MLNLP community is a well-known machine learning and natural language processing community in China and abroad, covering NLP master’s and doctoral students, university teachers, and corporate researchers. The Vision of the Community is to promote communication and progress between the academic and industrial circles of natural language processing and machine learning in China and abroad, … Read more
Follow our public account to discover the beauty of CV technology This article is adapted from Machine Heart, edited by Dan Jiang. Recently, diffusion models have become a research hotspot in the AI field. Researchers from Google Research and UT-Austin have fully considered the ‘corruption’ process in their latest study, proposing a design framework for … Read more
0. Introduction This article reviews deep generative models, particularly diffusion models, and how they endow machines with human-like imagination. Diffusion models show great potential in generating realistic samples, overcoming the posterior distribution alignment obstacles in variational autoencoders and alleviating the instability of adversarial objectives in generative adversarial networks. Diffusion models consist of two interconnected processes: … Read more
MLNLP community is a well-known machine learning and natural language processing community both domestically and internationally, covering NLP master’s and doctoral students, university teachers, and corporate researchers. The vision of the community is to promote communication and progress between academia, industry, and enthusiasts in natural language processing and machine learning, especially for beginners. Reprinted from … Read more
Professor Zhu Jun from Tsinghua University’s Computer Science Department, leading the TSAIL team, proposed DPM-Solver (NeurIPS 2022 Oral, approximately the top 1.7%) and DPM-Solver++, which has pushed the rapid sampling algorithm of diffusion models to the extreme: achieving high-quality sampling with only 10 to 25 steps without additional training. This year, one of the most … Read more
Paper Title: SemCity: Semantic Scene Generation with Triplane Diffusion Authors: Jumin Lee1, Sebin Lee1, Changho Jo, Woobin Im, Juhyeong Seon, Sung-Eui Yoon Project Address: https://sglab.kaist.ac.kr/SemCity/ Compiler: Babata Reviewer: Los Introduction: This paper has been accepted by CVPR24 and proposes a 3D diffusion model for generating outdoor real scenes. Utilizing the Triplane Diffusion method, it not … Read more
Follow our WeChat public account to discover the beauty of CV technology This article shares the NeruIPS 2023 paper SegRefiner: Towards Model-Agnostic Segmentation Refinement with Discrete Diffusion Process, which achieves high-precision image segmentation through diffusion. Details are as follows: Paper link: https://arxiv.org/abs/2312.12425 Open-source code: https://github.com/MengyuWang826/SegRefiner Background Introduction Although image segmentation has been widely researched and … Read more
Follow our public account to discover the beauty of CV technology This article introduces the latest 4D generation diffusion model proposed by teams from the University of Toronto, Beijing Jiaotong University, the University of Texas at Austin, and the University of Cambridge. This method can achieve spatio-temporal consistent 4D content generation in just a few … Read more
MLNLP community is a well-known machine learning and natural language processing community both domestically and internationally, covering NLP master’s and doctoral students, university teachers, and industry researchers. 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 … Read more
Follow our public account to discover the beauty of CV technology This article shares the AAAI 2024 paper FontDiffuser: One-Shot Font Generation via Denoising Diffusion with Multi-Scale Content Aggregation and Style Contrastive Learning, detailing the use of diffusion models for generating complex characters in any style. Detailed information is as follows: Authors: Yang Zhenhua, Peng … Read more