10 research outputs found
Diverse Semantic Image Editing with Style Codes
Semantic image editing requires inpainting pixels following a semantic map.
It is a challenging task since this inpainting requires both harmony with the
context and strict compliance with the semantic maps. The majority of the
previous methods proposed for this task try to encode the whole information
from erased images. However, when an object is added to a scene such as a car,
its style cannot be encoded from the context alone. On the other hand, the
models that can output diverse generations struggle to output images that have
seamless boundaries between the generated and unerased parts. Additionally,
previous methods do not have a mechanism to encode the styles of visible and
partially visible objects differently for better performance. In this work, we
propose a framework that can encode visible and partially visible objects with
a novel mechanism to achieve consistency in the style encoding and final
generations. We extensively compare with previous conditional image generation
and semantic image editing algorithms. Our extensive experiments show that our
method significantly improves over the state-of-the-art. Our method not only
achieves better quantitative results but also provides diverse results. Please
refer to the project web page for the released code and demo:
https://github.com/hakansivuk/DivSem
Dual Contrastive Loss and Attention for GANs
Generative Adversarial Networks (GANs) produce impressive results on
unconditional image generation when powered with large-scale image datasets.
Yet generated images are still easy to spot especially on datasets with high
variance (e.g. bedroom, church). In this paper, we propose various improvements
to further push the boundaries in image generation. Specifically, we propose a
novel dual contrastive loss and show that, with this loss, discriminator learns
more generalized and distinguishable representations to incentivize generation.
In addition, we revisit attention and extensively experiment with different
attention blocks in the generator. We find attention to be still an important
module for successful image generation even though it was not used in the
recent state-of-the-art models. Lastly, we study different attention
architectures in the discriminator, and propose a reference attention
mechanism. By combining the strengths of these remedies, we improve the
compelling state-of-the-art Fr\'{e}chet Inception Distance (FID) by at least
17.5% on several benchmark datasets. We obtain even more significant
improvements on compositional synthetic scenes (up to 47.5% in FID)
Synthetic Datasets for Autonomous Driving: A Survey
Autonomous driving techniques have been flourishing in recent years while
thirsting for huge amounts of high-quality data. However, it is difficult for
real-world datasets to keep up with the pace of changing requirements due to
their expensive and time-consuming experimental and labeling costs. Therefore,
more and more researchers are turning to synthetic datasets to easily generate
rich and changeable data as an effective complement to the real world and to
improve the performance of algorithms. In this paper, we summarize the
evolution of synthetic dataset generation methods and review the work to date
in synthetic datasets related to single and multi-task categories for to
autonomous driving study. We also discuss the role that synthetic dataset plays
the evaluation, gap test, and positive effect in autonomous driving related
algorithm testing, especially on trustworthiness and safety aspects. Finally,
we discuss general trends and possible development directions. To the best of
our knowledge, this is the first survey focusing on the application of
synthetic datasets in autonomous driving. This survey also raises awareness of
the problems of real-world deployment of autonomous driving technology and
provides researchers with a possible solution.Comment: 19 pages, 5 figure