Large language models cross-domain named entity recognition task in the face of the scarcity of large language labeled data in a specific domain,due to the entity bias arising from the variation of entity information ...
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Large language models cross-domain named entity recognition task in the face of the scarcity of large language labeled data in a specific domain,due to the entity bias arising from the variation of entity information between different domains,which makes large language models prone to spurious correlations problems when dealing with specific domains and *** order to solve this problem,this paper proposes a cross-domain named entity recognition method based on causal graph structure enhancement,which captures the cross-domain invariant causal structural representations between feature representations of text sequences and annotation sequences by establishing a causal learning and intervention module,so as to improve the utilization of causal structural features by the large languagemodels in the target domains,and thus effectively alleviate the false entity bias triggered by the false relevance problem;meanwhile,through the semantic feature fusion module,the semantic information of the source and target domains is effectively *** results show an improvement of 2.47%and 4.12%in the political and medical domains,respectively,compared with the benchmark model,and an excellent performance in small-sample scenarios,which proves the effectiveness of causal graph structural enhancement in improving the accuracy of cross-domain entity recognition and reducing false correlations.
Recently, many compression algorithms are applied to decrease the cost of video storage and transmission. This will introduce undesirable artifacts, which severely degrade visual quality. Therefore, Video Compression ...
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Generalized Zero-Shot Learning (GZSL) is characterized as a training process that comprises visual samples from seen classes and semantic samples from seen and unseen classes, followed by a testing process that classi...
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Variational Autoencoder (VAE) is now popular in modeling and language generation tasks, which need to pay attention to the diversity of generation results. The existing models are insufficient in capturing the built-i...
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In this research, we introduce an innovative saliency detection algorithm, comprising three essential steps. Firstly, leveraging fully convolutional networks with aggregation interaction modules, we generate an initia...
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Multitask learning (MTL) is a promising field in machine learning owing to its capability to improve the generalization performance of all tasks by sharing knowledge among the related tasks. MTL has attracted a large ...
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In this paper, both the marginal and joint statistics of second generation Orthogonal bandelet transform (OBT) coefficients of natural images are firstly studied, and the highly non-Gaussian marginal statistics and st...
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In this paper, both the marginal and joint statistics of second generation Orthogonal bandelet transform (OBT) coefficients of natural images are firstly studied, and the highly non-Gaussian marginal statistics and strong interscale, interlocation and interdirection dependencies among OBT coefficients are found. Then a Hidden Markov tree (HMT) model in OBT domain which can effectively capture all dependencies across scales, locations and directions is developed. The main contribution of this paper is that it exploits the edge direction information of OBT coefficients, and proposes an image denoising algorithm (B-HMT) based on HMT model in OBT domain. We apply B-HMT to denoise natural images which contaminated by additive Gaussian white noise, and experimental results show that B-HMT outperforms the Wavelet HMT (W-HMT) and Contourlet HMT (C-HMT) in terms of visual effect and objective evaluation criteria.
The networked radar system can synthesize different echo signals received by various radars and realize the cooperative detection of multiple radars, becoming more and more critical for data fusion sharing and network...
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Contour extraction is an important task in imageprocessing and computer vision. The contextual modulation is a universal phenomenon in the primary visual cortex (V1). A biologically motivated computational model is p...
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Contour extraction is an important task in imageprocessing and computer vision. The contextual modulation is a universal phenomenon in the primary visual cortex (V1). A biologically motivated computational model is presented for contour extraction in this paper. Two mechanisms of contextual modulation, surround suppression and collinear facilitation, are integrated in this model. We obtain good results via this model to extract contours from images with noise and texture backgrounds. This work provides a biologically motivated approach with great potential for computer vision.
At present, deep learning technology is widely used in ship target detection in synthetic aperture radar (SAR) images. However, high-resolution remote sensing SAR images cover a larger area and have larger image sizes...
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