and stable video object segmentation.
  • MTCM offers a significant advancement for transformer-based RVOS methods, with code publicly available for further research.