1. 论文和代码地址 Variational Transformer: A Framework Beyond the Trade-off between Accuracy and Diversity for Image Captioning 论文地址:https://arxiv.org/abs/2205.14458[1]代码地址:未开源 2. Motivation 在图像字幕中,生成多样化和准确的字幕是一项具有挑战性的任务,尽管付出了最大努力,但尚未完成。
CBTIC Model 首先,预训练的目标探测器将图像I表示为一组区域特征。给定图像区域特征,CBTIC旨在充分利用双向的属性来生成标注。模型的结构如图所示,其中包括编码器和解码器,编码器采用Transformer模型的编码器,这里不再赘述。 Captioning Decoder 该解码器采用上下文区域特征和每个图像的一对L2R和R2L单词序列作为输入,输出...
1. 论文和代码地址 Variational Transformer: A Framework Beyond the Trade-off between Accuracy and Diversity for Image Captioning 论文地址:https://arxiv.org/abs/2205.14458[1] 代码地址:未开源 2. Motivation 在图像字幕中,生成多样化和准确的字幕是一项具有挑战性的任务,尽管付出了最大努力,但尚未完成。虽然...
captioning 模型不同,MT不使用RNN,完全依赖注意力机制,使用深度 encoder-decoder来同时获得每个模态的 self-attention 和跨模态的 co-attention 针对最后一点: 使用multi-view feature learning 以适应对齐和非对齐的 multi-view visual features Multimodal Transformer The Transformer Model Transformer的核心构成是scaled d...
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') # conditional image captioning text = "a photography of" inputs = processor(raw_image, text,return_tensors="pt").to("cuda") out = model.generate(**inputs) ...
I am trying to produce a model that will produce a caption for an image using resnet as the encoder, transformer as the decoder and COCO as the database.After training my model for 10 epochs, my model failed to produce anything other than the word <pad> which implies that ...
模型验证过程主要应用了nn,Model,context,ImageNet,CrossEntropySmooth和vit_b_16等接口。通过改变Image...
从上表结果来看,Encoder-only模型的效果更好。但是Encoder-Decoder模型更灵活,可以解决image captioning等...
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Image captioningThe Transformer model has achieved very good results in machine translation tasks. In this paper, we adopt the Transformer model for the image captioning task. To promote the performance of image captioning, we improve the Transformer model from two aspects. First, we augment the ...