Proposal-Free Volumetric Instance Segmentation from Latent Single-Instance Masks

• URL: http://arxiv.org/abs/2009.04998v1
• Date: Thu, 10 Sep 2020 17:09:23 GMT
• Title: Proposal-Free Volumetric Instance Segmentation from Latent Single-Instance Masks
• Authors: Alberto Bailoni, Constantin Pape, Steffen Wolf, Anna Kreshuk, Fred A. Hamprecht
• Abstract summary: This work introduces a new proposal-free instance segmentation method. It builds on single-instance segmentation masks predicted across the entire image in a sliding window style. In contrast to related approaches, our method concurrently predicts all masks, one for each pixel, and thus resolves any conflict jointly across the entire image.
• Score: 16.217524435617744
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work introduces a new proposal-free instance segmentation method that builds on single-instance segmentation masks predicted across the entire image in a sliding window style. In contrast to related approaches, our method concurrently predicts all masks, one for each pixel, and thus resolves any conflict jointly across the entire image. Specifically, predictions from overlapping masks are combined into edge weights of a signed graph that is subsequently partitioned to obtain all final instances concurrently. The result is a parameter-free method that is strongly robust to noise and prioritizes predictions with the highest consensus across overlapping masks. All masks are decoded from a low dimensional latent representation, which results in great memory savings strictly required for applications to large volumetric images. We test our method on the challenging CREMI 2016 neuron segmentation benchmark where it achieves competitive scores.

Related papers

• Variance-insensitive and Target-preserving Mask Refinement for Interactive Image Segmentation [68.16510297109872]
  Point-based interactive image segmentation can ease the burden of mask annotation in applications such as semantic segmentation and image editing. We introduce a novel method, Variance-Insensitive and Target-Preserving Mask Refinement to enhance segmentation quality with fewer user inputs. Experiments on GrabCut, Berkeley, SBD, and DAVIS datasets demonstrate our method's state-of-the-art performance in interactive image segmentation.
  arXiv  Detail & Related papers  (2023-12-22T02:31:31Z)
• Segment (Almost) Nothing: Prompt-Agnostic Adversarial Attacks on Segmentation Models [61.46999584579775]
  General purpose segmentation models are able to generate (semantic) segmentation masks from a variety of prompts. In particular, input images are pre-processed by an image encoder to obtain embedding vectors which are later used for mask predictions. We show that even imperceptible perturbations of radius $epsilon=1/255$ are often sufficient to drastically modify the masks predicted with point, box and text prompts.
  arXiv  Detail & Related papers  (2023-11-24T12:57:34Z)
• Mask2Anomaly: Mask Transformer for Universal Open-set Segmentation [29.43462426812185]
  We propose a paradigm change by shifting from a per-pixel classification to a mask classification. Our mask-based method, Mask2Anomaly, demonstrates the feasibility of integrating a mask-classification architecture. By comprehensive qualitative and qualitative evaluation, we show Mask2Anomaly achieves new state-of-the-art results.
  arXiv  Detail & Related papers  (2023-09-08T20:07:18Z)
• Unmasking Anomalies in Road-Scene Segmentation [18.253109627901566]
  Anomaly segmentation is a critical task for driving applications. We propose a paradigm change by shifting from a per-pixel classification to a mask classification. Mask2Anomaly demonstrates the feasibility of integrating an anomaly detection method in a mask-classification architecture.
  arXiv  Detail & Related papers  (2023-07-25T08:23:10Z)
• Discovering Object Masks with Transformers for Unsupervised Semantic Segmentation [75.00151934315967]
  MaskDistill is a novel framework for unsupervised semantic segmentation. Our framework does not latch onto low-level image cues and is not limited to object-centric datasets.
  arXiv  Detail & Related papers  (2022-06-13T17:59:43Z)
• Few-shot semantic segmentation via mask aggregation [5.886986014593717]
  Few-shot semantic segmentation aims to recognize novel classes with only very few labelled data. Previous works have typically regarded it as a pixel-wise classification problem. We introduce a mask-based classification method for addressing this problem.
  arXiv  Detail & Related papers  (2022-02-15T07:13:09Z)
• Per-Pixel Classification is Not All You Need for Semantic Segmentation [184.2905747595058]
  Mask classification is sufficiently general to solve both semantic- and instance-level segmentation tasks. We propose MaskFormer, a simple mask classification model which predicts a set of binary masks. Our method outperforms both current state-of-the-art semantic (55.6 mIoU on ADE20K) and panoptic segmentation (52.7 PQ on COCO) models.
  arXiv  Detail & Related papers  (2021-07-13T17:59:50Z)
• 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)
• Mask Encoding for Single Shot Instance Segmentation [97.99956029224622]
  We propose a simple singleshot instance segmentation framework, termed mask encoding based instance segmentation (MEInst) Instead of predicting the two-dimensional mask directly, MEInst distills it into a compact and fixed-dimensional representation vector. We show that the much simpler and flexible one-stage instance segmentation method, can also achieve competitive performance.
  arXiv  Detail & Related papers  (2020-03-26T02:51:17Z)

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.