Paper Group ANR 644
Drive Video Analysis for the Detection of Traffic Near-Miss Incidents. Analyzing Self-Driving Cars on Twitter. Achieving Connectivity Between Wide Areas Through Self-Organising Robot Swarm Using Embodied Evolution. Attacking Speaker Recognition With Deep Generative Models. VizWiz Grand Challenge: Answering Visual Questions from Blind People. Adapti …
Drive Video Analysis for the Detection of Traffic Near-Miss Incidents
| Title | Drive Video Analysis for the Detection of Traffic Near-Miss Incidents |
| Authors | Hirokatsu Kataoka, Teppei Suzuki, Shoko Oikawa, Yasuhiro Matsui, Yutaka Satoh |
| Abstract | Because of their recent introduction, self-driving cars and advanced driver assistance system (ADAS) equipped vehicles have had little opportunity to learn, the dangerous traffic (including near-miss incident) scenarios that provide normal drivers with strong motivation to drive safely. Accordingly, as a means of providing learning depth, this paper presents a novel traffic database that contains information on a large number of traffic near-miss incidents that were obtained by mounting driving recorders in more than 100 taxis over the course of a decade. The study makes the following two main contributions: (i) In order to assist automated systems in detecting near-miss incidents based on database instances, we created a large-scale traffic near-miss incident database (NIDB) that consists of video clip of dangerous events captured by monocular driving recorders. (ii) To illustrate the applicability of NIDB traffic near-miss incidents, we provide two primary database-related improvements: parameter fine-tuning using various near-miss scenes from NIDB, and foreground/background separation into motion representation. Then, using our new database in conjunction with a monocular driving recorder, we developed a near-miss recognition method that provides automated systems with a performance level that is comparable to a human-level understanding of near-miss incidents (64.5% vs. 68.4% at near-miss recognition, 61.3% vs. 78.7% at near-miss detection). |
| Tasks | Self-Driving Cars |
| Published | 2018-04-07 |
| URL | http://arxiv.org/abs/1804.02555v1 |
| http://arxiv.org/pdf/1804.02555v1.pdf | |
| PWC | https://paperswithcode.com/paper/drive-video-analysis-for-the-detection-of |
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Analyzing Self-Driving Cars on Twitter
| Title | Analyzing Self-Driving Cars on Twitter |
| Authors | Rizwan Sadiq, Mohsin Khan |
| Abstract | This paper studies users’ perception regarding a controversial product, namely self-driving (autonomous) cars. To find people’s opinion regarding this new technology, we used an annotated Twitter dataset, and extracted the topics in positive and negative tweets using an unsupervised, probabilistic model known as topic modeling. We later used the topics, as well as linguist and Twitter specific features to classify the sentiment of the tweets. Regarding the opinions, the result of our analysis shows that people are optimistic and excited about the future technology, but at the same time they find it dangerous and not reliable. For the classification task, we found Twitter specific features, such as hashtags as well as linguistic features such as emphatic words among top attributes in classifying the sentiment of the tweets. |
| Tasks | Self-Driving Cars |
| Published | 2018-04-05 |
| URL | http://arxiv.org/abs/1804.04058v1 |
| http://arxiv.org/pdf/1804.04058v1.pdf | |
| PWC | https://paperswithcode.com/paper/analyzing-self-driving-cars-on-twitter |
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Achieving Connectivity Between Wide Areas Through Self-Organising Robot Swarm Using Embodied Evolution
| Title | Achieving Connectivity Between Wide Areas Through Self-Organising Robot Swarm Using Embodied Evolution |
| Authors | Erik Aaron Hansen, Stefano Nichele, Anis Yazidi, Hårek Haugerud, Asieh Abolpour Mofrad, Alex Alcocer |
| Abstract | Abruptions to the communication infrastructure happens occasionally, where manual dedicated personnel will go out to fix the interruptions, restoring communication abilities. However, sometimes this can be dangerous to the personnel carrying out the task, which can be the case in war situations, environmental disasters like earthquakes or toxic spills or in the occurrence of fire. Therefore, human casualties can be minimised if autonomous robots are deployed that can achieve the same outcome: to establish a communication link between two previously distant but connected sites. In this paper we investigate the deployment of mobile ad hoc robots which relay traffic between them. In order to get the robots to locate themselves appropriately, we take inspiration from self-organisation and emergence in artificial life, where a common overall goal may be achieved if the correct local rules on the agents in system are invoked. We integrate the aspect of connectivity between two sites into the multirobot simulation platform known as JBotEvolver. The robot swarm is composed of Thymio II robots. In addition, we compare three heuristics, of which one uses neuroevolution (evolution of neural networks) to show how self-organisation and embodied evolution can be used within the integration. Our use of embodiment in robotic controllers shows promising results and provide solid knowledge and guidelines for further investigations. |
| Tasks | Artificial Life |
| Published | 2018-07-12 |
| URL | http://arxiv.org/abs/1807.04505v1 |
| http://arxiv.org/pdf/1807.04505v1.pdf | |
| PWC | https://paperswithcode.com/paper/achieving-connectivity-between-wide-areas |
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Attacking Speaker Recognition With Deep Generative Models
| Title | Attacking Speaker Recognition With Deep Generative Models |
| Authors | Wilson Cai, Anish Doshi, Rafael Valle |
| Abstract | In this paper we investigate the ability of generative adversarial networks (GANs) to synthesize spoofing attacks on modern speaker recognition systems. We first show that samples generated with SampleRNN and WaveNet are unable to fool a CNN-based speaker recognition system. We propose a modification of the Wasserstein GAN objective function to make use of data that is real but not from the class being learned. Our semi-supervised learning method is able to perform both targeted and untargeted attacks, raising questions related to security in speaker authentication systems. |
| Tasks | Speaker Recognition |
| Published | 2018-01-08 |
| URL | http://arxiv.org/abs/1801.02384v1 |
| http://arxiv.org/pdf/1801.02384v1.pdf | |
| PWC | https://paperswithcode.com/paper/attacking-speaker-recognition-with-deep |
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VizWiz Grand Challenge: Answering Visual Questions from Blind People
| Title | VizWiz Grand Challenge: Answering Visual Questions from Blind People |
| Authors | Danna Gurari, Qing Li, Abigale J. Stangl, Anhong Guo, Chi Lin, Kristen Grauman, Jiebo Luo, Jeffrey P. Bigham |
| Abstract | The study of algorithms to automatically answer visual questions currently is motivated by visual question answering (VQA) datasets constructed in artificial VQA settings. We propose VizWiz, the first goal-oriented VQA dataset arising from a natural VQA setting. VizWiz consists of over 31,000 visual questions originating from blind people who each took a picture using a mobile phone and recorded a spoken question about it, together with 10 crowdsourced answers per visual question. VizWiz differs from the many existing VQA datasets because (1) images are captured by blind photographers and so are often poor quality, (2) questions are spoken and so are more conversational, and (3) often visual questions cannot be answered. Evaluation of modern algorithms for answering visual questions and deciding if a visual question is answerable reveals that VizWiz is a challenging dataset. We introduce this dataset to encourage a larger community to develop more generalized algorithms that can assist blind people. |
| Tasks | Question Answering, Visual Question Answering |
| Published | 2018-02-22 |
| URL | http://arxiv.org/abs/1802.08218v4 |
| http://arxiv.org/pdf/1802.08218v4.pdf | |
| PWC | https://paperswithcode.com/paper/vizwiz-grand-challenge-answering-visual |
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Adaptive Graph Convolutional Neural Networks
| Title | Adaptive Graph Convolutional Neural Networks |
| Authors | Ruoyu Li, Sheng Wang, Feiyun Zhu, Junzhou Huang |
| Abstract | Graph Convolutional Neural Networks (Graph CNNs) are generalizations of classical CNNs to handle graph data such as molecular data, point could and social networks. Current filters in graph CNNs are built for fixed and shared graph structure. However, for most real data, the graph structures varies in both size and connectivity. The paper proposes a generalized and flexible graph CNN taking data of arbitrary graph structure as input. In that way a task-driven adaptive graph is learned for each graph data while training. To efficiently learn the graph, a distance metric learning is proposed. Extensive experiments on nine graph-structured datasets have demonstrated the superior performance improvement on both convergence speed and predictive accuracy. |
| Tasks | Metric Learning |
| Published | 2018-01-10 |
| URL | http://arxiv.org/abs/1801.03226v1 |
| http://arxiv.org/pdf/1801.03226v1.pdf | |
| PWC | https://paperswithcode.com/paper/adaptive-graph-convolutional-neural-networks |
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Latitude: A Model for Mixed Linear-Tropical Matrix Factorization
| Title | Latitude: A Model for Mixed Linear-Tropical Matrix Factorization |
| Authors | Sanjar Karaev, James Hook, Pauli Miettinen |
| Abstract | Nonnegative matrix factorization (NMF) is one of the most frequently-used matrix factorization models in data analysis. A significant reason to the popularity of NMF is its interpretability and the parts of whole' interpretation of its components. Recently, max-times, or subtropical, matrix factorization (SMF) has been introduced as an alternative model with equally interpretable winner takes it all’ interpretation. In this paper we propose a new mixed linear–tropical model, and a new algorithm, called Latitude, that combines NMF and SMF, being able to smoothly alternate between the two. In our model, the data is modeled using the latent factors and latent parameters that control whether the factors are interpreted as NMF or SMF features, or their mixtures. We present an algorithm for our novel matrix factorization. Our experiments show that our algorithm improves over both baselines, and can yield interpretable results that reveal more of the latent structure than either NMF or SMF alone. |
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| Published | 2018-01-18 |
| URL | http://arxiv.org/abs/1801.06136v1 |
| http://arxiv.org/pdf/1801.06136v1.pdf | |
| PWC | https://paperswithcode.com/paper/latitude-a-model-for-mixed-linear-tropical |
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Conversational Analysis using Utterance-level Attention-based Bidirectional Recurrent Neural Networks
| Title | Conversational Analysis using Utterance-level Attention-based Bidirectional Recurrent Neural Networks |
| Authors | Chandrakant Bothe, Sven Magg, Cornelius Weber, Stefan Wermter |
| Abstract | Recent approaches for dialogue act recognition have shown that context from preceding utterances is important to classify the subsequent one. It was shown that the performance improves rapidly when the context is taken into account. We propose an utterance-level attention-based bidirectional recurrent neural network (Utt-Att-BiRNN) model to analyze the importance of preceding utterances to classify the current one. In our setup, the BiRNN is given the input set of current and preceding utterances. Our model outperforms previous models that use only preceding utterances as context on the used corpus. Another contribution of the article is to discover the amount of information in each utterance to classify the subsequent one and to show that context-based learning not only improves the performance but also achieves higher confidence in the classification. We use character- and word-level features to represent the utterances. The results are presented for character and word feature representations and as an ensemble model of both representations. We found that when classifying short utterances, the closest preceding utterances contributes to a higher degree. |
| Tasks | |
| Published | 2018-05-16 |
| URL | http://arxiv.org/abs/1805.06242v2 |
| http://arxiv.org/pdf/1805.06242v2.pdf | |
| PWC | https://paperswithcode.com/paper/conversational-analysis-using-utterance-level |
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SemEval 2019 Shared Task: Cross-lingual Semantic Parsing with UCCA - Call for Participation
| Title | SemEval 2019 Shared Task: Cross-lingual Semantic Parsing with UCCA - Call for Participation |
| Authors | Daniel Hershcovich, Leshem Choshen, Elior Sulem, Zohar Aizenbud, Ari Rappoport, Omri Abend |
| Abstract | We announce a shared task on UCCA parsing in English, German and French, and call for participants to submit their systems. UCCA is a cross-linguistically applicable framework for semantic representation, which builds on extensive typological work and supports rapid annotation. UCCA poses a challenge for existing parsing techniques, as it exhibits reentrancy (resulting in DAG structures), discontinuous structures and non-terminal nodes corresponding to complex semantic units. Given the success of recent semantic parsing shared tasks (on SDP and AMR), we expect the task to have a significant contribution to the advancement of UCCA parsing in particular, and semantic parsing in general. Furthermore, existing applications for semantic evaluation that are based on UCCA will greatly benefit from better automatic methods for UCCA parsing. The competition website is https://competitions.codalab.org/competitions/19160 |
| Tasks | Semantic Parsing |
| Published | 2018-05-31 |
| URL | http://arxiv.org/abs/1805.12386v2 |
| http://arxiv.org/pdf/1805.12386v2.pdf | |
| PWC | https://paperswithcode.com/paper/semeval-2019-shared-task-cross-lingual |
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A Nonparametric Approach to High-dimensional k-sample Comparison Problems
| Title | A Nonparametric Approach to High-dimensional k-sample Comparison Problems |
| Authors | Subhadeep, Mukhopadhyay, Kaijun Wang |
| Abstract | High-dimensional k-sample comparison is a common applied problem. We construct a class of easy-to-implement nonparametric distribution-free tests based on new tools and unexplored connections with spectral graph theory. The test is shown to possess various desirable properties along with a characteristic exploratory flavor that has practical consequences. The numerical examples show that our method works surprisingly well under a broad range of realistic situations. |
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| Published | 2018-10-03 |
| URL | https://arxiv.org/abs/1810.01724v2 |
| https://arxiv.org/pdf/1810.01724v2.pdf | |
| PWC | https://paperswithcode.com/paper/nonparametric-high-dimensional-k-sample |
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Learning Collaborative Generation Correction Modules for Blind Image Deblurring and Beyond
| Title | Learning Collaborative Generation Correction Modules for Blind Image Deblurring and Beyond |
| Authors | Risheng Liu, Yi He, Shichao Cheng, Xin Fan, Zhongxuan Luo |
| Abstract | Blind image deblurring plays a very important role in many vision and multimedia applications. Most existing works tend to introduce complex priors to estimate the sharp image structures for blur kernel estimation. However, it has been verified that directly optimizing these models is challenging and easy to fall into degenerate solutions. Although several experience-based heuristic inference strategies, including trained networks and designed iterations, have been developed, it is still hard to obtain theoretically guaranteed accurate solutions. In this work, a collaborative learning framework is established to address the above issues. Specifically, we first design two modules, named Generator and Corrector, to extract the intrinsic image structures from the data-driven and knowledge-based perspectives, respectively. By introducing a collaborative methodology to cascade these modules, we can strictly prove the convergence of our image propagations to a deblurring-related optimal solution. As a nontrivial byproduct, we also apply the proposed method to address other related tasks, such as image interpolation and edge-preserved smoothing. Plenty of experiments demonstrate that our method can outperform the state-of-the-art approaches on both synthetic and real datasets. |
| Tasks | Blind Image Deblurring, Deblurring |
| Published | 2018-07-31 |
| URL | http://arxiv.org/abs/1807.11706v1 |
| http://arxiv.org/pdf/1807.11706v1.pdf | |
| PWC | https://paperswithcode.com/paper/learning-collaborative-generation-correction |
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Exploring Partially Observed Networks with Nonparametric Bandits
| Title | Exploring Partially Observed Networks with Nonparametric Bandits |
| Authors | Kaushalya Madhawa, Tsuyoshi Murata |
| Abstract | Real-world networks such as social and communication networks are too large to be observed entirely. Such networks are often partially observed such that network size, network topology, and nodes of the original network are unknown. In this paper we formalize the Adaptive Graph Exploring problem. We assume that we are given an incomplete snapshot of a large network and additional nodes can be discovered by querying nodes in the currently observed network. The goal of this problem is to maximize the number of observed nodes within a given query budget. Querying which set of nodes maximizes the size of the observed network? We formulate this problem as an exploration-exploitation problem and propose a novel nonparametric multi-arm bandit (MAB) algorithm for identifying which nodes to be queried. Our contributions include: (1) $i$KNN-UCB, a novel nonparametric MAB algorithm, applies $k$-nearest neighbor UCB to the setting when the arms are presented in a vector space, (2) provide theoretical guarantee that $i$KNN-UCB algorithm has sublinear regret, and (3) applying $i$KNN-UCB algorithm on synthetic networks and real-world networks from different domains, we show that our method discovers up to 40% more nodes compared to existing baselines. |
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| Published | 2018-04-19 |
| URL | http://arxiv.org/abs/1804.07059v1 |
| http://arxiv.org/pdf/1804.07059v1.pdf | |
| PWC | https://paperswithcode.com/paper/exploring-partially-observed-networks-with |
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Data-driven Analytics for Business Architectures: Proposed Use of Graph Theory
| Title | Data-driven Analytics for Business Architectures: Proposed Use of Graph Theory |
| Authors | Lei Huang, Guangjie Ren, Shun Jiang, Raphael Arar, Eric Young Liu |
| Abstract | Business Architecture (BA) plays a significant role in helping organizations understand enterprise structures and processes, and align them with strategic objectives. However, traditional BAs are represented in fixed structure with static model elements and fail to dynamically capture business insights based on internal and external data. To solve this problem, this paper introduces the graph theory into BAs with aim of building extensible data-driven analytics and automatically generating business insights. We use IBM’s Component Business Model (CBM) as an example to illustrate various ways in which graph theory can be leveraged for data-driven analytics, including what and how business insights can be obtained. Future directions for applying graph theory to business architecture analytics are discussed. |
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| Published | 2018-06-05 |
| URL | http://arxiv.org/abs/1806.03168v1 |
| http://arxiv.org/pdf/1806.03168v1.pdf | |
| PWC | https://paperswithcode.com/paper/data-driven-analytics-for-business |
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Binocular Tone Mapping with Improved Overall Contrast and Local Details
| Title | Binocular Tone Mapping with Improved Overall Contrast and Local Details |
| Authors | Zhuming Zhang, Xinghong Hu, Xueting Liu, Tien-Tsin Wong |
| Abstract | Tone mapping is a commonly used technique that maps the set of colors in high-dynamic-range (HDR) images to another set of colors in low-dynamic-range (LDR) images, to fit the need for print-outs, LCD monitors and projectors. Unfortunately, during the compression of dynamic range, the overall contrast and local details generally cannot be preserved simultaneously. Recently, with the increased use of stereoscopic devices, the notion of binocular tone mapping has been proposed in the existing research study. However, the existing research lacks the binocular perception study and is unable to generate the optimal binocular pair that presents the most visual content. In this paper, we propose a novel perception-based binocular tone mapping method, that can generate an optimal binocular image pair (generating left and right images simultaneously) from an HDR image that presents the most visual content by designing a binocular perception metric. Our method outperforms the existing method in terms of both visual and time performance. |
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| Published | 2018-09-17 |
| URL | http://arxiv.org/abs/1809.06036v1 |
| http://arxiv.org/pdf/1809.06036v1.pdf | |
| PWC | https://paperswithcode.com/paper/binocular-tone-mapping-with-improved-overall |
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Convolutional Neural Networks Analyzed via Inverse Problem Theory and Sparse Representations
| Title | Convolutional Neural Networks Analyzed via Inverse Problem Theory and Sparse Representations |
| Authors | Cem Tarhan, Gozde Bozdagi Akar |
| Abstract | Inverse problems in imaging such as denoising, deblurring, superresolution (SR) have been addressed for many decades. In recent years, convolutional neural networks (CNNs) have been widely used for many inverse problem areas. Although their indisputable success, CNNs are not mathematically validated as to how and what they learn. In this paper, we prove that during training, CNN elements solve for inverse problems which are optimum solutions stored as CNN neuron filters. We discuss the necessity of mutual coherence between CNN layer elements in order for a network to converge to the optimum solution. We prove that required mutual coherence can be provided by the usage of residual learning and skip connections. We have set rules over training sets and depth of networks for better convergence, i.e. performance. |
| Tasks | Deblurring, Denoising |
| Published | 2018-07-20 |
| URL | http://arxiv.org/abs/1807.07998v2 |
| http://arxiv.org/pdf/1807.07998v2.pdf | |
| PWC | https://paperswithcode.com/paper/convolutional-neural-networks-analyzed-via |
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