# TRECVID 2019: An Evaluation Campaign to Benchmark Video Activity Detection, Video Captioning and Matching, and Video Search & Retrieval

arXiv: https://arxiv.org/abs/2009.09984 · HTML: https://mlsanity.com/paper/2009.09984/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2020.

## Built on by (most cited first, 16 of 16)

- [MovieChat: From Dense Token to Sparse Memory for Long Video Understanding](https://mlsanity.com/paper/2307.16449/) (2023)
- [MovieChat+: Question-aware Sparse Memory for Long Video Question Answering](https://mlsanity.com/paper/2404.17176/) (2024)
- [Face, Body, Voice: Video Person-Clustering with Multiple Modalities](https://mlsanity.com/paper/2105.09939/) (2021)
- [InfiniBench: A Benchmark for Large Multi-Modal Models in Long-Form Movies and TV Shows](https://mlsanity.com/paper/2406.19875/) (2024)
- [An Annotated Video Dataset for Computing Video Memorability](https://mlsanity.com/paper/2112.02303/) (2021)
- [MultiVENT: Multilingual Videos of Events with Aligned Natural Text](https://mlsanity.com/paper/2307.03153/) (2023)
- [Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner](https://mlsanity.com/paper/2412.18086/) (2024)
- [A Comprehensive Review of the Video-to-Text Problem](https://mlsanity.com/paper/2103.14785/) (2021)
- [Overview of The MediaEval 2021 Predicting Media Memorability Task](https://mlsanity.com/paper/2112.05982/) (2021)
- [Interpretable Embedding for Ad-hoc Video Search](https://mlsanity.com/paper/2402.11812/) (2024)
- [BERTHA: Video Captioning Evaluation Via Transfer-Learned Human Assessment](https://mlsanity.com/paper/2201.10243/) (2022)
- [Experiences from the MediaEval Predicting Media Memorability Task](https://mlsanity.com/paper/2212.03955/) (2022)
- [Extended Self-Critical Pipeline for Transforming Videos to Text (TRECVID-VTT Task 2021) -- Team: MMCUniAugsburg](https://mlsanity.com/paper/2112.14100/) (2021)
- [Multimodal LLM-based Query Paraphrasing for Video Search](https://mlsanity.com/paper/2407.12341/) (2024)
- [Learning Partially-Decorrelated Common Spaces for Ad-hoc Video Search](https://mlsanity.com/paper/2508.02340/) (2025)
- [Adaptive Multi-Agent Reasoning for Text-to-Video Retrieval](https://mlsanity.com/paper/2602.19040/) (2025)

## Abstract (arXiv)

The TREC Video Retrieval Evaluation (TRECVID) 2019 was a TREC-style video analysis and retrieval evaluation, the goal of which remains to promote progress in research and development of content-based exploitation and retrieval of information from digital video via open, metrics-based evaluation. Over the last nineteen years this effort has yielded a better understanding of how systems can effectively accomplish such processing and how one can reliably benchmark their performance. TRECVID has been funded by NIST (National Institute of Standards and Technology) and other US government agencies. In addition, many organizations and individuals worldwide contribute significant time and effort. TRECVID 2019 represented a continuation of four tasks from TRECVID 2018. In total, 27 teams from various research organizations worldwide completed one or more of the following four tasks: 1. Ad-hoc Video Search (AVS) 2. Instance Search (INS) 3. Activities in Extended Video (ActEV) 4. Video to Text Description (VTT) This paper is an introduction to the evaluation framework, tasks, data, and measures used in the workshop.