623 research outputs found
Road Segmentation for Remote Sensing Images using Adversarial Spatial Pyramid Networks
Road extraction in remote sensing images is of great importance for a wide
range of applications. Because of the complex background, and high density,
most of the existing methods fail to accurately extract a road network that
appears correct and complete. Moreover, they suffer from either insufficient
training data or high costs of manual annotation. To address these problems, we
introduce a new model to apply structured domain adaption for synthetic image
generation and road segmentation. We incorporate a feature pyramid network into
generative adversarial networks to minimize the difference between the source
and target domains. A generator is learned to produce quality synthetic images,
and the discriminator attempts to distinguish them. We also propose a feature
pyramid network that improves the performance of the proposed model by
extracting effective features from all the layers of the network for describing
different scales objects. Indeed, a novel scale-wise architecture is introduced
to learn from the multi-level feature maps and improve the semantics of the
features. For optimization, the model is trained by a joint reconstruction loss
function, which minimizes the difference between the fake images and the real
ones. A wide range of experiments on three datasets prove the superior
performance of the proposed approach in terms of accuracy and efficiency. In
particular, our model achieves state-of-the-art 78.86 IOU on the Massachusetts
dataset with 14.89M parameters and 86.78B FLOPs, with 4x fewer FLOPs but higher
accuracy (+3.47% IOU) than the top performer among state-of-the-art approaches
used in the evaluation
A survey of exemplar-based texture synthesis
Exemplar-based texture synthesis is the process of generating, from an input
sample, new texture images of arbitrary size and which are perceptually
equivalent to the sample. The two main approaches are statistics-based methods
and patch re-arrangement methods. In the first class, a texture is
characterized by a statistical signature; then, a random sampling conditioned
to this signature produces genuinely different texture images. The second class
boils down to a clever "copy-paste" procedure, which stitches together large
regions of the sample. Hybrid methods try to combine ideas from both approaches
to avoid their hurdles. The recent approaches using convolutional neural
networks fit to this classification, some being statistical and others
performing patch re-arrangement in the feature space. They produce impressive
synthesis on various kinds of textures. Nevertheless, we found that most real
textures are organized at multiple scales, with global structures revealed at
coarse scales and highly varying details at finer ones. Thus, when confronted
with large natural images of textures the results of state-of-the-art methods
degrade rapidly, and the problem of modeling them remains wide open.Comment: v2: Added comments and typos fixes. New section added to describe
FRAME. New method presented: CNNMR
Pedestrian Attribute Recognition: A Survey
Recognizing pedestrian attributes is an important task in computer vision
community due to it plays an important role in video surveillance. Many
algorithms has been proposed to handle this task. The goal of this paper is to
review existing works using traditional methods or based on deep learning
networks. Firstly, we introduce the background of pedestrian attributes
recognition (PAR, for short), including the fundamental concepts of pedestrian
attributes and corresponding challenges. Secondly, we introduce existing
benchmarks, including popular datasets and evaluation criterion. Thirdly, we
analyse the concept of multi-task learning and multi-label learning, and also
explain the relations between these two learning algorithms and pedestrian
attribute recognition. We also review some popular network architectures which
have widely applied in the deep learning community. Fourthly, we analyse
popular solutions for this task, such as attributes group, part-based,
\emph{etc}. Fifthly, we shown some applications which takes pedestrian
attributes into consideration and achieve better performance. Finally, we
summarized this paper and give several possible research directions for
pedestrian attributes recognition. The project page of this paper can be found
from the following website:
\url{https://sites.google.com/view/ahu-pedestrianattributes/}.Comment: Check our project page for High Resolution version of this survey:
https://sites.google.com/view/ahu-pedestrianattributes
- …