CLIP的模型结构其实非常简单:包括两个部分,即文本编码器(Text Encoder)和图像编码器(Image Encoder)。Text Encoder选择的是Text Transformer模型;Image Encoder选择了两种模型,一是基于CNN的ResNet(对比了不同层数的ResNet),二是基于Transformer的ViT。 CLIP在文本-图像对数据集上的训练过程如下: 假设DataLoader中的一个...
Text Encoder是一个transformer结构; t是一个learnable parameter,初始化为nn.Parameter(torch.ones([])∗np.log(1/0.07)),作用是避免logits超过100,从而避免训练过程的不稳定。 图3、Zero-shot的CLIP在很多数据集上表现都超过完全supervised的baseline。 包括在zero-shot,supervised,representation learning,feature的...
CLIPImageEncoder is an image encoder that wraps the image embedding functionality using the CLIP model from huggingface transformers. This encoder is meant to be used in conjunction with the CLIPTextEncoder, as it can embed text and images to the same latent space. For more information on the ...
虚线以下部分表示利用CLIP的text encoder生成图片的过程,在获取输入文本描述的text embedding之后,将其输入一个prior(autoregressive or diffusion),来获取image embedding,然后将image embedding送入diffusion model(decoder,改进版GLIDE)来生成图像。 prior网络的训练过程,对一个图片文本对 和已经训练好的CLIP模型(text enco...
Once the model is fit, you can pass an image into the image encoder to retrieve the text description that best fits the image – or, vice versa, you can pass a text description into the model to retrieve an image, as you'll see in some of the applications below!
UnCLIP的image/text embedding是使用CLIP encoder提取的,CLIP encoder是用(image, text)的对比学习训练出来的,两种embedding天然就在同一个latent space LDM是图像encoder是通过AE的图像重建Loss训练出来的,text embedding是通过外挂language model提取,DM Prior学习过程还得兼顾将两种embedding空间对齐。
(image, text) training examples. At test time the learned text encoder synthesizes a zero-shot linear classifier by embedding the names or descriptions of the target dataset’s classes. For pre-training, CLIP is trained to predict which of the $N X N$ possible (image, text) pairings ...
Text Encoder 对于输入T∈RLT∈RL,通过Transfomer提取到的特征为Ft∈RL×CFt∈RL×C。全局文本表示Fs∈RC′Fs∈RC′。其中C,C′C,C′是特征维度,LL是referring expression的长度。 Cross-modal Neck 给定多个视觉特征和全局文本表示FsFs,可以通过融合Fv4Fv4以及FsFs得到简单的多模态特征Fm4∈RH16×W16×CFm4∈RH...
In the second stage, the ID-specific text tokens and their encoder become static, providing constraints for fine-tuning the image encoder. With the help of the designed loss in the downstream task, the image encoder is able to represent data as vectors in the feature embedding accurately. The...
首先训练一个diffusion decoder用于逆转clip的image encoder,然后训练了一个prior model用于从给定文本生成clip的image embedding,二者一串就是个文生图新框架。对比了上一代dalle和GLIDE,结论是吊打。 Decoder: 调整了之前GLIDE的结构,把clip emb加入到existing时间步的emb里面去,也把clip emb投影成4个额外的token concat...