aid stringlengths 9 15 | mid stringlengths 7 10 | abstract stringlengths 78 2.56k | related_work stringlengths 92 1.77k | ref_abstract dict |
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1901.09590 | 2914592219 | Knowledge graphs are structured representations of real world facts. However, they typically contain only a small subset of all possible facts. Link prediction is a task of inferring missing facts based on existing ones. We propose TuckER, a relatively simple but powerful linear model based on Tucker decomposition of t... | DistMult DistMult @cite_15 is a special case of RESCAL with a diagonal matrix per relation, so the number of parameters of DistMult grows linearly with respect to the embedding dimension, reducing overfitting. However, the linear transformation performed on subject entity embedding vectors in DistMult is limited to a s... | {
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"Abstract: We consider learning representations of entities and relations in KBs using the neural-embedding approach. We show that most existing models, including NTN (, 2013) and TransE (, 2013b), can be generalized under a unifie... |
1901.09590 | 2914592219 | Knowledge graphs are structured representations of real world facts. However, they typically contain only a small subset of all possible facts. Link prediction is a task of inferring missing facts based on existing ones. We propose TuckER, a relatively simple but powerful linear model based on Tucker decomposition of t... | ComplEx ComplEx @cite_1 extends DistMult to the complex domain. Even though each relation matrix of ComplEx is still diagonal, subject and object entity embeddings for the same entity are no longer equivalent, but complex conjugates, which introduces asymmetry into the tensor decomposition and thus enables ComplEx to m... | {
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"In statistical relational learning, the link prediction problem is key to automatically understand the structure of large knowledge bases. As in previous studies, we propose to solve this problem through latent factorization. Howev... |
1901.09590 | 2914592219 | Knowledge graphs are structured representations of real world facts. However, they typically contain only a small subset of all possible facts. Link prediction is a task of inferring missing facts based on existing ones. We propose TuckER, a relatively simple but powerful linear model based on Tucker decomposition of t... | ConvE ConvE @cite_32 is the first non-linear model that significantly outperformed the preceding linear models. In ConvE, a global 2D convolution operation is performed on the subject entity and relation embedding vectors, after they are reshaped to matrices and concatenated. The obtained feature maps are flattened, tr... | {
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"Link prediction for knowledge graphs is the task of predicting missing relationships between entities. Previous work on link prediction has focused on shallow, fast models which can scale to large knowledge graphs. However, these ... |
1901.09590 | 2914592219 | Knowledge graphs are structured representations of real world facts. However, they typically contain only a small subset of all possible facts. Link prediction is a task of inferring missing facts based on existing ones. We propose TuckER, a relatively simple but powerful linear model based on Tucker decomposition of t... | HypER HypER @cite_9 is a simplified convolutional model, that uses a hypernetwork to generate 1D convolutional filters for each relation, extracting relation-specific features from subject entity embeddings. The authors show that convolution is a way of introducing sparsity and parameter tying and that HypER can be und... | {
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"Knowledge graphs are graphical representations of large databases of facts, which typically suffer from incompleteness. Inferring missing relations (links) between entities (nodes) is the task of link prediction. A recent state-of-... |
1901.09891 | 2913012226 | Data augmentation is usually adopted to increase the amount of training data, prevent overfitting and improve the performance of deep models. However, in practice, random data augmentation, such as random image cropping, is low-efficiency and might introduce many uncontrolled background noises. In this paper, we propos... | Random data augmentation suffers from low efficiency and generating much uncontrolled noise data. To overcome these issue, a few methods have been proposed to take dataset distribution into consideration and augment data according to the feedback of the training dataset, which is more effective than a random distributi... | {
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"Random data augmentation is a critical technique to avoid overfitting in training deep models. Yet, data augmentation and network training are often two isolated processes in most settings, yieldi... |
1901.09891 | 2913012226 | Data augmentation is usually adopted to increase the amount of training data, prevent overfitting and improve the performance of deep models. However, in practice, random data augmentation, such as random image cropping, is low-efficiency and might introduce many uncontrolled background noises. In this paper, we propos... | To focus on the local features, many methods rely on the annotations of parts location or attribute. Part R-CNN @cite_44 extended R-CNN @cite_32 to detect objects and localize their parts under a geometric prior, then predicted a fine-grained category from a pose-normalized representation. @cite_33 proposed a feedback-... | {
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1901.09891 | 2913012226 | Data augmentation is usually adopted to increase the amount of training data, prevent overfitting and improve the performance of deep models. However, in practice, random data augmentation, such as random image cropping, is low-efficiency and might introduce many uncontrolled background noises. In this paper, we propos... | Weakly supervised learning is an umbrella term that covers a variety of studies that attempt to construct predictive models by learning with weak supervision @cite_0 , which mainly consists of incomplete, inexact and inaccurate supervision. Localizing object or its parts only by image-level annotation belongs to the in... | {
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"Supervised learning techniques construct predictive models by learning from a large number of training examples, where each training example has a label indicating its ground-truth output. Though current techniques have achieved gr... |
1901.09891 | 2913012226 | Data augmentation is usually adopted to increase the amount of training data, prevent overfitting and improve the performance of deep models. However, in practice, random data augmentation, such as random image cropping, is low-efficiency and might introduce many uncontrolled background noises. In this paper, we propos... | Accurately locating the object or its parts only by image-level supervision is very challenging. Early work @cite_7 @cite_13 usually generate class-specific localization maps by Global Average Pooling (GAP) @cite_3 . The activation area can reflect the location of an object. However, training by softmax cross entropy l... | {
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"In this work, we propose Adversarial Complementary Learning (ACoL) to automatically localize integral objec... |
1901.09888 | 2913777072 | The increasing interest in user privacy is leading to new privacy preserving machine learning paradigms. In the Federated Learning paradigm, a master machine learning model is distributed to user clients, the clients use their locally stored data and model for both inference and calculating model updates. The model upd... | This work lies at the intersection of three research topics: (i) matrix factorization, (ii) parallel & distributed and, (iii) federated learning. Recently, Alternating Least Squares (ALS) and Stochastic Gradient Descent (SGD) have gained much interest and have become the most popular algorithms for matrix factorization... | {
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1901.09888 | 2913777072 | The increasing interest in user privacy is leading to new privacy preserving machine learning paradigms. In the Federated Learning paradigm, a master machine learning model is distributed to user clients, the clients use their locally stored data and model for both inference and calculating model updates. The model upd... | Federated Learning, on the other hand, a distributed learning paradigm essentially assumes user data is not available on central servers and is private and confidential. A prominent direction of research in this domain is based on the weighted averaging of the model parameters @cite_17 @cite_24 . In practice, a master ... | {
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1901.09774 | 2912208857 | Facial attributes are important since they provide a detailed description and determine the visual appearance of human faces. In this paper, we aim at converting a face image to a sketch while simultaneously generating facial attributes. To this end, we propose a novel Attribute-Guided Sketch Generative Adversarial Net... | Based on CGANs, @cite_46 have developed a generic framework Pix2pix'', which is suitable for different generative tasks. In Pix2pix, one conditional image is adopted as a reference during the training time. The generator in Pix2pix is a U-net, which tries to synthesize a fake image conditioned on the given conditional ... | {
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"We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to tr... |
1901.09892 | 2914520039 | Neural networks play an increasingly important role in the field of machine learning and are included in many applications in society. Unfortunately, neural networks suffer from adversarial samples generated to attack them. However, most of the generation approaches either assume that the attacker has full knowledge of... | Some recent research aimed to defend against the attack of adversarial samples and proposed approaches such as defensive distillatione @cite_20 @cite_10 @cite_31 @cite_28 . However, experiment results show that these approaches do not perform well in particular situations due to not being able to defend against adversa... | {
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"Machine learning and deep learning in particular has advanced tremendously on perceptual tasks ... |
1907.09987 | 2963313981 | Bayesian inference is used extensively to infer and to quantify the uncertainty in a field of interest from a measurement of a related field when the two are linked by a physical model. Despite its many applications, Bayesian inference faces challenges when inferring fields that have discrete representations of large d... | The solution of an inverse problem using sample-based priors has a rich history (see @cite_6 @cite_33 for example). As does the idea of reducing the dimension of the parameter space by mapping it to a lower-dimensional space @cite_17 @cite_27 . However, the use of GANs in these tasks is novel. | {
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"A greedy algorithm for the construction of a reduced model with reduction in both parameter and state is developed for an efficie... |
1907.09987 | 2963313981 | Bayesian inference is used extensively to infer and to quantify the uncertainty in a field of interest from a measurement of a related field when the two are linked by a physical model. Despite its many applications, Bayesian inference faces challenges when inferring fields that have discrete representations of large d... | Recently, a number of authors have considered the use machine learning-based methods for solving inverse problems. These include the use of convolutional neural networks (CNNs) to solve physics-driven inverse problems @cite_18 @cite_23 @cite_46 , and GANs to solve problems in computer vision @cite_57 @cite_65 @cite_29 ... | {
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1907.09987 | 2963313981 | Bayesian inference is used extensively to infer and to quantify the uncertainty in a field of interest from a measurement of a related field when the two are linked by a physical model. Despite its many applications, Bayesian inference faces challenges when inferring fields that have discrete representations of large d... | More recently, the approach described in @cite_36 utilizes GANs in a Bayesian setting; however the GAN is trained to approximate the posterior distribution (and not the prior, as in our case), and training is done in a supervised fashion. That is, paired samples of the measurement @math and the corresponding true solut... | {
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"Characterizing statistical properties of solutions of inverse problems is essential for decision making. Bayesian inversion offers a tractable framework for this purpose, but current approaches are computationally unfeasible for m... |
1907.09798 | 2972934250 | Motivated by the success of encoding multi-scale contextual information for image analysis, we propose our PointAtrousGraph (PAG) - a deep permutation-invariant hierarchical encoder-decoder for efficiently exploiting multi-scale edge features in point clouds. Our PAG is constructed by several novel modules, such as Poi... | Deep hierarchical encoder-decoder architectures are widely and successfully used for many image-based tasks, such as human pose estimation @cite_41 @cite_80 , semantic segmentation @cite_66 @cite_3 @cite_11 @cite_60 @cite_48 @cite_73 @cite_64 @cite_33 @cite_46 @cite_72 , optical flow estimation @cite_53 @cite_49 , and ... | {
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1907.09798 | 2972934250 | Motivated by the success of encoding multi-scale contextual information for image analysis, we propose our PointAtrousGraph (PAG) - a deep permutation-invariant hierarchical encoder-decoder for efficiently exploiting multi-scale edge features in point clouds. Our PAG is constructed by several novel modules, such as Poi... | Unorganized point cloud is a simple and straight-forward representation of 3D structures. @cite_45 . The pioneering work PointNet @cite_45 achieves permutation-invariance by applying symmetric functions. Inspired by PointNet, many following works @cite_65 @cite_82 @cite_59 @cite_21 propose more complicated symmetric op... | {
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1907.09760 | 2963048191 | We present NOLBO, a variational observation model estimation for 3D multi-object from 2D single shot. Previous probabilistic instance-level understandings mainly consider the single-object image, not single shot with multi-object; relations between objects and the entire scene are out of their focus. The objectness of ... | With the recent advent of neural networks, a number of single object classification and detection methods with high performance have been proposed @cite_31 @cite_36 @cite_39 . Beyond obtaining one feature vector from one image for an object, several multi-object detection techniques from single shot have been developed... | {
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1907.09760 | 2963048191 | We present NOLBO, a variational observation model estimation for 3D multi-object from 2D single shot. Previous probabilistic instance-level understandings mainly consider the single-object image, not single shot with multi-object; relations between objects and the entire scene are out of their focus. The objectness of ... | Various studies have also been conducted to understand the instance-level representation from 2D images such as object shape, orientation or bounding box. @cite_4 @cite_2 @cite_55 and @cite_48 estimate the orientation of the object by viewpoint classification with discretized bins. In addition, 3D bounding box regressi... | {
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1907.09760 | 2963048191 | We present NOLBO, a variational observation model estimation for 3D multi-object from 2D single shot. Previous probabilistic instance-level understandings mainly consider the single-object image, not single shot with multi-object; relations between objects and the entire scene are out of their focus. The objectness of ... | Through multi-object detection and instance-level understanding altogether, learning the disentangled representation of multi-object becomes achievable. @cite_6 exploits the yolov2 structure @cite_51 to estimate the 3D bounding box and center of the multi-object and obtains the orientations. In @cite_48 , they estimate... | {
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1907.09760 | 2963048191 | We present NOLBO, a variational observation model estimation for 3D multi-object from 2D single shot. Previous probabilistic instance-level understandings mainly consider the single-object image, not single shot with multi-object; relations between objects and the entire scene are out of their focus. The objectness of ... | These studies are efficient because they mainly concern direct and accurate estimation of the object characteristics through network modeling; on the other hand, the probabilistic observation models are relatively less considered. Although they exploit the neural network for nonlinear regression, approximating the intr... | {
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1907.09760 | 2963048191 | We present NOLBO, a variational observation model estimation for 3D multi-object from 2D single shot. Previous probabilistic instance-level understandings mainly consider the single-object image, not single shot with multi-object; relations between objects and the entire scene are out of their focus. The objectness of ... | To handle the intractable target distribution, latent variables can be adopted @cite_46 @cite_18 @cite_34 @cite_13 . In order to understand and utilize the latent space, @cite_58 @cite_9 have studied the relations between latent variables and object visualization by using VAE @cite_34 . However, it is still challenging... | {
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1907.09825 | 2964205377 | Efficient behavior and trajectory planning is one of the major challenges for automated driving. Especially intersection scenarios are very demanding due to their complexity arising from the variety of maneuver possibilities and other traffic participants. A key challenge is to generate behaviors which optimize the com... | A method for producing courteous behavior is to use a game-theoretic interaction model @cite_15 . The problem is modeled in a way that all agents try to optimize their own behavior. By predicting and considering the reaction to the ego trajectory, courteous behavior can be generated. It is shown that this courtesy lead... | {
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"Typically, autonomous cars optimize for a combination of safety, efficiency, and driving quality. But as we get better at this optimization, we start seeing behavior go from too conservative to too aggressive. The car's behavior e... |
1907.09945 | 2963509006 | Affective computing is a field of great interest in many computer vision applications, including video surveillance, behaviour analysis, and human-robot interaction. Most of the existing literature has addressed this field by analysing different sets of face features. However, in the last decade, several studies have s... | The most accurate affect recognition systems are based on electroencephalography @cite_27 . Although these systems achieve excellent results, they are limited by the use of dedicated sensors that require a controlled environment. The computer vision applications, on the other hand, are more suitable to work in real and... | {
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1907.09883 | 2963801192 | All public blockchains are secured by a proof of opportunity cost among block producers. For example, the security offered by proof-of-work (PoW) systems, like Bitcoin, is due to spent computation; it is work precisely because it cannot be performed for free. In general, more resources provably lost in producing blocks... | Also closely related is the work of @cite_6 who apply the theory of Potential Games @cite_23 to the problem of miner hash rate allocation across multiple blockchains. They prove that, regardless of individual hash rate and coinbase rewards for each of the blockchains, hash rate allocation will converge to a pure equili... | {
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"We model the competition over several blockchains characterizing multiple cryptocurrencies as a non-cooperative game. Then, we specialize our results to two instances of the... |
1907.09883 | 2963801192 | All public blockchains are secured by a proof of opportunity cost among block producers. For example, the security offered by proof-of-work (PoW) systems, like Bitcoin, is due to spent computation; it is work precisely because it cannot be performed for free. In general, more resources provably lost in producing blocks... | Several authors have sought to determine the optimal hash rate allocation between blockchains for miners or mining pools. @cite_7 argue that miners allocate their hash rate between multiple blockchains so as to minimize the risk associated with fluctuations in coin price. @cite_25 make a similar argument except that th... | {
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"Mining is a central operation of all proof-of-work (PoW) based cryptocurrencies. The vast majority of miners today participate in \"mining pools\" instead of \"solo... |
1907.09883 | 2963801192 | All public blockchains are secured by a proof of opportunity cost among block producers. For example, the security offered by proof-of-work (PoW) systems, like Bitcoin, is due to spent computation; it is work precisely because it cannot be performed for free. In general, more resources provably lost in producing blocks... | @cite_22 devised a Markov Decision Process (MDP) for discovering optimal selfish mining @cite_15 strategies. @cite_12 expanded the model to incorporate adjustable network parameters and include analysis of doublespend attacks. @cite_21 extend the MDP of @cite_12 to model mining difficulty adjustment. The biggest differ... | {
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"The Bitcoin cryptocurrency records its transactions in a public log called the blockchain. Its security rests critically on the ... |
1907.09786 | 2963372556 | We propose a novel single-step training strategy that allows convolutional encoder-decoder networks that use skip connections, to complete partially observed data by means of hallucination. This strategy is demonstrated for the task of completing 2-D road layouts as well as 3-D vehicle shapes. As input, it takes data f... | Closely related to our task of hallucinating the road layout is image inpainting. In recent years, deep convolutional neural networks (CNNs) enable the possibility of image inpainting with large missing areas, as CNNs can extract abstract semantic information from the observable context. The Context Encoder (CE) @cite_... | {
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1907.09786 | 2963372556 | We propose a novel single-step training strategy that allows convolutional encoder-decoder networks that use skip connections, to complete partially observed data by means of hallucination. This strategy is demonstrated for the task of completing 2-D road layouts as well as 3-D vehicle shapes. As input, it takes data f... | If the ground truth is not available, one has to hallucinate the region to be completed. Srikantha and Gall @cite_23 propose a system to hallucinate a depth map and a semantic map, given an RGB image and a noisy, incomplete depth map, which is able to remove the foreground objects. Schulter al @cite_1 proposes a CNN to... | {
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"3D shape completion from partial point clouds is a fundamental problem in computer vision and computer graphics. Recent approaches can be characterized as either da... |
1907.09786 | 2963372556 | We propose a novel single-step training strategy that allows convolutional encoder-decoder networks that use skip connections, to complete partially observed data by means of hallucination. This strategy is demonstrated for the task of completing 2-D road layouts as well as 3-D vehicle shapes. As input, it takes data f... | Road layout hallucinating can be seen as a specific approach of road layout understanding, which is an important task for robot and intelligent vehicle navigation. One challenge is that the ego-centric sensory data usually contains occlusions of the foreground objects, which makes roads visually incomplete. Many works ... | {
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"This paper is about the detection and inference of road boundaries from mono-images. Our goal is to trace out, in an image, the projection of road boundaries irresp... |
1907.09815 | 2963714798 | The interaction between language and visual information has been emphasized in visual question answering (VQA) with the help of attention mechanism. However, the relationship between words in question has been underestimated, which makes it hard to answer questions that involve the relationship between multiple entitie... | : VQA is a task to answer the given question based on the input image. The question is usually embedded into a vector with LSTM @cite_6 , and the image is represented by the fixed-size grid features from ResNet @cite_4 . Recently, @cite_28 focuses on bottom-up attention of image features and proposes a set of salient i... | {
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"Top-down visual attention mechanisms have been used extensively in image captioning and visual question answering (VQA) to enable deeper imag... |
1907.09815 | 2963714798 | The interaction between language and visual information has been emphasized in visual question answering (VQA) with the help of attention mechanism. However, the relationship between words in question has been underestimated, which makes it hard to answer questions that involve the relationship between multiple entitie... | Based on the fusion methods of the two features, we can classify VQA models into two categories: early fusion models and later fusion models. Early fusion models try to fine-tune the image classification network with the intervention of the question, they insert the question embedding into the batch normalization layer... | {
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1907.09871 | 2963864879 | The paper details the first successful attempt at using model-checking techniques to verify the correctness of distributed algorithms for robots evolving in a environment. The study focuses on the problem of rendezvous of two robots with lights. There exist many different rendezvous algorithms that aim at finding the m... | Designing and proving mobile robot protocols is notoriously difficult. Formal methods encompass a long-lasting path of research that is meant to overcome errors of human origin. Unsurprisingly, this mechanized approach to protocol correctness was used in the context of mobile robots @cite_11 @cite_32 @cite_5 @cite_18 @... | {
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1907.09871 | 2963864879 | The paper details the first successful attempt at using model-checking techniques to verify the correctness of distributed algorithms for robots evolving in a environment. The study focuses on the problem of rendezvous of two robots with lights. There exist many different rendezvous algorithms that aim at finding the m... | When robots move freely in a continuous two-dimensional Euclidean space (as considered in this paper), to the best of our knowledge the only formal framework available is Pactole. http: pactole.lri.fr It relies on higher-order logic to certify impossibility results @cite_18 @cite_27 @cite_2 , as well as the correctness... | {
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1907.09871 | 2963864879 | The paper details the first successful attempt at using model-checking techniques to verify the correctness of distributed algorithms for robots evolving in a environment. The study focuses on the problem of rendezvous of two robots with lights. There exist many different rendezvous algorithms that aim at finding the m... | On the other side, model checking and its derivatives (automatic program synthesis, parameterized model checking) hint at more automation once a suitable model has been defined with the input language of the model checker. In particular, model-checking proved useful to find bugs (usually in the setting) @cite_5 @cite_1... | {
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1907.09939 | 2963333474 | Understanding large amounts of spatiotemporal data from particle-based simulations, such as molecular dynamics, often relies on the computation and analysis of aggregate measures. These, however, by virtue of aggregation, hide structural information about the space time localization of the studied phenomena. This leads... | This paper contributes to the analysis of data that usually would be treated as a time series of spatially aggregated values. A good overview of visualization techniques for time-dependent data can be found in a book by @cite_57 . They focus primarily on time-dependent data in general, without specifically addressing s... | {
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"Time is an exceptional dimension that is common to many application domains such as medicine, engineering, business, or science. Due to the distinct characteristic... |
1907.09939 | 2963333474 | Understanding large amounts of spatiotemporal data from particle-based simulations, such as molecular dynamics, often relies on the computation and analysis of aggregate measures. These, however, by virtue of aggregation, hide structural information about the space time localization of the studied phenomena. This leads... | Our approach is based on the concept of the space-time cube, coined by H "a gerstrand @cite_16 in 1970. Since then it was repeatedly used, either explicitly or as an underlying concept. Recently, @cite_35 presented a useful overview of related techniques. They describe the theoretical concept of a generalized space-tim... | {
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1907.09939 | 2963333474 | Understanding large amounts of spatiotemporal data from particle-based simulations, such as molecular dynamics, often relies on the computation and analysis of aggregate measures. These, however, by virtue of aggregation, hide structural information about the space time localization of the studied phenomena. This leads... | Space reformation techniques are valuable tools for gaining insight into the spatial structure of the data. Recent publications present methods for a decomposition using a space-filling curve in MotionRugs @cite_53 and Dynamic Volume Lines @cite_1 . These works transform continuous volume data into one-dimensional repr... | {
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1907.09939 | 2963333474 | Understanding large amounts of spatiotemporal data from particle-based simulations, such as molecular dynamics, often relies on the computation and analysis of aggregate measures. These, however, by virtue of aggregation, hide structural information about the space time localization of the studied phenomena. This leads... | Substantial work has been done on the visualization of trajectory-based simulation data, including projects such as OVITO @cite_34 , Trillion Particles @cite_23 , Multiscale HIV @cite_13 . These are primarily concerned with the sheer volume of the data and its direct visualization, rather than the computation of derive... | {
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"The Open Visualization Tool (OVITO) is a new 3D visualization software designed for post-processing atomistic data obtained from molecular dynamics or Monte Carlo... |
1907.09939 | 2963333474 | Understanding large amounts of spatiotemporal data from particle-based simulations, such as molecular dynamics, often relies on the computation and analysis of aggregate measures. These, however, by virtue of aggregation, hide structural information about the space time localization of the studied phenomena. This leads... | Focusing on the trajectories, hierarchical particle grouping for large datasets has been done by @cite_33 . @cite_43 extract prominent trajectories from large particle data. present a specialized tool for exploring Monte Carlo simulations of photo-voltaic cells. | {
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"An effective means for flow visualization is the depiction of particle trajectories. When rendering large amounts of these pathlines, standard visualization techniques suffer from several weakness... |
1907.09939 | 2963333474 | Understanding large amounts of spatiotemporal data from particle-based simulations, such as molecular dynamics, often relies on the computation and analysis of aggregate measures. These, however, by virtue of aggregation, hide structural information about the space time localization of the studied phenomena. This leads... | @cite_47 present a visual analytics tool for exploring molecular structures based on the Solvent Accessible Surface (SAS). This is a geometric approach to the extraction of the spatial configuration of a protein--ligand interaction. MoleCollar and Tunnel Heat Map @cite_24 are works by By s where they reform the propert... | {
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"abstract": [
"Studying the characteristics of proteins and their inner void space, including their geometry, physico-chemical properties and dynamics are instrumental for evalu... |
1907.09834 | 2963026274 | We extend the Mobile Server Problem, introduced in SPAA'17, to a model where k identical mobile resources, here named servers, answer requests appearing at points in the Euclidean space. In order to reduce communication costs, the positions of the servers can be adapted by a limited distance m_s per round for each serv... | In the classical @math -Server Problem as introduced by @cite_8 , @math identical servers are located in a metric space and requests are answered by moving at least one of the servers to the point of the request. The associated costs are equal to the total distance moved. showed that no online algorithm could be better... | {
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"abstract": [
"Abstract The k-server problem is that of planning the motion of k mobile servers on the vertices of a graph under a sequence of requests for service. Each request consists of the name of a vertex, and is satisfied by placing a serv... |
1907.09834 | 2963026274 | We extend the Mobile Server Problem, introduced in SPAA'17, to a model where k identical mobile resources, here named servers, answer requests appearing at points in the Euclidean space. In order to reduce communication costs, the positions of the servers can be adapted by a limited distance m_s per round for each serv... | Since its introduction, many algorithms have been designed for special cases of the problem. Most notable is the Double-Coverage Algorithm @cite_10 , which is @math -competitive on trees. For general metrics, the best known result is the Work-Function Algorithm, which is shown to be @math -competitive @cite_3 . Althoug... | {
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"abstract": [
"This paper deals with the work function algorithm (WFA) for solving the on-line k-server problem. The pape... |
1907.09834 | 2963026274 | We extend the Mobile Server Problem, introduced in SPAA'17, to a model where k identical mobile resources, here named servers, answer requests appearing at points in the Euclidean space. In order to reduce communication costs, the positions of the servers can be adapted by a limited distance m_s per round for each serv... | The study of randomized online algorithms was initiated by @cite_4 who gave a @math -competitive algorithm for the complete graph. It is speculated that this factor can be obtained for all metrics, however the question is still open. For general metrics, the first algorithm with polylogarithmic competitive ratio was an... | {
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"abstract": [
"We exhibit a poly(log k)-competitive randomized algorithm for the k-server problem on any metric space. The best previous result i... |
1907.09834 | 2963026274 | We extend the Mobile Server Problem, introduced in SPAA'17, to a model where k identical mobile resources, here named servers, answer requests appearing at points in the Euclidean space. In order to reduce communication costs, the positions of the servers can be adapted by a limited distance m_s per round for each serv... | Regarding the Page Migration Problem @cite_2 (also known as File Migration Problem), most results focus on online algorithms which handle only a single page. Contrary to the @math -Server Problem, the design of such algorithms is not trivial for the Page Migration Problem. To the best of our knowledge, the current best... | {
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"abstract": [
"In this paper, we construct a deterministic 4-competitive algorithm for the online file migration problem, beating the currently bes... |
1901.09513 | 2913397231 | Underwater robots are subject to position drift due to the effect of ocean currents and the lack of accurate localisation while submerged. We are interested in exploiting such position drift to estimate the ocean current in the surrounding area, thereby assisting navigation and planning. We present a Gaussian process (... | To estimate ocean currents online, one approach is to consider current as a low-frequency disturbance and then apply an extended Kalman filter (EKF) @cite_7 or nonlinear observer @cite_30 in conjunction with acoustic sensors. However, modelling current as a temporal phenomenon clearly overlooks its spatial structure, a... | {
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"As applications for autonomous ocean vehicles expand into more dynamic and constrained environments, such as shallow, coastal areas, the benefits of using more pre... |
1901.09513 | 2913397231 | Underwater robots are subject to position drift due to the effect of ocean currents and the lack of accurate localisation while submerged. We are interested in exploiting such position drift to estimate the ocean current in the surrounding area, thereby assisting navigation and planning. We present a Gaussian process (... | An approach that does consider the spatial nature of the problem is presented in @cite_0 . The authors examine the feasibility of ocean current estimation through simply calculating the average current velocity by dividing the position drift by time. Unsurprisingly, the estimate is increasingly unreliable as distance b... | {
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"We consider the potential for making current measurements from gliders, and present data from a deployment in early 2007 of 1000 m Slocum electric gliders in the North West Mediterranean Sea. Three types of current measurement are ... |
1901.09482 | 2913508739 | What is the current state-of-the-art for image restoration and enhancement applied to degraded images acquired under less than ideal circumstances? Can the application of such algorithms as a pre-processing step to improve image interpretability for manual analysis or automatic visual recognition to classify scene cont... | The areas of image restoration and enhancement have a long history in computational photography, with associated benchmark datasets that are mainly used for the qualitative evaluation of image appearance. These include very small test image sets such as Set5 @cite_130 and Set14 @cite_44 , the set of blurred images intr... | {
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... |
1901.09482 | 2913508739 | What is the current state-of-the-art for image restoration and enhancement applied to degraded images acquired under less than ideal circumstances? Can the application of such algorithms as a pre-processing step to improve image interpretability for manual analysis or automatic visual recognition to classify scene cont... | With respect to data collected by aerial vehicles, the VIRAT Video Dataset @cite_80 contains realistic, natural and challenging (in terms of its resolution, background clutter, diversity in scenes)" imagery for event recognition, while the VisDrone2018 Dataset @cite_4 is designed for object detection and tracking. Othe... | {
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"In this paper we present a large-scale visual object detection and tracking benchma... |
1901.09482 | 2913508739 | What is the current state-of-the-art for image restoration and enhancement applied to degraded images acquired under less than ideal circumstances? Can the application of such algorithms as a pre-processing step to improve image interpretability for manual analysis or automatic visual recognition to classify scene cont... | Intuitively, if an image has been corrupted, then employing restoration techniques should improve performance of recognizing objects in the image. An early attempt at unifying a high-level task like object recognition with a low-level task like deblurring was performed by Zeiler al through deconvolutional networks @cit... | {
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1901.09482 | 2913508739 | What is the current state-of-the-art for image restoration and enhancement applied to degraded images acquired under less than ideal circumstances? Can the application of such algorithms as a pre-processing step to improve image interpretability for manual analysis or automatic visual recognition to classify scene cont... | Sajjadi al @cite_87 argue that the use of traditional metrics such as Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), or the Information Fidelity Criterion (IFC) might not reflect the performance of some models, and propose the use of object recognition performance as an evaluation metric. They o... | {
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1901.09482 | 2913508739 | What is the current state-of-the-art for image restoration and enhancement applied to degraded images acquired under less than ideal circumstances? Can the application of such algorithms as a pre-processing step to improve image interpretability for manual analysis or automatic visual recognition to classify scene cont... | While the above approaches employ object recognition in addition to visual enhancement, there are approaches designed to overlook the visual appearance of the image and instead make use of enhancement techniques to exclusively improve the object recognition performance. Sharma al @cite_79 make use of dynamic enhancemen... | {
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"Convolutional neural networks rely on image texture and structure to serve as discriminative features to classify the image content. Image enhancement techniques ... |
1901.09237 | 2950847180 | Digitally retouching images has become a popular trend, with people posting altered images on social media and even magazines posting flawless facial images of celebrities. Further, with advancements in Generative Adversarial Networks (GANs), now changing attributes and retouching have become very easy. Such synthetic ... | Retouching, makeup detection, face spoofing and morphing are widely studied areas, that can be considered similar to retouching detection. Recent work by @cite_2 makes use of supervised deep Boltzmann machine algorithm for detecting retouching on the ND-IIITD database. It also introduces the ND-IIITD dataset which cons... | {
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"Digital retouching of face images is becoming more widespread due to the introduction of software packages that automate the task. Several researchers have introdu... |
1901.09244 | 2912003633 | Video recognition models have progressed significantly over the past few years, evolving from shallow classifiers trained on hand-crafted features to deep spatiotemporal networks. However, labeled video data required to train such models have not been able to keep up with the ever-increasing depth and sophistication of... | Video understanding, specifically for the task of human action recognition, is a well studied problem in computer vision. Analogously to the progress of image-based recognition methods, which have advanced from hand-crafted features @cite_14 @cite_34 to modern deep networks @cite_2 @cite_11 @cite_32 , video understandi... | {
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1901.09244 | 2912003633 | Video recognition models have progressed significantly over the past few years, evolving from shallow classifiers trained on hand-crafted features to deep spatiotemporal networks. However, labeled video data required to train such models have not been able to keep up with the ever-increasing depth and sophistication of... | Until recently, video datasets have either been well-labeled but small @cite_47 @cite_24 @cite_7 , or large but weakly-labeled @cite_44 @cite_22 . A recently introduced dataset, Kinetics @cite_35 , is currently the largest well-annotated dataset, with around 300K videos labeled into 400 categories (we note a larger ver... | {
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1901.09244 | 2912003633 | Video recognition models have progressed significantly over the past few years, evolving from shallow classifiers trained on hand-crafted features to deep spatiotemporal networks. However, labeled video data required to train such models have not been able to keep up with the ever-increasing depth and sophistication of... | The challenge in generating large-scale well-labeled video datasets stems from the fact that a human annotator has to spend much longer to label a video compared to a single image. Previous work has attempted to reduce this labeling effort through heuristics @cite_8 , but these methods still require a human annotator t... | {
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1901.09244 | 2912003633 | Video recognition models have progressed significantly over the past few years, evolving from shallow classifiers trained on hand-crafted features to deep spatiotemporal networks. However, labeled video data required to train such models have not been able to keep up with the ever-increasing depth and sophistication of... | The question we pose is: since labeling images is faster, and since we already have large, well-labeled image datasets such as ImageNet, can we instead use these to bootstrap the learning of spatiotemporal video architectures? Unsurprisingly, various previous approaches have attempted this. The popular two-stream archi... | {
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"Deep convolutional networks have achieved great success for visual recognition in still images. However, for action recognition in videos, the advantage over traditional methods is not so evident.... |
1901.09244 | 2912003633 | Video recognition models have progressed significantly over the past few years, evolving from shallow classifiers trained on hand-crafted features to deep spatiotemporal networks. However, labeled video data required to train such models have not been able to keep up with the ever-increasing depth and sophistication of... | However, such initializations are only applicable to video models that use 2D convolutions, analogous to those applied in CNNs for still-images. What about more complex, truly spatiotemporal models, such as 3D convolutional architectures @cite_26 ? Until recently, such models have largely been limited to pre-training o... | {
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"abstract": [
"Convolutional Neural Networks (CNNs) have been established as a powerful class of models for image recognition problems. Encoura... |
1907.09597 | 2963065757 | In this paper we explore how actor-critic methods in deep reinforcement learning, in particular Asynchronous Advantage Actor-Critic (A3C), can be extended with agent modeling. Inspired by recent works on representation learning and multiagent deep reinforcement learning, we propose two architectures to perform agent mo... | Deep Reinforcement Opponent Network (DRON) @cite_5 was the first DRL work that performed opponent modeling. DRON's idea is to have two networks: one learns @math values (similar to DQN @cite_2 ) and a second learns a representation of the opponent's policy. DRON used hand-crafted features to define the opponent network... | {
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"abstract": [
"We present DPIQN, a deep policy inference Q-network that targets multi-agent systems composed of ... |
1907.09597 | 2963065757 | In this paper we explore how actor-critic methods in deep reinforcement learning, in particular Asynchronous Advantage Actor-Critic (A3C), can be extended with agent modeling. Inspired by recent works on representation learning and multiagent deep reinforcement learning, we propose two architectures to perform agent mo... | Deep Cognitive Hierarchies @cite_11 is an algorithm that aims to avoid overfitting in two-player games. It uses deep reinforcement learning to compute best responses to a distribution over policies and empirical game-theoretic analysis to compute new meta-strategy distributions. Theory of Mind Network @cite_16 tackles ... | {
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"",
"In multiagent domains, coping with non-stationary agents that change behaviors constantly is a challenging problem, where an agent is usually required to ... |
1907.09624 | 2964198301 | Object classes that surround us have a natural tendency to emerge at varying levels of abstraction. We propose a Bayesian approach to zero-shot learning (ZSL) that introduces the notion of meta-classes and implements a Bayesian hierarchy around these classes to effectively blend data likelihood with local and global pr... | Generative models for zero-shot learning. Although most of the early work focused on discriminative models there are a few studies that use generative models to tackle ZSL @cite_22 @cite_0 . The study in @cite_22 uses Normal distributions to model both image features and semantic vectors and learns a multimodal mapping... | {
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"We present a simple generative framework for learning to predict previously unseen classes, based on estimating class-attribute-gated class-conditional distribution... |
1907.09624 | 2964198301 | Object classes that surround us have a natural tendency to emerge at varying levels of abstraction. We propose a Bayesian approach to zero-shot learning (ZSL) that introduces the notion of meta-classes and implements a Bayesian hierarchy around these classes to effectively blend data likelihood with local and global pr... | Another close line of work leveraging hierarchical Bayesian model is done in @cite_3 . Their method is similar to ours in the way priors are used to achieve knowledge transfer across classes. However, unlike ours, no semantic information is used when establishing the Bayesian hierarchy in @cite_3 and class discovery is... | {
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1907.09658 | 2962765560 | Although skeleton-based action recognition has achieved great success in recent years, most of the existing methods may suffer from a large model size and slow execution speed. To alleviate this issue, we analyze skeleton sequence properties to propose a Double-feature Double-motion Network (DD-Net) for skeleton-based ... | Nowadays, with the fast advancement of deep learning, skeleton acquisition is not limited to use motion capture systems @cite_25 and depth cameras @cite_37 . The RGB data, for instance, can be used to infer 2D skeletons @cite_27 @cite_30 or 3D skeletons @cite_38 @cite_43 in real time. Moreover, even WiFi signals can be... | {
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1907.09658 | 2962765560 | Although skeleton-based action recognition has achieved great success in recent years, most of the existing methods may suffer from a large model size and slow execution speed. To alleviate this issue, we analyze skeleton sequence properties to propose a Double-feature Double-motion Network (DD-Net) for skeleton-based ... | In general, in order to achieve a better performance for skeleton-based action recognition, previous studies attempt to work on two aspects: introduce new features for skeleton sequences @cite_28 @cite_7 @cite_33 @cite_18 @cite_36 @cite_14 @cite_35 , and, propose novel neural network architectures @cite_2 @cite_34 @cit... | {
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1907.09658 | 2962765560 | Although skeleton-based action recognition has achieved great success in recent years, most of the existing methods may suffer from a large model size and slow execution speed. To alleviate this issue, we analyze skeleton sequence properties to propose a Double-feature Double-motion Network (DD-Net) for skeleton-based ... | A good skeleton-sequence representation should contain global motion information and be location-viewpoint invariant. However, it is challenging to satisfy both requirements in one feature. The studies @cite_7 @cite_18 @cite_42 @cite_35 focused on global motions without considering the location-viewpoint variation in t... | {
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1907.09658 | 2962765560 | Although skeleton-based action recognition has achieved great success in recent years, most of the existing methods may suffer from a large model size and slow execution speed. To alleviate this issue, we analyze skeleton sequence properties to propose a Double-feature Double-motion Network (DD-Net) for skeleton-based ... | Although Recurrent Neural Networks (RNNs) are commonly used in skeleton-based action recognition @cite_26 @cite_24 @cite_20 @cite_23 @cite_21 @cite_45 , we argue that it is relatively slow and difficult for parallel computing, compared with methods @cite_2 @cite_41 @cite_35 that use Convolutional Neural Networks (CNNs)... | {
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1907.09682 | 2964161024 | Knowledge distillation is a widely applicable technique for training a student neural network under the guidance of a trained teacher network. For example, in neural network compression, a high-capacity teacher is distilled to train a compact student; in privileged learning, a teacher trained with privileged data is di... | We presented in this paper a novel distillation loss for capturing and transferring knowledge from a teacher network to a student network. Several prior alternatives @cite_44 @cite_36 @cite_45 @cite_14 are described in the introduction and some key differences are highlighted in Section . In addition to the knowledge c... | {
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1907.09682 | 2964161024 | Knowledge distillation is a widely applicable technique for training a student neural network under the guidance of a trained teacher network. For example, in neural network compression, a high-capacity teacher is distilled to train a compact student; in privileged learning, a teacher trained with privileged data is di... | State-of-the-art network compression methods can achieve significant reductions in network size, in some cases by an order of magnitude, but often require specialized software or hardware support. For example, unstructured pruning requires optimized sparse matrix multiplication routines to realize practical acceleratio... | {
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1907.09702 | 2963448607 | Temporal action proposal generation is an challenging and promising task which aims to locate temporal regions in real-world videos where action or event may occur. Current bottom-up proposal generation methods can generate proposals with precise boundary, but cannot efficiently generate adequately reliable confidence ... | Action recognition is a fundamental and important task of video understanding area. Hand-crafted features such as HOG, HOF and MBH are widely used in earlier works, such as improved Dense Trajectory (iDT) @cite_21 @cite_30 . Recently, deep learning models have achieved significantly performance promotion in action reco... | {
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1907.09702 | 2963448607 | Temporal action proposal generation is an challenging and promising task which aims to locate temporal regions in real-world videos where action or event may occur. Current bottom-up proposal generation methods can generate proposals with precise boundary, but cannot efficiently generate adequately reliable confidence ... | Correlation matching algorithms are widely used in many computer vision tasks, such as image registration, action recognition and stereo matching. Specifically, stereo matching aims to find corresponding pixels from stereo images. For each pixel in left image of a rectified image pair, the stereo matching method need t... | {
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1907.09702 | 2963448607 | Temporal action proposal generation is an challenging and promising task which aims to locate temporal regions in real-world videos where action or event may occur. Current bottom-up proposal generation methods can generate proposals with precise boundary, but cannot efficiently generate adequately reliable confidence ... | As aforementioned, the goal of temporal action detection task is to detect action instances in untrimmed videos with temporal boundaries and action categories, which can be divided into temporal proposal generation and action classification stages. These two stages are taken apart in most detection methods @cite_2 @cit... | {
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1907.09706 | 2970623248 | Currently, the visually impaired rely on either a sighted human, guide dog, or white cane to safely navigate. However, the training of guide dogs is extremely expensive, and canes cannot provide essential information regarding the color of traffic lights and direction of crosswalks. In this paper, we propose a deep lea... | Some industrialized countries have developed acoustic pedestrian traffic lights that produce sound when the light is green, and is used as a signal for the visually impaired to know when to cross the street @cite_14 @cite_17 @cite_3 . However, for less economically developed countries, crossing streets is still a probl... | {
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1907.09706 | 2970623248 | Currently, the visually impaired rely on either a sighted human, guide dog, or white cane to safely navigate. However, the training of guide dogs is extremely expensive, and canes cannot provide essential information regarding the color of traffic lights and direction of crosswalks. In this paper, we propose a deep lea... | The task of detecting traffic light for autonomous driving has been explored by many and has developed over the years @cite_2 @cite_15 @cite_1 @cite_7 . @cite_8 created a model that is able to detect traffic lights as small as @math pixels and with relatively high accuracy. Though most models for traffic lights have a ... | {
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1907.09706 | 2970623248 | Currently, the visually impaired rely on either a sighted human, guide dog, or white cane to safely navigate. However, the training of guide dogs is extremely expensive, and canes cannot provide essential information regarding the color of traffic lights and direction of crosswalks. In this paper, we propose a deep lea... | A limitation that many attempts faced was the speed of hardware. Thus, @cite_5 created an algorithm specifically for mobile devices with an accelerator to detect pedestrian traffic lights in real time. @cite_4 incorporated external servers to remove the limitation of hardware and provide more accurate information. Thou... | {
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1907.09706 | 2970623248 | Currently, the visually impaired rely on either a sighted human, guide dog, or white cane to safely navigate. However, the training of guide dogs is extremely expensive, and canes cannot provide essential information regarding the color of traffic lights and direction of crosswalks. In this paper, we propose a deep lea... | Direction is another factor to consider when helping the visually impaired cross the street. Though the visually impaired can have a good sense of the general direction to cross the road in familiar environments, relying on one's memory has its limitations @cite_6 . Therefore, solutions to provide specific direction ha... | {
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1907.09642 | 2963590785 | Image smoothing is a fundamental procedure in applications of both computer vision and graphics. The required smoothing properties can be different or even contradictive among different tasks. Nevertheless, the inherent smoothing nature of one smoothing operator is usually fixed and thus cannot meet the various require... | In terms of structure-preserving smoothing, Zhang et al. @cite_28 proposed to smooth structures of different scales with a rolling guidance filter (RGF). Cho et al. @cite_27 modified the original BLF with local patch-based analysis of texture features and obtained a bilateral texture filter (BTF) for image texture remo... | {
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1907.09478 | 2964056701 | Digital histology images are amenable to the application of convolutional neural network (CNN) for analysis due to the sheer size of pixel data present in them. CNNs are generally used for representation learning from small image patches (e.g. 224x224) extracted from digital histology images due to computational and me... | In literature, various different approaches have been presented to incorporate the contextual information for the classification of histology images. Some researchers @cite_28 @cite_29 @cite_7 used image down-sampling, a common practice followed in natural image classification, to capture the context from larger histol... | {
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1907.09478 | 2964056701 | Digital histology images are amenable to the application of convolutional neural network (CNN) for analysis due to the sheer size of pixel data present in them. CNNs are generally used for representation learning from small image patches (e.g. 224x224) extracted from digital histology images due to computational and me... | Awan @cite_32 presented a method for two-tier CRC grading based on the extent of deviation of the gland from its normal shape (circular elliptical). They proposed a novel Best Alignment Metric (BAM) for this purpose. As a pre-processing step, CNN based gland segmentation was performed, followed by the calculation of BA... | {
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1907.09695 | 2962736944 | The problem of a deep learning model losing performance on a previously learned task when fine-tuned to a new one is a phenomenon known as Catastrophic forgetting. There are two major ways to mitigate this problem: either preserving activations of the initial network during training with a new task; or restricting the ... | The most common way to learn a new task from a model trained on another is to fine-tune it @cite_15 @cite_11 . Fine-tuning works generally very well for the new task, but at the price of a drop in accuracy for the former, since the weights are modified and tuned for the new task. A first possible solution is to keep a ... | {
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1907.09695 | 2962736944 | The problem of a deep learning model losing performance on a previously learned task when fine-tuned to a new one is a phenomenon known as Catastrophic forgetting. There are two major ways to mitigate this problem: either preserving activations of the initial network during training with a new task; or restricting the ... | The issue of accessing the data of previous tasks is mitigated to a large extent in the Learning without Forgetting' (LwF) framework @cite_5 @cite_24 . LwF combines fine-tuning and distillation networks @cite_2 , where a knowledge distillation loss @cite_2 tries to preserve the output of the former classifier on data f... | {
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1907.09695 | 2962736944 | The problem of a deep learning model losing performance on a previously learned task when fine-tuned to a new one is a phenomenon known as Catastrophic forgetting. There are two major ways to mitigate this problem: either preserving activations of the initial network during training with a new task; or restricting the ... | An alternative direction to the above is the idea of removing redundant parameters by neural network compression @cite_18 . The authors report good results but only use a fixed pruning percentage for all the tasks, irrespective of the complexity of the data involved. Other works have used masks on networks' weights, ei... | {
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1907.09595 | 2963037463 | Depthwise convolution is becoming increasingly popular in modern efficient ConvNets, but its kernel size is often overlooked. In this paper, we systematically study the impact of different kernel sizes, and observe that combining the benefits of multiple kernel sizes can lead to better accuracy and efficiency. Based on... | In recent years, significant efforts have been spent on improving ConvNet efficiency, from more efficient convolutional operations @cite_30 @cite_5 @cite_32 , bottleneck layers @cite_15 @cite_0 , to more efficient architectures @cite_17 @cite_26 @cite_13 . In particular, depthwise convolution has been increasingly popu... | {
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1907.09595 | 2963037463 | Depthwise convolution is becoming increasingly popular in modern efficient ConvNets, but its kernel size is often overlooked. In this paper, we systematically study the impact of different kernel sizes, and observe that combining the benefits of multiple kernel sizes can lead to better accuracy and efficiency. Based on... | Our idea shares a lot of similarities to prior multi-branch ConvNets, such as Inceptions @cite_1 @cite_7 , Inception-ResNet @cite_31 , ResNeXt @cite_0 , and NASNet @cite_6 . By using multiple branches in each layer, these ConvNets are able to utilize different operations (such as convolution and pooling) in a single la... | {
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1907.09705 | 2962957458 | Scene text recognition has been an important, active research topic in computer vision for years. Previous approaches mainly consider text as 1D signals and cast scene text recognition as a sequence prediction problem, by feat of CTC or attention based encoder-decoder framework, which is originally designed for speech ... | Another admirable direction of frame-wise prediction alignment is attention-based sequence encoder and decoder framework @cite_24 @cite_5 @cite_22 @cite_36 @cite_0 @cite_32 @cite_37 . The models focus on one position and predict the corresponding character at each time step, but suffer from the problems of misalignment... | {
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1907.09705 | 2962957458 | Scene text recognition has been an important, active research topic in computer vision for years. Previous approaches mainly consider text as 1D signals and cast scene text recognition as a sequence prediction problem, by feat of CTC or attention based encoder-decoder framework, which is originally designed for speech ... | In contrast to the aforementioned methods, Liao al @cite_19 recently propose to utilize instance segmentation to simultaneously predict character locations and recognition results, avoiding the problem of attention misalignment. They also notice the conflict between the 2D image feature and collapsed sequence presentat... | {
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1907.09705 | 2962957458 | Scene text recognition has been an important, active research topic in computer vision for years. Previous approaches mainly consider text as 1D signals and cast scene text recognition as a sequence prediction problem, by feat of CTC or attention based encoder-decoder framework, which is originally designed for speech ... | In concern of both accuracy and efficiency, 2D-CTC recognizes text from a 2D perspective similar to @cite_19 , but trained without any character-level annotations. By extending vanilla CTC, 2D-CTC achieves state-of-the-art performance, while retaining the high efficiency of CTC models. | {
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1907.09656 | 2962692517 | Tactile perception is an essential ability of intelligent robots in interaction with their surrounding environments. This perception as an intermediate level acts between sensation and action and has to be defined properly to generate suitable action in response to sensed data. In this paper, we propose a feedback appr... | In this paper, we aim to propose an interactive tactile perception with use of force-torque sensing for a grasping task. Grasping task can be initiated by either visual or tactile perception. Although the first one is powerful in terms of object recognition, a continuous processing of visual data during interaction is ... | {
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1907.09656 | 2962692517 | Tactile perception is an essential ability of intelligent robots in interaction with their surrounding environments. This perception as an intermediate level acts between sensation and action and has to be defined properly to generate suitable action in response to sensed data. In this paper, we propose a feedback appr... | Grasping is an essential and complex daily activity. Through this task, humans show an intention to affect surrounding environment in a controllable manner. Humans primarily utilize a combination of control strategy and learning from repetitive experiments to anticipate grasping in different situations @cite_12 . In th... | {
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1907.09656 | 2962692517 | Tactile perception is an essential ability of intelligent robots in interaction with their surrounding environments. This perception as an intermediate level acts between sensation and action and has to be defined properly to generate suitable action in response to sensed data. In this paper, we propose a feedback appr... | Tactile sensation is a very informative feature to recognize object properties. In @cite_19 , the authors proposed a tactile perception strategy to measure tactile features for mobile robots. Tactile sensing also is used to propose a robust controller for reliable grasping @cite_17 and slipping avoidance @cite_0 . Visu... | {
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1907.09656 | 2962692517 | Tactile perception is an essential ability of intelligent robots in interaction with their surrounding environments. This perception as an intermediate level acts between sensation and action and has to be defined properly to generate suitable action in response to sensed data. In this paper, we propose a feedback appr... | A lower dimensional representation of tactile data is also more useful for object material classification @cite_16 . With more processing approaches such as bag-of-words, identification of objects would be possible in advance @cite_22 . Extraction of object pose via touch based perception can be used for manipulation @... | {
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1907.09511 | 2963009244 | Most state-of-the-art person re-identification (re-id) methods depend on supervised model learning with a large set of cross-view identity labelled training data. Even worse, such trained models are limited to only the same-domain deployment with significantly degraded cross-domain generalization capability, i.e. "doma... | Unsupervised domain adaptation person re-id. The limitation of supervised learning re-id methods in cross-domain scalability can be addressed by using unsupervised domain adaptation (UDA) techniques. Existing UDA re-id methods generally fall into two categories: (1) image synthesis @cite_25 @cite_50 @cite_43 @cite_11 ,... | {
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1907.09607 | 2964204277 | The success of deep learning in medical imaging is mostly achieved at the cost of a large labeled data set. Semi-supervised learning (SSL) provides a promising solution by leveraging the structure of unlabeled data to improve learning from a small set of labeled data. Self-ensembling is a simple approach used in SSL to... | This work is mostly related to two lines of research: 1) SSL based on regularization with random transformations, and 2) disentangled representation learning and its use in SSL. In the former, consistency-based regularization is applied on ensemble predictions obtained by randomization techniques such as random data au... | {
"cite_N": [
"@cite_9",
"@cite_10",
"@cite_8"
],
"mid": [
"2789122432",
"2963229033",
"2530816535"
],
"abstract": [
"This paper introduces a novel measure-theoretic learning theory to analyze generalization behaviors of practical interest. The proposed learning theory has the foll... |
1907.09607 | 2964204277 | The success of deep learning in medical imaging is mostly achieved at the cost of a large labeled data set. Semi-supervised learning (SSL) provides a promising solution by leveraging the structure of unlabeled data to improve learning from a small set of labeled data. Self-ensembling is a simple approach used in SSL to... | Besideds the approaches discussed above, there is also an active line of research in GAN-based SSL methods @cite_7 @cite_5 . The general idea is to add a classification objective to the original mini-max game and increases the capacity of the discriminator to associate the inputs to the corresponding labels. The presen... | {
"cite_N": [
"@cite_5",
"@cite_7"
],
"mid": [
"2806321514",
"2548275288"
],
"abstract": [
"Deep learning algorithms require large amounts of labeled data which is difficult to attain for medical imaging. Even if a particular dataset is accessible, a learned classifier struggles to maintai... |
1907.09607 | 2964204277 | The success of deep learning in medical imaging is mostly achieved at the cost of a large labeled data set. Semi-supervised learning (SSL) provides a promising solution by leveraging the structure of unlabeled data to improve learning from a small set of labeled data. Self-ensembling is a simple approach used in SSL to... | An increased interest in SSL has also been seen in medical image anlaysis. The use of an unsupervised representation learning for better generalization has been investigated for the task of myocardial segmentation @cite_1 . In @cite_5 , SSL was used in a similar X-ray data set, although the scope was limited to binary ... | {
"cite_N": [
"@cite_5",
"@cite_1"
],
"mid": [
"2806321514",
"2963721223"
],
"abstract": [
"Deep learning algorithms require large amounts of labeled data which is difficult to attain for medical imaging. Even if a particular dataset is accessible, a learned classifier struggles to maintai... |
1907.09591 | 2964224524 | Modern trajectory optimization based approaches to motion planning are fast, easy to implement, and effective on a wide range of robotics tasks. However, trajectory optimization algorithms have parameters that are typically set in advance (and rarely discussed in detail). Setting these parameters properly can have a si... | Recent work in structured learning techniques offer avenues towards contending with these challenges. Several methods have focused on incorporating optimization within neural network architectures. For example, @cite_8 implicitly learns to perform nonlinear least squares optimization by learning an RNN that predicts it... | {
"cite_N": [
"@cite_18",
"@cite_8",
"@cite_9",
"@cite_6",
"@cite_16",
"@cite_10"
],
"mid": [
"2963755523",
"2895289727",
"2890290306",
"2963970238",
"2963775850",
"2963414638"
],
"abstract": [
"We introduce a new family of deep neural network models. Instea... |
1907.09133 | 2963230693 | Sensors producing 3D point clouds such as 3D laser scanners and RGB-D cameras are widely used in robotics, be it for autonomous driving or manipulation. Aligning point clouds produced by these sensors is a vital component in such applications to perform tasks such as model registration, pose estimation, and SLAM. Itera... | Selecting corresponding points in the source and reference point clouds is an important step in ICP, as evidenced by the various ICP variants that have been proposed in the literature to improve this part of ICP. @cite_7 use simulated annealing to obtain a good initial starting point. Masuda and Yokoyo @cite_16 combine... | {
"cite_N": [
"@cite_16",
"@cite_7"
],
"mid": [
"2176551120",
"2136391352"
],
"abstract": [
"Registration and segmentation of multiple range images are important problems in range image analysis. We propose a new algorithm of range data registration and segmentation that is robust in the p... |
1907.09133 | 2963230693 | Sensors producing 3D point clouds such as 3D laser scanners and RGB-D cameras are widely used in robotics, be it for autonomous driving or manipulation. Aligning point clouds produced by these sensors is a vital component in such applications to perform tasks such as model registration, pose estimation, and SLAM. Itera... | Chen and Medioni @cite_2 propose a robust version of ICP which minimises the distance between points and planes (point-to-plane ICP) instead of the traditional point-to-point distance. In point-to-plane ICP, matching points in the two point clouds are determined by intersecting a ray from the source point in direction ... | {
"cite_N": [
"@cite_18",
"@cite_8",
"@cite_1",
"@cite_2",
"@cite_20"
],
"mid": [
"2119851068",
"2271206385",
"",
"1883517952",
"2118631462"
],
"abstract": [
"The ICP (Iterative Closest Point) algorithm is widely used for geometric alignment of three-dimensional mod... |
1907.09133 | 2963230693 | Sensors producing 3D point clouds such as 3D laser scanners and RGB-D cameras are widely used in robotics, be it for autonomous driving or manipulation. Aligning point clouds produced by these sensors is a vital component in such applications to perform tasks such as model registration, pose estimation, and SLAM. Itera... | Once the corresponding point pairs are selected the next challenge is to use this information to find the optimal transformation. Several optimisation techniques are used to minimise ICP's cost function. Fitzgibbon @cite_3 presents a method which uses a non-linear Levenberg-Marquardt method @cite_6 which combines batch... | {
"cite_N": [
"@cite_5",
"@cite_6",
"@cite_3"
],
"mid": [
"2071992612",
"2432517183",
"2004312117"
],
"abstract": [
"Concerns the problem of range image registration for the purpose of building surface models of 3D objects. The registration task involves finding the translation and... |
1907.09133 | 2963230693 | Sensors producing 3D point clouds such as 3D laser scanners and RGB-D cameras are widely used in robotics, be it for autonomous driving or manipulation. Aligning point clouds produced by these sensors is a vital component in such applications to perform tasks such as model registration, pose estimation, and SLAM. Itera... | Another avenue of research are methods that improve the ability of ICP to handle an unknown degree of overlap between the point clouds. This includes the use of a trimmed square cost function to estimate optimal transformation @cite_21 , rejection of corresponding point pairs based on a threshold defined by the standar... | {
"cite_N": [
"@cite_9",
"@cite_15",
"@cite_21"
],
"mid": [
"2130109198",
"1602060479",
"2140711847"
],
"abstract": [
"A method for matching 3-D curves under Euclidean motions is presented. Our approach uses a semi-differential invariant description requiring only first derivatives... |
1907.09189 | 2963689651 | This paper is concerned with evaluating different multiagent learning (MAL) algorithms in problems where individual agents may be heterogenous, in the sense of utilizing different learning strategies, without the opportunity for prior agreements or information regarding coordination. Such a situation arises in ad hoc t... | Harsanyi pioneered the study of incomplete information games. In his 1967 paper @cite_31 , he describes the , a game in which players have beliefs about missing information. He develops the concept of the Bayesian Nash equilibrium @cite_34 in which each player plays a best response against the other players, based on t... | {
"cite_N": [
"@cite_31",
"@cite_34",
"@cite_17"
],
"mid": [
"",
"2102794165",
"2129760941"
],
"abstract": [
"",
"Part I of this paper has described a new theory for the analysis of games with incomplete information. It has been shown that, if the various players' subjective pr... |
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