| Project/Area Number |
19K24344
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| Research Category |
Grant-in-Aid for Research Activity Start-up
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| Allocation Type | Multi-year Fund |
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| Review Section |
1001:Information science, computer engineering, and related fields
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| Research Institution | Kobe University |
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Principal Investigator |
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| Project Period (FY) |
2019-08-30 – 2021-03-31
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| Project Status |
Granted (Fiscal Year 2019)
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| Budget Amount *help |
¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
Fiscal Year 2020: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2019: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Keywords | Deep learning / Computational efficiency / Semantic representation / Computer vision / Object recognition |
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| Outline of Research at the Start |
This study focuses on deriving new principles for optimization and semantic feature learning applied to generic object recognition. On the optimization front, we will focus on improving the computational and algorithmic efficiency of training deep learning models. In order to expand our search space by enabling quicker iteration over different architectural designs. On the semantic learning front, we aim to achieve a better understanding of the visual features that can be derived from semantic data, which we believe to be the key missing element to enable practical Zero-Shot recognition.
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