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arxiv: 2606.21949 · v1 · pith:T46CC2VTnew · submitted 2026-06-20 · 💻 cs.CV · cs.CL

CapRiCorn-1K: A Comprehensive Benchmark for Video Captioning and Subject Referential Consistency Across Temporal Scales

Xinlong Chen , Jiafu Tang , Yue Ding , Yizhuo Jia , Bozhou Li , Bohan Zeng , Yang Shi , Shihao Li
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Yiyan Ji Qiang Liu Weihong Lin Yuanxing Zhang Pengfei Wan Liang Wang Tieniu Tan
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Pith reviewed 2026-06-26 12:07 UTC · model grok-4.3

classification 💻 cs.CV cs.CL
keywords video captioningbenchmarksubject referential consistencytemporal scalesvideo understandingdownstream tasksmultimodal evaluation
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The pith

The CapRiCorn-1K benchmark shows current video captioning models lose accuracy and subject reference consistency as videos grow longer.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces CapRiCorn-1K to test how well video captioning models produce accurate, comprehensive captions while keeping consistent references to subjects across different video lengths and domains. It supports evaluation in both audiovisual and visual-only settings. Experiments with existing models find they generally fail at these tasks, and results worsen as video duration increases. The benchmark's metrics also track closely with how well the generated captions support other video understanding and generation work.

Core claim

CapRiCorn-1K is a benchmark for video captioning quality and subject referential consistency across long temporal horizons and diverse domains. It shows that current models struggle to generate accurate and comprehensive captions while maintaining consistent subject references, with both quality and consistency declining as video duration increases. The benchmark works in audiovisual and visual-only modes, and its metrics correlate strongly with performance on downstream tasks that use the captions.

What carries the argument

CapRiCorn-1K benchmark, which measures caption accuracy, comprehensiveness, and subject referential consistency across temporal scales.

If this is right

  • Models require advances in temporal handling to keep subject references stable in longer videos.
  • The benchmark metrics can serve as predictors for caption usefulness in other tasks.
  • Support for both audiovisual and visual-only inputs allows targeted testing of different input types.
  • Observed drops with longer durations indicate limits in current approaches to tracking subjects over time.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Model training could shift toward including more long-form videos to address the length-related declines.
  • Emphasis on referential consistency may lead to new architectures focused on entity tracking in video narratives.
  • The benchmark could be applied to test whether gains in consistency directly improve tasks like video summarization.

Load-bearing premise

The videos, annotations, and metrics selected for CapRiCorn-1K give an objective measure of caption quality and consistency that applies beyond the benchmark itself.

What would settle it

Finding a captioning model that scores high on CapRiCorn-1K yet shows no improvement or even worse results on downstream tasks when its captions are used would challenge the benchmark's value.

Figures

Figures reproduced from arXiv: 2606.21949 by Bohan Zeng, Bozhou Li, Jiafu Tang, Liang Wang, Pengfei Wan, Qiang Liu, Shihao Li, Tieniu Tan, Weihong Lin, Xinlong Chen, Yang Shi, Yiyan Ji, Yizhuo Jia, Yuanxing Zhang, Yue Ding.

Figure 1
Figure 1. Figure 1: The impact of ambiguous or inconsistent subject references. In the latter half of the baseline caption, the [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Evaluation pipeline of CapRiCorn-1K: (1) determining the mention status of all keypoints to assess overall [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Statistics of CapRiCorn-1K: (a) Diverse cate [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Correlation between evaluation metrics on [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Screenshot of the annotation system interface. [PITH_FULL_IMAGE:figures/full_fig_p013_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Further analysis of captioning performance [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Error analysis for clothing-change scenarios. Keypoints marked with [PITH_FULL_IMAGE:figures/full_fig_p015_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Error analysis in multi-subject scenarios. Keypoints marked with [PITH_FULL_IMAGE:figures/full_fig_p016_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Error analysis in multi-scene scenarios. Keypoints marked with [PITH_FULL_IMAGE:figures/full_fig_p017_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Prompts to jointly evaluate the mention status of subject-related keypoints and extract subject descriptions [PITH_FULL_IMAGE:figures/full_fig_p018_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Prompts to evaluate the mention status of other keypoints not related to the subjects. [PITH_FULL_IMAGE:figures/full_fig_p019_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Prompts for clustering descriptions of the same ground-truth subject. [PITH_FULL_IMAGE:figures/full_fig_p020_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: List of prompts used to evaluate the audiovisual video captioning models. During evaluation, prompts [PITH_FULL_IMAGE:figures/full_fig_p021_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: List of prompts used to evaluate the vision-only video captioning models. During evaluation, prompts [PITH_FULL_IMAGE:figures/full_fig_p022_14.png] view at source ↗
read the original 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. The project is available at https://github.com/xlchen0205/CapRiCorn-1K .

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 3 minor

Summary. The manuscript introduces CapRiCorn-1K, a benchmark for video captioning that evaluates both overall caption quality (accuracy and comprehensiveness) and subject referential consistency across temporal scales and diverse domains. It supports audiovisual and visual-only settings. Experiments on the benchmark show that current models struggle with these properties, that performance declines as video duration increases, and that the proposed evaluation metrics correlate strongly with downstream understanding and generation task performance.

Significance. A benchmark explicitly targeting subject referential consistency over long temporal horizons addresses a recognized gap in video captioning evaluation. If the dataset curation, metric definitions, and reported correlations are robust and reproducible, the work could provide a useful standardized testbed for model development in this area. The public GitHub release supports reproducibility.

minor comments (3)
  1. [Abstract / Experiments] The abstract states that the metrics 'exhibit strong correlations' with downstream tasks; the main text should include the exact correlation coefficients, p-values, and the number of models/tasks used to support this claim (e.g., in the experiments or results section).
  2. [Methods / Evaluation Metrics] Clarify the precise definition and computation of 'subject referential consistency' (e.g., how coreference chains are identified and scored across frames) in the methods or evaluation-metrics subsection.
  3. [Dataset Construction] Provide more detail on video selection criteria, duration binning, and domain coverage to allow readers to assess potential selection bias.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive assessment of CapRiCorn-1K and the recommendation of minor revision. The referee summary accurately captures the benchmark's focus on caption quality, subject referential consistency, and correlations with downstream tasks. No specific major comments were raised in the report.

Circularity Check

0 steps flagged

No significant circularity

full rationale

This is a benchmark paper whose central claims consist of empirical observations (model performance struggles, duration-dependent decline, metric-downstream correlations) obtained by running existing captioning models on the newly introduced CapRiCorn-1K dataset and metrics. No derivation chain, equations, fitted parameters renamed as predictions, or self-citation load-bearing steps appear in the supplied text. The benchmark design, annotations, and metrics are presented as independent contributions rather than quantities derived from the evaluated models themselves. The paper is therefore self-contained against external benchmarks and receives the default non-circularity finding.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available, so the ledger is necessarily incomplete. The benchmark likely rests on standard computer-vision assumptions about caption quality and referential consistency; no invented entities are mentioned. Free parameters would include any thresholds or weighting schemes inside the proposed metrics, but these cannot be enumerated without the full text.

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discussion (0)

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Reference graph

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    They contain the same subject name. In this case, attribute differences must be ignored, and **all descriptions with the same subject name must always be grouped into a single cluster**

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    a girl” should be treated as distinct subjects, because the only feature

    Based on the video caption, it can be reasonably and clearly inferred that the descriptions refer to the same subject. Guidelines: - Note that identical descriptions do not necessarily refer to the same subject. - For example, multiple generic references such as “a girl” should be treated as distinct subjects, because the only feature "girl" is too vague ...

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    Be sure to include as much of the audio information as possible, and ensure that your descriptions of the audio and video are closely aligned

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    Include as much information from the audio as possible, and ensure that the descriptions of both audio and video are well- coordinated

    Thoroughly describe everything in the video, capturing every detail. Include as much information from the audio as possible, and ensure that the descriptions of both audio and video are well- coordinated. Ensure coherence in the description of the same subject throughout

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    As you describe, you should also describe as much of the information in the audio as possible, and pay attention to the synchronization between the audio and video descriptions

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    Additionally, include as much of the audio content as you can, and make sure your descriptions of the audio and video are synchronized

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  27. [27]

    Pay attention to the identities and appearances of recurring subjects, their actions, interactions, locations, and how the scene evolves over time

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    Describe what happens from beginning to end, and maintain consistent references to the same subjects throughout the description

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    Give a detailed account of the visual content in the video, capturing the subjects, objects, actions, backgrounds, scene transitions, and temporal order of events. Ensure that recurring subjects are described coherently and consistently. Ensure coherence in the description of the same subject throughout. Figure 14: List of prompts used to evaluate the vis...