论文在Transformer的基础上,对于Image Caption任务,提出了一个全新的fully-attentive网络。同时借鉴了之前任务提出的两个key novelties: 以multi-level 的方式进行encode,在对low-level relation 和 high-level relation进行建模时,通过使用持久的内存向量来学习和编码先验知识。 做语句生成时,交叉使用了encoder中不同的lay...
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,输出一对预测单词...
Over the past decade, the field of Image captioning has witnessed a lot of intensive research interests. This paper proposes "GlosysIC Framework: Transformer for Image Captioning with Sequential Attention" to build a novel framework that harnesses the combination of Convolutional Neural Network (CNN)...
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。
Multimodal Transformer for Image Captioning MT架构被描述为由image encoder和textual decoder组成 image encoder输入一张图像,使用预训练的Faster R-CNN提取region-based visual features,然后送入encoder通过self-attention learning获得attended visual representation ...
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。虽然目前的captioning最新...
Image captioning attempts to generate a description given an image, usually taking Convolutional Neural Network as the encoder to extract the visual features and a sequence model, among which the self-attention mechanism has achieved advanced progress recently, as the decoder to generate descriptions. ...
Dual Global Enhanced Transformer for image captioning Reinforcement learningTransformer-based architectures have shown great success in image captioning, where self-attention module can model source and target ... T Xian,Z Li,C Zhang,... - 《Neural Networks the Official Journal of the International ...
[11] Improving Image Captioning by Leveraging Intra- and Inter-layer Global Representation in Transformer Network [12] Dual-Level Collaborative Transformer for Image Captioning [13] Dynamic Graph Representation Learning for Video Dialog via Multi-Modal Shuffled Transformers ...
(arXiv 2021.01) CPTR: FULL TRANSFORMER NETWORK FOR IMAGE CAPTIONING, (arXiv 2021.01) Trans2Seg: Transparent Object Segmentation with Transformer (arXiv 2021.01) Scheduled Sampling in Vision-Language Pretraining with Decoupled Encoder-Decoder Network ...