# An overview on the evaluated video retrieval tasks at TRECVID 2022

arXiv: https://arxiv.org/abs/2306.13118 · HTML: https://mlsanity.com/paper/2306.13118/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 43 references that are in the map.
- First posted on arXiv 22 Jun 2023.

## Builds on (most cited first, 11 of 11)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [SPICE: Semantic Propositional Image Caption Evaluation](https://mlsanity.com/paper/1607.08822/) (2016)
- [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)
- [HLVU : A New Challenge to Test Deep Understanding of Movies the Way Humans do](https://mlsanity.com/paper/2005.00463/) (2020)
- [Large Scale Organization and Inference of an Imagery Dataset for Public Safety](https://mlsanity.com/paper/1908.09006/) (2019)

## Built on by (most cited first, 7 of 7)

- [Tarsier: Recipes for Training and Evaluating Large Video Description Models](https://mlsanity.com/paper/2407.00634/) (2024)
- [Tarsier2: Advancing Large Vision-Language Models from Detailed Video Description to Comprehensive Video Understanding](https://mlsanity.com/paper/2501.07888/) (2025)
- [LADI v2: Multi-label Dataset and Classifiers for Low-Altitude Disaster Imagery](https://mlsanity.com/paper/2406.02780/) (2024)
- ["Previously on ..." From Recaps to Story Summarization](https://mlsanity.com/paper/2405.11487/) (2024)
- [LFS: Learnable Frame Selector for Event-Aware and Temporally Diverse Video Captioning](https://mlsanity.com/paper/2601.14594/) (2026)
- [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) 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, tasks-based evaluation supported by metrology. Over the last twenty-one 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 2022 planned for the following six tasks: Ad-hoc video search, Video to text captioning, Disaster scene description and indexing, Activity in extended videos, deep video understanding, and movie summarization. In total, 35 teams from various research organizations worldwide signed up to join the evaluation campaign this year. This paper introduces the tasks, datasets used, evaluation frameworks and metrics, as well as a high-level results overview.