3 accepted papers
Few-Shot Text Classification (FSTC) imitates humans to learn a new text classifier efficiently with only few examples, by leveraging prior knowledge from historical tasks. However, most prior works assume that all the tasks are sampled from a single data source, which cannot adapt to real-world scen…
Recent works suggest that transformer models are capable of multi-tasking on diverse NLP tasks and adapt to new tasks efficiently. However, the potential of these multi-task models may be limited as they use the same set of parameters for all tasks. In contrast, humans tackle tasks in a more flexibl…
Cross-resolution face recognition (CRFR) aims to learn the matching of a low-resolution (LR) probe image with a database of high-resolution (HR) gallery images. Existing methods including super resolution and projection-based algorithms are not recognition-oriented and computationally expensive, or…