InterRVOS: Interaction-aware Referring Video Object Segmentation

• URL: http://arxiv.org/abs/2506.02356v2
• Date: Wed, 04 Jun 2025 09:23:48 GMT
• Title: InterRVOS: Interaction-aware Referring Video Object Segmentation
• Authors: Woojeong Jin, Seongchan Kim, Seungryong Kim,
• Abstract summary: Referring video object segmentation aims to segment the object in a video corresponding to a given natural language expression.<n>In comprehensive video understanding, an object's role is often defined by its interactions with other entities.<n>We introduce Interaction-aware referring video object sgementation, a new task that requires segmenting both actor and target entities involved in an interaction.
• Score: 37.53744746544299
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Referring video object segmentation aims to segment the object in a video corresponding to a given natural language expression. While prior works have explored various referring scenarios, including motion-centric or multi-instance expressions, most approaches still focus on localizing a single target object in isolation. However, in comprehensive video understanding, an object's role is often defined by its interactions with other entities, which are largely overlooked in existing datasets and models. In this work, we introduce Interaction-aware referring video object sgementation (InterRVOS), a new task that requires segmenting both actor and target entities involved in an interaction. Each interactoin is described through a pair of complementary expressions from different semantic perspectives, enabling fine-grained modeling of inter-object relationships. To tackle this task, we propose InterRVOS-8K, the large-scale and automatically constructed dataset containing diverse interaction-aware expressions with corresponding masks, including challenging cases such as motion-only multi-instance expressions. We also present a baseline architecture, ReVIOSa, designed to handle actor-target segmentation from a single expression, achieving strong performance in both standard and interaction-focused settings. Furthermore, we introduce an actor-target-aware evalaution setting that enables a more targeted assessment of interaction understanding. Experimental results demonstrate that our approach outperforms prior methods in modeling complex object interactions for referring video object segmentation task, establishing a strong foundation for future research in interaction-centric video understanding. Our project page is available at https://cvlab-kaist.github.io/InterRVOS.

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