1,905 research outputs found
Discriminative Region Proposal Adversarial Networks for High-Quality Image-to-Image Translation
Image-to-image translation has been made much progress with embracing
Generative Adversarial Networks (GANs). However, it's still very challenging
for translation tasks that require high quality, especially at high-resolution
and photorealism. In this paper, we present Discriminative Region Proposal
Adversarial Networks (DRPAN) for high-quality image-to-image translation. We
decompose the procedure of image-to-image translation task into three iterated
steps, first is to generate an image with global structure but some local
artifacts (via GAN), second is using our DRPnet to propose the most fake region
from the generated image, and third is to implement "image inpainting" on the
most fake region for more realistic result through a reviser, so that the
system (DRPAN) can be gradually optimized to synthesize images with more
attention on the most artifact local part. Experiments on a variety of
image-to-image translation tasks and datasets validate that our method
outperforms state-of-the-arts for producing high-quality translation results in
terms of both human perceptual studies and automatic quantitative measures.Comment: ECCV 201
Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
Contrastive Transformer: Contrastive Learning Scheme with Transformer innate Patches
This paper presents Contrastive Transformer, a contrastive learning scheme
using the Transformer innate patches. Contrastive Transformer enables existing
contrastive learning techniques, often used for image classification, to
benefit dense downstream prediction tasks such as semantic segmentation. The
scheme performs supervised patch-level contrastive learning, selecting the
patches based on the ground truth mask, subsequently used for hard-negative and
hard-positive sampling. The scheme applies to all vision-transformer
architectures, is easy to implement, and introduces minimal additional memory
footprint. Additionally, the scheme removes the need for huge batch sizes, as
each patch is treated as an image.
We apply and test Contrastive Transformer for the case of aerial image
segmentation, known for low-resolution data, large class imbalance, and similar
semantic classes. We perform extensive experiments to show the efficacy of the
Contrastive Transformer scheme on the ISPRS Potsdam aerial image segmentation
dataset. Additionally, we show the generalizability of our scheme by applying
it to multiple inherently different Transformer architectures. Ultimately, the
results show a consistent increase in mean IoU across all classes.Comment: 7 pages, 3 figure
- …