Deep Learning Techniques for Video Segmentation: A Survey

In this paper, the authors propose a new approach to deep learning called attention-based neural networks. These networks use something called attention to focus on specific parts of the input data, rather than treating all parts equally. This allows the network to make more informed predictions and improve its performance.
To understand attention, imagine you are trying to translate a sentence from one language to another. You could try to do this by looking at every single word in both languages and translating them individually, but this would be very time-consuming and may lead to errors. Instead, you can use attention to focus on the most important words in each sentence and translate those first. Once you have translated the important words, you can use their translations to help you translate the rest of the sentence.
Attention works in a similar way for deep learning. Instead of looking at every single part of the input data, the network uses attention to focus on the most important parts and make predictions based on those. This allows the network to be more efficient and accurate.
The authors also propose a new type of attention called asymmetric attention, which allows the network to focus on different parts of the input data at different times. This can be useful in situations where the importance of certain parts of the input data changes over time.
Overall, the paper provides a comprehensive overview of attention-based neural networks and their applications in deep learning. It also demonstrates the effectiveness of attention in improving network performance and efficiency.