5,555 research outputs found
Distributed Stochastic Optimization over Time-Varying Noisy Network
This paper is concerned with distributed stochastic multi-agent optimization
problem over a class of time-varying network with slowly decreasing
communication noise effects. This paper considers the problem in composite
optimization setting which is more general in noisy network optimization. It is
noteworthy that existing methods for noisy network optimization are Euclidean
projection based. We present two related different classes of non-Euclidean
methods and investigate their convergence behavior. One is distributed
stochastic composite mirror descent type method (DSCMD-N) which provides a more
general algorithm framework than former works in this literature. As a
counterpart, we also consider a composite dual averaging type method (DSCDA-N)
for noisy network optimization. Some main error bounds for DSCMD-N and DSCDA-N
are obtained. The trade-off among stepsizes, noise decreasing rates,
convergence rates of algorithm is analyzed in detail. To the best of our
knowledge, this is the first work to analyze and derive convergence rates of
optimization algorithm in noisy network optimization. We show that an optimal
rate of in nonsmooth convex optimization can be obtained for
proposed methods under appropriate communication noise condition. Moveover,
convergence rates in different orders are comprehensively derived in both
expectation convergence and high probability convergence sense.Comment: 27 page
Equivalence of Two Approaches for Quantum-Classical Hybrid Systems
We discuss two approaches that are used frequently to describe
quantum-classical hybrid system. One is the well-known mean-field theory and
the other adopts a set of hybrid brackets which is a mixture of quantum
commutators and classical Poisson brackets. We prove that these two approaches
are equivalent.Comment: 9 page
RSVG: Exploring Data and Models for Visual Grounding on Remote Sensing Data
In this paper, we introduce the task of visual grounding for remote sensing
data (RSVG). RSVG aims to localize the referred objects in remote sensing (RS)
images with the guidance of natural language. To retrieve rich information from
RS imagery using natural language, many research tasks, like RS image visual
question answering, RS image captioning, and RS image-text retrieval have been
investigated a lot. However, the object-level visual grounding on RS images is
still under-explored. Thus, in this work, we propose to construct the dataset
and explore deep learning models for the RSVG task. Specifically, our
contributions can be summarized as follows. 1) We build the new large-scale
benchmark dataset of RSVG, termed RSVGD, to fully advance the research of RSVG.
This new dataset includes image/expression/box triplets for training and
evaluating visual grounding models. 2) We benchmark extensive state-of-the-art
(SOTA) natural image visual grounding methods on the constructed RSVGD dataset,
and some insightful analyses are provided based on the results. 3) A novel
transformer-based Multi-Level Cross-Modal feature learning (MLCM) module is
proposed. Remotely-sensed images are usually with large scale variations and
cluttered backgrounds. To deal with the scale-variation problem, the MLCM
module takes advantage of multi-scale visual features and multi-granularity
textual embeddings to learn more discriminative representations. To cope with
the cluttered background problem, MLCM adaptively filters irrelevant noise and
enhances salient features. In this way, our proposed model can incorporate more
effective multi-level and multi-modal features to boost performance.
Furthermore, this work also provides useful insights for developing better RSVG
models. The dataset and code will be publicly available at
https://github.com/ZhanYang-nwpu/RSVG-pytorch.Comment: 12 pages, 10 figure
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