HandsOff: Labeled Dataset Generation With No Additional Human
Annotations
- URL: http://arxiv.org/abs/2212.12645v2
- Date: Thu, 30 Mar 2023 19:38:05 GMT
- Title: HandsOff: Labeled Dataset Generation With No Additional Human
Annotations
- Authors: Austin Xu, Mariya I. Vasileva, Achal Dave, Arjun Seshadri
- Abstract summary: We introduce the HandsOff framework, a technique capable of producing an unlimited number of synthetic images and corresponding labels.
Our framework avoids the practical drawbacks of prior work by unifying the field of GAN inversion with dataset generation.
We generate datasets with rich pixel-wise labels in multiple challenging domains such as faces, cars, full-body human poses, and urban driving scenes.
- Score: 13.11411442720668
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recent work leverages the expressive power of generative adversarial networks
(GANs) to generate labeled synthetic datasets. These dataset generation methods
often require new annotations of synthetic images, which forces practitioners
to seek out annotators, curate a set of synthetic images, and ensure the
quality of generated labels. We introduce the HandsOff framework, a technique
capable of producing an unlimited number of synthetic images and corresponding
labels after being trained on less than 50 pre-existing labeled images. Our
framework avoids the practical drawbacks of prior work by unifying the field of
GAN inversion with dataset generation. We generate datasets with rich
pixel-wise labels in multiple challenging domains such as faces, cars,
full-body human poses, and urban driving scenes. Our method achieves
state-of-the-art performance in semantic segmentation, keypoint detection, and
depth estimation compared to prior dataset generation approaches and transfer
learning baselines. We additionally showcase its ability to address broad
challenges in model development which stem from fixed, hand-annotated datasets,
such as the long-tail problem in semantic segmentation. Project page:
austinxu87.github.io/handsoff.
Related papers
- Enhanced Generative Data Augmentation for Semantic Segmentation via Stronger Guidance [1.2923961938782627]
We introduce an effective data augmentation method for semantic segmentation using the Controllable Diffusion Model.
Our proposed method includes efficient prompt generation using Class-Prompt Appending and Visual Prior Combination.
We evaluate our method on the PASCAL VOC datasets and found it highly effective for synthesizing images in semantic segmentation.
arXiv Detail & Related papers (2024-09-09T19:01:14Z) - Scribbles for All: Benchmarking Scribble Supervised Segmentation Across Datasets [51.74296438621836]
We introduce Scribbles for All, a label and training data generation algorithm for semantic segmentation trained on scribble labels.
The main limitation of scribbles as source for weak supervision is the lack of challenging datasets for scribble segmentation.
Scribbles for All provides scribble labels for several popular segmentation datasets and provides an algorithm to automatically generate scribble labels for any dataset with dense annotations.
arXiv Detail & Related papers (2024-08-22T15:29:08Z) - Unlocking Pre-trained Image Backbones for Semantic Image Synthesis [29.688029979801577]
We propose a new class of GAN discriminators for semantic image synthesis that generates highly realistic images.
Our model, which we dub DP-SIMS, achieves state-of-the-art results in terms of image quality and consistency with the input label maps on ADE-20K, COCO-Stuff, and Cityscapes.
arXiv Detail & Related papers (2023-12-20T09:39:19Z) - Dataset Diffusion: Diffusion-based Synthetic Dataset Generation for
Pixel-Level Semantic Segmentation [6.82236459614491]
We propose a novel method for generating pixel-level semantic segmentation labels using the text-to-image generative model Stable Diffusion.
By utilizing the text prompts, cross-attention, and self-attention of SD, we introduce three new techniques: class-prompt appending, class-prompt cross-attention, and self-attention exponentiation.
These techniques enable us to generate segmentation maps corresponding to synthetic images.
arXiv Detail & Related papers (2023-09-25T17:19:26Z) - 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) - Is synthetic data from generative models ready for image recognition? [69.42645602062024]
We study whether and how synthetic images generated from state-of-the-art text-to-image generation models can be used for image recognition tasks.
We showcase the powerfulness and shortcomings of synthetic data from existing generative models, and propose strategies for better applying synthetic data for recognition tasks.
arXiv Detail & Related papers (2022-10-14T06:54:24Z) - Data Generation for Satellite Image Classification Using Self-Supervised
Representation Learning [0.0]
We introduce the self-supervised learning technique to create the synthetic labels for satellite image patches.
These synthetic labels can be used as the training dataset for the existing supervised learning techniques.
In our experiments, we show that the models trained on the synthetic labels give similar performance to the models trained on the real labels.
arXiv Detail & Related papers (2022-05-28T12:54:34Z) - A Shared Representation for Photorealistic Driving Simulators [83.5985178314263]
We propose to improve the quality of generated images by rethinking the discriminator architecture.
The focus is on the class of problems where images are generated given semantic inputs, such as scene segmentation maps or human body poses.
We aim to learn a shared latent representation that encodes enough information to jointly do semantic segmentation, content reconstruction, along with a coarse-to-fine grained adversarial reasoning.
arXiv Detail & Related papers (2021-12-09T18:59:21Z) - DatasetGAN: Efficient Labeled Data Factory with Minimal Human Effort [117.41383937100751]
Current deep networks are extremely data-hungry, benefiting from training on large-scale datasets.
We show how the GAN latent code can be decoded to produce a semantic segmentation of the image.
These generated datasets can then be used for training any computer vision architecture just as real datasets are.
arXiv Detail & Related papers (2021-04-13T20:08:29Z) - 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)
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.