JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection (2004.08515v1)

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/.

• The paper presents a novel joint learning and densely-cooperative fusion framework that integrates RGB and depth features effectively.
• It employs a Siamese network architecture to achieve concurrent feature extraction, enhancing the robustness of salient object detection.
• Experiments report a 1.9% improvement in the S-measure metric over state-of-the-art models, underlining its practical impact.

Joint Learning and Densely-Cooperative Fusion for RGB-D Salient Object Detection

The paper presents JL-DCF, a sophisticated deep learning framework designed to enhance the efficacy of salient object detection utilizing both RGB and depth inputs. The architecture adopts a novel joint learning strategy coupled with a unique densely-cooperative fusion mechanism to address the challenges inherent in RGB-D salient object detection tasks. The framework effectively exploits the complementary nature of RGB and depth information to improve detection accuracy and robustness.

JL-DCF diverges from traditional RGB-D models which typically process RGB and depth data through separate networks. Instead, it integrates these modalities through a Siamese network. This integration facilitates the concurrent learning of RGB and depth inputs, leveraging shared feature extraction pathways. The JL-DCF architecture introduces two core innovations: the Joint Learning (JL) module, which enables robust feature learning across modalities, and the Densely-Cooperative Fusion (DCF) component, responsible for harmonizing extracted features to maximize their synergistic potential.

The architecture's efficacy is validated through comprehensive experiments across multiple datasets, demonstrating significant improvements over state-of-the-art alternatives, particularly in the S-measure metric, with an average enhancement of approximately 1.9% over the top-performing D3Net model. This confirms JL-DCF's superior capacity to capture nuanced saliency cues within RGB-D data and its potential applicability to real-world scenarios.

The implications of this research extend both practically and theoretically. The practical benefits include enhanced performance in salient object detection applications such as autonomous driving, human-computer interaction, and robotics, where both visual and depth cues are vital. Theoretically, the research offers insights into cross-modal feature learning and integration, potentially informing future developments in related domains like multispectral image processing and multimodal machine learning.

JL-DCF represents a significant advancement in addressing the complexities of RGB-D salient object detection. The incorporation of a shared neural network for feature extraction across modalities illustrates a streamlined yet effective approach to enhancing cross-modal learning. Future research could extend this work by exploring alternative backbone architectures, further refining the feature fusion processes, or applying similar strategies to other areas of computer vision that leverage multi-source inputs for improved performance. Such developments could continue to bridge the gap between theoretical research and practical, real-world applications of AI technologies.