CodeCap-Bench: Can Code Serve as a Good Video Caption?
The paper proposes Code as Video Caption, where executable programs describe video content using graphical primitives, layout rules, and functions of time. Compared with natural language captions, the authors report that code captions achieve superior reasoning accuracy on downstream question answering and higher video reconstruction fidelity. To test whether current multimodal LLMs can generate such captions, they introduce CodeCap-Bench, with 323 videos across five categories of structured 2D content, fine-grained annotations, and a multi-level evaluation framework. Leading proprietary models show promising code captioning, but struggle with complex temporal dynamics, and open-source models lag substantially.
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- Primary sourceHugging Face Daily Papers
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The paper proposes Code as Video Caption, where executable programs describe video content using graphical primitives, layout rules, and functions of time. Compared with natural language captions, the authors report that code captions achieve superior reasoning accuracy on downstream question answering and higher video reconstruction fidelity. To test whether current multimodal LLMs can generate such captions, they introduce CodeCap-Bench, with 323 videos across five categories of structured 2D content, fine-grained annotations, and a multi-level evaluation framework. Leading proprietary models show promising code captioning, but struggle with complex temporal dynamics, and open-source models lag substantially.
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Key findings from the stored brief
- Code captions achieved superior reasoning accuracy and reconstruction fidelity compared with natural language captions in the reported experiments.
- CodeCap-Bench contains 323 videos spanning five categories of structured 2D content, with fine-grained annotations.
- Leading proprietary MLLMs show promising code captioning but still struggle to faithfully capture complex temporal dynamics.
- Open-source models lag substantially behind proprietary models on code captioning.
Source claims not independently checked; results are reported by the authors on a benchmark they introduced.
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Why this may matter
Code could offer a more precise, less ambiguous representation of visual content than lengthy natural language descriptions. CodeCap-Bench provides a way to measure whether multimodal models can produce faithful code captions, which matters for video understanding and reconstruction.
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