On Pursuit of Designing Multi-modal Transformer for Video Grounding (2021.emnlp-main)
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| Challenge: | Existing methods for video grounding are not end-to-end, i.e., they rely on time-consuming post-processing steps to refine predictions. |
| Approach: | They propose an end-to-end multi-modal Transformer model that uses two encoders and a cross-modal decoder for grounding prediction. |
| Outcome: | The proposed model is 4.9% faster than existing models and is based on a set of encodings and decoders. |
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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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| Challenge: | Existing work on video-grounded dialogue systems is limited by feature space and semantic information. |
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| Challenge: | Existing work has focused on what is captured by multi-modal architectures. |
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Grounded-VideoLLM: Sharpening Fine-grained Temporal Grounding in Video Large Language Models (2025.findings-emnlp)
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Haibo Wang, Zhiyang Xu, Yu Cheng, Shizhe Diao, Yufan Zhou, Yixin Cao, Qifan Wang, Weifeng Ge, Lifu Huang
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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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Yuwei Fang, Willi Menapace, Aliaksandr Siarohin, Tsai-Shien Chen, Kuan-Chieh Wang, Ivan Skorokhodov, Graham Neubig, Sergey Tulyakov
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| Challenge: | Existing models for natural language video localization are top-down and bottom-up . however, both approaches suffer several limitations, leading to performance degradation . |
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Temporally Grounding Natural Sentence in Video (D18-1)
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| Challenge: | Existing methods for grounding natural sentences in video are limited to a single pass. |
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