CodeCap-Bench asks whether code captions preserve video content
Original title: CodeCap-Bench: Can Code Serve as a Good Video Caption?
On 323 structured 2D videos, code captions beat natural language on question answering and reconstruction, but models still miss complex motion.
The paper proposes executable programs as video captions, using graphical primitives, layout rules, and functions of time instead of only natural language. Comparing the two on question answering and video reconstruction, the authors report higher reasoning accuracy and reconstruction fidelity for code captions. CodeCap-Bench has 323 videos in five categories of structured 2D content, fine-grained annotations, and a multi-level evaluation. Leading proprietary multimodal models show promising code captioning, but the paper says they still fail to capture complex temporal dynamics, with open-source models substantially behind.
Unconfirmed: The paper does not name the specific proprietary or open-source models in this summary text.
- The benchmark covers five categories of structured 2D video.
- Evaluation includes downstream question answering and reconstruction.
- Proprietary MLLMs lead; open-source models lag substantially.
- Faithful capture of complex temporal dynamics remains weak.
From the original article
1 min read at huggingface.co
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.
By Jiafu Tang, Yifan Xu, Eric Tang · huggingface.co
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