435 research outputs found
Nonconvex Nonsmooth Low-Rank Minimization via Iteratively Reweighted Nuclear Norm
The nuclear norm is widely used as a convex surrogate of the rank function in
compressive sensing for low rank matrix recovery with its applications in image
recovery and signal processing. However, solving the nuclear norm based relaxed
convex problem usually leads to a suboptimal solution of the original rank
minimization problem. In this paper, we propose to perform a family of
nonconvex surrogates of -norm on the singular values of a matrix to
approximate the rank function. This leads to a nonconvex nonsmooth minimization
problem. Then we propose to solve the problem by Iteratively Reweighted Nuclear
Norm (IRNN) algorithm. IRNN iteratively solves a Weighted Singular Value
Thresholding (WSVT) problem, which has a closed form solution due to the
special properties of the nonconvex surrogate functions. We also extend IRNN to
solve the nonconvex problem with two or more blocks of variables. In theory, we
prove that IRNN decreases the objective function value monotonically, and any
limit point is a stationary point. Extensive experiments on both synthesized
data and real images demonstrate that IRNN enhances the low-rank matrix
recovery compared with state-of-the-art convex algorithms
Generalized Nonconvex Nonsmooth Low-Rank Minimization
As surrogate functions of -norm, many nonconvex penalty functions have
been proposed to enhance the sparse vector recovery. It is easy to extend these
nonconvex penalty functions on singular values of a matrix to enhance low-rank
matrix recovery. However, different from convex optimization, solving the
nonconvex low-rank minimization problem is much more challenging than the
nonconvex sparse minimization problem. We observe that all the existing
nonconvex penalty functions are concave and monotonically increasing on
. Thus their gradients are decreasing functions. Based on this
property, we propose an Iteratively Reweighted Nuclear Norm (IRNN) algorithm to
solve the nonconvex nonsmooth low-rank minimization problem. IRNN iteratively
solves a Weighted Singular Value Thresholding (WSVT) problem. By setting the
weight vector as the gradient of the concave penalty function, the WSVT problem
has a closed form solution. In theory, we prove that IRNN decreases the
objective function value monotonically, and any limit point is a stationary
point. Extensive experiments on both synthetic data and real images demonstrate
that IRNN enhances the low-rank matrix recovery compared with state-of-the-art
convex algorithms.Comment: IEEE International Conference on Computer Vision and Pattern
Recognition, 201
Concurrence-Aware Long Short-Term Sub-Memories for Person-Person Action Recognition
Recently, Long Short-Term Memory (LSTM) has become a popular choice to model
individual dynamics for single-person action recognition due to its ability of
modeling the temporal information in various ranges of dynamic contexts.
However, existing RNN models only focus on capturing the temporal dynamics of
the person-person interactions by naively combining the activity dynamics of
individuals or modeling them as a whole. This neglects the inter-related
dynamics of how person-person interactions change over time. To this end, we
propose a novel Concurrence-Aware Long Short-Term Sub-Memories (Co-LSTSM) to
model the long-term inter-related dynamics between two interacting people on
the bounding boxes covering people. Specifically, for each frame, two
sub-memory units store individual motion information, while a concurrent LSTM
unit selectively integrates and stores inter-related motion information between
interacting people from these two sub-memory units via a new co-memory cell.
Experimental results on the BIT and UT datasets show the superiority of
Co-LSTSM compared with the state-of-the-art methods
Image-Specific Information Suppression and Implicit Local Alignment for Text-based Person Search
Text-based person search (TBPS) is a challenging task that aims to search
pedestrian images with the same identity from an image gallery given a query
text. In recent years, TBPS has made remarkable progress and state-of-the-art
methods achieve superior performance by learning local fine-grained
correspondence between images and texts. However, most existing methods rely on
explicitly generated local parts to model fine-grained correspondence between
modalities, which is unreliable due to the lack of contextual information or
the potential introduction of noise. Moreover, existing methods seldom consider
the information inequality problem between modalities caused by image-specific
information. To address these limitations, we propose an efficient joint
Multi-level Alignment Network (MANet) for TBPS, which can learn aligned
image/text feature representations between modalities at multiple levels, and
realize fast and effective person search. Specifically, we first design an
image-specific information suppression module, which suppresses image
background and environmental factors by relation-guided localization and
channel attention filtration respectively. This module effectively alleviates
the information inequality problem and realizes the alignment of information
volume between images and texts. Secondly, we propose an implicit local
alignment module to adaptively aggregate all pixel/word features of image/text
to a set of modality-shared semantic topic centers and implicitly learn the
local fine-grained correspondence between modalities without additional
supervision and cross-modal interactions. And a global alignment is introduced
as a supplement to the local perspective. The cooperation of global and local
alignment modules enables better semantic alignment between modalities.
Extensive experiments on multiple databases demonstrate the effectiveness and
superiority of our MANet
Erasing, Transforming, and Noising Defense Network for Occluded Person Re-Identification
Occlusion perturbation presents a significant challenge in person
re-identification (re-ID), and existing methods that rely on external visual
cues require additional computational resources and only consider the issue of
missing information caused by occlusion. In this paper, we propose a simple yet
effective framework, termed Erasing, Transforming, and Noising Defense Network
(ETNDNet), which treats occlusion as a noise disturbance and solves occluded
person re-ID from the perspective of adversarial defense. In the proposed
ETNDNet, we introduce three strategies: Firstly, we randomly erase the feature
map to create an adversarial representation with incomplete information,
enabling adversarial learning of identity loss to protect the re-ID system from
the disturbance of missing information. Secondly, we introduce random
transformations to simulate the position misalignment caused by occlusion,
training the extractor and classifier adversarially to learn robust
representations immune to misaligned information. Thirdly, we perturb the
feature map with random values to address noisy information introduced by
obstacles and non-target pedestrians, and employ adversarial gaming in the
re-ID system to enhance its resistance to occlusion noise. Without bells and
whistles, ETNDNet has three key highlights: (i) it does not require any
external modules with parameters, (ii) it effectively handles various issues
caused by occlusion from obstacles and non-target pedestrians, and (iii) it
designs the first GAN-based adversarial defense paradigm for occluded person
re-ID. Extensive experiments on five public datasets fully demonstrate the
effectiveness, superiority, and practicality of the proposed ETNDNet. The code
will be released at \url{https://github.com/nengdong96/ETNDNet}
Dexmedetomidine reduces inflammation in mice with acute pancreatitis by inhibiting NLRP3 inflammasome and sympathetic nerve activity
Purpose: To study the anti-inflammatory influence of dexmedetomidine (DEX) in mice with acute pancreatitis (AP), and to determine the underlying mechanism.Methods: A total of 75 healthy ICR male mice were randomly divided into control, mild acute pancreatitis (MAP), MAP+DEX, severe acute pancreatitis (SAP), and SAP+DEX groups, with 15 mice/group. Blood levels of inflammatory factors (TNF-α and IL-1β) and norepinephrine were assayed ineach group. Western blotting was used to assay the protein expressions of NLRP3 and norepinephrine transporter (NET) in the pancreatic tissue of each group.Results: The levels of inflammatory factors in the MAP+DEX group were markedly lower than those in the MAP group after 10 h of MAP induction (p < 0.01). Mice in MAP+DEX group had significantly lower expression of NLRP3 in pancreatic tissue, and significantly higher NET protein level, relative to the MAP mice. Following 10 h of SAP, concentrations of the inflammatory factors and the pancreatic expression of NLRP3 were lower in SAP+DEX-treated mice than in SAP mice, while NET protein was significantly higher in SAP mice (p < 0.01).Conclusion: DEX reduces the expressions of inflammation-related factors TNF-α and IL-1β, and inhibits inflammatory response in mice with AP via downregulation of NET protein expression via inhibition of NLRP3 and early sympathetic events.
Keywords: Dexmedetomidine, NLRP3 inflammasome, Sympathetic nerve, Acute pancreatitis, Inflammatory respons
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