与“CNN+Transformer”的设计范式相比,该模型可以从一开始就在每个编码器层上建模全局上下文,并且完全没有卷积。 FrameWork Encoder 如上图所示,作者没有像之前的方法那样使用预先训练的CNN或Faster R-CNN模型来提取空间特征或自下而上的特征,而是选择对输入图像进行排序,并将图像标注作为序列到序列预测任务。具体地说,...
In this paper, we consider the image captioning task from a new sequence-to-sequence prediction perspective and propose Caption TransformeR (CPTR) which takes the sequentialized raw images as the input to Transformer. Compared to the "CNN+Transformer" design paradigm, our model can model global ...
In this work, an image captioning method is proposed that uses discrete wavelet decomposition along with convolutional neural network (WCNN) for extracting the spectral information in addition to the spatial and semantic features of the image. An attempt is made to enhance the visual modelling of ...
Attention on Attention for Image Captioning https://arxiv.org/abs/1908.06954 This paper proposes to add a gated linear unit at the end of the attention layer, further gated by the original queries. Although this is not widely used outside of visual question / answering, I suspect it should...
7. The network takes into account both efficiency and accuracy by utilizing a backbone network to extract multi-source information from the original image. This extracted information is subsequently input into two fully connected layers, forming the decision layer that outputs the specific vegetable ...
There are many interesting vision-language datasets labeled for tasks such as visual question answering, image captioning, and text-image retrieval, to name a few. Vision-language Data Augmentation schemes such as Vokenization look to be a very promising area of research. A recent trend in Image...
Self-Critical Sequence Training for Image Captioning Steven J. Rennie, Etienne Marcheret, Youssef Mroueh, Jerret Ross, Vaibhava Goel Analyzing Humans 1 Spotlight 1-2B Crossing Nets: Combining GANs and VAEs With a Shared Latent Space for Hand Pose Estimation ...
The Transformer-based approach represents the state-of-the-art in image captioning. However, existing studies have shown Transformer has a problem that irrelevant tokens with overlapping neighbors incorrectly attend to each other with relatively large at
Full size image KNN numberk In this test, we scalekandfsindividually, and Fig.7shows the joint error rate. According to the PHYAlert detector algorithm,kandfsdetermine the number of accepted frames used for detection. In the stationary scenarios, as shown in Fig.7a and b, both the FPE and...
import torch from x_transformers import TransformerWrapper, Decoder model = TransformerWrapper( num_tokens = 20000, max_seq_len = 1024, attn_layers = Decoder( dim = 512, depth = 6, heads = 8, attn_one_kv_head = True ) ) Attention on Attention for Image Captioning https://arxiv.org/...