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

arXiv: https://arxiv.org/abs/2606.21949 · HTML: https://mlsanity.com/paper/2606.21949/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 38 references that are in the map.
- First posted on arXiv 20 Jun 2026.

## Builds on (most cited first, 29 of 29)

- [InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency](https://mlsanity.com/paper/2508.18265/) (2025)
- [Tarsier: Recipes for Training and Evaluating Large Video Description Models](https://mlsanity.com/paper/2407.00634/) (2024)
- [AuroraCap: Efficient, Performant Video Detailed Captioning and a New Benchmark](https://mlsanity.com/paper/2410.03051/) (2024)
- [video-SALMONN: Speech-Enhanced Audio-Visual Large Language Models](https://mlsanity.com/paper/2406.15704/) (2024)
- [Video ReCap: Recursive Captioning of Hour-Long Videos](https://mlsanity.com/paper/2402.13250/) (2024)
- [Tarsier2: Advancing Large Vision-Language Models from Detailed Video Description to Comprehensive Video Understanding](https://mlsanity.com/paper/2501.07888/) (2025)
- [X-InstructBLIP: A Framework for aligning X-Modal instruction-aware representations to LLMs and Emergent Cross-modal Reasoning](https://mlsanity.com/paper/2311.18799/) (2023)
- [Audio-Visual LLM for Video Understanding](https://mlsanity.com/paper/2312.06720/) (2023)
- [OmniVinci: Enhancing Architecture and Data for Omni-Modal Understanding LLM](https://mlsanity.com/paper/2510.15870/) (2025)
- [CAT: Enhancing Multimodal Large Language Model to Answer Questions in Dynamic Audio-Visual Scenarios](https://mlsanity.com/paper/2403.04640/) (2024)
- [LongVALE: Vision-Audio-Language-Event Benchmark Towards Time-Aware Omni-Modal Perception of Long Videos](https://mlsanity.com/paper/2411.19772/) (2024)
- [OneStory: Coherent Multi-Shot Video Generation with Adaptive Memory](https://mlsanity.com/paper/2512.07802/) (2025)
- [Omni-Captioner: Data Pipeline, Models, and Benchmark for Omni Detailed Perception](https://mlsanity.com/paper/2510.12720/) (2025)
- [VidCapBench: A Comprehensive Benchmark of Video Captioning for Controllable Text-to-Video Generation](https://mlsanity.com/paper/2502.12782/) (2025)
- [CaReBench: A Fine-Grained Benchmark for Video Captioning and Retrieval](https://mlsanity.com/paper/2501.00513/) (2024)
- [UGC-VideoCaptioner: An Omni UGC Video Detail Caption Model and New Benchmarks](https://mlsanity.com/paper/2507.11336/) (2025)
- [StoryTeller: Improving Long Video Description through Global Audio-Visual Character Identification](https://mlsanity.com/paper/2411.07076/) (2024)
- [VidBridge-R1: Bridging QA and Captioning for RL-based Video Understanding Models with Intermediate Proxy Tasks](https://mlsanity.com/paper/2506.09079/) (2025)
- [OwlCap: Harmonizing Motion-Detail for Video Captioning via HMD-270K and Caption Set Equivalence Reward](https://mlsanity.com/paper/2508.18634/) (2025)
- [LongCaptioning: Unlocking the Power of Long Video Caption Generation in Large Multimodal Models](https://mlsanity.com/paper/2502.15393/) (2025)
- [Towards Universal Video MLLMs with Attribute-Structured and Quality-Verified Instructions](https://mlsanity.com/paper/2602.13013/) (2026)
- [Towards Long Form Audio-visual Video Understanding](https://mlsanity.com/paper/2306.09431/) (2023)
- [Fine-Grained Captioning of Long Videos through Scene Graph Consolidation](https://mlsanity.com/paper/2502.16427/) (2025)
- [VC4VG: Optimizing Video Captions for Text-to-Video Generation](https://mlsanity.com/paper/2510.24134/) (2025)
- [Script-a-Video: Deep Structured Audio-visual Captions via Factorized Streams and Relational Grounding](https://mlsanity.com/paper/2604.11244/) (2026)
- [FIOVA: A Multi-Annotator Benchmark for Human-Aligned Video Captioning](https://mlsanity.com/paper/2410.15270/) (2024)
- [D-ORCA: Dialogue-Centric Optimization for Robust Audio-Visual Captioning](https://mlsanity.com/paper/2602.07960/) (2026)
- [OmniScript: Towards Audio-Visual Script Generation for Long-Form Cinematic Video](https://mlsanity.com/paper/2604.11102/) (2026)
- [Addressing the ID-Matching Challenge in Long Video Captioning](https://mlsanity.com/paper/2510.06973/) (2025)

## Built on by (most cited first, 2 of 2)

- [RefCaptioner: Multi-Reference Image-Grounded Video Captioning](https://mlsanity.com/paper/2607.28509/) (2026)
- [PIGEONS: Incremental Long-Video Captioning with Persistent Identity Gallery and Evolving Overarching Narrative Summary](https://mlsanity.com/paper/2610.16066/) (2026)

## Abstract (arXiv)

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 .