2,907 research outputs found
Active Object Localization in Visual Situations
We describe a method for performing active localization of objects in
instances of visual situations. A visual situation is an abstract
concept---e.g., "a boxing match", "a birthday party", "walking the dog",
"waiting for a bus"---whose image instantiations are linked more by their
common spatial and semantic structure than by low-level visual similarity. Our
system combines given and learned knowledge of the structure of a particular
situation, and adapts that knowledge to a new situation instance as it actively
searches for objects. More specifically, the system learns a set of probability
distributions describing spatial and other relationships among relevant
objects. The system uses those distributions to iteratively sample object
proposals on a test image, but also continually uses information from those
object proposals to adaptively modify the distributions based on what the
system has detected. We test our approach's ability to efficiently localize
objects, using a situation-specific image dataset created by our group. We
compare the results with several baselines and variations on our method, and
demonstrate the strong benefit of using situation knowledge and active
context-driven localization. Finally, we contrast our method with several other
approaches that use context as well as active search for object localization in
images.Comment: 14 page
Conceptual spatial representations for indoor mobile robots
We present an approach for creating conceptual representations of human-made indoor environments using mobile
robots. The concepts refer to spatial and functional properties of typical indoor environments. Following ļ¬ndings
in cognitive psychology, our model is composed of layers representing maps at diļ¬erent levels of abstraction. The
complete system is integrated in a mobile robot endowed with laser and vision sensors for place and object recognition.
The system also incorporates a linguistic framework that actively supports the map acquisition process, and which
is used for situated dialogue. Finally, we discuss the capabilities of the integrated system
Adversarially Tuned Scene Generation
Generalization performance of trained computer vision systems that use
computer graphics (CG) generated data is not yet effective due to the concept
of 'domain-shift' between virtual and real data. Although simulated data
augmented with a few real world samples has been shown to mitigate domain shift
and improve transferability of trained models, guiding or bootstrapping the
virtual data generation with the distributions learnt from target real world
domain is desired, especially in the fields where annotating even few real
images is laborious (such as semantic labeling, and intrinsic images etc.). In
order to address this problem in an unsupervised manner, our work combines
recent advances in CG (which aims to generate stochastic scene layouts coupled
with large collections of 3D object models) and generative adversarial training
(which aims train generative models by measuring discrepancy between generated
and real data in terms of their separability in the space of a deep
discriminatively-trained classifier). Our method uses iterative estimation of
the posterior density of prior distributions for a generative graphical model.
This is done within a rejection sampling framework. Initially, we assume
uniform distributions as priors on the parameters of a scene described by a
generative graphical model. As iterations proceed the prior distributions get
updated to distributions that are closer to the (unknown) distributions of
target data. We demonstrate the utility of adversarially tuned scene generation
on two real-world benchmark datasets (CityScapes and CamVid) for traffic scene
semantic labeling with a deep convolutional net (DeepLab). We realized
performance improvements by 2.28 and 3.14 points (using the IoU metric) between
the DeepLab models trained on simulated sets prepared from the scene generation
models before and after tuning to CityScapes and CamVid respectively.Comment: 9 pages, accepted at CVPR 201
10371 Abstracts Collection -- Dynamic Maps
From September 12th to 17th, 2010, the Dagstuhl Seminar 10371 ``Dynamic Maps \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
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