# What Matters for Ad-hoc Video Search? A Large-scale Evaluation on TRECVID

arXiv: https://arxiv.org/abs/2109.01774 · HTML: https://mlsanity.com/paper/2109.01774/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2021.

## Built on by (most cited first, 4 of 4)

- [Lightweight Attentional Feature Fusion: A New Baseline for Text-to-Video Retrieval](https://mlsanity.com/paper/2112.01832/) (2021)
- [Accommodating Audio Modality in CLIP for Multimodal Processing](https://mlsanity.com/paper/2303.06591/) (2023)
- [Learn to Understand Negation in Video Retrieval](https://mlsanity.com/paper/2205.00132/) (2022)
- [Are All Combinations Equal? Combining Textual and Visual Features with Multiple Space Learning for Text-Based Video Retrieval](https://mlsanity.com/paper/2211.11351/) (2022)

## Abstract (arXiv)

For quantifying progress in Ad-hoc Video Search (AVS), the annual TRECVID AVS task is an important international evaluation. Solutions submitted by the task participants vary in terms of their choices of cross-modal matching models, visual features and training data. As such, what one may conclude from the evaluation is at a high level that is insufficient to reveal the influence of the individual components. In order to bridge the gap between the current solution-level comparison and the desired component-wise comparison, we propose in this paper a large-scale and systematic evaluation on TRECVID. By selected combinations of state-of-the-art matching models, visual features and (pre-)training data, we construct a set of 25 different solutions and evaluate them on the TRECVID AVS tasks 2016--2020. The presented evaluation helps answer the key question of what matters for AVS. The resultant observations and learned lessons are also instructive for developing novel AVS solutions.