第一阶段是Image Quantization,借助ViT,将256x256的图片编码成32x32的离散latent codes,codebook size是8192,这里为了提升训练效果,用到了logit-laplace loss, l2 loss, adversarial loss and perceptual loss等loss。第二阶段是Vector-quantized Image Modeling,用第一阶段的模型得到的32x32共1024个tokens,让Transformer ...
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...
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 ...
In “Vector-Quantized Image Modeling with Improved VQGAN”, we propose a two-stage model that reconceives traditional image quantization techniques to yield improved performance on image generation and image understanding tasks. In the first stage, an image quantization model, calledVQGAN, encodes an...
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 27 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 ...
基于MIM (Masked image modeling)的自监督表示学习方法已经有很不错的结果, 这种自监督的目标主要是恢复corrupted image patches. 但是已有的工作, 基本都是在low-level的 image pixel的层面上, 但是对于high level的语义方面很少有研究. 那么这个工作就是要做到这件事. ...
话说回来,从上面这个概览图可以看出,VQGAN 和 VQ-VAE 的流程完全一致——先学习 codebook、再学习 prior. 学习 codebook 的部分与 VQ-VAE 大同小异,不同之处在于:加了一个 Patch Discriminator 做对抗训练,以及把重构损失的 L2 loss 换成了 perceptual loss. 实验证明 VQ-VAE 的重构非常模糊,而 VQGAN 能保留...
Most of the successful time series models focus on modeling correlation structure at different length scales, such as the traditional autoregressive models. In this paper, instead of following the standard approach to compress a signal in a single batch, we propose a new modeling approach based on...
Herein we present an unsupervised anomaly detection approach for OCTA images, based on two complementary deep learning models: A Vector-Quantized Variational Auto-Encoder (VQ-VAE) connected with Auto-Regressive (AR) modeling, and a Bayesian U-Net for blood vessel segmentation. Both models are train...