Progressive Guided Fusion Network With Multi-Modal and Multi-Scale Attention for RGB-D Salient Object Detection Article Swipe
Jiajia Wu
,
Guangliang Han
,
Haining Wang
,
Hang Yang
,
Qingqing Li
,
Dongxu Liu
,
Fangjian Ye
,
Peixun Liu
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/access.2021.3126338
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/access.2021.3126338
The depth map contains abundant spatial structure cues, which makes it extensively introduced into saliency detection tasks for improving the detection accuracy. Nevertheless, the acquired depth map is often with uneven quality, due to the interference of depth sensors and external environments, posing a challenge when trying to minimize the disturbances from low-quality depth maps during the fusion process. In this article, to mitigate such issues and highlight the salient objects, we propose a progressive guided fusion network (PGFNet) with multi-modal and multi-scale attention for RGB-D salient object detection. Particularly, we first present a multi-modal and multi-scale attention fusion model (MMAFM) to fully mine and utilize the complementarity of features at different scales and modalities for achieving optimal fusion. Then, to strengthen the semantic expressiveness of the shallow-layer features, we design a multi-modal feature refinement mechanism (MFRM), which exploits the high-level fusion feature to guide the enhancement of the shallow-layer original RGB and depth features before they are fused. Moreover, a residual prediction module (RPM) is applied to further suppress background elements. Our entire network adopts a top-down strategy to progressively excavate and integrate valuable information. Compared with the state-of-the-art methods, experimental results demonstrate the effectiveness of our proposed method both qualitatively and quantitatively on eight challenging benchmark datasets.
Related Topics
Concepts
Computer science
RGB color model
Artificial intelligence
Fusion
Feature (linguistics)
Fusion mechanism
Salient
Benchmark (surveying)
Residual
Computer vision
Modal
Robustness (evolution)
Process (computing)
Pattern recognition (psychology)
Object detection
Scale (ratio)
Algorithm
Operating system
Gene
Geography
Quantum mechanics
Polymer chemistry
Linguistics
Geodesy
Physics
Philosophy
Lipid bilayer fusion
Chemistry
Biochemistry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2021.3126338
- OA Status
- gold
- Cited By
- 2
- References
- 81
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3213066273
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3213066273Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/access.2021.3126338Digital Object Identifier
- Title
-
Progressive Guided Fusion Network With Multi-Modal and Multi-Scale Attention for RGB-D Salient Object DetectionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-01Full publication date if available
- Authors
-
Jiajia Wu, Guangliang Han, Haining Wang, Hang Yang, Qingqing Li, Dongxu Liu, Fangjian Ye, Peixun LiuList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2021.3126338Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1109/access.2021.3126338Direct OA link when available
- Concepts
-
Computer science, RGB color model, Artificial intelligence, Fusion, Feature (linguistics), Fusion mechanism, Salient, Benchmark (surveying), Residual, Computer vision, Modal, Robustness (evolution), Process (computing), Pattern recognition (psychology), Object detection, Scale (ratio), Algorithm, Operating system, Gene, Geography, Quantum mechanics, Polymer chemistry, Linguistics, Geodesy, Physics, Philosophy, Lipid bilayer fusion, Chemistry, BiochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 1, 2022: 1Per-year citation counts (last 5 years)
- References (count)
-
81Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3213066273 |
|---|---|
| doi | https://doi.org/10.1109/access.2021.3126338 |
| ids.doi | https://doi.org/10.1109/access.2021.3126338 |
| ids.mag | 3213066273 |
| ids.openalex | https://openalex.org/W3213066273 |
| fwci | 0.20443896 |
| type | article |
| title | Progressive Guided Fusion Network With Multi-Modal and Multi-Scale Attention for RGB-D Salient Object Detection |
| awards[0].id | https://openalex.org/G1793137068 |
| awards[0].funder_id | https://openalex.org/F4320321001 |
| awards[0].display_name | |
| awards[0].funder_award_id | 61602432 |
| awards[0].funder_display_name | National Natural Science Foundation of China |
| awards[1].id | https://openalex.org/G1121076708 |
| awards[1].funder_id | https://openalex.org/F4320321001 |
| awards[1].display_name | |
| awards[1].funder_award_id | 61401425 |
| awards[1].funder_display_name | National Natural Science Foundation of China |
| biblio.issue | |
| biblio.volume | 9 |
| biblio.last_page | 150622 |
| biblio.first_page | 150608 |
| topics[0].id | https://openalex.org/T11605 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 1.0 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1707 |
| topics[0].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[0].display_name | Visual Attention and Saliency Detection |
| topics[1].id | https://openalex.org/T10036 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9934999942779541 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1707 |
| topics[1].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[1].display_name | Advanced Neural Network Applications |
| topics[2].id | https://openalex.org/T11659 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9742000102996826 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2214 |
| topics[2].subfield.display_name | Media Technology |
| topics[2].display_name | Advanced Image Fusion Techniques |
| funders[0].id | https://openalex.org/F4320321001 |
| funders[0].ror | https://ror.org/01h0zpd94 |
| funders[0].display_name | National Natural Science Foundation of China |
| is_xpac | False |
| apc_list.value | 1850 |
| apc_list.currency | USD |
| apc_list.value_usd | 1850 |
| apc_paid.value | 1850 |
| apc_paid.currency | USD |
| apc_paid.value_usd | 1850 |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.8100318908691406 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C82990744 |
| concepts[1].level | 2 |
| concepts[1].score | 0.7112476229667664 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q166194 |
| concepts[1].display_name | RGB color model |
| concepts[2].id | https://openalex.org/C154945302 |
| concepts[2].level | 1 |
| concepts[2].score | 0.6798108816146851 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[2].display_name | Artificial intelligence |
| concepts[3].id | https://openalex.org/C158525013 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5434584617614746 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q2593739 |
| concepts[3].display_name | Fusion |
| concepts[4].id | https://openalex.org/C2776401178 |
| concepts[4].level | 2 |
| concepts[4].score | 0.529798686504364 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q12050496 |
| concepts[4].display_name | Feature (linguistics) |
| concepts[5].id | https://openalex.org/C173414695 |
| concepts[5].level | 4 |
| concepts[5].score | 0.5236560702323914 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q5510276 |
| concepts[5].display_name | Fusion mechanism |
| concepts[6].id | https://openalex.org/C2780719617 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5186785459518433 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q1030752 |
| concepts[6].display_name | Salient |
| concepts[7].id | https://openalex.org/C185798385 |
| concepts[7].level | 2 |
| concepts[7].score | 0.5044506788253784 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q1161707 |
| concepts[7].display_name | Benchmark (surveying) |
| concepts[8].id | https://openalex.org/C155512373 |
| concepts[8].level | 2 |
| concepts[8].score | 0.4831923544406891 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q287450 |
| concepts[8].display_name | Residual |
| concepts[9].id | https://openalex.org/C31972630 |
| concepts[9].level | 1 |
| concepts[9].score | 0.45730477571487427 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[9].display_name | Computer vision |
| concepts[10].id | https://openalex.org/C71139939 |
| concepts[10].level | 2 |
| concepts[10].score | 0.4485906958580017 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q910194 |
| concepts[10].display_name | Modal |
| concepts[11].id | https://openalex.org/C63479239 |
| concepts[11].level | 3 |
| concepts[11].score | 0.4444854259490967 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q7353546 |
| concepts[11].display_name | Robustness (evolution) |
| concepts[12].id | https://openalex.org/C98045186 |
| concepts[12].level | 2 |
| concepts[12].score | 0.4404396414756775 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q205663 |
| concepts[12].display_name | Process (computing) |
| concepts[13].id | https://openalex.org/C153180895 |
| concepts[13].level | 2 |
| concepts[13].score | 0.4269748032093048 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[13].display_name | Pattern recognition (psychology) |
| concepts[14].id | https://openalex.org/C2776151529 |
| concepts[14].level | 3 |
| concepts[14].score | 0.41760358214378357 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q3045304 |
| concepts[14].display_name | Object detection |
| concepts[15].id | https://openalex.org/C2778755073 |
| concepts[15].level | 2 |
| concepts[15].score | 0.4130188226699829 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q10858537 |
| concepts[15].display_name | Scale (ratio) |
| concepts[16].id | https://openalex.org/C11413529 |
| concepts[16].level | 1 |
| concepts[16].score | 0.10151931643486023 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[16].display_name | Algorithm |
| concepts[17].id | https://openalex.org/C111919701 |
| concepts[17].level | 1 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q9135 |
| concepts[17].display_name | Operating system |
| concepts[18].id | https://openalex.org/C104317684 |
| concepts[18].level | 2 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q7187 |
| concepts[18].display_name | Gene |
| concepts[19].id | https://openalex.org/C205649164 |
| concepts[19].level | 0 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q1071 |
| concepts[19].display_name | Geography |
| concepts[20].id | https://openalex.org/C62520636 |
| concepts[20].level | 1 |
| concepts[20].score | 0.0 |
| concepts[20].wikidata | https://www.wikidata.org/wiki/Q944 |
| concepts[20].display_name | Quantum mechanics |
| concepts[21].id | https://openalex.org/C188027245 |
| concepts[21].level | 1 |
| concepts[21].score | 0.0 |
| concepts[21].wikidata | https://www.wikidata.org/wiki/Q750446 |
| concepts[21].display_name | Polymer chemistry |
| concepts[22].id | https://openalex.org/C41895202 |
| concepts[22].level | 1 |
| concepts[22].score | 0.0 |
| concepts[22].wikidata | https://www.wikidata.org/wiki/Q8162 |
| concepts[22].display_name | Linguistics |
| concepts[23].id | https://openalex.org/C13280743 |
| concepts[23].level | 1 |
| concepts[23].score | 0.0 |
| concepts[23].wikidata | https://www.wikidata.org/wiki/Q131089 |
| concepts[23].display_name | Geodesy |
| concepts[24].id | https://openalex.org/C121332964 |
| concepts[24].level | 0 |
| concepts[24].score | 0.0 |
| concepts[24].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[24].display_name | Physics |
| concepts[25].id | https://openalex.org/C138885662 |
| concepts[25].level | 0 |
| concepts[25].score | 0.0 |
| concepts[25].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[25].display_name | Philosophy |
| concepts[26].id | https://openalex.org/C103038307 |
| concepts[26].level | 3 |
| concepts[26].score | 0.0 |
| concepts[26].wikidata | https://www.wikidata.org/wiki/Q6556360 |
| concepts[26].display_name | Lipid bilayer fusion |
| concepts[27].id | https://openalex.org/C185592680 |
| concepts[27].level | 0 |
| concepts[27].score | 0.0 |
| concepts[27].wikidata | https://www.wikidata.org/wiki/Q2329 |
| concepts[27].display_name | Chemistry |
| concepts[28].id | https://openalex.org/C55493867 |
| concepts[28].level | 1 |
| concepts[28].score | 0.0 |
| concepts[28].wikidata | https://www.wikidata.org/wiki/Q7094 |
| concepts[28].display_name | Biochemistry |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.8100318908691406 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/rgb-color-model |
| keywords[1].score | 0.7112476229667664 |
| keywords[1].display_name | RGB color model |
| keywords[2].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[2].score | 0.6798108816146851 |
| keywords[2].display_name | Artificial intelligence |
| keywords[3].id | https://openalex.org/keywords/fusion |
| keywords[3].score | 0.5434584617614746 |
| keywords[3].display_name | Fusion |
| keywords[4].id | https://openalex.org/keywords/feature |
| keywords[4].score | 0.529798686504364 |
| keywords[4].display_name | Feature (linguistics) |
| keywords[5].id | https://openalex.org/keywords/fusion-mechanism |
| keywords[5].score | 0.5236560702323914 |
| keywords[5].display_name | Fusion mechanism |
| keywords[6].id | https://openalex.org/keywords/salient |
| keywords[6].score | 0.5186785459518433 |
| keywords[6].display_name | Salient |
| keywords[7].id | https://openalex.org/keywords/benchmark |
| keywords[7].score | 0.5044506788253784 |
| keywords[7].display_name | Benchmark (surveying) |
| keywords[8].id | https://openalex.org/keywords/residual |
| keywords[8].score | 0.4831923544406891 |
| keywords[8].display_name | Residual |
| keywords[9].id | https://openalex.org/keywords/computer-vision |
| keywords[9].score | 0.45730477571487427 |
| keywords[9].display_name | Computer vision |
| keywords[10].id | https://openalex.org/keywords/modal |
| keywords[10].score | 0.4485906958580017 |
| keywords[10].display_name | Modal |
| keywords[11].id | https://openalex.org/keywords/robustness |
| keywords[11].score | 0.4444854259490967 |
| keywords[11].display_name | Robustness (evolution) |
| keywords[12].id | https://openalex.org/keywords/process |
| keywords[12].score | 0.4404396414756775 |
| keywords[12].display_name | Process (computing) |
| keywords[13].id | https://openalex.org/keywords/pattern-recognition |
| keywords[13].score | 0.4269748032093048 |
| keywords[13].display_name | Pattern recognition (psychology) |
| keywords[14].id | https://openalex.org/keywords/object-detection |
| keywords[14].score | 0.41760358214378357 |
| keywords[14].display_name | Object detection |
| keywords[15].id | https://openalex.org/keywords/scale |
| keywords[15].score | 0.4130188226699829 |
| keywords[15].display_name | Scale (ratio) |
| keywords[16].id | https://openalex.org/keywords/algorithm |
| keywords[16].score | 0.10151931643486023 |
| keywords[16].display_name | Algorithm |
| language | en |
| locations[0].id | doi:10.1109/access.2021.3126338 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S2485537415 |
| locations[0].source.issn | 2169-3536 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2169-3536 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | IEEE Access |
| locations[0].source.host_organization | https://openalex.org/P4310319808 |
| locations[0].source.host_organization_name | Institute of Electrical and Electronics Engineers |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310319808 |
| locations[0].source.host_organization_lineage_names | Institute of Electrical and Electronics Engineers |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | IEEE Access |
| locations[0].landing_page_url | https://doi.org/10.1109/access.2021.3126338 |
| locations[1].id | pmh:oai:doaj.org/article:a36210973e84426688a7859caa3fc9f1 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306401280 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[1].source.host_organization | |
| locations[1].source.host_organization_name | |
| locations[1].license | cc-by-sa |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | https://openalex.org/licenses/cc-by-sa |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | IEEE Access, Vol 9, Pp 150608-150622 (2021) |
| locations[1].landing_page_url | https://doaj.org/article/a36210973e84426688a7859caa3fc9f1 |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5101605865 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-7667-4878 |
| authorships[0].author.display_name | Jiajia Wu |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I19820366, https://openalex.org/I4210088164 |
| authorships[0].affiliations[0].raw_affiliation_string | Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences , Changchun, China |
| authorships[0].affiliations[1].institution_ids | https://openalex.org/I19820366, https://openalex.org/I4210088164 |
| authorships[0].affiliations[1].raw_affiliation_string | Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, China |
| authorships[0].institutions[0].id | https://openalex.org/I4210088164 |
| authorships[0].institutions[0].ror | https://ror.org/012rct222 |
| authorships[0].institutions[0].type | facility |
| authorships[0].institutions[0].lineage | https://openalex.org/I19820366, https://openalex.org/I4210088164 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Changchun Institute of Optics, Fine Mechanics and Physics |
| authorships[0].institutions[1].id | https://openalex.org/I19820366 |
| authorships[0].institutions[1].ror | https://ror.org/034t30j35 |
| authorships[0].institutions[1].type | government |
| authorships[0].institutions[1].lineage | https://openalex.org/I19820366 |
| authorships[0].institutions[1].country_code | CN |
| authorships[0].institutions[1].display_name | Chinese Academy of Sciences |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Jiajia Wu |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences , Changchun, China, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, China |
| authorships[1].author.id | https://openalex.org/A5088872755 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-3757-7447 |
| authorships[1].author.display_name | Guangliang Han |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I19820366, https://openalex.org/I4210088164 |
| authorships[1].affiliations[0].raw_affiliation_string | Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China. |
| authorships[1].institutions[0].id | https://openalex.org/I4210088164 |
| authorships[1].institutions[0].ror | https://ror.org/012rct222 |
| authorships[1].institutions[0].type | facility |
| authorships[1].institutions[0].lineage | https://openalex.org/I19820366, https://openalex.org/I4210088164 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Changchun Institute of Optics, Fine Mechanics and Physics |
| authorships[1].institutions[1].id | https://openalex.org/I19820366 |
| authorships[1].institutions[1].ror | https://ror.org/034t30j35 |
| authorships[1].institutions[1].type | government |
| authorships[1].institutions[1].lineage | https://openalex.org/I19820366 |
| authorships[1].institutions[1].country_code | CN |
| authorships[1].institutions[1].display_name | Chinese Academy of Sciences |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Guangliang Han |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China. |
| authorships[2].author.id | https://openalex.org/A5100664238 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-5843-793X |
| authorships[2].author.display_name | Haining Wang |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I37448385 |
| authorships[2].affiliations[0].raw_affiliation_string | School of Police Administration, People's Public Security University of China, Beijing 100038, China. |
| authorships[2].institutions[0].id | https://openalex.org/I37448385 |
| authorships[2].institutions[0].ror | https://ror.org/05twya590 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I37448385 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | China People's Public Security University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Haining Wang |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | School of Police Administration, People's Public Security University of China, Beijing 100038, China. |
| authorships[3].author.id | https://openalex.org/A5088740924 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-6027-1337 |
| authorships[3].author.display_name | Hang Yang |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I19820366, https://openalex.org/I4210088164 |
| authorships[3].affiliations[0].raw_affiliation_string | Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China. |
| authorships[3].institutions[0].id | https://openalex.org/I4210088164 |
| authorships[3].institutions[0].ror | https://ror.org/012rct222 |
| authorships[3].institutions[0].type | facility |
| authorships[3].institutions[0].lineage | https://openalex.org/I19820366, https://openalex.org/I4210088164 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | Changchun Institute of Optics, Fine Mechanics and Physics |
| authorships[3].institutions[1].id | https://openalex.org/I19820366 |
| authorships[3].institutions[1].ror | https://ror.org/034t30j35 |
| authorships[3].institutions[1].type | government |
| authorships[3].institutions[1].lineage | https://openalex.org/I19820366 |
| authorships[3].institutions[1].country_code | CN |
| authorships[3].institutions[1].display_name | Chinese Academy of Sciences |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Hang Yang |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China. |
| authorships[4].author.id | https://openalex.org/A5100411167 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-2339-2399 |
| authorships[4].author.display_name | Qingqing Li |
| authorships[4].countries | CN |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I4210088164, https://openalex.org/I4210165038 |
| authorships[4].affiliations[0].raw_affiliation_string | Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China and School of Optoelectronics, University of Chinese Academy of Sciences, Beijing 100049, China. |
| authorships[4].institutions[0].id | https://openalex.org/I4210088164 |
| authorships[4].institutions[0].ror | https://ror.org/012rct222 |
| authorships[4].institutions[0].type | facility |
| authorships[4].institutions[0].lineage | https://openalex.org/I19820366, https://openalex.org/I4210088164 |
| authorships[4].institutions[0].country_code | CN |
| authorships[4].institutions[0].display_name | Changchun Institute of Optics, Fine Mechanics and Physics |
| authorships[4].institutions[1].id | https://openalex.org/I4210165038 |
| authorships[4].institutions[1].ror | https://ror.org/05qbk4x57 |
| authorships[4].institutions[1].type | education |
| authorships[4].institutions[1].lineage | https://openalex.org/I19820366, https://openalex.org/I4210165038 |
| authorships[4].institutions[1].country_code | CN |
| authorships[4].institutions[1].display_name | University of Chinese Academy of Sciences |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Qingqing Li |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China and School of Optoelectronics, University of Chinese Academy of Sciences, Beijing 100049, China. |
| authorships[5].author.id | https://openalex.org/A5100386802 |
| authorships[5].author.orcid | https://orcid.org/0000-0003-0691-4025 |
| authorships[5].author.display_name | Dongxu Liu |
| authorships[5].countries | CN |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I4210088164, https://openalex.org/I4210165038 |
| authorships[5].affiliations[0].raw_affiliation_string | Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China and School of Optoelectronics, University of Chinese Academy of Sciences, Beijing 100049, China. |
| authorships[5].institutions[0].id | https://openalex.org/I4210088164 |
| authorships[5].institutions[0].ror | https://ror.org/012rct222 |
| authorships[5].institutions[0].type | facility |
| authorships[5].institutions[0].lineage | https://openalex.org/I19820366, https://openalex.org/I4210088164 |
| authorships[5].institutions[0].country_code | CN |
| authorships[5].institutions[0].display_name | Changchun Institute of Optics, Fine Mechanics and Physics |
| authorships[5].institutions[1].id | https://openalex.org/I4210165038 |
| authorships[5].institutions[1].ror | https://ror.org/05qbk4x57 |
| authorships[5].institutions[1].type | education |
| authorships[5].institutions[1].lineage | https://openalex.org/I19820366, https://openalex.org/I4210165038 |
| authorships[5].institutions[1].country_code | CN |
| authorships[5].institutions[1].display_name | University of Chinese Academy of Sciences |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Dongxu Liu |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China and School of Optoelectronics, University of Chinese Academy of Sciences, Beijing 100049, China. |
| authorships[6].author.id | https://openalex.org/A5048633196 |
| authorships[6].author.orcid | |
| authorships[6].author.display_name | Fangjian Ye |
| authorships[6].countries | CN |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I1302611135, https://openalex.org/I4210139944 |
| authorships[6].affiliations[0].raw_affiliation_string | Institute of Forensic Science, Ministry of Public Security, Beijing 100038, China. |
| authorships[6].institutions[0].id | https://openalex.org/I4210139944 |
| authorships[6].institutions[0].ror | https://ror.org/04ry60e05 |
| authorships[6].institutions[0].type | facility |
| authorships[6].institutions[0].lineage | https://openalex.org/I4210089256, https://openalex.org/I4210127390, https://openalex.org/I4210139944 |
| authorships[6].institutions[0].country_code | CN |
| authorships[6].institutions[0].display_name | Institute of Forensic Science |
| authorships[6].institutions[1].id | https://openalex.org/I1302611135 |
| authorships[6].institutions[1].ror | https://ror.org/00bt9we26 |
| authorships[6].institutions[1].type | government |
| authorships[6].institutions[1].lineage | https://openalex.org/I1302611135 |
| authorships[6].institutions[1].country_code | CN |
| authorships[6].institutions[1].display_name | Ministry of Public Security of the People's Republic of China |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Fangjian Ye |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | Institute of Forensic Science, Ministry of Public Security, Beijing 100038, China. |
| authorships[7].author.id | https://openalex.org/A5103123580 |
| authorships[7].author.orcid | https://orcid.org/0000-0002-5611-7502 |
| authorships[7].author.display_name | Peixun Liu |
| authorships[7].countries | CN |
| authorships[7].affiliations[0].institution_ids | https://openalex.org/I19820366, https://openalex.org/I4210088164 |
| authorships[7].affiliations[0].raw_affiliation_string | Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China. |
| authorships[7].institutions[0].id | https://openalex.org/I4210088164 |
| authorships[7].institutions[0].ror | https://ror.org/012rct222 |
| authorships[7].institutions[0].type | facility |
| authorships[7].institutions[0].lineage | https://openalex.org/I19820366, https://openalex.org/I4210088164 |
| authorships[7].institutions[0].country_code | CN |
| authorships[7].institutions[0].display_name | Changchun Institute of Optics, Fine Mechanics and Physics |
| authorships[7].institutions[1].id | https://openalex.org/I19820366 |
| authorships[7].institutions[1].ror | https://ror.org/034t30j35 |
| authorships[7].institutions[1].type | government |
| authorships[7].institutions[1].lineage | https://openalex.org/I19820366 |
| authorships[7].institutions[1].country_code | CN |
| authorships[7].institutions[1].display_name | Chinese Academy of Sciences |
| authorships[7].author_position | last |
| authorships[7].raw_author_name | Peixun Liu |
| authorships[7].is_corresponding | False |
| authorships[7].raw_affiliation_strings | Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China. |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.1109/access.2021.3126338 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Progressive Guided Fusion Network With Multi-Modal and Multi-Scale Attention for RGB-D Salient Object Detection |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11605 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 1.0 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1707 |
| primary_topic.subfield.display_name | Computer Vision and Pattern Recognition |
| primary_topic.display_name | Visual Attention and Saliency Detection |
| related_works | https://openalex.org/W2988498288, https://openalex.org/W1991764124, https://openalex.org/W2786308501, https://openalex.org/W2012247345, https://openalex.org/W4318719019, https://openalex.org/W2145243726, https://openalex.org/W2463550667, https://openalex.org/W2351649588, https://openalex.org/W4378188910, https://openalex.org/W2437857815 |
| cited_by_count | 2 |
| counts_by_year[0].year | 2023 |
| counts_by_year[0].cited_by_count | 1 |
| counts_by_year[1].year | 2022 |
| counts_by_year[1].cited_by_count | 1 |
| locations_count | 2 |
| best_oa_location.id | doi:10.1109/access.2021.3126338 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S2485537415 |
| best_oa_location.source.issn | 2169-3536 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2169-3536 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | IEEE Access |
| best_oa_location.source.host_organization | https://openalex.org/P4310319808 |
| best_oa_location.source.host_organization_name | Institute of Electrical and Electronics Engineers |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310319808 |
| best_oa_location.source.host_organization_lineage_names | Institute of Electrical and Electronics Engineers |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | IEEE Access |
| best_oa_location.landing_page_url | https://doi.org/10.1109/access.2021.3126338 |
| primary_location.id | doi:10.1109/access.2021.3126338 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S2485537415 |
| primary_location.source.issn | 2169-3536 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2169-3536 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | IEEE Access |
| primary_location.source.host_organization | https://openalex.org/P4310319808 |
| primary_location.source.host_organization_name | Institute of Electrical and Electronics Engineers |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319808 |
| primary_location.source.host_organization_lineage_names | Institute of Electrical and Electronics Engineers |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | IEEE Access |
| primary_location.landing_page_url | https://doi.org/10.1109/access.2021.3126338 |
| publication_date | 2021-01-01 |
| publication_year | 2021 |
| referenced_works | https://openalex.org/W3006465601, https://openalex.org/W3108812909, https://openalex.org/W3049194477, https://openalex.org/W2963897031, https://openalex.org/W2909381593, https://openalex.org/W2957414648, https://openalex.org/W3010616503, https://openalex.org/W3108421143, https://openalex.org/W3092729213, https://openalex.org/W3106587394, https://openalex.org/W2798857366, https://openalex.org/W3034320133, https://openalex.org/W2948300571, https://openalex.org/W3108822985, https://openalex.org/W2910219960, https://openalex.org/W3114152269, https://openalex.org/W3035357085, https://openalex.org/W3002301267, https://openalex.org/W2887486131, https://openalex.org/W2945809413, https://openalex.org/W3113392965, https://openalex.org/W2124321291, https://openalex.org/W2128254161, https://openalex.org/W1974536722, https://openalex.org/W2558002256, https://openalex.org/W2337762808, https://openalex.org/W2597742770, https://openalex.org/W2620958690, https://openalex.org/W2969377765, https://openalex.org/W2887522866, https://openalex.org/W2520640394, https://openalex.org/W3011305844, https://openalex.org/W2998449272, https://openalex.org/W6631190155, https://openalex.org/W3097053213, https://openalex.org/W6684191040, https://openalex.org/W6766978945, https://openalex.org/W2907643346, https://openalex.org/W2963529609, https://openalex.org/W2963868681, https://openalex.org/W2039298799, https://openalex.org/W1986670485, https://openalex.org/W2765838470, https://openalex.org/W2405748702, https://openalex.org/W2514453564, https://openalex.org/W2766315367, https://openalex.org/W2962741298, https://openalex.org/W2739012628, https://openalex.org/W2767775878, https://openalex.org/W1976409045, https://openalex.org/W1938386764, https://openalex.org/W2461758788, https://openalex.org/W3085685449, https://openalex.org/W2523532944, https://openalex.org/W1976754232, https://openalex.org/W6648922525, https://openalex.org/W1966025376, https://openalex.org/W2963091558, https://openalex.org/W2000946514, https://openalex.org/W20683899, https://openalex.org/W6637373629, https://openalex.org/W2194775991, https://openalex.org/W6739901393, https://openalex.org/W3136838953, https://openalex.org/W3086388316, https://openalex.org/W3118710621, https://openalex.org/W3003376220, https://openalex.org/W3115482207, https://openalex.org/W3097336090, https://openalex.org/W2964121744, https://openalex.org/W1993713494, https://openalex.org/W1522301498, https://openalex.org/W3099871687, https://openalex.org/W1686810756, https://openalex.org/W4295312788, https://openalex.org/W2163605009, https://openalex.org/W3101839051, https://openalex.org/W2963403868, https://openalex.org/W2970971581, https://openalex.org/W3035687312, https://openalex.org/W4385245566 |
| referenced_works_count | 81 |
| abstract_inverted_index.a | 43, 73, 93, 131, 160, 176 |
| abstract_inverted_index.In | 59 |
| abstract_inverted_index.at | 110 |
| abstract_inverted_index.is | 27, 165 |
| abstract_inverted_index.it | 10 |
| abstract_inverted_index.of | 36, 108, 125, 147, 196 |
| abstract_inverted_index.on | 204 |
| abstract_inverted_index.to | 33, 47, 62, 101, 120, 143, 167, 179 |
| abstract_inverted_index.we | 71, 90, 129 |
| abstract_inverted_index.Our | 172 |
| abstract_inverted_index.RGB | 151 |
| abstract_inverted_index.The | 0 |
| abstract_inverted_index.and | 39, 66, 81, 95, 104, 113, 152, 182, 202 |
| abstract_inverted_index.are | 157 |
| abstract_inverted_index.due | 32 |
| abstract_inverted_index.for | 17, 84, 115 |
| abstract_inverted_index.map | 2, 26 |
| abstract_inverted_index.our | 197 |
| abstract_inverted_index.the | 19, 23, 34, 49, 56, 68, 106, 122, 126, 139, 145, 148, 188, 194 |
| abstract_inverted_index.both | 200 |
| abstract_inverted_index.from | 51 |
| abstract_inverted_index.into | 13 |
| abstract_inverted_index.maps | 54 |
| abstract_inverted_index.mine | 103 |
| abstract_inverted_index.such | 64 |
| abstract_inverted_index.they | 156 |
| abstract_inverted_index.this | 60 |
| abstract_inverted_index.when | 45 |
| abstract_inverted_index.with | 29, 79, 187 |
| abstract_inverted_index.(RPM) | 164 |
| abstract_inverted_index.RGB-D | 85 |
| abstract_inverted_index.Then, | 119 |
| abstract_inverted_index.cues, | 7 |
| abstract_inverted_index.depth | 1, 25, 37, 53, 153 |
| abstract_inverted_index.eight | 205 |
| abstract_inverted_index.first | 91 |
| abstract_inverted_index.fully | 102 |
| abstract_inverted_index.guide | 144 |
| abstract_inverted_index.makes | 9 |
| abstract_inverted_index.model | 99 |
| abstract_inverted_index.often | 28 |
| abstract_inverted_index.tasks | 16 |
| abstract_inverted_index.which | 8, 137 |
| abstract_inverted_index.adopts | 175 |
| abstract_inverted_index.before | 155 |
| abstract_inverted_index.design | 130 |
| abstract_inverted_index.during | 55 |
| abstract_inverted_index.entire | 173 |
| abstract_inverted_index.fused. | 158 |
| abstract_inverted_index.fusion | 57, 76, 98, 141 |
| abstract_inverted_index.guided | 75 |
| abstract_inverted_index.issues | 65 |
| abstract_inverted_index.method | 199 |
| abstract_inverted_index.module | 163 |
| abstract_inverted_index.object | 87 |
| abstract_inverted_index.posing | 42 |
| abstract_inverted_index.scales | 112 |
| abstract_inverted_index.trying | 46 |
| abstract_inverted_index.uneven | 30 |
| abstract_inverted_index.(MFRM), | 136 |
| abstract_inverted_index.(MMAFM) | 100 |
| abstract_inverted_index.applied | 166 |
| abstract_inverted_index.feature | 133, 142 |
| abstract_inverted_index.further | 168 |
| abstract_inverted_index.fusion. | 118 |
| abstract_inverted_index.network | 77, 174 |
| abstract_inverted_index.optimal | 117 |
| abstract_inverted_index.present | 92 |
| abstract_inverted_index.propose | 72 |
| abstract_inverted_index.results | 192 |
| abstract_inverted_index.salient | 69, 86 |
| abstract_inverted_index.sensors | 38 |
| abstract_inverted_index.spatial | 5 |
| abstract_inverted_index.utilize | 105 |
| abstract_inverted_index.(PGFNet) | 78 |
| abstract_inverted_index.Compared | 186 |
| abstract_inverted_index.abundant | 4 |
| abstract_inverted_index.acquired | 24 |
| abstract_inverted_index.article, | 61 |
| abstract_inverted_index.contains | 3 |
| abstract_inverted_index.excavate | 181 |
| abstract_inverted_index.exploits | 138 |
| abstract_inverted_index.external | 40 |
| abstract_inverted_index.features | 109, 154 |
| abstract_inverted_index.methods, | 190 |
| abstract_inverted_index.minimize | 48 |
| abstract_inverted_index.mitigate | 63 |
| abstract_inverted_index.objects, | 70 |
| abstract_inverted_index.original | 150 |
| abstract_inverted_index.process. | 58 |
| abstract_inverted_index.proposed | 198 |
| abstract_inverted_index.quality, | 31 |
| abstract_inverted_index.residual | 161 |
| abstract_inverted_index.saliency | 14 |
| abstract_inverted_index.semantic | 123 |
| abstract_inverted_index.strategy | 178 |
| abstract_inverted_index.suppress | 169 |
| abstract_inverted_index.top-down | 177 |
| abstract_inverted_index.valuable | 184 |
| abstract_inverted_index.Moreover, | 159 |
| abstract_inverted_index.accuracy. | 21 |
| abstract_inverted_index.achieving | 116 |
| abstract_inverted_index.attention | 83, 97 |
| abstract_inverted_index.benchmark | 207 |
| abstract_inverted_index.challenge | 44 |
| abstract_inverted_index.datasets. | 208 |
| abstract_inverted_index.detection | 15, 20 |
| abstract_inverted_index.different | 111 |
| abstract_inverted_index.elements. | 171 |
| abstract_inverted_index.features, | 128 |
| abstract_inverted_index.highlight | 67 |
| abstract_inverted_index.improving | 18 |
| abstract_inverted_index.integrate | 183 |
| abstract_inverted_index.mechanism | 135 |
| abstract_inverted_index.structure | 6 |
| abstract_inverted_index.background | 170 |
| abstract_inverted_index.detection. | 88 |
| abstract_inverted_index.high-level | 140 |
| abstract_inverted_index.introduced | 12 |
| abstract_inverted_index.modalities | 114 |
| abstract_inverted_index.prediction | 162 |
| abstract_inverted_index.refinement | 134 |
| abstract_inverted_index.strengthen | 121 |
| abstract_inverted_index.challenging | 206 |
| abstract_inverted_index.demonstrate | 193 |
| abstract_inverted_index.enhancement | 146 |
| abstract_inverted_index.extensively | 11 |
| abstract_inverted_index.low-quality | 52 |
| abstract_inverted_index.multi-modal | 80, 94, 132 |
| abstract_inverted_index.multi-scale | 82, 96 |
| abstract_inverted_index.progressive | 74 |
| abstract_inverted_index.disturbances | 50 |
| abstract_inverted_index.experimental | 191 |
| abstract_inverted_index.information. | 185 |
| abstract_inverted_index.interference | 35 |
| abstract_inverted_index.Nevertheless, | 22 |
| abstract_inverted_index.Particularly, | 89 |
| abstract_inverted_index.effectiveness | 195 |
| abstract_inverted_index.environments, | 41 |
| abstract_inverted_index.progressively | 180 |
| abstract_inverted_index.qualitatively | 201 |
| abstract_inverted_index.shallow-layer | 127, 149 |
| abstract_inverted_index.expressiveness | 124 |
| abstract_inverted_index.quantitatively | 203 |
| abstract_inverted_index.complementarity | 107 |
| abstract_inverted_index.state-of-the-art | 189 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile.value | 0.51992717 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |