Research map / Detection and segmentation / CNN features
JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use JL-DCF instead of the strongest reported baseline | RGB-D salient object detection · NJU2K, NLPR, STERE, RGBD135, LFSD, SIP | S-measure: 0.895 -> .903; 0.906 -> .925; 0.891 -> .905; 0.900 -> .929; 0.839 -> .854; 0.864 -> .879 | Helped |
| replace ResNet-101 with VGG-16 (ablation) | JL-DCF · NJU2K | S-measure: .903 -> .897 | Hurt |
| replace cooperative fusion modules with concatenation (ablation) | JL-DCF · NJU2K | S-measure: .903 -> .900 | Hurt |
| use RGB input only instead of RGB and depth (ablation) | JL-DCF · most datasets | S-measure | Hurt |
| use depth input only instead of RGB and depth (ablation) | JL-DCF · most datasets | S-measure | Hurt |
| use RGB and depth together rather than RGB alone (ablation) | JL-DCF · STERE | S-measure: .909 -> .905 | Hurt |
| use separate RGB and depth backbones instead of joint learning (ablation) | JL-DCF · six RGB-D datasets | S-measure: 1.1% overall drop on S_alpha | Hurt |
Closest papers by meaning
- Progressively Complementarity-Aware Fusion Network for RGB-D Salient Object Detection (CVPR 2018 · similarity 0.90)
- RGB-D Salient Object Detection via 3D Convolutional Neural Networks (AAAI 2021 · similarity 0.90)
- Promoting Saliency From Depth: Deep Unsupervised RGB-D Saliency Detection (ICLR 2022 · similarity 0.90)
- Deep RGB-D Saliency Detection With Depth-Sensitive Attention and Automatic Multi-Modal Fusion (CVPR 2021 · similarity 0.90)
- DiMSOD: A Diffusion-Based Framework for Multi-Modal Salient Object Detection (AAAI 2025 · similarity 0.88)
- Alignment-Free RGB-T Salient Object Detection: A Large-Scale Dataset and Progressive Correlation Network (AAAI 2025 · similarity 0.87)
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