1,764 research outputs found
Gradient Harmonized Single-stage Detector
Despite the great success of two-stage detectors, single-stage detector is
still a more elegant and efficient way, yet suffers from the two well-known
disharmonies during training, i.e. the huge difference in quantity between
positive and negative examples as well as between easy and hard examples. In
this work, we first point out that the essential effect of the two disharmonies
can be summarized in term of the gradient. Further, we propose a novel gradient
harmonizing mechanism (GHM) to be a hedging for the disharmonies. The
philosophy behind GHM can be easily embedded into both classification loss
function like cross-entropy (CE) and regression loss function like smooth-
() loss. To this end, two novel loss functions called GHM-C and GHM-R are
designed to balancing the gradient flow for anchor classification and bounding
box refinement, respectively. Ablation study on MS COCO demonstrates that
without laborious hyper-parameter tuning, both GHM-C and GHM-R can bring
substantial improvement for single-stage detector. Without any whistles and
bells, our model achieves 41.6 mAP on COCO test-dev set which surpasses the
state-of-the-art method, Focal Loss (FL) + , by 0.8.Comment: To appear at AAAI 201
Processing and analysis of transient data from permanent down-hole gauges (PDG)
The Permanent Downhole Gauge (PDG) can monitor the reservoir in real time over a
long period of time. This produces a huge amount of real time data which can
potentially provide more information about wells and reservoirs. However, processing
large numbers of data and extracting useful information from these data brings new
challenges for industry and engineers.
A new workflow for processing the PDG data is proposed in this study. The new
approach processes PDG data from the view of gauge, well and reservoir. The gauge
information is first filtered with data preprocessing and outlier removal. Then, the
well event is identified using an improved wavelet approach. The further processing
step of data denoise and data reduction is carried out before analyzing the reservoir
information.
The accurate production history is very essential for data analysis. However, the
accurate production rate is hard to be acquired. Therefore, a new approach is created
to recover flow rate history from the accumulated production and PDG pressure data.
This new approach is based on the theory that the relation between production rate and
the amplitude of detail coefficient are in direct proportion after wavelet transform.
With accurate pressure and rate data, traditional well testing is applied to analyze the
PDG pressure data to get dynamic reservoir parameters. The numerical well testing
approach is also carried out to analyze more complex reservoir model with a new
toolbox. However, these two approaches all suffer from the nonlinear problem of PDG
pressure. So, a dynamic forward modelling approach is proposed to analyze PDG
pressure data. The new approach uses the deconvolution method to diagnose the linear
region in the nonlinear system. The nonlinear system can be divided into different
linear systems which can be analyzed with the numerical well testing approach.
Finally, a toolbox which includes a PDG data processing module and PDG data analysis
module is designed with Matlab
ViP-CNN: Visual Phrase Guided Convolutional Neural Network
As the intermediate level task connecting image captioning and object
detection, visual relationship detection started to catch researchers'
attention because of its descriptive power and clear structure. It detects the
objects and captures their pair-wise interactions with a
subject-predicate-object triplet, e.g. person-ride-horse. In this paper, each
visual relationship is considered as a phrase with three components. We
formulate the visual relationship detection as three inter-connected
recognition problems and propose a Visual Phrase guided Convolutional Neural
Network (ViP-CNN) to address them simultaneously. In ViP-CNN, we present a
Phrase-guided Message Passing Structure (PMPS) to establish the connection
among relationship components and help the model consider the three problems
jointly. Corresponding non-maximum suppression method and model training
strategy are also proposed. Experimental results show that our ViP-CNN
outperforms the state-of-art method both in speed and accuracy. We further
pretrain ViP-CNN on our cleansed Visual Genome Relationship dataset, which is
found to perform better than the pretraining on the ImageNet for this task.Comment: 10 pages, 5 figures, accepted by CVPR 201
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