image_to_text=pipeline("image-to-text",model="nlpconnect/vit-gpt2-image-captioning")output=image_to_text("./parrots.png")print(output) 执行后,自动下载模型文件并进行识别: 2.5 模型排名 在huggingface上,我们将图片转文本(image-to-text)模型按
A text-to-image model is an artificial intelligence (AI) system that takes textual descriptions as input and generates corresponding visual representations as output. These models combine natural language processing (NLP) and computer vision advancements to bridge the gap between words and visuals. It...
作为一个AI绘画模型深度使用者,就个人感受而言,AI绘画工具的表现确实让人耳目一新,而其本质其实是一种生成符合给定文本描述的真实图像(text-to-image)的崭新交互方式。 文本到图像模型(Text-to-image model) 文本到图像模型(Text-to-image model)是一种机器学习模型,它将自然语言描述作为输入并生成与该描述匹配的...
值得注意的是,通用多媒体大型语言模型LLaVA[32]无法捕捉到与另外两个专门训练在图像字幕任务上的模型相当的性能,论文在附录A.3中提供了详细分析。 论文标题:CoMat: Aligning Text-to-Image Diffusion Model with Image-to-Text Concept Matching 论文链接:arxiv.org/pdf/2404.0365...
A TensorFlow implementation of the image-to-text model described in the paper: "Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge." Oriol Vinyals, Alexander Toshev, Samy Bengio, Dumitru Erhan. IEEE transactions on pattern analysis and machine intelligence (2016). ...
3. After clicking on an image an asynchronous request will be sent to a HuggingFaceSalesforce/blip-image-captioning-baseImageToText model to process and generate a description of the image, it may take a few seconds. 4. Since HuggingFace with its inference API creates a common interface for ...
text pairs to encapsulate these meaningful embeddings. If a tag or attribute accurately describes an image, their embeddings should be relatively close in this space. To generate corresponding tags or attributes, a list of potential tags can be inputted into...
Paddle Multimodal Integration and eXploration, supporting mainstream multi-modal tasks, including end-to-end large-scale multi-modal pretrain models and diffusion model toolbox. Equipped with high performance and flexibility. image-to-textcliptext-to-imageditmultimodalsoratext-to-videoaigcstable-diffusion...
可以看出,总损失的第一项LG,原理与StackGAN中的无条件+有条件结构相似,无条件损失确定图像是真实的还是假的,条件损失确定图像和句子是否相符。 没看StackGAN++可以点击->:Text to image论文精读 StackGAN++ 而损失函数的第二项LDAMSM是由DAMSM计算的字符级细粒度图像-文本匹配损失,这部分在本博文的第七节中介绍。
text-to-image diffusion model采样公式文本到图像的扩散模型采样公式主要是通过定义F_{\phi}left(x_t, y, t \right) = abla_{x_{t}} log p_{\phi}\left(y \mid x_{t}\right) 来实现的,其中x_t代表初始噪声,y是目标数据,t表示时间。采样过程可以通过调整 F_{\phi}\left(x_t, y, t \...