Cross-domain few-shot segmentation (CD-FSS) aims to tackle the dual challenge of recognizing novel classes and adapting to unseen domains with limited annotations. However, encoder features often entangle domain-relevant and category-relevant information, limiting both generalization and rapid adaptation to new domains. To address this issue, we propose a Divide-and-Conquer Decoupled Network (DCDNet). In the training stage, to tackle feature entanglement that impedes cross-domain generalization and rapid adaptation, we propose the Adversarial-Contrastive Feature Decomposition (ACFD) module. It decouples backbone features into category-relevant private and domain-relevant shared representations via contrastive learning and adversarial learning. Then, to mitigate the potential degradation caused by the disentanglement, the Matrix-Guided Dynamic Fusion (MGDF) module adaptively integrates base, shared, and private features under spatial guidance, maintaining structural coherence. In addition, in the fine-tuning stage, to enhanced model generalization, the Cross-Adaptive Modulation (CAM) module is placed before the MGDF, where shared features guide private features via modulation ensuring effective integration of domain-relevant information. Extensive experiments on four challenging datasets show that DCDNet outperforms existing CD-FSS methods, setting a new state-of-the-art for cross-domain generalization and few-shot adaptation.

Remote sensing images captured from aerial perspectives often exhibit significant scale variations and complex backgrounds, posing challenges for salient object detection (SOD). Existing methods typically extract multi-level features at a single scale using uniform attention mechanisms, leading to suboptimal representations and incomplete detection results. To address these issues, we propose a GeoGran-Aware Hierarchical Feature Fusion Network (G2HFNet) that fully exploits geometric and granular cues in optical remote sensing images. Specifically, G2HFNet adopts Swin Transformer as the backbone to extract multi-level features and integrates three key modules: the multi-scale detail enhancement (MDE) module to handle object scale variations and enrich fine details, the dual-branch geo-gran complementary (DGC) module to jointly capture fine-grained details and positional information in mid-level features, and the deep semantic perception (DSP) module to refine high-level positional cues via self-attention. Additionally, a local-global guidance fusion (LGF) module is introduced to replace traditional convolutions for effective multi-level feature integration. Extensive experiments demonstrate that G2HFNet achieves high-quality saliency maps and significantly improves detection performance in challenging remote sensing scenarios.

This paper focuses on the inconsistency in salient regions between RGB and thermal images. To address this issue, we propose the Region-guided Selective Optimization Network for RGB-T Salient Object Detection, which consists of the region guidance stage and saliency generation stage. In the region guidance stage, three parallel branches with same encoder-decoder structure equipped with the context interaction (CI) module and spatial-aware fusion (SF) module are designed to generate the guidance maps which are leveraged to calculate similarity scores. Then, in the saliency generation stage, the selective optimization (SO) module fuses RGB and thermal features based on the previously obtained similarity values to mitigate the impact of inconsistent distribution of salient targets between the two modalities. After that, to generate high-quality detection result, the dense detail enhancement (DDE) module which adopts the multiple dense connections and visual state space blocks is applied to low-level features for optimizing the detail information. In addition, the mutual interaction semantic (MIS) module is placed in the high-level features to dig the location cues by the mutual fusion strategy. We conduct extensive experiments on the RGB-T dataset, and the results demonstrate that the proposed RSONet achieves competitive performance against 27 state-of-the-art SOD methods.

This article 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.

Recently, salient object detection in optical remote-sensing images (RSIs) has received more and more attention. To tackle the challenges of RSIs including large-scale variation of objects, cluttered background, irregular shape of objects, and big difference in illumination, the cutting-edge convolutional neural network (CNN)-based models are proposed and have achieved an encouraging performance. However, the performance of the top-level models usually depends on the large model size and high computational cost, which limits their practical applications. To remedy the issue, we introduce a fully squeezed multiscale (FSM) module to equip the entire network. Specifically, the FSM module squeezes the feature maps from high dimension to low dimension and introduces the multiscale strategy to endow the capability of feature characterization with different receptive fields and different contexts. Based on the FSM module, we build the FSM inference network (FSMI-Net) to pop-out salient objects from optical RSIs, which is with fewer parameters and fast inference speed. Particularly, the proposed FSMI-Net only contains 3.6M parameters, and its GPU running speed is about 28 fps for$384 \times 384$inputs, which is superior to the existing saliency models targeting optical RSIs. Extensive comparisons are performed on two public optical RSIs datasets, and our FSMI-Net achieves comparable detection accuracy when compared with the state-of-the-art models, where our model realizes a balance between the computational cost and detection performance.

The spreading factor is considered as a key parameter that controls the concentration of the directional distribution of the wave energy. It has been confirmed by many scholars that there is a certain relationship between spreading factor and sea surface wind. In the application of high frequency surface wave radar (HFSWR), spreading factor is extracted from the ratio (RB) of power spectrum density (PSD) of positive (PB+) and negative (PB-) Bragg peaks. To extract accurate spreading factor, the premise is that the PSD of detection unit is as little as possible affected by the adjacent detection units. For narrow-beam radar, digital beamforming (DBF) is easy to meet requirements. But for broad-beam radar, it is very difficult. In this paper, a new scheme is proposed to extract spreading factor from broad-beam HFSWR data with the MUSIC-APES algorithm. Different from spatial filtering by DBF, MUSIC-APES directly estimates the azimuth of positive or negative Bragg waves and their echo amplitudes. For broad-beam radar, this scheme can still achieve high azimuth resolution and accurate amplitude estimation at the same time. It solves the biggest obstacle to extract the spreading factor from broad-beam HFSWR data. To verify the feasibility of this scheme, simulations and experiments are carried out to compare with DBF. The extraction accuracy is improved greatly. The results are very surprising. It shows that spreading factor and wind speed are highly relevant. This may be a new way to extract wind speed in the application of HFSWR.

Abstract Application reconfiguration is essential to achieving flexibility and adaptability of wireless sensor networks (WSNs) used in environment monitoring. In this paper, we present an integrated reconfiguration scheme (IRS) for implementing environment adaptive application reconfiguration (EAAR) in WSNs. In our scheme, application reconfiguration is implemented with the push‐based paradigm for densely distributed nodes and the cluster‐based hybrid reconfiguration (CHR) paradigm for sparsely distributed nodes. We demonstrate the energy‐efficiency and scalability of our scheme by analyzing the energy consumption based on a randomly deployed sensor network. Moreover, we derive the density threshold of reconfiguration nodes (RNs) for determining if the nodes are densely or sparsely distributed, and choose the mode of operation for IRS. We use extensive simulation experiments to demonstrate the effectiveness of our scheme. Copyright © 2009 John Wiley & Sons, Ltd.

Signature verification is a challenging task, because only a small set of genuine samples can be acquired and usually no forgeries are available in real application. In this paper, we propose a new two-stage statistical system for automatic on-line signature verification. Our system is composed of a simplified GMM model for global signature features, and a discrete HMM model for local signature features. To be practical, we introduce specific simplification strategies for model building and training. Our system requires only 5 genuine samples for new users and relies on only 3 global parameters for quick and efficient system tuning. Experiments are conducted to verify the effectiveness of our system.