pwc-archive/papers-with-abstracts huggingface dataset:
Dynamics Are Important for the Recognition of Equine Pain in Video
Authors: Sofia Broome,  Karina Bech Gleerup,  Pia Haubro Andersen,  Hedvig Kjellstrom
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6

A prerequisite to successfully alleviate pain in animals is to recognize it, which is a great challenge in non-verbal species. Furthermore, prey animals such as horses tend to hide their pain. In this study, we propose a deep recurrent two-stream architecture for the task of distinguishing pain from non-pain in videos of horses. Different models are evaluated on a unique dataset showing horses under controlled trials with moderate pain induction, which has been presented in earlier work. Sequential models are experimentally compared to single-frame models, showing the importance of the temporal dimension of the data, and are benchmarked against a veterinary expert classification of the data. We additionally perform baseline comparisons with generalized versions of state-of-the-art human pain recognition methods. While equine pain detection in machine learning is a novel field, our results surpass veterinary expert performance and outperform pain detection results reported for other larger non-human species.
Motion Estimation of Non-Holonomic Ground Vehicles From a Single Feature Correspondence Measured Over N Views
Authors: Kun Huang,  Yifu Wang,  Laurent Kneip
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Motion Estimation

The planar motion of ground vehicles is often non-holonomic, which enables a solution of the two-view relative pose problem from a single point feature correspondence. Man-made environments such as underground parking lots are however dominated by line features. Inspired by the planar tri-focal tensor and its ability to handle lines, we establish an n-linear constraint on the locally circular motion of non-holonomic vehicles able to handle an arbitrarily large and dense window of views. We prove that this stays a uni-variate problem under the assumption of locally constant vehicle speed, and it can transparently handle both point and vertical line correspondences. In particular, we prove that an application of Viete's formulas for extrapolating trigonometric functions of angle multiples and the Weierstrass substitution casts the problem as one that merely seeks the roots of a uni-variate polynomial. We present the complete theory of this novel solver, and test it on both simulated and real data. Our results prove that it successfully handles a variety of relevant scenarios, eventually outperforming the 1-point two-view solver.
Large Scale High-Resolution Land Cover Mapping With Multi-Resolution Data
Authors: Caleb Robinson,  Le Hou,  Kolya Malkin,  Rachel Soobitsky,  Jacob Czawlytko,  Bistra Dilkina,  Nebojsa Jojic
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Vocal Bursts Intensity Prediction

In this paper we propose multi-resolution data fusion methods for deep learning-based high-resolution land cover mapping from aerial imagery. The land cover mapping problem, at country-level scales, is challenging for common deep learning methods due to the scarcity of high-resolution labels, as well as variation in geography and quality of input images. On the other hand, multiple satellite imagery and low-resolution ground truth label sources are widely available, and can be used to improve model training efforts. Our methods include: introducing low-resolution satellite data to smooth quality differences in high-resolution input, exploiting low-resolution labels with a dual loss function, and pairing scarce high-resolution labels with inputs from several points in time. We train models that are able to generalize from a portion of the Northeast United States, where we have high-resolution land cover labels, to the rest of the US. With these models, we produce the first high-resolution (1-meter) land cover map of the contiguous US, consisting of over 8 trillion pixels. We demonstrate the robustness and potential applications of this data in a case study with domain experts and develop a web application to share our results. This work is practically useful, and can be applied to other locations over the earth as high-resolution imagery becomes more widely available even as high-resolution labeled land cover data remains sparse.
Leveraging Heterogeneous Auxiliary Tasks to Assist Crowd Counting
Authors: Muming Zhao,  Jian Zhang,  Chongyang Zhang,  Wenjun Zhang
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Crowd Counting, Density Estimation

Crowd counting is a challenging task in the presence of drastic scale variations, the clutter background, and severe occlusions, etc. Existing CNN-based counting methods tackle these challenges mainly by fusing either multi-scale or multi-context features to generate robust representations. In this paper, we propose to address these issues by leveraging the heterogeneous attributes compounded in the density map. We identify three geometric/semantic/numeric attributes essentially important to the density estimation, and demonstrate how to effectively utilize these heterogeneous attributes to assist the crowd counting by formulating them into multiple auxiliary tasks. With the multi-fold regularization effects induced by the auxiliary tasks, the backbone CNN model is driven to embed desired properties explicitly and thus gains robust representations towards more accurate density estimation. Extensive experiments on three challenging crowd counting datasets have demonstrated the effectiveness of the proposed approach.
Attack and Anomaly Detection in IoT Sensors in IoT Sites Using Machine Learning Approaches
Authors: Mahmudul Hasan, Md. Milon Islam, Ishrak Islam, M. M. A. Hashem
Date: 2019-05-11 00:00:00
Proceeding: journal 2019 5
Tasks: Anomaly Detection, BIG-bench Machine Learning

Attack and Anomaly detection in the Internet of Things (IoT) infrastructure is a rising concern in the domain of IoT. With the increased use of IoT infrastructure in every domain, threats and attacks in these infrastructures are also growing commensurately. Denial of Service, Data Type Probing, Malicious Control, Malicious Operation, Scan, Spying and Wrong Setup are such attacks and anomalies which can cause an IoT system failure. In this paper, performances of several machine learning models have been compared to predict attacks and anomalies on the IoT systems accurately. The machine learning (ML) algorithms that have been used here are Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN). The evaluation metrics used in the comparison of performance are accuracy, precision, recall, f1 score, and area under the Receiver Operating Characteristic Curve. The system obtained 99.4% test accuracy for Decision Tree, Random Forest, and ANN. Though these techniques have the same accuracy, other metrics prove that Random Forest performs comparatively better.
Correlative Multi-Label Multi-Instance Image Annotation
Authors: Xiangyang Xue, Wei zhang, Jie Zhang, Bin Wu, Jianping Fan, Yao Lu
Date: 2011-01-01 00:00:00
Proceeding: IEEE International Conference on Computer Vision 2011 1

In this paper, each image is viewed as a bag of local re-gions, as well as it is investigated globally. A novel methodis developed for achieving multi-label multi-instance im-age annotation, where image-level (bag-level) labels andregion-level (instance-level) labels are both obtained. Theassociations between semantic concepts and visual featuresare mined both at the image level and at the region level.Inter-label correlations are captured by a co-occurence ma-trix of concept pairs. The cross-level label coherence en-codes the consistency between the labels at the image leveland the labels at the region level.  The associations be-tween visual features and semantic concepts, the corre-lations among the multiple labels, and the cross-level la-bel coherence are sufficiently leveraged to improve anno-tation performance.  Structural max-margin technique isused to formulate the proposed model and multiple inter-related classifiers are learned jointly. To leverage the avail-able image-level labeled samples for the model training, theregion-level label identification on the training set is firstlyaccomplished by building the correspondences between themultiple bag-level labels and the image regions. JEC dis-tance based kernels are employed to measure the similari-ties both between images and between regions. Experimen-tal results on real image datasets MSRC and Corel demon-strate the effectiveness of our method
Navigating through the R packages for movement
Authors: Rocio Joo, Matthew E. Boone, Thomas A. Clay, Samantha C. Patrick, Susana Clusella-Trullas, Mathieu Basille
Date: 2019-01-17 00:00:00
Tasks: Data Visualization

The advent of miniaturized biologging devices has provided ecologists with
unparalleled opportunities to record animal movement across scales, and led to
the collection of ever-increasing quantities of tracking data. In parallel,
sophisticated tools to process, visualize and analyze tracking data have been
developed in abundance. Within the R software alone, we listed 57 focused on
these tasks, called here tracking packages. Here, we reviewed these tracking
packages, as an introduction to this set of packages for researchers, and to
provide feedback and recommendations to package developers, from a user
perspective. We described each package based on a workflow centered around
tracking data (i.e. (x,y,t)), broken down in three stages: pre-processing,
post-processing, and analysis (data visualization, track description, path
reconstruction, behavioral pattern identification, space use characterization,
trajectory simulation and others).
  Supporting documentation is key to the accessibility of a package for users.
Based on a user survey, we reviewed the quality of packages' documentation, and
identified $12$ packages with good or excellent documentation. Links between
packages were assessed through a network graph analysis. Although a large group
of packages shows some degree of connectivity (either depending on functions or
suggesting the use of another tracking package), a third of tracking packages
work on isolation, reflecting a fragmentation in the R Movement-Ecology
programming community.
  Finally, we provide recommendations for users to choose packages, and for
developers to maximize usefulness of their contribution and strengthen the
links between the programming community.
SharpNet: Fast and Accurate Recovery of Occluding Contours in Monocular Depth Estimation
Authors: Michaël Ramamonjisoa, Vincent Lepetit
Date: 2019-05-21 00:00:00
Tasks: Depth Estimation, Depth Prediction, Monocular Depth Estimation, Monocular Reconstruction, Object Recognition

We introduce SharpNet, a method that predicts an accurate depth map for an input color image, with a particular attention to the reconstruction of occluding contours: Occluding contours are an important cue for object recognition, and for realistic integration of virtual objects in Augmented Reality, but they are also notoriously difficult to reconstruct accurately. For example, they are a challenge for stereo-based reconstruction methods, as points around an occluding contour are visible in only one image. Inspired by recent methods that introduce normal estimation to improve depth prediction, we introduce a novel term that constrains depth and occluding contours predictions. Since ground truth depth is difficult to obtain with pixel-perfect accuracy along occluding contours, we use synthetic images for training, followed by fine-tuning on real data. We demonstrate our approach on the challenging NYUv2-Depth dataset, and show that our method outperforms the state-of-the-art along occluding contours, while performing on par with the best recent methods for the rest of the images. Its accuracy along the occluding contours is actually better than the `ground truth' acquired by a depth camera based on structured light. We show this by introducing a new benchmark based on NYUv2-Depth for evaluating occluding contours in monocular reconstruction, which is our second contribution.
The self-tuned sensitivity of circadian clocks
Authors: Kabir Husain, Weerapat Pittayakanchit, Gopal Pattanayak, Michael J Rust, Arvind Murugan
Date: 2019-03-17 00:00:00
Tasks: Sensitivity

Living organisms need to be sensitive to a changing environment while also
ignoring uninformative environmental fluctuations. Here, we show that the
circadian clock in \textit{Synechococcus elongatus} can naturally tune its
environmental sensitivity, through a clock-metabolism coupling quantified in
recent experiments. The metabolic coupling can detect mismatch between clock
predictions and the day-night light cycle, and temporarily raise the clock's
sensitivity to light changes and thus entrain faster. We also analyze analogous
behavior in recent experiments on switching between slow and fast osmotic
stress response pathways in yeast. In both cases, cells can raise their
sensitivity to new external information in epochs of frequent challenging
stress, much like a Kalman filter with adaptive gain in signal processing. Our
work suggests a new class of experiments that probe the history-dependence of
environmental sensitivity in biophysical sensing mechanisms.
A Comparative Analysis of Distributional Term Representations for Author Profiling in Social Media
Authors: Miguel Á. Álvarez-Carmona, Esaú Villatoro-Tello, Manuel Montes-y-Gómez, Luis Villaseñor-Pienda
Date: 2019-05-21 00:00:00
Tasks: Author Profiling

Author Profiling (AP) aims at predicting specific characteristics from a group of authors by analyzing their written documents. Many research has been focused on determining suitable features for modeling writing patterns from authors. Reported results indicate that content-based features continue to be the most relevant and discriminant features for solving this task. Thus, in this paper, we present a thorough analysis regarding the appropriateness of different distributional term representations (DTR) for the AP task. In this regard, we introduce a novel framework for supervised AP using these representations and, supported on it. We approach a comparative analysis of representations such as DOR, TCOR, SSR, and word2vec in the AP problem. We also compare the performance of the DTRs against classic approaches including popular topic-based methods. The obtained results indicate that DTRs are suitable for solving the AP task in social media domains as they achieve competitive results while providing meaningful interpretability.
Textured Neural Avatars
Authors: Aliaksandra Shysheya, Egor Zakharov, Kara-Ali Aliev, Renat Bashirov, Egor Burkov, Karim Iskakov, Aleksei Ivakhnenko, Yury Malkov, Igor Pasechnik, Dmitry Ulyanov, Alexander Vakhitov, Victor Lempitsky
Date: 2019-05-21 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image-to-Image Translation, Translation

We present a system for learning full-body neural avatars, i.e. deep networks that produce full-body renderings of a person for varying body pose and camera position. Our system takes the middle path between the classical graphics pipeline and the recent deep learning approaches that generate images of humans using image-to-image translation. In particular, our system estimates an explicit two-dimensional texture map of the model surface. At the same time, it abstains from explicit shape modeling in 3D. Instead, at test time, the system uses a fully-convolutional network to directly map the configuration of body feature points w.r.t. the camera to the 2D texture coordinates of individual pixels in the image frame. We show that such a system is capable of learning to generate realistic renderings while being trained on videos annotated with 3D poses and foreground masks. We also demonstrate that maintaining an explicit texture representation helps our system to achieve better generalization compared to systems that use direct image-to-image translation.
High-Resolution Road Vehicle Collision Prediction for the City of Montreal
Authors: Antoine Hébert, Timothée Guédon, Tristan Glatard, Brigitte Jaumard
Date: 2019-05-21 00:00:00
Tasks: BIG-bench Machine Learning, Prediction, Vocal Bursts Intensity Prediction

Road accidents are an important issue of our modern societies, responsible for millions of deaths and injuries every year in the world. In Quebec only, in 2018, road accidents are responsible for 359 deaths and 33 thousands of injuries. In this paper, we show how one can leverage open datasets of a city like Montreal, Canada, to create high-resolution accident prediction models, using big data analytics. Compared to other studies in road accident prediction, we have a much higher prediction resolution, i.e., our models predict the occurrence of an accident within an hour, on road segments defined by intersections. Such models could be used in the context of road accident prevention, but also to identify key factors that can lead to a road accident, and consequently, help elaborate new policies. We tested various machine learning methods to deal with the severe class imbalance inherent to accident prediction problems. In particular, we implemented the Balanced Random Forest algorithm, a variant of the Random Forest machine learning algorithm in Apache Spark. Interestingly, we found that in our case, Balanced Random Forest does not perform significantly better than Random Forest. Experimental results show that 85% of road vehicle collisions are detected by our model with a false positive rate of 13%. The examples identified as positive are likely to correspond to high-risk situations. In addition, we identify the most important predictors of vehicle collisions for the area of Montreal: the count of accidents on the same road segment during previous years, the temperature, the day of the year, the hour and the visibility.
Toward Learning a Unified Many-to-Many Mapping for Diverse Image Translation
Authors: Wenju Xu, Shawn Keshmiri, Guanghui Wang
Date: 2019-05-21 00:00:00
Tasks: Attribute, Generative Adversarial Network, Image-to-Image Translation, Translation

Image-to-image translation, which translates input images to a different domain with a learned one-to-one mapping, has achieved impressive success in recent years. The success of translation mainly relies on the network architecture to reserve the structural information while modify the appearance slightly at the pixel level through adversarial training. Although these networks are able to learn the mapping, the translated images are predictable without exclusion. It is more desirable to diversify them using image-to-image translation by introducing uncertainties, i.e., the generated images hold potential for variations in colors and textures in addition to the general similarity to the input images, and this happens in both the target and source domains. To this end, we propose a novel generative adversarial network (GAN) based model, InjectionGAN, to learn a many-to-many mapping. In this model, the input image is combined with latent variables, which comprise of domain-specific attribute and unspecific random variations. The domain-specific attribute indicates the target domain of the translation, while the unspecific random variations introduce uncertainty into the model. A unified framework is proposed to regroup these two parts and obtain diverse generations in each domain. Extensive experiments demonstrate that the diverse generations have high quality for the challenging image-to-image translation tasks where no pairing information of the training dataset exits. Both quantitative and qualitative results prove the superior performance of InjectionGAN over the state-of-the-art approaches.
Performance Analysis of Deep Learning Workloads on Leading-edge Systems
Authors: Yi-Hui Ren, Shinjae Yoo, Adolfy Hoisie
Date: 2019-05-21 00:00:00
Tasks: Deep Learning, GPU

This work examines the performance of leading-edge systems designed for machine learning computing, including the NVIDIA DGX-2, Amazon Web Services (AWS) P3, IBM Power System Accelerated Compute Server AC922, and a consumer-grade Exxact TensorEX TS4 GPU server. Representative deep learning workloads from the fields of computer vision and natural language processing are the focus of the analysis. Performance analysis is performed along with a number of important dimensions. Performance of the communication interconnects and large and high-throughput deep learning models are considered. Different potential use models for the systems as standalone and in the cloud also are examined. The effect of various optimization of the deep learning models and system configurations is included in the analysis.
Sampling from Stochastic Finite Automata with Applications to CTC Decoding
Authors: Martin Jansche, Alexander Gutkin
Date: 2019-05-21 00:00:00

Stochastic finite automata arise naturally in many language and speech processing tasks. They include stochastic acceptors, which represent certain probability distributions over random strings. We consider the problem of efficient sampling: drawing random string variates from the probability distribution represented by stochastic automata and transformations of those. We show that path-sampling is effective and can be efficient if the epsilon-graph of a finite automaton is acyclic. We provide an algorithm that ensures this by conflating epsilon-cycles within strongly connected components. Sampling is also effective in the presence of non-injective transformations of strings. We illustrate this in the context of decoding for Connectionist Temporal Classification (CTC), where the predictive probabilities yield auxiliary sequences which are transformed into shorter labeling strings. We can sample efficiently from the transformed labeling distribution and use this in two different strategies for finding the most probable CTC labeling.
RIU-Net: Embarrassingly simple semantic segmentation of 3D LiDAR point cloud
Authors: Pierre Biasutti, Aurélie Bugeau, Jean-François Aujol, Mathieu Brédif
Date: 2019-05-21 00:00:00
Tasks: 3D Object Detection, GPU, object-detection, Object Detection, Segmentation, Semantic Segmentation, valid

This paper proposes RIU-Net (for Range-Image U-Net), the adaptation of a popular semantic segmentation network for the semantic segmentation of a 3D LiDAR point cloud. The point cloud is turned into a 2D range-image by exploiting the topology of the sensor. This image is then used as input to a U-net. This architecture has already proved its efficiency for the task of semantic segmentation of medical images. We demonstrate how it can also be used for the accurate semantic segmentation of a 3D LiDAR point cloud and how it represents a valid bridge between image processing and 3D point cloud processing. Our model is trained on range-images built from KITTI 3D object detection dataset. Experiments show that RIU-Net, despite being very simple, offers results that are comparable to the state-of-the-art of range-image based methods. Finally, we demonstrate that this architecture is able to operate at 90fps on a single GPU, which enables deployment for real-time segmentation.
Limitation of capsule networks
Authors: David Peer, Sebastian Stabinger, Antonio Rodriguez-Sanchez
Date: 2019-05-21 00:00:00

A recently proposed method in deep learning groups multiple neurons to capsules such that each capsule represents an object or part of an object. Routing algorithms route the output of capsules from lower-level layers to upper-level layers. In this paper, we prove that state-of-the-art routing procedures decrease the expressivity of capsule networks. More precisely, it is shown that EM-routing and routing-by-agreement prevent capsule networks from distinguishing inputs and their negative counterpart. Therefore, only symmetric functions can be expressed by capsule networks, and it can be concluded that they are not universal approximators. We also theoretically motivate and empirically show that this limitation affects the training of deep capsule networks negatively. Therefore, we present an incremental improvement for state-of-the-art routing algorithms that solves the aforementioned limitation and stabilizes the training of capsule networks.