Active Learning Inspired ControlNet Guidance for Augmenting Semantic Segmentation Datasets

• URL: http://arxiv.org/abs/2503.09221v1
• Date: Wed, 12 Mar 2025 10:09:27 GMT
• Title: Active Learning Inspired ControlNet Guidance for Augmenting Semantic Segmentation Datasets
• Authors: Hannah Kniesel, Pedro Hermosilla, Timo Ropinski,
• Abstract summary: ControlNet enables precise alignment between ground truth segmentation masks and the generated image content.<n>We propose the first approach to integrate active learning-based selection metrics into the backward diffusion process.<n>We show that segmentation models trained with guided synthetic data outperform those trained on non-guided synthetic data.
• Score: 15.786823017952122
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in conditional image generation from diffusion models have shown great potential in achieving impressive image quality while preserving the constraints introduced by the user. In particular, ControlNet enables precise alignment between ground truth segmentation masks and the generated image content, allowing the enhancement of training datasets in segmentation tasks. This raises a key question: Can ControlNet additionally be guided to generate the most informative synthetic samples for a specific task? Inspired by active learning, where the most informative real-world samples are selected based on sample difficulty or model uncertainty, we propose the first approach to integrate active learning-based selection metrics into the backward diffusion process for sample generation. Specifically, we explore uncertainty, query by committee, and expected model change, which are commonly used in active learning, and demonstrate their application for guiding the sample generation process through gradient approximation. Our method is training-free, modifying only the backward diffusion process, allowing it to be used on any pretrained ControlNet. Using this process, we show that segmentation models trained with guided synthetic data outperform those trained on non-guided synthetic data. Our work underscores the need for advanced control mechanisms for diffusion-based models, which are not only aligned with image content but additionally downstream task performance, highlighting the true potential of synthetic data generation.

Related papers

• Prompting Forgetting: Unlearning in GANs via Textual Guidance [4.3562145620596215]
  We propose Text-to-Unlearn, a novel framework that selectively unlearns concepts from pre-trained GANs using only text prompts. Our approach guides the unlearning process without requiring additional datasets or supervised fine-tuning. To our knowledge, Text-to-Unlearn is the first cross-modal unlearning framework for GANs.
  arXiv  Detail & Related papers  (2025-04-01T22:18:40Z)
• Learn from the Learnt: Source-Free Active Domain Adaptation via Contrastive Sampling and Visual Persistence [60.37934652213881]
  Domain Adaptation (DA) facilitates knowledge transfer from a source domain to a related target domain. This paper investigates a practical DA paradigm, namely Source data-Free Active Domain Adaptation (SFADA), where source data becomes inaccessible during adaptation. We present learn from the learnt (LFTL), a novel paradigm for SFADA to leverage the learnt knowledge from the source pretrained model and actively iterated models without extra overhead.
  arXiv  Detail & Related papers  (2024-07-26T17:51:58Z)
• Adversarial Robustification via Text-to-Image Diffusion Models [56.37291240867549]
  Adrial robustness has been conventionally believed as a challenging property to encode for neural networks. We develop a scalable and model-agnostic solution to achieve adversarial robustness without using any data.
  arXiv  Detail & Related papers  (2024-07-26T10:49:14Z)
• Pre-Trained Vision-Language Models as Partial Annotators [40.89255396643592]
  Pre-trained vision-language models learn massive data to model unified representations of images and natural languages. In this paper, we investigate a novel "pre-trained annotating - weakly-supervised learning" paradigm for pre-trained model application and experiment on image classification tasks.
  arXiv  Detail & Related papers  (2024-05-23T17:17:27Z)
• EfficientTrain++: Generalized Curriculum Learning for Efficient Visual Backbone Training [79.96741042766524]
  We reformulate the training curriculum as a soft-selection function. We show that exposing the contents of natural images can be readily achieved by the intensity of data augmentation. The resulting method, EfficientTrain++, is simple, general, yet surprisingly effective.
  arXiv  Detail & Related papers  (2024-05-14T17:00:43Z)
• Few-shot Online Anomaly Detection and Segmentation [29.693357653538474]
  This paper focuses on addressing the challenging yet practical few-shot online anomaly detection and segmentation (FOADS) task. Under the FOADS framework, models are trained on a few-shot normal dataset, followed by inspection and improvement of their capabilities by leveraging unlabeled streaming data containing both normal and abnormal samples simultaneously. In order to achieve improved performance with limited training samples, we employ multi-scale feature embedding extracted from a CNN pre-trained on ImageNet to obtain a robust representation.
  arXiv  Detail & Related papers  (2024-03-27T02:24:00Z)
• Diffusion-Based Neural Network Weights Generation [80.89706112736353]
  D2NWG is a diffusion-based neural network weights generation technique that efficiently produces high-performing weights for transfer learning. Our method extends generative hyper-representation learning to recast the latent diffusion paradigm for neural network weights generation. Our approach is scalable to large architectures such as large language models (LLMs), overcoming the limitations of current parameter generation techniques.
  arXiv  Detail & Related papers  (2024-02-28T08:34:23Z)
• Denoising Diffusion Probabilistic Models for Generation of Realistic Fully-Annotated Microscopy Image Data Sets [1.07539359851877]
  In this study, we demonstrate that diffusion models can effectively generate fully-annotated microscopy image data sets. The proposed pipeline helps to reduce the reliance on manual annotations when training deep learning-based segmentation approaches.
  arXiv  Detail & Related papers  (2023-01-02T14:17:08Z)
• Self-Distillation for Further Pre-training of Transformers [83.84227016847096]
  We propose self-distillation as a regularization for a further pre-training stage. We empirically validate the efficacy of self-distillation on a variety of benchmark datasets for image and text classification tasks.
  arXiv  Detail & Related papers  (2022-09-30T02:25:12Z)
• Texture-guided Saliency Distilling for Unsupervised Salient Object Detection [67.10779270290305]
  We propose a novel USOD method to mine rich and accurate saliency knowledge from both easy and hard samples. Our method achieves state-of-the-art USOD performance on RGB, RGB-D, RGB-T, and video SOD benchmarks.
  arXiv  Detail & Related papers  (2022-07-13T02:01:07Z)
• Negative Data Augmentation [127.28042046152954]
  We show that negative data augmentation samples provide information on the support of the data distribution. We introduce a new GAN training objective where we use NDA as an additional source of synthetic data for the discriminator. Empirically, models trained with our method achieve improved conditional/unconditional image generation along with improved anomaly detection capabilities.
  arXiv  Detail & Related papers  (2021-02-09T20:28:35Z)
• Unlabeled Data Guided Semi-supervised Histopathology Image Segmentation [34.45302976822067]
  Semi-supervised learning (SSL) based on generative methods has been proven to be effective in utilizing diverse image characteristics. We propose a new data guided generative method for histopathology image segmentation by leveraging the unlabeled data distributions. Our method is evaluated on glands and nuclei datasets.
  arXiv  Detail & Related papers  (2020-12-17T02:54:19Z)
• Omni-supervised Facial Expression Recognition via Distilled Data [120.11782405714234]
  We propose omni-supervised learning to exploit reliable samples in a large amount of unlabeled data for network training. We experimentally verify that the new dataset can significantly improve the ability of the learned FER model. To tackle this, we propose to apply a dataset distillation strategy to compress the created dataset into several informative class-wise images.
  arXiv  Detail & Related papers  (2020-05-18T09:36:51Z)

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.