Adaptive Proposal Generation Network for Temporal Sentence Localization in Videos (2021.emnlp-main)
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| Challenge: | Temporal sentence localization in videos is an important yet challenging task in natural language processing. |
| Approach: | They propose an Adaptive Proposal Generation Network to maintain the segment-level interaction while speeding up the efficiency. |
| Outcome: | The proposed model outperforms state-of-the-art methods on three challenging benchmarks. |
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| Challenge: | Existing work on temporal sentence grounding rely on expensive video-query paired annotations . despite this, there are no ground-truth annotations in the current work . |
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| Challenge: | Existing methods for temporal sentence grounding do not capture subtle details of small objects. |
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| Challenge: | Existing zero-shot pipelines generate event proposals and then generate a pseudo query for each event proposal. |
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| Challenge: | Existing methods for temporal sentence localization in videos focus on visual content, but they are insufficient to model complex video contents. |
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Towards Parameter-Efficient Integration of Pre-Trained Language Models In Temporal Video Grounding (2023.findings-acl)
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Erica Kido Shimomoto, Edison Marrese-Taylor, Hiroya Takamura, Ichiro Kobayashi, Hideki Nakayama, Yusuke Miyao
| Challenge: | Recent studies have improved query inputs with pre-trained language models, but the effects of this integration are unclear. |
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