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

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