What architecture would best perform image material segmentation?
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I want to perform semantic/stuff segmentation, but then classify and segment with respect to the material properties of objects in an image, rather than the objects themselves. This means that, instead of the network classifying a tree trunk as "tree", I would like to have it classified as "wood", and I want to classify "bricks" instead of "wall". What would currently be the best way to tackle this? Is there a state-of-the-art for this? COCO has the Stuff Segmentation task, but it seems severely underpopulated (why is this?).
What do you think is currently the best way to material segment? What direction should I take on this? Does the architecture actually matter, or can I just take the COCO frontrunner for panoptic segmentation and need I only concern myself with finding the right dataset (containing material segmentations)? I.e. the network will segment based on color/texture rather than shape/size. What would you do? Any advice and comments are greatly appreciated!
dataset cnn computer-vision
asked 11 mins ago
EmielBossEmielBoss
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$begingroup$
I want to perform semantic/stuff segmentation, but then classify and segment with respect to the material properties of objects in an image, rather than the objects themselves. This means that, instead of the network classifying a tree trunk as "tree", I would like to have it classified as "wood", and I want to classify "bricks" instead of "wall". What would currently be the best way to tackle this? Is there a state-of-the-art for this? COCO has the Stuff Segmentation task, but it seems severely underpopulated (why is this?).
What do you think is currently the best way to material segment? What direction should I take on this? Does the architecture actually matter, or can I just take the COCO frontrunner for panoptic segmentation and need I only concern myself with finding the right dataset (containing material segmentations)? I.e. the network will segment based on color/texture rather than shape/size. What would you do? Any advice and comments are greatly appreciated!
dataset cnn computer-vision
asked 11 mins ago
EmielBossEmielBoss
1
New contributor
EmielBoss is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I want to perform semantic/stuff segmentation, but then classify and segment with respect to the material properties of objects in an image, rather than the objects themselves. This means that, instead of the network classifying a tree trunk as "tree", I would like to have it classified as "wood", and I want to classify "bricks" instead of "wall". What would currently be the best way to tackle this? Is there a state-of-the-art for this? COCO has the Stuff Segmentation task, but it seems severely underpopulated (why is this?).
What do you think is currently the best way to material segment? What direction should I take on this? Does the architecture actually matter, or can I just take the COCO frontrunner for panoptic segmentation and need I only concern myself with finding the right dataset (containing material segmentations)? I.e. the network will segment based on color/texture rather than shape/size. What would you do? Any advice and comments are greatly appreciated!
dataset cnn computer-vision
asked 11 mins ago
EmielBossEmielBoss
1
New contributor
EmielBoss is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I want to perform semantic/stuff segmentation, but then classify and segment with respect to the material properties of objects in an image, rather than the objects themselves. This means that, instead of the network classifying a tree trunk as "tree", I would like to have it classified as "wood", and I want to classify "bricks" instead of "wall". What would currently be the best way to tackle this? Is there a state-of-the-art for this? COCO has the Stuff Segmentation task, but it seems severely underpopulated (why is this?).
What do you think is currently the best way to material segment? What direction should I take on this? Does the architecture actually matter, or can I just take the COCO frontrunner for panoptic segmentation and need I only concern myself with finding the right dataset (containing material segmentations)? I.e. the network will segment based on color/texture rather than shape/size. What would you do? Any advice and comments are greatly appreciated!
dataset cnn computer-vision
dataset cnn computer-vision
asked 11 mins ago
EmielBossEmielBoss
1
New contributor
EmielBoss is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 11 mins ago
EmielBossEmielBoss
1
New contributor
EmielBoss is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 11 mins ago
EmielBossEmielBoss
1
New contributor
EmielBoss is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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asked 11 mins ago
EmielBossEmielBoss
1
asked 11 mins ago
EmielBossEmielBoss
1
1
New contributor
EmielBoss is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
EmielBoss is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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