arXiv subjects

Jie Guo

Publications and source records attributed to Jie Guo.

98 records · Page 6Linked to original sources

arXiv2018

Attention-Mechanism-based Tracking Method for Intelligent Internet of Vehicles

Vehicle tracking task plays an important role on the internet of vehicles and intelligent transportation system. Beyond the traditional GPS sensor, the image sensor can capture different kinds of vehicles, analyze their driving situation and can interact with them. Aiming at the problem that the traditional convolutional neural network is vulnerable to background interference, this paper proposes vehicle tracking method based on human attention mechanism for self-selection of deep features with an inter-channel fully connected layer. It mainly includes the following contents: 1) A fully convolutional neural network fused attention mechanism with the selection of the deep features for convolution. 2) A separation method for template and semantic background region to separate target vehicles from the background in the initial frame adaptively. 3) A two-stage method for model training using our traffic dataset. The experimental results show that the proposed method improves the tracking accuracy without an increase in tracking time. Meanwhile, it strengthens the robustness of algorithm under the condition of the complex background region. The success rate of the proposed method in overall traffic datasets is higher than Siamese network by about 10 percent, and the overall precision is higher than Siamese network by 8 percent.

cs.CV↗

arXiv2018

Stochastic Channel Decorrelation Network and Its Application to Visual Tracking

Deep convolutional neural networks (CNNs) have dominated many computer vision domains because of their great power to extract good features automatically. However, many deep CNNs-based computer vison tasks suffer from lack of training data while there are millions of parameters in the deep models. Obviously, these two biphase violation facts will result in parameter redundancy of many poorly designed deep CNNs. Therefore, we look deep into the existing CNNs and find that the redundancy of network parameters comes from the correlation between features in different channels within a convolutional layer. To solve this problem, we propose the stochastic channel decorrelation (SCD) block which, in every iteration, randomly selects multiple pairs of channels within a convolutional layer and calculates their normalized cross correlation (NCC). Then a squared max-margin loss is proposed as the objective of SCD to suppress correlation and keep diversity between channels explicitly. The proposed SCD is very flexible and can be applied to any current existing CNN models simply. Based on the SCD and the Fully-Convolutional Siamese Networks, we proposed a visual tracking algorithm to verify the effectiveness of SCD.

cs.CV↗

arXiv2018

Fused Text Segmentation Networks for Multi-oriented Scene Text Detection

In this paper, we introduce a novel end-end framework for multi-oriented scene text detection from an instance-aware semantic segmentation perspective. We present Fused Text Segmentation Networks, which combine multi-level features during the feature extracting as text instance may rely on finer feature expression compared to general objects. It detects and segments the text instance jointly and simultaneously, leveraging merits from both semantic segmentation task and region proposal based object detection task. Not involving any extra pipelines, our approach surpasses the current state of the art on multi-oriented scene text detection benchmarks: ICDAR2015 Incidental Scene Text and MSRA-TD500 reaching Hmean 84.1% and 82.0% respectively. Morever, we report a baseline on total-text containing curved text which suggests effectiveness of the proposed approach.

cs.CV↗

arXiv2018

FPAN: Fine-grained and Progressive Attention Localization Network for Data Retrieval

The Localization of the target object for data retrieval is a key issue in the Intelligent and Connected Transportation Systems (ICTS). However, due to lack of intelligence in the traditional transportation system, it can take tremendous resources to manually retrieve and locate the queried objects among a large number of images. In order to solve this issue, we propose an effective method to query-based object localization that uses artificial intelligence techniques to automatically locate the queried object in the complex background. The presented method is termed as Fine-grained and Progressive Attention Localization Network (FPAN), which uses an image and a queried object as input to accurately locate the target object in the image. Specifically, the fine-grained attention module is naturally embedded into each layer of the convolution neural network (CNN), thereby gradually suppressing the regions that are irrelevant to the queried object and eventually shrinking attention to the target area. We further employ top-down attentions fusion algorithm operated by a learnable cascade up-sampling structure to establish the connection between the attention map and the exact location of the queried object in the original image. Furthermore, the FPAN is trained by multi-task learning with box segmentation loss and cosine loss. At last, we conduct comprehensive experiments on both queried-based digit localization and object tracking with synthetic and benchmark datasets, respectively. The experimental results show that our algorithm is far superior to other algorithms in the synthesis datasets and outperforms most existing trackers on the OTB and VOT datasets.

cs.CV↗

arXiv2018

Thermal Conductivity of Suspended Few-Layer MoS2

Modifying phonon thermal conductivity in nanomaterials is important not only for fundamental research but also for practical applications. However, the experiments on tailoring the thermal conductivity in nanoscale, especially in two-dimensional materials, are rare due to technical challenges. In this work, we demonstrate in-situ thermal conduction measurement of MoS2 and find that its thermal conductivity can be continuously tuned to a required value from crystalline to amorphous limits. The reduction of thermal conductivity is understood from phonon-defects scatterings that decrease the phonon transmission coefficient. Beyond a threshold, a sharp drop in thermal conductivity is observed, which is believed to be a crystalline-amorphous transition. Our method and results provide guidance for potential applications in thermoelectrics, photoelectronics, and energy harvesting where thermal management is critical with further integration and miniaturization.

cond-mat.mes-hall↗

arXiv2018

Dimensional crossover of heat conduction in amorphous Polyimide nanofibers

The mechanism of thermal conductivity in amorphous polymers, especially polymer fibers, is unclear in comparison with that in inorganic materials. Here, we report the observation of across over of heat conduction behavior from three dimensions (3D) to quasi-one dimension (1D) in Polyimide(PI) nanofibers at a given temperature. A theoretical model based on the random walk theory has been proposed to quantitatively describe the interplay between the inter-chain hopping and the intra-chain hopping in nanofibers. This model explains well the diameter dependence of thermal conductivity and also speculates the upper limit of thermal conductivity of amorphous polymers in the quasi-1D limit.

physics.app-ph↗

arXiv2017

Thermal conduction across a boron nitride and silicon oxide interface

The needs for efficient heat removal and superior thermal conduction in nano/micro devices have triggered tremendous studies in low-dimensional materials with high thermal conductivity. Hexagonal boron nitride (h-BN) is believed to be one of the candidates for thermal management and heat dissipation due to its novel physical properties, i.e. thermal conductor and electrical insulator. Here we reported interfacial thermal resistance between few-layer h-BN and its silicon oxide substrate using differential 3 omega method. The measured interfacial thermal resistance is around ~1.6*10-8 m2K/W for monolayer h-BN and ~3.4*10-8 m2K/W for 12.8nm-thick h-BN in metal/h-BN/SiO2 interfaces. Our results suggest that the voids and gaps between substrate and thick h-BN flakes limit the interfacial thermal conduction. This work provides a deeper understanding of utilizing h-BN flake as lateral heat spreader in electronic and optoelectronic nano/micro devices with further miniaturization and integration.

cond-mat.mes-hall↗

arXiv2016

Superior thermal conductivity in suspended bilayer hexagonal boron nitride

We reported the basal-plane thermal conductivity in exfoliated bilayer hexagonal boron nitride h-BN that was measured using suspended prepatterned microstructures. The h-BN sample suitable for thermal measurements was fabricated by dry-transfer method, whose sample quality, due to less polymer residues on surfaces, is believed to be superior to that of PMMA-mediated samples. The measured room temperature thermal conductivity is around 484 Wm-1K-1(+141 Wm-1K-1/ -24 Wm-1K-1) which exceeds that in bulk h-BN, providing experimental observation of the thickness-dependent thermal conductivity in suspended few-layer h-BN.

cond-mat.mes-hall↗