HiDAnet: RGB-D Salient Object Detection via Hierarchical Depth Awareness
This work addresses a specific challenge in computer vision for applications like robotics and augmented reality, offering an incremental improvement over existing methods.
The paper tackles the problem of RGB-D salient object detection by proposing HiDAnet, a hierarchical depth awareness network that improves the fusion of RGB and depth features to better distinguish objects with similar appearances but different distances, achieving state-of-the-art performance on benchmark datasets with significant margins.
RGB-D saliency detection aims to fuse multi-modal cues to accurately localize salient regions. Existing works often adopt attention modules for feature modeling, with few methods explicitly leveraging fine-grained details to merge with semantic cues. Thus, despite the auxiliary depth information, it is still challenging for existing models to distinguish objects with similar appearances but at distinct camera distances. In this paper, from a new perspective, we propose a novel Hierarchical Depth Awareness network (HiDAnet) for RGB-D saliency detection. Our motivation comes from the observation that the multi-granularity properties of geometric priors correlate well with the neural network hierarchies. To realize multi-modal and multi-level fusion, we first use a granularity-based attention scheme to strengthen the discriminatory power of RGB and depth features separately. Then we introduce a unified cross dual-attention module for multi-modal and multi-level fusion in a coarse-to-fine manner. The encoded multi-modal features are gradually aggregated into a shared decoder. Further, we exploit a multi-scale loss to take full advantage of the hierarchical information. Extensive experiments on challenging benchmark datasets demonstrate that our HiDAnet performs favorably over the state-of-the-art methods by large margins.