模型介绍:广泛应用的CLIP模型是典型的双流模型。它的image encoder有ViT和Resnet这2种版本,它的Text encoder是GPT的encoder。在预训练阶段只有1个任务:ITC图文对比学习,其优化目标包括2部分:在mini-batch中,最大化匹配的图文对余弦相似度分数,最小化不匹配图文对的分数。它的预训练任务本身不复杂,但胜在训练数据规...
在vit模块里面的github.com/huggingface/里面的ViTPatchEmbeddings的self.projection=nn.Conv2d(num_channels,hidden_size,kernel_size=patch_size,stride=patch_size)。本质上clip里面的image-encoder部分就是vit。vit对图片进行分割成多个patch,就是巧妙的使用conv2d的步长和窗口尺寸,来进行窗口滑动。 以上图为例,假设...
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 ...
CLIP (Contrastive Language-Image Pre-Training) is a neural network trained on a variety of (image, text) pairs. It can be instructed in natural language to predict the most relevant text snippet, given an image, without directly optimizing for the task, similarly to the zero-shot capabilities...
BuiltInStandardEncoderPreset CbcsDrmConfiguration CencDrmConfiguration ChannelMapping CheckNameAvailabilityInput ClearKeyEncryptionConfiguration ClipTime Codec CommonEncryptionCbcs CommonEncryptionCenc Complexity ContentKeyPolicies ContentKeyPolicy ContentKeyPolicy.Definition ContentKeyPolicy.DefinitionStages ContentKeyPolicy....
ClipRect The region of the destination in which to write data. DebugDescription A developer-meaningful description of this object. (Inherited from NSObject) Description Description of the object, the Objective-C version of ToString. (Inherited from NSObject) Device Gets the device for which...
其中, l 为用于引导的文本, E_L 表示文本encoder。 x_t 为图像, E_I^{\prime} 表示图像encoder。因此,CLIP中图像encoder必须要使用噪声图像finetune。其效果如下 Image Guidance Image Guidance的 F_{\phi}\left(x_t, x_t^\prime, t \right) 定义如下: F_{\phi}\left(x_t, x_t^\prime, t \...
Specifically, given an image without text labels, we first extract the embedding of the image in the united language-vision embedding space with the image encoder of CLIP. Next, we convert the image into a sequence of discrete tokens in the VQGAN codebook space (the VQGAN model can be ...
首先将原始图片通过CLIP的image encoder ,将不同层的编码向量(p x p x d)分别单独输入到Global mapping(三层线性层)中,得到对应数量的N个embedding。然后将这N个embedding当作S*的初始化,与文本prompt进行拼接过完text encoder后,当作条件输入到Diffusion model的cross attention 中。 这里需要注意的是,训练阶段的Gl...
This paper first finds out that simply fine-tuning the visual model initialized by the image encoder in CLIP, has already obtained competitive performances in various ReID tasks. Then we propose a two-stage strategy to facilitate a better visual representation. The key idea is to fully exploit ...