I'm fascinated by you and your team members' craft, with tweaks on loss, ideas of encodings and sufficient amount of data, applications will be huge. I would love to learn and explore those possibilities, Isn’t there anyway to be a part of your team or contribute to any related projects?
Excellent Explanation. But I want to know the most important thing in this video, How did you create those cool animations like @1:58-@2:20 and @8:00-@8:05
Nice work! A small correction to what you said: "Semantic segmentation labels each pixel in the whole image. It is not restricted to only pixels in the background".
@@nicolascarion3111 Can we then say that: "Panoptic Segmentation= Instance Segmentation+Semantic Segmentation minus annotations of foreground classes" ?
This is an end to end neural network defined by functions which all have derivatives. In the R-CNN family of algorithms you have one procedure that produces a bunch of region proposals, then you crop out these regions and feed them to a classifier, and then you run another algorithm to prune out overlapping and low confidence predictions. Since there are multiple steps that have logical rather than mathematical implementations, you can't take derivatives all the way through to back propagate information through the whole system.
The transformer isn't aware of the 2D structure of the image, because 1) we flatten it and 2) permuting the inputs of a transformer simply permutes its outputs (permutation equivariance). That's why we add 2D positional encodings. This is similar to what is done in NLP, to retain the order of the sentence.
@@nicolascarion3111 Thanks for your explanation. I have another question: Right now DETR because of rectangle bboxes of COCO-dataset produces rectangle-bboxes outputs, if we had polygon bboxes (8 points), which parts of the architecture must be modified to output a polygon shape bboxes?
@@ZobeirRaisi Well you need to modify the regression head as well as the loss and matching function (GiOU may not make sense anymore, so you'll likely have to stick to L1). For this kind of questions, it's best to open an issue on our github. Thanks!
I'm fascinated by you and your team members' craft, with tweaks on loss, ideas of encodings and sufficient amount of data, applications will be huge. I would love to learn and explore those possibilities, Isn’t there anyway to be a part of your team or contribute to any related projects?
A very nice presentation with clear visualizations and easy-to-understand explanations! Great Work!!🌟🌟🌟🌟🌟
Smooth animations 👌
Outstanding work. I’m also very interested in the, arguably more difficult, small object detection problem.
Excellent Explanation.
But I want to know the most important thing in this video,
How did you create those cool animations like @1:58-@2:20 and @8:00-@8:05
I'm expecting this answer too!
@@praveen9083 me 2!
anyone has idea?
Nice work!
A small correction to what you said: "Semantic segmentation labels each pixel in the whole image. It is not restricted to only pixels in the background".
You're right, my statement is imprecise. I meant that semantic annotations of foreground classes are not used in the panoptic task.
@@nicolascarion3111 merci infiniment :)
@@nicolascarion3111 Can we then say that: "Panoptic Segmentation= Instance Segmentation+Semantic Segmentation minus annotations of foreground classes" ?
Thanks for sharing!
Could you please explain what you mean by full differentiable and how other methods might not be fully differentiable?
This is an end to end neural network defined by functions which all have derivatives. In the R-CNN family of algorithms you have one procedure that produces a bunch of region proposals, then you crop out these regions and feed them to a classifier, and then you run another algorithm to prune out overlapping and low confidence predictions. Since there are multiple steps that have logical rather than mathematical implementations, you can't take derivatives all the way through to back propagate information through the whole system.
WoW thankyou for your contribution!
Thank you for the great work and the presentation!
i realize it is quite off topic but do anyone know of a good website to watch new movies online ?
@Kace Angelo try Flixzone. Just google for it =)
Really good work!
Elegant explanation. liked it
Amazing! What was the main motivation behind using a sequence model for an object detection?
It is not a sequence model. It was successfully used for sequences, but it's not a sequence model by definition.
What this mean?: "since the transformer is a permutation
equivalent some extra care is required to retain
the 2d structure of the image."
The transformer isn't aware of the 2D structure of the image, because 1) we flatten it and 2) permuting the inputs of a transformer simply permutes its outputs (permutation equivariance). That's why we add 2D positional encodings. This is similar to what is done in NLP, to retain the order of the sentence.
@@nicolascarion3111 Thanks for your explanation. I have another question: Right now DETR because of rectangle bboxes of COCO-dataset produces rectangle-bboxes outputs, if we had polygon bboxes (8 points), which parts of the architecture must be modified to output a polygon shape bboxes?
@@ZobeirRaisi Well you need to modify the regression head as well as the loss and matching function (GiOU may not make sense anymore, so you'll likely have to stick to L1). For this kind of questions, it's best to open an issue on our github. Thanks!