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Zero-shot recognition of generic objects

Research Project

Project/Area Number 19K24344
Research Category

Grant-in-Aid for Research Activity Start-up

Allocation TypeMulti-year Fund
Review Section 1001:Information science, computer engineering, and related fields
Research InstitutionKobe University

Principal Investigator

HASCOET TRISTAN  神戸大学, 経営学研究科, 助教 (60848448)

Project Period (FY) 2019-08-30 – 2021-03-31
Project Status Granted (Fiscal Year 2019)
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)
KeywordsDeep learning / Computational efficiency / Semantic representation / Computer vision / Object recognition
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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Published: 2019-09-03   Modified: 2019-10-15  

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