Large deep learning models are impressive, but they struggle when real-time data is not
available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for …

Taking time to identify expected products and waiting for the checkout in a retail store are
common scenes we all encounter in our daily lives. The realization of automatic product …

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 …

Our work focuses on tackling the challenging but natural visual recognition task of long-
tailed data distribution (ie, a few classes occupy most of the data, while most classes have …

The task of multi-label image recognition is to predict a set of object labels that present in an
image. As objects normally co-occur in an image, it is desirable to model the label …

While image retrieval and instance recognition techniques are progressing rapidly, there is a
need for challenging datasets to accurately measure their performance--while posing novel …

Deep learning models are vulnerable to adversarial examples. As a more threatening type
for practical deep learning systems, physical adversarial examples have received extensive …

Adversarial examples are inputs with imperceptible perturbations that easily misleading
deep neural networks (DNNs). Recently, adversarial patch, with noise confined to a small …

Nowadays, customer's demands for E-commerce are more diversified, which introduces
more complications to the product retrieval industry. Previous methods are either subject to …

The efficient identification of rice pests and diseases is crucial for preventing crop damage.
To address the limitations of traditional manual detection methods and machine learning …