Multi-interactive Dual-decoder for RGB-thermal Salient Object Detection

Abstract

RGB-thermal salient object detection (SOD) aims to segment the common prominent regions of visible image and corresponding thermal infrared image that we call it RGBT SOD. Existing methods don't fully explore and exploit the potentials of complementarity of different modalities and multi-type cues of image contents, which play a vital role in achieving accurate results. In this paper, we propose a multi-interactive dual-decoder to mine and model the multi-type interactions for accurate RGBT SOD. In specific, we first encode two modalities into multi-level multi-modal feature representations. Then, we design a novel dual-decoder to conduct the interactions of multi-level features, two modalities and global contexts. With these interactions, our method works well in diversely challenging scenarios even in the presence of invalid modality. Finally, we carry out extensive experiments on public RGBT and RGBD SOD datasets, and the results show that the proposed method achieves the outstanding performance against state-of-the-art algorithms. The source code has been released at:https://github.com/lz118/Multi-interactive-Dual-decoder.

Accepted by IEEE TIP

References in corpus (2)

• RGBT Tracking via Multi-Adapter Network with Hierarchical Divergence Loss
• Bilateral Attention Network for RGB-D Salient Object Detection

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• Glass Segmentation with RGB-Thermal Image Pairs
• Position-Aware Relation Learning for RGB-Thermal Salient Object Detection
• VST++: Efficient and Stronger Visual Saliency Transformer
• UniRGB-IR: A Unified Framework for Visible-Infrared Semantic Tasks via Adapter Tuning
• Deep Fourier-embedded Network for RGB and Thermal Salient Object Detection
• Efficient Fourier Filtering Network with Contrastive Learning for AAV-based Unaligned Bimodal Salient Object Detection
• LEAF-Mamba: Local Emphatic and Adaptive Fusion State Space Model for RGB-D Salient Object Detection