22,206 research outputs found
Multi-Image Semantic Matching by Mining Consistent Features
This work proposes a multi-image matching method to estimate semantic
correspondences across multiple images. In contrast to the previous methods
that optimize all pairwise correspondences, the proposed method identifies and
matches only a sparse set of reliable features in the image collection. In this
way, the proposed method is able to prune nonrepeatable features and also
highly scalable to handle thousands of images. We additionally propose a
low-rank constraint to ensure the geometric consistency of feature
correspondences over the whole image collection. Besides the competitive
performance on multi-graph matching and semantic flow benchmarks, we also
demonstrate the applicability of the proposed method for reconstructing
object-class models and discovering object-class landmarks from images without
using any annotation.Comment: CVPR 201
Scale-Adaptive Neural Dense Features: Learning via Hierarchical Context Aggregation
How do computers and intelligent agents view the world around them? Feature
extraction and representation constitutes one the basic building blocks towards
answering this question. Traditionally, this has been done with carefully
engineered hand-crafted techniques such as HOG, SIFT or ORB. However, there is
no ``one size fits all'' approach that satisfies all requirements. In recent
years, the rising popularity of deep learning has resulted in a myriad of
end-to-end solutions to many computer vision problems. These approaches, while
successful, tend to lack scalability and can't easily exploit information
learned by other systems. Instead, we propose SAND features, a dedicated deep
learning solution to feature extraction capable of providing hierarchical
context information. This is achieved by employing sparse relative labels
indicating relationships of similarity/dissimilarity between image locations.
The nature of these labels results in an almost infinite set of dissimilar
examples to choose from. We demonstrate how the selection of negative examples
during training can be used to modify the feature space and vary it's
properties. To demonstrate the generality of this approach, we apply the
proposed features to a multitude of tasks, each requiring different properties.
This includes disparity estimation, semantic segmentation, self-localisation
and SLAM. In all cases, we show how incorporating SAND features results in
better or comparable results to the baseline, whilst requiring little to no
additional training. Code can be found at:
https://github.com/jspenmar/SAND_featuresComment: CVPR201
Manipulating Attributes of Natural Scenes via Hallucination
In this study, we explore building a two-stage framework for enabling users
to directly manipulate high-level attributes of a natural scene. The key to our
approach is a deep generative network which can hallucinate images of a scene
as if they were taken at a different season (e.g. during winter), weather
condition (e.g. in a cloudy day) or time of the day (e.g. at sunset). Once the
scene is hallucinated with the given attributes, the corresponding look is then
transferred to the input image while preserving the semantic details intact,
giving a photo-realistic manipulation result. As the proposed framework
hallucinates what the scene will look like, it does not require any reference
style image as commonly utilized in most of the appearance or style transfer
approaches. Moreover, it allows to simultaneously manipulate a given scene
according to a diverse set of transient attributes within a single model,
eliminating the need of training multiple networks per each translation task.
Our comprehensive set of qualitative and quantitative results demonstrate the
effectiveness of our approach against the competing methods.Comment: Accepted for publication in ACM Transactions on Graphic
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