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 …

Real-world data often follow a long-tailed distribution as the frequency of each class is
typically different. For example, a dataset can have a large number of under-represented …

Fine-grained visual classification (FGVC) is much more challenging than traditional
classification tasks due to the inherently subtle intra-class object variations. Recent works …

The goal of gait recognition is to learn the unique spatio-temporal pattern about the human
body shape from its temporal changing characteristics. As different body parts behave …

The core for tackling the fine-grained visual categorization (FGVC) is to learn subtle yet
discriminative features. Most previous works achieve this by explicitly selecting the …

Vision transformers (ViTs), which have demonstrated a state-of-the-art performance in image
classification, can also visualize global interpretations through attention-based contributions …

Food recognition plays an important role in food choice and intake, which is essential to the
health and well‐being of humans. It is thus of importance to the computer vision community …