11,527 research outputs found
Contextual Object Detection with a Few Relevant Neighbors
A natural way to improve the detection of objects is to consider the
contextual constraints imposed by the detection of additional objects in a
given scene. In this work, we exploit the spatial relations between objects in
order to improve detection capacity, as well as analyze various properties of
the contextual object detection problem. To precisely calculate context-based
probabilities of objects, we developed a model that examines the interactions
between objects in an exact probabilistic setting, in contrast to previous
methods that typically utilize approximations based on pairwise interactions.
Such a scheme is facilitated by the realistic assumption that the existence of
an object in any given location is influenced by only few informative locations
in space. Based on this assumption, we suggest a method for identifying these
relevant locations and integrating them into a mostly exact calculation of
probability based on their raw detector responses. This scheme is shown to
improve detection results and provides unique insights about the process of
contextual inference for object detection. We show that it is generally
difficult to learn that a particular object reduces the probability of another,
and that in cases when the context and detector strongly disagree this learning
becomes virtually impossible for the purposes of improving the results of an
object detector. Finally, we demonstrate improved detection results through use
of our approach as applied to the PASCAL VOC and COCO datasets
Unsupervised Understanding of Location and Illumination Changes in Egocentric Videos
Wearable cameras stand out as one of the most promising devices for the
upcoming years, and as a consequence, the demand of computer algorithms to
automatically understand the videos recorded with them is increasing quickly.
An automatic understanding of these videos is not an easy task, and its mobile
nature implies important challenges to be faced, such as the changing light
conditions and the unrestricted locations recorded. This paper proposes an
unsupervised strategy based on global features and manifold learning to endow
wearable cameras with contextual information regarding the light conditions and
the location captured. Results show that non-linear manifold methods can
capture contextual patterns from global features without compromising large
computational resources. The proposed strategy is used, as an application case,
as a switching mechanism to improve the hand-detection problem in egocentric
videos.Comment: Submitted for publicatio
The More You Know: Using Knowledge Graphs for Image Classification
One characteristic that sets humans apart from modern learning-based computer
vision algorithms is the ability to acquire knowledge about the world and use
that knowledge to reason about the visual world. Humans can learn about the
characteristics of objects and the relationships that occur between them to
learn a large variety of visual concepts, often with few examples. This paper
investigates the use of structured prior knowledge in the form of knowledge
graphs and shows that using this knowledge improves performance on image
classification. We build on recent work on end-to-end learning on graphs,
introducing the Graph Search Neural Network as a way of efficiently
incorporating large knowledge graphs into a vision classification pipeline. We
show in a number of experiments that our method outperforms standard neural
network baselines for multi-label classification.Comment: CVPR 201
Finding Streams in Knowledge Graphs to Support Fact Checking
The volume and velocity of information that gets generated online limits
current journalistic practices to fact-check claims at the same rate.
Computational approaches for fact checking may be the key to help mitigate the
risks of massive misinformation spread. Such approaches can be designed to not
only be scalable and effective at assessing veracity of dubious claims, but
also to boost a human fact checker's productivity by surfacing relevant facts
and patterns to aid their analysis. To this end, we present a novel,
unsupervised network-flow based approach to determine the truthfulness of a
statement of fact expressed in the form of a (subject, predicate, object)
triple. We view a knowledge graph of background information about real-world
entities as a flow network, and knowledge as a fluid, abstract commodity. We
show that computational fact checking of such a triple then amounts to finding
a "knowledge stream" that emanates from the subject node and flows toward the
object node through paths connecting them. Evaluation on a range of real-world
and hand-crafted datasets of facts related to entertainment, business, sports,
geography and more reveals that this network-flow model can be very effective
in discerning true statements from false ones, outperforming existing
algorithms on many test cases. Moreover, the model is expressive in its ability
to automatically discover several useful path patterns and surface relevant
facts that may help a human fact checker corroborate or refute a claim.Comment: Extended version of the paper in proceedings of ICDM 201
Adaptive Nonparametric Image Parsing
In this paper, we present an adaptive nonparametric solution to the image
parsing task, namely annotating each image pixel with its corresponding
category label. For a given test image, first, a locality-aware retrieval set
is extracted from the training data based on super-pixel matching similarities,
which are augmented with feature extraction for better differentiation of local
super-pixels. Then, the category of each super-pixel is initialized by the
majority vote of the -nearest-neighbor super-pixels in the retrieval set.
Instead of fixing as in traditional non-parametric approaches, here we
propose a novel adaptive nonparametric approach which determines the
sample-specific k for each test image. In particular, is adaptively set to
be the number of the fewest nearest super-pixels which the images in the
retrieval set can use to get the best category prediction. Finally, the initial
super-pixel labels are further refined by contextual smoothing. Extensive
experiments on challenging datasets demonstrate the superiority of the new
solution over other state-of-the-art nonparametric solutions.Comment: 11 page
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