442 research outputs found
Cylindrical Cloak with Axial Permittivity/Permeability Spatially Invariant
In order to reduce the difficulties in the experimental realizations of the
cloak but still keep good performance of invisibility, we proposed a perfect
cylindrical invisibility cloak with spatially invariant axial material
parameters. The advantage of this kind of TE (or TM) cloak is that only rho and
phi components of mu (or epsilon) are spatially variant, which makes it
possible to realize perfect invisibility with two-dimensional (2D) magnetic (or
electric) metamaterials. The effects of perturbations of the parameters on the
performance of this cloak are quantitatively analyzed by scattering theory. Our
work provides a simple and feasible solution to the experimental realization of
cloaks with ideal parameters
Online Multi-Object Tracking Using CNN-based Single Object Tracker with Spatial-Temporal Attention Mechanism
In this paper, we propose a CNN-based framework for online MOT. This
framework utilizes the merits of single object trackers in adapting appearance
models and searching for target in the next frame. Simply applying single
object tracker for MOT will encounter the problem in computational efficiency
and drifted results caused by occlusion. Our framework achieves computational
efficiency by sharing features and using ROI-Pooling to obtain individual
features for each target. Some online learned target-specific CNN layers are
used for adapting the appearance model for each target. In the framework, we
introduce spatial-temporal attention mechanism (STAM) to handle the drift
caused by occlusion and interaction among targets. The visibility map of the
target is learned and used for inferring the spatial attention map. The spatial
attention map is then applied to weight the features. Besides, the occlusion
status can be estimated from the visibility map, which controls the online
updating process via weighted loss on training samples with different occlusion
statuses in different frames. It can be considered as temporal attention
mechanism. The proposed algorithm achieves 34.3% and 46.0% in MOTA on
challenging MOT15 and MOT16 benchmark dataset respectively.Comment: Accepted at International Conference on Computer Vision (ICCV) 201
Second-Harmonic Generation and Spectrum Modulation by Active Nonlinear Metamaterial
The nonlinear properties of a metamaterial sample composed of double-layer
metallic patterns and voltage controllable diodes are experimentally
investigated. Second harmonics and spectrum modulations are clearly observed in
a wide band of microwave frequencies, showing that this kind of metamaterial is
not only tunable by low DC bias voltage, but also behaves strong nonlinear
property under a small power incidence. These properties are difficult to be
found in normal, naturally occurring materials.Comment: 14 pages, 4 figure
Origin Distribution Visualization of Floating Population and Determinants Analysis: A Case study of Yiwu City
AbstractBased on registered individual floating population data from 2005 to 2008 of Yiwu, the phenomena that population floating to Yiwu City from 34 province and 91 counties in Jiangxi provinces is analyzed. The study aims at analyzing the “pull” forces of Yiwu City and developing migration models for understanding determinants factors of population migration/floating into Yiwu City from other areas in China. The spatial layout of Yiwu's pull forces is proved as a V-shaped pattern consisting of the two axes by using explorative spatial data analysis and map visualization method. The migration models with (model 3) or without (model 2) migration stock are presented and estimated using standard linear regression model, spatial error model as well as spatial lag model at the county scale in Jiangxi province. Based on the likelihood statistics, the AIC and the Moran's I statistics of residuals, the model with migration stock provides an improved fit over the model without migration stock. The correlation between migration ratio and man land ratio is significant at the 0.5 level according to estimates of model 3 and spatial version of model 2. All the three estimates of model 2 and the OLS results of model 3 confirm the distance-decay effect while results from the spatial version of model 3 failed to support the distance rule in population floating. Contrary to the previous studies at the provincial level, the correlation between per capital net income of rural labor forces and migration ratio is not significant according to the three versions of the two models due to the small disparities of income within the counties in Jiangxi. Examination of specification tests in spatial version of model 3 indicates that there is less significant spatial error dependence in the spatial lag models than spatial lag dependence in the error models, further suggesting a preference for the lag model. Model 2 does not suggest any preference for choosing spatial error model and spatial lag model
Decoupled DETR: Spatially Disentangling Localization and Classification for Improved End-to-End Object Detection
The introduction of DETR represents a new paradigm for object detection.
However, its decoder conducts classification and box localization using shared
queries and cross-attention layers, leading to suboptimal results. We observe
that different regions of interest in the visual feature map are suitable for
performing query classification and box localization tasks, even for the same
object. Salient regions provide vital information for classification, while the
boundaries around them are more favorable for box regression. Unfortunately,
such spatial misalignment between these two tasks greatly hinders DETR's
training. Therefore, in this work, we focus on decoupling localization and
classification tasks in DETR. To achieve this, we introduce a new design scheme
called spatially decoupled DETR (SD-DETR), which includes a task-aware query
generation module and a disentangled feature learning process. We elaborately
design the task-aware query initialization process and divide the
cross-attention block in the decoder to allow the task-aware queries to match
different visual regions. Meanwhile, we also observe that the prediction
misalignment problem for high classification confidence and precise
localization exists, so we propose an alignment loss to further guide the
spatially decoupled DETR training. Through extensive experiments, we
demonstrate that our approach achieves a significant improvement in MSCOCO
datasets compared to previous work. For instance, we improve the performance of
Conditional DETR by 4.5 AP. By spatially disentangling the two tasks, our
method overcomes the misalignment problem and greatly improves the performance
of DETR for object detection.Comment: accepted by ICCV202
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