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Speech2MaskTrack achieves runner-up in LSVOS Challenge

By PulseAugur Editorial · [1 sources] ·

Researchers have developed Speech2MaskTrack, a novel approach for speech-guided referring video object segmentation. This method connects speech recognition with temporal grounding and mask tracking to identify objects based on spoken motion descriptions. Speech2MaskTrack achieved second place in the MeViS-Audio track of the 8th LSVOS Challenge by transcribing spoken queries into structured constraints and ranking instance tracks using motion and relation evidence. AI

IMPACT This research advances speech-guided video object segmentation, potentially improving human-AI interaction in video analysis tasks.

RANK_REASON The item is an academic paper detailing a runner-up solution for a challenge. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Speech2MaskTrack achieves runner-up in LSVOS Challenge

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The item is an academic paper detailing a runner-up solution for a challenge. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Jinxing Zhou, Suiyi Zhao, Yanghao Zhou, Ruohao Guo ·

    Motion-Aware Reasoning from Speech to Mask Tracks: Runner-up Solution for the MeViS-Audio Track of the 8th LSVOS Challenge 2026

    arXiv:2608.22337v1 Announce Type: cross Abstract: Speech-guided referring video object segmentation aims to recover the mask tracks of objects specified by a spoken motion description. Here, speech carries a linguistic instruction rather than acoustic evidence from a sounding obj…

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