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CONE: An Efficient COarse-to-fiNE Alignment Framework for Long Video Temporal Grounding.

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(Un)likelihood Training for Interpretable Embedding., , , and . CoRR, (2022)vireoJD-MM at Activity Detection in Extended Videos., , , , , , and . CoRR, (2019)GroundNLQ @ Ego4D Natural Language Queries Challenge 2023., , , , , , , , , and . CoRR, (2023)An Efficient COarse-to-fiNE Alignment Framework @ Ego4D Natural Language Queries Challenge 2022., , , , , , , , and . CoRR, (2022)VireoJD-MM @ TRECVid 2019: Activities in Extended Video (ActEV)., , , and . TRECVID, National Institute of Standards and Technology (NIST), (2019)CONE: An Efficient COarse-to-fiNE Alignment Framework for Long Video Temporal Grounding., , , , , , , , and . CoRR, (2022)Reinforcement Learning-Based Interactive Video Search., , , and . MMM (2), volume 13142 of Lecture Notes in Computer Science, page 549-555. Springer, (2022)Conjectural variation based learning of generator's behavior in electricity market., , , , and . ISCAS (3), page 403-406. IEEE, (2003)CONE: An Efficient COarse-to-fiNE Alignment Framework for Long Video Temporal Grounding., , , , , , , , and . ACL (1), page 8013-8028. Association for Computational Linguistics, (2023)CONQUER: Contextual Query-aware Ranking for Video Corpus Moment Retrieval., , and . ACM Multimedia, page 3900-3908. ACM, (2021)