2,305 research outputs found
STLGRU: Spatio-Temporal Lightweight Graph GRU for Traffic Flow Prediction
Reliable forecasting of traffic flow requires efficient modeling of traffic
data. Different correlations and influences arise in a dynamic traffic network,
making modeling a complicated task. Existing literature has proposed many
different methods to capture the complex underlying spatial-temporal relations
of traffic networks. However, methods still struggle to capture different local
and global dependencies of long-range nature. Also, as more and more
sophisticated methods are being proposed, models are increasingly becoming
memory-heavy and, thus, unsuitable for low-powered devices. In this paper, we
focus on solving these problems by proposing a novel deep learning framework -
STLGRU. Specifically, our proposed STLGRU can effectively capture both local
and global spatial-temporal relations of a traffic network using
memory-augmented attention and gating mechanism. Instead of employing separate
temporal and spatial components, we show that our memory module and gated unit
can learn the spatial-temporal dependencies successfully, allowing for reduced
memory usage with fewer parameters. We extensively experiment on several
real-world traffic prediction datasets to show that our model performs better
than existing methods while the memory footprint remains lower. Code is
available at \url{https://github.com/Kishor-Bhaumik/STLGRU}
GFF: Gated Fully Fusion for Semantic Segmentation
Semantic segmentation generates comprehensive understanding of scenes through
densely predicting the category for each pixel. High-level features from Deep
Convolutional Neural Networks already demonstrate their effectiveness in
semantic segmentation tasks, however the coarse resolution of high-level
features often leads to inferior results for small/thin objects where detailed
information is important. It is natural to consider importing low level
features to compensate for the lost detailed information in high-level
features.Unfortunately, simply combining multi-level features suffers from the
semantic gap among them. In this paper, we propose a new architecture, named
Gated Fully Fusion (GFF), to selectively fuse features from multiple levels
using gates in a fully connected way. Specifically, features at each level are
enhanced by higher-level features with stronger semantics and lower-level
features with more details, and gates are used to control the propagation of
useful information which significantly reduces the noises during fusion. We
achieve the state of the art results on four challenging scene parsing datasets
including Cityscapes, Pascal Context, COCO-stuff and ADE20K.Comment: accepted by AAAI-2020(oral
Attentional Encoder Network for Targeted Sentiment Classification
Targeted sentiment classification aims at determining the sentimental
tendency towards specific targets. Most of the previous approaches model
context and target words with RNN and attention. However, RNNs are difficult to
parallelize and truncated backpropagation through time brings difficulty in
remembering long-term patterns. To address this issue, this paper proposes an
Attentional Encoder Network (AEN) which eschews recurrence and employs
attention based encoders for the modeling between context and target. We raise
the label unreliability issue and introduce label smoothing regularization. We
also apply pre-trained BERT to this task and obtain new state-of-the-art
results. Experiments and analysis demonstrate the effectiveness and lightweight
of our model.Comment: 7 page
Graph-Segmenter: Graph Transformer with Boundary-aware Attention for Semantic Segmentation
The transformer-based semantic segmentation approaches, which divide the
image into different regions by sliding windows and model the relation inside
each window, have achieved outstanding success. However, since the relation
modeling between windows was not the primary emphasis of previous work, it was
not fully utilized. To address this issue, we propose a Graph-Segmenter,
including a Graph Transformer and a Boundary-aware Attention module, which is
an effective network for simultaneously modeling the more profound relation
between windows in a global view and various pixels inside each window as a
local one, and for substantial low-cost boundary adjustment. Specifically, we
treat every window and pixel inside the window as nodes to construct graphs for
both views and devise the Graph Transformer. The introduced boundary-aware
attention module optimizes the edge information of the target objects by
modeling the relationship between the pixel on the object's edge. Extensive
experiments on three widely used semantic segmentation datasets (Cityscapes,
ADE-20k and PASCAL Context) demonstrate that our proposed network, a Graph
Transformer with Boundary-aware Attention, can achieve state-of-the-art
segmentation performance
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