# TRECVID 2020: A comprehensive campaign for evaluating video retrieval tasks across multiple application domains

arXiv: https://arxiv.org/abs/2104.13473 · HTML: https://mlsanity.com/paper/2104.13473/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 29 references that are in the map.
- First posted on arXiv 27 Apr 2021.

## Builds on (most cited first, 5 of 5)

- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [SPICE: Semantic Propositional Image Caption Evaluation](https://mlsanity.com/paper/1607.08822/) (2016)
- [Summarizing Videos with Attention](https://mlsanity.com/paper/1812.01969/) (2018)
- [V3C - a Research Video Collection](https://mlsanity.com/paper/1810.04401/) (2018)
- [Evaluation of Automatic Video Captioning Using Direct Assessment](https://mlsanity.com/paper/1710.10586/) (2017)

## 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)
- [WTS: A Pedestrian-Centric Traffic Video Dataset for Fine-grained Spatial-Temporal Understanding](https://mlsanity.com/paper/2407.15350/) (2024)
- [MDMMT-2: Multidomain Multimodal Transformer for Video Retrieval, One More Step Towards Generalization](https://mlsanity.com/paper/2203.07086/) (2022)
- [ActionHub: A Large-scale Action Video Description Dataset for Zero-shot Action Recognition](https://mlsanity.com/paper/2401.11654/) (2024)
- [Argus++: Robust Real-time Activity Detection for Unconstrained Video Streams with Overlapping Cube Proposals](https://mlsanity.com/paper/2201.05290/) (2022)
- [MMSys'22 Grand Challenge on AI-based Video Production for Soccer](https://mlsanity.com/paper/2202.01031/) (2022)
- [Video Summarization Overview](https://mlsanity.com/paper/2210.11707/) (2022)
- [BERTHA: Video Captioning Evaluation Via Transfer-Learned Human Assessment](https://mlsanity.com/paper/2201.10243/) (2022)
- [Improving Interpretable Embeddings for Ad-hoc Video Search with Generative Captions and Multi-word Concept Bank](https://mlsanity.com/paper/2404.06173/) (2024)
- [Synchronized Audio-Visual Frames with Fractional Positional Encoding for Transformers in Video-to-Text Translation](https://mlsanity.com/paper/2112.14088/) (2021)
- [LADI v2: Multi-label Dataset and Classifiers for Low-Altitude Disaster Imagery](https://mlsanity.com/paper/2406.02780/) (2024)
- [Extended Self-Critical Pipeline for Transforming Videos to Text (TRECVID-VTT Task 2021) -- Team: MMCUniAugsburg](https://mlsanity.com/paper/2112.14100/) (2021)
- [Academic competitions](https://mlsanity.com/paper/2312.00268/) (2023)
- [Towards Multi-Task Multi-Modal Models: A Video Generative Perspective](https://mlsanity.com/paper/2405.16728/) (2024)
- [Learning Partially-Decorrelated Common Spaces for Ad-hoc Video Search](https://mlsanity.com/paper/2508.02340/) (2025)

## Abstract (arXiv)

The TREC Video Retrieval Evaluation (TRECVID) is a TREC-style video analysis and retrieval evaluation with the goal of promoting progress in research and development of content-based exploitation and retrieval of information from digital video via open, metrics-based evaluation. Over the last twenty 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 2020 represented a continuation of four tasks and the addition of two new tasks. In total, 29 teams from various research organizations worldwide completed one or more of the following six tasks: 1. Ad-hoc Video Search (AVS), 2. Instance Search (INS), 3. Disaster Scene Description and Indexing (DSDI), 4. Video to Text Description (VTT), 5. Activities in Extended Video (ActEV), 6. Video Summarization (VSUM). This paper is an introduction to the evaluation framework, tasks, data, and measures used in the evaluation campaign.