Lightweight RGB-D Salient Object Detection from a Speed-Accuracy Tradeoff Perspective (2505.04758v1)

Abstract: Current RGB-D methods usually leverage large-scale backbones to improve accuracy but sacrifice efficiency. Meanwhile, several existing lightweight methods are difficult to achieve high-precision performance. To balance the efficiency and performance, we propose a Speed-Accuracy Tradeoff Network (SATNet) for Lightweight RGB-D SOD from three fundamental perspectives: depth quality, modality fusion, and feature representation. Concerning depth quality, we introduce the Depth Anything Model to generate high-quality depth maps,which effectively alleviates the multi-modal gaps in the current datasets. For modality fusion, we propose a Decoupled Attention Module (DAM) to explore the consistency within and between modalities. Here, the multi-modal features are decoupled into dual-view feature vectors to project discriminable information of feature maps. For feature representation, we develop a Dual Information Representation Module (DIRM) with a bi-directional inverted framework to enlarge the limited feature space generated by the lightweight backbones. DIRM models texture features and saliency features to enrich feature space, and employ two-way prediction heads to optimal its parameters through a bi-directional backpropagation. Finally, we design a Dual Feature Aggregation Module (DFAM) in the decoder to aggregate texture and saliency features. Extensive experiments on five public RGB-D SOD datasets indicate that the proposed SATNet excels state-of-the-art (SOTA) CNN-based heavyweight models and achieves a lightweight framework with 5.2 M parameters and 415 FPS.

Lightweight RGB-D Salient Object Detection: A Speed-Accuracy Tradeoff Network Approach

The discussed paper explores the domain of RGB-D salient object detection (SOD), focusing on the equilibrium between computational efficiency and detection accuracy, particularly for applications constrained by resources. Typical RGB-D SOD methodologies, while advancing detection accuracy, incur prohibitive computational costs due to reliance on complex models and extensive datasets. This paper presents a novel approach that converges on a lightweight framework known as the Speed-Accuracy Tradeoff Network (SATNet), which strategically addresses three main aspects: depth map quality, modality fusion, and feature representation.

Key Methodological Innovations

1. Depth Quality Enhancement: Acknowledging the limitation imposed by low-quality depth maps in existing datasets, the paper introduces the Depth Anything Model. This vision foundation model is employed to produce high-quality pseudo depth maps that bridge the gap between RGB and depth modalities, thereby enhancing model performance in RGB-D SOD tasks.
2. Modality Fusion through Decoupled Attention Module (DAM): The paper proposes the DAM, a lightweight yet effective fusion strategy. The DAM facilitates cross-modal integration by decoupling features into horizontal and vertical vectors, enabling the model to efficiently learn discriminative attributes through a transformed attention mechanism tailored for resource-efficient models.
3. Enhanced Feature Representation via Dual Information Representation Module (DIRM): By employing a bi-directional inverted framework, DIRM aims to expand the feature space capabilities within the constraints of lightweight backbones. This involves a dual-path approach to capture texture and saliency features, optimizing parameter learning through bi-directional pathways that augment model capacity without substantial resource consumption.
4. Dual Feature Aggregation Module (DFAM): The final stage of the SATNet architecture utilizes DFAM in the decoder, merging texture and saliency features. This module is critical for the effective generation of detailed saliency maps, ensuring the integration of varied receptive fields through advanced convolutional techniques, such as asymmetric and dilated convolutions.

Empirical Evaluation

SATNet's efficacy was rigorously tested on five RGB-D SOD benchmarks, yielding superior performance compared to state-of-the-art CNN-based models. The results emphasize SATNet's capability to significantly outperform these models while maintaining a lean architecture with only 5.2 million parameters and executing at a remarkable 415 frames per second (FPS). Such metrics underscore the model’s potential in real-world scenarios demanding high accuracy and speed on compact hardware.

Theoretical and Practical Implications

The paper's contributions reflect a substantial step toward integrating high-efficiency models in the field of RGB-D SOD. The innovative use of a vision foundation model for depth estimation and the adaptation of attention mechanisms suitable for lightweight frameworks offers a promising path for future research. The practical implications of this work are vast, facilitating applications in real-time detection systems such as autonomous vehicles and mobile devices, where resource management is pivotal.

Speculations on Future Developments

Going forward, the integration of foundation models like SAM within the SOD domain could further elevate the capabilities of such systems, potentially achieving unprecedented levels of generalization across varying tasks. This could mitigate issues related to over-segmentation and enhance the model’s robustness against ambiguity. Refinement strategies such as fine-tuning through large-scale data synthesis and knowledge distillation would serve as potential areas for exploration, aiming to cater to the evolving demands of dynamic AI environments.

In essence, this research delineates a structured pathway towards achieving a balanced synergy between accuracy and speed, paving the way for more adaptable and efficient RGB-D SOD systems adaptable to a spectrum of computational ecosystems.