NDLI: Flow driven attention network for video salient object detection

Content Provider	IET Digital Library
Author	Zhou, Feng Shuai, Hui Liu, Qingshan Guo, Guodong
Abstract	Salient object detection has been revolutionised by convolutional neural network (CNN) recently. However, it is hard to transfer the state-of-the-art still-image based saliency detectors to videos directly, owing to the neglect of temporal contexts between frames. In this study, the authors propose a flow-driven attention network (FDAN) to exploit motion information for video salient object detection. FDAN consists of an appearance feature extractor, a motion-guided attention module and a saliency map regression module. It extracts the appearance feature per frame, refines appearance feature with optical flow and infers the ultimate saliency map, respectively. Motion-guided attention module is the core of FDAN, which extracts motion information in the form of attention. This attention mechanism is a two-branch CNN, fusing optical flow and appearance features. In addition, a shortcut connection is applied to the attention multiplied feature map for noise suppression intensively. Experimental results show that the proposed method can achieve performance on par with the state-of-the-art method flow-guided recurrent neural encoder on challenging benchmarks of Densely Annotated Video Segmentation and Freiburg–Berkeley Motion Segmentation while being two times faster in detection.
Starting Page	997
Ending Page	1004
Page Count	8
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 6, May (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0836
Journal	IET Image Processing
Publisher Date	2019-12-11
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Appearance Feature Extractor Appearance Features Attention Mechanism Computer Vision And Image Processing Technique Convolutional Neural Network Densely Annotated Video Segmentation FDAN Feature Extraction Feature Map Flow Driven Attention Network Flow-driven Attention Network Freiburg–Berkeley Motion Segmentation Fusing Optical Flow Image Motion Analysis Image Segmentation Image Sequence Infers Motion Information Motion-guided Attention Module Neural Nets Object Detection Optical, Image And Video Signal Processing Saliency Map Regression Module State-of-the-art Method Flow-guided Recurrent Neural Encoder Still-image Based Saliency Detector Ultimate Saliency Map Video Salient Object Detection Video Signal Processing
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Particle filter framework for salient object detection in videos

Salient object detection via reciprocal function filter

Video object segmentation via attention-modulating networks

Multi-scale deep neural network for salient object detection

Moving visual focus in salient object segmentation

Salient object detection via reliability-based depth compactness and depth contrast

Salient object detection via reliable boundary seeds and saliency refinement

MOSnet: moving object segmentation with convolutional networks

MOTION-DECISION BASED SPATIOTEMPORAL SALIENCY FOR VIDEO SEQUENCES

Flow driven attention network for video salient object detection

Similar Documents

Particle filter framework for salient object detection in videos

Salient object detection via reciprocal function filter

Video object segmentation via attention-modulating networks

Multi-scale deep neural network for salient object detection

Moving visual focus in salient object segmentation

Salient object detection via reliability-based depth compactness and depth contrast

Salient object detection via reliable boundary seeds and saliency refinement

MOSnet: moving object segmentation with convolutional networks

MOTION-DECISION BASED SPATIOTEMPORAL SALIENCY FOR VIDEO SEQUENCES

Flow driven attention network for video salient object detection