Vector-quantized Image Modeling (VIM)。训练transformer来自回归地预测32x32=1024个token,若是 class-conditioned 图片生成,跟vqgan一样把类别id的token放在图片token前面(输入模型)。加分类头是为了评估无监督学习的质量。 跟vqgan的差别: 阶段1的CNN换成ViT,因此解码器先将预测的每个token转换回8x8的图片patch,再...
一、用VIM提升image generation和image understanding任务的关键点在于一个好的image quantizer 二、发现在stage2用更大的计算量并且保持stage1中transformer的轻量级是有益的 Method 一、Vector-Quantized Images with ViT-VQGAN 二、Vector-Quantized Image Modeling Experiment 一、重建 二、生成 三、无监督学习 论文地...
Vector-quantized Image Modeling 训好的ViT-VQGAN可以把图片encode然后得到一系列codebook的id,然后就可以用decoder-only的Transformer来autoregressively地学图像数据的分布,the density of image data P(x) ,如公式(3)所示。最终的目标是优化负对数似然(negative log-likelihood) L = \mathbb{E}_{x \in X}(-\...
Systems and methods are provided for vector-quantized image modeling using vision transformers and improved codebook handling. In particular, the present disclosure provides a Vector-quantized Image Modeling (VIM) approach that involves pretraining a machine learning model (e.g., Transformer model) to...
Motivated by this success, we explore a Vector-quantized Image Modeling (VIM) approach that involves pretraining a Transformer to predict rasterized image tokens autoregressively. The discrete image tokens are encoded from a learned Vision-Transformer-based VQGAN (ViT-VQGAN). We first propose ...
Vector Quantized Generative Adversarial Networks (VQGAN) is a generative model for image modeling. It was introduced in Taming Transformers for High-Resolution Image Synthesis. The concept is build upon two stages. The first stage learns in an autoencoder-like fashion by encoding images into a low...
1. VQVAE《Neural discrete representation learning》NeurIPS 2017 2. VQGAN 《Taming Transformers for High-Resolution Image Synthesis》CVPR 2021 3. ViT-VQGAN 《Vector-quantized Image Modeling with Impr…
(2022 arxiv) BEIT V2- Masked Image Modeling with Vector-Quantized Visual Tokenizers 笔记 鹦鹉丛中笑 28 人赞同了该文章 目录 收起 1.论文动机 问题分析 解决方案 2.具体做法 2-1.码本的构建——VQ-KD 2-2. BEITv2部分 3.实验结果 3-1.分类任务 3-2.消融实验 4.结论 5.引用 作者列表 ...
"Vector quantized diffusion model for text-to-image synthesis." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10696-10706. 2022. ^Tang, Zhicong, Shuyang Gu, Jianmin Bao, Dong Chen, and Fang Wen. "Improved vector quantized diffusion models." arXiv...