Methods › Computer Vision › RoI Feature Extractors › Position-Sensitive RoI Pooling › Papers where code ran, page 1
Position-Sensitive RoI Pooling
Papers archive 2025-07-28
archive papers tagged: 32 · with a code link: 12 · where Syntology ran a sample: 3 (2 with a run with no instrument failure, 1 where every run was a failure of Syntology's instrument) Syntology
Show: all tagged papersonly where code ran (3 of 32 tagged: 2 with a run with no instrument failure, 1 where every run was a failure of Syntology's instrument)
Syntology We ran code from the paper's repository; we did not isolate this method inside it.
Page 1 of 1: papers 1 to 3 of the 3 tagged papers where Syntology ran at least one harvested sample (2 with a run with no instrument failure, 1 where every run was a failure of Syntology's instrument), newest first by the archive's date (ties by arXiv id). This is a filter on Syntology's record ordered by date only, not a ranking; a run is not a correctness claim. A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.
Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code, as “N ran (of which C constructed an object rather than computing a result; K with no instrument failure: H honoured, V violated, P with no contract checked; I where Syntology's instrument failed) · U unverified”; the instrument figure counts failures of Syntology's instrument, not of the code. It is per sample and not a correctness claim. When the archive marks a repository official for the paper, the line starts with that repository's state (the archive's flag, not a verdict on who wrote the code; “community repositories only” when every sample that ran came from a community repository, “official: no sample here; runs from other or unrecorded repositories” when some came from a repository the paper names or has in its text, or from none recorded); hover it for the repositories the samples that ran came from.
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Cascade R-CNN: Delving into High Quality Object Detection 3 Dec 2017 · 8 repositories · arXiv:1712.00726Syntology official: no sample here; runs from other or unrecorded repositories · 2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified (of 2 harvested samples) · 2 pointer-only (licence)
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Soft-NMS -- Improving Object Detection With One Line of Code 14 Apr 2017 · 8 repositories · arXiv:1704.04503Syntology 4 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified (of 4 harvested samples)
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R-FCN: Object Detection via Region-based Fully Convolutional Networks 20 May 2016 · 48 repositories · arXiv:1605.06409Syntology community repositories only · 7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified (of 11 harvested samples) · 1 pointer-only (licence)