titleCLIP-CC-Bench: Evaluating Paragraph-Level Video Descriptions in Video-Language Models
authorsMukhtiar Ali, Harsh Dubey, Sugam Mishra, Chulwoo Pack
date2026-08-05
venuearXiv (preprint, not peer reviewed)
idarXiv:2608.04302
affiliationMINT Lab
upvotes2 (Hugging Face Daily Papers, 2026-11-01)
linksabs pdf hf code
abstractBenchmarking video-language models has largely focused on short clips and single-sentence metrics, leaving open whether current systems can generate accurate long-form, paragraph-level descriptions. We introduce CLIP-CC-Bench, an evaluation suite for long-form video description built from 5 hours of movie content segmented into 90-second clips, each paired with an expert-written paragraph-style reference. The evaluation suite employs an ensemble of five state-of-the-art LLM-based embedding models to increase reliability and mitigate single-model bias, and applies two complementary methodologies: (i) coarse-grained semantic matching and (ii) fine-grained semantic matching to compare model-generated descriptions against CLIP-CC-Bench references. Using this framework, we evaluate 17 state-of-the-art video-language models and report both their Borda-aggregated rankings and their average scores on CLIP-CC-Bench. We further quantify the protocol's internal reliability through inter-judge agreement and bootstrap ranking stability. We release standardized evaluation scripts, model outputs, and aggregation tools at https://github.com/Multimodal-Intelligence-Lab/CLIP-CC-Bench to support reproducibility. CLIP-CC-Bench provides a practical evaluation framework for long-form video description, filling a gap left by existing short-clip and QA-only benchmarks.

cite

@misc{ali2026clip,
  title={CLIP-CC-Bench: Evaluating Paragraph-Level Video Descriptions in Video-Language Models},
  author={Mukhtiar Ali and Harsh Dubey and Sugam Mishra and Chulwoo Pack},
  year={2026},
  eprint={2608.04302},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2608.04302}
}