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

arXiv: https://arxiv.org/abs/1608.04959 · HTML: https://mlsanity.com/paper/1608.04959/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2016.

## Built on by (most cited first, 5 of 5)

- [A Comprehensive Review of the Video-to-Text Problem](https://mlsanity.com/paper/2103.14785/) (2021)
- [Video Captioning: a comparative review of where we are and which could be the route](https://mlsanity.com/paper/2204.05976/) (2022)
- [Edit As You Wish: Video Caption Editing with Multi-grained User Control](https://mlsanity.com/paper/2305.08389/) (2023)
- [Video Captioning in Compressed Video](https://mlsanity.com/paper/2101.00359/) (2021)
- [Visual Subtitle Feature Enhanced Video Outline Generation](https://mlsanity.com/paper/2208.11307/) (2022)

## Abstract (arXiv)

We present our submission to the Microsoft Video to Language Challenge of generating short captions describing videos in the challenge dataset. Our model is based on the encoder--decoder pipeline, popular in image and video captioning systems. We propose to utilize two different kinds of video features, one to capture the video content in terms of objects and attributes, and the other to capture the motion and action information. Using these diverse features we train models specializing in two separate input sub-domains. We then train an evaluator model which is used to pick the best caption from the pool of candidates generated by these domain expert models. We argue that this approach is better suited for the current video captioning task, compared to using 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 MSR Video to Language Challenge, as per human evaluation. Additionally, we were ranked second in the automatic evaluation metrics based table.