9,483 research outputs found
Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification
Person re-identification (re-id) aims to match pedestrians observed by
disjoint camera views. It attracts increasing attention in computer vision due
to its importance to surveillance system. To combat the major challenge of
cross-view visual variations, deep embedding approaches are proposed by
learning a compact feature space from images such that the Euclidean distances
correspond to their cross-view similarity metric. However, the global Euclidean
distance cannot faithfully characterize the ideal similarity in a complex
visual feature space because features of pedestrian images exhibit unknown
distributions due to large variations in poses, illumination and occlusion.
Moreover, intra-personal training samples within a local range are robust to
guide deep embedding against uncontrolled variations, which however, cannot be
captured by a global Euclidean distance. In this paper, we study the problem of
person re-id by proposing a novel sampling to mine suitable \textit{positives}
(i.e. intra-class) within a local range to improve the deep embedding in the
context of large intra-class variations. Our method is capable of learning a
deep similarity metric adaptive to local sample structure by minimizing each
sample's local distances while propagating through the relationship between
samples to attain the whole intra-class minimization. To this end, a novel
objective function is proposed to jointly optimize similarity metric learning,
local positive mining and robust deep embedding. This yields local
discriminations by selecting local-ranged positive samples, and the learned
features are robust to dramatic intra-class variations. Experiments on
benchmarks show state-of-the-art results achieved by our method.Comment: Published on Pattern Recognitio
Hierarchical relational models for document networks
We develop the relational topic model (RTM), a hierarchical model of both
network structure and node attributes. We focus on document networks, where the
attributes of each document are its words, that is, discrete observations taken
from a fixed vocabulary. For each pair of documents, the RTM models their link
as a binary random variable that is conditioned on their contents. The model
can be used to summarize a network of documents, predict links between them,
and predict words within them. We derive efficient inference and estimation
algorithms based on variational methods that take advantage of sparsity and
scale with the number of links. We evaluate the predictive performance of the
RTM for large networks of scientific abstracts, web documents, and
geographically tagged news.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS309 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Anarchy and Hierarchy
We advocate a new approach to study models of fermion masses and mixings,
namely anarchy proposed in hep-ph/9911341. In this approach, we scan the O(1)
coefficients randomly. We argue that this is the correct approach when the
fundamental theory is sufficiently complicated. Assuming there is no physical
distinction among three generations of neutrinos, the probability distributions
in MNS mixing angles can be predicted independent of the choice of the measure.
This is because the mixing angles are distributed according to the Haar measure
of the Lie groups whose elements diagonalize the mass matrices. The
near-maximal mixings, as observed in the atmospheric neutrino data and as
required in the LMA solution to the solar neutrino problem, are highly
probable. A small hierarchy between the Delta m^2 for the atmospheric and the
solar neutrinos is obtained very easily; the complex seesaw case gives a
hierarchy of a factor of 20 as the most probable one, even though this
conclusion is more measure-dependent. U_{e3} has to be just below the current
limit from the CHOOZ experiment. The CP-violating parameter sin delta is
preferred to be maximal. We present a simple SU(5)-like extension of anarchy to
the charged-lepton and quark sectors which works well phenomenologically.Comment: 26 page
Long Text Generation via Adversarial Training with Leaked Information
Automatically generating coherent and semantically meaningful text has many
applications in machine translation, dialogue systems, image captioning, etc.
Recently, by combining with policy gradient, Generative Adversarial Nets (GAN)
that use a discriminative model to guide the training of the generative model
as a reinforcement learning policy has shown promising results in text
generation. However, the scalar guiding signal is only available after the
entire text has been generated and lacks intermediate information about text
structure during the generative process. As such, it limits its success when
the length of the generated text samples is long (more than 20 words). In this
paper, we propose a new framework, called LeakGAN, to address the problem for
long text generation. We allow the discriminative net to leak its own
high-level extracted features to the generative net to further help the
guidance. The generator incorporates such informative signals into all
generation steps through an additional Manager module, which takes the
extracted features of current generated words and outputs a latent vector to
guide the Worker module for next-word generation. Our extensive experiments on
synthetic data and various real-world tasks with Turing test demonstrate that
LeakGAN is highly effective in long text generation and also improves the
performance in short text generation scenarios. More importantly, without any
supervision, LeakGAN would be able to implicitly learn sentence structures only
through the interaction between Manager and Worker.Comment: 14 pages, AAAI 201
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