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 …

Emotion recognition plays a significant role in cognitive psychology research. However,
measuring emotions is a challenging task. Thus, several approaches have been designed …

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 …

Given new tasks with very little data---such as new classes in a classification problem or a
domain shift in the input---performance of modern vision systems degrades remarkably …

Prototype-based methods use interpretable representations to address the black-box nature
of deep learning models, in contrast to post-hoc explanation methods that only approximate …

Abstract Deep Convolutional Neural Network (CNN) is a special type of Neural Networks,
which has shown exemplary performance on several competitions related to Computer …