YouMakeup: A Large-Scale Domain-Specific Multimodal Dataset for Fine-Grained Semantic Comprehension (D19-1)
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| Challenge: | Multimodal semantic comprehension has attracted increasing research interest recently such as visual question answering and caption generation. |
| Approach: | They propose to use a large-scale multimodal instructional video dataset to support fine-grained comprehension research in specific domain. |
| Outcome: | The proposed dataset contains 2,800 videos from YouTube, spanning more than 420 hours in total. |
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| Challenge: | Existing text-rich image understanding benchmarks lack scale and fragmented scenarios . a new full-image structured output format is proposed to enable fine-grained evaluation of perception and reasoning capabilities. |
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| Challenge: | Existing methods for audio captioning lack fine-grained detail and contextual accuracy due to limited unimodal or superficial information. |
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Multimodal Pretraining for Dense Video Captioning (2020.aacl-main)
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Te-Lin Wu, Zi-Yi Dou, Qingyuan Hu, Yu Hou, Nischal Chandra, Marjorie Freedman, Ralph Weischedel, Nanyun Peng
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MM-AVS: A Full-Scale Dataset for Multi-modal Summarization (2021.naacl-main)
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| Challenge: | Multimodal summarization materials lacking a holistic organization by integrating resources from various modalities. |
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| Challenge: | resurgence of multimodal datasets has attracted significant research interest, but there is no comprehensive survey for this task. |
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| Challenge: | Recent advances in text-video retrieval neglect the crucial user perspective, leading to discrepancies between user queries and content retrieved. |
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