SAMDWICH: Moment-aware Video-text Alignment for Referring Video Object Segmentation

• URL: http://arxiv.org/abs/2508.11955v1
• Date: Sat, 16 Aug 2025 07:34:43 GMT
• Title: SAMDWICH: Moment-aware Video-text Alignment for Referring Video Object Segmentation
• Authors: Seunghun Lee, Jiwan Seo, Jeonghoon Kim, Siwon Kim, Haeun Yun, Hyogyeong Jeon, Wonhyeok Choi, Jaehoon Jeong, Zane Durante, Sang Hyun Park, Sunghoon Im,
• Abstract summary: Referring Video Object (RVOS) aims to segment and track objects in videos based on natural language expressions.<n>Current methods often suffer from semantic misalignment due to indiscriminate frame sampling and supervision of all visible objects during training.<n>We introduce a moment-aware RVOS framework named SAMDWICH, along with a newly annotated dataset, MeViS-M, built upon the challenging MeViS benchmark.
• Score: 16.11630169710364
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Referring Video Object Segmentation (RVOS) aims to segment and track objects in videos based on natural language expressions, requiring precise alignment between visual content and textual queries. However, existing methods often suffer from semantic misalignment, largely due to indiscriminate frame sampling and supervision of all visible objects during training -- regardless of their actual relevance to the expression. To address this, we introduce a moment-aware RVOS framework named SAMDWICH, along with a newly annotated dataset, MeViS-M, built upon the challenging MeViS benchmark. We manually annotate temporal moments indicating when each object is referred to by the expression, enabling semantically grounded supervision that strengthens video-text alignment. SAMDWICH leverages these aligned text-to-clip pairs to guide training, significantly enhancing referential understanding. Building upon this framework, we propose Moment-guided Dual-path Propagation (MDP), a moment-aware propagation strategy that improves both object grounding and tracking by training on both relevant and irrelevant frames through a moment-centric memory mechanism. In addition, we introduce Object-level Selective Supervision (OSS), an object-level filtering strategy that supervises only the objects temporally aligned with the expression in each training clip. This selective supervision reduces semantic noise and reinforces language-conditioned learning. Extensive experiments show that SAMDWICH achieves state-of-the-art performance on challenging MeViS benchmark, particularly excelling in complex scenarios involving diverse expressions.

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