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Non-autoregressive video captioning methods generate visual words in parallel but often overlook semantic correlations among them, especially regarding verbs, leading to lower caption quality. To address this, we integrate action information of highlighted objects to enhance semantic connections among visual words. Our proposed Action-aware Language Skeleton Optimization Network (ALSO-Net) tackles the challenge of extracting action information across frames, improving understanding of complex context-dependent video actions and reducing sentence inconsistencies. ALSO-Net incorporates a linguistic skeleton tag generator to refine semantic correlations and a video action predictor to enhance verb prediction accuracy in video captions. We also address issues of unsatisfactory caption length and quality by jointly optimizing different levels of motion prediction loss. Experimental evaluation on prominent video captioning datasets demonstrates that ALSO-Net outperforms baseline methods by a significant margin and achieves competitive performance compared to state-of-the-art autoregressive methods with smaller model complexity and faster inference time.
Chen et al. (Sat,) studied this question.