Overview A mix of beginner and advanced-level books to suit various learning needs.Each book blends theory with practical ...

Introduction: Weeds compete with crops for water, nutrients, and light, negatively impacting maize yield and quality. To enhance weed identification accuracy and meet the requirements of precision ...

Semantic segmentation of remote sensing images is pivotal for comprehensive Earth observation, but the demand for interpreting new object categories, coupled with the high expense of manual annotation ...

While I found the config files and code for training and distilling DinoV3, as well as training the classification head and doing the text alignment, I didn't find training code for semantic ...

Abstract: Variations in scene complexity and image quality across remote sensing images lead to inconsistent performance when applying pretrained semantic segmentation models. To ensure quality ...

Modern software engineering faces growing challenges in accurately retrieving and understanding code across diverse programming languages and large-scale codebases. Existing embedding models often ...

Abstract: 4D LiDAR semantic segmentation classifies the semantic category of each LiDAR point and detects whether it is dynamic, a critical ability for tasks like obstacle avoidance and autonomous ...

Python libraries are pre-written collections of code designed to simplify programming by providing ready-made functions for specific tasks. They eliminate the need to write repetitive code and cover ...