PCAMs: Weakly Supervised Semantic Segmentation Using Point Supervision

• URL: http://arxiv.org/abs/2007.05615v1
• Date: Fri, 10 Jul 2020 21:25:27 GMT
• Title: PCAMs: Weakly Supervised Semantic Segmentation Using Point Supervision
• Authors: R. Austin McEver and B.S. Manjunath
• Abstract summary: This paper presents a novel procedure for producing semantic segmentation from images given some point level annotations. We propose training a CNN that is normally fully supervised using our pseudo labels in place of ground truth labels. Our method achieves state of the art results for point supervised semantic segmentation on the PASCAL VOC 2012 dataset citeeveringham2010pascal, even outperforming state of the art methods for stronger bounding box and squiggle supervision.
• Score: 12.284208932393073
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current state of the art methods for generating semantic segmentation rely heavily on a large set of images that have each pixel labeled with a class of interest label or background. Coming up with such labels, especially in domains that require an expert to do annotations, comes at a heavy cost in time and money. Several methods have shown that we can learn semantic segmentation from less expensive image-level labels, but the effectiveness of point level labels, a healthy compromise between all pixels labelled and none, still remains largely unexplored. This paper presents a novel procedure for producing semantic segmentation from images given some point level annotations. This method includes point annotations in the training of a convolutional neural network (CNN) for producing improved localization and class activation maps. Then, we use another CNN for predicting semantic affinities in order to propagate rough class labels and create pseudo semantic segmentation labels. Finally, we propose training a CNN that is normally fully supervised using our pseudo labels in place of ground truth labels, which further improves performance and simplifies the inference process by requiring just one CNN during inference rather than two. Our method achieves state of the art results for point supervised semantic segmentation on the PASCAL VOC 2012 dataset \cite{everingham2010pascal}, even outperforming state of the art methods for stronger bounding box and squiggle supervision.

Related papers

• Scribble Hides Class: Promoting Scribble-Based Weakly-Supervised Semantic Segmentation with Its Class Label [16.745019028033518]
  We propose a class-driven scribble promotion network, which utilizes both scribble annotations and pseudo-labels informed by image-level classes and global semantics for supervision. Experiments on the ScribbleSup dataset with different qualities of scribble annotations outperform all the previous methods, demonstrating the superiority and robustness of our method.
  arXiv  Detail & Related papers  (2024-02-27T14:51:56Z)
• Learning Semantic Segmentation with Query Points Supervision on Aerial Images [57.09251327650334]
  We present a weakly supervised learning algorithm to train semantic segmentation algorithms. Our proposed approach performs accurate semantic segmentation and improves efficiency by significantly reducing the cost and time required for manual annotation.
  arXiv  Detail & Related papers  (2023-09-11T14:32:04Z)
• Distilling Self-Supervised Vision Transformers for Weakly-Supervised Few-Shot Classification & Segmentation [58.03255076119459]
  We address the task of weakly-supervised few-shot image classification and segmentation, by leveraging a Vision Transformer (ViT) Our proposed method takes token representations from the self-supervised ViT and leverages their correlations, via self-attention, to produce classification and segmentation predictions. Experiments on Pascal-5i and COCO-20i demonstrate significant performance gains in a variety of supervision settings.
  arXiv  Detail & Related papers  (2023-07-07T06:16:43Z)
• Exploring Structured Semantic Prior for Multi Label Recognition with Incomplete Labels [60.675714333081466]
  Multi-label recognition (MLR) with incomplete labels is very challenging. Recent works strive to explore the image-to-label correspondence in the vision-language model, ie, CLIP, to compensate for insufficient annotations. We advocate remedying the deficiency of label supervision for the MLR with incomplete labels by deriving a structured semantic prior.
  arXiv  Detail & Related papers  (2023-03-23T12:39:20Z)
• ISLE: A Framework for Image Level Semantic Segmentation Ensemble [5.137284292672375]
  Conventional semantic segmentation networks require massive pixel-wise annotated labels to reach state-of-the-art prediction quality. We propose ISLE, which employs an ensemble of the "pseudo-labels" for a given set of different semantic segmentation techniques on a class-wise level. We reach up to 2.4% improvement over ISLE's individual components.
  arXiv  Detail & Related papers  (2023-03-14T13:36:36Z)
• LESS: Label-Efficient Semantic Segmentation for LiDAR Point Clouds [62.49198183539889]
  We propose a label-efficient semantic segmentation pipeline for outdoor scenes with LiDAR point clouds. Our method co-designs an efficient labeling process with semi/weakly supervised learning. Our proposed method is even highly competitive compared to the fully supervised counterpart with 100% labels.
  arXiv  Detail & Related papers  (2022-10-14T19:13:36Z)
• Leveraging Auxiliary Tasks with Affinity Learning for Weakly Supervised Semantic Segmentation [88.49669148290306]
  We propose a novel weakly supervised multi-task framework called AuxSegNet to leverage saliency detection and multi-label image classification as auxiliary tasks. Inspired by their similar structured semantics, we also propose to learn a cross-task global pixel-level affinity map from the saliency and segmentation representations. The learned cross-task affinity can be used to refine saliency predictions and propagate CAM maps to provide improved pseudo labels for both tasks.
  arXiv  Detail & Related papers  (2021-07-25T11:39:58Z)
• Universal Weakly Supervised Segmentation by Pixel-to-Segment Contrastive Learning [28.498782661888775]
  We formulate weakly supervised segmentation as a semi-supervised metric learning problem. We propose 4 types of contrastive relationships between pixels and segments in the feature space. We deliver a universal weakly supervised segmenter with significant gains on Pascal VOC and DensePose.
  arXiv  Detail & Related papers  (2021-05-03T15:49:01Z)
• A Closer Look at Self-training for Zero-Label Semantic Segmentation [53.4488444382874]
  Being able to segment unseen classes not observed during training is an important technical challenge in deep learning. Prior zero-label semantic segmentation works approach this task by learning visual-semantic embeddings or generative models. We propose a consistency regularizer to filter out noisy pseudo-labels by taking the intersections of the pseudo-labels generated from different augmentations of the same image.
  arXiv  Detail & Related papers  (2021-04-21T14:34:33Z)
• Discovering Latent Classes for Semi-Supervised Semantic Segmentation [18.5909667833129]
  This paper studies the problem of semi-supervised semantic segmentation. We learn latent classes consistent with semantic classes on labeled images. We show that the proposed method achieves state of the art results for semi-supervised semantic segmentation.
  arXiv  Detail & Related papers  (2019-12-30T14:16:24Z)

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.