Narrative Action Evaluation with Prompt-Guided Multimodal Interaction

• URL: http://arxiv.org/abs/2404.14471v2
• Date: Fri, 26 Apr 2024 14:35:32 GMT
• Title: Narrative Action Evaluation with Prompt-Guided Multimodal Interaction
• Authors: Shiyi Zhang, Sule Bai, Guangyi Chen, Lei Chen, Jiwen Lu, Junle Wang, Yansong Tang,
• Abstract summary: Narrative action evaluation (NAE) aims to generate professional commentary that evaluates the execution of an action. NAE is a more challenging task because it requires both narrative flexibility and evaluation rigor. We propose a prompt-guided multimodal interaction framework to facilitate the interaction between different modalities of information.
• Score: 60.281405999483
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we investigate a new problem called narrative action evaluation (NAE). NAE aims to generate professional commentary that evaluates the execution of an action. Unlike traditional tasks such as score-based action quality assessment and video captioning involving superficial sentences, NAE focuses on creating detailed narratives in natural language. These narratives provide intricate descriptions of actions along with objective evaluations. NAE is a more challenging task because it requires both narrative flexibility and evaluation rigor. One existing possible solution is to use multi-task learning, where narrative language and evaluative information are predicted separately. However, this approach results in reduced performance for individual tasks because of variations between tasks and differences in modality between language information and evaluation information. To address this, we propose a prompt-guided multimodal interaction framework. This framework utilizes a pair of transformers to facilitate the interaction between different modalities of information. It also uses prompts to transform the score regression task into a video-text matching task, thus enabling task interactivity. To support further research in this field, we re-annotate the MTL-AQA and FineGym datasets with high-quality and comprehensive action narration. Additionally, we establish benchmarks for NAE. Extensive experiment results prove that our method outperforms separate learning methods and naive multi-task learning methods. Data and code are released at https://github.com/shiyi-zh0408/NAE_CVPR2024.

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