  Video captioning automatically generates short descriptions of the video
content, usually in form of a single sentence. Many methods have been proposed
for solving this task. A large dataset called MSR Video to Text (MSR-VTT) is
often used as the benchmark dataset for testing the performance of the methods.
However, we found that the human annotations, i.e., the descriptions of video
contents in the dataset are quite noisy, e.g., there are many duplicate
captions and many captions contain grammatical problems. These problems may
pose difficulties to video captioning models for learning underlying patterns.
We cleaned the MSR-VTT annotations by removing these problems, then tested
several typical video captioning models on the cleaned dataset. Experimental
results showed that data cleaning boosted the performances of the models
measured by popular quantitative metrics. We recruited subjects to evaluate the
results of a model trained on the original and cleaned datasets. The human
behavior experiment demonstrated that trained on the cleaned dataset, the model
generated captions that were more coherent and more relevant to the contents of
the video clips.
