Exploring a CLIP-Enhanced Automated Approach for Video Description Generation

Visual storytelling is a learned ability that humans have developed through the course of their evolution. In contrast to written records and descriptions, people are now sharing glimpses of their lives through short videos. Human conversations largely rely on visual and auditory inputs, followed by corresponding feedbacks. For machines, the conversion of images to texts serves as a bridge between visual and linguistic information, enabling machine-human interactions more naturally. Video captioning-involving automatically generating textual descriptions from videos-is one of the core technologies that enable such applications. In this work, we present CLIP-CAP, an automatic method for transforming visual contents to concise textual descriptions. We investigate the CLIP pretraining model as well as its potential in this task. Through experiments on the ActivityNet Captions dataset, we show that the proposed CLIP-CAP model outperforms existing video captioning methods in terms of several different metrics.