Hierarchical Cross-modal Transformer for RGB-D Salient Object Detection

• URL: http://arxiv.org/abs/2302.08052v1
• Date: Thu, 16 Feb 2023 03:23:23 GMT
• Title: Hierarchical Cross-modal Transformer for RGB-D Salient Object Detection
• Authors: Hao Chen and Feihong Shen
• Abstract summary: We propose the Hierarchical Cross-modal Transformer (HCT), a new multi-modal transformer, to tackle this problem. Unlike previous multi-modal transformers that directly connecting all patches from two modalities, we explore the cross-modal complementarity hierarchically. We present a Feature Pyramid module for Transformer (FPT) to boost informative cross-scale integration as well as a consistency-complementarity module to disentangle the multi-modal integration path.
• Score: 6.385624548310884
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most of existing RGB-D salient object detection (SOD) methods follow the CNN-based paradigm, which is unable to model long-range dependencies across space and modalities due to the natural locality of CNNs. Here we propose the Hierarchical Cross-modal Transformer (HCT), a new multi-modal transformer, to tackle this problem. Unlike previous multi-modal transformers that directly connecting all patches from two modalities, we explore the cross-modal complementarity hierarchically to respect the modality gap and spatial discrepancy in unaligned regions. Specifically, we propose to use intra-modal self-attention to explore complementary global contexts, and measure spatial-aligned inter-modal attention locally to capture cross-modal correlations. In addition, we present a Feature Pyramid module for Transformer (FPT) to boost informative cross-scale integration as well as a consistency-complementarity module to disentangle the multi-modal integration path and improve the fusion adaptivity. Comprehensive experiments on a large variety of public datasets verify the efficacy of our designs and the consistent improvement over state-of-the-art models.

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