CONQUER_RVMR

This repository contains the XML model for the baseline of the Ranked Video Moment Retrieval (RVMR) task. The associated paper is titled "Video Moment Retrieval in Practical Setting: A Dataset of Ranked Moments for Imprecise Queries."

The main repository of the paper is TVR-Ranking, and this model is adapted from CONQUER. The environment setup is the same as for RelocNet_RVMR, as detailed in the TVR-Ranking repository.

CONQUER leverages video retrieval results from HERO. We continue to use these results when training on our TVR-Ranking dataset. Note that, because the HERO results are obtained from the TVR dataset, there could be a data leak issue in our task setting. However, this issue is negligible for two reasons: (i) the queries used in our setting is imprecise query with query re-written, and (ii) a query has multiple ground truth moments in our task setting, which was not annotated in the original TVR dataset.

Performance

Model	Train Set Top N	IoU=0.3		IoU=0.5		IoU=0.7
		Val	Test	Val	Test	Val	Test
NDCG@10
CONQUER	1	0.0999	0.0859	0.0844	0.0709	0.0530	0.0512
CONQUER	20	0.2406	0.2249	0.2222	0.2104	0.1672	0.1517
CONQUER	40	0.2450	0.2219	0.2262	0.2085	0.1670	0.1515
NDCG@20
CONQUER	1	0.0952	0.0835	0.0808	0.0687	0.0526	0.0484
CONQUER	20	0.2130	0.1995	0.1976	0.1867	0.1527	0.1368
CONQUER	40	0.2183	0.1968	0.2022	0.1851	0.1524	0.1365
NDCG@40
CONQUER	1	0.0974	0.0866	0.0832	0.0718	0.0557	0.0510
CONQUER	20	0.2029	0.1906	0.1891	0.1788	0.1476	0.1326
CONQUER	40	0.2080	0.1885	0.1934	0.1775	0.1473	0.1323

Quick Start

Modify the path in run_disjoint_top20.sh and then execute the script:

sh run_disjoint_top20.sh

Feel free to contribute or raise issues for any problems encountered.