  For video captioning, "pre-training and fine-tuning" has become a de facto
paradigm, where ImageNet Pre-training (INP) is usually used to encode the video
content, then a task-oriented network is fine-tuned from scratch to cope with
caption generation. This paper first investigates the impact of the recently
proposed CLIP (Contrastive Language-Image Pre-training) on video captioning.
Through the empirical study on INP vs. CLIP, we identify the potential
deficiencies of INP and explore the key factors for accurate description
generation. The results show that the INP-based model is tricky to capture
concepts' semantics and sensitive to irrelevant background information. By
contrast, the CLIP-based model significantly improves the caption quality and
highlights the importance of concept-aware representation learning. With these
findings, we propose Dual Concept Detection (DCD) further to inject concept
knowledge into the model during training. DCD is an auxiliary task that
requires a caption model to learn the correspondence between video content and
concepts and the co-occurrence relations between concepts. Experiments on
MSR-VTT and VATEX demonstrate the effectiveness of DCD, and the visualization
results further reveal the necessity of learning concept-aware representations.
