Skip to Main content Skip to Navigation
Journal articles

Overview of deep-learning based methods for salient object detection in videos

Abstract : Video salient object detection is a challenging and important problem in computer vision domain. In recent years, deep-learning based methods have contributed to significant improvements in this domain. This paper provides an overview of recent developments in this domain and compares the corresponding methods up to date, including 1) Classification of the state-of-the-art methods and their frameworks; 2) summary of the benchmark datasets and commonly used evaluation metrics; 3) experimental comparison of the performances of the state-of-the-art methods; 4) suggestions of some promising future works for unsolved challenges.
Document type :
Journal articles
Complete list of metadatas

Cited literature [53 references]  Display  Hide  Download

https://hal-univ-rennes1.archives-ouvertes.fr/hal-02796892
Contributor : Laurent Jonchère <>
Submitted on : Tuesday, June 23, 2020 - 3:03:00 PM
Last modification on : Monday, October 5, 2020 - 9:50:30 AM
Long-term archiving on: : Thursday, September 24, 2020 - 5:46:47 PM

File

Wang et al-2020-Overview of de...
Files produced by the author(s)

Identifiers

Citation

Qiong Wang, Lu Zhang, Yan Li, Kidiyo Kpalma. Overview of deep-learning based methods for salient object detection in videos. Pattern Recognition, Elsevier, 2020, 104, pp.107340. ⟨10.1016/j.patcog.2020.107340⟩. ⟨hal-02796892⟩

Share

Metrics

Record views

35

Files downloads

6