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231226s2022 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2022.3205212
|2 doi
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|a pubmed25n1153.xml
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|a DE-627
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Zeng, Pengpeng
|e verfasserin
|4 aut
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| 245 |
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|a Video Question Answering With Prior Knowledge and Object-Sensitive Learning
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|c 2022
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|a Text
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|a ƒaComputermedien
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|a Date Completed 19.09.2022
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|a Date Revised 19.09.2022
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a Video Question Answering (VideoQA), which explores spatial-temporal visual information of videos given a linguistic query, has received unprecedented attention over recent years. One of the main challenges lies in locating relevant visual and linguistic information, and therefore various attention-based approaches are proposed. Despite the impressive progress, two aspects are not fully explored by current methods to get proper attention. Firstly, prior knowledge, which in the human cognitive process plays an important role in assisting the reasoning process of VideoQA, is not fully utilized. Secondly, structured visual information (e.g., object) instead of the raw video is underestimated. To address the above two issues, we propose a Prior Knowledge and Object-sensitive Learning (PKOL) by exploring the effect of prior knowledge and learning object-sensitive representations to boost the VideoQA task. Specifically, we first propose a Prior Knowledge Exploring (PKE) module that aims to acquire and integrate prior knowledge into a question feature for feature enriching, where an information retriever is constructed to retrieve related sentences as prior knowledge from the massive corpus. In addition, we propose an Object-sensitive Representation Learning (ORL) module to generate object-sensitive features by interacting object-level features with frame and clip-level features. Our proposed PKOL achieves consistent improvements on three competitive benchmarks (i.e., MSVD-QA, MSRVTT-QA, and TGIF-QA) and gains state-of-the-art performance. The source code is available at https://github.com/zchoi/PKOL
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| 650 |
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4 |
|a Journal Article
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| 700 |
1 |
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|a Zhang, Haonan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Gao, Lianli
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Song, Jingkuan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Shen, Heng Tao
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 31(2022) vom: 09., Seite 5936-5948
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|x 1941-0042
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| 773 |
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|g volume:31
|g year:2022
|g day:09
|g pages:5936-5948
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|u http://dx.doi.org/10.1109/TIP.2022.3205212
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