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Journal article

Sequential optimization for efficient high-quality object proposal generation

Abstract:

We are motivated by the need for a generic object proposal generation algorithm which achieves good balance between object detection recall, proposal localization quality and computational efficiency. We propose a novel object proposal algorithm, BING++, which inherits the virtue of good computational efficiency of BING [1] but significantly improves its proposal localization quality. At high level we formulate the problem of object proposal generation from a novel probabilistic perspective, ...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Accepted Manuscript

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Publisher copy:
10.1109/TPAMI.2017.2707492

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ORCID:
0000-0002-0921-8298
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ORCID:
0000-0001-5550-8758
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Publisher:
IEEE Publisher's website
Journal:
IEEE Transactions on Pattern Analysis and Machine Intelligence Journal website
Volume:
40
Issue:
5
Pages:
1209-1223
Publication date:
2017-05-23
DOI:
EISSN:
1939-3539
ISSN:
0162-8828
Pubs id:
pubs:811747
URN:
uri:dd14c48e-23c3-43e7-befb-9052f8f74f50
UUID:
uuid:dd14c48e-23c3-43e7-befb-9052f8f74f50
Local pid:
pubs:811747

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