Contour-based Interactive Segmentation
- URL: http://arxiv.org/abs/2302.06353v2
- Date: Tue, 5 Dec 2023 11:32:00 GMT
- Title: Contour-based Interactive Segmentation
- Authors: Danil Galeev, Polina Popenova, Anna Vorontsova and Anton Konushin
- Abstract summary: We consider a natural form of user interaction as a loose contour, and introduce a contour-based interactive segmentation method.
We demonstrate that a single contour provides the same accuracy as multiple clicks, thus reducing the required amount of user interactions.
- Score: 4.164728134421114
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Recent advances in interactive segmentation (IS) allow speeding up and
simplifying image editing and labeling greatly. The majority of modern IS
approaches accept user input in the form of clicks. However, using clicks may
require too many user interactions, especially when selecting small objects,
minor parts of an object, or a group of objects of the same type. In this
paper, we consider such a natural form of user interaction as a loose contour,
and introduce a contour-based IS method. We evaluate the proposed method on the
standard segmentation benchmarks, our novel UserContours dataset, and its
subset UserContours-G containing difficult segmentation cases. Through
experiments, we demonstrate that a single contour provides the same accuracy as
multiple clicks, thus reducing the required amount of user interactions.
Related papers
- RClicks: Realistic Click Simulation for Benchmarking Interactive Segmentation [37.44155289954746]
We conduct a large crowdsourcing study of click patterns in an interactive segmentation scenario and collect 475K real-user clicks.
Using our model and dataset, we propose RClicks benchmark for a comprehensive comparison of existing interactive segmentation methods on realistic clicks.
According to our benchmark, in real-world usage interactive segmentation models may perform worse than it has been reported in the baseline benchmark, and most of the methods are not robust.
arXiv Detail & Related papers (2024-10-15T15:55:00Z) - TETRIS: Towards Exploring the Robustness of Interactive Segmentation [39.1981941213761]
We propose a methodology for finding extreme user inputs by a direct optimization in a white-box adversarial attack on the interactive segmentation model.
We report the results of an extensive evaluation of dozens of models.
arXiv Detail & Related papers (2024-02-09T01:36:21Z) - Explore Synergistic Interaction Across Frames for Interactive Video
Object Segmentation [70.93295323156876]
We propose a framework that can accept multiple frames simultaneously and explore synergistic interaction across frames (SIAF)
Our SwinB-SIAF achieves new state-of-the-art performance on DAVIS 2017 (89.6%, J&F@60)
Our R50-SIAF is more than 3 faster than the state-of-the-art competitor under challenging multi-object scenarios.
arXiv Detail & Related papers (2024-01-23T04:19:15Z) - SimpSON: Simplifying Photo Cleanup with Single-Click Distracting Object
Segmentation Network [70.89436857471887]
We propose an interactive distractor selection method that is optimized to achieve the task with just a single click.
Our method surpasses the precision and recall achieved by the traditional method of running panoptic segmentation.
Our experiments demonstrate that the model can effectively and accurately segment unknown distracting objects interactively and in groups.
arXiv Detail & Related papers (2023-05-28T04:05:24Z) - DynaMITe: Dynamic Query Bootstrapping for Multi-object Interactive
Segmentation Transformer [58.95404214273222]
Most state-of-the-art instance segmentation methods rely on large amounts of pixel-precise ground-truth for training.
We introduce a more efficient approach, called DynaMITe, in which we represent user interactions as-temporal queries.
Our architecture also alleviates any need to re-compute image features during refinement, and requires fewer interactions for segmenting multiple instances in a single image.
arXiv Detail & Related papers (2023-04-13T16:57:02Z) - The Minority Matters: A Diversity-Promoting Collaborative Metric
Learning Algorithm [154.47590401735323]
Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems.
This paper focuses on a challenging scenario where a user has multiple categories of interests.
We propose a novel method called textitDiversity-Promoting Collaborative Metric Learning (DPCML)
arXiv Detail & Related papers (2022-09-30T08:02:18Z) - UCP-Net: Unstructured Contour Points for Instance Segmentation [2.105564340986074]
We propose a novel approach to interactive segmentation based on unconstrained contour clicks for initial segmentation and segmentation refinement.
Our method is class-agnostic and produces accurate segmentation masks (IoU > 85%) for a lower number of user interactions than state-of-the-art methods on popular segmentation datasets.
arXiv Detail & Related papers (2021-09-15T22:03:37Z) - Modular Interactive Video Object Segmentation: Interaction-to-Mask,
Propagation and Difference-Aware Fusion [68.45737688496654]
We present a modular interactive VOS framework which decouples interaction-to-mask and mask propagation.
We show that our method outperforms current state-of-the-art algorithms while requiring fewer frame interactions.
arXiv Detail & Related papers (2021-03-14T14:39:08Z) - Localized Interactive Instance Segmentation [24.55415554455844]
We propose a clicking scheme wherein user interactions are restricted to the proximity of the object.
We demonstrate the effectiveness of our proposed clicking scheme and localization strategy through detailed experimentation.
arXiv Detail & Related papers (2020-10-18T23:24:09Z) - FAIRS -- Soft Focus Generator and Attention for Robust Object
Segmentation from Extreme Points [70.65563691392987]
We present a new approach to generate object segmentation from user inputs in the form of extreme points and corrective clicks.
We demonstrate our method's ability to generate high-quality training data as well as its scalability in incorporating extreme points, guiding clicks, and corrective clicks in a principled manner.
arXiv Detail & Related papers (2020-04-04T22:25:47Z)
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.