Box-supervised Instance Segmentation with Level Set Evolution
- URL: http://arxiv.org/abs/2207.09055v1
- Date: Tue, 19 Jul 2022 03:59:44 GMT
- Title: Box-supervised Instance Segmentation with Level Set Evolution
- Authors: Wentong Li, Wenyu Liu, Jianke Zhu, Miaomiao Cui, Xiansheng Hua, Lei
Zhang
- Abstract summary: We propose a box-supervised instance segmentation approach, which integrates the classical level set model with deep neural network delicately.
A simple mask supervised SOLOv2 model is adapted to predict the instance-aware mask map as the level set for each instance.
The experimental results on four challenging benchmarks demonstrate the leading performance of our proposed approach.
- Score: 41.19797478617953
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In contrast to the fully supervised methods using pixel-wise mask labels,
box-supervised instance segmentation takes advantage of the simple box
annotations, which has recently attracted a lot of research attentions. In this
paper, we propose a novel single-shot box-supervised instance segmentation
approach, which integrates the classical level set model with deep neural
network delicately. Specifically, our proposed method iteratively learns a
series of level sets through a continuous Chan-Vese energy-based function in an
end-to-end fashion. A simple mask supervised SOLOv2 model is adapted to predict
the instance-aware mask map as the level set for each instance. Both the input
image and its deep features are employed as the input data to evolve the level
set curves, where a box projection function is employed to obtain the initial
boundary. By minimizing the fully differentiable energy function, the level set
for each instance is iteratively optimized within its corresponding bounding
box annotation. The experimental results on four challenging benchmarks
demonstrate the leading performance of our proposed approach to robust instance
segmentation in various scenarios. The code is available at:
https://github.com/LiWentomng/boxlevelset.
Related papers
- Extreme Point Supervised Instance Segmentation [28.191795758445352]
This paper introduces a novel approach to learning instance segmentation using extreme points, i.e., the topmost, leftmost, bottommost, and rightmost points, of each object.
These points are readily available in the modern bounding box annotation process while offering strong clues for precise segmentation.
Our model generates high-quality masks when a target object is separated into multiple parts, where previous box-supervised methods often fail.
arXiv Detail & Related papers (2024-05-31T09:37:39Z) - Box2Mask: Box-supervised Instance Segmentation via Level-set Evolution [38.88010537144528]
This paper presents a novel single-shot instance segmentation approach, namely Box2Mask.
Box2Mask integrates the classical level-set evolution model into deep neural network learning to achieve accurate mask prediction with only bounding box supervision.
arXiv Detail & Related papers (2022-12-03T09:32:14Z) - Sparse Instance Activation for Real-Time Instance Segmentation [72.23597664935684]
We propose a conceptually novel, efficient, and fully convolutional framework for real-time instance segmentation.
SparseInst has extremely fast inference speed and achieves 40 FPS and 37.9 AP on the COCO benchmark.
arXiv Detail & Related papers (2022-03-24T03:15:39Z) - Deep Level Set for Box-supervised Instance Segmentation in Aerial Images [27.659592291045414]
We propose a novel aerial instance segmentation approach, which drives the network to learn a series of level set functions for the aerial objects.
The experimental results demonstrate that the proposed approach outperforms the state-of-the-art box-supervised instance segmentation methods.
arXiv Detail & Related papers (2021-12-07T02:27:58Z) - Point Cloud Instance Segmentation with Semi-supervised Bounding-Box
Mining [17.69745159912481]
We introduce the first semi-supervised point cloud instance segmentation framework (SPIB) using both labeled and unlabelled bounding boxes as supervision.
Our method can achieve competitive performance compared with the recent fully-supervised methods.
arXiv Detail & Related papers (2021-11-30T08:40:40Z) - 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) - Pointly-Supervised Instance Segmentation [81.34136519194602]
We propose point-based instance-level annotation, a new form of weak supervision for instance segmentation.
It combines the standard bounding box annotation with labeled points that are uniformly sampled inside each bounding box.
In our experiments, Mask R-CNN models trained on COCO, PASCAL VOC, Cityscapes, and LVIS with only 10 annotated points per object achieve 94%--98% of their fully-supervised performance.
arXiv Detail & Related papers (2021-04-13T17:59:40Z) - Mask-guided sample selection for Semi-Supervised Instance Segmentation [13.091166009687058]
We propose a sample selection approach to decide which samples to annotate for semi-supervised instance segmentation.
Our method consists in first predicting pseudo-masks for the unlabeled pool of samples, together with a score predicting the quality of the mask.
We study which samples are better to annotate given the quality score, and show how our approach outperforms a random selection.
arXiv Detail & Related papers (2020-08-25T14:44:58Z) - UniT: Unified Knowledge Transfer for Any-shot Object Detection and
Segmentation [52.487469544343305]
Methods for object detection and segmentation rely on large scale instance-level annotations for training.
We propose an intuitive and unified semi-supervised model that is applicable to a range of supervision.
arXiv Detail & Related papers (2020-06-12T22:45:47Z) - 1st Place Solutions for OpenImage2019 -- Object Detection and Instance
Segmentation [116.25081559037872]
This article introduces the solutions of the two champion teams, MMfruit' for the detection track and MMfruitSeg' for the segmentation track, in OpenImage Challenge 2019.
It is commonly known that for an object detector, the shared feature at the end of the backbone is not appropriate for both classification and regression.
We propose the Decoupling Head (DH) to disentangle the object classification and regression via the self-learned optimal feature extraction.
arXiv Detail & Related papers (2020-03-17T06:45:07Z)
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.