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• W3197021677 abstract "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." @default.
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• W3197021677 date "2021-09-03" @default.
• W3197021677 modified "2023-09-24" @default.
• W3197021677 title "What Matters for Ad-hoc Video Search? A Large-scale Evaluation on TRECVID" @default.
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• W3197021677 doi "https://doi.org/10.48550/arxiv.2109.01774" @default.
• W3197021677 hasPublicationYear "2021" @default.
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