Self-attention is a useful mechanism to build generative models for language and images. It
determines the importance of context elements by comparing each element to the current …

This paper presents a multi-level matching and aggregation network (MLMAN) for few-shot
relation classification. Previous studies on this topic adopt prototypical networks, which …

Transformers were initially introduced for natural language processing, leveraging the self-
attention mechanism. They require minimal inductive biases in their design and can function …

Convolution operator is the core of convolutional neural networks (CNNs) and occupies the
most computation cost. To make CNNs more efficient, many methods have been proposed …

Boundary information is essential for the semantic segmentation of remote sensing images.
However, most existing methods were designed to establish strong contextual information …

Many RGB-T trackers attempt to attain robust feature representation by utilizing an adaptive
weighting scheme (or attention mechanism). Different from these works, we propose a new …

Pseudo-code written by natural language is helpful for novice developers' program
comprehension. However, writing such pseudo-code is time-consuming and laborious …

The goal of natural language inference (NLI) is to judge the logical relationship between
sentence pairs, including entailment, contradiction, and neutral. At present, many …

The introduction of large-scale audio datasets, such as AudioSet, paved the way for
Transformers to conquer the audio domain and replace CNNs as the state-of-the-art neural …

Text matching aims to establish the matching relationship between two texts. It is an
important operation in some information retrieval related tasks such as question duplicate …