# BERTHA: Video Captioning Evaluation Via Transfer-Learned Human Assessment

arXiv: https://arxiv.org/abs/2201.10243 · HTML: https://mlsanity.com/paper/2201.10243/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 27 references that are in the map.
- First posted on arXiv 25 Jan 2022.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Microsoft COCO Captions: Data Collection and Evaluation Server](https://mlsanity.com/paper/1504.00325/) (2015)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books](https://mlsanity.com/paper/1506.06724/) (2015)
- [SPICE: Semantic Propositional Image Caption Evaluation](https://mlsanity.com/paper/1607.08822/) (2016)
- [COIN: A Large-scale Dataset for Comprehensive Instructional Video Analysis](https://mlsanity.com/paper/1903.02874/) (2019)
- [Learning to Evaluate Image Captioning](https://mlsanity.com/paper/1806.06422/) (2018)
- [TRECVID 2019: An Evaluation Campaign to Benchmark Video Activity Detection, Video Captioning and Matching, and Video Search & Retrieval](https://mlsanity.com/paper/2009.09984/) (2020)
- [TRECVID 2020: A comprehensive campaign for evaluating video retrieval tasks across multiple application domains](https://mlsanity.com/paper/2104.13473/) (2021)
- [Evaluation of Automatic Video Captioning Using Direct Assessment](https://mlsanity.com/paper/1710.10586/) (2017)
- [Machine Translation Evaluation with BERT Regressor](https://mlsanity.com/paper/1907.12679/) (2019)

## Built on by (most cited first, 3 of 3)

- [A Review of Deep Learning for Video Captioning](https://mlsanity.com/paper/2304.11431/) (2023)
- [Audio Description Generation in the Era of LLMs and VLMs: A Review of Transferable Generative AI Technologies](https://mlsanity.com/paper/2410.08860/) (2024)
- [Putting Captions to the Test: Evaluating Video Caption Quality through Multiple-Choice Question Answering](https://mlsanity.com/paper/2609.09973/) (2026)

## Abstract (arXiv)

Evaluating video captioning systems is a challenging task as there are multiple factors to consider; for instance: the fluency of the caption, multiple actions happening in a single scene, and the human bias of what is considered important. Most metrics try to measure how similar the system generated captions are to a single or a set of human-annotated captions. This paper presents a new method based on a deep learning model to evaluate these systems. The model is based on BERT, which is a language model that has been shown to work well in multiple NLP tasks. The aim is for the model to learn to perform an evaluation similar to that of a human. To do so, we use a dataset that contains human evaluations of system generated captions. The dataset consists of the human judgments of the captions produce by the system participating in various years of the TRECVid video to text task. These annotations will be made publicly available. BERTHA obtain favourable results, outperforming the commonly used metrics in some setups.