364 research outputs found
MR-GNN: Multi-Resolution and Dual Graph Neural Network for Predicting Structured Entity Interactions
Predicting interactions between structured entities lies at the core of
numerous tasks such as drug regimen and new material design. In recent years,
graph neural networks have become attractive. They represent structured
entities as graphs and then extract features from each individual graph using
graph convolution operations. However, these methods have some limitations: i)
their networks only extract features from a fix-sized subgraph structure (i.e.,
a fix-sized receptive field) of each node, and ignore features in substructures
of different sizes, and ii) features are extracted by considering each entity
independently, which may not effectively reflect the interaction between two
entities. To resolve these problems, we present MR-GNN, an end-to-end graph
neural network with the following features: i) it uses a multi-resolution based
architecture to extract node features from different neighborhoods of each
node, and, ii) it uses dual graph-state long short-term memory networks
(L-STMs) to summarize local features of each graph and extracts the interaction
features between pairwise graphs. Experiments conducted on real-world datasets
show that MR-GNN improves the prediction of state-of-the-art methods.Comment: Accepted by IJCAI 201
Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition
Recognizing irregular text in natural scene images is challenging due to the
large variance in text appearance, such as curvature, orientation and
distortion. Most existing approaches rely heavily on sophisticated model
designs and/or extra fine-grained annotations, which, to some extent, increase
the difficulty in algorithm implementation and data collection. In this work,
we propose an easy-to-implement strong baseline for irregular scene text
recognition, using off-the-shelf neural network components and only word-level
annotations. It is composed of a -layer ResNet, an LSTM-based
encoder-decoder framework and a 2-dimensional attention module. Despite its
simplicity, the proposed method is robust and achieves state-of-the-art
performance on both regular and irregular scene text recognition benchmarks.
Code is available at: https://tinyurl.com/ShowAttendReadComment: Accepted to Proc. AAAI Conference on Artificial Intelligence 201
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