Multi-Level Visual Representation with Semantic-Reinforced Learning for Video Captioning

Abstract

This paper describes our bronze-medal solution for the video captioning task of the ACMMM2021 Pre-Training for Video Understanding Challenge. We depart from the Bottom-Up-Top-Down model, with technical improvements on both video content encoding and caption decoding. For encoding, we propose to extract multi-level video features that describe holistic scenes and fine-grained key objects, respectively. The scene-level and object-level features are enhanced separately by multi-head self-attention mechanisms before feeding them into the decoding module. Towards generating content-relevant and human-like captions, we train our network end-to-end by semantic-reinforced learning. Finally, in order to select the best caption from captions produced by distinct models, we perform caption reranking by cross-modal matching between a given video and each candidate caption. Both internal experiments on the MSR-VTT test set and external evaluations by the challenge organizers justify the viability of the proposed solution.