29,862 research outputs found
On Filter Size in Graph Convolutional Networks
Recently, many researchers have been focusing on the definition of neural
networks for graphs. The basic component for many of these approaches remains
the graph convolution idea proposed almost a decade ago. In this paper, we
extend this basic component, following an intuition derived from the well-known
convolutional filters over multi-dimensional tensors. In particular, we derive
a simple, efficient and effective way to introduce a hyper-parameter on graph
convolutions that influences the filter size, i.e. its receptive field over the
considered graph. We show with experimental results on real-world graph
datasets that the proposed graph convolutional filter improves the predictive
performance of Deep Graph Convolutional Networks.Comment: arXiv admin note: text overlap with arXiv:1811.0693
MLGCN: An Ultra Efficient Graph Convolution Neural Model For 3D Point Cloud Analysis
The analysis of 3D point clouds has diverse applications in robotics, vision
and graphics. Processing them presents specific challenges since they are
naturally sparse, can vary in spatial resolution and are typically unordered.
Graph-based networks to abstract features have emerged as a promising
alternative to convolutional neural networks for their analysis, but these can
be computationally heavy as well as memory inefficient. To address these
limitations we introduce a novel Multi-level Graph Convolution Neural (MLGCN)
model, which uses Graph Neural Networks (GNN) blocks to extract features from
3D point clouds at specific locality levels. Our approach employs precomputed
graph KNNs, where each KNN graph is shared between GCN blocks inside a GNN
block, making it both efficient and effective compared to present models. We
demonstrate the efficacy of our approach on point cloud based object
classification and part segmentation tasks on benchmark datasets, showing that
it produces comparable results to those of state-of-the-art models while
requiring up to a thousand times fewer floating-point operations (FLOPs) and
having significantly reduced storage requirements. Thus, our MLGCN model could
be particular relevant to point cloud based 3D shape analysis in industrial
applications when computing resources are scarce
clcNet: Improving the Efficiency of Convolutional Neural Network using Channel Local Convolutions
Depthwise convolution and grouped convolution has been successfully applied
to improve the efficiency of convolutional neural network (CNN). We suggest
that these models can be considered as special cases of a generalized
convolution operation, named channel local convolution(CLC), where an output
channel is computed using a subset of the input channels. This definition
entails computation dependency relations between input and output channels,
which can be represented by a channel dependency graph(CDG). By modifying the
CDG of grouped convolution, a new CLC kernel named interlaced grouped
convolution (IGC) is created. Stacking IGC and GC kernels results in a
convolution block (named CLC Block) for approximating regular convolution. By
resorting to the CDG as an analysis tool, we derive the rule for setting the
meta-parameters of IGC and GC and the framework for minimizing the
computational cost. A new CNN model named clcNet is then constructed using CLC
blocks, which shows significantly higher computational efficiency and fewer
parameters compared to state-of-the-art networks, when being tested using the
ImageNet-1K dataset. Source code is available at
https://github.com/dqzhang17/clcnet.torch
Exploring Context with Deep Structured models for Semantic Segmentation
State-of-the-art semantic image segmentation methods are mostly based on
training deep convolutional neural networks (CNNs). In this work, we proffer to
improve semantic segmentation with the use of contextual information. In
particular, we explore `patch-patch' context and `patch-background' context in
deep CNNs. We formulate deep structured models by combining CNNs and
Conditional Random Fields (CRFs) for learning the patch-patch context between
image regions. Specifically, we formulate CNN-based pairwise potential
functions to capture semantic correlations between neighboring patches.
Efficient piecewise training of the proposed deep structured model is then
applied in order to avoid repeated expensive CRF inference during the course of
back propagation. For capturing the patch-background context, we show that a
network design with traditional multi-scale image inputs and sliding pyramid
pooling is very effective for improving performance. We perform comprehensive
evaluation of the proposed method. We achieve new state-of-the-art performance
on a number of challenging semantic segmentation datasets including ,
-, , -, -,
-, and datasets. Particularly, we report an
intersection-over-union score of on the - dataset.Comment: 16 pages. Accepted to IEEE T. Pattern Analysis & Machine
Intelligence, 2017. Extended version of arXiv:1504.0101
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