# Adversarial Inference for Multi-Sentence Video Description

## TL;DR

This paper introduces an adversarial inference approach with multiple discriminators to generate more accurate, diverse, and coherent multi-sentence video descriptions, addressing challenges in fluency, relevance, and redundancy.

## Contribution

It proposes a novel multi-discriminator adversarial inference method that evaluates different aspects of video descriptions to improve quality and coherence.

## Key findings

- Enhanced description relevance, diversity, and coherence.
- Improved automatic and human evaluation scores.
- Effective multi-discriminator design for video captioning.

## Abstract

While significant progress has been made in the image captioning task, video description is still in its infancy due to the complex nature of video data. Generating multi-sentence descriptions for long videos is even more challenging. Among the main issues are the fluency and coherence of the generated descriptions, and their relevance to the video. Recently, reinforcement and adversarial learning based methods have been explored to improve the image captioning models; however, both types of methods suffer from a number of issues, e.g. poor readability and high redundancy for RL and stability issues for GANs. In this work, we instead propose to apply adversarial techniques during inference, designing a discriminator which encourages better multi-sentence video description. In addition, we find that a multi-discriminator "hybrid" design, where each discriminator targets one aspect of a description, leads to the best results. Specifically, we decouple the discriminator to evaluate on three criteria: 1) visual relevance to the video, 2) language diversity and fluency, and 3) coherence across sentences. Our approach results in more accurate, diverse, and coherent multi-sentence video descriptions, as shown by automatic as well as human evaluation on the popular ActivityNet Captions dataset.