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Chao Peng

9 papers hereh-index 138k citations15 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author2
  • middle author7

Across the 9 of 9 papers where every author was matched, so the position is known.

fields
  • cs.CV8
  • eess.IV1
same name
  • Chao Peng — 23 papers, h 10
  • Chao Peng — 12 papers, h 16
  • Chao Peng — 9 papers, h 3
  • Chao Peng — 4 papers, h 2
  • Chao Peng — 4 papers, h 0
  • Chao Peng — 3 papers, h 6

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20172020
most citedLight-Head R-CNN: In Defense of Two-Stage Object Detector

284 citations · 916 across the 9 of their papers we have counts for

collaborators
Showing 2018Show all

4 papers · 1 filter

cs.CV2018★ 124 cited

BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation

Changqian Yu, Jingbo Wang, Chao Peng +3

Semantic segmentation requires both rich spatial information and sizeable receptive field. However, modern approaches usually compromise spatial resolution to achieve real-time inf…

cs.CV2018★ 89 cited

Learning a Discriminative Feature Network for Semantic Segmentation

Changqian Yu, Jingbo Wang, Chao Peng +3

Most existing methods of semantic segmentation still suffer from two aspects of challenges: intra-class inconsistency and inter-class indistinction. To tackle these two problems, w…

cs.CV2018★ 243 cited

DetNet: A Backbone network for Object Detection

Zeming Li, Chao Peng, Gang Yu +3

Recent CNN based object detectors, no matter one-stage methods like YOLO, SSD, and RetinaNe or two-stage detectors like Faster R-CNN, R-FCN and FPN are usually trying to directly f…

cs.CV2018★ 55 cited

ExFuse: Enhancing Feature Fusion for Semantic Segmentation

Zhenli Zhang, Xiangyu Zhang, Chao Peng +2

Modern semantic segmentation frameworks usually combine low-level and high-level features from pre-trained backbone convolutional models to boost performance. In this paper, we fir…

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