Visual Prompt Engineering: Adapting General Models for Various Tasks

Visual Prompt Engineering: Adapting General Models for Various Tasks

MLNLP community is a well-known machine learning and natural language processing community in China and abroad, covering NLP graduate students, university teachers, and researchers in enterprises. The vision of the community is to promote communication and progress between the academic and industrial circles of natural language processing and machine learning, especially for beginners. Reprinted from … Read more

CM-GAN: Realistic Image Inpainting with Global Structure and Texture Detail

CM-GAN: Realistic Image Inpainting with Global Structure and Texture Detail

Machine Heart reports Machine Heart Editorial Team Researchers from the University of Rochester and Adobe Research have proposed a new generative network CM-GAN, which synthesizes overall structure and local details very well, significantly outperforming existing SOTA methods such as CoModGAN and LaMa in both quantitative and qualitative evaluations. Image inpainting refers to the process of … Read more

Visual Prompt Engineering: No Fine-Tuning Required

Visual Prompt Engineering: No Fine-Tuning Required

↑ ClickBlue Text Follow the Jishi platform Author丨Tech Beast Editor丨Jishi Platform Jishi Guide How to adapt a pre-trained visual model to new downstream tasks without specific task fine-tuning or any model modifications? >> Join the Jishi CV technology exchange group and stay at the forefront of computer vision Table of Contents 1 Completing Visual Prompting … Read more

Implementing Image Inpainting with TensorFlow and Deep Learning

Implementing Image Inpainting with TensorFlow and Deep Learning

Big Data Digest article, see the end of the article for reposting requirements Author | Brandon Amos Translation | Molly, Han Xiaoyang Table of Contents ■ Introduction ■ Step 1: Understanding Images as Samples from a Probability Distribution How do you fill in missing information? But how do you start with statistics? These are all … Read more