30,083 research outputs found
Semantic Object Parsing with Graph LSTM
By taking the semantic object parsing task as an exemplar application
scenario, we propose the Graph Long Short-Term Memory (Graph LSTM) network,
which is the generalization of LSTM from sequential data or multi-dimensional
data to general graph-structured data. Particularly, instead of evenly and
fixedly dividing an image to pixels or patches in existing multi-dimensional
LSTM structures (e.g., Row, Grid and Diagonal LSTMs), we take each
arbitrary-shaped superpixel as a semantically consistent node, and adaptively
construct an undirected graph for each image, where the spatial relations of
the superpixels are naturally used as edges. Constructed on such an adaptive
graph topology, the Graph LSTM is more naturally aligned with the visual
patterns in the image (e.g., object boundaries or appearance similarities) and
provides a more economical information propagation route. Furthermore, for each
optimization step over Graph LSTM, we propose to use a confidence-driven scheme
to update the hidden and memory states of nodes progressively till all nodes
are updated. In addition, for each node, the forgets gates are adaptively
learned to capture different degrees of semantic correlation with neighboring
nodes. Comprehensive evaluations on four diverse semantic object parsing
datasets well demonstrate the significant superiority of our Graph LSTM over
other state-of-the-art solutions.Comment: 18 page
Attributed Multi-order Graph Convolutional Network for Heterogeneous Graphs
Heterogeneous graph neural networks aim to discover discriminative node
embeddings and relations from multi-relational networks.One challenge of
heterogeneous graph learning is the design of learnable meta-paths, which
significantly influences the quality of learned embeddings.Thus, in this paper,
we propose an Attributed Multi-Order Graph Convolutional Network (AMOGCN),
which automatically studies meta-paths containing multi-hop neighbors from an
adaptive aggregation of multi-order adjacency matrices. The proposed model
first builds different orders of adjacency matrices from manually designed node
connections. After that, an intact multi-order adjacency matrix is attached
from the automatic fusion of various orders of adjacency matrices. This process
is supervised by the node semantic information, which is extracted from the
node homophily evaluated by attributes. Eventually, we utilize a one-layer
simplifying graph convolutional network with the learned multi-order adjacency
matrix, which is equivalent to the cross-hop node information propagation with
multi-layer graph neural networks. Substantial experiments reveal that AMOGCN
gains superior semi-supervised classification performance compared with
state-of-the-art competitors
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