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CapMap-MS-TTA system ranks 3rd in LSVOS Challenge multimodal track

By PulseAugur Editorial · [1 sources] ·

A research paper details CapMap-MS-TTA, a system that achieved third place in the MUMU track of the 8th LSVOS Challenge at ECCV 2026. This track required a single multimodal model to perform image tagging, open-vocabulary object detection, and English captioning within strict resource limitations. The CapMap-MS-TTA solution, built upon Microsoft Florence-2-base, utilized a training-free approach with caption keyword mapping and multi-scale flip test-time augmentation to improve performance. AI

IMPACT Demonstrates advancements in multimodal AI for complex vision tasks under resource constraints.

RANK_REASON The cluster describes a research paper detailing a system's performance in a specific challenge. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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

CapMap-MS-TTA system ranks 3rd in LSVOS Challenge multimodal track

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The cluster describes a research paper detailing a system's performance in a specific challenge. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Chengfeng Qiu, Kaifeng Wei ·

    CapMap-MS-TTA: 3rd Place Solution for the MUMU Track of the 8th LSVOS Challenge at ECCV 2026

    arXiv:2609.18206v1 Announce Type: cross Abstract: The MUMU track of the 8th Large-scale Video Object Segmentation (LSVOS) Challenge requires a single unified multimodal model to jointly solve image tagging (Task A), open-vocabulary object detection (Task B), and English captionin…

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