TextToImage+generateImage(text: String)GAN-discriminator-generator+train(data: Dataset)VAE+encode(data: Dataset)+decode(z: LatentVector) 特性拆解 在扩展能力方面,文字转图片技术能够与多个工具链协作,以满足不同的需求。 TextToImagestringtextImageGeneratorstringimageUserstringusernamerequestsgenerates 隐藏高级...
text = pytesseract.image_to_string(img, lang='chi_sim') print("文字识别结果如下:",text) print(f"解析获取当前的系统初始化日期:{text.split('初始化日期')[-1].split(' ')[1]}") 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. ...
在本文中,我们将结合图像和文本处理来构建一个有用的深度学习应用程序,即图像字幕(Image Captioning)。它是指从图像生成文本描述的过程,主要根据图像中物体和物体的动作。例如: 这个应用在现实生活中有很多潜在的应用场景。要注意保存图像的文本描述,以便在稍后的阶段就可以根据此描述轻松检索。
(127,127,127))/0.5defect_net.setInput(blob)res=defect_net.forward()idx=np.argmax(np.reshape(res,(6)))defect_txt=defect_labels[idx]cv.putText(image,defect_txt,(10,25),cv.FONT_HERSHEY_SIMPLEX,1,(255,0,0),2)cv.imshow("input",image)print(f,defect_txt)cv.waitKey(0)cv.destroyAll...
(False, False, True), layer_cross_attns = False ) # imagen, which contains the unets above (base unet and super resoluting ones) imagen = Imagen( condition_on_text = False, # this must be set to False for unconditional Imagen unets = (unet1, unet2), image_sizes = (64, 128),...
@inproceedings{qin2024noisy, title={Noisy-Correspondence Learning for Text-to-Image Person Re-identification}, author={Qin, Yang and Chen, Yingke and Peng, Dezhong and Peng, Xi and Zhou, Joey Tianyi and Hu, Peng}, booktitle={IEEE International Conference on Computer Vision and Pattern Recognit...
Implementation of Muse: Text-to-Image Generation via Masked Generative Transformers, in Pytorch - lucidrains/muse-maskgit-pytorch
ODISE: Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models ODISE: Open-vocabulary DIffusion-based panoptic SEgmentation exploits pre-trained text-image diffusion and discriminative models to perform open-vocabulary panoptic segmentation. It leverages the frozen representation of both the...
让AI画图有了成熟的解决方案,GAN就是是一个好办法,但是它通畅并不能按要求随心所欲造出图像。 而微软和京东AI研究院合作提出的ObjGAN就能做到这一点。ObjGAN可以理解一段说明文字,生成草图布局,并根据确切描述完善图像细节。 他们的文章《Object-driven Text-to-Image Synthesis via Adversarial Training》已经被正在...
texts) def __getitem__(self, idx): # 获取第idx个文本和标签 text = self.texts[idx] label = self.labels[idx] # 将文本转化为向量(词袋模型) text_vector = self.vectorizer.transform([text]).toarray() return torch.tensor(text_vector, dtype=torch.float32), torch.tensor(label, dtype=torch...