Video Corpus Moment Retrieval
11 papers with code • 1 benchmarks • 1 datasets
TVR
Most implemented papers
HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training
linjieli222/HERO
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EMNLP 2020
We present HERO, a novel framework for large-scale video+language omni-representation learning.
Finding Moments in Video Collections Using Natural Language
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.
TVR: A Large-Scale Dataset for Video-Subtitle Moment Retrieval
jayleicn/TVRetrieval
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ECCV 2020
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.
Video Corpus Moment Retrieval with Contrastive Learning
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.
CONQUER: Contextual Query-aware Ranking for Video Corpus Moment Retrieval
This task is essential because advanced video retrieval applications should enable users to retrieve a precise moment from a large video corpus.
Partially Relevant Video Retrieval
HuiGuanLab/ms-sl
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To fill the gap, we propose in this paper a novel T2VR subtask termed Partially Relevant Video Retrieval (PRVR).
Selective Query-guided Debiasing for Video Corpus Moment Retrieval
Video moment retrieval (VMR) aims to localize target moments in untrimmed videos pertinent to a given textual query.
Modal-specific Pseudo Query Generation for Video Corpus Moment Retrieval
minjoong507/MPGN
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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.
MVMR: A New Framework for Evaluating Faithfulness of Video Moment Retrieval against Multiple Distractors
yny0506/massive-videos-moment-retrieval
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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.
Improving Video Corpus Moment Retrieval with Partial Relevance Enhancement
We argue that effectively capturing the partial relevance between the query and video is essential for the VCMR task.
