Papers › SPSN: Superpixel Prototype Sampling Network for RGB-D Salient Object Detection

SPSN: Superpixel Prototype Sampling Network for RGB-D Salient Object Detection

16 Jul 2022arXiv:2207.07898archive 2025-07-28

Minhyeok Lee, Chaewon Park, Suhwan Cho, Sangyoun Lee

RGB-D salient object detection (SOD) has been in the spotlight recently because it is an important preprocessing operation for various vision tasks. However, despite advances in deep learning-based methods, RGB-D SOD is still challenging due to the large domain gap between an RGB image and the depth map and low-quality depth maps. To solve this problem, we propose a novel superpixel prototype sampling network (SPSN) architecture. The proposed model splits the input RGB image and depth map into component superpixels to generate component prototypes. We design a prototype sampling network so that the network only samples prototypes corresponding to salient objects. In addition, we propose a reliance selection module to recognize the quality of each RGB and depth feature map and adaptively weight them in proportion to their reliability. The proposed method makes the model robust to inconsistencies between RGB images and depth maps and eliminates the influence of non-salient objects. Our method is evaluated on five popular datasets, achieving state-of-the-art performance. We prove the effectiveness of the proposed method through comparative experiments.

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Transformer Hydragon516/SPSN/model.py official repository ran · metamorphic tier: deterministic MIT (permissive) · 0504eaf29f4d4f34 · report
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Tasks

Object DetectionRGB-D Salient Object DetectionSalient Object DetectionSuperpixelsobject-detection

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
RGB-D Salient Object Detection DES SPSN Average MAE 0.016 #4 of 13 Archive leaderboard report
RGB-D Salient Object Detection DES SPSN S-Measure 93.8 #4 of 13 Archive leaderboard report
RGB-D Salient Object Detection DES SPSN max E-Measure 97.6 #4 of 13 Archive leaderboard report
RGB-D Salient Object Detection DES SPSN max F-Measure 94.3 #4 of 13 Archive leaderboard report
RGB-D Salient Object Detection NJU2K SPSN Average MAE 0.032 #4 of 27 Archive leaderboard report
RGB-D Salient Object Detection NJU2K SPSN S-Measure 91.8 #4 of 27 Archive leaderboard report
RGB-D Salient Object Detection NJU2K SPSN max E-Measure 95.0 #4 of 27 Archive leaderboard report
RGB-D Salient Object Detection NJU2K SPSN max F-Measure 92.0 #4 of 27 Archive leaderboard report
RGB-D Salient Object Detection NLPR SPSN Average MAE 0.022 #5 of 14 Archive leaderboard report
RGB-D Salient Object Detection NLPR SPSN S-Measure 92.6 #5 of 14 Archive leaderboard report
RGB-D Salient Object Detection NLPR SPSN max E-Measure 96.2 #5 of 14 Archive leaderboard report
RGB-D Salient Object Detection NLPR SPSN max F-Measure 91.4 #5 of 14 Archive leaderboard report
RGB-D Salient Object Detection SIP SPSN Average MAE 0.042 #5 of 16 Archive leaderboard report
RGB-D Salient Object Detection SIP SPSN S-Measure 89.2 #5 of 16 Archive leaderboard report
RGB-D Salient Object Detection SIP SPSN max E-Measure 93.4 #5 of 16 Archive leaderboard report
RGB-D Salient Object Detection SIP SPSN max F-Measure 89.9 #5 of 16 Archive leaderboard report
RGB-D Salient Object Detection STERE SPSN Average MAE 0.035 #7 of 14 Archive leaderboard report
RGB-D Salient Object Detection STERE SPSN S-Measure 90.7 #7 of 14 Archive leaderboard report
RGB-D Salient Object Detection STERE SPSN max E-Measure 94.3 #7 of 14 Archive leaderboard report
RGB-D Salient Object Detection STERE SPSN max F-Measure 90.0 #7 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.

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