Concept-Aware Video Captioning: Describing Videos With Effective Prior Information

Area of Science:

• Artificial Intelligence
• Computer Vision
• Natural Language Processing

Background:

• Video captioning aims to generate textual descriptions for video content.
• Existing methods struggle with inaccurate concept detection and underutilization, leading to suboptimal caption quality.
• Concepts, as meaningful words, can bridge the gap between visual and textual information.

Purpose of the Study:

• To propose a novel framework, Concept-awARE video captioning (CARE), for more plausible video caption generation.
• To address limitations in concept detection precision and utilization in current video captioning models.
• To enhance the prior information provided to caption generation models.

Main Methods:

• CARE employs an encoder-decoder structure with multimodal-driven concept detection (MCD) for precise concept identification.
• Global-local semantic guidance (G-LSG) is utilized to effectively leverage detected concepts for caption generation.
• Knowledge transfer from a contrastive vision-language pre-trained model (CLIP) is central to CARE's visual understanding and retrieval capabilities.

Main Results:

• CARE demonstrates superior performance compared to strong CLIP-based video captioning baselines.
• The framework achieves state-of-the-art results on benchmark datasets including MSVD, MSR-VTT, and VATEX.
• CARE shows versatility, performing well with different encoder-decoder architectures.

Conclusions:

• The proposed CARE framework effectively improves video captioning by enhancing concept detection and utilization.
• Leveraging CLIP's knowledge transfer significantly boosts the model's performance.
• CARE offers a promising direction for generating more accurate and relevant video captions.