Cited by 282Advanced Neural Network ApplicationsAdvanced Image and Video Retrieval TechniquesVisual Attention and Saliency Detection

Revisiting RCNN: On Awakening the Classification Power of Faster RCNN

Open access
Cheng, Bowen, Wei, Yunchao, Shi, Honghui, Feris, Rogerio, Xiong, Jinjun, Huang, Thomas·2018·Lecture Notes in Computer Science

Integrity signals

Registry

Crossref · 10.1007/978-3-030-01267-0_28

Cites

32 of 32 references verified0 citation errors · 0 not checkable

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Recomputing p-values needs the paper’s full text.

Claims the paper makes

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Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Jointly learning localization and classification may cause sub-optimal solutions for both tasks, and decoupling localization and classification is helpful.

    “As pointed out by recent works [7, 43], jointly learning of localization and classification may cause sub-optimal solutions for both tasks, and it is helpful to decouple localization and classification.”

    From Learning Spatio-Temporal Transformer for Visual Tracking (2021) · cites this paper as [7]

Cites

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2 of the 34 references couldn’t be matched to a known paper.

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Authors
Cheng, B., Wei, Y., Shi, H., Feris, R., Xiong, J., & Huang, T.
Affiliations
  • University of Illinois Urbana-Champaign (US)
  • IBM (United States) (US)
Corresponding author
Wei, Yunchao
Venue
Lecture Notes in Computer Science
Published
2018
Pages
473-490
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
OpenAlex
W2962731685
Cited by
282
References
34
Citations by year
2026: 8 · 2025: 33 · 2024: 39 · 2023: 36 · 2022: 28 · 2021: 52 · 2020: 43 · 2019: 32 · 2018: 11
FWCI
10.40
Type
conference paper
Language
en
Open access
Locations
  • Lecture notes in computer science (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
Keywords
Faster R-CNN, region-based object detection, shared feature representation, multi-task learning, small object detection, receptive fields, object classification

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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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