Deep learning is thought of as a promising mean to identify maize diseases. However, the
drawback of deep learning is the huge sample data and low accuracy. In this paper, we …

Abstract Class Activation Map (CAM) is one of the most popular approaches to visually
explain the convolutional neural networks (CNNs). To obtain fine-grained saliency maps …

The zero-shot semantic segmentation requires models with a strong image understanding
ability. The majority of current solutions are based on direct mapping or generation. These …

Weakly supervised semantic segmentation with only image-level labels is an essential
application since it reduces the considerable human effort to fully annotate image. Most state …

Currently, weakly supervised semantic segmentation approaches adopt the Class Activation
Map (CAM) to generate the initial attention maps from the standard classification backbone …

Human parsing is a fine-grained semantic segmentation task, which needs to understand
human semantic parts. Most existing methods model human parsing as a general semantic …

Weakly supervised semantic segmentation is a challenging task, utilizing only low-cost weak
supervision to produce pixel-level predictions. Existing transformer-based methods for …

With the rapid development of deep convolutional neural networks, the results of image
semantic segmentation are remarkable, and the segmentation effect is greatly improved …

During the past couple of decades, machine learning and deep learning methods have
achieved remarkable results in many real-world applications. However, it is difficult to …