CLIP-CC-Bench: Evaluating Paragraph-Level Video Descriptions in Video-Language Models
Benchmarking 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-...
| Authors | Mukhtiar Ali, Harsh Dubey, Sugam Mishra et al. |
|---|---|
| Primary category | cs.CV |
| Published | 5 August 2026 |
| Last updated at source | 5 August 2026 |
Models and datasets referencing this paper
- MINT-SDSU/CLIP-CCDataset
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.