ScribbleGen: Generative Data Augmentation Improves Scribble-supervised Semantic Segmentation
- URL: http://arxiv.org/abs/2311.17121v2
- Date: Tue, 16 Apr 2024 22:41:26 GMT
- Title: ScribbleGen: Generative Data Augmentation Improves Scribble-supervised Semantic Segmentation
- Authors: Jacob Schnell, Jieke Wang, Lu Qi, Vincent Tao Hu, Meng Tang,
- Abstract summary: We propose ScribbleGen, a generative data augmentation method for scribble-supervised semantic segmentation.
We leverage a ControlNet diffusion model conditioned on semantic scribbles to produce high-quality training data.
We show that our framework significantly improves segmentation performance on small datasets, even surpassing fully-supervised segmentation.
- Score: 10.225021032417589
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recent advances in generative models, such as diffusion models, have made generating high-quality synthetic images widely accessible. Prior works have shown that training on synthetic images improves many perception tasks, such as image classification, object detection, and semantic segmentation. We are the first to explore generative data augmentations for scribble-supervised semantic segmentation. We propose ScribbleGen, a generative data augmentation method that leverages a ControlNet diffusion model conditioned on semantic scribbles to produce high-quality training data. However, naive implementations of generative data augmentations may inadvertently harm the performance of the downstream segmentor rather than improve it. We leverage classifier-free diffusion guidance to enforce class consistency and introduce encode ratios to trade off data diversity for data realism. Using the guidance scale and encode ratio, we can generate a spectrum of high-quality training images. We propose multiple augmentation schemes and find that these schemes significantly impact model performance, especially in the low-data regime. Our framework further reduces the gap between the performance of scribble-supervised segmentation and that of fully-supervised segmentation. We also show that our framework significantly improves segmentation performance on small datasets, even surpassing fully-supervised segmentation. The code is available at https://github.com/mengtang-lab/scribblegen.
Related papers
- Unsupervised Class Generation to Expand Semantic Segmentation Datasets [9.144948836224078]
We introduce novel samples into the training data without modifications to the underlying algorithms.
We show how models can not only effectively learn how to segment novel classes, with an average performance of 51% IoU, but also reduce errors for other, already existing classes.
arXiv Detail & Related papers (2025-01-04T11:53:13Z) - DreamMask: Boosting Open-vocabulary Panoptic Segmentation with Synthetic Data [61.62554324594797]
We propose DreamMask, which explores how to generate training data in the open-vocabulary setting, and how to train the model with both real and synthetic data.
In general, DreamMask significantly simplifies the collection of large-scale training data, serving as a plug-and-play enhancement for existing methods.
For instance, when trained on COCO and tested on ADE20K, the model equipped with DreamMask outperforms the previous state-of-the-art by a substantial margin of 2.1% mIoU.
arXiv Detail & Related papers (2025-01-03T19:00:00Z) - Adaptive Masking Enhances Visual Grounding [12.793586888511978]
We propose IMAGE, Interpretative MAsking with Gaussian radiation modEling, to enhance vocabulary grounding in low-shot learning scenarios.
We evaluate the efficacy of our approach on benchmark datasets, including COCO and ODinW, demonstrating its superior performance in zero-shot and few-shot tasks.
arXiv Detail & Related papers (2024-10-04T05:48:02Z) - SatSynth: Augmenting Image-Mask Pairs through Diffusion Models for Aerial Semantic Segmentation [69.42764583465508]
We explore the potential of generative image diffusion to address the scarcity of annotated data in earth observation tasks.
To the best of our knowledge, we are the first to generate both images and corresponding masks for satellite segmentation.
arXiv Detail & Related papers (2024-03-25T10:30:22Z) - A Lightweight Clustering Framework for Unsupervised Semantic
Segmentation [28.907274978550493]
Unsupervised semantic segmentation aims to categorize each pixel in an image into a corresponding class without the use of annotated data.
We propose a lightweight clustering framework for unsupervised semantic segmentation.
Our framework achieves state-of-the-art results on PASCAL VOC and MS COCO datasets.
arXiv Detail & Related papers (2023-11-30T15:33:42Z) - DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion
Models [61.906934570771256]
We present a generic dataset generation model that can produce diverse synthetic images and perception annotations.
Our method builds upon the pre-trained diffusion model and extends text-guided image synthesis to perception data generation.
We show that the rich latent code of the diffusion model can be effectively decoded as accurate perception annotations using a decoder module.
arXiv Detail & Related papers (2023-08-11T14:38:11Z) - Graph Masked Autoencoder for Sequential Recommendation [10.319298705782058]
We propose a Graph Masked AutoEncoder-enhanced sequential Recommender system (MAERec) that adaptively and dynamically distills global item transitional information for self-supervised augmentation.
Our method significantly outperforms state-of-the-art baseline models and can learn more accurate representations against data noise and sparsity.
arXiv Detail & Related papers (2023-05-08T10:57:56Z) - Adversarial Feature Augmentation and Normalization for Visual
Recognition [109.6834687220478]
Recent advances in computer vision take advantage of adversarial data augmentation to ameliorate the generalization ability of classification models.
Here, we present an effective and efficient alternative that advocates adversarial augmentation on intermediate feature embeddings.
We validate the proposed approach across diverse visual recognition tasks with representative backbone networks.
arXiv Detail & Related papers (2021-03-22T20:36:34Z) - Group-Wise Semantic Mining for Weakly Supervised Semantic Segmentation [49.90178055521207]
This work addresses weakly supervised semantic segmentation (WSSS), with the goal of bridging the gap between image-level annotations and pixel-level segmentation.
We formulate WSSS as a novel group-wise learning task that explicitly models semantic dependencies in a group of images to estimate more reliable pseudo ground-truths.
In particular, we devise a graph neural network (GNN) for group-wise semantic mining, wherein input images are represented as graph nodes.
arXiv Detail & Related papers (2020-12-09T12:40:13Z) - Robust Optimization as Data Augmentation for Large-scale Graphs [117.2376815614148]
We propose FLAG (Free Large-scale Adversarial Augmentation on Graphs), which iteratively augments node features with gradient-based adversarial perturbations during training.
FLAG is a general-purpose approach for graph data, which universally works in node classification, link prediction, and graph classification tasks.
arXiv Detail & Related papers (2020-10-19T21:51:47Z)
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.