Thank you for a clearly, patiently explained video. You explained the concepts with a perfect blend of clear language and technical background without hiding behind acronyms and algorithm names without explanation. Well done!
Interesting. Does Non-Local offers advantage to transformers? This is a very difficult topic, do you have a sequence of materials to get deeper in this subject?
That’s a very good question. I think of Non-local Module as a generalization of scaled dot-product attention. The proposed non-local block can be added to existing architectures for capturing long-range dependencies. But Transformers are different. We will cover different vision transformer models in this series . The previous introductory video shows the topics that will be covered
In the scope of this series, I plan to begin with discussing the evolution of attention mechanisms in the image domain, followed by an exploration into vision transformers. It's important to note that the Non-Local Module (NLM) is distinct from vision transformers. NLMs, particularly when coupled with residual connections, can be integrated into any pre-trained model, such as ResNet, as demonstrated in the original paper. This integration is designed to enhance the model without altering its fundamental behavior. Stay tuned as we delve deeper into Vision Transformers later on, and will see how self-attention mechanism is utilized in ViTs.
Thanks for your concise and insightful description.🙏
Thank you for a clearly, patiently explained video. You explained the concepts with a perfect blend of clear language and technical background without hiding behind acronyms and algorithm names without explanation. Well done!
Thanks for the nice comment, glad you enjoyed it!
What a great channel, brow keep going i just find your channel
Interesting. Does Non-Local offers advantage to transformers? This is a very difficult topic, do you have a sequence of materials to get deeper in this subject?
That’s a very good question. I think of Non-local Module as a generalization of scaled dot-product attention. The proposed non-local block can be added to existing architectures for capturing long-range dependencies. But Transformers are different. We will cover different vision transformer models in this series . The previous introductory video shows the topics that will be covered
I’d like more explanation about how this self attention mechanism plugs into the large language models.
In the scope of this series, I plan to begin with discussing the evolution of attention mechanisms in the image domain, followed by an exploration into vision transformers. It's important to note that the Non-Local Module (NLM) is distinct from vision transformers. NLMs, particularly when coupled with residual connections, can be integrated into any pre-trained model, such as ResNet, as demonstrated in the original paper. This integration is designed to enhance the model without altering its fundamental behavior.
Stay tuned as we delve deeper into Vision Transformers later on, and will see how self-attention mechanism is utilized in ViTs.