11,943 research outputs found
Toward a Taxonomy and Computational Models of Abnormalities in Images
The human visual system can spot an abnormal image, and reason about what
makes it strange. This task has not received enough attention in computer
vision. In this paper we study various types of atypicalities in images in a
more comprehensive way than has been done before. We propose a new dataset of
abnormal images showing a wide range of atypicalities. We design human subject
experiments to discover a coarse taxonomy of the reasons for abnormality. Our
experiments reveal three major categories of abnormality: object-centric,
scene-centric, and contextual. Based on this taxonomy, we propose a
comprehensive computational model that can predict all different types of
abnormality in images and outperform prior arts in abnormality recognition.Comment: To appear in the Thirtieth AAAI Conference on Artificial Intelligence
(AAAI 2016
Detecting Visual Relationships with Deep Relational Networks
Relationships among objects play a crucial role in image understanding.
Despite the great success of deep learning techniques in recognizing individual
objects, reasoning about the relationships among objects remains a challenging
task. Previous methods often treat this as a classification problem,
considering each type of relationship (e.g. "ride") or each distinct visual
phrase (e.g. "person-ride-horse") as a category. Such approaches are faced with
significant difficulties caused by the high diversity of visual appearance for
each kind of relationships or the large number of distinct visual phrases. We
propose an integrated framework to tackle this problem. At the heart of this
framework is the Deep Relational Network, a novel formulation designed
specifically for exploiting the statistical dependencies between objects and
their relationships. On two large datasets, the proposed method achieves
substantial improvement over state-of-the-art.Comment: To be appeared in CVPR 2017 as an oral pape
Recognizing point clouds using conditional random fields
Detecting objects in cluttered scenes is a necessary step for many robotic tasks and facilitates the interaction of the robot with its environment. Because of the availability of efficient 3D sensing devices as the Kinect, methods for the recognition of objects in 3D point clouds have gained importance during the last years. In this paper, we propose a new supervised learning approach for the recognition of objects from 3D point clouds using Conditional Random Fields, a type of discriminative, undirected probabilistic graphical model. The various features and contextual relations of the objects are described by the potential functions in the graph. Our method allows for learning and inference from unorganized point clouds of arbitrary sizes and shows significant benefit in terms of computational speed during prediction when compared to a state-of-the-art approach based on constrained optimization.Peer ReviewedPostprint (author’s final draft
Visual Representations: Defining Properties and Deep Approximations
Visual representations are defined in terms of minimal sufficient statistics
of visual data, for a class of tasks, that are also invariant to nuisance
variability. Minimal sufficiency guarantees that we can store a representation
in lieu of raw data with smallest complexity and no performance loss on the
task at hand. Invariance guarantees that the statistic is constant with respect
to uninformative transformations of the data. We derive analytical expressions
for such representations and show they are related to feature descriptors
commonly used in computer vision, as well as to convolutional neural networks.
This link highlights the assumptions and approximations tacitly assumed by
these methods and explains empirical practices such as clamping, pooling and
joint normalization.Comment: UCLA CSD TR140023, Nov. 12, 2014, revised April 13, 2015, November
13, 2015, February 28, 201
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