Vision Transformer and its Applications

Vision transformer is a recent breakthrough in the area of computer vision. While transformer-based models have dominated the field of natural language processing since 2017, CNN-based models are still demonstrating state-of-the-art performances in vision problems. Last year, a group of researchers from Google figured out how to make a transformer work on recognition. They called it "vision transformer". The follow-up works by the community demonstrated superior performance of vision transformers not only in recognition but also in other downstream tasks such as detection, segmentation, multi-modal learning and scene text recognition to mention a few.
In this talk, Rowel Atienza will go into a deeper understanding of the model architecture of vision transformers. Most importantly, Rowel will focus on the concept of self-attention and its role in vision. Then, he will present different model implementations utilizing the vision transformer as the main backbone.
Since self-attention can be applied beyond transformers, Rowel Atienza will also discuss a promising direction in building general-purpose model architectures. In particular, networks that can process a variety of data formats such as text, audio, image and video.
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[@DrAIScience]

Very very very nice explanation!!! I like learning the foundation/origin of the concepts where models are derived..

[@jhjbm1959]

This video provides a clear step by step explanation how to get from images to input features for Transformer encoders, which has proven hard to find anywhere else.
Thank you.

[@crapadopalese]

10:46 - this is a mistake; the convolution is not equivariant to scaling - if the bird is scaled, the output of the convolution will not be simply a scaling of the original output. That would only be true if you also rescale the filters.

[@PrestonRahim]

Super helpful. Was very lost on the process from image patch to embedded vector until I watched this.

[@xXMaDGaMeR]

amazing lecture, thank you sir!

[@rikki146]

20:17 I think the encoder blocks are stacked in parallel fashion rather than sequential?

[@DrAIScience]

Do you have a video about beit or dino?

[@sahil-vz8or]

you said 196 patches in imagenet data. No of matches will depend on the input image size and the patch size. For eg: if the input image is of 400X400 and patch size of 8X8, then no of patches will be (400X400/8X8) = 50X50 =2500.

[@ailinhasanpour]

thanks for sharing , it was extremely helpful 💯

[@OpenDataScienceCon]

Thank you!

[@scottkorman4953]

What exactly is happening in the self-attention and MLP blocks of the encoder module? Could you describe it in a simplistic way?

[@muhammadshahzaibiqbal7658]

Thanks for sharing.

[@PRASHANTKUMAR-ze6mj]

thanks for sharing

[@anirudhgangadhar6158]

Great resource!

[@user-co6pu8zv3v]

Thank you, sir

[@DrAIScience]

Are you the channel owner??

[@hoangtrung.aiengineer]

Thank you for making such a great video

[@capocianni1043]

Thank you for this genuine knowledge.

[@mohammedrakib3736]

Fantastic Video! Really loved the detailed explanation step-by-step.

[@liangcheng9856]

awesome

[@saimasideeq7254]

thankyou much clearer

[@improvement_developer8995]

Tax evader 🤮

[@improvement_developer8995]

🤮