Learning Panoptic Segmentation from Instance Contours
- URL: http://arxiv.org/abs/2010.11681v2
- Date: Tue, 6 Apr 2021 01:09:26 GMT
- Title: Learning Panoptic Segmentation from Instance Contours
- Authors: Sumanth Chennupati, Venkatraman Narayanan, Ganesh Sistu, Senthil
Yogamani and Samir A Rawashdeh
- Abstract summary: Panopticpixel aims to provide an understanding of background (stuff) and instances of objects (things) at a pixel level.
It combines the separate tasks of semantic segmentation (level classification) and instance segmentation to build a single unified scene understanding task.
We present a fully convolution neural network that learns instance segmentation from semantic segmentation and instance contours.
- Score: 9.347742071428918
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Panoptic Segmentation aims to provide an understanding of background (stuff)
and instances of objects (things) at a pixel level. It combines the separate
tasks of semantic segmentation (pixel level classification) and instance
segmentation to build a single unified scene understanding task. Typically,
panoptic segmentation is derived by combining semantic and instance
segmentation tasks that are learned separately or jointly (multi-task
networks). In general, instance segmentation networks are built by adding a
foreground mask estimation layer on top of object detectors or using instance
clustering methods that assign a pixel to an instance center. In this work, we
present a fully convolution neural network that learns instance segmentation
from semantic segmentation and instance contours (boundaries of things).
Instance contours along with semantic segmentation yield a boundary aware
semantic segmentation of things. Connected component labeling on these results
produces instance segmentation. We merge semantic and instance segmentation
results to output panoptic segmentation. We evaluate our proposed method on the
CityScapes dataset to demonstrate qualitative and quantitative performances
along with several ablation studies. Our overview video can be accessed from
url:https://youtu.be/wBtcxRhG3e0.
Related papers
- Synthetic Instance Segmentation from Semantic Image Segmentation Masks [15.477053085267404]
We propose a novel paradigm called Synthetic Instance (SISeg)
SISeg instance segmentation results by leveraging image masks generated by existing semantic segmentation models.
In other words, the proposed model does not need extra manpower or higher computational expenses.
arXiv Detail & Related papers (2023-08-02T05:13:02Z) - SegGPT: Segmenting Everything In Context [98.98487097934067]
We present SegGPT, a model for segmenting everything in context.
We unify various segmentation tasks into a generalist in-context learning framework.
SegGPT can perform arbitrary segmentation tasks in images or videos via in-context inference.
arXiv Detail & Related papers (2023-04-06T17:59:57Z) - Tag-Based Attention Guided Bottom-Up Approach for Video Instance
Segmentation [83.13610762450703]
Video instance is a fundamental computer vision task that deals with segmenting and tracking object instances across a video sequence.
We introduce a simple end-to-end train bottomable-up approach to achieve instance mask predictions at the pixel-level granularity, instead of the typical region-proposals-based approach.
Our method provides competitive results on YouTube-VIS and DAVIS-19 datasets, and has minimum run-time compared to other contemporary state-of-the-art performance methods.
arXiv Detail & Related papers (2022-04-22T15:32:46Z) - 3D Compositional Zero-shot Learning with DeCompositional Consensus [102.7571947144639]
We argue that part knowledge should be composable beyond the observed object classes.
We present 3D Compositional Zero-shot Learning as a problem of part generalization from seen to unseen object classes.
arXiv Detail & Related papers (2021-11-29T16:34:53Z) - Robust 3D Scene Segmentation through Hierarchical and Learnable
Part-Fusion [9.275156524109438]
3D semantic segmentation is a fundamental building block for several scene understanding applications such as autonomous driving, robotics and AR/VR.
Previous methods have utilized hierarchical, iterative methods to fuse semantic and instance information, but they lack learnability in context fusion.
This paper presents Segment-Fusion, a novel attention-based method for hierarchical fusion of semantic and instance information.
arXiv Detail & Related papers (2021-11-16T13:14:47Z) - SOLO: A Simple Framework for Instance Segmentation [84.00519148562606]
"instance categories" assigns categories to each pixel within an instance according to the instance's location.
"SOLO" is a simple, direct, and fast framework for instance segmentation with strong performance.
Our approach achieves state-of-the-art results for instance segmentation in terms of both speed and accuracy.
arXiv Detail & Related papers (2021-06-30T09:56:54Z) - Object-Guided Instance Segmentation With Auxiliary Feature Refinement
for Biological Images [58.914034295184685]
Instance segmentation is of great importance for many biological applications, such as study of neural cell interactions, plant phenotyping, and quantitatively measuring how cells react to drug treatment.
Box-based instance segmentation methods capture objects via bounding boxes and then perform individual segmentation within each bounding box region.
Our method first detects the center points of the objects, from which the bounding box parameters are then predicted.
The segmentation branch reuses the object features as guidance to separate target object from the neighboring ones within the same bounding box region.
arXiv Detail & Related papers (2021-06-14T04:35:36Z) - Exploring Cross-Image Pixel Contrast for Semantic Segmentation [130.22216825377618]
We propose a pixel-wise contrastive framework for semantic segmentation in the fully supervised setting.
The core idea is to enforce pixel embeddings belonging to a same semantic class to be more similar than embeddings from different classes.
Our method can be effortlessly incorporated into existing segmentation frameworks without extra overhead during testing.
arXiv Detail & Related papers (2021-01-28T11:35:32Z) - Instance segmentation of buildings using keypoints [26.220921532554136]
We propose a novel instance segmentation network for building segmentation in high-resolution remote sensing images.
The detected keypoints are subsequently reformulated as a closed polygon, which is the semantic boundary of the building.
Our network is a bottom-up instance segmentation method that could well preserve geometric details.
arXiv Detail & Related papers (2020-06-06T13:11:37Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.