@inproceedings{hong-etal-2026-target,
title = "The Target Is a Predicate: Predicate-State Grounding for Semantic-Level Video Reasoning Segmentation",
author = "Hong, Yuhuan and
Ren, Yiming and
Lin, Zhiqiang and
Wu, Feize and
Xingyun, Liu and
Xie, Yitong and
Zhou, Lei and
Wu, Lisheng and
Wang, Junjie and
Yang, Yujiu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.938/",
pages = "19783--19797",
ISBN = "979-8-89176-462-0",
abstract = "In this paper, we introduce Predicate-State Video Reasoning Segmentation (PS-VRS), where a query defines a dynamic predicate $P(o,t)$ over objects and time rather than a fixed object identity. Existing methods typically treat the target as permanently active once identified, leading to over-emission of masks during vacant intervals and failure to handle target switching. To address this, we construct PS-Bench, a large-scale benchmark with 2,077 videos and 16,250 queries annotated with explicit active/vacant predicate states, temporal intervals, and pixel masks. We further propose Predicate-State Grounding (PSG), a State-Bind-Emit pipeline that: (1) predicts [ACTIVE]/[VACANT] state tokens; (2) aligns them to video frames via dense cross-modal similarity; and (3) emits masks via SAM2 only for active states. Experiments show that PSG outperforms existing baselines by $+9.32$ $\mathcal{J}$ $\mathcal{F} $on PS-Bench and significantly reduces false-positive mask emissions. Our results demonstrate the effectiveness of explicit predicate-state modeling for video reasoning segmentation with dynamic visual witnesses."
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<abstract>In this paper, we introduce Predicate-State Video Reasoning Segmentation (PS-VRS), where a query defines a dynamic predicate P(o,t) over objects and time rather than a fixed object identity. Existing methods typically treat the target as permanently active once identified, leading to over-emission of masks during vacant intervals and failure to handle target switching. To address this, we construct PS-Bench, a large-scale benchmark with 2,077 videos and 16,250 queries annotated with explicit active/vacant predicate states, temporal intervals, and pixel masks. We further propose Predicate-State Grounding (PSG), a State-Bind-Emit pipeline that: (1) predicts [ACTIVE]/[VACANT] state tokens; (2) aligns them to video frames via dense cross-modal similarity; and (3) emits masks via SAM2 only for active states. Experiments show that PSG outperforms existing baselines by +9.32 \mathcalJ \mathcalF on PS-Bench and significantly reduces false-positive mask emissions. Our results demonstrate the effectiveness of explicit predicate-state modeling for video reasoning segmentation with dynamic visual witnesses.</abstract>
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%0 Conference Proceedings
%T The Target Is a Predicate: Predicate-State Grounding for Semantic-Level Video Reasoning Segmentation
%A Hong, Yuhuan
%A Ren, Yiming
%A Lin, Zhiqiang
%A Wu, Feize
%A Xingyun, Liu
%A Xie, Yitong
%A Zhou, Lei
%A Wu, Lisheng
%A Wang, Junjie
%A Yang, Yujiu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F hong-etal-2026-target
%X In this paper, we introduce Predicate-State Video Reasoning Segmentation (PS-VRS), where a query defines a dynamic predicate P(o,t) over objects and time rather than a fixed object identity. Existing methods typically treat the target as permanently active once identified, leading to over-emission of masks during vacant intervals and failure to handle target switching. To address this, we construct PS-Bench, a large-scale benchmark with 2,077 videos and 16,250 queries annotated with explicit active/vacant predicate states, temporal intervals, and pixel masks. We further propose Predicate-State Grounding (PSG), a State-Bind-Emit pipeline that: (1) predicts [ACTIVE]/[VACANT] state tokens; (2) aligns them to video frames via dense cross-modal similarity; and (3) emits masks via SAM2 only for active states. Experiments show that PSG outperforms existing baselines by +9.32 \mathcalJ \mathcalF on PS-Bench and significantly reduces false-positive mask emissions. Our results demonstrate the effectiveness of explicit predicate-state modeling for video reasoning segmentation with dynamic visual witnesses.
%U https://aclanthology.org/2026.findings-emnlp.938/
%P 19783-19797
Markdown (Informal)
[The Target Is a Predicate: Predicate-State Grounding for Semantic-Level Video Reasoning Segmentation](https://aclanthology.org/2026.findings-emnlp.938/) (Hong et al., Findings 2026)
ACL
- Yuhuan Hong, Yiming Ren, Zhiqiang Lin, Feize Wu, Liu Xingyun, Yitong Xie, Lei Zhou, Lisheng Wu, Junjie Wang, and Yujiu Yang. 2026. The Target Is a Predicate: Predicate-State Grounding for Semantic-Level Video Reasoning Segmentation. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 19783–19797, Budapest, Hungary. Association for Computational Linguistics.