Abstract Monocular depth estimation (MDE) has made great progress with the development of convolutional neural networks (CNNs). However, these approaches suffer from essential shortsightedness due to the utilization of insufficient feature-based reasoning. To this end, we propose an effective parallel CNNs and Transformer model for MDE via dual attention (PCTDepth). Specifically, we use two stream backbones to extract features, where ResNet and Swin Transformer are utilized to obtain local detail features and global long-range dependencies, respectively. Furthermore, a hierarchical fusion module (HFM) is designed to actively exchange beneficial information for the complementation of each representation during the intermediate fusion. Finally, a dual attention module is incorporated for each fused feature in the decoder stage to improve the accuracy of the model by enhancing inter-channel correlations and focusing on relevant spatial locations. Comprehensive experiments on the KITTI dataset demonstrate that the proposed model consistently outperforms the other state-of-the-art methods.

Multi-modality complementary information brings new impetus and innovation to saliency object detection (SOD). However, most existing RGB-D SOD methods either indiscriminately handle RGB features and depth features or only take depth features as additional information of RGB subnet-work, ignoring the different roles of two modalities for SOD tasks. To tackle this issue, we propose a novel multi-modality diversity fusion network with SwinTransformer (M2DFNet) for RGB-D SOD from the perspective of the different status of multi-modality, which adequately explores the roles of RGB and depth modalities. To this end, a triple-diversity supervision mechanism (TDSM) and a diversity fusion module (DFM) are designed to parse the function of different modalities. Besides, we designed a dense decoder (DSD) to integrate multi-scale features and transfer gain information from top to bottom, which can improve the performance of SOD. Extensive experiments on five benchmark datasets demonstrate that the proposed M2DFNet outperforms 17 other state-of-the-art (SOTA) RGB-D SOD methods.

Multi-scale context is crucial for the accurate salient object detection (SOD) in the real-world scenes. Although current contextual information-based SOD methods have achieved great progress, they may fail to generate precise saliency maps due to their seldom considering the correlation of different scale context during the extraction process. To address these issues, we propose an Efficient Multi-Scale Context Exploration Network (EMCENet) for SOD. Specifically, a progressive multi-scale context extraction (PMCE) module is designed to progressively capture strongly correlated multi-scale context by using multi-receptive-field convolution operations. Afterwards, a hierarchical feature hybrid interaction (HFHI) module is introduced to generate powerful feature representations by adaptively aggregating multi-level features in a hybrid interaction strategy. Extensive experimental results on six public datasets demonstrate that the proposed EMCENet method without any post-processing performs favorably against 13 state-of-the-art SOD methods.

In this study, the authors propose a distinctive bottom‐up visual saliency detection algorithm based on a new background prior and a new reinforcement. Inspired by genetic algorithm, the final map is obtained with three steps. First of all, the authors construct a background‐based saliency map by manifold ranking via superior image corners selected by convex‐hull as background prior, which is different from most of the existing background prior‐based methods treated all image boundaries as background. Then, a better result is obtained by ranking the relevance of the image elements with foreground seeds extracted from the preliminary saliency map. Furthermore, a novel optimisation framework is introduced with the intention of refining the map, which integrates an energy function with a guided filter. Experimental results on three public datasets indicate that the proposed method performs favourably against the state‐of‐the‐art algorithms.