favorite2 Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman.
favorite2Figure 2: The first zoom increases the IoU by a small amount and therefore results in a positive reward, but also cuts off a large portion of the ground truth, which cannot be covered anymore in the future using the 1-stage approach..
favorite117Even though the correctness of a detection is evaluated using the IoU between the bounding box and the ground truth, using a zoom reward which is positive if a zoom increases the IoU and negative otherwise, does often not result in a favorable zoom action (see figure 2).
favorite10They use deep Q-learning for class-specific object detection which allows an agent to stepwise deform a bounding box in its size, position and aspect ratio to fit an object.
favorite4The best performing approach comprises a zoom stage and a refinement stage, uses aspect-ratio modifying actions and is trained using a combination of three different reward metrics.
favorite3Tang, "Deep Convolutional Network Cascade for Facial Key Point Detection," in Conference on Computer Vision and Pattern Recognition, 2013.
favorite14Both figure 15 and figure 16 show that both train and validation RMSE decrease over time and they end up being close enough in the final check-pointed model.
favorite0Both figure 13 and figure 14 show that both train and validation RMSE decrease over time with small wiggles and end up being close in the check-pointed model, which can be seen 30 epochs before the last epoch on x-axis.
favorite6Both figure 5 and figure 6 show that both train and validation RMSE decrease over time with small wiggles along the curve showcasing that the batch size was rightly chosen and end up being close enough, thus showing that the two NaimishNet models generalized well.
favorite13 addressed FKPs detection by first applying histogram stretching for image contrast enhancement, followed by principal component analysis for noise reduction and mean patch search algorithm with correlation scoring and mutual information scoring for predicting left and right eye centers.
favorite9Facial Key Points Detection using Deep Convolutional Neural Network - NaimishNet. Naimish Agarwal, IIIT-Allahabad (email@example.com) Artus Krohn-Grimberghe, University of Paderborn (firstname.lastname@example.org) Ranjana Vyas, IIIT-Allahabad (email@example.com).
favorite149Table 5: Selected pipeline and evaluation score for different datasets We also used AutoCompete in the AutoML Challenge.
favorite40These five datasets selected differ a lot from each other in terms of the number of variables, kind of data, machine learning task type to be applied and selection of evaluation metrics.
favorite0Table 1: Classification and regression modules present in the current AutoCompete framework We propose two different selectors for selection of model and the corresponding hyperparameters: (a) random search, (b) grid-search on a given parameter space.
favorite10Figure 1 shows the performance of our human expert supported by earlier versions of this framework in selected machine learning competitions.
favorite14The proposed system helps in identifying data types, choosing a machine learning model, tuning hyper-parameters, avoiding over-fitting and optimization for a provided evaluation metric.