Humans can naturally and effectively find salient regions in complex scenes. Motivated by
this observation, attention mechanisms were introduced into computer vision with the aim of …

Attention is the important ability to flexibly control limited computational resources. It has
been studied in conjunction with many other topics in neuroscience and psychology …

Face forgery by deepfake is widely spread over the internet and has raised severe societal
concerns. Recently, how to detect such forgery contents has become a hot research topic …

Dynamic neural network is an emerging research topic in deep learning. Compared to static
models which have fixed computational graphs and parameters at the inference stage …

Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer
vision and pattern recognition, and underpins a diverse set of real-world applications. The …

Recently, self-attention mechanisms have shown impressive performance in various NLP
and CV tasks, which can help capture sequential characteristics and derive global …

Attention mechanism has demonstrated great potential in fine-grained visual recognition
tasks. In this paper, we present a counterfactual attention learning method to learn more …

Fine-grained visual classification (FGVC) which aims at recognizing objects from
subcategories is a very challenging task due to the inherently subtle inter-class differences …

A novel coronavirus (COVID-19) recently emerged as an acute respiratory syndrome, and
has caused a pneumonia outbreak world-widely. As the COVID-19 continues to spread …

Few-shot fine-grained recognition (FS-FGR) aims to distinguish several highly similar
objects from different sub-categories with limited supervision. However, traditional few-shot …