Paper Group ANR 984
Collaborative Learning for Weakly Supervised Object Detection. AMUSE: Multilingual Semantic Parsing for Question Answering over Linked Data. Visual Object Tracking based on Adaptive Siamese and Motion Estimation Network. AI Learns to Recognize Bengali Handwritten Digits: Bengali.AI Computer Vision Challenge 2018. Histogram of Oriented Depth Gradien …
Collaborative Learning for Weakly Supervised Object Detection
| Title | Collaborative Learning for Weakly Supervised Object Detection |
| Authors | Jiajie Wang, Jiangchao Yao, Ya Zhang, Rui Zhang |
| Abstract | Weakly supervised object detection has recently received much attention, since it only requires image-level labels instead of the bounding-box labels consumed in strongly supervised learning. Nevertheless, the save in labeling expense is usually at the cost of model accuracy. In this paper, we propose a simple but effective weakly supervised collaborative learning framework to resolve this problem, which trains a weakly supervised learner and a strongly supervised learner jointly by enforcing partial feature sharing and prediction consistency. For object detection, taking WSDDN-like architecture as weakly supervised detector sub-network and Faster-RCNN-like architecture as strongly supervised detector sub-network, we propose an end-to-end Weakly Supervised Collaborative Detection Network. As there is no strong supervision available to train the Faster-RCNN-like sub-network, a new prediction consistency loss is defined to enforce consistency of predictions between the two sub-networks as well as within the Faster-RCNN-like sub-networks. At the same time, the two detectors are designed to partially share features to further guarantee the model consistency at perceptual level. Extensive experiments on PASCAL VOC 2007 and 2012 data sets have demonstrated the effectiveness of the proposed framework. |
| Tasks | Object Detection, Weakly Supervised Object Detection |
| Published | 2018-02-10 |
| URL | http://arxiv.org/abs/1802.03531v1 |
| http://arxiv.org/pdf/1802.03531v1.pdf | |
| PWC | https://paperswithcode.com/paper/collaborative-learning-for-weakly-supervised |
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AMUSE: Multilingual Semantic Parsing for Question Answering over Linked Data
| Title | AMUSE: Multilingual Semantic Parsing for Question Answering over Linked Data |
| Authors | Sherzod Hakimov, Soufian Jebbara, Philipp Cimiano |
| Abstract | The task of answering natural language questions over RDF data has received wide interest in recent years, in particular in the context of the series of QALD benchmarks. The task consists of mapping a natural language question to an executable form, e.g. SPARQL, so that answers from a given KB can be extracted. So far, most systems proposed are i) monolingual and ii) rely on a set of hard-coded rules to interpret questions and map them into a SPARQL query. We present the first multilingual QALD pipeline that induces a model from training data for mapping a natural language question into logical form as probabilistic inference. In particular, our approach learns to map universal syntactic dependency representations to a language-independent logical form based on DUDES (Dependency-based Underspecified Discourse Representation Structures) that are then mapped to a SPARQL query as a deterministic second step. Our model builds on factor graphs that rely on features extracted from the dependency graph and corresponding semantic representations. We rely on approximate inference techniques, Markov Chain Monte Carlo methods in particular, as well as Sample Rank to update parameters using a ranking objective. Our focus lies on developing methods that overcome the lexical gap and present a novel combination of machine translation and word embedding approaches for this purpose. As a proof of concept for our approach, we evaluate our approach on the QALD-6 datasets for English, German & Spanish. |
| Tasks | Machine Translation, Question Answering, Semantic Parsing |
| Published | 2018-02-26 |
| URL | http://arxiv.org/abs/1802.09296v1 |
| http://arxiv.org/pdf/1802.09296v1.pdf | |
| PWC | https://paperswithcode.com/paper/amuse-multilingual-semantic-parsing-for |
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Visual Object Tracking based on Adaptive Siamese and Motion Estimation Network
| Title | Visual Object Tracking based on Adaptive Siamese and Motion Estimation Network |
| Authors | Hossein Kashiani, Shahriar B. Shokouhi |
| Abstract | Recently, convolutional neural network (CNN) has attracted much attention in different areas of computer vision, due to its powerful abstract feature representation. Visual object tracking is one of the interesting and important areas in computer vision that achieves remarkable improvements in recent years. In this work, we aim to improve both the motion and observation models in visual object tracking by leveraging representation power of CNNs. To this end, a motion estimation network (named MEN) is utilized to seek the most likely locations of the target and prepare a further clue in addition to the previous target position. Hence the motion estimation would be enhanced by generating a small number of candidates near two plausible positions. The generated candidates are then fed into a trained Siamese network to detect the most probable candidate. Each candidate is compared to an adaptable buffer, which is updated under a predefined condition. To take into account the target appearance changes, a weighting CNN (called WCNN) adaptively assigns weights to the final similarity scores of the Siamese network using sequence-specific information. Evaluation results on well-known benchmark datasets (OTB100, OTB50 and OTB2013) prove that the proposed tracker outperforms the state-of-the-art competitors. |
| Tasks | Motion Estimation, Object Tracking, Visual Object Tracking |
| Published | 2018-09-29 |
| URL | http://arxiv.org/abs/1810.00119v1 |
| http://arxiv.org/pdf/1810.00119v1.pdf | |
| PWC | https://paperswithcode.com/paper/visual-object-tracking-based-on-adaptive |
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AI Learns to Recognize Bengali Handwritten Digits: Bengali.AI Computer Vision Challenge 2018
| Title | AI Learns to Recognize Bengali Handwritten Digits: Bengali.AI Computer Vision Challenge 2018 |
| Authors | Sharif Amit Kamran, Ahmed Imtiaz Humayun, Samiul Alam, Rashed Mohammad Doha, Manash Kumar Mandal, Tahsin Reasat, Fuad Rahman |
| Abstract | Solving problems with Artificial intelligence in a competitive manner has long been absent in Bangladesh and Bengali-speaking community. On the other hand, there has not been a well structured database for Bengali Handwritten digits for mass public use. To bring out the best minds working in machine learning and use their expertise to create a model which can easily recognize Bengali Handwritten digits, we organized Bengali.AI Computer Vision Challenge.The challenge saw both local and international teams participating with unprecedented efforts. |
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| Published | 2018-10-10 |
| URL | http://arxiv.org/abs/1810.04452v1 |
| http://arxiv.org/pdf/1810.04452v1.pdf | |
| PWC | https://paperswithcode.com/paper/ai-learns-to-recognize-bengali-handwritten |
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Histogram of Oriented Depth Gradients for Action Recognition
| Title | Histogram of Oriented Depth Gradients for Action Recognition |
| Authors | Nachwa Abou Bakr, James Crowley |
| Abstract | In this paper, we report on experiments with the use of local measures for depth motion for visual action recognition from MPEG encoded RGBD video sequences. We show that such measures can be combined with local space-time video descriptors for appearance to provide a computationally efficient method for recognition of actions. Fisher vectors are used for encoding and concatenating a depth descriptor with existing RGB local descriptors. We then employ a linear SVM for recognizing manipulation actions using such vectors. We evaluate the effectiveness of such measures by comparison to the state-of-the-art using two recent datasets for action recognition in kitchen environments. |
| Tasks | Temporal Action Localization |
| Published | 2018-01-29 |
| URL | http://arxiv.org/abs/1801.09477v1 |
| http://arxiv.org/pdf/1801.09477v1.pdf | |
| PWC | https://paperswithcode.com/paper/histogram-of-oriented-depth-gradients-for |
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Spiking Neural Algorithms for Markov Process Random Walk
| Title | Spiking Neural Algorithms for Markov Process Random Walk |
| Authors | William Severa, Rich Lehoucq, Ojas Parekh, James B. Aimone |
| Abstract | The random walk is a fundamental stochastic process that underlies many numerical tasks in scientific computing applications. We consider here two neural algorithms that can be used to efficiently implement random walks on spiking neuromorphic hardware. The first method tracks the positions of individual walkers independently by using a modular code inspired by the grid cell spatial representation in the brain. The second method tracks the densities of random walkers at each spatial location directly. We analyze the scaling complexity of each of these methods and illustrate their ability to model random walkers under different probabilistic conditions. |
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| Published | 2018-05-01 |
| URL | http://arxiv.org/abs/1805.00509v1 |
| http://arxiv.org/pdf/1805.00509v1.pdf | |
| PWC | https://paperswithcode.com/paper/spiking-neural-algorithms-for-markov-process |
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Training Machine Learning Models by Regularizing their Explanations
| Title | Training Machine Learning Models by Regularizing their Explanations |
| Authors | Andrew Slavin Ross |
| Abstract | Neural networks are among the most accurate supervised learning methods in use today. However, their opacity makes them difficult to trust in critical applications, especially when conditions in training may differ from those in practice. Recent efforts to develop explanations for neural networks and machine learning models more generally have produced tools to shed light on the implicit rules behind predictions. These tools can help us identify when models are right for the wrong reasons. However, they do not always scale to explaining predictions for entire datasets, are not always at the right level of abstraction, and most importantly cannot correct the problems they reveal. In this thesis, we explore the possibility of training machine learning models (with a particular focus on neural networks) using explanations themselves. We consider approaches where models are penalized not only for making incorrect predictions but also for providing explanations that are either inconsistent with domain knowledge or overly complex. These methods let us train models which can not only provide more interpretable rationales for their predictions but also generalize better when training data is confounded or meaningfully different from test data (even adversarially so). |
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| Published | 2018-09-29 |
| URL | http://arxiv.org/abs/1810.00869v1 |
| http://arxiv.org/pdf/1810.00869v1.pdf | |
| PWC | https://paperswithcode.com/paper/training-machine-learning-models-by |
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Hierarchical Expertise-Level Modeling for User Specific Robot-Behavior Explanations
| Title | Hierarchical Expertise-Level Modeling for User Specific Robot-Behavior Explanations |
| Authors | Sarath Sreedharan, Siddharth Srivastava, Subbarao Kambhampati |
| Abstract | There is a growing interest within the AI research community to develop autonomous systems capable of explaining their behavior to users. One aspect of the explanation generation problem that has yet to receive much attention is the task of explaining plans to users whose level of expertise differ from that of the explainer. We propose an approach for addressing this problem by representing the user’s model as an abstraction of the domain model that the planner uses. We present algorithms for generating minimal explanations in cases where this abstract human model is not known. We reduce the problem of generating explanation to a search over the space of abstract models and investigate possible greedy approximations for minimal explanations. We also empirically show that our approach can efficiently compute explanations for a variety of problems. |
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| Published | 2018-02-19 |
| URL | http://arxiv.org/abs/1802.06895v1 |
| http://arxiv.org/pdf/1802.06895v1.pdf | |
| PWC | https://paperswithcode.com/paper/hierarchical-expertise-level-modeling-for |
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Handwriting styles: benchmarks and evaluation metrics
| Title | Handwriting styles: benchmarks and evaluation metrics |
| Authors | Omar Mohammed, Gerard Bailly, Damien Pellier |
| Abstract | Evaluating the style of handwriting generation is a challenging problem, since it is not well defined. It is a key component in order to develop in developing systems with more personalized experiences with humans. In this paper, we propose baseline benchmarks, in order to set anchors to estimate the relative quality of different handwriting style methods. This will be done using deep learning techniques, which have shown remarkable results in different machine learning tasks, learning classification, regression, and most relevant to our work, generating temporal sequences. We discuss the challenges associated with evaluating our methods, which is related to evaluation of generative models in general. We then propose evaluation metrics, which we find relevant to this problem, and we discuss how we evaluate the evaluation metrics. In this study, we use IRON-OFF dataset. To the best of our knowledge, there is no work done before in generating handwriting (either in terms of methodology or the performance metrics), our in exploring styles using this dataset. |
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| Published | 2018-09-04 |
| URL | http://arxiv.org/abs/1809.00862v1 |
| http://arxiv.org/pdf/1809.00862v1.pdf | |
| PWC | https://paperswithcode.com/paper/handwriting-styles-benchmarks-and-evaluation |
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A Deep Neural Network Surrogate for High-Dimensional Random Partial Differential Equations
| Title | A Deep Neural Network Surrogate for High-Dimensional Random Partial Differential Equations |
| Authors | Mohammad Amin Nabian, Hadi Meidani |
| Abstract | Developing efficient numerical algorithms for the solution of high dimensional random Partial Differential Equations (PDEs) has been a challenging task due to the well-known curse of dimensionality. We present a new solution framework for these problems based on a deep learning approach. Specifically, the random PDE is approximated by a feed-forward fully-connected deep residual network, with either strong or weak enforcement of initial and boundary constraints. The framework is mesh-free, and can handle irregular computational domains. Parameters of the approximating deep neural network are determined iteratively using variants of the Stochastic Gradient Descent (SGD) algorithm. The satisfactory accuracy of the proposed frameworks is numerically demonstrated on diffusion and heat conduction problems, in comparison with the converged Monte Carlo-based finite element results. |
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| Published | 2018-06-08 |
| URL | http://arxiv.org/abs/1806.02957v2 |
| http://arxiv.org/pdf/1806.02957v2.pdf | |
| PWC | https://paperswithcode.com/paper/a-deep-neural-network-surrogate-for-high |
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Orbital Petri Nets: A Novel Petri Net Approach
| Title | Orbital Petri Nets: A Novel Petri Net Approach |
| Authors | Mohamed Yorky, Aboul Ella Hassanien |
| Abstract | Petri Nets is very interesting tool for studying and simulating different behaviors of information systems. It can be used in different applications based on the appropriate class of Petri Nets whereas it is classical, colored or timed Petri Nets. In this paper we introduce a new approach of Petri Nets called orbital Petri Nets (OPN) for studying the orbital rotating systems within a specific domain. The study investigated and analyzed OPN with highlighting the problem of space debris collision problem as a case study. The mathematical investigation results of two OPN models proved that space debris collision problem can be prevented based on the new method of firing sequence in OPN. By this study, new smart algorithms can be implemented and simulated by orbital Petri Nets for mitigating the space debris collision problem as a next work. |
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| Published | 2018-06-08 |
| URL | http://arxiv.org/abs/1806.03267v1 |
| http://arxiv.org/pdf/1806.03267v1.pdf | |
| PWC | https://paperswithcode.com/paper/orbital-petri-nets-a-novel-petri-net-approach |
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Blind Pre-Processing: A Robust Defense Method Against Adversarial Examples
| Title | Blind Pre-Processing: A Robust Defense Method Against Adversarial Examples |
| Authors | Adnan Siraj Rakin, Zhezhi He, Boqing Gong, Deliang Fan |
| Abstract | Deep learning algorithms and networks are vulnerable to perturbed inputs which is known as the adversarial attack. Many defense methodologies have been investigated to defend against such adversarial attack. In this work, we propose a novel methodology to defend the existing powerful attack model. We for the first time introduce a new attacking scheme for the attacker and set a practical constraint for white box attack. Under this proposed attacking scheme, we present the best defense ever reported against some of the recent strong attacks. It consists of a set of nonlinear function to process the input data which will make it more robust over the adversarial attack. However, we make this processing layer completely hidden from the attacker. Blind pre-processing improves the white box attack accuracy of MNIST from 94.3% to 98.7%. Even with increasing defense when others defenses completely fail, blind pre-processing remains one of the strongest ever reported. Another strength of our defense is that it eliminates the need for adversarial training as it can significantly increase the MNIST accuracy without adversarial training as well. Additionally, blind pre-processing can also increase the inference accuracy in the face of a powerful attack on CIFAR-10 and SVHN data set as well without much sacrificing clean data accuracy. |
| Tasks | Adversarial Attack |
| Published | 2018-02-05 |
| URL | http://arxiv.org/abs/1802.01549v2 |
| http://arxiv.org/pdf/1802.01549v2.pdf | |
| PWC | https://paperswithcode.com/paper/blind-pre-processing-a-robust-defense-method |
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Comparing Multilayer Perceptron and Multiple Regression Models for Predicting Energy Use in the Balkans
| Title | Comparing Multilayer Perceptron and Multiple Regression Models for Predicting Energy Use in the Balkans |
| Authors | Radmila Janković, Alessia Amelio |
| Abstract | Global demographic and economic changes have a critical impact on the total energy consumption, which is why demographic and economic parameters have to be taken into account when making predictions about the energy consumption. This research is based on the application of a multiple linear regression model and a neural network model, in particular multilayer perceptron, for predicting the energy consumption. Data from five Balkan countries has been considered in the analysis for the period 1995-2014. Gross domestic product, total number of population, and CO2 emission were taken as predictor variables, while the energy consumption was used as the dependent variable. The analyses showed that CO2 emissions have the highest impact on the energy consumption, followed by the gross domestic product, while the population number has the lowest impact. The results from both analyses are then used for making predictions on the same data, after which the obtained values were compared with the real values. It was observed that the multilayer perceptron model predicts better the energy consumption than the regression model. |
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| Published | 2018-10-26 |
| URL | http://arxiv.org/abs/1810.11333v1 |
| http://arxiv.org/pdf/1810.11333v1.pdf | |
| PWC | https://paperswithcode.com/paper/comparing-multilayer-perceptron-and-multiple |
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Refining Coarse-grained Spatial Data using Auxiliary Spatial Data Sets with Various Granularities
| Title | Refining Coarse-grained Spatial Data using Auxiliary Spatial Data Sets with Various Granularities |
| Authors | Yusuke Tanaka, Tomoharu Iwata, Toshiyuki Tanaka, Takeshi Kurashima, Maya Okawa, Hiroyuki Toda |
| Abstract | We propose a probabilistic model for refining coarse-grained spatial data by utilizing auxiliary spatial data sets. Existing methods require that the spatial granularities of the auxiliary data sets are the same as the desired granularity of target data. The proposed model can effectively make use of auxiliary data sets with various granularities by hierarchically incorporating Gaussian processes. With the proposed model, a distribution for each auxiliary data set on the continuous space is modeled using a Gaussian process, where the representation of uncertainty considers the levels of granularity. The fine-grained target data are modeled by another Gaussian process that considers both the spatial correlation and the auxiliary data sets with their uncertainty. We integrate the Gaussian process with a spatial aggregation process that transforms the fine-grained target data into the coarse-grained target data, by which we can infer the fine-grained target Gaussian process from the coarse-grained data. Our model is designed such that the inference of model parameters based on the exact marginal likelihood is possible, in which the variables of fine-grained target and auxiliary data are analytically integrated out. Our experiments on real-world spatial data sets demonstrate the effectiveness of the proposed model. |
| Tasks | Gaussian Processes |
| Published | 2018-09-21 |
| URL | https://arxiv.org/abs/1809.07952v2 |
| https://arxiv.org/pdf/1809.07952v2.pdf | |
| PWC | https://paperswithcode.com/paper/refining-coarse-grained-spatial-data-using |
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UH-PRHLT at SemEval-2016 Task 3: Combining Lexical and Semantic-based Features for Community Question Answering
| Title | UH-PRHLT at SemEval-2016 Task 3: Combining Lexical and Semantic-based Features for Community Question Answering |
| Authors | Marc Franco-Salvador, Sudipta Kar, Thamar Solorio, Paolo Rosso |
| Abstract | In this work we describe the system built for the three English subtasks of the SemEval 2016 Task 3 by the Department of Computer Science of the University of Houston (UH) and the Pattern Recognition and Human Language Technology (PRHLT) research center - Universitat Politecnica de Valencia: UH-PRHLT. Our system represents instances by using both lexical and semantic-based similarity measures between text pairs. Our semantic features include the use of distributed representations of words, knowledge graphs generated with the BabelNet multilingual semantic network, and the FrameNet lexical database. Experimental results outperform the random and Google search engine baselines in the three English subtasks. Our approach obtained the highest results of subtask B compared to the other task participants. |
| Tasks | Community Question Answering, Knowledge Graphs, Question Answering |
| Published | 2018-07-30 |
| URL | http://arxiv.org/abs/1807.11584v1 |
| http://arxiv.org/pdf/1807.11584v1.pdf | |
| PWC | https://paperswithcode.com/paper/uh-prhlt-at-semeval-2016-task-3-combining |
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