1st Place Solution for 5th LSVOS Challenge: Referring Video Object Segmentation

• URL: http://arxiv.org/abs/2401.00663v1
• Date: Mon, 1 Jan 2024 04:24:48 GMT
• Title: 1st Place Solution for 5th LSVOS Challenge: Referring Video Object Segmentation
• Authors: Zhuoyan Luo, Yicheng Xiao, Yong Liu, Yitong Wang, Yansong Tang, Xiu Li, Yujiu Yang
• Abstract summary: We integrate strengths of leading RVOS models to build up an effective paradigm. To improve the consistency and quality of masks, we propose Two-Stage Multi-Model Fusion strategy. Our method achieves 75.7% J&F on Ref-Youtube-VOS validation set and 70% J&F on test set, which ranks 1st place on 5th Large-scale Video Object Challenge (ICCV 2023) track 3.
• Score: 65.45702890457046
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The recent transformer-based models have dominated the Referring Video Object Segmentation (RVOS) task due to the superior performance. Most prior works adopt unified DETR framework to generate segmentation masks in query-to-instance manner. In this work, we integrate strengths of that leading RVOS models to build up an effective paradigm. We first obtain binary mask sequences from the RVOS models. To improve the consistency and quality of masks, we propose Two-Stage Multi-Model Fusion strategy. Each stage rationally ensembles RVOS models based on framework design as well as training strategy, and leverages different video object segmentation (VOS) models to enhance mask coherence by object propagation mechanism. Our method achieves 75.7% J&F on Ref-Youtube-VOS validation set and 70% J&F on test set, which ranks 1st place on 5th Large-scale Video Object Segmentation Challenge (ICCV 2023) track 3. Code is available at https://github.com/RobertLuo1/iccv2023_RVOS_Challenge.

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