# Video captioning with recurrent networks based on frame- and video-level features and visual content classification

Source: [Video captioning with recurrent networks based on frame- and video-level features and visual content classification](<https://www.sota2.com/research/paper/68fa62321e209131ce9f82fb>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:15:30.656Z

Authors: Rakshith Shetty, Jorma Laaksonen

Published: 2015-12-09T17:17:29.000Z

## Abstract

In this paper, we describe the system for generating textual descriptions of short video clips using recurrent neural networks (RNN), which we used while participating in the Large Scale Movie Description Challenge 2015 in ICCV 2015. Our work builds on static image captioning systems with RNN based language models and extends this framework to videos utilizing both static image features and video-specific features. In addition, we study the usefulness of visual content classifiers as a source of additional information for caption generation. With experimental results we show that utilizing keyframe based features, dense trajectory video features and content classifier outputs together gives better performance than any one of them individually.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Video Annotation on LSMDC 2016 (challenge)](<https://www.sota2.com/research/sota/video-annotation-on-lsmdc-2016-challenge.md>): Video Annotation; LSMDC 2016 (challenge); CIDEr-D: 0.044.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1512.02949>)

[PDF](<https://arxiv.org/pdf/1512.02949.pdf>)