42,183 research outputs found
On the Importance of Visual Context for Data Augmentation in Scene Understanding
Performing data augmentation for learning deep neural networks is known to be
important for training visual recognition systems. By artificially increasing
the number of training examples, it helps reducing overfitting and improves
generalization. While simple image transformations can already improve
predictive performance in most vision tasks, larger gains can be obtained by
leveraging task-specific prior knowledge. In this work, we consider object
detection, semantic and instance segmentation and augment the training images
by blending objects in existing scenes, using instance segmentation
annotations. We observe that randomly pasting objects on images hurts the
performance, unless the object is placed in the right context. To resolve this
issue, we propose an explicit context model by using a convolutional neural
network, which predicts whether an image region is suitable for placing a given
object or not. In our experiments, we show that our approach is able to improve
object detection, semantic and instance segmentation on the PASCAL VOC12 and
COCO datasets, with significant gains in a limited annotation scenario, i.e.
when only one category is annotated. We also show that the method is not
limited to datasets that come with expensive pixel-wise instance annotations
and can be used when only bounding boxes are available, by employing
weakly-supervised learning for instance masks approximation.Comment: Updated the experimental section. arXiv admin note: substantial text
overlap with arXiv:1807.0742
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
Hierarchical Object Parsing from Structured Noisy Point Clouds
Object parsing and segmentation from point clouds are challenging tasks
because the relevant data is available only as thin structures along object
boundaries or other features, and is corrupted by large amounts of noise. To
handle this kind of data, flexible shape models are desired that can accurately
follow the object boundaries. Popular models such as Active Shape and Active
Appearance models lack the necessary flexibility for this task, while recent
approaches such as the Recursive Compositional Models make model
simplifications in order to obtain computational guarantees. This paper
investigates a hierarchical Bayesian model of shape and appearance in a
generative setting. The input data is explained by an object parsing layer,
which is a deformation of a hidden PCA shape model with Gaussian prior. The
paper also introduces a novel efficient inference algorithm that uses informed
data-driven proposals to initialize local searches for the hidden variables.
Applied to the problem of object parsing from structured point clouds such as
edge detection images, the proposed approach obtains state of the art parsing
errors on two standard datasets without using any intensity information.Comment: 13 pages, 16 figure
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