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-$L_1$ ($SL_1$) 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) + $SL_1$, 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