pwc-archive/papers-with-abstracts huggingface dataset:
Understanding the Radical Mind: Identifying Signals to Detect Extremist Content on Twitter
Authors: Mariam Nouh, Jason R. C. Nurse, Michael Goldsmith
Date: 2019-05-15 00:00:00

The Internet and, in particular, Online Social Networks have changed the way that terrorist and extremist groups can influence and radicalise individuals. Recent reports show that the mode of operation of these groups starts by exposing a wide audience to extremist material online, before migrating them to less open online platforms for further radicalization. Thus, identifying radical content online is crucial to limit the reach and spread of the extremist narrative. In this paper, our aim is to identify measures to automatically detect radical content in social media. We identify several signals, including textual, psychological and behavioural, that together allow for the classification of radical messages. Our contribution is three-fold: (1) we analyze propaganda material published by extremist groups and create a contextual text-based model of radical content, (2) we build a model of psychological properties inferred from these material, and (3) we evaluate these models on Twitter to determine the extent to which it is possible to automatically identify online radical tweets. Our results show that radical users do exhibit distinguishable textual, psychological, and behavioural properties. We find that the psychological properties are among the most distinguishing features. Additionally, our results show that textual models using vector embedding features significantly improves the detection over TF-IDF features. We validate our approach on two experiments achieving high accuracy. Our findings can be utilized as signals for detecting online radicalization activities.
Relation Structure-Aware Heterogeneous Information Network Embedding
Authors: Yuanfu Lu, Chuan Shi, Linmei Hu, Zhiyuan Liu
Date: 2019-05-15 00:00:00
Tasks: Clustering, Link Prediction, Network Embedding, Node Classification, Node Clustering, Relation

Heterogeneous information network (HIN) embedding aims to embed multiple types of nodes into a low-dimensional space. Although most existing HIN embedding methods consider heterogeneous relations in HINs, they usually employ one single model for all relations without distinction, which inevitably restricts the capability of network embedding. In this paper, we take the structural characteristics of heterogeneous relations into consideration and propose a novel Relation structure-aware Heterogeneous Information Network Embedding model (RHINE). By exploring the real-world networks with thorough mathematical analysis, we present two structure-related measures which can consistently distinguish heterogeneous relations into two categories: Affiliation Relations (ARs) and Interaction Relations (IRs). To respect the distinctive characteristics of relations, in our RHINE, we propose different models specifically tailored to handle ARs and IRs, which can better capture the structures and semantics of the networks. At last, we combine and optimize these models in a unified and elegant manner. Extensive experiments on three real-world datasets demonstrate that our model significantly outperforms the state-of-the-art methods in various tasks, including node clustering, link prediction, and node classification.
Power of the Few: Analyzing the Impact of Influential Users in Collaborative Recommender Systems
Authors: Farzad Eskandanian, Nasim Sonboli, Bamshad Mobasher
Date: 2019-05-14 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Like other social systems, in collaborative filtering a small number of "influential" users may have a large impact on the recommendations of other users, thus affecting the overall behavior of the system. Identifying influential users and studying their impact on other users is an important problem because it provides insight into how small groups can inadvertently or intentionally affect the behavior of the system as a whole. Modeling these influences can also shed light on patterns and relationships that would otherwise be difficult to discern, hopefully leading to more transparency in how the system generates personalized content. In this work we first formalize the notion of "influence" in collaborative filtering using an Influence Discrimination Model. We then empirically identify and characterize influential users and analyze their impact on the system under different underlying recommendation algorithms and across three different recommendation domains: job, movie and book recommendations. Insights from these experiments can help in designing systems that are not only optimized for accuracy, but are also tuned to mitigate the impact of influential users when it might lead to potential imbalance or unfairness in the system's outcomes.
On Estimating Maximum Sum Rate of MIMO Systems with Successive Zero-Forcing Dirty Paper Coding and Per-antenna Power Constraint
Authors: Thuy M. Pham, Ronan Farrell, Le-Nam Tran
Date: 2019-05-14 00:00:00

In this paper, we study the sum rate maximization for successive zero-forcing dirty-paper coding (SZFDPC) with per-antenna power constraint (PAPC). Although SZFDPC is a low-complexity alternative to the optimal dirty paper coding (DPC), efficient algorithms to compute its sum rate are still open problems especially under practical PAPC. The existing solution to the considered problem is computationally inefficient due to employing high-complexity interior-point method. In this study, we propose two new low-complexity approaches to this important problem. More specifically, the first algorithm achieves the optimal solution by transforming the original problem in the broadcast channel into an equivalent problem in the multiple access channel, then the resulting problem is solved by alternating optimization together with successive convex approximation. We also derive a suboptimal solution based on machine learning to which simple linear regressions are applicable. The approaches are analyzed and validated extensively to demonstrate their superiors over the existing approach.
AutoAugment: Learning Augmentation Strategies From Data
Authors: Ekin D. Cubuk,  Barret Zoph,  Dandelion Mane,  Vijay Vasudevan,  Quoc V. Le
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Data Augmentation, Domain Generalization

Data augmentation is an effective technique for improving the accuracy of modern image classifiers. However, current data augmentation implementations are manually designed. In this paper, we describe a simple procedure called AutoAugment to automatically search for improved data augmentation policies. In our implementation, we have designed a search space where a policy consists of many sub-policies, one of which is randomly chosen for each image in each mini-batch. A sub-policy consists of two operations, each operation being an image processing function such as translation, rotation, or shearing, and the probabilities and magnitudes with which the functions are applied.  We use a search algorithm to find the best policy such that the neural network yields the highest validation accuracy on a target dataset. Our method achieves state-of-the-art accuracy on CIFAR-10, CIFAR-100, SVHN, and ImageNet (without additional data). On ImageNet, we attain a Top-1 accuracy of 83.5% which is 0.4% better than the previous record of 83.1%. On CIFAR-10, we achieve an error rate of 1.5%, which is 0.6% better than the previous state-of-the-art. Augmentation policies we find are  transferable between datasets.  The policy learned on ImageNet transfers well to achieve significant improvements on other datasets, such as Oxford Flowers, Caltech-101, Oxford-IIT Pets, FGVC Aircraft, and Stanford Cars.
SDRSAC: Semidefinite-Based Randomized Approach for Robust Point Cloud Registration Without Correspondences
Authors: Huu M. Le,  Thanh-Toan Do,  Tuan Hoang,  Ngai-Man Cheung
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Graph Matching, Point Cloud Registration

This paper presents a novel randomized algorithm for robust point cloud registration without correspondences. Most existing registration approaches require a set of putative correspondences obtained by extracting invariant descriptors. However, such descriptors could become unreliable in noisy and contaminated settings. In these settings, methods that directly handle input point sets are preferable.  Without correspondences, however,   conventional randomized techniques require a very large number of samples in order to reach satisfactory solutions. In this paper, we propose a novel approach to address this problem. In particular, our work enables the use of randomized methods for point cloud registration without the need of putative correspondences. By considering point cloud alignment as a special instance of graph matching and employing an efficient semi-definite relaxation, we propose a novel sampling mechanism, in which the size of the sampled subsets can be larger-than-minimal. Our tight relaxation scheme enables fast rejection of the outliers in the sampled sets, resulting in high quality hypotheses. We conduct extensive experiments to demonstrate that our approach outperforms other state-of-the-art methods. Importantly, our proposed method serves as a generic framework which can be extended to problems with known correspondences.
BAD SLAM: Bundle Adjusted Direct RGB-D SLAM
Authors: Thomas Schops,  Torsten Sattler,  Marc Pollefeys
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Simultaneous Localization and Mapping

A key component of Simultaneous Localization and Mapping (SLAM) systems is the joint optimization of the estimated 3D map and camera trajectory. Bundle adjustment (BA) is the gold standard for this. Due to the large number of variables in dense RGB-D SLAM, previous work has focused on approximating BA. In contrast, in this paper we present a novel, fast direct BA formulation which we implement in a real-time dense RGB-D SLAM algorithm. In addition, we show that direct RGB-D SLAM systems are highly sensitive to rolling shutter, RGB and depth sensor synchronization, and calibration errors. In order to facilitate state-of-the-art research on direct RGB-D SLAM, we propose a novel, well-calibrated benchmark for this task that uses synchronized global shutter RGB and depth cameras. It includes a training set, a test set without public ground truth, and an online evaluation service. We observe that the ranking of methods changes on this dataset compared to existing ones, and our proposed algorithm outperforms all other evaluated SLAM methods. Our benchmark and our open source SLAM algorithm are available at: www.eth3d.net
Strand-Accurate Multi-View Hair Capture
Authors: Giljoo Nam,  Chenglei Wu,  Min H. Kim,  Yaser Sheikh
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6

Hair is one of the most challenging objects to reconstruct due to its micro-scale structure and a large number of repeated strands with heavy occlusions. In this paper, we present the first method to capture high-fidelity hair geometry with strand-level accuracy. Our method takes three stages to achieve this. In the first stage, a new multi-view stereo method with a slanted support line is proposed to solve the hair correspondences between different views. In detail, we contribute a novel cost function consisting of both photo-consistency term and geometric term that reconstructs each hair pixel as a 3D line. By merging all the depth maps, a point cloud, as well as local line directions for each point, is obtained. Thus, in the second stage, we feature a novel strand reconstruction method with the mean-shift to convert the noisy point data to a set of strands. Lastly, we grow the hair strands with multi-view geometric constraints to elongate the short strands and recover the missing strands, thus significantly increasing the reconstruction completeness. We evaluate our method on both synthetic data and real captured data, showing that our method can reconstruct hair strands with sub-millimeter accuracy.
LAF-Net: Locally Adaptive Fusion Networks for Stereo Confidence Estimation
Authors: Sunok Kim,  Seungryong Kim,  Dongbo Min,  Kwanghoon Sohn
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6

We present a novel method that estimates confidence map of an initial disparity by making full use of tri-modal input, including matching cost, disparity, and color image through deep networks.  The proposed network, termed as Locally Adaptive Fusion Networks (LAF-Net), learns locally-varying attention and scale maps to fuse the tri-modal confidence features. The attention inference networks encode the importance of tri-modal confidence features and then concatenate them using the attention maps in an adaptive and dynamic fashion. This enables us to make an optimal fusion of the heterogeneous features, compared to a simple concatenation technique that is commonly used in conventional approaches. In addition, to encode the confidence features with locally-varying receptive fields, the scale inference networks learn the scale map and warp the fused confidence features through convolutional spatial transformer networks. Finally, the confidence map is progressively estimated in the recursive refinement networks to enforce a spatial context and local consistency. Experimental results show that this model outperforms the state-of-the-art methods on various benchmarks.
Coordinate-Free Carlsson-Weinshall Duality and Relative Multi-View Geometry
Authors: Matthew Trager,  Martial Hebert,  Jean Ponce
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6

We present a coordinate-free description of Carlsson-Weinshall duality between scene points and camera pinholes and use it to derive a new characterization of primal/dual multi-view geometry. In the case of three views, a particular set of reduced trilinearities provide a novel parameterization of camera geometry that, unlike existing ones, is subject only to very simple internal constraints. These trilinearities lead to new "quasi-linear" algorithms for primal and dual structure from motion. We include some preliminary experiments with real and synthetic data.
Which Way Are You Going? Imitative Decision Learning for Path Forecasting in Dynamic Scenes
Authors: Yuke Li
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6

Path forecasting is a pivotal step toward understanding dynamic scenes and an emerging topic in the computer vi- sion field. This task is challenging due to the multimodal nature of the future, namely, given a partial history, there is more than one plausible prediction. Yet, the state-of-the-art methods seem not fully responsive to this innate variabil- ity. Hence, how to better foresee the forthcoming trajectory in dynamic scenes has to be more thoroughly pursued. To this end, we propose a novel Imitative Decision Learning (IDL) approach. It delves deeper into the key that inher- ently characterizes the multimodality - the latent decision. The proposed IDL first infers the distribution of such latent decisions by learning from moving histories. A policy is then generated by taking the sampled latent decision into account to predict the future. Different plausible upcoming paths corresponds to each sampled latent decision. This ap- proach significantly differs from the mainstream literature that relies on a predefined latent variable to extrapolate di- verse predictions. In order to augment the understanding of the latent decision and resultant mutimodal future, we in- vestigate their connection through mutual information op- timization. Moreover, the proposed IDL integrates spatial and temporal dependencies into one single framework, in contrast to handling them with two-step settings. As a re- sult, our approach enables simultaneous anticipation of the paths of all pedestrians in the scene. We assess our pro- posal on the large-scale SAP, ETH and UCY datasets. The experiments show that IDL introduces considerable margin improvements with respect to recent leading studies.
Language-Driven Temporal Activity Localization: A Semantic Matching Reinforcement Learning Model
Authors: Weining Wang,  Yan Huang,  Liang Wang
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Action Detection, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sentence

Current studies on action detection in untrimmed videos are mostly designed for action classes, where an action is described at word level such as jumping, tumbling, swing, etc. This paper focuses on a rarely investigated problem of localizing an activity via a sentence query which would be more challenging and practical. Considering that current methods are generally time-consuming due to the dense frame-processing manner, we propose a recurrent neural network based reinforcement learning model which selectively observes a sequence of frames and associates the given sentence with video content in a matching-based manner. However, directly matching sentences with video content performs poorly due to the large visual-semantic discrepancy. Thus, we extend the method to a semantic matching reinforcement learning (SM-RL) model  by extracting semantic concepts of videos and then fusing them with global context features. Extensive experiments on three benchmark datasets, TACoS, Charades-STA and DiDeMo, show that our method achieves the state-of-the-art performance with a high detection speed, demonstrating both effectiveness and efficiency of our method.
TopNet: Structural Point Cloud Decoder
Authors: Lyne P. Tchapmi,  Vineet Kosaraju,  Hamid Rezatofighi,  Ian Reid,  Silvio Savarese
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Decoder, Object, Point Cloud Completion

3D point cloud generation is of great use for 3D scene modeling and understanding.  Real-world 3D object point clouds  can  be  properly  described  by  a  collection  of  low-level and high-level structures such as surfaces, geometric primitives, semantic parts,etc. In fact, there exist many different representations of a 3D object point cloud as a set of point groups.  Existing frameworks for point cloud genera-ion either do not consider structure in their proposed solutions, or assume and enforce a specific structure/topology,e.g.  a  collection  of  manifolds  or  surfaces,  for  the  generated  point  cloud  of  a  3D  object.   In  this  work,  we  pro-pose a novel decoder that generates a structured point cloud without assuming any specific structure or topology on the underlying point set.  Our decoder is softly constrained to generate a point cloud following a hierarchical rooted tree structure.  We show that given enough capacity and allowing for redundancies, the proposed decoder is very flexible and able to learn any arbitrary grouping of points including any topology on the point set.  We evaluate our decoder on the task of point cloud generation for 3D point cloud shape completion.  Combined with encoders from existing frameworks, we show that our proposed decoder significantly outperforms state-of-the-art 3D point cloud completion methods on the Shapenet dataset
Spherical Fractal Convolutional Neural Networks for Point Cloud Recognition
Authors: Yongming Rao,  Jiwen Lu,  Jie Zhou
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Object Classification, 3D Part Segmentation, General Classification, Retrieval

We present a generic, flexible and 3D rotation invariant framework based on spherical symmetry for point cloud recognition. By introducing regular icosahedral lattice and its fractals to approximate and discretize sphere,  convolution can be easily implemented to process 3D points. Based on the fractal structure, a hierarchical feature learning framework together with an adaptive sphere projection module is proposed to learn deep feature in an end-to-end manner. Our framework not only inherits the strong representation power and generalization capability from convolutional neural networks for image recognition, but also extends CNN to learn robust feature resistant to rotations and perturbations. The proposed model is effective yet robust. Comprehensive experimental study demonstrates that our approach can achieve competitive performance compared to state-of-the-art techniques on both 3D object classification and part segmentation tasks, meanwhile, outperform other rotation invariant models on rotated 3D object classification and retrieval tasks by a large margin.
Co-Occurrent Features in Semantic Segmentation
Authors: Hang Zhang,  Han Zhang,  Chenguang Wang,  Junyuan Xie
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Segmentation, Semantic Segmentation

Recent work has achieved great success in utilizing global contextual information for semantic segmentation, including increasing the receptive field and aggregating pyramid feature representations. In this paper, we go beyond global context and explore the fine-grained representation using co-occurrent features by introducing Co-occurrent Feature Model, which predicts the distribution of  co-occurrent features for a given target. To leverage the semantic context in the co-occurrent features, we build an Aggregated Co-occurrent Feature (ACF) Module by aggregating the probability of the co-occurrent feature with the co-occurrent context. ACF Module learns a fine-grained spatial invariant representation to capture co-occurrent context information across the scene. Our approach significantly improves the segmentation results using FCN and achieves superior performance 54.0% mIoU on Pascal Context, 87.2% mIoU on Pascal VOC 2012 and 44.89% mIoU on ADE20K datasets. The source code and complete system will be publicly available upon publication.
Learning Channel-Wise Interactions for Binary Convolutional Neural Networks
Authors: Ziwei Wang,  Jiwen Lu,  Chenxin Tao,  Jie Zhou,  Qi Tian
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Quantization, Reinforcement Learning

In this paper, we propose a channel-wise interaction based binary convolutional neural network learning method (CI-BCNN) for efficient inference. Conventional methods apply xnor and bitcount operations in binary convolution with notable quantization error, which usually obtains inconsistent signs in binary feature maps compared with their full-precision counterpart and leads to significant information loss. In contrast, our CI-BCNN mines the channel-wise interactions, through which prior knowledge is provided to alleviate inconsistency of signs in binary feature maps and preserves the information of input samples during inference. Specifically, we mine the channel-wise interactions by a reinforcement learning model, and impose channel-wise priors on the intermediate feature maps through the interacted bitcount function. Extensive experiments on the CIFAR-10 and ImageNet datasets show that our method outperforms the state-of-the-art binary convolutional neural networks with less computational and storage cost.