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This paper introduces CLIP-CC-Bench, an evaluation suite designed to assess paragraph-level video descriptions generated by video-language models, addressing the limitations of existing benchmarks that focus on short clips and single-sentence metrics. By utilizing a dataset of 90-second movie clips paired with expert-written paragraph references, the authors employ a dual methodology of coarse- and fine-grained semantic matching using five state-of-the-art LLM-based embedding models to enhance evaluation reliability. The results reveal significant insights into the performance of 17 leading video-language models, providing a comprehensive ranking and demonstrating the internal reliability of the evaluation protocol.
CLIP-CC-Bench reveals that current video-language models struggle with generating coherent long-form descriptions, highlighting a critical gap in their capabilities.
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-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.
