15,904 research outputs found
Prototypical Kernel Learning and Open-set Foreground Perception for Generalized Few-shot Semantic Segmentation
Generalized Few-shot Semantic Segmentation (GFSS) extends Few-shot Semantic
Segmentation (FSS) to simultaneously segment unseen classes and seen classes
during evaluation. Previous works leverage additional branch or prototypical
aggregation to eliminate the constrained setting of FSS. However,
representation division and embedding prejudice, which heavily results in poor
performance of GFSS, have not been synthetical considered. We address the
aforementioned problems by jointing the prototypical kernel learning and
open-set foreground perception. Specifically, a group of learnable kernels is
proposed to perform segmentation with each kernel in charge of a stuff class.
Then, we explore to merge the prototypical learning to the update of base-class
kernels, which is consistent with the prototype knowledge aggregation of
few-shot novel classes. In addition, a foreground contextual perception module
cooperating with conditional bias based inference is adopted to perform
class-agnostic as well as open-set foreground detection, thus to mitigate the
embedding prejudice and prevent novel targets from being misclassified as
background. Moreover, we also adjust our method to the Class Incremental
Few-shot Semantic Segmentation (CIFSS) which takes the knowledge of novel
classes in a incremental stream. Extensive experiments on PASCAL-5i and
COCO-20i datasets demonstrate that our method performs better than previous
state-of-the-art.Comment: Accepted by ICCV202
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
Toward open sharing of task-based fMRI data: the OpenfMRI project
The large-scale sharing of task-based functional neuroimaging data has the potential to allow novel insights into the organization of mental function in the brain, but the field of neuroimaging has lagged behind other areas of bioscience in the development of data sharing resources. This paper describes the OpenFMRI project (accessible online at http://www.openfmri.org), which aims to provide the neuroimaging community with a resource to support open sharing of task-based fMRI studies. We describe the motivation behind the project, focusing particularly on how this project addresses some of the well-known challenges to sharing of task-based fMRI data. Results from a preliminary analysis of the current database are presented, which demonstrate the ability to classify between task contrasts with high generalization accuracy across subjects, and the ability to identify individual subjects from their activation maps with moderately high accuracy. Clustering analyses show that the similarity relations between statistical maps have a somewhat orderly relation to the mental functions engaged by the relevant tasks. These results highlight the potential of the project to support large-scale multivariate analyses of the relation between mental processes and brain function
3D-Aware Scene Manipulation via Inverse Graphics
We aim to obtain an interpretable, expressive, and disentangled scene
representation that contains comprehensive structural and textural information
for each object. Previous scene representations learned by neural networks are
often uninterpretable, limited to a single object, or lacking 3D knowledge. In
this work, we propose 3D scene de-rendering networks (3D-SDN) to address the
above issues by integrating disentangled representations for semantics,
geometry, and appearance into a deep generative model. Our scene encoder
performs inverse graphics, translating a scene into a structured object-wise
representation. Our decoder has two components: a differentiable shape renderer
and a neural texture generator. The disentanglement of semantics, geometry, and
appearance supports 3D-aware scene manipulation, e.g., rotating and moving
objects freely while keeping the consistent shape and texture, and changing the
object appearance without affecting its shape. Experiments demonstrate that our
editing scheme based on 3D-SDN is superior to its 2D counterpart.Comment: NeurIPS 2018. Code: https://github.com/ysymyth/3D-SDN Website:
http://3dsdn.csail.mit.edu
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