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Sheng Yang

5 papers hereh-index 7630 citations8 works total

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

author position
  • first author2
  • middle author3

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • cs.MM1
same name
  • Sheng Yang — 24 papers, h 22
  • Sheng Yang — 24 papers, h 17
  • Sheng Yang — 15 papers, h 12
  • Sheng Yang — 12 papers, h 7
  • Sheng Yang — 8 papers, h 3
  • Sheng Yang — 7 papers, h 7

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
20182021
most citedA Dilated Inception Network for Visual Saliency Prediction

10 citations · 20 across the 3 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2021

Progressive Self-Guided Loss for Salient Object Detection

Sheng Yang, Weisi Lin, Guosheng Lin +2

We present a simple yet effective progressive self-guided loss function to facilitate deep learning-based salient object detection (SOD) in images. The saliency maps produced by th…

cs.CV2019★ 10 cited

A Dilated Inception Network for Visual Saliency Prediction

Sheng Yang, Guosheng Lin, Qiuping Jiang +1

Recently, with the advent of deep convolutional neural networks (DCNN), the improvements in visual saliency prediction research are impressive. One possible direction to approach t…

cs.CV2019★ 6 cited

Towards Robust Curve Text Detection with Conditional Spatial Expansion

Zichuan Liu, Guosheng Lin, Sheng Yang +3

It is challenging to detect curve texts due to their irregular shapes and varying sizes. In this paper, we first investigate the deficiency of the existing curve detection methods…

cs.CV2018

Learning Markov Clustering Networks for Scene Text Detection

Zichuan Liu, Guosheng Lin, Sheng Yang +3

A novel framework named Markov Clustering Network (MCN) is proposed for fast and robust scene text detection. MCN predicts instance-level bounding boxes by firstly converting an im…

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