Papers › Bifurcated backbone strategy for RGB-D salient object detection

Bifurcated backbone strategy for RGB-D salient object detection

6 Jul 2020arXiv:2007.02713archive 2025-07-28

Yingjie Zhai, Deng-Ping Fan, Jufeng Yang, Ali Borji, Ling Shao, Junwei Han, Liang Wang

Multi-level feature fusion is a fundamental topic in computer vision. It has been exploited to detect, segment and classify objects at various scales. When multi-level features meet multi-modal cues, the optimal feature aggregation and multi-modal learning strategy become a hot potato. In this paper, we leverage the inherent multi-modal and multi-level nature of RGB-D salient object detection to devise a novel cascaded refinement network. In particular, first, we propose to regroup the multi-level features into teacher and student features using a bifurcated backbone strategy (BBS). Second, we introduce a depth-enhanced module (DEM) to excavate informative depth cues from the channel and spatial views. Then, RGB and depth modalities are fused in a complementary way. Our architecture, named Bifurcated Backbone Strategy Network (BBS-Net), is simple, efficient, and backbone-independent. Extensive experiments show that BBS-Net significantly outperforms eighteen SOTA models on eight challenging datasets under five evaluation measures, demonstrating the superiority of our approach (∼4 % improvement in S-measure vs. the top-ranked model: DMRA-iccv2019). In addition, we provide a comprehensive analysis on the generalization ability of different RGB-D datasets and provide a powerful training set for future research.

PaperPDFCodeCode Syntology ran

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="2007.02713")

Code

Syntology Ran 3 of 5 code samples harvested from 2 repositories linked to this paper; 2 have no recorded run. Of those that ran: 1 ran · our draft was wrong; 2 ran with no contract checked.

By repository: official repository: 4 samples from 1 repository, 3 ran; community (archive-listed): 1 sample from 1 repository, 0 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

zyjwuyan/BBS-Net officialmentioned in papermentioned on GitHubpytorchMIT report
DengPingFan/BBS-Net mentioned on GitHubpytorchMIT report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

5 samples harvested; 3 ran; 0 honoured the contract we drafted; 2 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

1ran · our draft was wrong
2ran
2unverified

Licence: 0 of the 5 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from 2 repositories linked to this paper, official or community; each sample names its own and says which. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

conv3x3 zyjwuyan/BBS-Net/models/BBSNet_model.py official repository ran · our draft was wrong MIT (permissive) · fac5364e2f53c6db · report
cv_random_flip zyjwuyan/BBS-Net/data.py official repository ran MIT (permissive) · c5900dcd35b2d0a3 · report
randomCrop zyjwuyan/BBS-Net/data.py official repository ran MIT (permissive) · 7edae4532113d306 · report
randomRotation zyjwuyan/BBS-Net/data.py official repository unverified MIT (permissive) · 2ce2ebcbdb96d573 · report
adjust_lr DengPingFan/BBS-Net/utils.py community (archive-listed) unverified MIT (permissive) · 1a4b7ff9d3c48bd7 · report

Tasks

ObjectObject DetectionRGB Salient Object DetectionRGB-D Salient Object DetectionSalient Object Detectionobject-detection

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
RGB-D Salient Object Detection DES BBS-Net Average MAE 0.021 #9 of 13 Archive leaderboard report
RGB-D Salient Object Detection DES BBS-Net S-Measure 93.3 #9 of 13 Archive leaderboard report
RGB-D Salient Object Detection DES BBS-Net max E-Measure 96.6 #9 of 13 Archive leaderboard report
RGB-D Salient Object Detection DES BBS-Net max F-Measure 92.7 #9 of 13 Archive leaderboard report
RGB-D Salient Object Detection LFSD BBS-Net Average MAE 0.072 #5 of 8 Archive leaderboard report
RGB-D Salient Object Detection LFSD BBS-Net S-Measure 86.4 #5 of 8 Archive leaderboard report
RGB-D Salient Object Detection LFSD BBS-Net max E-Measure 90.1 #5 of 8 Archive leaderboard report
RGB-D Salient Object Detection LFSD BBS-Net max F-Measure 85.8 #5 of 8 Archive leaderboard report
RGB-D Salient Object Detection NLPR BBS-Net Average MAE 0.023 #3 of 14 Archive leaderboard report
RGB-D Salient Object Detection NLPR BBS-Net S-Measure 93.0 #3 of 14 Archive leaderboard report
RGB-D Salient Object Detection NLPR BBS-Net max E-Measure 96.1 #3 of 14 Archive leaderboard report
RGB-D Salient Object Detection NLPR BBS-Net max F-Measure 91.8 #3 of 14 Archive leaderboard report
RGB-D Salient Object Detection RGBD135 BBS-Net Average MAE 0.044 #2 of 5 Archive leaderboard report
RGB-D Salient Object Detection RGBD135 BBS-Net S-Measure 88.2 #2 of 5 Archive leaderboard report
RGB-D Salient Object Detection RGBD135 BBS-Net max E-Measure 91.9 #2 of 5 Archive leaderboard report
RGB-D Salient Object Detection RGBD135 BBS-Net max F-Measure 85.9 #2 of 5 Archive leaderboard report
RGB-D Salient Object Detection SIP BBS-Net Average MAE 0.055 #10 of 16 Archive leaderboard report
RGB-D Salient Object Detection SIP BBS-Net S-Measure 87.9 #10 of 16 Archive leaderboard report
RGB-D Salient Object Detection SIP BBS-Net max E-Measure 92.2 #10 of 16 Archive leaderboard report
RGB-D Salient Object Detection SIP BBS-Net max F-Measure 88.3 #10 of 16 Archive leaderboard report
RGB-D Salient Object Detection STERE BBS-Net Average MAE 0.041 #6 of 14 Archive leaderboard report
RGB-D Salient Object Detection STERE BBS-Net S-Measure 90.8 #6 of 14 Archive leaderboard report
RGB-D Salient Object Detection STERE BBS-Net max E-Measure 94.2 #6 of 14 Archive leaderboard report
RGB-D Salient Object Detection STERE BBS-Net max F-Measure 90.3 #6 of 14 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections