Multi-label zero-shot learning (ML-ZSL), with the difficulty of both multi-label learning and zero-shot learning, aims to recognize various unseen objects that are not observed during training. Previous methods mainly...
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Deep neural networks have been widely and successfully applied to many critical tasks, such as imagepatternrecognition, natural language processing.and autonomous driving, and large amounts of training data and incr...
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The need for robust machine learning models is particularly evident in the realm of biological patternrecognition. Traditional centralized methods often struggle, as they frequently depend on large datasets that are ...
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In this paper, we address the classification of medical actions with only one single sample by developing a novel one-shot learning framework which contains both cross-attention and dynamic time warping (DTW) modules....
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As the birthplace of soybeans, China has become the world's largest importer and consumer of soybeans. The supply and demand imbalance of soybeans has become one of the significant food and oil security concerns i...
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With the continuous progress of mankind, the level of sports is particularly important in today's society. Mobile communication system is one of the key parts. This paper proposes a method of human biological sign...
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The use of eye movements for identifying intrinsic human psychological changes is a hot topic, and the identification of eye states in specific scenarios is the basis for analyzing the test taker's instantaneous p...
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images form the basis of human vision and are an important source of information for both human perception and machine patternrecognition. Since the development of the image quality evaluation field, a large number o...
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Sketch-based image retrieval (SBIR) has undergone an increasing interest in the community of computer vision bringing high impact in real applications. For instance, SBIR brings an increased benefit to eCommerce searc...
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ISBN:
(纸本)9781665448994
Sketch-based image retrieval (SBIR) has undergone an increasing interest in the community of computer vision bringing high impact in real applications. For instance, SBIR brings an increased benefit to eCommerce search engines because it allows users to formulate a query just by drawing what they need to buy. However, current methods showing high precision in retrieval work in a high dimensional space, which negatively affects aspects like memory consumption and time processing. Although some authors have also proposed compact representations, these drastically degrade the performance in a low dimension. Therefore in this work, we present different results of evaluating methods for producing compact embeddings in the context of sketch-based image retrieval. Our main interest is in strategies aiming to keep the local structure of the original space. The recent unsupervised local-topology preserving dimension reduction method UMAP fits our requirements and shows outstanding performance, improving even the precision achieved by SOTA methods. We evaluate six methods in two different datasets. We use Flickr15K and eCommerce datasets;the latter is another contribution of this work. We show that UMAP allows us to have feature vectors of 16 bytes improving precision by more than 35%.
Under the vigorous promotion of global energy Internet, China's AC/DC power grid construction has been developed rapidly and both artificial intelligence technology and machine learning technology have been develo...
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