Accurate prediction of aeroengine Remaining Useful Life (RUL) is critical for ensuring flight safety, minimizing maintenance costs, and improving operational efficiency. This study proposes a novel model, the Fourier-...
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Knowledge-based Vision Question Answering (KBVQA) systems aim to answer natural language questions grounded in image contents by retrieving and integrating relevant knowledge from external knowledge bases to generate ...
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From successfully embedding the KNN classifier into a network using prototype vectors, to exploring various active learning methods in deep learning to find uncertain and representative samples for active learning, fe...
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With the rapid development of the transportation industry, pedestrian detection algorithms on highway roads have become an important research direction. To solve the problem of a large number of model parameters and a...
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This paper primarily focuses on resolving the issue of reconstructing weakly textured areas in indoor environments in multi-view 3D reconstruction. Previous works could yield impressive reconstruction results on objec...
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The main aim of this survey is to provide wider insight about the use of MEMS (Micro-Electro-Mechanical-System) technology in various interdisciplinary fields. The areas include IoT (Internet-Of-Things) for smart auto...
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Understanding temporal information in video sequences is crucial for various computer vision tasks, such as action recognition. Transformer-based methods and GCNs can effectively handle temporal information, but they ...
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As many countries face the challenges of an aging population and declining birth rates, the demand for labor, particularly for assisting the elderly, is increasing. Traditional robots, being standardized products, req...
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Domain adaptive semantic segmentation enables robust pixel- wise understanding in real-world driving scenes. Source-free domain adaptation, as a more practical technique, addresses the concerns of data privacy and sto...
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Domain adaptive semantic segmentation enables robust pixel- wise understanding in real-world driving scenes. Source-free domain adaptation, as a more practical technique, addresses the concerns of data privacy and storage limitations in typical unsupervised domain adaptation methods, making it especially relevant in the context of intelligent vehicles. It utilizes a well-trained source model and unlabeled target data to achieve adaptation in the target domain. However, in the absence of source data and target labels, current solutions cannot sufficiently reduce the impact of domain shift and fully leverage the information from the target data. In this paper, we propose an end-to-end source-free domain adaptation semantic segmentation method via Importance-Aware and Prototype-Contrast (IAPC) learning. The proposed IAPC framework effectively extracts domain-invariant knowledge from the well-trained source model and learns domain-specific knowledge from the unlabeled target domain. Specifically, considering the problem of domain shift in the prediction of the target domain by the source model, we put forward an importance-aware mechanism for the biased target prediction probability distribution to extract domain-invariant knowledge from the source model. We further introduce a prototype-contrast strategy, which includes a prototype-symmetric cross-entropy loss and a prototype-enhanced cross-entropy loss, to learn target intra-domain knowledge without relying on labels. A comprehensive variety of experiments on two domain adaptive semantic segmentation benchmarks demonstrates that the proposed end-to-end IAPC solution outperforms existing state-of-the-art methods. The source code is publicly available at https://***/yihong-97/Source-free-IAPC. IEEE
In the process of multi-UAVs cooperative reconnaissance operations, due to the limited battery capacity and computing resources of the unmanned aerial vehicle (UAV), processing tasks can not only lead to excessive del...
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