SwinNet: Swin Transformer drives edge-aware RGB-D and RGB-T salient object detection (2204.05585v1)

Abstract: Convolutional neural networks (CNNs) are good at extracting contexture features within certain receptive fields, while transformers can model the global long-range dependency features. By absorbing the advantage of transformer and the merit of CNN, Swin Transformer shows strong feature representation ability. Based on it, we propose a cross-modality fusion model SwinNet for RGB-D and RGB-T salient object detection. It is driven by Swin Transformer to extract the hierarchical features, boosted by attention mechanism to bridge the gap between two modalities, and guided by edge information to sharp the contour of salient object. To be specific, two-stream Swin Transformer encoder first extracts multi-modality features, and then spatial alignment and channel re-calibration module is presented to optimize intra-level cross-modality features. To clarify the fuzzy boundary, edge-guided decoder achieves inter-level cross-modality fusion under the guidance of edge features. The proposed model outperforms the state-of-the-art models on RGB-D and RGB-T datasets, showing that it provides more insight into the cross-modality complementarity task.

• The paper introduces SwinNet, a two-stream Swin Transformer-based model that significantly advances cross-modality salient object detection for both RGB-D and RGB-T inputs.
• It employs a spatial alignment and channel re-calibration module with an edge-guided decoder to enhance precision in object boundary delineation.
• Extensive experiments on benchmark datasets demonstrate SwinNet’s superiority via metrics like S-measure, F-measure, and MAE, indicating strong potential for practical applications.

SwinNet: Advancements in Cross-Modality Salient Object Detection

The paper "SwinNet: Swin Transformer Drives Edge-Aware RGB-D and RGB-T Salient Object Detection" presents a novel approach to solving challenges in salient object detection (SOD) across RGB-D and RGB-T modalities. The fundamental proposition lies in leveraging the Swin Transformer architecture to enhance feature representation and providing a more robust detection mechanism compared to traditional convolutional neural networks (CNNs).

Methodology Overview

The SwinNet model operates on the premise of integrating the merits of both transformers and CNNs to effectively manage the cross-modality complementarity challenges in SOD. The Swin Transformer is utilized as a backbone, capitalizing on its ability to maintain local contextuality while handling global semantic dependencies.

The model architecture comprises several key components:

1. Two-stream Swin Transformer Encoder: This setup facilitates the extraction of multi-modality hierarchical features. The simultaneous processing through dual streams (RGB-D and RGB-T) allows for efficient handling of disparate data types.
2. Spatial Alignment and Channel Re-calibration Module: This module optimizes intra-level cross-modality features through attentional mechanisms that align and recalibrate spatial and channel information.
3. Edge-Guided Decoder: Operating under edge-aware constraints, this decoder ensures inter-level cross-modality fusion is sharp and precise, refining the contours of the salient objects.

Numerical and Qualitative Results

The empirical evaluations demonstrate that SwinNet achieves superior performance, outperforming state-of-the-art models across several established datasets, namely NLPR, NJU2K, STERE, DES, SIP, and DUT for RGB-D SOD, and VT821, VT1000, and VT5000 for RGB-T SOD. The improvement is distinctly captured through metrics such as S-measure, F-measure, E-measure, and MAE, showcasing SwinNet’s effectiveness in decomposing complex scenes into precise saliency maps.

Qualitative analyses further solidify these numeric results, as SwinNet delivers clearer and more defined boundaries in challenging scenarios, including those with similar foreground and background, complex scenes, and varying illuminance conditions.

Implications and Future Directions

The implications of SwinNet extend across both practical and theoretical domains. Practically, it advances the capabilities of SOD in applications where multiple modalities are involved, particularly in surveillance and robotics, where understanding environmental contexts in-depth is critical. Theoretically, it reinforces the utility of transformer architectures in vision tasks that traditionally relied on CNNs, suggesting a paradigm shift.

Future explorations could focus on optimizing transformer-based networks for real-time applications, addressing computational complexity, and incorporating additional modalities. Moreover, extending this approach to other computer vision tasks such as semantic segmentation and object tracking could further evaluate the adaptability and robustness of transformer-driven frameworks.

In conclusion, SwinNet represents a substantial contribution to the field of multi-modality vision tasks, providing a scalable and effective solution to the intricate challenge of salient object detection.