https://padlet.com/bumandu2/1fhm3zv5oqnf2ce1 en-us 2021-09-28 09:31:18 UTC 2021-09-29 13:11:48 UTC hello@padlet.com bumandu2 https://padlet.com/bumandu2/1fhm3zv5oqnf2ce1/wish/1774039443 This paper propose Panoptic Segmentation task and metric for the proposed task. Panoptic segmentation unifies the typically distinct tasks of semantic segmentation(assign a class label to each pixel) and instance segmentation(detect and segment each object instance). They propose panoptic quality metric for task to captures performance for all classes in an interpretable and unified manner. What I like about this paper is requesting new task on computer vision.]]> 2021-09-28 09:34:56 UTC https://padlet.com/bumandu2/1fhm3zv5oqnf2ce1/wish/1774039443 bumandu2 https://padlet.com/bumandu2/1fhm3zv5oqnf2ce1/wish/1774085984 This paper is about the model to perform panoptic segmentation task. They use single deep neural network for which the goal is to provide each individual pixel of an input image with a class label, as in semantic segmentation, as well as a unique identifier for specific objects in an image. They propose two methods to improve model's performance by leveraging information exchange and improving the merging heuristics. What I like about this paper is using single network for panoptic segmentation task. Previous methods train two networks jointly.]]> 2021-09-28 09:57:01 UTC https://padlet.com/bumandu2/1fhm3zv5oqnf2ce1/wish/1774085984 bumandu2 https://padlet.com/bumandu2/1fhm3zv5oqnf2ce1/wish/1774139690 This paper present a new operator for panoptic segmentation, called Instance Mask Projection(IMP), which projects a predicted Instance Segmentation as a new feature for semantic segmentation. Using proposed method show a dramatic improvement of 20.4%. What I like about this paper is novel idea to improve performance.]]> 2021-09-28 10:27:08 UTC https://padlet.com/bumandu2/1fhm3zv5oqnf2ce1/wish/1774139690 bumandu2 https://padlet.com/bumandu2/1fhm3zv5oqnf2ce1/wish/1777934430 This paper propose a novel attention-guided dense-upsampling network(AUNet) for panoptic segmentation. In AU Net, they employ an asymmetrical encoder-decoder structure and propose an effective upsampling block, attention-guided dense-upsampling block(AU Block). Using these methods achieves state-of-the-art performance in panoptic segmentation. What I liked about this paper is that proposing novelty methods applying attention manner to segmentation architecture.]]> 2021-09-29 13:05:08 UTC https://padlet.com/bumandu2/1fhm3zv5oqnf2ce1/wish/1777934430 bumandu2 https://padlet.com/bumandu2/1fhm3zv5oqnf2ce1/wish/1777956454  This paper present a single-shot, bottom-up approach for panoptic segmentation. The proposed model generalizes the tasks of semantic segmentation for 'stuff' classes and instance segmentation for 'thing' classes, assigning both semantic and instance labels to every pixel in an image by using a single-shot manner, resulting in a streamlined system. What I liked about paper is that the model attains a good trade-off between accuracy and speed.]]> 2021-09-29 13:11:48 UTC https://padlet.com/bumandu2/1fhm3zv5oqnf2ce1/wish/1777956454