@inproceedings{chen-etal-2026-capricorn,
title = "{C}ap{R}i{C}orn-1{K}: A Comprehensive Benchmark for Video Captioning and Subject Referential Consistency Across Temporal Scales",
author = "Chen, Xinlong and
Tang, Jiafu and
Ding, Yue and
Jia, Yizhuo and
Li, Bozhou and
Zeng, Bohan and
Shi, Yang and
Li, Shihao and
Ji, Yiyan and
Liu, Qiang and
Lin, Weihong and
Zhang, Yuanxing and
Wan, Pengfei and
Wang, Liang and
Tan, Tieniu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.272/",
pages = "6171--6197",
ISBN = "979-8-89176-459-0",
abstract = "Accurate and comprehensive video captions with consistent subject references are critical for downstream understanding and generation tasks. However, few existing benchmarks can objectively and comprehensively evaluate these properties across diverse durations and scenarios, thereby hindering the advancement of video captioning models. To bridge this gap, we propose CapRiCorn-1K, a comprehensive benchmark designed to evaluate both video captioning quality and subject referential consistency across long temporal horizons and diverse video domains. To accommodate varied evaluation needs, our benchmark supports both audiovisual and visual-only settings. Extensive experiments on CapRiCorn-1K reveal that current models generally struggle to generate accurate and comprehensive captions while maintaining consistent subject references. Moreover, as video duration increases, both the overall caption quality and subject referential consistency decline. Notably, our evaluation metrics exhibit strong correlations with the performance of downstream understanding and generation tasks conditioned on the generated captions, further validating their effectiveness. CapRiCorn-1K will be fully open-sourced to facilitate future research."
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<abstract>Accurate and comprehensive video captions with consistent subject references are critical for downstream understanding and generation tasks. However, few existing benchmarks can objectively and comprehensively evaluate these properties across diverse durations and scenarios, thereby hindering the advancement of video captioning models. To bridge this gap, we propose CapRiCorn-1K, a comprehensive benchmark designed to evaluate both video captioning quality and subject referential consistency across long temporal horizons and diverse video domains. To accommodate varied evaluation needs, our benchmark supports both audiovisual and visual-only settings. Extensive experiments on CapRiCorn-1K reveal that current models generally struggle to generate accurate and comprehensive captions while maintaining consistent subject references. Moreover, as video duration increases, both the overall caption quality and subject referential consistency decline. Notably, our evaluation metrics exhibit strong correlations with the performance of downstream understanding and generation tasks conditioned on the generated captions, further validating their effectiveness. CapRiCorn-1K will be fully open-sourced to facilitate future research.</abstract>
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%0 Conference Proceedings
%T CapRiCorn-1K: A Comprehensive Benchmark for Video Captioning and Subject Referential Consistency Across Temporal Scales
%A Chen, Xinlong
%A Tang, Jiafu
%A Ding, Yue
%A Jia, Yizhuo
%A Li, Bozhou
%A Zeng, Bohan
%A Shi, Yang
%A Li, Shihao
%A Ji, Yiyan
%A Liu, Qiang
%A Lin, Weihong
%A Zhang, Yuanxing
%A Wan, Pengfei
%A Wang, Liang
%A Tan, Tieniu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F chen-etal-2026-capricorn
%X Accurate and comprehensive video captions with consistent subject references are critical for downstream understanding and generation tasks. However, few existing benchmarks can objectively and comprehensively evaluate these properties across diverse durations and scenarios, thereby hindering the advancement of video captioning models. To bridge this gap, we propose CapRiCorn-1K, a comprehensive benchmark designed to evaluate both video captioning quality and subject referential consistency across long temporal horizons and diverse video domains. To accommodate varied evaluation needs, our benchmark supports both audiovisual and visual-only settings. Extensive experiments on CapRiCorn-1K reveal that current models generally struggle to generate accurate and comprehensive captions while maintaining consistent subject references. Moreover, as video duration increases, both the overall caption quality and subject referential consistency decline. Notably, our evaluation metrics exhibit strong correlations with the performance of downstream understanding and generation tasks conditioned on the generated captions, further validating their effectiveness. CapRiCorn-1K will be fully open-sourced to facilitate future research.
%U https://aclanthology.org/2026.emnlp-main.272/
%P 6171-6197
Markdown (Informal)
[CapRiCorn-1K: A Comprehensive Benchmark for Video Captioning and Subject Referential Consistency Across Temporal Scales](https://aclanthology.org/2026.emnlp-main.272/) (Chen et al., EMNLP 2026)
ACL
- Xinlong Chen, Jiafu Tang, Yue Ding, Yizhuo Jia, Bozhou Li, Bohan Zeng, Yang Shi, Shihao Li, Yiyan Ji, Qiang Liu, Weihong Lin, Yuanxing Zhang, Pengfei Wan, Liang Wang, and Tieniu Tan. 2026. CapRiCorn-1K: A Comprehensive Benchmark for Video Captioning and Subject Referential Consistency Across Temporal Scales. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 6171–6197, Budapest, Hungary. Association for Computational Linguistics.