transformer in Image Caption Ctrl CV keep learningimage caption的目标就是根据提供的图像,输出对应的文字描述。 对于图片描述任务,应该尽可能写实,即不需要华丽的语句,只需要陈述图片所展现的事实即可。根据常识,可以知道该任务一般分为两个部分,一是图片编码,二是文本生成,基于此后续的模型也都是encoder-decoder的...
visual signal和hidden state进行了融合，用来预测生成的单词（如上图所示），具体计算如下所示：...
随着深度学习技术的飞速发展,图像描述(Image Caption)作为计算机视觉与自然语言处理(CV与NLP)交叉领域的重要任务,受到了广泛的关注。Transformer模型,自其诞生以来,便在NLP领域取得了巨大成功,其独特的自注意力机制更是为处理序列数据提供了全新的视角。本文将探讨Transformer模型在图像描述任务中的革新应用,揭示其背后的技...
Image caption generation has emerged as a remarkable development that bridges the gap between Natural Language Processing (NLP) and Computer Vision (CV). It lies at the intersection of these fields and presents unique challenges, particularly when dealing with low-resource languages such as Urdu. ...
Automatic Image Captioning is a task that involves two prominent areas of Deep Learning research, i.e., image processing and language generation. Over the years, we have achieved a lot of success in being able to generate syntactically and semantically m
Image Captioning Transformer This projects extendspytorch/fairseqwithTransformer-based image captioning models. It is stillwork in progressand inspired by the following papers: Only baseline models are available at the moment, incl.pre-trained models. Theirarchitectureis based on a vanilla Transformer. ...
Multifaceted Feature Coding Image Caption Generation Algorithm Based on Transformer HENG Hongjun, FAN Yuchen, WANG Jialiang RichHTML 25 PDF 297 摘要/Abstract 摘要: 由目标检测算法提取的目标特征在图像描述生成任务中发挥重要作用,但仅使用对图像进行目标检测的特征作为图像描述任务的输入会导致除关键目标信息...
region-based visual features 可能无法涵盖图中所有的 object,不足的视觉表达导致无法产生精准的 caption 针对前两点: 使用MT(Multimodal Transformer)model for image captioning,与CNN-RNN 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...
objection:Transformer基的模型在其他地方state-of-the-art,但是在image caption探索的还比较少。为了fill the gap ,我们提出了M^2(Meshed Transformer with Memory) idea: 1.学习不同层级表示之间的关系得到先验知识 2.在encoder和decoder间建立mesh-like connectivity去挖掘高层和底层特征。
self).__init__()self.image_size=image_sizeself.patch_size=patch_sizeself.num_patches=(image_...