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Knowledge-Routed Visual Question Reasoning: Challenges for Deep Representation Embedding


Dec 14, 2020
Qingxing Cao, Bailin Li, Xiaodan Liang, Keze Wang, Liang Lin

* To appear in TNNLS 2021. Considering that a desirable VQA model should correctly perceive the image context, understand the question, and incorporate its learned knowledge, our proposed dataset aims to cutoff the shortcut learning exploited by the current deep embedding models and push the research boundary of the knowledge-based visual question reasoning 

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EagleEye: Fast Sub-net Evaluation for Efficient Neural Network Pruning


Jul 06, 2020
Bailin Li, Bowen Wu, Jiang Su, Guangrun Wang, Liang Lin

* Accepted in ECCV 2020(Oral). Codes are available on https://github.com/anonymous47823493/EagleEye 

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Explainable High-order Visual Question Reasoning: A New Benchmark and Knowledge-routed Network


Sep 23, 2019
Qingxing Cao, Bailin Li, Xiaodan Liang, Liang Lin


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Interpretable Visual Question Answering by Reasoning on Dependency Trees


Sep 06, 2018
Qingxing Cao, Xiaodan Liang, Bailin Li, Liang Lin

* 14 pages, 10 figures. arXiv admin note: text overlap with arXiv:1804.00105 

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