CLIP原文

CLIP（Contrastive Language-Image Pre-training）是由OpenAI提出的一种多模态模型，其核心目标是通过对比学习方法训练一个能够理解图像和文本之间关系的联合嵌入空间。该模型的原始论文名为《Learning Transferable Visual Models From Natural Language Supervision》，详细描述了CLIP的设计理念、架构以及其实验结果。 在CLIP模型中，视觉部分可以采用改进版的ResNet或者基于Transformer的结构来提取图像特征[^2]，而文本处理则依赖于定制化的Transformer网络完成语义表示的学习。整个框架旨在将输入数据映射到统一的空间内，并利用余弦相似度衡量两者间的匹配程度。这种设计使得CLIP不仅适用于零样本分类场景下的表现评估[^1]，而且对于少量标注情况也有良好的泛化能力。 以下是CLIP源码中的关键组成部分之一——`model.py` 文件的部分代码展示： ```python class Bottleneck(nn.Module): """ An implementation detail of the architecture """ class AttentionPool2d(nn.Module): """ A module that applies attention over spatial dimensions """ class ModifiedResNet(nn.Module): """ A modified version of standard ResNets with some adjustments made specifically for this task.""" class LayerNorm(nn.LayerNorm): """ Subclassing PyTorch's native layer normalization class to add support for fp16 training.""" class QuickGELU(nn.Module): """ Custom Gated Linear Unit activation function optimized for speed""" class ResidualAttentionBlock(nn.Module): """ Building block used within both text & vision encoders consisting primarily out residual connections alongside multi-head self attentions layers""" class Transformer(nn.Module): """ Text encoder built upon stackings multiple instances above mentioned blocks together forming complete transformer backbone." class VisualTransformer(nn.Module): """ Vision counterpart utilizing either pretrained resnets as feature extractors OR entirely learned parameters via vit architectures depending on configuration choices during initialization phase." class CLIP(nn.Module): """ Combines all previously defined components into single cohesive unit capable performing cross modal embeddings generation tasks efficiently while maintaining high accuracy standards set forth throughout development process thus far..." ```