Yongcheng Wang

Also published as: Yong Cheng Wang, Yong-Cheng Wang


2019

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YouMakeup: A Large-Scale Domain-Specific Multimodal Dataset for Fine-Grained Semantic Comprehension
Weiying Wang | Yongcheng Wang | Shizhe Chen | Qin Jin
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Multimodal semantic comprehension has attracted increasing research interests recently such as visual question answering and caption generation. However, due to the data limitation, fine-grained semantic comprehension has not been well investigated, which requires to capture semantic details of multimodal contents. In this work, we introduce “YouMakeup”, a large-scale multimodal instructional video dataset to support fine-grained semantic comprehension research in specific domain. YouMakeup contains 2,800 videos from YouTube, spanning more than 420 hours in total. Each video is annotated with a sequence of natural language descriptions for instructional steps, grounded in temporal video range and spatial facial areas. The annotated steps in a video involve subtle difference in actions, products and regions, which requires fine-grained understanding and reasoning both temporally and spatially. In order to evaluate models’ ability for fined-grained comprehension, we further propose two groups of tasks including generation tasks and visual question answering from different aspects. We also establish a baseline of step caption generation for future comparison. The dataset will be publicly available at https://github.com/AIM3-RUC/YouMakeup to support research investigation in fine-grained semantic comprehension.

2010

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Automatic Generation of Semantic Fields for Annotating Web Images
Gang Wang | Tat Seng Chua | Chong-Wah Ngo | Yong Cheng Wang
Coling 2010: Posters

2003

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Extracting Key Semantic Terms from Chinese Speech Query for Web Searches
Gang Wang | Tat-Seng Chua | Yong-Cheng Wang
Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics