FeVOS: Foresight Expression Video Object Segmentation

• URL: http://arxiv.org/abs/2606.25585v1
• Date: Wed, 24 Jun 2026 08:56:48 GMT
• Title: FeVOS: Foresight Expression Video Object Segmentation
• Abstract summary: Video Object tasks focus on expressions describing events, actions or appearances of relevant objects within the observed frames.<n>We propose Foresight Expression, referring Video Object frames that queries future events in upcoming video segments as visual answers.<n>We introduce FeVOS, a dataset with 968 video clips, 14,525 expressions and 2,904 chain-of-thought annotations.<n>We develop FeVOS-1, an explicit MLLM-based model trained on our dataset via a two-stage pipeline of supervised fine-tuning and learning.
• Score: 46.1622421004227
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing Referring Video Object Segmentation tasks focus on referring expressions describing events, actions or appearances of relevant objects within the observed frames, lacking evaluation in scenarios that require pre-decisive spatio-temporal reasoning, thereby limiting their applicability. To address this, we propose Foresight Expression Video Object Segmentation, a task that queries future events in upcoming video segments and requires masks of the objects in the observed frames as visual answers. For example, in ego-centric scenes, the question "What tool will be used?" demands reasoning over spatio-temporal cues to predict the masks of the next tool to be used, which helps with the understanding of future actions and decisions. To support this task, we introduce FeVOS, a dataset with 968 video clips, 14,525 foresight expressions, and 2,904 chain-of-thought annotations to provide explicit and interpretable reasoning steps. We further develop FeVOS-R1, an MLLM-based model trained on our dataset via a two-stage pipeline of supervised fine-tuning and reinforcement learning. FeVOS-R1 not only achieves state-of-the-art performance on FeVOS, but also demonstrates strong generalization to existing RVOS benchmarks. We hope this work can inspire more research on predictive reasoning in video perception.

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