5,140 research outputs found
Stopping Set Distributions of Some Linear Codes
Stopping sets and stopping set distribution of an low-density parity-check
code are used to determine the performance of this code under iterative
decoding over a binary erasure channel (BEC). Let be a binary
linear code with parity-check matrix , where the rows of may be
dependent. A stopping set of with parity-check matrix is a subset
of column indices of such that the restriction of to does not
contain a row of weight one. The stopping set distribution
enumerates the number of stopping sets with size of with parity-check
matrix . Note that stopping sets and stopping set distribution are related
to the parity-check matrix of . Let be the parity-check matrix
of which is formed by all the non-zero codewords of its dual code
. A parity-check matrix is called BEC-optimal if
and has the smallest number of rows. On the
BEC, iterative decoder of with BEC-optimal parity-check matrix is an
optimal decoder with much lower decoding complexity than the exhaustive
decoder. In this paper, we study stopping sets, stopping set distributions and
BEC-optimal parity-check matrices of binary linear codes. Using finite geometry
in combinatorics, we obtain BEC-optimal parity-check matrices and then
determine the stopping set distributions for the Simplex codes, the Hamming
codes, the first order Reed-Muller codes and the extended Hamming codes.Comment: 33 pages, submitted to IEEE Trans. Inform. Theory, Feb. 201
Multi-scale Deep Learning Architectures for Person Re-identification
Person Re-identification (re-id) aims to match people across non-overlapping
camera views in a public space. It is a challenging problem because many people
captured in surveillance videos wear similar clothes. Consequently, the
differences in their appearance are often subtle and only detectable at the
right location and scales. Existing re-id models, particularly the recently
proposed deep learning based ones match people at a single scale. In contrast,
in this paper, a novel multi-scale deep learning model is proposed. Our model
is able to learn deep discriminative feature representations at different
scales and automatically determine the most suitable scales for matching. The
importance of different spatial locations for extracting discriminative
features is also learned explicitly. Experiments are carried out to demonstrate
that the proposed model outperforms the state-of-the art on a number of
benchmarksComment: 9 pages, 3 figures, accepted by ICCV 201
Tucker Bilinear Attention Network for Multi-scale Remote Sensing Object Detection
Object detection on VHR remote sensing images plays a vital role in
applications such as urban planning, land resource management, and rescue
missions. The large-scale variation of the remote-sensing targets is one of the
main challenges in VHR remote-sensing object detection. Existing methods
improve the detection accuracy of high-resolution remote sensing objects by
improving the structure of feature pyramids and adopting different attention
modules. However, for small targets, there still be seriously missed detections
due to the loss of key detail features. There is still room for improvement in
the way of multiscale feature fusion and balance. To address this issue, this
paper proposes two novel modules: Guided Attention and Tucker Bilinear
Attention, which are applied to the stages of early fusion and late fusion
respectively. The former can effectively retain clean key detail features, and
the latter can better balance features through semantic-level correlation
mining. Based on two modules, we build a new multi-scale remote sensing object
detection framework. No bells and whistles. The proposed method largely
improves the average precisions of small objects and achieves the highest mean
average precisions compared with 9 state-of-the-art methods on DOTA, DIOR, and
NWPU VHR-10.Code and models are available at
https://github.com/Shinichict/GTNet.Comment: arXiv admin note: text overlap with arXiv:1705.06676,
arXiv:2209.13351 by other author
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