RGB-D Salient Object Detection Methods Research Articles

BMFNet: Bifurcated multi-modal fusion network for RGB-D salient object detection

Although deep learning-based RGB-D salient object detection methods have achieved impressive results in the recent years, there are still some issues need to be addressed including multi-modal fusion and multi-level aggregation. In this paper, we propose a bifurcated multi-modal fusion network (BMFNet) to address these two issues cooperatively. First, we design a multi-modal feature interaction (MFI) module to fully capture the complementary information between the RGB and depth features by leveraging the channel attention and spatial attention. Second, unlike the widely used layer-by-layer progressive fusion, we adopt a bifurcated fusion strategy for all the multi-level unimodal and cross-modal features to effectively reduce the gaps between features at different levels. For the intra-group feature aggregation, a multi-modal feature fusion (MFF) module is designed to integrate the intra-group multi-modal features to produce a low-level/high-level saliency feature. For the inter-group aggregation, a multi-scale feature learning (MFL) module is introduced to exploit the contextual interactions between different scales to boost fusion performance. Experimental results on five public RGB-D datasets demonstrate the effectiveness and superiority of our proposed network. The code and prediction maps will be available at https://github.com/ZhangQing0329/BMFNet

Channel-overcomplete convolutional architectures for RGB-D salient object detection

It is a challenging task to detect small salient objects by fusing color information and spatial information from RGB-D images. Most RGB-D salient object detection (SOD) methods use U-Net or its variants because they tend to extract high-level features that can quickly determine the location of salient objects. However, it is also indispensable to effectively learn low-level features to detect fine boundaries and small structures. To this end, we propose a novel two-branch SOD network (COSU), which includes channel-overcomplete (CO) and spatial-undercomplete (SU) branches. First of all, the CO branch pays attention to low-level features by transform the spatial distribution into the channel connection and restricting the receptive field of filters. Secondly, SU branch uses increased receptive field to capture the high-level features. Finally, a multi-scale modal correction strategy is designed to fully fuse the multi-level features from the two branches and mitigate the modal divergence from RGB-D features. Experimental results on seven widely used datasets show that the proposed COSU outperforms ten state-of-the-art RGB-D SOD methods.

CDNet: Complementary Depth Network for RGB-D Salient Object Detection.

Current RGB-D salient object detection (SOD) methods utilize the depth stream as complementary information to the RGB stream. However, the depth maps are usually of low-quality in existing RGB-D SOD datasets. Most RGB-D SOD networks trained with these datasets would produce error-prone results. In this paper, we propose a novel Complementary Depth Network (CDNet) to well exploit saliency-informative depth features for RGB-D SOD. To alleviate the influence of low-quality depth maps to RGB-D SOD, we propose to select saliency-informative depth maps as the training targets and leverage RGB features to estimate meaningful depth maps. Besides, to learn robust depth features for accurate prediction, we propose a new dynamic scheme to fuse the depth features extracted from the original and estimated depth maps with adaptive weights. What's more, we design a two-stage cross-modal feature fusion scheme to well integrate the depth features with the RGB ones, further improving the performance of our CDNet on RGB-D SOD. Experiments on seven benchmark datasets demonstrate that our CDNet outperforms state-of-the-art RGB-D SOD methods. The code is publicly available at https://github.com/blanclist/CDNet.

RGB-D Salient Object Detection With Ubiquitous Target Awareness.

Conventional RGB-D salient object detection methods aim to leverage depth as complementary information to find the salient regions in both modalities. However, the salient object detection results heavily rely on the quality of captured depth data which sometimes are unavailable. In this work, we make the first attempt to solve the RGB-D salient object detection problem with a novel depth-awareness framework. This framework only relies on RGB data in the testing phase, utilizing captured depth data as supervision for representation learning. To construct our framework as well as achieving accurate salient detection results, we propose a Ubiquitous Target Awareness (UTA) network to solve three important challenges in RGB-D SOD task: 1) a depth awareness module to excavate depth information and to mine ambiguous regions via adaptive depth-error weights, 2) a spatial-aware cross-modal interaction and a channel-aware cross-level interaction, exploiting the low-level boundary cues and amplifying high-level salient channels, and 3) a gated multi-scale predictor module to perceive the object saliency in different contextual scales. Besides its high performance, our proposed UTA network is depth-free for inference and runs in real-time with 43 FPS. Experimental evidence demonstrates that our proposed network not only surpasses the state-of-the-art methods on five public RGB-D SOD benchmarks by a large margin, but also verifies its extensibility on five public RGB SOD benchmarks.

Depth-Quality-Aware Salient Object Detection.

The existing fusion-based RGB-D salient object detection methods usually adopt the bistream structure to strike a balance in the fusion trade-off between RGB and depth (D). While the D quality usually varies among the scenes, the state-of-the-art bistream approaches are depth-quality-unaware, resulting in substantial difficulties in achieving complementary fusion status between RGB and D and leading to poor fusion results for low-quality D. Thus, this paper attempts to integrate a novel depth-quality-aware subnet into the classic bistream structure in order to assess the depth quality prior to conducting the selective RGB-D fusion. Compared to the SOTA bistream methods, the major advantage of our method is its ability to lessen the importance of the low-quality, no-contribution, or even negative-contribution D regions during RGB-D fusion, achieving a much improved complementary status between RGB and D. Our source code and data are available online at https://github.com/qdu1995/DQSD.

Data-Level Recombination and Lightweight Fusion Scheme for RGB-D Salient Object Detection.

Existing RGB-D salient object detection methods treat depth information as an independent component to complement RGB and widely follow the bistream parallel network architecture. To selectively fuse the CNN features extracted from both RGB and depth as a final result, the state-of-the-art (SOTA) bistream networks usually consist of two independent subbranches: one subbranch is used for RGB saliency, and the other aims for depth saliency. However, depth saliency is persistently inferior to the RGB saliency because the RGB component is intrinsically more informative than the depth component. The bistream architecture easily biases its subsequent fusion procedure to the RGB subbranch, leading to a performance bottleneck. In this paper, we propose a novel data-level recombination strategy to fuse RGB with D (depth) before deep feature extraction, where we cyclically convert the original 4-dimensional RGB-D into DGB, RDB and RGD. Then, a newly lightweight designed triple-stream network is applied over these novel formulated data to achieve an optimal channel-wise complementary fusion status between the RGB and D, achieving a new SOTA performance.

Three-stream Attention-aware Network for RGB-D Salient Object Detection.

Previous RGB-D fusion systems based on convolutional neural networks (CNNs) typically employ a two-stream architecture, in which RGB and depth inputs are learnt independently. The multi-modal fusion stage is typically performed by concatenating the deep features from each stream in the inference process. The traditional two-stream architecture might experience insufficient multi-modal fusion due to two following limitations: (1) The cross-modal complementarity is rarely studied in the bottom-up path, wherein we believe the crossmodal complements can be combined to learn new discriminative features to enlarge the RGB-D representation community; (2) The cross-modal channels are typically combined by undifferentiated concatenation, which appears ambiguous to select cross-modal complementary features. In this work, we address these two limitations by proposing a novel three-stream attention-aware multi-modal fusion network. In the proposed architecture, a cross-modal distillation stream, accompanying the RGB-specific and depth-specific streams, is introduced to extract new RGB-D features in each level in the bottom-up path. Furthermore, the channel-wise attention mechanism is innovatively introduced to the cross-modal cross-level fusion problem to adaptively select complementary feature maps from each modality in each level. Extensive experiments report the effectiveness of the proposed architecture and the significant improvement over state-of-theart RGB-D salient object detection methods.