Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
119 tokens/sec
GPT-4o
56 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

DPANet: Depth Potentiality-Aware Gated Attention Network for RGB-D Salient Object Detection (2003.08608v4)

Published 19 Mar 2020 in cs.CV

Abstract: There are two main issues in RGB-D salient object detection: (1) how to effectively integrate the complementarity from the cross-modal RGB-D data; (2) how to prevent the contamination effect from the unreliable depth map. In fact, these two problems are linked and intertwined, but the previous methods tend to focus only on the first problem and ignore the consideration of depth map quality, which may yield the model fall into the sub-optimal state. In this paper, we address these two issues in a holistic model synergistically, and propose a novel network named DPANet to explicitly model the potentiality of the depth map and effectively integrate the cross-modal complementarity. By introducing the depth potentiality perception, the network can perceive the potentiality of depth information in a learning-based manner, and guide the fusion process of two modal data to prevent the contamination occurred. The gated multi-modality attention module in the fusion process exploits the attention mechanism with a gate controller to capture long-range dependencies from a cross-modal perspective. Experimental results compared with 15 state-of-the-art methods on 8 datasets demonstrate the validity of the proposed approach both quantitatively and qualitatively.

User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (4)
  1. Zuyao Chen (7 papers)
  2. Runmin Cong (59 papers)
  3. Qianqian Xu (74 papers)
  4. Qingming Huang (168 papers)
Citations (160)
View on Semantic Scholar

Summary

DPANet: Depth Potentiality-Aware Gated Attention Network for RGB-D Salient Object Detection

The paper introduces DPANet, a novel network designed to tackle the challenges of RGB-D salient object detection (SOD). Specifically, it aims to resolve two critical issues: the effective integration of complementary cross-modal RGB-D data and the contamination caused by unreliable depth maps. By proposing the DPANet, the authors offer a holistic solution that synergistically addresses these interconnected problems.

Methodology

DPANet employs a two-stream encoder-decoder architecture that processes both RGB and depth information. The network introduces depth potentiality perception, a learning-driven approach to estimate the reliability of depth information. This estimation guides the fusion process, preventing contamination from poor quality depth inputs. The network incorporates a Gated Multi-modality Attention (GMA) module that leverages attention mechanisms with gate controllers to capture long-range dependencies across modalities.

The GMA module is pivotal, utilizing depth information to refine RGB features and vice versa. The gated approach ensures selective integration, controlled by the estimated potentiality of the depth map. This prevents deterioration in performance due to unreliable depth data.

Additionally, DPANet introduces multi-scale and multi-modality feature fusion strategies. These strategies are designed to systematically integrate features of varying levels from both RGB and depth modalities, enriching the salient object detection process through enhanced feature representation.

Results and Implications

The network was evaluated against 16 state-of-the-art methods using eight datasets. It demonstrated superior performance both quantitatively and qualitatively, achieving significant gains across precision, recall, F-measure, S-measure, and MAE metrics. This emphasizes the network's efficacy in handling diverse scenarios, including background disturbances, complex scenes, and low contrast settings, without reliance on pre- or post-processing techniques.

DPANet's approach to address RGB-D SOD through depth potentiality perception and gated attention modules enhances robustness against depth inaccuracies, a common stumbling block in RGB-D integration. This innovation paves the way for improved saliency detection in complex multimodal settings.

Future Developments

The implications of the research are profound for AI-driven image processing. The methodology introduces a paradigm shift in dealing with multimodal datasets, focusing on quality assessment and selective data integration. Future work could explore extending these ideas to real-time applications, further optimizing the network architecture, or integrating similar concepts into other cross-modal learning domains, such as video analysis or augmented reality systems.

Additionally, as new hardware continues to make depth data more accessible, such as the proliferation of devices like Microsoft Kinect and iPhone XR, DPANet's foundational concepts may become increasingly pertinent, offering resilience and accuracy in complex detection tasks.

In conclusion, DPANet offers a robust framework for RGB-D salient object detection, leveraging depth potentiality awareness and gated attention mechanisms to deliver state-of-the-art performance across a multitude of non-trivial scenarios. The paper underscores the importance of depth map quality consideration and offers valuable insights that are poised to influence future developments in AI-driven multimodal analysis.