5,776 research outputs found
Dynamic Feature Integration for Simultaneous Detection of Salient Object, Edge and Skeleton
In this paper, we solve three low-level pixel-wise vision problems, including
salient object segmentation, edge detection, and skeleton extraction, within a
unified framework. We first show some similarities shared by these tasks and
then demonstrate how they can be leveraged for developing a unified framework
that can be trained end-to-end. In particular, we introduce a selective
integration module that allows each task to dynamically choose features at
different levels from the shared backbone based on its own characteristics.
Furthermore, we design a task-adaptive attention module, aiming at
intelligently allocating information for different tasks according to the image
content priors. To evaluate the performance of our proposed network on these
tasks, we conduct exhaustive experiments on multiple representative datasets.
We will show that though these tasks are naturally quite different, our network
can work well on all of them and even perform better than current
single-purpose state-of-the-art methods. In addition, we also conduct adequate
ablation analyses that provide a full understanding of the design principles of
the proposed framework. To facilitate future research, source code will be
released
S4Net: Single Stage Salient-Instance Segmentation
We consider an interesting problem-salient instance segmentation in this
paper. Other than producing bounding boxes, our network also outputs
high-quality instance-level segments. Taking into account the
category-independent property of each target, we design a single stage salient
instance segmentation framework, with a novel segmentation branch. Our new
branch regards not only local context inside each detection window but also its
surrounding context, enabling us to distinguish the instances in the same scope
even with obstruction. Our network is end-to-end trainable and runs at a fast
speed (40 fps when processing an image with resolution 320x320). We evaluate
our approach on a publicly available benchmark and show that it outperforms
other alternative solutions. We also provide a thorough analysis of the design
choices to help readers better understand the functions of each part of our
network. The source code can be found at
\url{https://github.com/RuochenFan/S4Net}
Two-phase Unsourced Random Access in Massive MIMO: Performance Analysis and Approximate Message Passing Decoder
In this paper, we design a novel two-phase unsourced random access (URA)
scheme in massive multiple input multiple output (MIMO). In the first phase, we
collect a sequence of information bits to jointly acquire the user channel
state information (CSI) and the associated information bits. In the second
phase, the residual information bits of all the users are partitioned into
sub-blocks with a very short length to exhibit a higher spectral efficiency and
a lower computational complexity than the existing transmission schemes in
massive MIMO URA. By using the acquired CSI in the first phase, the sub-block
recovery in the second phase is cast as a compressed sensing (CS) problem. From
the perspective of the statistical physics, we provide a theoretical framework
for our proposed URA scheme to analyze the induced problem based on the replica
method.
The analytical results show that the performance metrics of our URA scheme
can be linked to the system parameters by a single-valued free entropy
function. An AMP-based recovery algorithm is designed to achieve the
performance indicated by the proposed theoretical framework. Simulations verify
that our scheme outperforms the most recent counterparts.Comment: 16pages,7 figure
A Fully Bayesian Approach for Massive MIMO Unsourced Random Access
In this paper, we propose a novel fully Bayesian approach for the massive
multiple-input multiple-output (MIMO) massive unsourced random access (URA).
The payload of each user device is coded by the sparse regression codes
(SPARCs) without redundant parity bits. A Bayesian model is established to
capture the probabilistic characteristics of the overall system. Particularly,
we adopt the core idea of the model-based learning approach to establish a
flexible Bayesian channel model to adapt the complex environments. Different
from the traditional divide-and-conquer or pilot-based massive MIMO URA
strategies, we propose a three-layer message passing (TLMP) algorithm to
jointly decode all the information blocks, as well as acquire the massive MIMO
channel, which adopts the core idea of the variational message passing and
approximate message passing. We verify that our proposed TLMP significantly
enhances the spectral efficiency compared with the state-of-the-arts baselines,
and is more robust to the possible codeword collisions
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