CodeCap-Bench Tests Code-Based Video Captions

Researchers introduced CodeCap-Bench, a benchmark for testing whether multimodal large language models can turn videos into executable code captions. The paper, published on October 27 and submitted to Hugging Face on October 28, evaluates code captions as an alternative representation to natural-language descriptions.

The researchers say natural-language captions can become lengthy or ambiguous when describing fine-grained spatial structures and temporal dynamics. Code captions instead use graphical primitives, layout rules and time-based functions to represent visual content in executable programs.

The paper compares code captions with natural-language captions on downstream question-answering and video-reconstruction tasks. It reports that code captions achieve higher reasoning accuracy and reconstruction fidelity, though it gives no numerical scores in the abstract.

CodeCap-Bench contains 323 videos across five categories of structured 2D content, with fine-grained annotations and a multi-level evaluation framework. Experiments found that leading proprietary multimodal models show promising code-captioning ability but still struggle to faithfully capture complex temporal dynamics. Open-source models lag substantially behind, according to the researchers. The benchmark is designed to assess how faithfully models produce code captions for video content. tarj?

The paper lists Jiafu Tang, Yifan Xu, Eric Tang, Junjie Zhao, Shipeng Luo, Te Sun, Haoxuan Hu, Yiyan Ji, Xinlong Chen, Zihao Wang, Yang Shi, Yuanxing Zhang, Pengfei Wan and Jiaheng Liu as authors.

Researchers introduced CodeCap-Bench, a benchmark for testing whether multimodal large language models can turn videos into executable code captions. The paper, published on October 27 and submitted to Hugging Face on October 28, evaluates code captions as an alternative representation to natural-language descriptions.

The researchers say natural-language captions can become lengthy or ambiguous when describing fine-grained spatial structures and temporal dynamics. Code captions instead use graphical primitives, layout rules and time-based functions to represent visual content in executable programs.

The paper compares code captions with natural-language captions on downstream question-answering and video-reconstruction tasks. It reports that code captions achieve higher reasoning accuracy and reconstruction fidelity, though it gives no numerical scores in the abstract.

CodeCap-Bench contains 323 videos across five categories of structured 2D content, with fine-grained annotations and a multi-level evaluation framework. Experiments found that leading proprietary multimodal models show promising code-captioning ability but still struggle to faithfully capture complex temporal dynamics. Open-source models lag substantially behind, according to the researchers. The benchmark is designed to assess how faithfully models produce code captions for video content. tarj?

The paper lists Jiafu Tang, Yifan Xu, Eric Tang, Junjie Zhao, Shipeng Luo, Te Sun, Haoxuan Hu, Yiyan Ji, Xinlong Chen, Zihao Wang, Yang Shi, Yuanxing Zhang, Pengfei Wan and Jiaheng Liu as authors.