Paper Group ANR 513
Femural Head Autosegmentation for 3D Radiotherapy Planning: Preliminary Results. MedTQ: Dynamic Topic Discovery and Query Generation for Medical Ontologies. Accurate and Efficient Similarity Search for Large Scale Face Recognition. Unsupervised 3D Shape Learning from Image Collections in the Wild. Attacks on State-of-the-Art Face Recognition using …
Femural Head Autosegmentation for 3D Radiotherapy Planning: Preliminary Results
| Title | Femural Head Autosegmentation for 3D Radiotherapy Planning: Preliminary Results |
| Authors | Bruno A. G. da Silva, Alvaro L. Fazenda, Fabiano C. Paixao |
| Abstract | Contouring of organs at risk is an important but time consuming part of radiotherapy treatment planning. Several authors proposed methods for automatic delineation but the clinical experts eye remains the gold standard method. In this paper, we present a totally visual software for automated delineation of the femural head. The software was successfully characterized in pelvic CT Scan of prostate patients (n=11). The automatic delineation was compared with manual and approved delineation through blind test evaluated by a panel of seniors radiation oncologists (n=9). Clinical experts evaluated that no any contouring correction were need in 77.8% and 67.8% of manual and automatic delineation respectively. Our results show that the software is robust, the automated delineation was reproducible in all patient, and its performance was similar to manually delineation. |
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| Published | 2018-12-11 |
| URL | http://arxiv.org/abs/1812.04682v1 |
| http://arxiv.org/pdf/1812.04682v1.pdf | |
| PWC | https://paperswithcode.com/paper/femural-head-autosegmentation-for-3d |
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MedTQ: Dynamic Topic Discovery and Query Generation for Medical Ontologies
| Title | MedTQ: Dynamic Topic Discovery and Query Generation for Medical Ontologies |
| Authors | Feichen Shen, Yugyung Lee |
| Abstract | Biomedical ontology refers to a shared conceptualization for a biomedical domain of interest that has vastly improved data management and data sharing through the open data movement. The rapid growth and availability of biomedical data make it impractical and computationally expensive to perform manual analysis and query processing with the large scale ontologies. The lack of ability in analyzing ontologies from such a variety of sources, and supporting knowledge discovery for clinical practice and biomedical research should be overcome with new technologies. In this study, we developed a Medical Topic discovery and Query generation framework (MedTQ), which was composed by a series of approaches and algorithms. A predicate neighborhood pattern-based approach introduced has the ability to compute the similarity of predicates (relations) in ontologies. Given a predicate similarity metric, machine learning algorithms have been developed for automatic topic discovery and query generation. The topic discovery algorithm, called the hierarchical K-Means algorithm was designed by extending an existing supervised algorithm (K-means clustering) for the construction of a topic hierarchy. In the hierarchical K-Means algorithm, a level-by-level optimization strategy was selected for consistent with the strongly association between elements within a topic. Automatic query generation was facilitated for discovered topic that could be guided users for interactive query design and processing. Evaluation was conducted to generate topic hierarchy for DrugBank ontology as a case study. Results demonstrated that the MedTQ framework can enhance knowledge discovery by capturing underlying structures from domain specific data and ontologies. |
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| Published | 2018-02-12 |
| URL | http://arxiv.org/abs/1802.03855v1 |
| http://arxiv.org/pdf/1802.03855v1.pdf | |
| PWC | https://paperswithcode.com/paper/medtq-dynamic-topic-discovery-and-query |
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Accurate and Efficient Similarity Search for Large Scale Face Recognition
| Title | Accurate and Efficient Similarity Search for Large Scale Face Recognition |
| Authors | Ce Qi, Zhizhong Liu, Fei Su |
| Abstract | Face verification is a relatively easy task with the help of discriminative features from deep neural networks. However, it is still a challenge to recognize faces on millions of identities while keeping high performance and efficiency. The challenge 2 of MS-Celeb-1M is a classification task. However, the number of identities is too large and it is not that elegant to treat the task as an image classification task. We treat the classification task as similarity search and do experiments on different similarity search strategies. Similarity search strategy accelerates the speed of searching and boosts the accuracy of final results. The model used for extracting features is a single deep neural network pretrained on CASIA-Webface, which is not trained on the base set or novel set offered by official. Finally, we rank \textbf{3rd}, while the speed of searching is 1ms/image. |
| Tasks | Face Recognition, Face Verification, Image Classification |
| Published | 2018-06-01 |
| URL | http://arxiv.org/abs/1806.00365v1 |
| http://arxiv.org/pdf/1806.00365v1.pdf | |
| PWC | https://paperswithcode.com/paper/accurate-and-efficient-similarity-search-for |
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Unsupervised 3D Shape Learning from Image Collections in the Wild
| Title | Unsupervised 3D Shape Learning from Image Collections in the Wild |
| Authors | Attila Szabó, Paolo Favaro |
| Abstract | We present a method to learn the 3D surface of objects directly from a collection of images. Previous work achieved this capability by exploiting additional manual annotation, such as object pose, 3D surface templates, temporal continuity of videos, manually selected landmarks, and foreground/background masks. In contrast, our method does not make use of any such annotation. Rather, it builds a generative model, a convolutional neural network, which, given a noise vector sample, outputs the 3D surface and texture of an object and a background image. These 3 components combined with an additional random viewpoint vector are then fed to a differential renderer to produce a view of the sampled object and background. Our general principle is that if the output of the renderer, the generated image, is realistic, then its input, the generated 3D and texture, should also be realistic. To achieve realism, the generative model is trained adversarially against a discriminator that tries to distinguish between the output of the renderer and real images from the given data set. Moreover, our generative model can be paired with an encoder and trained as an autoencoder, to automatically extract the 3D shape, texture and pose of the object in an image. Our trained generative model and encoder show promising results both on real and synthetic data, which demonstrate for the first time that fully unsupervised 3D learning from image collections is possible. |
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| Published | 2018-11-26 |
| URL | http://arxiv.org/abs/1811.10519v2 |
| http://arxiv.org/pdf/1811.10519v2.pdf | |
| PWC | https://paperswithcode.com/paper/unsupervised-3d-shape-learning-from-image |
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Attacks on State-of-the-Art Face Recognition using Attentional Adversarial Attack Generative Network
| Title | Attacks on State-of-the-Art Face Recognition using Attentional Adversarial Attack Generative Network |
| Authors | Qing Song, Yingqi Wu, Lu Yang |
| Abstract | With the broad use of face recognition, its weakness gradually emerges that it is able to be attacked. So, it is important to study how face recognition networks are subject to attacks. In this paper, we focus on a novel way to do attacks against face recognition network that misleads the network to identify someone as the target person not misclassify inconspicuously. Simultaneously, for this purpose, we introduce a specific attentional adversarial attack generative network to generate fake face images. For capturing the semantic information of the target person, this work adds a conditional variational autoencoder and attention modules to learn the instance-level correspondences between faces. Unlike traditional two-player GAN, this work introduces face recognition networks as the third player to participate in the competition between generator and discriminator which allows the attacker to impersonate the target person better. The generated faces which are hard to arouse the notice of onlookers can evade recognition by state-of-the-art networks and most of them are recognized as the target person. |
| Tasks | Adversarial Attack, Face Recognition |
| Published | 2018-11-29 |
| URL | http://arxiv.org/abs/1811.12026v2 |
| http://arxiv.org/pdf/1811.12026v2.pdf | |
| PWC | https://paperswithcode.com/paper/attacks-on-state-of-the-art-face-recognition |
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Recurrent Convolutions for Causal 3D CNNs
| Title | Recurrent Convolutions for Causal 3D CNNs |
| Authors | Gurkirt Singh, Fabio Cuzzolin |
| Abstract | Recently, three dimensional (3D) convolutional neural networks (CNNs) have emerged as dominant methods to capture spatiotemporal representations in videos, by adding to pre-existing 2D CNNs a third, temporal dimension. Such 3D CNNs, however, are anti-causal (i.e., they exploit information from both the past and the future frames to produce feature representations, thus preventing their use in online settings), constrain the temporal reasoning horizon to the size of the temporal convolution kernel, and are not temporal resolution-preserving for video sequence-to-sequence modelling, as, for instance, in action detection. To address these serious limitations, here we present a new 3D CNN architecture for the causal/online processing of videos. Namely, we propose a novel Recurrent Convolutional Network (RCN), which relies on recurrence to capture the temporal context across frames at each network level. Our network decomposes 3D convolutions into (1) a 2D spatial convolution component, and (2) an additional hidden state $1\times 1$ convolution, applied across time. The hidden state at any time $t$ is assumed to depend on the hidden state at $t-1$ and on the current output of the spatial convolution component. As a result, the proposed network: (i) produces causal outputs, (ii) provides flexible temporal reasoning, (iii) preserves temporal resolution. Our experiments on the large-scale large Kinetics and MultiThumos datasets show that the proposed method performs comparably to anti-causal 3D CNNs, while being causal and using fewer parameters. |
| Tasks | Action Detection |
| Published | 2018-11-17 |
| URL | https://arxiv.org/abs/1811.07157v2 |
| https://arxiv.org/pdf/1811.07157v2.pdf | |
| PWC | https://paperswithcode.com/paper/recurrence-to-the-rescue-towards-causal |
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Use of recurrent infomax to improve the memory capability of input-driven recurrent neural networks
| Title | Use of recurrent infomax to improve the memory capability of input-driven recurrent neural networks |
| Authors | Hisashi Iwade, Kohei Nakajima, Takuma Tanaka, Toshio Aoyagi |
| Abstract | The inherent transient dynamics of recurrent neural networks (RNNs) have been exploited as a computational resource in input-driven RNNs. However, the information processing capability varies from RNN to RNN, depending on their properties. Many authors have investigated the dynamics of RNNs and their relevance to the information processing capability. In this study, we present a detailed analysis of the information processing capability of an RNN optimized by recurrent infomax (RI), which is an unsupervised learning scheme that maximizes the mutual information of RNNs by adjusting the connection strengths of the network. Thus, we observe that a delay-line structure emerges from the RI and the network optimized by the RI possesses superior short-term memory, which is the ability to store the temporal information of the input stream in its transient dynamics. |
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| Published | 2018-02-14 |
| URL | http://arxiv.org/abs/1803.05383v1 |
| http://arxiv.org/pdf/1803.05383v1.pdf | |
| PWC | https://paperswithcode.com/paper/use-of-recurrent-infomax-to-improve-the |
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Finding your Lookalike: Measuring Face Similarity Rather than Face Identity
| Title | Finding your Lookalike: Measuring Face Similarity Rather than Face Identity |
| Authors | Amir Sadovnik, Wassim Gharbi, Thanh Vu, Andrew Gallagher |
| Abstract | Face images are one of the main areas of focus for computer vision, receiving on a wide variety of tasks. Although face recognition is probably the most widely researched, many other tasks such as kinship detection, facial expression classification and facial aging have been examined. In this work we propose the new, subjective task of quantifying perceived face similarity between a pair of faces. That is, we predict the perceived similarity between facial images, given that they are not of the same person. Although this task is clearly correlated with face recognition, it is different and therefore justifies a separate investigation. Humans often remark that two persons look alike, even in cases where the persons are not actually confused with one another. In addition, because face similarity is different than traditional image similarity, there are challenges in data collection and labeling, and dealing with diverging subjective opinions between human labelers. We present evidence that finding facial look-alikes and recognizing faces are two distinct tasks. We propose a new dataset for facial similarity and introduce the Lookalike network, directed towards similar face classification, which outperforms the ad hoc usage of a face recognition network directed at the same task. |
| Tasks | Face Recognition |
| Published | 2018-06-13 |
| URL | http://arxiv.org/abs/1806.05252v1 |
| http://arxiv.org/pdf/1806.05252v1.pdf | |
| PWC | https://paperswithcode.com/paper/finding-your-lookalike-measuring-face |
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Fully-Automated Analysis of Body Composition from CT in Cancer Patients Using Convolutional Neural Networks
| Title | Fully-Automated Analysis of Body Composition from CT in Cancer Patients Using Convolutional Neural Networks |
| Authors | Christopher P. Bridge, Michael Rosenthal, Bradley Wright, Gopal Kotecha, Florian Fintelmann, Fabian Troschel, Nityanand Miskin, Khanant Desai, William Wrobel, Ana Babic, Natalia Khalaf, Lauren Brais, Marisa Welch, Caitlin Zellers, Neil Tenenholtz, Mark Michalski, Brian Wolpin, Katherine Andriole |
| Abstract | The amounts of muscle and fat in a person’s body, known as body composition, are correlated with cancer risks, cancer survival, and cardiovascular risk. The current gold standard for measuring body composition requires time-consuming manual segmentation of CT images by an expert reader. In this work, we describe a two-step process to fully automate the analysis of CT body composition using a DenseNet to select the CT slice and U-Net to perform segmentation. We train and test our methods on independent cohorts. Our results show Dice scores (0.95-0.98) and correlation coefficients (R=0.99) that are favorable compared to human readers. These results suggest that fully automated body composition analysis is feasible, which could enable both clinical use and large-scale population studies. |
| Tasks | |
| Published | 2018-08-11 |
| URL | http://arxiv.org/abs/1808.03844v1 |
| http://arxiv.org/pdf/1808.03844v1.pdf | |
| PWC | https://paperswithcode.com/paper/fully-automated-analysis-of-body-composition |
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Optimal Continuous State POMDP Planning with Semantic Observations: A Variational Approach
| Title | Optimal Continuous State POMDP Planning with Semantic Observations: A Variational Approach |
| Authors | Luke Burks, Ian Loefgren, Nisar Ahmed |
| Abstract | This work develops novel strategies for optimal planning with semantic observations using continuous state partially observable markov decision processes (CPOMDPs). Two major innovations are presented in relation to Gaussian mixture (GM) CPOMDP policy approximation methods. While existing methods have many desirable theoretical properties, they are unable to efficiently represent and reason over hybrid continuous-discrete probabilistic models. The first major innovation is the derivation of closed-form variational Bayes GM approximations of Point-Based Value Iteration Bellman policy backups, using softmax models of continuous-discrete semantic observation probabilities. A key benefit of this approach is that dynamic decision-making tasks can be performed with complex non-Gaussian uncertainties, while also exploiting continuous dynamic state space models (thus avoiding cumbersome and costly discretization). The second major innovation is a new clustering-based technique for mixture condensation that scales well to very large GM policy functions and belief functions. Simulation results for a target search and interception task with semantic observations show that the GM policies resulting from these innovations are more effective than those produced by other state of the art policy approximations, but require significantly less modeling overhead and online runtime cost. Additional results show the robustness of this approach to model errors and scaling to higher dimensions. |
| Tasks | Decision Making |
| Published | 2018-07-22 |
| URL | https://arxiv.org/abs/1807.08229v2 |
| https://arxiv.org/pdf/1807.08229v2.pdf | |
| PWC | https://paperswithcode.com/paper/optimal-continuous-state-pomdp-planning-with |
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Active Learning for Regression Using Greedy Sampling
| Title | Active Learning for Regression Using Greedy Sampling |
| Authors | Dongrui Wu, Chin-Teng Lin, Jian Huang |
| Abstract | Regression problems are pervasive in real-world applications. Generally a substantial amount of labeled samples are needed to build a regression model with good generalization ability. However, many times it is relatively easy to collect a large number of unlabeled samples, but time-consuming or expensive to label them. Active learning for regression (ALR) is a methodology to reduce the number of labeled samples, by selecting the most beneficial ones to label, instead of random selection. This paper proposes two new ALR approaches based on greedy sampling (GS). The first approach (GSy) selects new samples to increase the diversity in the output space, and the second (iGS) selects new samples to increase the diversity in both input and output spaces. Extensive experiments on 12 UCI and CMU StatLib datasets from various domains, and on 15 subjects on EEG-based driver drowsiness estimation, verified their effectiveness and robustness. |
| Tasks | Active Learning, EEG |
| Published | 2018-08-08 |
| URL | http://arxiv.org/abs/1808.04245v1 |
| http://arxiv.org/pdf/1808.04245v1.pdf | |
| PWC | https://paperswithcode.com/paper/active-learning-for-regression-using-greedy |
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RGB-D Object Detection and Semantic Segmentation for Autonomous Manipulation in Clutter
| Title | RGB-D Object Detection and Semantic Segmentation for Autonomous Manipulation in Clutter |
| Authors | Max Schwarz, Anton Milan, Arul Selvam Periyasamy, Sven Behnke |
| Abstract | Autonomous robotic manipulation in clutter is challenging. A large variety of objects must be perceived in complex scenes, where they are partially occluded and embedded among many distractors, often in restricted spaces. To tackle these challenges, we developed a deep-learning approach that combines object detection and semantic segmentation. The manipulation scenes are captured with RGB-D cameras, for which we developed a depth fusion method. Employing pretrained features makes learning from small annotated robotic data sets possible. We evaluate our approach on two challenging data sets: one captured for the Amazon Picking Challenge 2016, where our team NimbRo came in second in the Stowing and third in the Picking task, and one captured in disaster-response scenarios. The experiments show that object detection and semantic segmentation complement each other and can be combined to yield reliable object perception. |
| Tasks | Object Detection, Semantic Segmentation |
| Published | 2018-10-01 |
| URL | http://arxiv.org/abs/1810.00818v1 |
| http://arxiv.org/pdf/1810.00818v1.pdf | |
| PWC | https://paperswithcode.com/paper/rgb-d-object-detection-and-semantic |
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Sleep Stage Classification: Scalability Evaluations of Distributed Approaches
| Title | Sleep Stage Classification: Scalability Evaluations of Distributed Approaches |
| Authors | Serife Acikalin, Suleyman Eken, Ahmet Sayar |
| Abstract | Processing and analyzing of massive clinical data are resource intensive and time consuming with traditional analytic tools. Electroencephalogram (EEG) is one of the major technologies in detecting and diagnosing various brain disorders, and produces huge volume big data to process. In this study, we propose a big data framework to diagnose sleep disorders by classifying the sleep stages from EEG signals. The framework is developed with open source SparkMlib Libraries. We also tested and evaluated the proposed framework by measuring the scalabilities of well-known classification algorithms on physionet sleep records. |
| Tasks | EEG |
| Published | 2018-09-01 |
| URL | http://arxiv.org/abs/1809.00233v1 |
| http://arxiv.org/pdf/1809.00233v1.pdf | |
| PWC | https://paperswithcode.com/paper/sleep-stage-classification-scalability |
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Conceptualization of Object Compositions Using Persistent Homology
| Title | Conceptualization of Object Compositions Using Persistent Homology |
| Authors | Christian A. Mueller, Andreas Birk |
| Abstract | A topological shape analysis is proposed and utilized to learn concepts that reflect shape commonalities. Our approach is two-fold: i) a spatial topology analysis of point cloud segment constellations within objects. Therein constellations are decomposed and described in an hierarchical manner - from single segments to segment groups until a single group reflects an entire object. ii) a topology analysis of the description space in which segment decompositions are exposed in. Inspired by Persistent Homology, hidden groups of shape commonalities are revealed from object segment decompositions. Experiments show that extracted persistent groups of commonalities can represent semantically meaningful shape concepts. We also show the generalization capability of the proposed approach considering samples of external datasets. |
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| Published | 2018-03-06 |
| URL | http://arxiv.org/abs/1803.02140v3 |
| http://arxiv.org/pdf/1803.02140v3.pdf | |
| PWC | https://paperswithcode.com/paper/conceptualization-of-object-compositions |
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Spatial Filtering for Brain Computer Interfaces: A Comparison between the Common Spatial Pattern and Its Variant
| Title | Spatial Filtering for Brain Computer Interfaces: A Comparison between the Common Spatial Pattern and Its Variant |
| Authors | He He, Dongrui Wu |
| Abstract | The electroencephalogram (EEG) is the most popular form of input for brain computer interfaces (BCIs). However, it can be easily contaminated by various artifacts and noise, e.g., eye blink, muscle activities, powerline noise, etc. Therefore, the EEG signals are often filtered both spatially and temporally to increase the signal-to-noise ratio before they are fed into a machine learning algorithm for recognition. This paper considers spatial filtering, particularly, the common spatial pattern (CSP) filters for EEG classification. In binary classification, CSP seeks a set of filters to maximize the variance for one class while minimizing it for the other. We first introduce the traditional solution, and then a new solution based on a slightly different objective function. We performed comprehensive experiments on motor imagery to compare the two approaches, and found that generally the traditional CSP solution still gives better results. We also showed that adding regularization to the covariance matrices can improve the final classification performance, no matter which objective function is used. |
| Tasks | EEG |
| Published | 2018-08-08 |
| URL | http://arxiv.org/abs/1808.06533v1 |
| http://arxiv.org/pdf/1808.06533v1.pdf | |
| PWC | https://paperswithcode.com/paper/spatial-filtering-for-brain-computer |
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