Improving Augmentation and Evaluation Schemes for Semantic Image Synthesis

• URL: http://arxiv.org/abs/2011.12636v3
• Date: Sat, 30 Jan 2021 09:43:15 GMT
• Title: Improving Augmentation and Evaluation Schemes for Semantic Image Synthesis
• Authors: Prateek Katiyar, Anna Khoreva
• Abstract summary: We introduce a novel augmentation scheme designed specifically for generative adversarial networks (GANs) We propose to randomly warp object shapes in the semantic label maps used as an input to the generator. The local shape discrepancies between the warped and non-warped label maps and images enable the GAN to learn better the structural and geometric details of the scene.
• Score: 16.097324852253912
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite data augmentation being a de facto technique for boosting the performance of deep neural networks, little attention has been paid to developing augmentation strategies for generative adversarial networks (GANs). To this end, we introduce a novel augmentation scheme designed specifically for GAN-based semantic image synthesis models. We propose to randomly warp object shapes in the semantic label maps used as an input to the generator. The local shape discrepancies between the warped and non-warped label maps and images enable the GAN to learn better the structural and geometric details of the scene and thus to improve the quality of generated images. While benchmarking the augmented GAN models against their vanilla counterparts, we discover that the quantification metrics reported in the previous semantic image synthesis studies are strongly biased towards specific semantic classes as they are derived via an external pre-trained segmentation network. We therefore propose to improve the established semantic image synthesis evaluation scheme by analyzing separately the performance of generated images on the biased and unbiased classes for the given segmentation network. Finally, we show strong quantitative and qualitative improvements obtained with our augmentation scheme, on both class splits, using state-of-the-art semantic image synthesis models across three different datasets. On average across COCO-Stuff, ADE20K and Cityscapes datasets, the augmented models outperform their vanilla counterparts by ~3 mIoU and ~10 FID points.

Related papers

• Is Synthetic Image Useful for Transfer Learning? An Investigation into Data Generation, Volume, and Utilization [62.157627519792946]
  We introduce a novel framework called bridged transfer, which initially employs synthetic images for fine-tuning a pre-trained model to improve its transferability. We propose dataset style inversion strategy to improve the stylistic alignment between synthetic and real images. Our proposed methods are evaluated across 10 different datasets and 5 distinct models, demonstrating consistent improvements.
  arXiv  Detail & Related papers  (2024-03-28T22:25:05Z)
• 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)
• Diversified in-domain synthesis with efficient fine-tuning for few-shot classification [64.86872227580866]
  Few-shot image classification aims to learn an image classifier using only a small set of labeled examples per class. We propose DISEF, a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. We validate our method in ten different benchmarks, consistently outperforming baselines and establishing a new state-of-the-art for few-shot classification.
  arXiv  Detail & Related papers  (2023-12-05T17:18:09Z)
• Diversify, Don't Fine-Tune: Scaling Up Visual Recognition Training with Synthetic Images [37.29348016920314]
  We present a new framework leveraging off-the-shelf generative models to generate synthetic training images. We address class name ambiguity, lack of diversity in naive prompts, and domain shifts. Our framework consistently enhances recognition model performance with more synthetic data.
  arXiv  Detail & Related papers  (2023-12-04T18:35:27Z)
• Few-shot Semantic Image Synthesis with Class Affinity Transfer [23.471210664024067]
  We propose a transfer method that leverages a model trained on a large source dataset to improve the learning ability on small target datasets. The class affinity matrix is introduced as a first layer to the source model to make it compatible with the target label maps. We apply our approach to GAN-based and diffusion-based architectures for semantic synthesis.
  arXiv  Detail & Related papers  (2023-04-05T09:24:45Z)
• SIAN: Style-Guided Instance-Adaptive Normalization for Multi-Organ Histopathology Image Synthesis [63.845552349914186]
  We propose a style-guided instance-adaptive normalization (SIAN) to synthesize realistic color distributions and textures for different organs. The four phases work together and are integrated into a generative network to embed image semantics, style, and instance-level boundaries.
  arXiv  Detail & Related papers  (2022-09-02T16:45:46Z)
• Semantic Image Synthesis via Diffusion Models [159.4285444680301]
  Denoising Diffusion Probabilistic Models (DDPMs) have achieved remarkable success in various image generation tasks. Recent work on semantic image synthesis mainly follows the emphde facto Generative Adversarial Nets (GANs)
  arXiv  Detail & Related papers  (2022-06-30T18:31:51Z)
• You Only Need Adversarial Supervision for Semantic Image Synthesis [84.83711654797342]
  We propose a novel, simplified GAN model, which needs only adversarial supervision to achieve high quality results. We show that images synthesized by our model are more diverse and follow the color and texture of real images more closely.
  arXiv  Detail & Related papers  (2020-12-08T23:00:48Z)
• Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation [84.7571086566595]
  We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
  arXiv  Detail & Related papers  (2020-03-31T11:50:43Z)

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.