文献阅读:LXMERT: Learning Cross-Modality Encoder Representations from Transformers 中都珍 背景 在LXMERT方法出现之前,人们通常使用其他方法来解决图像与语言之间的交互问题。一种常见的方法是使用卷积神经网络(CNN)和循环神经网络(RNN)等深度学习方法进行端到端的图像与语言处理。这些方法可以通过联合训练来学习图像和语...
self-learning,我们使用音频信息来提高视频翻译的效率,分别使用单视频和mix翻译得到最终文本的概率分别为pu和pm,使用jsd正则化来规范他俩。 jsd是干啥的 JS散度度量了两个概率分布的相似度,是KL散度的变体,JS散度解决了KL散度非对称的问题。一般地,JS散度是对称的,其取值是0到1之间 要让两种方式翻译出的概率分布...
Image captioningCross-modality alignmentSentence embeddingsImage captioning is a challenging task in the research area of vision and language. Traditionally in a deep learning-based image captioning model, two types of input features are utilized for generating the token of the current inference step, ...
To solve this problem, we propose a cross-modality consistency learning network, which jointly considers crossmodal learning and distillation learning. It consists of two associated components: the feature adaptation network (FANet) and the modality learning module (MLM). The FANet combines global and...
LXMERT: Learning Cross-Modality Encoder Representations from Transformers 2020-12-24 14:24:05 Paper:EMNLP 2019 Code:github 1. Background and Motivation: 本文提出一种 image-language pre-trained model,特征编码层面有三个 encoder,即: an object relationship encoder, ...
Compared with previous robot behavior learning methods, this method has the property of learning a new behavior while at the same time preserving prior learned behaviors. Moreover, visual information and audio information are integrated to form a unified percept of the observed behavior, which ...
Introduction (1)Motivation: 解决跨模态reid的方法主要有两类:模态共享特征学习(modality-shared feature learning)、模态特定特征补偿(modality-specific feature compensation)。模态共享特征学习旨在将不
Cross-Modality 在深度学习(Deep Learning)的回答 返回所有回答 神经网络为什么可以(理论上)拟合任何函数? Cross-Modality 机器学习工程师 这个问题实际上对大多数研究生没什么实用意义,尤其是使用深度学习方法做具体领域应用的研究生。我的结论可能会让你觉得莫名其妙,为什么这么说?还请听我细细道来。 在读研时,我对...
2019LXMERT:Learning Cross-Modality Encoder Representations from Transformers,程序员大本营,技术文章内容聚合第一站。
“bright-field holography” method through the snapshot imaging of bioaerosols distributed in 3D, matching the artifact-free image contrast and axial sectioning performance of a high-NA bright-field microscope. This data-driven deep-learning-based imaging method bridges the contrast gap between ...