197 research outputs found
A Bilevel Formalism for the Peer-Reviewing Problem
Due to the large number of submissions that more and more conferences
experience, finding an automatized way to well distribute the submitted papers
among reviewers has become necessary. We model the peer-reviewing matching
problem as a {\it bilevel programming (BP)} formulation. Our model consists of
a lower-level problem describing the reviewers' perspective and an upper-level
problem describing the editors'. Every reviewer is interested in minimizing
their overall effort, while the editors are interested in finding an allocation
that maximizes the quality of the reviews and follows the reviewers'
preferences the most. To the best of our knowledge, the proposed model is the
first one that formulates the peer-reviewing matching problem by considering
two objective functions, one to describe the reviewers' viewpoint and the other
to describe the editors' viewpoint. We demonstrate that both the upper-level
and lower-level problems are feasible and that our BP model admits a solution
under mild assumptions. After studying the properties of the solutions, we
propose a heuristic to solve our model and compare its performance with the
relevant state-of-the-art methods. Extensive numerical results show that our
approach can find fairer solutions with competitive quality and less effort
from the reviewers.Comment: 14 pages, 7 figure
PanoVOS: Bridging Non-panoramic and Panoramic Views with Transformer for Video Segmentation
Panoramic videos contain richer spatial information and have attracted
tremendous amounts of attention due to their exceptional experience in some
fields such as autonomous driving and virtual reality. However, existing
datasets for video segmentation only focus on conventional planar images. To
address the challenge, in this paper, we present a panoramic video dataset,
PanoVOS. The dataset provides 150 videos with high video resolutions and
diverse motions. To quantify the domain gap between 2D planar videos and
panoramic videos, we evaluate 15 off-the-shelf video object segmentation (VOS)
models on PanoVOS. Through error analysis, we found that all of them fail to
tackle pixel-level content discontinues of panoramic videos. Thus, we present a
Panoramic Space Consistency Transformer (PSCFormer), which can effectively
utilize the semantic boundary information of the previous frame for pixel-level
matching with the current frame. Extensive experiments demonstrate that
compared with the previous SOTA models, our PSCFormer network exhibits a great
advantage in terms of segmentation results under the panoramic setting. Our
dataset poses new challenges in panoramic VOS and we hope that our PanoVOS can
advance the development of panoramic segmentation/tracking
Optimization of Forcemyography Sensor Placement for Arm Movement Recognition
How to design an optimal wearable device for human movement recognition is
vital to reliable and accurate human-machine collaboration. Previous works
mainly fabricate wearable devices heuristically. Instead, this paper raises an
academic question: can we design an optimization algorithm to optimize the
fabrication of wearable devices such as figuring out the best sensor
arrangement automatically? Specifically, this work focuses on optimizing the
placement of Forcemyography (FMG) sensors for FMG armbands in the application
of arm movement recognition. Firstly, based on graph theory, the armband is
modeled considering sensors' signals and connectivity. Then, a Graph-based
Armband Modeling Network (GAM-Net) is introduced for arm movement recognition.
Afterward, the sensor placement optimization for FMG armbands is formulated and
an optimization algorithm with greedy local search is proposed. To study the
effectiveness of our optimization algorithm, a dataset for mechanical
maintenance tasks using FMG armbands with 16 sensors is collected. Our
experiments show that using only 4 sensors optimized with our algorithm can
help maintain a comparable recognition accuracy to using all sensors. Finally,
the optimized sensor placement result is verified from a physiological view.
This work would like to shed light on the automatic fabrication of wearable
devices considering downstream tasks, such as human biological signal
collection and movement recognition. Our code and dataset are available at
https://github.com/JerryX1110/IROS22-FMG-Sensor-OptimizationComment: 6 pages, 10 figures, Accepted by IROS22 (The 2022 IEEE/RSJ
International Conference on Intelligent Robots and Systems (IROS
HR-Pro: Point-supervised Temporal Action Localization via Hierarchical Reliability Propagation
Point-supervised Temporal Action Localization (PSTAL) is an emerging research
direction for label-efficient learning. However, current methods mainly focus
on optimizing the network either at the snippet-level or the instance-level,
neglecting the inherent reliability of point annotations at both levels. In
this paper, we propose a Hierarchical Reliability Propagation (HR-Pro)
framework, which consists of two reliability-aware stages: Snippet-level
Discrimination Learning and Instance-level Completeness Learning, both stages
explore the efficient propagation of high-confidence cues in point annotations.
For snippet-level learning, we introduce an online-updated memory to store
reliable snippet prototypes for each class. We then employ a Reliability-aware
Attention Block to capture both intra-video and inter-video dependencies of
snippets, resulting in more discriminative and robust snippet representation.
For instance-level learning, we propose a point-based proposal generation
approach as a means of connecting snippets and instances, which produces
high-confidence proposals for further optimization at the instance level.
Through multi-level reliability-aware learning, we obtain more reliable
confidence scores and more accurate temporal boundaries of predicted proposals.
Our HR-Pro achieves state-of-the-art performance on multiple challenging
benchmarks, including an impressive average mAP of 60.3% on THUMOS14. Notably,
our HR-Pro largely surpasses all previous point-supervised methods, and even
outperforms several competitive fully supervised methods. Code will be
available at https://github.com/pipixin321/HR-Pro.Comment: 12 pages, 8 figure
Msk is required for nuclear import of TGF-{beta}/BMP-activated Smads
Nuclear translocation of Smad proteins is a critical step in signal transduction of transforming growth factor beta (TGF-beta) and bone morphogenetic proteins (BMPs). Using nuclear accumulation of the Drosophila Smad Mothers against Decapentaplegic (Mad) as the readout, we carried out a whole-genome RNAi screening in Drosophila cells. The screen identified moleskin (msk) as important for the nuclear import of phosphorylated Mad. Genetic evidence in the developing eye imaginal discs also demonstrates the critical functions of msk in regulating phospho-Mad. Moreover, knockdown of importin 7 and 8 (Imp7 and 8), the mammalian orthologues of Msk, markedly impaired nuclear accumulation of Smad1 in response to BMP2 and of Smad2/3 in response to TGF-beta. Biochemical studies further suggest that Smads are novel nuclear import substrates of Imp7 and 8. We have thus identified new evolutionarily conserved proteins that are important in the signal transduction of TGF-beta and BMP into the nucleus
GlanceVAD: Exploring Glance Supervision for Label-efficient Video Anomaly Detection
In recent years, video anomaly detection has been extensively investigated in
both unsupervised and weakly supervised settings to alleviate costly temporal
labeling. Despite significant progress, these methods still suffer from
unsatisfactory results such as numerous false alarms, primarily due to the
absence of precise temporal anomaly annotation. In this paper, we present a
novel labeling paradigm, termed "glance annotation", to achieve a better
balance between anomaly detection accuracy and annotation cost. Specifically,
glance annotation is a random frame within each abnormal event, which can be
easily accessed and is cost-effective. To assess its effectiveness, we manually
annotate the glance annotations for two standard video anomaly detection
datasets: UCF-Crime and XD-Violence. Additionally, we propose a customized
GlanceVAD method, that leverages gaussian kernels as the basic unit to compose
the temporal anomaly distribution, enabling the learning of diverse and robust
anomaly representations from the glance annotations. Through comprehensive
analysis and experiments, we verify that the proposed labeling paradigm can
achieve an excellent trade-off between annotation cost and model performance.
Extensive experimental results also demonstrate the effectiveness of our
GlanceVAD approach, which significantly outperforms existing advanced
unsupervised and weakly supervised methods. Code and annotations will be
publicly available at https://github.com/pipixin321/GlanceVAD.Comment: 21 page
Msk is required for nuclear import of TGF-β/BMP-activated Smads
Nuclear translocation of Smad proteins is a critical step in signal transduction of transforming growth factor β (TGF-β) and bone morphogenetic proteins (BMPs). Using nuclear accumulation of the Drosophila Smad Mothers against Decapentaplegic (Mad) as the readout, we carried out a whole-genome RNAi screening in Drosophila cells. The screen identified moleskin (msk) as important for the nuclear import of phosphorylated Mad. Genetic evidence in the developing eye imaginal discs also demonstrates the critical functions of msk in regulating phospho-Mad. Moreover, knockdown of importin 7 and 8 (Imp7 and 8), the mammalian orthologues of Msk, markedly impaired nuclear accumulation of Smad1 in response to BMP2 and of Smad2/3 in response to TGF-β. Biochemical studies further suggest that Smads are novel nuclear import substrates of Imp7 and 8. We have thus identified new evolutionarily conserved proteins that are important in the signal transduction of TGF-β and BMP into the nucleus
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