CodeCap-Bench: 可执行代码能否作为优质视频字幕？
CodeCap-Bench: Can Code Serve as a Good Video Caption?
CodeCap-Bench 通过比较代码字幕与自然语言字幕在视频问答和重建任务上的表现，证明代码字幕具有更优的推理准确性和重建保真度。该基准包含 323 个结构化 2D 内容视频，涵盖五个类别，评估发现主流闭源 MLLM 在代码字幕生成上虽表现良好，但难以捕捉复杂时序动态，开源模型差距显著。
Published on Oct 27
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Abstract
Executable code representations of video content improve reasoning and reconstruction over natural language, yet current multimodal models still struggle to generate faithful code captions for complex temporal dynamics.
Video captioning aims to preserve visual information through compact descriptions, predominantly using natural language. However, precisely expressing fine-grained spatial structures and temporal dynamics in natural language often leads to lengthy and potentially ambiguous descriptions. In this work, we explore Code as Video Caption, a paradigm where executable programs serve as a complementary representation of visual content through graphical primitives, layout rules, and functions of time. To examine whether code captions effectively preserve video information, we compare them with natural language captions on downstream question answering and video reconstruction tasks. Our results show that code captions achieve superior reasoning accuracy and reconstruction fidelity. Despite these advantages, realizing the potential of code captioning depends on whether current multimodal large language models (MLLMs) can generate faithful code captions. To systematically assess this capability, we introduce CodeCap-Bench, which comprises 323 videos spanning five categories of structured 2D content, along with fine-grained annotations and a multi-level evaluation framework. Extensive experiments reveal that while leading proprietary MLLMs demonstrate promising code captioning capabilities, they still struggle to faithfully capture complex temporal dynamics, with open-source models lagging substantially behind.
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来源: Hugging Face Daily Papers · huggingface.co