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Few-shot classification aims to recognize the query sample through a limited amount of support data, where a prototype classifier is commonly applied. However, although the prototype classifier is simple and non-parametric, it does not fully utilize the ...

Remote sensing semantic segmentation technology has been widely applied in various fields such as urban planning, land source management and agriculture. Due to the large number of ground objects with different scales and boundaries in remote sensing ...

Manually annotating datasets for training deep models is very labor-intensive and time-consuming. To overcome such inferiority, directly leveraging web images to conduct training data becomes a natural choice. Nevertheless, the presence of label ...

Multimodal movie genre classification has always been regarded as a demanding multi-label classification task due to the diversity of multimodal data such as posters, plot summaries, trailers and metadata. Although existing works have made great progress ...

Collaborative representation based techniques have shown promising results for face recognition; however, most of them code the samples by taking the overall samples as a dictionary, which may contain much noise information. To tackle ...

The lack of sufficient training data has been one obstacle to fine-grained visual classification research because labeling subcategories generally requires specialist knowledge. As one optional approach to alleviating the data-hunger problem, leveraging ...

To distinguish the subtle differences among fine-grained categories, a large amount of well-labeled images are typically required. However, manual annotations for fine-grained categories is an extremely difficult task as it usually has a high ...

Coronaviruses are comprised of a single-stranded RNA genome that is ready to be translated by the host ribosome.

Fine-grained visual recognition tasks typically require training data with reliable acquisition and annotation processes. Acquiring such datasets with precise fine-grained annotations is very expensive and time-consuming. Conversely, a vast amount of ...

Constructing fine-grained image datasets typically requires domain-specific expert knowledge, which is not always available for crowd-sourcing platform annotators. Accordingly, learning directly from web images becomes an alternative method for fine-...

In order to solve the problem that the traditional methods can't accurately measure the localization of multiple acoustic sources in far field, this paper presents a new multiple acoustic sources localization method using in the near field and the far ...