JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection

• URL: http://arxiv.org/abs/2004.08515v1
• Date: Sat, 18 Apr 2020 03:22:40 GMT
• Title: JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection
• Authors: Keren Fu and Deng-Ping Fan and Ge-Peng Ji and Qijun Zhao
• Abstract summary: This paper proposes a novel joint learning and densely-cooperative fusion (JL-DCF) architecture for RGB-D salient object detection. Our JL-DCF learns from both RGB and depth inputs through a Siamese network. Experiments show that the designed framework yields a robust RGB-D saliency detector with good generalization.
• Score: 39.125777418630136
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper proposes a novel joint learning and densely-cooperative fusion (JL-DCF) architecture for RGB-D salient object detection. Existing models usually treat RGB and depth as independent information and design separate networks for feature extraction from each. Such schemes can easily be constrained by a limited amount of training data or over-reliance on an elaborately-designed training process. In contrast, our JL-DCF learns from both RGB and depth inputs through a Siamese network. To this end, we propose two effective components: joint learning (JL), and densely-cooperative fusion (DCF). The JL module provides robust saliency feature learning, while the latter is introduced for complementary feature discovery. Comprehensive experiments on four popular metrics show that the designed framework yields a robust RGB-D saliency detector with good generalization. As a result, JL-DCF significantly advances the top-1 D3Net model by an average of ~1.9% (S-measure) across six challenging datasets, showing that the proposed framework offers a potential solution for real-world applications and could provide more insight into the cross-modality complementarity task. The code will be available at https://github.com/kerenfu/JLDCF/.

Related papers

• Salient Object Detection in RGB-D Videos [11.805682025734551]
  This paper makes two primary contributions: the dataset and the model. We construct the RDVS dataset, a new RGB-D VSOD dataset with realistic depth. We introduce DCTNet+, a three-stream network tailored for RGB-D VSOD.
  arXiv  Detail & Related papers  (2023-10-24T03:18:07Z)
• HODINet: High-Order Discrepant Interaction Network for RGB-D Salient Object Detection [4.007827908611563]
  RGB-D salient object detection (SOD) aims to detect the prominent regions by jointly modeling RGB and depth information. Most RGB-D SOD methods apply the same type of backbones and fusion modules to identically learn the multimodality and multistage features. In this paper, we propose a high-order discrepant interaction network (HODINet) for RGB-D SOD.
  arXiv  Detail & Related papers  (2023-07-03T11:56:21Z)
• Middle-level Fusion for Lightweight RGB-D Salient Object Detection [81.43951906434175]
  A novel lightweight RGB-D SOD model is presented in this paper. With IMFF and L modules incorporated in the middle-level fusion structure, our proposed model has only 3.9M parameters and runs at 33 FPS. The experimental results on several benchmark datasets verify the effectiveness and superiority of the proposed method over some state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-04-23T11:37:15Z)
• Self-Supervised Representation Learning for RGB-D Salient Object Detection [93.17479956795862]
  We use Self-Supervised Representation Learning to design two pretext tasks: the cross-modal auto-encoder and the depth-contour estimation. Our pretext tasks require only a few and un RGB-D datasets to perform pre-training, which make the network capture rich semantic contexts. For the inherent problem of cross-modal fusion in RGB-D SOD, we propose a multi-path fusion module.
  arXiv  Detail & Related papers  (2021-01-29T09:16:06Z)
• Siamese Network for RGB-D Salient Object Detection and Beyond [113.30063105890041]
  A novel framework is proposed to learn from both RGB and depth inputs through a shared network backbone. Comprehensive experiments using five popular metrics show that the designed framework yields a robust RGB-D saliency detector. We also link JL-DCF to the RGB-D semantic segmentation field, showing its capability of outperforming several semantic segmentation models.
  arXiv  Detail & Related papers  (2020-08-26T06:01:05Z)
• Hierarchical Dynamic Filtering Network for RGB-D Salient Object Detection [91.43066633305662]
  The main purpose of RGB-D salient object detection (SOD) is how to better integrate and utilize cross-modal fusion information. In this paper, we explore these issues from a new perspective. We implement a kind of more flexible and efficient multi-scale cross-modal feature processing.
  arXiv  Detail & Related papers  (2020-07-13T07:59:55Z)
• Cross-Modal Weighting Network for RGB-D Salient Object Detection [76.0965123893641]
  We propose a novel Cross-Modal Weighting (CMW) strategy to encourage comprehensive interactions between RGB and depth channels for RGB-D SOD. Specifically, three RGB-depth interaction modules, named CMW-L, CMW-M and CMW-H, are developed to deal with respectively low-, middle- and high-level cross-modal information fusion. CMWNet consistently outperforms 15 state-of-the-art RGB-D SOD methods on seven popular benchmarks.
  arXiv  Detail & Related papers  (2020-07-09T16:01:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.