12,949 research outputs found
Unsupervised learning of clutter-resistant visual representations from natural videos
Populations of neurons in inferotemporal cortex (IT) maintain an explicit
code for object identity that also tolerates transformations of object
appearance e.g., position, scale, viewing angle [1, 2, 3]. Though the learning
rules are not known, recent results [4, 5, 6] suggest the operation of an
unsupervised temporal-association-based method e.g., Foldiak's trace rule [7].
Such methods exploit the temporal continuity of the visual world by assuming
that visual experience over short timescales will tend to have invariant
identity content. Thus, by associating representations of frames from nearby
times, a representation that tolerates whatever transformations occurred in the
video may be achieved. Many previous studies verified that such rules can work
in simple situations without background clutter, but the presence of visual
clutter has remained problematic for this approach. Here we show that temporal
association based on large class-specific filters (templates) avoids the
problem of clutter. Our system learns in an unsupervised way from natural
videos gathered from the internet, and is able to perform a difficult
unconstrained face recognition task on natural images: Labeled Faces in the
Wild [8]
Person Re-identification by Local Maximal Occurrence Representation and Metric Learning
Person re-identification is an important technique towards automatic search
of a person's presence in a surveillance video. Two fundamental problems are
critical for person re-identification, feature representation and metric
learning. An effective feature representation should be robust to illumination
and viewpoint changes, and a discriminant metric should be learned to match
various person images. In this paper, we propose an effective feature
representation called Local Maximal Occurrence (LOMO), and a subspace and
metric learning method called Cross-view Quadratic Discriminant Analysis
(XQDA). The LOMO feature analyzes the horizontal occurrence of local features,
and maximizes the occurrence to make a stable representation against viewpoint
changes. Besides, to handle illumination variations, we apply the Retinex
transform and a scale invariant texture operator. To learn a discriminant
metric, we propose to learn a discriminant low dimensional subspace by
cross-view quadratic discriminant analysis, and simultaneously, a QDA metric is
learned on the derived subspace. We also present a practical computation method
for XQDA, as well as its regularization. Experiments on four challenging person
re-identification databases, VIPeR, QMUL GRID, CUHK Campus, and CUHK03, show
that the proposed method improves the state-of-the-art rank-1 identification
rates by 2.2%, 4.88%, 28.91%, and 31.55% on the four databases, respectively.Comment: This paper has been accepted by CVPR 2015. For source codes and
extracted features please visit
http://www.cbsr.ia.ac.cn/users/scliao/projects/lomo_xqda
Semantic Visual Localization
Robust visual localization under a wide range of viewing conditions is a
fundamental problem in computer vision. Handling the difficult cases of this
problem is not only very challenging but also of high practical relevance,
e.g., in the context of life-long localization for augmented reality or
autonomous robots. In this paper, we propose a novel approach based on a joint
3D geometric and semantic understanding of the world, enabling it to succeed
under conditions where previous approaches failed. Our method leverages a novel
generative model for descriptor learning, trained on semantic scene completion
as an auxiliary task. The resulting 3D descriptors are robust to missing
observations by encoding high-level 3D geometric and semantic information.
Experiments on several challenging large-scale localization datasets
demonstrate reliable localization under extreme viewpoint, illumination, and
geometry changes
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