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Underwater salient object detection seeks to pinpoint the most vital elements in underwater environments, offering considerable promise for underwater exploration. Considering the preference for low-complexity algorithms in underwater applications ...

Recently, large pre-trained vision-language models, such as CLIP, have demonstrated significant potential in zero-/few-shot anomaly detection tasks. However, existing methods not only rely on expert knowledge to manually craft extensive text prompts but ...

Multi-focus image fusion (MFIF) aims to combine multiple images with different focused regions into a single all-in-focus image. Existing unsupervised deep learning-based methods only fuse structural information of images in the spatial domain, ...

Generating diverse plausible outputs from a single input is crucial for addressing visual ambiguities, exemplified in medical imaging where experts may provide varying semantic segmentation annotations for the same image.Existing methods handles ...

Despite deep learning based intelligent diagnosis has become an essential means of perceiving the health status of rotating machinery, existing diagnostic models struggle to efficiently extract domain-invariant representations for cross-domain ...

• DRSN-SCW network is proposed to handle vibration signals with strong noise, enhancing the feature extraction capability.
• A self-supervised Siamese framework is designed for end-to-end representation learning.
• The pseudo-labeling ...

Vehicle re-identification (ReID) aims to match and track vehicles in a surveillance system across non-overlapping camera views. Despite great advances have been achieved in intra-domain and cross-domain vehicle ReID, most existing methods still ...

• A novel framework uses attribute visual information to improve the performance of Vehicle ReID.
• Attribute Representation Disentangle Branch is proposed to promote attribute learning flow.
• Our model achieves superior results in both ...

Pan-sharpening aims to leverage the high-frequency signal of the panchromatic (PAN) image to enhance the resolution of its corresponding multi-spectral (MS) image. However, deep neural networks (DNNs) tend to prioritize learning the low-frequency ...

Pan-sharpening is a task that aims to super-resolve the low-resolution multispectral (LRMS) image with the guidance of a corresponding high-resolution panchromatic (PAN) image. The key challenge in pan-sharpening is to accurately modeling the ...

Acoustic beamforming methods based on microphone arrays have been widely used for sound source localization in various industrial fields. The conventional methods such as Delay and Sum (DAS) beamforming are limited by poor spatial resolution ...

• The propose DAMAS-FISTA-LASSO algorithm can significantly reduce the runtime requirement of DAMAS.
• We further design DFLNet, which adopts the structure of the DAMAS-FISTA-LASSO and inherits the relevant acoustic domain knowledge.
• ...

Domain generalization (DG), which doesn’t require any data from target domains during training, is more challenging but practical than unsupervised domain adaptation (UDA). Since different vehicles of the same type have a similar appearance, neural ...

Recent advances in Domain Adaptive Object Detection (DAOD) have vastly restrained the performance degradation caused by distribution shift. However, DAOD relies on the strong assumption of accessible target domain during the learning procedure, ...

• Combined with a complex Bayesian compressive sensing framework, our method allows the components to adaptively be extracted via an iterative optimization framework.
• This algorithm can directly estimate the instantaneous amplitude and ...

The decomposition of non-stationary signals remains a challenge in a wide variety of fields. Especially, the impulse or cross-mode signals are difficult to be reconstructed by recent methods due to their transient characteristic. Moreover, most ...

Pan-sharpening aims to utilize the high-resolution panchromatic (PAN) image as a guidance to super-resolve the spatial resolution of the low-resolution multispectral (MS) image. The key challenge in pan-sharpening is how to effectively and precisely ...

Pan-sharpening aims to spatially enhance the low-resolution multispectral image (LRMS) by transferring high-frequency details from a panchromatic image (PAN) while preserving the spectral characteristics of LRMS. Previous arts mainly focus on how to ...

Pan-sharpening is a technique that fuses a high-resolution panchromatic (PAN) image with its corresponding low-resolution multispectral (MS) image to create a high-resolution multispectral image. Due to the powerful representation ability of ...

Infrared small-dim object detection is a challenging task due to the small size, weak features, lack of prominent structural information, and vulnerability to background interference. During the process of deep learning-based feature extraction, ...

Centralized aerial imagery analysis techniques face two challenges. The first one is the data silos problem, where data is located at different organizations separately. The second challenge is the class imbalance in the overall distribution of ...

Synthetic Aperture Radar (SAR) has become a research hotspot due to its ability to identify targets in all weather conditions and at all times. To achieve satisfactory recognition performance in most existing automatic target recognition (ATR) ...

With the rapid development of deep learning technology, significant progress has been made in the field of synthetic aperture radar (SAR) target recognition algorithms. However, deep neural networks are vulnerable to adversarial attacks in ...

Non-intrusive load monitoring (NILM) is able to analyze and predict users’ power consumption behaviors for further improving the power consumption efficiency of the grid. Neural network-based techniques have been developed for NILM. However, the ...