Sparse Frame Grouping Network with Action Centered for Untrimmed Video Paragraph Captioning

Guorui Yu, Yimin Hu, Yuejie Zhang, Rui Feng, Tao Zhang, Shang Gao

Submission Type: Regular Long Paper

Submission Track: Language Grounding to Vision, Robotics and Beyond

Submission Track 2: Natural Language Generation

Keywords: video paragraph captioning, transformer, grouping, action centered, contrastive learning

Abstract: Generating paragraph captions for untrimmed videos without event annotations is challenging, especially when aiming to enhance precision and minimize repetition at the same time. To address this challenge, we propose a module called Sparse Frame Grouping (SFG). It dynamically groups event information with the help of action information for the entire video and excludes redundant frames within pre-defined clips. To enhance the performance, an Intra Contrastive Learning technique is designed to align the SFG module with the core event content in the paragraph, and an Inter Contrastive Learning technique is employed to learn action-guided context with reduced static noise simultaneously. Extensive experiments are conducted on two benchmark datasets (ActivityNet Captions and YouCook2). Results demonstrate that SFG outperforms the state-of-the-art methods on all metrics.

Submission Number: 2301