The convolutional neural networks (CNN) like AlexNet, GoogleNet, VGGNet, etc. have been proven as the very discriminative feature descriptor for many computer vision problems. The trained CNN model over one dataset performs reasonably well over another dataset of similar type and outperforms the hand-designed feature descriptor. The Rectified Linear Unit (ReLU) layer discards some information in order to introduce the non-linearity. In this paper, it is proposed that the discriminative ability of deep image representation using trained model can be improved by Average Biased ReLU (AB-ReLU) at last few layers. Basically, AB-ReLU improves the discriminative ability by two ways: 1) it also exploits some of the discriminative and discarded negative information of ReLU and 2) it kills the irrelevant and positive information used by ReLU. The VGGFace model already trained in MatConvNet over the VGG-Face dataset is used as the feature descriptor for face retrieval over other face datasets. The proposed approach is tested over six challenging unconstrained and robust face datasets like PubFig, LFW, PaSC, AR, etc. in retrieval framework. It is observed that AB-ReLU is consistently performed better than ReLU using VGGFace pretrained model over face datasets.
The rectified linear unit (ReLU) in a neural network is used to introduce the non-linearity @cite_6 . The ReLU simply works like a filter, ignores the negative signals and pass the positive signals. Consider @math is the input volume to ReLU at @math layer of any network and @math is the output volume of ReLU for @math layer. Suppose the input volume @math is @math dimensional and @math is the size of the input volume in @math dimension @math . Then, an element at position @math of output volume @math is computed from the corresponding element of input volume @math as follows, where @math is @math -dimensional, @math is the size of @math in @math dimension, @math @math . The ReLU function is illustrated in Fig. . It is linear in the @math range, whereas zero in the @math range. The main drawback with ReLU is that it passes all @math values even it might not be important and blocks all @math values even it might be important. This problem is solved in the next section by introducing a data dependent ReLU.
@cite_6
1910776219
Cross-scene crowd counting is a challenging task where no laborious data annotation is required for counting people in new target surveillance crowd scenes unseen in the training set. The performance of most existing crowd counting methods drops significantly when they are applied to an unseen scene. To address this problem, we propose a deep convolutional neural network (CNN) for crowd counting, and it is trained alternatively with two related learning objectives, crowd density and crowd count. This proposed switchable learning approach is able to obtain better local optimum for both objectives. To handle an unseen target crowd scene, we present a data-driven method to finetune the trained CNN model for the target scene. A new dataset including 108 crowd scenes with nearly 200,000 head annotations is introduced to better evaluate the accuracy of cross-scene crowd counting methods. Extensive experiments on the proposed and another two existing datasets demonstrate the effectiveness and reliability of our approach.