Deep Learning, as a crucial branch of artificial intelligence, has achieved significant progress in recent years across various fields such as computer vision and natural language processing. However, the success of deep learning largely depends on the choice of loss functions and performance metrics. Loss functions are used to measure the differen...

Application of Automation Technology in Embryo Trophectoderm Cell Biopsy Academic Background Embryo biopsy is a crucial step in assisted reproductive technology (In Vitro Fertilization, IVF), especially in Preimplantation Genetic Testing (PGT). Through embryo biopsy, doctors can extract a small number of cells from embryos for genetic analysis to a...

Pseudo-Plane Regularized Signed Distance Field for Neural Indoor Scene Reconstruction Academic Background 3D reconstruction of indoor scenes is a significant task in computer vision with broad applications, such as computer graphics and virtual reality. Traditional 3D reconstruction methods often rely on expensive 3D ground truth data. In recent ye...

Selective Frequency Interaction Network for Improved Aerial Object Detection Background and Problem Statement With the advancements in computer vision, aerial object detection has become a critical research focus in remote sensing. This task aims to identify targets such as vehicles or buildings from aerial images captured at varying angles and alt...

StyleAdapter: A Unified Stylized Image Generation Model In recent years, advancements in text-to-image (T2I) generation and deep learning models have significantly driven progress in artificial intelligence for image generation. However, integrating specific styles from reference images into high-quality images generated from textual prompts remain...