194 research outputs found
RGB-T salient object detection via fusing multi-level CNN features
RGB-induced salient object detection has recently witnessed substantial progress, which is attributed to the superior feature learning capability of deep convolutional neural networks (CNNs). However, such detections suffer from challenging scenarios characterized by cluttered backgrounds, low-light conditions and variations in illumination. Instead of improving RGB based saliency detection, this paper takes advantage of the complementary benefits of RGB and thermal infrared images. Specifically, we propose a novel end-to-end network for multi-modal salient object detection, which turns the challenge of RGB-T saliency detection to a CNN feature fusion problem. To this end, a backbone network (e.g., VGG-16) is first adopted to extract the coarse features from each RGB or thermal infrared image individually, and then several adjacent-depth feature combination (ADFC) modules are designed to extract multi-level refined features for each single-modal input image, considering that features captured at different depths differ in semantic information and visual details. Subsequently, a multi-branch group fusion (MGF) module is employed to capture the cross-modal features by fusing those features from ADFC modules for a RGB-T image pair at each level. Finally, a joint attention guided bi-directional message passing (JABMP) module undertakes the task of saliency prediction via integrating the multi-level fused features from MGF modules. Experimental results on several public RGB-T salient object detection datasets demonstrate the superiorities of our proposed algorithm over the state-of-the-art approaches, especially under challenging conditions, such as poor illumination, complex background and low contrast
Hierarchical Cross-modal Transformer for RGB-D Salient Object Detection
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.Comment: 10 pages, 10 figure
CIR-Net: Cross-modality Interaction and Refinement for RGB-D Salient Object Detection
Focusing on the issue of how to effectively capture and utilize
cross-modality information in RGB-D salient object detection (SOD) task, we
present a convolutional neural network (CNN) model, named CIR-Net, based on the
novel cross-modality interaction and refinement. For the cross-modality
interaction, 1) a progressive attention guided integration unit is proposed to
sufficiently integrate RGB-D feature representations in the encoder stage, and
2) a convergence aggregation structure is proposed, which flows the RGB and
depth decoding features into the corresponding RGB-D decoding streams via an
importance gated fusion unit in the decoder stage. For the cross-modality
refinement, we insert a refinement middleware structure between the encoder and
the decoder, in which the RGB, depth, and RGB-D encoder features are further
refined by successively using a self-modality attention refinement unit and a
cross-modality weighting refinement unit. At last, with the gradually refined
features, we predict the saliency map in the decoder stage. Extensive
experiments on six popular RGB-D SOD benchmarks demonstrate that our network
outperforms the state-of-the-art saliency detectors both qualitatively and
quantitatively.Comment: Accepted by IEEE Transactions on Image Processing 2022, 16 pages, 11
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