FINE-GRAINED AND LAYERED OBJECT RECOGNITION
2012 ◽
Vol 26
(02)
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pp. 1255006
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Keyword(s):
This paper presents a novel research on promoting the performance and enriching the functionalities of object recognition. Instead of simply fitting various data to a few predefined semantic object categories, we propose to generate proper results for different object instances based on their actual visual appearances. The results can be fine-grained and layered categorization along with absolute or relative localization. We present a generic model based on structured prediction and an efficient online learning algorithm to solve it. Experiments on a new benchmark dataset demonstrate the effectiveness of our model and its superiority against traditional recognition methods.
2021 ◽
Vol 0
(0)
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Keyword(s):
Keyword(s):
2017 ◽
Vol 10
(13)
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pp. 284
2018 ◽
Vol 65
(11)
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pp. 1788-1792
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2019 ◽
Vol 33
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pp. 3232-3239
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2019 ◽
Vol 356
(13)
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pp. 7548-7570
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