Meaning guided video captioning

TL;DR

This paper introduces a meaning-guided video captioning model that incorporates object detection and semantic similarity metrics to generate more accurate and meaningful captions, outperforming previous models on the MSDV dataset.

Contribution

It proposes a novel framework that combines object detection with a sequence-to-sequence model and semantic similarity learning for improved video captioning.

Findings

Significantly better performance than baseline models.

Effective integration of object detection into captioning.

Demonstrated on MSDV dataset with improved metrics.

Abstract

Current video captioning approaches often suffer from problems of missing objects in the video to be described, while generating captions semantically similar with ground truth sentences. In this paper, we propose a new approach to video captioning that can describe objects detected by object detection, and generate captions having similar meaning with correct captions. Our model relies on S2VT, a sequence-to-sequence model for video captioning. Given a sequence of video frames, the encoding RNN takes a frame as well as detected objects in the frame in order to incorporate the information of the objects in the scene. The following decoding RNN outputs are then fed into an attention layer and then to a decoder for generating captions. The caption is compared with the ground truth by learning metric so that vector representations of generated captions are semantically similar to those of ground truth. Experimental results with the MSDV dataset demonstrate that the performance of the proposed approach is much better than the model without the proposed meaning-guided framework, showing the effectiveness of the proposed model. Code are publicly available at https://github.com/captanlevi/Meaning-guided-video-captioning-.