Video Corpus Moment Retrieval

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

1 dataset whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

11 shown of 11 papers with code (17 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training
  1 May 2020 3 repositories listed Syntology ran 8 of 13 samples · 5 unverified · 8 pointer-only (licence)
  We present HERO, a novel framework for large-scale video+language omni-representation learning.
• TVR: A Large-Scale Dataset for Video-Subtitle Moment Retrieval
  24 Jan 2020 2 repositories listed Syntology ran 15 of 19 samples · 4 unverified
  The queries are also labeled with query types that indicate whether each of them is more related to video or subtitle or both, allowing for in-depth analysis of the dataset and the methods that built on top of it.
• Finding Moments in Video Collections Using Natural Language
  30 Jul 2019 2 repositories listed Syntology ran 10 of 11 samples · 1 unverified
  We evaluate our approach on two recently proposed datasets for temporal localization of moments in video with natural language (DiDeMo and Charades-STA) extended to our video corpus moment retrieval setting.
• VERIFIED: A Video Corpus Moment Retrieval Benchmark for Fine-Grained Video Understanding
  11 Oct 2024 1 repository listed
  In this paper, we propose a more challenging fine-grained VCMR benchmark requiring methods to localize the best-matched moment from the corpus with other partially matched candidates.
• Improving Video Corpus Moment Retrieval with Partial Relevance Enhancement
  21 Feb 2024 1 repository listed
  We argue that effectively capturing the partial relevance between the query and video is essential for the VCMR task.
• MVMR: A New Framework for Evaluating Faithfulness of Video Moment Retrieval against Multiple Distractors
  15 Aug 2023 1 repository listed
  However, the existing VMR framework evaluates video moment retrieval performance, assuming that a video is given, which may not reveal whether the models exhibit overconfidence in the falsely given video.
• Modal-specific Pseudo Query Generation for Video Corpus Moment Retrieval
  23 Oct 2022 1 repository listed
  Video corpus moment retrieval (VCMR) is the task to retrieve the most relevant video moment from a large video corpus using a natural language query.
• Selective Query-guided Debiasing for Video Corpus Moment Retrieval
  17 Oct 2022 1 repository listed Syntology ran 6 of 24 samples · 18 unverified
  Video moment retrieval (VMR) aims to localize target moments in untrimmed videos pertinent to a given textual query.
• Partially Relevant Video Retrieval
  26 Aug 2022 1 repository listed
  To fill the gap, we propose in this paper a novel T2VR subtask termed Partially Relevant Video Retrieval (PRVR).
• CONQUER: Contextual Query-aware Ranking for Video Corpus Moment Retrieval
  21 Sep 2021 1 repository listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  This task is essential because advanced video retrieval applications should enable users to retrieve a precise moment from a large video corpus.
• Video Corpus Moment Retrieval with Contrastive Learning
  13 May 2021 1 repository listed Syntology ran 10 of 14 samples · 4 unverified
  We adopt the first approach and introduce two contrastive learning objectives to refine video encoder and text encoder to learn video and text representations separately but with better alignment for VCMR.

Syntology lines on 6 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-25.