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pdf2markdown/marker/data/examples/json/multicolcnn.json

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"html": "<p block-type=\"Text\"><i>We propose the use of dilated filters to construct an ag</i><i>gregation module in a multicolumn convolutional neural</i> <i>network for perspective-free counting. Counting is a com</i><i>mon problem in computer vision (e.g. traffic on the street or</i> <i>pedestrians in a crowd). Modern approaches to the count</i><i>ing problem involve the production of a density map via re</i><i>gression whose integral is equal to the number of objects</i> <i>in the image. However, objects in the image can occur at</i> <i>different scales (e.g. due to perspective effects) which can</i> <i>make it difficult for a learning agent to learn the proper</i> <i>density map. While the use of multiple columns to extract</i> <i>multiscale information from images has been shown be</i><i>fore, our approach aggregates the multiscale information</i> <i>gathered by the multicolumn convolutional neural network</i> <i>to improve performance. Our experiments show that our</i> <i>proposed network outperforms the state-of-the-art on many</i> <i>benchmark datasets, and also that using our aggregation</i> <i>module in combination with a higher number of columns is</i> <i>beneficial for multiscale counting.</i></p>",
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"html": "<p block-type=\"Text\">Learning to count the number of objects in an image is a deceptively difficult problem with many interesting applications, such as surveillance <a href=\"#page-8-0\">[20]</a>, traffic monitoring <a href=\"#page-8-1\">[14]</a> and medical image analysis <a href=\"#page-8-2\">[22]</a>. In many of these application areas, the objects to be counted vary widely in appearance, size and shape, and labeled training data is typically sparse. These factors pose a significant computer vision and machine learning challenge.</p>",
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"html": "<p block-type=\"Text\">Lempitsky et al. <a href=\"#page-8-3\">[15]</a> showed that it is possible to learn to count without learning to explicitly detect and localize individual objects. Instead, they propose learning to predict a density map whose integral over the image equals the number of objects in the image. This approach has been adopted by many later works (Cf. <a href=\"#page-8-4\">[18,</a> <a href=\"#page-9-0\">28]</a>).</p>",
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"html": "<p block-type=\"Text\">Jonathan Ventura University of Colorado Colorado Springs jventura@uccs.edu</p>",
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"html": "<blockquote><p block-type=\"Text\">counting cells in a microscope image, pedestrians in a crowd, or vehicles in a traffic jam, regressors trained on a single image scale are not reliable <a href=\"#page-8-4\">[18]</a>. This is due to a variety of challenges including overlap of objects and perspective effects which cause significant variance in object shape, size and appearance.</p></blockquote>",
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"html": "<blockquote><p block-type=\"Text\">The most successful recent approaches address this issue by explicitly incorporating multi-scale information in the network <a href=\"#page-8-4\">[18,</a><a href=\"#page-9-0\">28]</a>. These approaches either combine multiple networks which take input patches of different sizes <a href=\"#page-8-4\">[18]</a> or combine multiple filtering paths (\"columns\") which have different size filters <a href=\"#page-9-0\">[28]</a>.</p></blockquote>",
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"html": "<blockquote><p block-type=\"Text\">Following on the intuition that multiscale integration is key to achieving good counting performance, we propose to incorporate dilated filters <a href=\"#page-8-5\">[25]</a> into a multicolumn convolutional neural network design <a href=\"#page-9-0\">[28]</a>. Dilated filters exponentially increase the network's receptive field without an exponential increase in parameters, allowing for efficient use of multiscale information. Convolutional neural networks with dilated filters have proven to provide competitive performance in image segmentation where multiscale analysis is also critical <a href=\"#page-8-5\">[25,</a> <a href=\"#page-8-6\">26]</a>. By incorporating dilated filters into the multicolumn network design, we greatly increase the ability of the network to selectively aggregate multiscale information, without the need for explicit perspective maps during training and testing. We propose the \"aggregated multicolumn dilated convolution network\" or AMDCN which uses dilations to aggregate multiscale information. Our extensive experimental evaluation shows that this proposed network outperforms previous methods on many benchmark datasets.</p></blockquote>",
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"html": "<p><span id=\"page-1-0\"></span>Figure 1. Fully convolutional architecture diagram (not to scale). Arrows show separate columns that all take the same input. At the end of the columns, the feature maps are merged (concatenated) together and passed to another series of dilated convolutions: the aggregator, which can aggregate the multiscale information collected by the columns <a href=\"#page-8-5\">[25]</a>. The input image is I with C channels. The output single channel density map is D, and integrating over this map (summing the pixels) results in the final count. Initial filter sizes are labeled with brackets or lines. Convolution operations are shown as flat rectangles, feature maps are shown as prisms. The number below each filter represents the dilation rate (1 means no dilation).</p>",
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"html": "<p block-type=\"Text\">our paper follow this method of producing a density map via regression. This is particularly advantageous because a sufficiently accurate regressor can also locate the objects in the image via this method. However, the Lempitsky paper ignores the issue of perspective scaling and other scaling issues. The work of <a href=\"#page-8-7\">[27]</a> introduces CNNs (convolutional neural networks) for the purposes of crowd counting, but performs regression on similarly scaled image patches.</p>",
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"html": "<p block-type=\"Text\">of the image is truly necessary in order to solve dense prediction problems via convolutional neural networks. Moreover, these approaches seem to saturate in performance at three columns, which means the network is extracting information from fewer scales. The work of <a href=\"#page-8-5\">[25]</a> proposes the use of dilated convolutions as a simpler alternative that does not require sampling of rescaled image patches to provide global, scale-aware information to the network. A fully convolutional approach to multiscale counting has been proposed by <a href=\"#page-9-0\">[28]</a>, in which a multicolumn convolutional network gathers features of different scales by using convolutions of increasing kernel sizes from column to column instead of scaling image patches. Further, DeepLab has used dilated convolutions in multiple columns to extract scale information for segmentation <a href=\"#page-8-13\">[8]</a>. We build on these approaches with our aggregator module as described in Section <a href=\"#page-2-0\">3.1,</a> which should allow for extracting information from more scales.</p>",
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"html": "<p>Figure 2. UCF sample results. Left: input counting image. Middle: Ground truth density map. Right: AMDCN prediction of density map on test image. The network never saw these images during training. All density maps are one channel only (i.e. grayscale), but are colored here for clarity.</p>",
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"html": "<p block-type=\"Text\">Looking at more scales should allow for more accurate regression of the density map. However, because not all scales will be relevant, we extend the network beyond a simple 1 × 1 convolution after the merged columns. Instead, we construct a second part of the network, the aggregator, which sets our method apart from <a href=\"#page-9-0\">[28]</a>, <a href=\"#page-8-13\">[8]</a>, and other multicolumn networks. This aggregator is another series of dilated convolutions that should appropriately consolidate the multiscale information collected by the columns. This is a capability of dilated convolutions observed by <a href=\"#page-8-5\">[25]</a>. While papers such as <a href=\"#page-9-0\">[28]</a> and <a href=\"#page-8-13\">[8]</a> have shown that multiple columns and dilated columns are useful in extracting multiscale information, we argue in this paper that the simple aggregator module built using dilated convolutions is able to effectively make use multiscale information from multiple columns. We show compelling evidence for these claims in Section <a href=\"#page-5-0\">4.5.</a></p>",
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"html": "<p block-type=\"Text\">The network as shown in Figure <a href=\"#page-1-0\">1</a> contains 5 columns. Note that dilations allow us to use more columns for counting than <a href=\"#page-9-0\">[28]</a> or <a href=\"#page-8-13\">[8]</a>. Each column looks at a larger scale than the previous (the exact dilations can also be seen in Figure <a href=\"#page-1-0\">1)</a>. There are 32 feature maps for each convolution, and all inputs are zero padded prior to each convolution in order to maintain the same data shape from input to output. That is, an image input to this network will result in a density map of the same dimensions. All activations in the specified network are ReLUs. Our input pixel values are floating point 32 bit values from 0 to 1. We center our inputs at 0 by subtracting the per channel mean from each channel. When</p>",
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"html": "<p><span id=\"page-2-1\"></span><sup>1</sup> Implementation available on <a href=\"https://github.com/diptodip/counting\">https://github.com/</a> <a href=\"https://github.com/diptodip/counting\">diptodip/counting</a>.</p>",
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"html": "<p block-type=\"Equation\"><math display=\"block\">L = \\frac{1}{n} \\sum_{i=1}^{n} |\\hat{y}_i - \\gamma y_i| \\qquad (3)</math></p>",
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"html": "<p block-type=\"TextInlineMath\">where γ is the scale factor, <math display=\"inline\">\\hat{y}_i </math>is the prediction, <math display=\"inline\">y_i </math>is the true value, and <math display=\"inline\">n</math> is the number of pixels. We use a scaled mean absolute error because the target values are so small that it is numerically unstable to regress to these values. At testing time, when retrieving the output density map from the network, we scale the pixel values by <math display=\"inline\">γ^{-1} </math>to obtain the correct value. This approach is more numerically stable and avoids having the network learn to output only zeros by weighting the nonzero values highly. For all our datasets, we set <math display=\"inline\">γ = 255. </math></p>",
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"html": "<p block-type=\"Text\">We evaluated the performance of dilated convolutions against various counting methods on a variety of common counting datasets: UCF50 crowd data, TRANCOS traffic data <a href=\"#page-8-4\">[18]</a>, UCSD crowd data <a href=\"#page-8-17\">[5]</a>, and WorldExpo crowd data <a href=\"#page-8-7\">[27]</a>. For each of these data sets, we used labels given by the corresponding density map for each image. An example of this is shown in Figure <a href=\"#page-2-2\">2.</a> We have performed experiments on the four different splits of the UCSD data as used in <a href=\"#page-8-4\">[18]</a> and the split of the UCSD data as used in <a href=\"#page-9-0\">[28]</a> (which we call the original split). We also evaluated the performance of our network on the TRANCOS traffic dataset <a href=\"#page-8-1\">[14]</a>. We have also experimented with higher density datasets for crowd counting, namely WorldExpo and UCF.</p>",
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"html": "<p block-type=\"Text\">We have observed that multicolumn dilations produce density maps (and therefore counts) that often have lower loss than those of HydraCNN <a href=\"#page-8-4\">[18]</a> and <a href=\"#page-9-0\">[28]</a>. We measure density map regression loss via a scaled mean absolute error loss during training. We compare accuracy of the counts via mean absolute error for the crowd datasets and the GAME metric in the TRANCOS dataset as explained in Section <a href=\"#page-3-0\">3.2.2.</a> Beyond the comparison to HydraCNN, we will also compare to other recent convolutional counting methods, especially those of <a href=\"#page-8-14\">[21]</a>, <a href=\"#page-8-15\">[24]</a>, and <a href=\"#page-8-16\">[4]</a> where possible.</p>",
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"html": "<p block-type=\"Text\">For all datasets, we generally use patched input images and ground truth density maps produced by summing a Gaussian of a fixed size (σ) for each object for training. This size varies from dataset to dataset, but remains constant within a dataset with the exception of cases in which a perspective map is used. This is explained per dataset. All experiments were performed using Keras with the Adam optimizer <a href=\"#page-8-18\">[10]</a>. The learning rates used are detailed per dataset. For testing, we also use patches that can either be directly pieced together or overlapped and averaged except in the case of UCF, for which we run our network on the full image.</p>",
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"html": "<p block-type=\"Text\">Furthermore, we performed a set of experiments in which we varied the number of columns from 1 to 5 (simply by including or not including the columns as specified in Figure <a href=\"#page-1-0\">1,</a> starting with the smallest filter column and adding larger filter columns one by one). Essentially, the network is allowed to extract information at larger and larger scales in addition to the smaller scales as we include each column. We then performed the same set of experiments, varying the number of columns, but with the aggregator module removed. We perform these experiments on the original split of UCSD as specified in Section <a href=\"#page-4-0\">3.2.3</a> and <a href=\"#page-8-17\">[5]</a>, the TRAN-COS dataset, and the WorldExpo dataset because these are relatively large and well defined datasets. We limit the number of epochs to 10 for all of these sets of experiments in order to control for the effect of learning time, and also compare all results using MAE for consistency. These experiments are key to determining the efficacy of the aggregator in effectively combining multiscale information and in providing evidence to support the use of multiple columns to extract multiscale information from images. We report the results of these ablation studies in Section <a href=\"#page-5-0\">4.5.</a></p>",
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"html": "<h4>3.2.1 UCF50 Crowd Counting</h4>",
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"html": "<p block-type=\"Text\">UCF is a particularly challenging crowd counting dataset. There are only 50 images in the whole dataset and they are all of varying sizes and from different scenes. The number of people also varies between images from less than 100 to the thousands. The average image has on the order of 1000 people. The difficulty is due to the combination of the very low number of images in the dataset and the fact that the images are all of varying scenes, making high quality generalization crucial. Furthermore, perspective effects are particularly noticeable for many images in this dataset. Despite this, there is no perspective information available for this dataset.</p>",
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"html": "<p block-type=\"Text\">We take 1600 random patches of size 150 × 150 for the training. For testing, we do not densely scan the image as in <a href=\"#page-8-4\">[18]</a> but instead test on the whole image. In order to standardize the image sizes, we pad each image out with zeros until all images are 1024 × 1024. We then suppress output in the regions where we added padding when testing. This provides a cleaner resulting density map for these large crowds. The ground truth density maps are produced by annotating each object with a Gaussian of σ = 15.</p>",
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"html": "<h4><span id=\"page-3-0\"></span>3.2.2 TRANCOS Traffic Counting</h4>",
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"html": "<p block-type=\"Text\">The UCSD crowd counting dataset consists of frames of video of a sidewalk. There are relatively few people in view at any given time (approximately 25 on average). Furthermore, because the dataset comes from a video, there are many nearly identical images in the dataset. For this dataset, there have been two different ways to split the data into train and test sets. Therefore, we report results using both methods of splitting the data. The first method consists of four different splits: maximal, downscale, upscale, and minimal. Minimal is particularly challenging as the train set contains only 10 images. Moreover, upscale appears to be the easiest for the majority of methods <a href=\"#page-8-4\">[18]</a>. The second method of splitting this data is much more succinct, leaving 1200 images in the testing set and 800 images in the training set <a href=\"#page-9-0\">[28]</a>. This split comes from the original paper, so we call it the original split <a href=\"#page-8-17\">[5]</a>.</p>",
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"html": "<p block-type=\"TextInlineMath\">For this dataset, each object is annotated with a 2D Gaussian of covariance Σ = 8 \n· 12x2. The ground truth map is produced by summing these. When we make use of the perspective maps provided, we divide Σ by the perspective map value at that pixel x, represented by M(x). The provided perspective map for UCSD contains both a horizontal and vertical direction so we take the square root of the provided combined value. For training, we take 1600 random 79 × 119 pixel patches and for testing, we split each test image up into quadrants (which have dimension 79 × 119). There are two different ways to split the dataset into training and testing sets. We have experimented on the split that gave [18] the best results as well as the split used in [28].</p>",
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"html": "<p block-type=\"Text\" class=\"has-continuation\">First, we split the dataset into four separate groups of training and testing sets as used in <a href=\"#page-8-4\">[18]</a> and originally defined by <a href=\"#page-8-0\">[20]</a>. These groups are \"upscale,\" \"maximal,\"</p>",
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"html": "<p block-type=\"Text\">\"minimal,\" and \"downscale.\" We see in Table <a href=\"#page-6-0\">3</a> that the \"upscale\" split and \"downscale\" split give us state of the art results on counting for this dataset. For this experiment, we sampled 1600 random patches of size 119 × 79 pixels (width and height respectively) for the training set and split the test set images into 119 × 79 quadrants that could be reconstructed by piecing them together without overlap. We also added left-right flips of each image to our training data.</p>",
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"html": "<p block-type=\"TextInlineMath\">We then evaluate the original split. For this experiment, we similarly sampled 1600 random patches of size 119 × 79 pixels (width and height respectively) for the training set and split the test set images into 119 × 79 quadrants that could be reconstructed by piecing them together without overlap.</p>",
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"html": "<p block-type=\"Text\">The WorldExpo dataset <a href=\"#page-8-7\">[27]</a> contains a larger number of people (approximately 50 on average, which is double that of UCSD) and contains images from multiple locations. Perspective effects are also much more noticeable in this dataset as compared to UCSD. These qualities of the dataset serve to increase the difficulty of counting. Like UCSD, the WorldExpo dataset was constructed from frames of video recordings of crowds. This means that, unlike UCF, this dataset contains a relatively large number of training and testing images. We experiment on this dataset with and without perspective information.</p>",
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"html": "<p block-type=\"Text\">When perspective maps are used, however, we follow the procedure as described in <a href=\"#page-8-7\">[27]</a>, which involves estimating a \"crowd density distribution kernel\" as the sum of two 2D Gaussians: a symmetric Gaussian for the head and an ellipsoid Gaussian for the body. These are scaled by the perspective map M provided, where M(x) gives the number of pixels that represents a meter at pixel x <a href=\"#page-8-7\">[27]</a>. Note that the meaning of this perspective map is distinct from the meaning of the perspective map provided for the UCSD dataset. Using this information, the density contribution from a person with head pixel x is given by the following sum of normalized Gaussians:</p>",
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"html": "<p block-type=\"Text\" class=\"has-continuation\">We have proposed the use of aggregated multicolumn dilated convolutions, the AMDCN, as an alternative to the HydraCNN <a href=\"#page-8-4\">[18]</a> or multicolumn CNN <a href=\"#page-9-0\">[28]</a> for the vision task of counting objects in images. Inspired by the multicolumn approach to multiscale problems, we also employ dilations to increase the receptive field of our columns. We</p>",
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"html": "<p block-type=\"Text\">then aggregate this multiscale information using another series of dilated convolutions to enable a wide network and detect features at more scales. This method takes advantage of the ability of dilated convolutions to provide exponentially increasing receptive fields. We have performed experiments on the challenging UCF crowd counting dataset, the TRANCOS traffic dataset, multiple splits of the UCSD crowd counting dataset, and the WorldExpo crowd counting dataset.</p>",
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"html": "<p block-type=\"Text\">We obtain superior or comparable results in most of these datasets. The AMDCN is capable of outperforming these approaches completely especially when perspective information is not provided, as in UCF and TRANCOS. These results show that the AMDCN performs surprisingly well and is also robust to scale effects. Further, our ablation study of removing the aggregator network shows that using more columns and an aggregator provides the best accuracy for counting — especially so when there is no perspective information.</p>",
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"html": "<p block-type=\"Text\" class=\"has-continuation\">In addition to an analysis of performance on counting, a density regressor can also be used to locate objects in the image. As mentioned previously, if the regressor is accurate and precise enough, the resulting density map can be used to locate the objects in the image. We expect that in order to do this, one must regress each object to a single point rather than a region specified by a Gaussian. Perhaps this might be</p>",
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"html": "<p block-type=\"Text\">This material is based upon work supported by the National Science Foundation under Grant No. 1359275 and 1659788. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. Furthermore, we acknowledge Kyle Yee and Sridhama Prakhya for their helpful conversations and insights during the research process.</p>",
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