What is cascade attention
Cascaded Attention is an attention mechanism used in natural language processing (NLP) tasks. It is a way to implement multiple layers of attention in deep learning models. In cascaded attention, the model uses one layer of attention to encode the input and passes the encoded output to the next layer of attention. Each layer of attention can focus on different information in the input, such as different words or phrases in a sentence. By using multi-layer cascaded attention, the model can better capture different levels of semantic information in the input, thereby improving the performance of the model.
The following is an example of a cascaded attention-based model: First, the model encodes the input sequence through a bidirectional recurrent neural network (Bi-LSTM). Next, the model passes the encoded output to an attention layer, which uses a vector to calculate an attention weight for each input. Finally, the model re-feeds the attention-weighted output into another Bi-LSTM for encoding, and then uses the attention layer again to generate the final output.
Overall, cascade attention is an effective mechanism that can help deep learning models better capture semantic information in input sequences. It has been widely used in a variety of natural language processing tasks, such as machine translation, text summarization, and sentiment analysis.
"Attentional Kernel Encoding Networks for Fine-Grained Visual Categorization" This article also uses cascaded attention to solve fine-grained image classification tasks. Fine-grained image classification refers to a classification task that requires distinguishing objects with similar appearance characteristics, such as different types of birds or dogs. In this article, the author proposes an Attentional Kernel Encoding Network (AKEN), which combines the characteristics of cascade attention and convolutional neural networks (CNN). AKEN models regions at different scales in images through cascaded attention, allowing for fine-grained classification of different kinds of objects. Specifically, AKEN divides the feature map of the input image into multiple scales and applies cascaded attention to each scale to generate scale-specific feature representations. These feature representations are then used to train a support vector machine (SVM) classifier to classify different kinds of objects. The experimental results of this article show that AKEN outperforms other methods on multiple fine-grained classification tasks, including some methods using CNN. This shows that cascaded attention is an effective mechanism in fine-grained image classification tasks, which can improve the performance of the model and help capture subtle differences in the input image.
