Paper Group ANR 328
Evaluation of trackers for Pan-Tilt-Zoom Scenarios. Human experts vs. machines in taxa recognition. Using Artificial Neural Networks (ANN) to Control Chaos. Computation-Performance Optimization of Convolutional Neural Networks with Redundant Kernel Removal. 3D Shape Segmentation via Shape Fully Convolutional Networks. Learning ReLUs via Gradient De …
Evaluation of trackers for Pan-Tilt-Zoom Scenarios
| Title | Evaluation of trackers for Pan-Tilt-Zoom Scenarios |
| Authors | Yucao Tang, Guillaume-Alexandre Bilodeau |
| Abstract | Tracking with a Pan-Tilt-Zoom (PTZ) camera has been a research topic in computer vision for many years. Compared to tracking with a still camera, the images captured with a PTZ camera are highly dynamic in nature because the camera can perform large motion resulting in quickly changing capture conditions. Furthermore, tracking with a PTZ camera involves camera control to position the camera on the target. For successful tracking and camera control, the tracker must be fast enough, or has to be able to predict accurately the next position of the target. Therefore, standard benchmarks do not allow to assess properly the quality of a tracker for the PTZ scenario. In this work, we use a virtual PTZ framework to evaluate different tracking algorithms and compare their performances. We also extend the framework to add target position prediction for the next frame, accounting for camera motion and processing delays. By doing this, we can assess if predicting can make long-term tracking more robust as it may help slower algorithms for keeping the target in the field of view of the camera. Results confirm that both speed and robustness are required for tracking under the PTZ scenario. |
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| Published | 2017-11-12 |
| URL | http://arxiv.org/abs/1711.04260v1 |
| http://arxiv.org/pdf/1711.04260v1.pdf | |
| PWC | https://paperswithcode.com/paper/evaluation-of-trackers-for-pan-tilt-zoom |
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Human experts vs. machines in taxa recognition
| Title | Human experts vs. machines in taxa recognition |
| Authors | Johanna Ärje, Jenni Raitoharju, Alexandros Iosifidis, Ville Tirronen, Kristian Meissner, Moncef Gabbouj, Serkan Kiranyaz, Salme Kärkkäinen |
| Abstract | The step of expert taxa recognition currently slows down the response time of many bioassessments. Shifting to quicker and cheaper state-of-the-art machine learning approaches is still met with expert scepticism towards the ability and logic of machines. In our study, we investigate both the differences in accuracy and in the identification logic of taxonomic experts and machines. We propose a systematic approach utilizing deep Convolutional Neural Nets with the transfer learning paradigm and extensively evaluate it over a multi-pose taxonomic dataset with hierarchical labels specifically created for this comparison. We also study the prediction accuracy on different ranks of taxonomic hierarchy in detail. Our results revealed that human experts using actual specimens yield the lowest classification error ($\overline{CE}=6.1%$). However, a much faster, automated approach using deep Convolutional Neural Nets comes close to human accuracy ($\overline{CE}=11.4%$). Contrary to previous findings in the literature, we find that for machines following a typical flat classification approach commonly used in machine learning performs better than forcing machines to adopt a hierarchical, local per parent node approach used by human taxonomic experts. Finally, we publicly share our unique dataset to serve as a public benchmark dataset in this field. |
| Tasks | Transfer Learning |
| Published | 2017-08-23 |
| URL | https://arxiv.org/abs/1708.06899v4 |
| https://arxiv.org/pdf/1708.06899v4.pdf | |
| PWC | https://paperswithcode.com/paper/human-experts-vs-machines-in-taxa-recognition |
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Using Artificial Neural Networks (ANN) to Control Chaos
| Title | Using Artificial Neural Networks (ANN) to Control Chaos |
| Authors | Ibrahim Ighneiwaa, Salwa Hamidatoua, Fadia Ben Ismaela |
| Abstract | Controlling Chaos could be a big factor in getting great stable amounts of energy out of small amounts of not necessarily stable resources. By definition, Chaos is getting huge changes in the system’s output due to unpredictable small changes in initial conditions, and that means we could take advantage of this fact and select the proper control system to manipulate system’s initial conditions and inputs in general and get a desirable output out of otherwise a Chaotic system. That was accomplished by first building some known chaotic circuit (Chua circuit) and the NI’s MultiSim was used to simulate the ANN control system. It was shown that this technique can also be used to stabilize some hard to stabilize electronic systems. |
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| Published | 2017-01-01 |
| URL | http://arxiv.org/abs/1701.00754v1 |
| http://arxiv.org/pdf/1701.00754v1.pdf | |
| PWC | https://paperswithcode.com/paper/using-artificial-neural-networks-ann-to |
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Computation-Performance Optimization of Convolutional Neural Networks with Redundant Kernel Removal
| Title | Computation-Performance Optimization of Convolutional Neural Networks with Redundant Kernel Removal |
| Authors | Chih-Ting Liu, Yi-Heng Wu, Yu-Sheng Lin, Shao-Yi Chien |
| Abstract | Deep Convolutional Neural Networks (CNNs) are widely employed in modern computer vision algorithms, where the input image is convolved iteratively by many kernels to extract the knowledge behind it. However, with the depth of convolutional layers getting deeper and deeper in recent years, the enormous computational complexity makes it difficult to be deployed on embedded systems with limited hardware resources. In this paper, we propose two computation-performance optimization methods to reduce the redundant convolution kernels of a CNN with performance and architecture constraints, and apply it to a network for super resolution (SR). Using PSNR drop compared to the original network as the performance criterion, our method can get the optimal PSNR under a certain computation budget constraint. On the other hand, our method is also capable of minimizing the computation required under a given PSNR drop. |
| Tasks | Super-Resolution |
| Published | 2017-05-30 |
| URL | http://arxiv.org/abs/1705.10748v3 |
| http://arxiv.org/pdf/1705.10748v3.pdf | |
| PWC | https://paperswithcode.com/paper/computation-performance-optimization-of |
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3D Shape Segmentation via Shape Fully Convolutional Networks
| Title | 3D Shape Segmentation via Shape Fully Convolutional Networks |
| Authors | Pengyu Wang, Yuan Gan, Panpan Shui, Fenggen Yu, Yan Zhang, Songle Chen, Zhengxing Sun |
| Abstract | We desgin a novel fully convolutional network architecture for shapes, denoted by Shape Fully Convolutional Networks (SFCN). 3D shapes are represented as graph structures in the SFCN architecture, based on novel graph convolution and pooling operations, which are similar to convolution and pooling operations used on images. Meanwhile, to build our SFCN architecture in the original image segmentation fully convolutional network (FCN) architecture, we also design and implement a generating operation} with bridging function. This ensures that the convolution and pooling operation we have designed can be successfully applied in the original FCN architecture. In this paper, we also present a new shape segmentation approach based on SFCN. Furthermore, we allow more general and challenging input, such as mixed datasets of different categories of shapes} which can prove the ability of our generalisation. In our approach, SFCNs are trained triangles-to-triangles by using three low-level geometric features as input. Finally, the feature voting-based multi-label graph cuts is adopted to optimise the segmentation results obtained by SFCN prediction. The experiment results show that our method can effectively learn and predict mixed shape datasets of either similar or different characteristics, and achieve excellent segmentation results. |
| Tasks | Semantic Segmentation |
| Published | 2017-02-28 |
| URL | http://arxiv.org/abs/1702.08675v3 |
| http://arxiv.org/pdf/1702.08675v3.pdf | |
| PWC | https://paperswithcode.com/paper/3d-shape-segmentation-via-shape-fully |
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Learning ReLUs via Gradient Descent
| Title | Learning ReLUs via Gradient Descent |
| Authors | Mahdi Soltanolkotabi |
| Abstract | In this paper we study the problem of learning Rectified Linear Units (ReLUs) which are functions of the form $max(0,<w,x>)$ with $w$ denoting the weight vector. We study this problem in the high-dimensional regime where the number of observations are fewer than the dimension of the weight vector. We assume that the weight vector belongs to some closed set (convex or nonconvex) which captures known side-information about its structure. We focus on the realizable model where the inputs are chosen i.i.d.~from a Gaussian distribution and the labels are generated according to a planted weight vector. We show that projected gradient descent, when initialization at 0, converges at a linear rate to the planted model with a number of samples that is optimal up to numerical constants. Our results on the dynamics of convergence of these very shallow neural nets may provide some insights towards understanding the dynamics of deeper architectures. |
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| Published | 2017-05-10 |
| URL | http://arxiv.org/abs/1705.04591v2 |
| http://arxiv.org/pdf/1705.04591v2.pdf | |
| PWC | https://paperswithcode.com/paper/learning-relus-via-gradient-descent |
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Feature base fusion for splicing forgery detection based on neuro fuzzy
| Title | Feature base fusion for splicing forgery detection based on neuro fuzzy |
| Authors | Habib Ghaffari Hadigheh, Ghazali bin sulong |
| Abstract | Most of researches on image forensics have been mainly focused on detection of artifacts introduced by a single processing tool. They lead in the development of many specialized algorithms looking for one or more particular footprints under specific settings. Naturally, the performance of such algorithms are not perfect, and accordingly the provided output might be noisy, inaccurate and only partially correct. Furthermore, a forged image in practical scenarios is often the result of utilizing several tools available by image-processing software systems. Therefore, reliable tamper detection requires developing more poweful tools to deal with various tempering scenarios. Fusion of forgery detection tools based on Fuzzy Inference System has been used before for addressing this problem. Adjusting the membership functions and defining proper fuzzy rules for attaining to better results are time-consuming processes. This can be accounted as main disadvantage of fuzzy inference systems. In this paper, a Neuro-Fuzzy inference system for fusion of forgery detection tools is developed. The neural network characteristic of these systems provides appropriate tool for automatically adjusting the membership functions. Moreover, initial fuzzy inference system is generated based on fuzzy clustering techniques. The proposed framework is implemented and validated on a benchmark image splicing data set in which three forgery detection tools are fused based on adaptive Neuro-Fuzzy inference system. The outcome of the proposed method reveals that applying Neuro Fuzzy inference systems could be a better approach for fusion of forgery detection tools. |
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| Published | 2017-01-29 |
| URL | http://arxiv.org/abs/1701.08374v1 |
| http://arxiv.org/pdf/1701.08374v1.pdf | |
| PWC | https://paperswithcode.com/paper/feature-base-fusion-for-splicing-forgery |
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Improving the Accuracy of the CogniLearn System for Cognitive Behavior Assessment
| Title | Improving the Accuracy of the CogniLearn System for Cognitive Behavior Assessment |
| Authors | Amir Ghaderi, Srujana Gattupalli, Dylan Ebert, Ali Sharifara, Vassilis Athitsos, Fillia Makedon |
| Abstract | HTKS is a game-like cognitive assessment method, designed for children between four and eight years of age. During the HTKS assessment, a child responds to a sequence of requests, such as “touch your head” or “touch your toes”. The cognitive challenge stems from the fact that the children are instructed to interpret these requests not literally, but by touching a different body part than the one stated. In prior work, we have developed the CogniLearn system, that captures data from subjects performing the HTKS game, and analyzes the motion of the subjects. In this paper we propose some specific improvements that make the motion analysis module more accurate. As a result of these improvements, the accuracy in recognizing cases where subjects touch their toes has gone from 76.46% in our previous work to 97.19% in this paper. |
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| Published | 2017-03-25 |
| URL | http://arxiv.org/abs/1703.08697v1 |
| http://arxiv.org/pdf/1703.08697v1.pdf | |
| PWC | https://paperswithcode.com/paper/improving-the-accuracy-of-the-cognilearn |
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Robot gains Social Intelligence through Multimodal Deep Reinforcement Learning
| Title | Robot gains Social Intelligence through Multimodal Deep Reinforcement Learning |
| Authors | Ahmed Hussain Qureshi, Yutaka Nakamura, Yuichiro Yoshikawa, Hiroshi Ishiguro |
| Abstract | For robots to coexist with humans in a social world like ours, it is crucial that they possess human-like social interaction skills. Programming a robot to possess such skills is a challenging task. In this paper, we propose a Multimodal Deep Q-Network (MDQN) to enable a robot to learn human-like interaction skills through a trial and error method. This paper aims to develop a robot that gathers data during its interaction with a human and learns human interaction behaviour from the high-dimensional sensory information using end-to-end reinforcement learning. This paper demonstrates that the robot was able to learn basic interaction skills successfully, after 14 days of interacting with people. |
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| Published | 2017-02-24 |
| URL | http://arxiv.org/abs/1702.07492v1 |
| http://arxiv.org/pdf/1702.07492v1.pdf | |
| PWC | https://paperswithcode.com/paper/robot-gains-social-intelligence-through |
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Estimate Exchange over Network is Good for Distributed Hard Thresholding Pursuit
| Title | Estimate Exchange over Network is Good for Distributed Hard Thresholding Pursuit |
| Authors | Ahmed Zaki, Partha P. Mitra, Lars K. Rasmussen, Saikat Chatterjee |
| Abstract | We investigate an existing distributed algorithm for learning sparse signals or data over networks. The algorithm is iterative and exchanges intermediate estimates of a sparse signal over a network. This learning strategy using exchange of intermediate estimates over the network requires a limited communication overhead for information transmission. Our objective in this article is to show that the strategy is good for learning in spite of limited communication. In pursuit of this objective, we first provide a restricted isometry property (RIP)-based theoretical analysis on convergence of the iterative algorithm. Then, using simulations, we show that the algorithm provides competitive performance in learning sparse signals vis-a-vis an existing alternate distributed algorithm. The alternate distributed algorithm exchanges more information including observations and system parameters. |
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| Published | 2017-09-22 |
| URL | http://arxiv.org/abs/1709.07731v1 |
| http://arxiv.org/pdf/1709.07731v1.pdf | |
| PWC | https://paperswithcode.com/paper/estimate-exchange-over-network-is-good-for |
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The empirical Christoffel function with applications in data analysis
| Title | The empirical Christoffel function with applications in data analysis |
| Authors | Jean-Bernard Lasserre, Edouard Pauwels |
| Abstract | We illustrate the potential applications in machine learning of the Christoffel function, or more precisely, its empirical counterpart associated with a counting measure uniformly supported on a finite set of points. Firstly, we provide a thresholding scheme which allows to approximate the support of a measure from a finite subset of its moments with strong asymptotic guaranties. Secondly, we provide a consistency result which relates the empirical Christoffel function and its population counterpart in the limit of large samples. Finally, we illustrate the relevance of our results on simulated and real world datasets for several applications in statistics and machine learning: (a) density and support estimation from finite samples, (b) outlier and novelty detection and (c) affine matching. |
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| Published | 2017-01-11 |
| URL | http://arxiv.org/abs/1701.02886v4 |
| http://arxiv.org/pdf/1701.02886v4.pdf | |
| PWC | https://paperswithcode.com/paper/the-empirical-christoffel-function-with |
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Unknowable Manipulators: Social Network Curator Algorithms
| Title | Unknowable Manipulators: Social Network Curator Algorithms |
| Authors | Samuel Albanie, Hillary Shakespeare, Tom Gunter |
| Abstract | For a social networking service to acquire and retain users, it must find ways to keep them engaged. By accurately gauging their preferences, it is able to serve them with the subset of available content that maximises revenue for the site. Without the constraints of an appropriate regulatory framework, we argue that a sufficiently sophisticated curator algorithm tasked with performing this process may choose to explore curation strategies that are detrimental to users. In particular, we suggest that such an algorithm is capable of learning to manipulate its users, for several qualitative reasons: 1. Access to vast quantities of user data combined with ongoing breakthroughs in the field of machine learning are leading to powerful but uninterpretable strategies for decision making at scale. 2. The availability of an effective feedback mechanism for assessing the short and long term user responses to curation strategies. 3. Techniques from reinforcement learning have allowed machines to learn automated and highly successful strategies at an abstract level, often resulting in non-intuitive yet nonetheless highly appropriate action selection. In this work, we consider the form that these strategies for user manipulation might take and scrutinise the role that regulation should play in the design of such systems. |
| Tasks | Decision Making |
| Published | 2017-01-17 |
| URL | http://arxiv.org/abs/1701.04895v1 |
| http://arxiv.org/pdf/1701.04895v1.pdf | |
| PWC | https://paperswithcode.com/paper/unknowable-manipulators-social-network |
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Optical Flow-based 3D Human Motion Estimation from Monocular Video
| Title | Optical Flow-based 3D Human Motion Estimation from Monocular Video |
| Authors | Thiemo Alldieck, Marc Kassubeck, Marcus Magnor |
| Abstract | We present a generative method to estimate 3D human motion and body shape from monocular video. Under the assumption that starting from an initial pose optical flow constrains subsequent human motion, we exploit flow to find temporally coherent human poses of a motion sequence. We estimate human motion by minimizing the difference between computed flow fields and the output of an artificial flow renderer. A single initialization step is required to estimate motion over multiple frames. Several regularization functions enhance robustness over time. Our test scenarios demonstrate that optical flow effectively regularizes the under-constrained problem of human shape and motion estimation from monocular video. |
| Tasks | Motion Estimation, Optical Flow Estimation |
| Published | 2017-03-01 |
| URL | http://arxiv.org/abs/1703.00177v2 |
| http://arxiv.org/pdf/1703.00177v2.pdf | |
| PWC | https://paperswithcode.com/paper/optical-flow-based-3d-human-motion-estimation |
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A Practically Competitive and Provably Consistent Algorithm for Uplift Modeling
| Title | A Practically Competitive and Provably Consistent Algorithm for Uplift Modeling |
| Authors | Yan Zhao, Xiao Fang, David Simchi-Levi |
| Abstract | Randomized experiments have been critical tools of decision making for decades. However, subjects can show significant heterogeneity in response to treatments in many important applications. Therefore it is not enough to simply know which treatment is optimal for the entire population. What we need is a model that correctly customize treatment assignment base on subject characteristics. The problem of constructing such models from randomized experiments data is known as Uplift Modeling in the literature. Many algorithms have been proposed for uplift modeling and some have generated promising results on various data sets. Yet little is known about the theoretical properties of these algorithms. In this paper, we propose a new tree-based ensemble algorithm for uplift modeling. Experiments show that our algorithm can achieve competitive results on both synthetic and industry-provided data. In addition, by properly tuning the “node size” parameter, our algorithm is proved to be consistent under mild regularity conditions. This is the first consistent algorithm for uplift modeling that we are aware of. |
| Tasks | Decision Making |
| Published | 2017-09-12 |
| URL | http://arxiv.org/abs/1709.03683v1 |
| http://arxiv.org/pdf/1709.03683v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-practically-competitive-and-provably |
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CycleGAN, a Master of Steganography
| Title | CycleGAN, a Master of Steganography |
| Authors | Casey Chu, Andrey Zhmoginov, Mark Sandler |
| Abstract | CycleGAN (Zhu et al. 2017) is one recent successful approach to learn a transformation between two image distributions. In a series of experiments, we demonstrate an intriguing property of the model: CycleGAN learns to “hide” information about a source image into the images it generates in a nearly imperceptible, high-frequency signal. This trick ensures that the generator can recover the original sample and thus satisfy the cyclic consistency requirement, while the generated image remains realistic. We connect this phenomenon with adversarial attacks by viewing CycleGAN’s training procedure as training a generator of adversarial examples and demonstrate that the cyclic consistency loss causes CycleGAN to be especially vulnerable to adversarial attacks. |
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| Published | 2017-12-08 |
| URL | http://arxiv.org/abs/1712.02950v2 |
| http://arxiv.org/pdf/1712.02950v2.pdf | |
| PWC | https://paperswithcode.com/paper/cyclegan-a-master-of-steganography |
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