Cited by 153Advanced Neural Network ApplicationsVisual Attention and Saliency DetectionAdvanced Image and Video Retrieval Techniques

Contextual Priming and Feedback for Faster R-CNN

Shrivastava, Abhinav, Gupta, Abhinav·2016·Lecture Notes in Computer Science

Integrity signals

Registry

Crossref · 10.1007/978-3-319-46448-0_20

Cites

78 of 78 references verified0 citation errors · 0 not checkable

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  • The simplest way to improve detection with segmentation is to think of the segmentation network as a fixed feature extractor and integrate it into a detector as auxiliary features.

    “To improve detection with segmentation, the simplest way is to think of the segmentation network as a fixed feature extractor and to integrate it into a detector as auxiliary features [83, 192, 193].”

    From Object Detection in 20 Years: A Survey (2023) · cites this paper as [193]

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Authors
Shrivastava, A., & Gupta, A.
Affiliations
Carnegie Mellon University (US)
Corresponding author
Shrivastava, Abhinav
Venue
Lecture Notes in Computer Science
Published
2016
Pages
330-348
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
OpenAlex
W2520951797
Cited by
153
References
78
Citations by year
2026: 2 · 2025: 4 · 2024: 9 · 2023: 9 · 2022: 12 · 2021: 29 · 2020: 27 · 2019: 23 · 2018: 26 · 2017: 10 · 2016: 2
FWCI
7.06
Type
conference paper
Language
en
Open access
closed
Locations
Lecture notes in computer science (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
Keywords
Faster R-CNN, object detection, semantic segmentation, iterative feedback, two-stage training, region proposal generation

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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