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We present our submission to the Microsoft Video to Language Challenge ofgenerating short captions describing videos in the challenge dataset. Our modelis based on the encoder--decoder pipeline, popular in image and videocaptioning systems. We propose to utilize two different kinds of videofeatures, one to capture the video content in terms of objects and attributes,and the other to capture the motion and action information. Using these diversefeatures we train models specializing in two separate input sub-domains. Wethen train an evaluator model which is used to pick the best caption from thepool of candidates generated by these domain expert models. We argue that thisapproach is better suited for the current video captioning task, compared tousing a single model, due to the diversity in the dataset.  Efficacy of our method is proven by the fact that it was rated best in MSRVideo to Language Challenge, as per human evaluation. Additionally, we wereranked second in the automatic evaluation metrics based table.

Frame- and Segment-Level Features and Candidate Pool Evaluation for Video Caption Generation