226,232 research outputs found
Age Progression/Regression by Conditional Adversarial Autoencoder
"If I provide you a face image of mine (without telling you the actual age
when I took the picture) and a large amount of face images that I crawled
(containing labeled faces of different ages but not necessarily paired), can
you show me what I would look like when I am 80 or what I was like when I was
5?" The answer is probably a "No." Most existing face aging works attempt to
learn the transformation between age groups and thus would require the paired
samples as well as the labeled query image. In this paper, we look at the
problem from a generative modeling perspective such that no paired samples is
required. In addition, given an unlabeled image, the generative model can
directly produce the image with desired age attribute. We propose a conditional
adversarial autoencoder (CAAE) that learns a face manifold, traversing on which
smooth age progression and regression can be realized simultaneously. In CAAE,
the face is first mapped to a latent vector through a convolutional encoder,
and then the vector is projected to the face manifold conditional on age
through a deconvolutional generator. The latent vector preserves personalized
face features (i.e., personality) and the age condition controls progression
vs. regression. Two adversarial networks are imposed on the encoder and
generator, respectively, forcing to generate more photo-realistic faces.
Experimental results demonstrate the appealing performance and flexibility of
the proposed framework by comparing with the state-of-the-art and ground truth.Comment: Accepted by The IEEE Conference on Computer Vision and Pattern
Recognition (CVPR 2017
Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models
We present a new deep learning architecture (called Kd-network) that is
designed for 3D model recognition tasks and works with unstructured point
clouds. The new architecture performs multiplicative transformations and share
parameters of these transformations according to the subdivisions of the point
clouds imposed onto them by Kd-trees. Unlike the currently dominant
convolutional architectures that usually require rasterization on uniform
two-dimensional or three-dimensional grids, Kd-networks do not rely on such
grids in any way and therefore avoid poor scaling behaviour. In a series of
experiments with popular shape recognition benchmarks, Kd-networks demonstrate
competitive performance in a number of shape recognition tasks such as shape
classification, shape retrieval and shape part segmentation.Comment: Spotlight at ICCV'1
Amorphous Placement and Informed Diffusion for Timely Monitoring by Autonomous, Resource-Constrained, Mobile Sensors
Personal communication devices are increasingly equipped with sensors for passive monitoring of encounters and surroundings. We envision the emergence of services that enable a community of mobile users carrying such resource-limited devices to query such information at remote locations in the field in which they collectively roam. One approach to implement such a service is directed placement and retrieval (DPR), whereby readings/queries about a specific location are routed to a node responsible for that location. In a mobile, potentially sparse setting, where end-to-end paths are unavailable, DPR is not an attractive solution as it would require the use of delay-tolerant (flooding-based store-carry-forward) routing of both readings and queries, which is inappropriate for applications with data freshness constraints, and which is incompatible with stringent device power/memory constraints. Alternatively, we propose the use of amorphous placement and retrieval (APR), in which routing and field monitoring are integrated through the use of a cache management scheme coupled with an informed exchange of cached samples to diffuse sensory data throughout the network, in such a way that a query answer is likely to be found close to the query origin. We argue that knowledge of the distribution of query targets could be used effectively by an informed cache management policy to maximize the utility of collective storage of all devices. Using a simple analytical model, we show that the use of informed cache management is particularly important when the mobility model results in a non-uniform distribution of users over the field. We present results from extensive simulations which show that in sparsely-connected networks, APR is more cost-effective than DPR, that it provides extra resilience to node failure and packet losses, and that its use of informed cache management yields superior performance
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