The internal structure and operation mechanism of large-scale language models are
analyzed theoretically, especially how Transformer and its derivative architectures can …

This paper explores the integration of strategic optimization methods in search advertising,
focusing on ad ranking and bidding mechanisms within E-commerce platforms. By …

This paper investigates convolutional neural network (CNN)-based approaches for image
classification and semantic segmentation, with a focus on addressing spatial detail loss and …

The accurate recognition and interpretation of multi-scale visual information is a critical focus
within contemporary computer vision research. To this end, this study explores and …

This paper introduces an implementation of scale invariant feature transform (SIFT)
algorithm with CUDA. Primary steps including building the Gaussian pyramid and the …

This paper presents a novel approach to enhance image-to-image generation by leveraging
the multimodal capabilities of the Large Language and Vision Assistant (LLaVA). We …

In this project, we present a novel approach to depth perception using a monocular camera
by incorporating information from both RGB and LiDAR modalities. Our primary objective is …

This paper explores an automatic news generation and fact-checking system based on
language processing, aimed at enhancing the efficiency and quality of news production …

Pulmonary nodules serve as critical indicators for early lung cancer diagnosis, making their
detection and classification essential. The prevalent use of transfer learning in recognition …

This paper proposes new methods to enhance clickthrough rate (CTR) prediction models
using the Deep Interest Network (DIN) model, specifically applied to the advertising system …