Few-shot Class-Incremental Semantic Segmentation via Pseudo-Labeling and Knowledge Distillation

• URL: http://arxiv.org/abs/2308.02790v1
• Date: Sat, 5 Aug 2023 05:05:37 GMT
• Title: Few-shot Class-Incremental Semantic Segmentation via Pseudo-Labeling and Knowledge Distillation
• Authors: Chengjia Jiang, Tao Wang, Sien Li, Jinyang Wang, Shirui Wang, Antonios Antoniou
• Abstract summary: We address the problem of learning new classes for semantic segmentation models from few examples. For learning from limited data, we propose a pseudo-labeling strategy to augment the few-shot training annotations. We integrate the above steps into a single convolutional neural network with a unified learning objective.
• Score: 3.4436201325139737
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We address the problem of learning new classes for semantic segmentation models from few examples, which is challenging because of the following two reasons. Firstly, it is difficult to learn from limited novel data to capture the underlying class distribution. Secondly, it is challenging to retain knowledge for existing classes and to avoid catastrophic forgetting. For learning from limited data, we propose a pseudo-labeling strategy to augment the few-shot training annotations in order to learn novel classes more effectively. Given only one or a few images labeled with the novel classes and a much larger set of unlabeled images, we transfer the knowledge from labeled images to unlabeled images with a coarse-to-fine pseudo-labeling approach in two steps. Specifically, we first match each labeled image to its nearest neighbors in the unlabeled image set at the scene level, in order to obtain images with a similar scene layout. This is followed by obtaining pseudo-labels within this neighborhood by applying classifiers learned on the few-shot annotations. In addition, we use knowledge distillation on both labeled and unlabeled data to retain knowledge on existing classes. We integrate the above steps into a single convolutional neural network with a unified learning objective. Extensive experiments on the Cityscapes and KITTI datasets validate the efficacy of the proposed approach in the self-driving domain. Code is available from https://github.com/ChasonJiang/FSCILSS.

Related papers

• Mixed Supervision Learning for Whole Slide Image Classification [88.31842052998319]
  We propose a mixed supervision learning framework for super high-resolution images. During the patch training stage, this framework can make use of coarse image-level labels to refine self-supervised learning. A comprehensive strategy is proposed to suppress pixel-level false positives and false negatives.
  arXiv  Detail & Related papers  (2021-07-02T09:46:06Z)
• 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)
• Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization [112.68171734288237]
  We propose a novel framework for discriminative pixel-level tasks using a generative model of both images and labels. We learn a generative adversarial network that captures the joint image-label distribution and is trained efficiently using a large set of unlabeled images. We demonstrate strong in-domain performance compared to several baselines, and are the first to showcase extreme out-of-domain generalization.
  arXiv  Detail & Related papers  (2021-04-12T21:41:25Z)
• Few Shot Learning With No Labels [28.91314299138311]
  Few-shot learners aim to recognize new categories given only a small number of training samples. The core challenge is to avoid overfitting to the limited data while ensuring good generalization to novel classes. Existing literature makes use of vast amounts of annotated data by simply shifting the label requirement from novel classes to base classes.
  arXiv  Detail & Related papers  (2020-12-26T14:40:12Z)
• Grafit: Learning fine-grained image representations with coarse labels [114.17782143848315]
  This paper tackles the problem of learning a finer representation than the one provided by training labels. By jointly leveraging the coarse labels and the underlying fine-grained latent space, it significantly improves the accuracy of category-level retrieval methods.
  arXiv  Detail & Related papers  (2020-11-25T19:06:26Z)
• Attention-Aware Noisy Label Learning for Image Classification [97.26664962498887]
  Deep convolutional neural networks (CNNs) learned on large-scale labeled samples have achieved remarkable progress in computer vision. The cheapest way to obtain a large body of labeled visual data is to crawl from websites with user-supplied labels, such as Flickr. This paper proposes the attention-aware noisy label learning approach to improve the discriminative capability of the network trained on datasets with potential label noise.
  arXiv  Detail & Related papers  (2020-09-30T15:45:36Z)
• SSKD: Self-Supervised Knowledge Distillation for Cross Domain Adaptive Person Re-Identification [25.96221714337815]
  Domain adaptive person re-identification (re-ID) is a challenging task due to the large discrepancy between the source domain and the target domain. Existing methods mainly attempt to generate pseudo labels for unlabeled target images by clustering algorithms. We propose a Self-Supervised Knowledge Distillation (SSKD) technique containing two modules, the identity learning and the soft label learning.
  arXiv  Detail & Related papers  (2020-09-13T10:12:02Z)
• Multi-label Zero-shot Classification by Learning to Transfer from External Knowledge [36.04579549557464]
  Multi-label zero-shot classification aims to predict multiple unseen class labels for an input image. This paper introduces a novel multi-label zero-shot classification framework by learning to transfer from external knowledge.
  arXiv  Detail & Related papers  (2020-07-30T17:26:46Z)
• Semi-supervised Learning for Few-shot Image-to-Image Translation [89.48165936436183]
  We propose a semi-supervised method for few-shot image translation, called SEMIT. Our method achieves excellent results on four different datasets using as little as 10% of the source labels.
  arXiv  Detail & Related papers  (2020-03-30T22:46:49Z)
• Continual Local Replacement for Few-shot Learning [13.956960291580938]
  The goal of few-shot learning is to learn a model that can recognize novel classes based on one or few training data. It is challenging mainly due to two aspects: (1) it lacks good feature representation of novel classes; (2) a few of labeled data could not accurately represent the true data distribution. A novel continual local replacement strategy is proposed to address the data deficiency problem.
  arXiv  Detail & Related papers  (2020-01-23T04:26:21Z)

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.