Human pose estimation has given rise to a broad spectrum of novel and compelling applications, including action recognition, sports analysis, as well as surveillance. However, accurate video pose estimation remains an...
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Human pose estimation in videos remains a challenge, largely due to the reliance on extensive manual annotation of large datasets, which is expensive and labor-intensive. Furthermore, existing approaches often struggl...
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Short text classification, as a research subtopic in natural language processing, is more challenging due to its semantic sparsity and insufficient labeled samples in practical scenarios. We propose a novel model name...
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Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) and data collection (DC) have been popular research issues. Different from existing works that consider MEC and DC scenarios separately, this paper in...
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A recent research work pointed out that the reversible data hiding algorithms proposed for gray-scale images can be implemented on the reconstructed palette images to improve embedding capacity and visual quality by r...
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As for social choice, all alternatives are ranked by agents to form preferences as linear orders. However, in applications, sometimes some alternatives cannot be ranked, or it is unnecessary to rank them, which leads ...
As for social choice, all alternatives are ranked by agents to form preferences as linear orders. However, in applications, sometimes some alternatives cannot be ranked, or it is unnecessary to rank them, which leads to unranked alternatives. Hence, without loss of generality, by dividing the set of alternatives into three ranked and unranked subsets, including top-k alternatives, intermediate-r alternatives, and last-l alternatives, the Mallows model on ranked and unranked preferences can be analyzed systematically. Technically, a repeated insertion model is adopted during sampling, and probability distributions are derived for ranked and unranked preferences of alternatives. Experimental results verify the accuracy of the probability distributions for different ranked and unranked preferences of alternatives. Furthermore, in order to solve the preference completion problem where agents have multiple partial rankings, a fuzzy preference completion algorithm, Fuzzy-Multi-Rankings, is proposed, which introduces a fuzzy ranking to complete the target agent’s preference in addition to the traditional nearest-neighbor-based methods. Based on the three ranked and unranked preferences, seven cases can be classified and analyzed for fuzzy preference completion. Experiments on the synthetic datasets and MovieLens dataset confirm the effectiveness and efficiency of our proposed Fuzzy-Multi-Rankings algorithm and also verify the accuracy of the evaluated probability distributions for the proposed seven cases.
Visual grounding aims to ground an image region through natural language, which heavily relies on cross-modal alignment. Most existing methods transfer visual/linguistic knowledge separately by fully fine-tuning uni-m...
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Utilizing the high-level semantic information of language to compensate for the limitations of vision information is a highly regarded approach in single-object tracking. However, most existing vision-language (VL) tr...
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In hyperspectral image (HSI) classification, convolutional neural networks (CNNs) excel at local feature modeling but are limited to Euclidean space. Transformers offer long-range dependency modeling but suffer from h...
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Deep learning (DL) has made significant advancements in tomographic imaging, particularly in low-dose computed tomography (LDCT) denoising. A recent trend involves servers training powerful models with enormous self-c...
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