21,629 research outputs found
Creating Capsule Wardrobes from Fashion Images
We propose to automatically create capsule wardrobes. Given an inventory of
candidate garments and accessories, the algorithm must assemble a minimal set
of items that provides maximal mix-and-match outfits. We pose the task as a
subset selection problem. To permit efficient subset selection over the space
of all outfit combinations, we develop submodular objective functions capturing
the key ingredients of visual compatibility, versatility, and user-specific
preference. Since adding garments to a capsule only expands its possible
outfits, we devise an iterative approach to allow near-optimal submodular
function maximization. Finally, we present an unsupervised approach to learn
visual compatibility from "in the wild" full body outfit photos; the
compatibility metric translates well to cleaner catalog photos and improves
over existing methods. Our results on thousands of pieces from popular fashion
websites show that automatic capsule creation has potential to mimic skilled
fashionistas in assembling flexible wardrobes, while being significantly more
scalable.Comment: Accepted to CVPR 201
Gradient-free activation maximization for identifying effective stimuli
A fundamental question for understanding brain function is what types of
stimuli drive neurons to fire. In visual neuroscience, this question has also
been posted as characterizing the receptive field of a neuron. The search for
effective stimuli has traditionally been based on a combination of insights
from previous studies, intuition, and luck. Recently, the same question has
emerged in the study of units in convolutional neural networks (ConvNets), and
together with this question a family of solutions were developed that are
generally referred to as "feature visualization by activation maximization."
We sought to bring in tools and techniques developed for studying ConvNets to
the study of biological neural networks. However, one key difference that
impedes direct translation of tools is that gradients can be obtained from
ConvNets using backpropagation, but such gradients are not available from the
brain. To circumvent this problem, we developed a method for gradient-free
activation maximization by combining a generative neural network with a genetic
algorithm. We termed this method XDream (EXtending DeepDream with real-time
evolution for activation maximization), and we have shown that this method can
reliably create strong stimuli for neurons in the macaque visual cortex (Ponce
et al., 2019). In this paper, we describe extensive experiments characterizing
the XDream method by using ConvNet units as in silico models of neurons. We
show that XDream is applicable across network layers, architectures, and
training sets; examine design choices in the algorithm; and provide practical
guides for choosing hyperparameters in the algorithm. XDream is an efficient
algorithm for uncovering neuronal tuning preferences in black-box networks
using a vast and diverse stimulus space.Comment: 16 pages, 8 figures, 3 table
Joint in-network video rate adaptation and measurement-based admission control: algorithm design and evaluation
The important new revenue opportunities that multimedia services offer to network and service providers come with important management challenges. For providers, it is important to control the video quality that is offered and perceived by the user, typically known as the quality of experience (QoE). Both admission control and scalable video coding techniques can control the QoE by blocking connections or adapting the video rate but influence each other's performance. In this article, we propose an in-network video rate adaptation mechanism that enables a provider to define a policy on how the video rate adaptation should be performed to maximize the provider's objective (e.g., a maximization of revenue or QoE). We discuss the need for a close interaction of the video rate adaptation algorithm with a measurement based admission control system, allowing to effectively orchestrate both algorithms and timely switch from video rate adaptation to the blocking of connections. We propose two different rate adaptation decision algorithms that calculate which videos need to be adapted: an optimal one in terms of the provider's policy and a heuristic based on the utility of each connection. Through an extensive performance evaluation, we show the impact of both algorithms on the rate adaptation, network utilisation and the stability of the video rate adaptation. We show that both algorithms outperform other configurations with at least 10 %. Moreover, we show that the proposed heuristic is about 500 times faster than the optimal algorithm and experiences only a performance drop of approximately 2 %, given the investigated video delivery scenario
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