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 (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...
改进版的VQGAN,受到NLP任务重autoregressive pretraining的启发,利用VIT进行离散化编码和解码,同时也优化了codebook的学习,极大地提升了vector-quantized Image modeling的效果 Method 方法主要分为两个阶段,如Figure 1所示。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 si...
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 ...
Masked image modeling (MIM) has demonstrated impressive results in self-supervised representation learning by recovering corrupted image patches. However, most existing studies operate on low-level image pixels, which hinders the exploitation of high-level semantics for representation models. In this work...
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...
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...
BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers Masked image modeling (MIM) has demonstrated impressive results in self-supervised representation learning by recovering corrupted image patches. However, ... Z Peng,L Dong,H Bao,... 被引量: 0发表: 2022年 Storage and ...
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…