Most cutting-edge video saliency prediction models rely on spatiotemporal features extracted by 3D convolutions due to its local contextual cues acquirement ability. However, the shortage of 3D convolutions is that it cannot effectively capture long-term spatiotemporal dependencies in videos. To address this limitation, we propose a novel Transformer-based Multi-scale Feature Integration Network (TMFI-Net) for video saliency prediction, where the proposed TMFI-Net consists of a semantic-guided encoder and a hierarchical decoder. Firstly, embarking on the Transformer-based multi-level spatiotemporal features, the semantic-guided encoder enhances the features by inserting the high-level feature into each level feature via a top-down pathway and a longitudinal connection, which endows the multi-level spatiotemporal features with rich contextual information. In this way, the features are steered to give more concerns to saliency regions. Secondly, the hierarchical decoder employs a multi-dimensional attention (MA) module to elevate features along channel, temporal, and spatial dimensions jointly. Successively, the hierarchical decoder deploys a progressive decoding block to conduct an initial saliency prediction, which provides a coarse localization of saliency regions. Lastly, considering the complementarity of different saliency predictions, we integrate all initial saliency prediction results into the final saliency map. Comprehensive experimental results on four video saliency datasets firmly demonstrate that our model achieves superior performance when compared with the state-of-the-art video saliency models. The code is available at https://github.com/wusonghe/TMFI-Net.

In the field of bearing fault diagnosis, it is difficult to obtain a large amount of labeled data for training, and therefore it is easy to overfit the model, which makes it less robust and less generalizable. To address this problem, this article offers a novel semi-supervised contrastive learning (SSCL) method based on a multiscale attention (MSA) mechanism and multitarget contrastive learning (MCL) for rolling bearing fault diagnosis under limited labeled samples. First, the proposed SSCL utilizes improved MCL to pretrain the model as a means of capturing potentially generic features in unlabeled data. Then, semi-supervised contrast learning is constructed and based on the pretrained model combining a limited amount of labeled data and a large amount of unlabeled data to jointly learn the feature mapping and further enhance the feature extraction capability of the model through an MSA mechanism. Case Western Reserve University, the Society for Mechanical Failure Prevention Techniques, and Paderborn University are common motor-bearing datasets that are used in this article for testing the proposed technique. As a result of the experimental findings, SSCL is demonstrated to be superior to other approaches in similar situations.

Depth images and thermal images contain the spatial geometry information and surface temperature information, which can act as complementary information for the RGB modality. However, the quality of the depth and thermal images is often unreliable in some challenging scenarios, which will result in the performance degradation of the two-modal based salient object detection (SOD). Meanwhile, some researchers pay attention to the triple-modal SOD task, namely the visible-depth-thermal (VDT) SOD, where they attempt to explore the complementarity of the RGB image, the depth image, and the thermal image. However, existing triple-modal SOD methods fail to perceive the quality of depth maps and thermal images, which leads to performance degradation when dealing with scenes with low-quality depth and thermal images. Therefore, in this paper, we propose a quality-aware selective fusion network (QSF-Net) to conduct VDT salient object detection, which contains three subnets including the initial feature extraction subnet, the quality-aware region selection subnet, and the region-guided selective fusion subnet. Firstly, except for extracting features, the initial feature extraction subnet can generate a preliminary prediction map from each modality via a shrinkage pyramid architecture, which is equipped with the multi-scale fusion (MSF) module. Then, we design the weakly-supervised quality-aware region selection subnet to generate the quality-aware maps. Concretely, we first find the high-quality and low-quality regions by using the preliminary predictions, which further constitute the pseudo label that can be used to train this subnet. Finally, the region-guided selective fusion subnet purifies the initial features under the guidance of the quality-aware maps, and then fuses the triple-modal features and refines the edge details of prediction maps through the intra-modality and inter-modality attention (IIA) module and the edge refinement (ER) module, respectively. Extensive experiments are performed on VDT-2048 dataset, and the results show that our saliency model consistently outperforms 13 state-of-the-art methods with a large margin. Our code and results are available at https://github.com/Lx-Bao/QSFNet .

This paper discusses the limitations of single- and two-modal salient object detection (SOD) methods and the emergence of multi-modal SOD techniques that integrate Visible, Depth, or Thermal information. However, current multi-modal methods often rely on simple fusion techniques such as addition, multiplication and concatenation, to combine the different modalities, which is ineffective for challenging scenes, such as low illumination and background messy. To address this issue, we propose a novel multi-modal feature fusion network (MFFNet) for V-D-T salient object detection, where the two key points are the triple-modal deep fusion encoder and the progressive feature enhancement decoder. The MFFNet's triple-modal deep fusion (TDF) module is designed to integrate the features of the three modalities and explore their complementarity by utilizing mutual optimization during the encoding phase. In addition, the progressive feature enhancement decoder consists of the weighted context-enhanced feature (WCF) module, region optimization (RO) module and boundary perception (BP) module to produce region-aware and contour-aware features. After that, a multi-scale fusion (MF) module is proposed to integrate these features and generate high-quality saliency maps. We conduct extensive experiments on the VDT-2048 dataset, and our results show that the proposed MFFNet outperforms 12 state-of-the-art multi-modal methods.

Salient object detection of surface defects is one of the surface defect detection tasks, which aims at highlighting the defect regions from the surface of strip steel, magnetic tale, road, and so on. However, the performance of existing methods degrades dramatically when dealing with complex scenarios, such as low contrast of defect regions and various defect shapes. Therefore, in this article, we propose a novel saliency model, namely, localizing, focus, and refinement network (LFRNet), which consists of the semantic-guided localizing module, the context-driven focus module, and the edge-aware refinement (ER) module. First, the semantic-guided localizing module deploys the graph reasoning (GR) unit and the global attention (GA) unit to localize the potential defect regions from a global view. Second, the context-driven focus module employs the split context (SC) unit and the mutual attention (MA) unit to perform the identification process via the introduction of spatial detail features. Finally, to further improve the accuracy of the detection results, we deploy the ER module, which introduces the boundary cues via the edge generation (EG) unit and aggregates the localizing result, the focus results, and the edge information into the high-quality detection map. Extensive experiments on four public defect datasets clearly show the effectiveness and superiority of the proposed LFRNet, where the LFRNet obtains an improvement of 4.1%, 5.7%, 1.0%, and 0.8% on F-measure (FM), weighted FM (WF), E-measure (EM), and structure-measure (SM), respectively, compared with the top-level method: AEP.

RGB-T semantic segmentation can effectively pop-out objects from challenging scenarios (e.g., low illumination and low contrast environments) by combining RGB and thermal infrared images. However, the existing cutting-edge RGB-T semantic segmentation methods often present insufficient exploration of multi-modal feature fusion, where they overlook the differences between the two modalities. In this paper, we propose an adaptive gated fusion network (AGFNet) to conduct RGB-T semantic segmentation, where the multi-modal features are combined via the gating mechanisms and the spatial details are enhanced via the introduction of edge information. Specifically, the AGFNet employs a cross-modal adaptive gated-attention fusion (CAGF) module to aggregate the RGB and thermal features, where we give a sufficient exploration of the complementarity between the two-modal features via the gated attention unit (GAU). Particularly, in GAU, the gates can be used to purify the features, and the channel and spatial attention mechanisms are further employed to enhance the two-modal features interactively. Then, we design an edge detection (ED) module to learn the object-related edge cues, which simultaneously incorporates local detail information from low-level features and global location information from high-level features. After that, we deploy the edge guidance (EG) module to emphasize the spatial details of the fused features. Next, we deploy the contextual elevation (CE) module to enrich the contextual information of features by iteratively introducing the sine and cosine functions. Finally, considering that the quality of thermal images is usually lower than that of RGB images, we progressively integrate the multi-level RGB encoder features with multi-level decoder features, thereby focusing more on appearance information. Following this way, we can acquire the final high-quality segmentation result. Extensive experiments are performed on three public datasets including MFNet, PST900 and FMB datasets, and the experimental results show that our method achieves competitive performance when compared with the 22 state-of-the-art methods.