BoundarySqueeze: Image Segmentation as Boundary Squeezing
- URL: http://arxiv.org/abs/2105.11668v1
- Date: Tue, 25 May 2021 04:58:51 GMT
- Title: BoundarySqueeze: Image Segmentation as Boundary Squeezing
- Authors: Hao He, Xiangtai Li, Kuiyuan Yang, Guangliang Cheng, Jianping Shi,
Yunhai Tong, Zhengjun Zha, Lubin Weng
- Abstract summary: We propose a novel method for fine-grained high-quality image segmentation of both objects and scenes.
Inspired by dilation and erosion from morphological image processing techniques, we treat the pixel level segmentation problems as squeezing object boundary.
Our method yields large gains on COCO, Cityscapes, for both instance and semantic segmentation and outperforms previous state-of-the-art PointRend in both accuracy and speed under the same setting.
- Score: 104.43159799559464
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We propose a novel method for fine-grained high-quality image segmentation of
both objects and scenes. Inspired by dilation and erosion from morphological
image processing techniques, we treat the pixel level segmentation problems as
squeezing object boundary. From this perspective, we propose \textbf{Boundary
Squeeze} module: a novel and efficient module that squeezes the object boundary
from both inner and outer directions which leads to precise mask
representation. To generate such squeezed representation, we propose a new
bidirectionally flow-based warping process and design specific loss signals to
supervise the learning process. Boundary Squeeze Module can be easily applied
to both instance and semantic segmentation tasks as a plug-and-play module by
building on top of existing models. We show that our simple yet effective
design can lead to high qualitative results on several different datasets and
we also provide several different metrics on boundary to prove the
effectiveness over previous work. Moreover, the proposed module is
light-weighted and thus has potential for practical usage. Our method yields
large gains on COCO, Cityscapes, for both instance and semantic segmentation
and outperforms previous state-of-the-art PointRend in both accuracy and speed
under the same setting. Code and model will be available.
Related papers
- Generalizable Entity Grounding via Assistance of Large Language Model [77.07759442298666]
We propose a novel approach to densely ground visual entities from a long caption.
We leverage a large multimodal model to extract semantic nouns, a class-a segmentation model to generate entity-level segmentation, and a multi-modal feature fusion module to associate each semantic noun with its corresponding segmentation mask.
arXiv Detail & Related papers (2024-02-04T16:06:05Z) - Skeleton-Guided Instance Separation for Fine-Grained Segmentation in
Microscopy [23.848474219551818]
One of the fundamental challenges in microscopy (MS) image analysis is instance segmentation (IS)
We propose a novel one-stage framework named A2B-IS to address this challenge and enhance the accuracy of IS in MS images.
Our method has been thoroughly validated on two large-scale MS datasets.
arXiv Detail & Related papers (2024-01-18T11:14:32Z) - SegRefiner: Towards Model-Agnostic Segmentation Refinement with Discrete
Diffusion Process [102.18226145874007]
We propose a model-agnostic solution called SegRefiner to enhance the quality of object masks produced by different segmentation models.
SegRefiner takes coarse masks as inputs and refines them using a discrete diffusion process.
It consistently improves both the segmentation metrics and boundary metrics across different types of coarse masks.
arXiv Detail & Related papers (2023-12-19T18:53:47Z) - ReFit: A Framework for Refinement of Weakly Supervised Semantic
Segmentation using Object Border Fitting for Medical Images [4.945138408504987]
Weakly Supervised Semantic (WSSS) relying only on image-level supervision is a promising approach to deal with the need for networks.
We propose our novel ReFit framework, which deploys state-of-the-art class activation maps combined with various post-processing techniques.
By applying our method to WSSS predictions, we achieved up to 10% improvement over the current state-of-the-art WSSS methods for medical imaging.
arXiv Detail & Related papers (2023-03-14T12:46:52Z) - PNM: Pixel Null Model for General Image Segmentation [17.971090313814447]
We present a prior model that weights each pixel according to its probability of being correctly classified by a random segmenter.
Experiments on semantic, instance, and panoptic segmentation tasks over three datasets confirm that PNM consistently improves the segmentation quality.
We propose a new metric, textitPNM IoU, which perceives the boundary sharpness and better reflects the model segmentation performance in error-prone regions.
arXiv Detail & Related papers (2022-03-13T15:17:41Z) - A Unified Efficient Pyramid Transformer for Semantic Segmentation [40.20512714144266]
We advocate a unified framework(UN-EPT) to segment objects by considering both context information and boundary artifacts.
We first adapt a sparse sampling strategy to incorporate the transformer-based attention mechanism for efficient context modeling.
We demonstrate promising performance on three popular benchmarks for semantic segmentation with low memory footprint.
arXiv Detail & Related papers (2021-07-29T17:47:32Z) - Enhanced Boundary Learning for Glass-like Object Segmentation [55.45473926510806]
This paper aims to solve the glass-like object segmentation problem via enhanced boundary learning.
In particular, we first propose a novel refined differential module for generating finer boundary cues.
An edge-aware point-based graph convolution network module is proposed to model the global shape representation along the boundary.
arXiv Detail & Related papers (2021-03-29T16:18:57Z) - The Devil is in the Boundary: Exploiting Boundary Representation for
Basis-based Instance Segmentation [85.153426159438]
We propose Basis based Instance(B2Inst) to learn a global boundary representation that can complement existing global-mask-based methods.
Our B2Inst leads to consistent improvements and accurately parses out the instance boundaries in a scene.
arXiv Detail & Related papers (2020-11-26T11:26:06Z) - Improving Semantic Segmentation via Decoupled Body and Edge Supervision [89.57847958016981]
Existing semantic segmentation approaches either aim to improve the object's inner consistency by modeling the global context, or refine objects detail along their boundaries by multi-scale feature fusion.
In this paper, a new paradigm for semantic segmentation is proposed.
Our insight is that appealing performance of semantic segmentation requires textitexplicitly modeling the object textitbody and textitedge, which correspond to the high and low frequency of the image.
We show that the proposed framework with various baselines or backbone networks leads to better object inner consistency and object boundaries.
arXiv Detail & Related papers (2020-07-20T12:11:22Z)
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.