BAISeg: Boundary Assisted Weakly Supervised Instance Segmentation

• URL: http://arxiv.org/abs/2406.18558v2
• Date: Tue, 19 Nov 2024 15:50:24 GMT
• Title: BAISeg: Boundary Assisted Weakly Supervised Instance Segmentation
• Authors: Tengbo Wang, Yu Bai,
• Abstract summary: How to extract instance-level masks without instance-level supervision is the main challenge of weakly supervised instance segmentation (WSIS) Popular WSIS methods estimate a displacement field (DF) via learning inter-pixel relations and perform clustering to identify instances. We propose Boundary-Assisted Instance (BAISeg), which is a novel paradigm for WSIS that realizes instance segmentation with pixel-level annotations.
• Score: 9.6046915661065
• License:
• Abstract: How to extract instance-level masks without instance-level supervision is the main challenge of weakly supervised instance segmentation (WSIS). Popular WSIS methods estimate a displacement field (DF) via learning inter-pixel relations and perform clustering to identify instances. However, the resulting instance centroids are inherently unstable and vary significantly across different clustering algorithms. In this paper, we propose Boundary-Assisted Instance Segmentation (BAISeg), which is a novel paradigm for WSIS that realizes instance segmentation with pixel-level annotations. BAISeg comprises an instance-aware boundary detection (IABD) branch and a semantic segmentation branch. The IABD branch identifies instances by predicting class-agnostic instance boundaries rather than instance centroids, therefore, it is different from previous DF-based approaches. In particular, we proposed the Cascade Fusion Module (CFM) and the Deep Mutual Attention (DMA) in the IABD branch to obtain rich contextual information and capture instance boundaries with weak responses. During the training phase, we employed Pixel-to-Pixel Contrast to enhance the discriminative capacity of the IABD branch. This further strengthens the continuity and closedness of the instance boundaries. Extensive experiments on PASCAL VOC 2012 and MS COCO demonstrate the effectiveness of our approach, and we achieve considerable performance with only pixel-level annotations. The code will be available at https://github.com/wsis-seg/BAISeg.

Related papers

• PANet: LiDAR Panoptic Segmentation with Sparse Instance Proposal and Aggregation [15.664835767712775]
  This work proposes a new LPS framework named PANet to eliminate the dependency on the offset branch. PaNet achieves state-of-the-art performance among published works on the Semantic KITII validation and nuScenes validation for the panoptic segmentation task.
  arXiv  Detail & Related papers  (2023-06-27T10:02:28Z)
• Collaborative Propagation on Multiple Instance Graphs for 3D Instance Segmentation with Single-point Supervision [63.429704654271475]
  We propose a novel weakly supervised method RWSeg that only requires labeling one object with one point. With these sparse weak labels, we introduce a unified framework with two branches to propagate semantic and instance information. Specifically, we propose a Cross-graph Competing Random Walks (CRW) algorithm that encourages competition among different instance graphs.
  arXiv  Detail & Related papers  (2022-08-10T02:14:39Z)
• Beyond the Prototype: Divide-and-conquer Proxies for Few-shot Segmentation [63.910211095033596]
  Few-shot segmentation aims to segment unseen-class objects given only a handful of densely labeled samples. We propose a simple yet versatile framework in the spirit of divide-and-conquer. Our proposed approach, named divide-and-conquer proxies (DCP), allows for the development of appropriate and reliable information.
  arXiv  Detail & Related papers  (2022-04-21T06:21:14Z)
• 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)
• Learning to Detect Instance-level Salient Objects Using Complementary Image Labels [55.049347205603304]
  We present the first weakly-supervised approach to the salient instance detection problem. We propose a novel weakly-supervised network with three branches: a Saliency Detection Branch leveraging class consistency information to locate candidate objects; a Boundary Detection Branch exploiting class discrepancy information to delineate object boundaries; and a Centroid Detection Branch using subitizing information to detect salient instance centroids.
  arXiv  Detail & Related papers  (2021-11-19T10:15:22Z)
• Semantic Attention and Scale Complementary Network for Instance Segmentation in Remote Sensing Images [54.08240004593062]
  We propose an end-to-end multi-category instance segmentation model, which consists of a Semantic Attention (SEA) module and a Scale Complementary Mask Branch (SCMB) SEA module contains a simple fully convolutional semantic segmentation branch with extra supervision to strengthen the activation of interest instances on the feature map. SCMB extends the original single mask branch to trident mask branches and introduces complementary mask supervision at different scales.
  arXiv  Detail & Related papers  (2021-07-25T08:53:59Z)
• 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)
• Weakly-supervised Salient Instance Detection [65.0408760733005]
  We present the first weakly-supervised approach to the salient instance detection problem. We propose a novel weakly-supervised network with three branches: a Saliency Detection Branch leveraging class consistency information to locate candidate objects; a Boundary Detection Branch exploiting class discrepancy information to delineate object boundaries; and a Centroid Detection Branch using subitizing information to detect salient instance centroids.
  arXiv  Detail & Related papers  (2020-09-29T09:47:23Z)
• Towards Bounding-Box Free Panoptic Segmentation [16.4548904544277]
  We introduce a new Bounding-Box Free Network (BBFNet) for panoptic segmentation. BBFNet predicts coarse watershed levels and uses them to detect large instance candidates where boundaries are well defined. For smaller instances, whose boundaries are less reliable, BBFNet also predicts instance centers by means of Hough voting followed by mean-shift to reliably detect small objects.
  arXiv  Detail & Related papers  (2020-02-18T16:34:01Z)

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.