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

, , , , , , , , and . ACL (1), page 8013-8028. Association for Computational Linguistics, (2023)

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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)From Two Graphs to N Questions: A VQA Dataset for Compositional Reasoning on Vision and Commonsense., , , and . CoRR, (2019)Env-QA: A Video Question Answering Benchmark for Comprehensive Understanding of Dynamic Environments., , , and . ICCV, page 1655-1665. IEEE, (2021)GazeVQA: A Video Question Answering Dataset for Multiview Eye-Gaze Task-Oriented Collaborations., , , , , , , and . EMNLP, page 10462-10479. Association for Computational Linguistics, (2023)ViT-Lens-2: Gateway to Omni-modal Intelligence., , , , , , , , and . CoRR, (2023)GEB+: A benchmark for generic event boundary captioning, grounding and text-based retrieval., , , , , and . CoRR, (2022)CONE: An Efficient COarse-to-fiNE Alignment Framework for Long Video Temporal Grounding., , , , , , , , and . CoRR, (2022)CRIC: A VQA Dataset for Compositional Reasoning on Vision and Commonsense., , , and . IEEE Trans. Pattern Anal. Mach. Intell., 45 (5): 5561-5578 (May 2023)Event Graph Guided Compositional Spatial-Temporal Reasoning for Video Question Answering., , , and . IEEE Trans. Image Process., (2024)