Paper Group ANR 268
Multi-level Contextual RNNs with Attention Model for Scene Labeling. Active Sensing of Social Networks. Improving the Accuracy of Stereo Visual Odometry Using Visual Illumination Estimation. Exploiting Facial Landmarks for Emotion Recognition in the Wild. On the Worst-case Communication Overhead for Distributed Data Shuffling. Programming in logic …
Multi-level Contextual RNNs with Attention Model for Scene Labeling
| Title | Multi-level Contextual RNNs with Attention Model for Scene Labeling |
| Authors | Heng Fan, Xue Mei, Danil Prokhorov, Haibin Ling |
| Abstract | Context in image is crucial for scene labeling while existing methods only exploit local context generated from a small surrounding area of an image patch or a pixel, by contrast long-range and global contextual information is ignored. To handle this issue, we in this work propose a novel approach for scene labeling by exploring multi-level contextual recurrent neural networks (ML-CRNNs). Specifically, we encode three kinds of contextual cues, i.e., local context, global context and image topic context in structural recurrent neural networks (RNNs) to model long-range local and global dependencies in image. In this way, our method is able to `see’ the image in terms of both long-range local and holistic views, and make a more reliable inference for image labeling. Besides, we integrate the proposed contextual RNNs into hierarchical convolutional neural networks (CNNs), and exploit dependence relationships in multiple levels to provide rich spatial and semantic information. Moreover, we novelly adopt an attention model to effectively merge multiple levels and show that it outperforms average- or max-pooling fusion strategies. Extensive experiments demonstrate that the proposed approach achieves new state-of-the-art results on the CamVid, SiftFlow and Stanford-background datasets. | |
| Tasks | Scene Labeling |
| Published | 2016-07-08 |
| URL | http://arxiv.org/abs/1607.02537v2 |
| http://arxiv.org/pdf/1607.02537v2.pdf | |
| PWC | https://paperswithcode.com/paper/multi-level-contextual-rnns-with-attention |
| Repo | |
| Framework | |
Active Sensing of Social Networks
| Title | Active Sensing of Social Networks |
| Authors | Hoi-To Wai, Anna Scaglione, Amir Leshem |
| Abstract | This paper develops an active sensing method to estimate the relative weight (or trust) agents place on their neighbors’ information in a social network. The model used for the regression is based on the steady state equation in the linear DeGroot model under the influence of stubborn agents, i.e., agents whose opinions are not influenced by their neighbors. This method can be viewed as a \emph{social RADAR}, where the stubborn agents excite the system and the latter can be estimated through the reverberation observed from the analysis of the agents’ opinions. The social network sensing problem can be interpreted as a blind compressed sensing problem with a sparse measurement matrix. We prove that the network structure will be revealed when a sufficient number of stubborn agents independently influence a number of ordinary (non-stubborn) agents. We investigate the scenario with a deterministic or randomized DeGroot model and propose a consistent estimator of the steady states for the latter scenario. Simulation results on synthetic and real world networks support our findings. |
| Tasks | |
| Published | 2016-01-21 |
| URL | http://arxiv.org/abs/1601.05834v2 |
| http://arxiv.org/pdf/1601.05834v2.pdf | |
| PWC | https://paperswithcode.com/paper/active-sensing-of-social-networks |
| Repo | |
| Framework | |
Improving the Accuracy of Stereo Visual Odometry Using Visual Illumination Estimation
| Title | Improving the Accuracy of Stereo Visual Odometry Using Visual Illumination Estimation |
| Authors | Lee Clement, Valentin Peretroukhin, Jonathan Kelly |
| Abstract | In the absence of reliable and accurate GPS, visual odometry (VO) has emerged as an effective means of estimating the egomotion of robotic vehicles. Like any dead-reckoning technique, VO suffers from unbounded accumulation of drift error over time, but this accumulation can be limited by incorporating absolute orientation information from, for example, a sun sensor. In this paper, we leverage recent work on visual outdoor illumination estimation to show that estimation error in a stereo VO pipeline can be reduced by inferring the sun position from the same image stream used to compute VO, thereby gaining the benefits of sun sensing without requiring a dedicated sun sensor or the sun to be visible to the camera. We compare sun estimation methods based on hand-crafted visual cues and Convolutional Neural Networks (CNNs) and demonstrate our approach on a combined 7.8 km of urban driving from the popular KITTI dataset, achieving up to a 43% reduction in translational average root mean squared error (ARMSE) and a 59% reduction in final translational drift error compared to pure VO alone. |
| Tasks | Visual Odometry |
| Published | 2016-09-15 |
| URL | https://arxiv.org/abs/1609.04705v3 |
| https://arxiv.org/pdf/1609.04705v3.pdf | |
| PWC | https://paperswithcode.com/paper/improving-the-accuracy-of-stereo-visual |
| Repo | |
| Framework | |
Exploiting Facial Landmarks for Emotion Recognition in the Wild
| Title | Exploiting Facial Landmarks for Emotion Recognition in the Wild |
| Authors | Matthew Day |
| Abstract | In this paper, we describe an entry to the third Emotion Recognition in the Wild Challenge, EmotiW2015. We detail the associated experiments and show that, through more accurately locating the facial landmarks, and considering only the distances between them, we can achieve a surprising level of performance. The resulting system is not only more accurate than the challenge baseline, but also much simpler. |
| Tasks | Emotion Recognition |
| Published | 2016-03-30 |
| URL | http://arxiv.org/abs/1603.09129v1 |
| http://arxiv.org/pdf/1603.09129v1.pdf | |
| PWC | https://paperswithcode.com/paper/exploiting-facial-landmarks-for-emotion |
| Repo | |
| Framework | |
On the Worst-case Communication Overhead for Distributed Data Shuffling
| Title | On the Worst-case Communication Overhead for Distributed Data Shuffling |
| Authors | Mohamed Attia, Ravi Tandon |
| Abstract | Distributed learning platforms for processing large scale data-sets are becoming increasingly prevalent. In typical distributed implementations, a centralized master node breaks the data-set into smaller batches for parallel processing across distributed workers to achieve speed-up and efficiency. Several computational tasks are of sequential nature, and involve multiple passes over the data. At each iteration over the data, it is common practice to randomly re-shuffle the data at the master node, assigning different batches for each worker to process. This random re-shuffling operation comes at the cost of extra communication overhead, since at each shuffle, new data points need to be delivered to the distributed workers. In this paper, we focus on characterizing the information theoretically optimal communication overhead for the distributed data shuffling problem. We propose a novel coded data delivery scheme for the case of no excess storage, where every worker can only store the assigned data batches under processing. Our scheme exploits a new type of coding opportunity and is applicable to any arbitrary shuffle, and for any number of workers. We also present an information theoretic lower bound on the minimum communication overhead for data shuffling, and show that the proposed scheme matches this lower bound for the worst-case communication overhead. |
| Tasks | |
| Published | 2016-09-30 |
| URL | http://arxiv.org/abs/1609.09823v1 |
| http://arxiv.org/pdf/1609.09823v1.pdf | |
| PWC | https://paperswithcode.com/paper/on-the-worst-case-communication-overhead-for |
| Repo | |
| Framework | |
Programming in logic without logic programming
| Title | Programming in logic without logic programming |
| Authors | Robert Kowalski, Fariba Sadri |
| Abstract | In previous work, we proposed a logic-based framework in which computation is the execution of actions in an attempt to make reactive rules of the form if antecedent then consequent true in a canonical model of a logic program determined by an initial state, sequence of events, and the resulting sequence of subsequent states. In this model-theoretic semantics, reactive rules are the driving force, and logic programs play only a supporting role. In the canonical model, states, actions and other events are represented with timestamps. But in the operational semantics, for the sake of efficiency, timestamps are omitted and only the current state is maintained. State transitions are performed reactively by executing actions to make the consequents of rules true whenever the antecedents become true. This operational semantics is sound, but incomplete. It cannot make reactive rules true by preventing their antecedents from becoming true, or by proactively making their consequents true before their antecedents become true. In this paper, we characterize the notion of reactive model, and prove that the operational semantics can generate all and only such models. In order to focus on the main issues, we omit the logic programming component of the framework. |
| Tasks | |
| Published | 2016-01-04 |
| URL | http://arxiv.org/abs/1601.00529v2 |
| http://arxiv.org/pdf/1601.00529v2.pdf | |
| PWC | https://paperswithcode.com/paper/programming-in-logic-without-logic |
| Repo | |
| Framework | |
Human Decision-Making under Limited Time
| Title | Human Decision-Making under Limited Time |
| Authors | Pedro A. Ortega, Alan A. Stocker |
| Abstract | Subjective expected utility theory assumes that decision-makers possess unlimited computational resources to reason about their choices; however, virtually all decisions in everyday life are made under resource constraints - i.e. decision-makers are bounded in their rationality. Here we experimentally tested the predictions made by a formalization of bounded rationality based on ideas from statistical mechanics and information-theory. We systematically tested human subjects in their ability to solve combinatorial puzzles under different time limitations. We found that our bounded-rational model accounts well for the data. The decomposition of the fitted model parameter into the subjects’ expected utility function and resource parameter provide interesting insight into the subjects’ information capacity limits. Our results confirm that humans gradually fall back on their learned prior choice patterns when confronted with increasing resource limitations. |
| Tasks | Decision Making |
| Published | 2016-10-06 |
| URL | http://arxiv.org/abs/1610.01698v1 |
| http://arxiv.org/pdf/1610.01698v1.pdf | |
| PWC | https://paperswithcode.com/paper/human-decision-making-under-limited-time |
| Repo | |
| Framework | |
Signature of Geometric Centroids for 3D Local Shape Description and Partial Shape Matching
| Title | Signature of Geometric Centroids for 3D Local Shape Description and Partial Shape Matching |
| Authors | Keke Tang, Peng Song, Xiaoping Chen |
| Abstract | Depth scans acquired from different views may contain nuisances such as noise, occlusion, and varying point density. We propose a novel Signature of Geometric Centroids descriptor, supporting direct shape matching on the scans, without requiring any preprocessing such as scan denoising or converting into a mesh. First, we construct the descriptor by voxelizing the local shape within a uniquely defined local reference frame and concatenating geometric centroid and point density features extracted from each voxel. Second, we compare two descriptors by employing only corresponding voxels that are both non-empty, thus supporting matching incomplete local shape such as those close to scan boundary. Third, we propose a descriptor saliency measure and compute it from a descriptor-graph to improve shape matching performance. We demonstrate the descriptor’s robustness and effectiveness for shape matching by comparing it with three state-of-the-art descriptors, and applying it to object/scene reconstruction and 3D object recognition. |
| Tasks | 3D Object Recognition, Denoising, Object Recognition |
| Published | 2016-12-26 |
| URL | http://arxiv.org/abs/1612.08408v1 |
| http://arxiv.org/pdf/1612.08408v1.pdf | |
| PWC | https://paperswithcode.com/paper/signature-of-geometric-centroids-for-3d-local |
| Repo | |
| Framework | |
A Learning Scheme for Microgrid Islanding and Reconnection
| Title | A Learning Scheme for Microgrid Islanding and Reconnection |
| Authors | Carter Lassetter, Eduardo Cotilla-Sanchez, Jinsub Kim |
| Abstract | This paper introduces a potential learning scheme that can dynamically predict the stability of the reconnection of sub-networks to a main grid. As the future electrical power systems tend towards smarter and greener technology, the deployment of self sufficient networks, or microgrids, becomes more likely. Microgrids may operate on their own or synchronized with the main grid, thus control methods need to take into account islanding and reconnecting of said networks. The ability to optimally and safely reconnect a portion of the grid is not well understood and, as of now, limited to raw synchronization between interconnection points. A support vector machine (SVM) leveraging real-time data from phasor measurement units (PMUs) is proposed to predict in real time whether the reconnection of a sub-network to the main grid would lead to stability or instability. A dynamics simulator fed with pre-acquired system parameters is used to create training data for the SVM in various operating states. The classifier was tested on a variety of cases and operating points to ensure diversity. Accuracies of approximately 85% were observed throughout most conditions when making dynamic predictions of a given network. |
| Tasks | |
| Published | 2016-11-15 |
| URL | http://arxiv.org/abs/1611.05317v2 |
| http://arxiv.org/pdf/1611.05317v2.pdf | |
| PWC | https://paperswithcode.com/paper/a-learning-scheme-for-microgrid-islanding-and |
| Repo | |
| Framework | |
Blind restoration for non-uniform aerial images using non-local Retinex model and shearlet-based higher-order regularization
| Title | Blind restoration for non-uniform aerial images using non-local Retinex model and shearlet-based higher-order regularization |
| Authors | Rui Chen, Huizhu Jia, Xiaodong Xie, Wen Gao |
| Abstract | Aerial images are often degraded by space-varying motion blur and simultaneous uneven illumination. To recover high-quality aerial image from its non-uniform version, we propose a novel patch-wise restoration approach based on a key observation that the degree of blurring is inevitably affected by the illuminated conditions. A non-local Retinex model is developed to accurately estimate the reflectance component from the degraded aerial image. Thereafter the uneven illumination is corrected well. And then non-uniform coupled blurring in the enhanced reflectance image is alleviated and transformed towards uniform distribution, which will facilitate the subsequent deblurring. For constructing the multi-scale sparsified regularizer, the discrete shearlet transform is improved to better represent anisotropic image features in term of directional sensitivity and selectivity. In addition, a new adaptive variant of total generalized variation is proposed for the structure-preserving regularizer. These complementary regularizers are elegantly integrated into an objective function. The final deblurred image with uniform illumination can be extracted by applying the fast alternating direction scheme to solve the derived function. The experimental results demonstrate that our algorithm can not only remove both the space-varying illumination and motion blur in the aerial image effectively but also recover the abundant details of aerial scenes with top-level objective and subjective quality, and outperforms other state-of-the-art restoration methods. |
| Tasks | Deblurring |
| Published | 2016-12-23 |
| URL | http://arxiv.org/abs/1612.08037v1 |
| http://arxiv.org/pdf/1612.08037v1.pdf | |
| PWC | https://paperswithcode.com/paper/blind-restoration-for-non-uniform-aerial |
| Repo | |
| Framework | |
Instrumenting an SMT Solver to Solve Hybrid Network Reachability Problems
| Title | Instrumenting an SMT Solver to Solve Hybrid Network Reachability Problems |
| Authors | Daniel Bryce, Sergiy Bogomolov, Alexander Heinz, Christian Schilling |
| Abstract | PDDL+ planning has its semantics rooted in hybrid automata (HA) and recent work has shown that it can be modeled as a network of HAs. Addressing the complexity of nonlinear PDDL+ planning as HAs requires both space and time efficient reasoning. Unfortunately, existing solvers either do not address nonlinear dynamics or do not natively support networks of automata. We present a new algorithm, called HNSolve, which guides the variable selection of the dReal Satisfiability Modulo Theories (SMT) solver while reasoning about network encodings of nonlinear PDDL+ planning as HAs. HNSolve tightly integrates with dReal by solving a discrete abstraction of the HA network. HNSolve finds composite runs on the HA network that ignore continuous variables, but respect mode jumps and synchronization labels. HNSolve admissibly detects dead-ends in the discrete abstraction, and posts conflict clauses that prune the SMT solver’s search. We evaluate the benefits of our HNSolve algorithm on PDDL+ benchmark problems and demonstrate its performance with respect to prior work. |
| Tasks | |
| Published | 2016-09-13 |
| URL | http://arxiv.org/abs/1609.03847v1 |
| http://arxiv.org/pdf/1609.03847v1.pdf | |
| PWC | https://paperswithcode.com/paper/instrumenting-an-smt-solver-to-solve-hybrid |
| Repo | |
| Framework | |
Road Detection through Supervised Classification
| Title | Road Detection through Supervised Classification |
| Authors | Yasamin Alkhorshid, Kamelia Aryafar, Sven Bauer, Gerd Wanielik |
| Abstract | Autonomous driving is a rapidly evolving technology. Autonomous vehicles are capable of sensing their environment and navigating without human input through sensory information such as radar, lidar, GNSS, vehicle odometry, and computer vision. This sensory input provides a rich dataset that can be used in combination with machine learning models to tackle multiple problems in supervised settings. In this paper we focus on road detection through gray-scale images as the sole sensory input. Our contributions are twofold: first, we introduce an annotated dataset of urban roads for machine learning tasks; second, we introduce a road detection framework on this dataset through supervised classification and hand-crafted feature vectors. |
| Tasks | Autonomous Driving, Autonomous Vehicles |
| Published | 2016-05-10 |
| URL | http://arxiv.org/abs/1605.03150v1 |
| http://arxiv.org/pdf/1605.03150v1.pdf | |
| PWC | https://paperswithcode.com/paper/road-detection-through-supervised |
| Repo | |
| Framework | |
A Variational Bayesian Approach for Image Restoration. Application to Image Deblurring with Poisson-Gaussian Noise
| Title | A Variational Bayesian Approach for Image Restoration. Application to Image Deblurring with Poisson-Gaussian Noise |
| Authors | Yosra Marnissi, Yuling Zheng, Emilie Chouzenoux, Jean-Christophe Pesquet |
| Abstract | In this paper, a methodology is investigated for signal recovery in the presence of non-Gaussian noise. In contrast with regularized minimization approaches often adopted in the literature, in our algorithm the regularization parameter is reliably estimated from the observations. As the posterior density of the unknown parameters is analytically intractable, the estimation problem is derived in a variational Bayesian framework where the goal is to provide a good approximation to the posterior distribution in order to compute posterior mean estimates. Moreover, a majorization technique is employed to circumvent the difficulties raised by the intricate forms of the non-Gaussian likelihood and of the prior density. We demonstrate the potential of the proposed approach through comparisons with state-of-the-art techniques that are specifically tailored to signal recovery in the presence of mixed Poisson-Gaussian noise. Results show that the proposed approach is efficient and achieves performance comparable with other methods where the regularization parameter is manually tuned from the ground truth. |
| Tasks | Deblurring, Image Restoration |
| Published | 2016-10-24 |
| URL | http://arxiv.org/abs/1610.07519v2 |
| http://arxiv.org/pdf/1610.07519v2.pdf | |
| PWC | https://paperswithcode.com/paper/a-variational-bayesian-approach-for-image |
| Repo | |
| Framework | |
Joint learning of ontology and semantic parser from text
| Title | Joint learning of ontology and semantic parser from text |
| Authors | Janez Starc, Dunja Mladenić |
| Abstract | Semantic parsing methods are used for capturing and representing semantic meaning of text. Meaning representation capturing all the concepts in the text may not always be available or may not be sufficiently complete. Ontologies provide a structured and reasoning-capable way to model the content of a collection of texts. In this work, we present a novel approach to joint learning of ontology and semantic parser from text. The method is based on semi-automatic induction of a context-free grammar from semantically annotated text. The grammar parses the text into semantic trees. Both, the grammar and the semantic trees are used to learn the ontology on several levels – classes, instances, taxonomic and non-taxonomic relations. The approach was evaluated on the first sentences of Wikipedia pages describing people. |
| Tasks | Semantic Parsing |
| Published | 2016-01-05 |
| URL | http://arxiv.org/abs/1601.00901v1 |
| http://arxiv.org/pdf/1601.00901v1.pdf | |
| PWC | https://paperswithcode.com/paper/joint-learning-of-ontology-and-semantic |
| Repo | |
| Framework | |
Regularized Dynamic Boltzmann Machine with Delay Pruning for Unsupervised Learning of Temporal Sequences
| Title | Regularized Dynamic Boltzmann Machine with Delay Pruning for Unsupervised Learning of Temporal Sequences |
| Authors | Sakyasingha Dasgupta, Takayuki Yoshizumi, Takayuki Osogami |
| Abstract | We introduce Delay Pruning, a simple yet powerful technique to regularize dynamic Boltzmann machines (DyBM). The recently introduced DyBM provides a particularly structured Boltzmann machine, as a generative model of a multi-dimensional time-series. This Boltzmann machine can have infinitely many layers of units but allows exact inference and learning based on its biologically motivated structure. DyBM uses the idea of conduction delays in the form of fixed length first-in first-out (FIFO) queues, with a neuron connected to another via this FIFO queue, and spikes from a pre-synaptic neuron travel along the queue to the post-synaptic neuron with a constant period of delay. Here, we present Delay Pruning as a mechanism to prune the lengths of the FIFO queues (making them zero) by setting some delay lengths to one with a fixed probability, and finally selecting the best performing model with fixed delays. The uniqueness of structure and a non-sampling based learning rule in DyBM, make the application of previously proposed regularization techniques like Dropout or DropConnect difficult, leading to poor generalization. First, we evaluate the performance of Delay Pruning to let DyBM learn a multidimensional temporal sequence generated by a Markov chain. Finally, we show the effectiveness of delay pruning in learning high dimensional sequences using the moving MNIST dataset, and compare it with Dropout and DropConnect methods. |
| Tasks | Time Series |
| Published | 2016-09-22 |
| URL | http://arxiv.org/abs/1610.01989v1 |
| http://arxiv.org/pdf/1610.01989v1.pdf | |
| PWC | https://paperswithcode.com/paper/regularized-dynamic-boltzmann-machine-with |
| Repo | |
| Framework | |
