2021 Fiscal Year Final Research Report
Zero-shot recognition of generic objects
| 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 – 2022-03-31
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| Keywords | Zero-Shot Learning / Self-Supervised Learning / Visual Representation / Feature Extraction / Semantic representations / Resource Efficiency / CNN / Computer vision |
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| Outline of Final Research Achievements |
Until a few years ago, computers could not recognize things in pictures. For example, computers were not capable to tell whether any human is in a given picture or not. Around ten years ago, computer programs became capable to recognize a number of things in pictures with high precision, including humans, dogs, cars, etc. The development of many technologies such as self-driving vehicles and robots were previously limited by the inability of computers to recognize such objects: for example, a self-driving car can not drive if it can not recognize a pedestrian on the road.
However, computers can currently only recognize a finite number of things such as "a man" or "a woman", while humans can recognize things with more details and nuance such as "a young asian woman on a bike". This research project has worked towards giving computers the ability to recognize more complex and less predefined things, in order to allow computers to take better decisions.
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| Free Research Field |
深層学習を用いたコンピュータービジョンのゼロショットラーニング
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| Academic Significance and Societal Importance of the Research Achievements |
本研究では、写真から不特定の物の秋類を認識するために必要な情報を研究しました。 視覚情報の特定の処理は、意味表現よりも重要であり、そのようなプログラムを生成する能力は、実行できる計算の量によって制限されることがわかりました。
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