8,857 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
Depth from Monocular Images using a Semi-Parallel Deep Neural Network (SPDNN) Hybrid Architecture
Deep neural networks are applied to a wide range of problems in recent years.
In this work, Convolutional Neural Network (CNN) is applied to the problem of
determining the depth from a single camera image (monocular depth). Eight
different networks are designed to perform depth estimation, each of them
suitable for a feature level. Networks with different pooling sizes determine
different feature levels. After designing a set of networks, these models may
be combined into a single network topology using graph optimization techniques.
This "Semi Parallel Deep Neural Network (SPDNN)" eliminates duplicated common
network layers, and can be further optimized by retraining to achieve an
improved model compared to the individual topologies. In this study, four SPDNN
models are trained and have been evaluated at 2 stages on the KITTI dataset.
The ground truth images in the first part of the experiment are provided by the
benchmark, and for the second part, the ground truth images are the depth map
results from applying a state-of-the-art stereo matching method. The results of
this evaluation demonstrate that using post-processing techniques to refine the
target of the network increases the accuracy of depth estimation on individual
mono images. The second evaluation shows that using segmentation data alongside
the original data as the input can improve the depth estimation results to a
point where performance is comparable with stereo depth estimation. The
computational time is also discussed in this study.Comment: 44 pages, 25 figure
Interpretable Structure-Evolving LSTM
This paper develops a general framework for learning interpretable data
representation via Long Short-Term Memory (LSTM) recurrent neural networks over
hierarchal graph structures. Instead of learning LSTM models over the pre-fixed
structures, we propose to further learn the intermediate interpretable
multi-level graph structures in a progressive and stochastic way from data
during the LSTM network optimization. We thus call this model the
structure-evolving LSTM. In particular, starting with an initial element-level
graph representation where each node is a small data element, the
structure-evolving LSTM gradually evolves the multi-level graph representations
by stochastically merging the graph nodes with high compatibilities along the
stacked LSTM layers. In each LSTM layer, we estimate the compatibility of two
connected nodes from their corresponding LSTM gate outputs, which is used to
generate a merging probability. The candidate graph structures are accordingly
generated where the nodes are grouped into cliques with their merging
probabilities. We then produce the new graph structure with a
Metropolis-Hasting algorithm, which alleviates the risk of getting stuck in
local optimums by stochastic sampling with an acceptance probability. Once a
graph structure is accepted, a higher-level graph is then constructed by taking
the partitioned cliques as its nodes. During the evolving process,
representation becomes more abstracted in higher-levels where redundant
information is filtered out, allowing more efficient propagation of long-range
data dependencies. We evaluate the effectiveness of structure-evolving LSTM in
the application of semantic object parsing and demonstrate its advantage over
state-of-the-art LSTM models on standard benchmarks.Comment: To appear in CVPR 2017 as a spotlight pape
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