Commonality-Parsing Network across Shape and Appearance for Partially Supervised Instance Segmentation

• URL: http://arxiv.org/abs/2007.12387v1
• Date: Fri, 24 Jul 2020 07:23:44 GMT
• Title: Commonality-Parsing Network across Shape and Appearance for Partially Supervised Instance Segmentation
• Authors: Qi Fan, Lei Ke, Wenjie Pei, Chi-Keung Tang, Yu-Wing Tai
• Abstract summary: We propose to learn the underlying class-agnostic commonalities that can be generalized from mask-annotated categories to novel categories. Our model significantly outperforms the state-of-the-art methods on both partially supervised setting and few-shot setting for instance segmentation on COCO dataset.
• Score: 71.59275788106622
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Partially supervised instance segmentation aims to perform learning on limited mask-annotated categories of data thus eliminating expensive and exhaustive mask annotation. The learned models are expected to be generalizable to novel categories. Existing methods either learn a transfer function from detection to segmentation, or cluster shape priors for segmenting novel categories. We propose to learn the underlying class-agnostic commonalities that can be generalized from mask-annotated categories to novel categories. Specifically, we parse two types of commonalities: 1) shape commonalities which are learned by performing supervised learning on instance boundary prediction; and 2) appearance commonalities which are captured by modeling pairwise affinities among pixels of feature maps to optimize the separability between instance and the background. Incorporating both the shape and appearance commonalities, our model significantly outperforms the state-of-the-art methods on both partially supervised setting and few-shot setting for instance segmentation on COCO dataset.

Related papers

• Self Supervised Correlation-based Permutations for Multi-View Clustering [7.972599673048582]
  We propose an end-to-end deep learning-based MVC framework for general data. Our approach involves learning meaningful fused data representations with a novel permutation-based canonical correlation objective. We demonstrate the effectiveness of our model using ten MVC benchmark datasets.
  arXiv  Detail & Related papers  (2024-02-26T08:08:30Z)
• Synthetic Instance Segmentation from Semantic Image Segmentation Masks [15.477053085267404]
  We propose a novel paradigm called Synthetic Instance (SISeg) SISeg instance segmentation results by leveraging image masks generated by existing semantic segmentation models. In other words, the proposed model does not need extra manpower or higher computational expenses.
  arXiv  Detail & Related papers  (2023-08-02T05:13:02Z)
• Harmonizing Base and Novel Classes: A Class-Contrastive Approach for Generalized Few-Shot Segmentation [78.74340676536441]
  We propose a class contrastive loss and a class relationship loss to regulate prototype updates and encourage a large distance between prototypes. Our proposed approach achieves new state-of-the-art performance for the generalized few-shot segmentation task on PASCAL VOC and MS COCO datasets.
  arXiv  Detail & Related papers  (2023-03-24T00:30:25Z)
• ContrastMask: Contrastive Learning to Segment Every Thing [18.265503138997794]
  We propose ContrastMask, which learns a mask segmentation model on both seen and unseen categories. Features from the mask regions (foreground) are pulled together, and are contrasted against those from the background, and vice versa. Exhaustive experiments on the COCO dataset demonstrate the superiority of our method.
  arXiv  Detail & Related papers  (2022-03-18T07:41:48Z)
• Learning Debiased and Disentangled Representations for Semantic Segmentation [52.35766945827972]
  We propose a model-agnostic and training scheme for semantic segmentation. By randomly eliminating certain class information in each training iteration, we effectively reduce feature dependencies among classes. Models trained with our approach demonstrate strong results on multiple semantic segmentation benchmarks.
  arXiv  Detail & Related papers  (2021-10-31T16:15:09Z)
• Discovering Relationships between Object Categories via Universal Canonical Maps [80.07703460198198]
  We tackle the problem of learning the geometry of multiple categories of deformable objects jointly. Recent work has shown that it is possible to learn a unified dense pose predictor for several categories of related objects. We show that improved correspondences can be learned automatically as a natural byproduct of learning category-specific dense pose predictors.
  arXiv  Detail & Related papers  (2021-06-17T18:38:18Z)
• Hierarchical Lov\'asz Embeddings for Proposal-free Panoptic Segmentation [25.065380488503262]
  State-of-the-art panoptic segmentation methods use complex models with a distinct stream for each task. We propose Hierarchical Lov'asz Embeddings, per pixel feature vectors that simultaneously encode instance- and category-level discriminative information. Our model achieves state-of-the-art results compared to existing proposal-free panoptic segmentation methods on Cityscapes, COCO, and Mapillary Vistas.
  arXiv  Detail & Related papers  (2021-06-08T17:43:54Z)
• Unsupervised Discovery of the Long-Tail in Instance Segmentation Using Hierarchical Self-Supervision [3.841232411073827]
  We propose a method that can perform unsupervised discovery of long-tail categories in instance segmentation. Our model is able to discover novel and more fine-grained objects than the common categories. We show that the model achieves competitive quantitative results on LVIS as compared to the supervised and partially supervised methods.
  arXiv  Detail & Related papers  (2021-04-02T22:05:03Z)
• Part-aware Prototype Network for Few-shot Semantic Segmentation [50.581647306020095]
  We propose a novel few-shot semantic segmentation framework based on the prototype representation. Our key idea is to decompose the holistic class representation into a set of part-aware prototypes. We develop a novel graph neural network model to generate and enhance the proposed part-aware prototypes.
  arXiv  Detail & Related papers  (2020-07-13T11:03:09Z)
• 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)

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.