676 research outputs found
PDANet: Pyramid Density-aware Attention Net for Accurate Crowd Counting
Crowd counting, i.e., estimating the number of people in a crowded area, has
attracted much interest in the research community. Although many attempts have
been reported, crowd counting remains an open real-world problem due to the
vast scale variations in crowd density within the interested area, and severe
occlusion among the crowd. In this paper, we propose a novel Pyramid
Density-Aware Attention-based network, abbreviated as PDANet, that leverages
the attention, pyramid scale feature and two branch decoder modules for
density-aware crowd counting. The PDANet utilizes these modules to extract
different scale features, focus on the relevant information, and suppress the
misleading ones. We also address the variation of crowdedness levels among
different images with an exclusive Density-Aware Decoder (DAD). For this
purpose, a classifier evaluates the density level of the input features and
then passes them to the corresponding high and low crowded DAD modules.
Finally, we generate an overall density map by considering the summation of low
and high crowded density maps as spatial attention. Meanwhile, we employ two
losses to create a precise density map for the input scene. Extensive
evaluations conducted on the challenging benchmark datasets well demonstrate
the superior performance of the proposed PDANet in terms of the accuracy of
counting and generated density maps over the well-known state of the arts
Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray
Pneumonia is a life-threatening disease, which occurs in the lungs caused by
either bacterial or viral infection. It can be life-endangering if not acted
upon in the right time and thus an early diagnosis of pneumonia is vital. The
aim of this paper is to automatically detect bacterial and viral pneumonia
using digital x-ray images. It provides a detailed report on advances made in
making accurate detection of pneumonia and then presents the methodology
adopted by the authors. Four different pre-trained deep Convolutional Neural
Network (CNN)- AlexNet, ResNet18, DenseNet201, and SqueezeNet were used for
transfer learning. 5247 Bacterial, viral and normal chest x-rays images
underwent preprocessing techniques and the modified images were trained for the
transfer learning based classification task. In this work, the authors have
reported three schemes of classifications: normal vs pneumonia, bacterial vs
viral pneumonia and normal, bacterial and viral pneumonia. The classification
accuracy of normal and pneumonia images, bacterial and viral pneumonia images,
and normal, bacterial and viral pneumonia were 98%, 95%, and 93.3%
respectively. This is the highest accuracy in any scheme than the accuracies
reported in the literature. Therefore, the proposed study can be useful in
faster-diagnosing pneumonia by the radiologist and can help in the fast airport
screening of pneumonia patients.Comment: 13 Figures, 5 tables. arXiv admin note: text overlap with
arXiv:2003.1314
Resiliency in Deep Convolutional Neural Networks
The enormous success and popularity of deep convolutional neural networks for object detection has prompted their deployment in various real-world applications. However, their performance in the presence of hardware faults or damage that could occur in the field has not been studied. This thesis explores the resiliency of six popular network architectures for image classification, AlexNet, VGG16, ResNet, GoogleNet, SqueezeNet and YOLO9000, when subjected to various degrees of failures. We introduce failures in a deep network by dropping a percentage of weights at each layer. We then assess the effects of these failures on classification performance. We find the fitness of the weights and then dropped from least fit to most fit weights. Finally, we determine the ability of the network to self-heal and recover its performance by retraining its healthy portions after partial damage. We try different methods to re-train the healthy portion by varying the optimizer. We also try to find the time and resources required for re-training. We also reduce the number of parameters in GoogleNet, VGG16 to the size of SqueezeNet and re-trained with varying percentage of dataset. This can be used as a network pruning method
Advancements in Image Classification using Convolutional Neural Network
Convolutional Neural Network (CNN) is the state-of-the-art for image
classification task. Here we have briefly discussed different components of
CNN. In this paper, We have explained different CNN architectures for image
classification. Through this paper, we have shown advancements in CNN from
LeNet-5 to latest SENet model. We have discussed the model description and
training details of each model. We have also drawn a comparison among those
models.Comment: 9 pages, 15 figures, 3 Tables. Submitted to 2018 Fourth International
Conference on Research in Computational Intelligence and Communication
Networks(ICRCICN 2018
Attending Category Disentangled Global Context for Image Classification
In this paper, we propose a general framework for image classification using
the attention mechanism and global context, which could incorporate with
various network architectures to improve their performance. To investigate the
capability of the global context, we compare four mathematical models and
observe the global context encoded in the category disentangled conditional
generative model could give more guidance as "know what is task irrelevant will
also know what is relevant". Based on this observation, we define a novel
Category Disentangled Global Context (CDGC) and devise a deep network to obtain
it. By attending CDGC, the baseline networks could identify the objects of
interest more accurately, thus improving the performance. We apply the
framework to many different network architectures and compare with the
state-of-the-art on four publicly available datasets. Extensive results
validate the effectiveness and superiority of our approach. Code will be made
public upon paper acceptance.Comment: Under revie
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