pwc-archive/papers-with-abstracts huggingface dataset:
Introducing the diagrammatic semiotic mode
Authors: Tuomo Hiippala, John A. Bateman
Date: 2020-01-30 00:00:00
Tasks: Diversity, General Classification

As the use and diversity of diagrams across many disciplines grows, there is an increasing interest in the diagrams research community concerning how such diversity might be documented and explained. In this article, we argue that one way of achieving increased reliability, coverage, and utility for a general classification of diagrams is to draw on recently developed semiotic principles developed within the field of multimodality. To this end, we sketch out the internal details of what may tentatively be termed the diagrammatic semiotic mode. This provides a natural account of how diagrammatic representations may integrate natural language, various forms of graphics, diagrammatic elements such as arrows, lines and other expressive resources into coherent organisations, while still respecting the crucial diagrammatic contributions of visual organisation. We illustrate the proposed approach using two recent diagram corpora and show how a multimodal approach supports the empirical analysis of diagrammatic representations, especially in identifying diagrammatic constituents and describing their interrelations in a manner that may be generalised across diagram types and be used to characterise distinct kinds of functionality.
How Does BN Increase Collapsed Neural Network Filters?
Authors: Sheng Zhou, Xinjiang Wang, Ping Luo, Litong Feng, Wenjie Li, Wei zhang
Date: 2020-01-30 00:00:00
Tasks: object-detection, Object Detection

Improving sparsity of deep neural networks (DNNs) is essential for network compression and has drawn much attention. In this work, we disclose a harmful sparsifying process called filter collapse, which is common in DNNs with batch normalization (BN) and rectified linear activation functions (e.g. ReLU, Leaky ReLU). It occurs even without explicit sparsity-inducing regularizations such as $L_1$. This phenomenon is caused by the normalization effect of BN, which induces a non-trainable region in the parameter space and reduces the network capacity as a result. This phenomenon becomes more prominent when the network is trained with large learning rates (LR) or adaptive LR schedulers, and when the network is finetuned. We analytically prove that the parameters of BN tend to become sparser during SGD updates with high gradient noise and that the sparsifying probability is proportional to the square of learning rate and inversely proportional to the square of the scale parameter of BN. To prevent the undesirable collapsed filters, we propose a simple yet effective approach named post-shifted BN (psBN), which has the same representation ability as BN while being able to automatically make BN parameters trainable again as they saturate during training. With psBN, we can recover collapsed filters and increase the model performance in various tasks such as classification on CIFAR-10 and object detection on MS-COCO2017.
TCMI: a non-parametric mutual-dependence estimator for multivariate continuous distributions
Authors: Benjamin Regler, Matthias Scheffler, Luca M. Ghiringhelli
Date: 2020-01-30 00:00:00
Tasks: feature selection

The identification of relevant features, i.e., the driving variables that determine a process or the properties of a system, is an essential part of the analysis of data sets with a large number of variables. A mathematical rigorous approach to quantifying the relevance of these features is mutual information. Mutual information determines the relevance of features in terms of their joint mutual dependence to the property of interest. However, mutual information requires as input probability distributions, which cannot be reliably estimated from continuous distributions such as physical quantities like lengths or energies. Here, we introduce total cumulative mutual information (TCMI), a measure of the relevance of mutual dependences that extends mutual information to random variables of continuous distribution based on cumulative probability distributions. TCMI is a non-parametric, robust, and deterministic measure that facilitates comparisons and rankings between feature sets with different cardinality. The ranking induced by TCMI allows for feature selection, i.e., the identification of variable sets that are nonlinear statistically related to a property of interest, taking into account the number of data samples as well as the cardinality of the set of variables. We evaluate the performance of our measure with simulated data, compare its performance with similar multivariate-dependence measures, and demonstrate the effectiveness of our feature-selection method on a set of standard data sets and a typical scenario in materials science.
Weakly Supervised Instance Segmentation by Deep Community Learning
Authors: Jaedong Hwang, Seohyun Kim, Jeany Son, Bohyung Han
Date: 2020-01-30 00:00:00
Tasks: Image-level Supervised Instance Segmentation, Instance Segmentation, object-detection, Object Detection, Segmentation, Semantic Segmentation, Weakly-supervised instance segmentation, Weakly Supervised Object Detection

We present a weakly supervised instance segmentation algorithm based on deep community learning with multiple tasks. This task is formulated as a combination of weakly supervised object detection and semantic segmentation, where individual objects of the same class are identified and segmented separately. We address this problem by designing a unified deep neural network architecture, which has a positive feedback loop of object detection with bounding box regression, instance mask generation, instance segmentation, and feature extraction. Each component of the network makes active interactions with others to improve accuracy, and the end-to-end trainability of our model makes our results more robust and reproducible. The proposed algorithm achieves state-of-the-art performance in the weakly supervised setting without any additional training such as Fast R-CNN and Mask R-CNN on the standard benchmark dataset. The implementation of our algorithm is available on the project webpage: https://cv.snu.ac.kr/research/WSIS_CL.
Image Embedded Segmentation: Uniting Supervised and Unsupervised Objectives for Segmenting Histopathological Images
Authors: C. T. Sari, C. Sokmensuer, C. Gunduz-Demir
Date: 2020-01-30 00:00:00
Tasks: Generative Adversarial Network, Image Reconstruction, Segmentation, Semantic Segmentation

This paper presents a new regularization method to train a fully convolutional network for semantic tissue segmentation in histopathological images. This method relies on the benefit of unsupervised learning, in the form of image reconstruction, for network training. To this end, it puts forward an idea of defining a new embedding that allows uniting the main supervised task of semantic segmentation and an auxiliary unsupervised task of image reconstruction into a single one and proposes to learn this united task by a single generative model. This embedding generates an output image by superimposing an input image on its segmentation map. Then, the method learns to translate the input image to this embedded output image using a conditional generative adversarial network, which is known as quite effective for image-to-image translations. This proposal is different than the existing approach that uses image reconstruction for the same regularization purpose. The existing approach considers segmentation and image reconstruction as two separate tasks in a multi-task network, defines their losses independently, and combines them in a joint loss function. However, the definition of such a function requires externally determining right contributions of the supervised and unsupervised losses that yield balanced learning between the segmentation and image reconstruction tasks. The proposed approach provides an easier solution to this problem by uniting these two tasks into a single one, which intrinsically combines their losses. We test our approach on three datasets of histopathological images. Our experiments demonstrate that it leads to better segmentation results in these datasets, compared to its counterparts.
Finite-Time Analysis of Round-Robin Kullback-Leibler Upper Confidence Bounds for Optimal Adaptive Allocation with Multiple Plays and Markovian Rewards
Authors: Vrettos Moulos
Date: 2020-01-30 00:00:00
Proceeding: NeurIPS 2020 12

We study an extension of the classic stochastic multi-armed bandit problem which involves multiple plays and Markovian rewards in the rested bandits setting. In order to tackle this problem we consider an adaptive allocation rule which at each stage combines the information from the sample means of all the arms, with the Kullback-Leibler upper confidence bound of a single arm which is selected in round-robin way. For rewards generated from a one-parameter exponential family of Markov chains, we provide a finite-time upper bound for the regret incurred from this adaptive allocation rule, which reveals the logarithmic dependence of the regret on the time horizon, and which is asymptotically optimal. For our analysis we devise several concentration results for Markov chains, including a maximal inequality for Markov chains, that may be of interest in their own right. As a byproduct of our analysis we also establish asymptotically optimal, finite-time guarantees for the case of multiple plays, and i.i.d. rewards drawn from a one-parameter exponential family of probability densities. Additionally, we provide simulation results that illustrate that calculating Kullback-Leibler upper confidence bounds in a round-robin way, is significantly more efficient than calculating them for every arm at each round, and that the expected regrets of those two approaches behave similarly.
Narrative Smoothing: Dynamic Conversational Network for the Analysis of TV Series Plots
Authors: Xavier Bost, Vincent Labatut, Serigne Gueye, Georges Linarès
Date: 2016-02-25 00:00:00

Modern popular TV series often develop complex storylines spanning several seasons, but are usually watched in quite a discontinuous way. As a result, the viewer generally needs a comprehensive summary of the previous season plot before the new one starts. The generation of such summaries requires first to identify and characterize the dynamics of the series subplots. One way of doing so is to study the underlying social network of interactions between the characters involved in the narrative. The standard tools used in the Social Networks Analysis field to extract such a network rely on an integration of time, either over the whole considered period, or as a sequence of several time-slices. However, they turn out to be inappropriate in the case of TV series, due to the fact the scenes showed onscreen alternatively focus on parallel storylines, and do not necessarily respect a traditional chronology. This makes existing extraction methods inefficient to describe the dynamics of relationships between characters, or to get a relevant instantaneous view of the current social state in the plot. This is especially true for characters shown as interacting with each other at some previous point in the plot but temporarily neglected by the narrative. In this article, we introduce narrative smoothing, a novel, still exploratory, network extraction method. It smooths the relationship dynamics based on the plot properties, aiming at solving some of the limitations present in the standard approaches. In order to assess our method, we apply it to a new corpus of 3 popular TV series, and compare it to both standard approaches. Our results are promising, showing narrative smoothing leads to more relevant observations when it comes to the characterization of the protagonists and their relationships. It could be used as a basis for further modeling the intertwined storylines constituting TV series plots.
A CNN With Multi-scale Convolution for Hyperspectral Image Classification using Target-Pixel-Orientation scheme
Authors: Jayasree Saha, Yuvraj Khanna, Jayanta Mukherjee
Date: 2020-01-30 00:00:00
Tasks: General Classification, Hyperspectral Image Classification, image-classification, Image Classification

Recently, CNN is a popular choice to handle the hyperspectral image classification challenges. In spite of having such large spectral information in Hyper-Spectral Image(s) (HSI), it creates a curse of dimensionality. Also, large spatial variability of spectral signature adds more difficulty in classification problem. Additionally, training a CNN in the end to end fashion with scarced training examples is another challenging and interesting problem. In this paper, a novel target-patch-orientation method is proposed to train a CNN based network. Also, we have introduced a hybrid of 3D-CNN and 2D-CNN based network architecture to implement band reduction and feature extraction methods, respectively. Experimental results show that our method outperforms the accuracies reported in the existing state of the art methods.
The Direction-Aware, Learnable, Additive Kernels and the Adversarial Network for Deep Floor Plan Recognition
Authors: Yuli Zhang, Yeyang He, Shaowen Zhu, Xinhan Di
Date: 2020-01-30 00:00:00
Tasks: Segmentation, Semantic Segmentation

This paper presents a new approach for the recognition of elements in floor plan layouts. Besides of elements with common shapes, we aim to recognize elements with irregular shapes such as circular rooms and inclined walls. Furthermore, the reduction of noise in the semantic segmentation of the floor plan is on demand. To this end, we propose direction-aware, learnable, additive kernels in the application of both the context module and common convolutional blocks. We apply them for high performance of elements with both common and irregular shapes. Besides, an adversarial network with two discriminators is proposed to further improve the accuracy of the elements and to reduce the noise of the semantic segmentation. Experimental results demonstrate the superiority and effectiveness of the proposed network over the state-of-the-art methods.
Automatic marker-free registration of tree point-cloud data based on rotating projection
Authors: Xiuxian Xu, Pei Wang, Xiaozheng Gan, Ya-Xin Li, Li Zhang, Qing Zhang, Mei Zhou, Yinghui Zhao, Xinwei Li
Date: 2020-01-30 00:00:00

Point-cloud data acquired using a terrestrial laser scanner (TLS) play an important role in digital forestry research. Multiple scans are generally used to overcome occlusion effects and obtain complete tree structural information. However, it is time-consuming and difficult to place artificial reflectors in a forest with complex terrain for marker-based registration, a process that reduces registration automation and efficiency. In this study, we propose an automatic coarse-to-fine method for the registration of point-cloud data from multiple scans of a single tree. In coarse registration, point clouds produced by each scan are projected onto a spherical surface to generate a series of two-dimensional (2D) images, which are used to estimate the initial positions of multiple scans. Corresponding feature-point pairs are then extracted from these series of 2D images. In fine registration, point-cloud data slicing and fitting methods are used to extract corresponding central stem and branch centers for use as tie points to calculate fine transformation parameters. To evaluate the accuracy of registration results, we propose a model of error evaluation via calculating the distances between center points from corresponding branches in adjacent scans. For accurate evaluation, we conducted experiments on two simulated trees and a real-world tree. Average registration errors of the proposed method were 0.26m around on simulated tree point clouds, and 0.05m around on real-world tree point cloud.
2018 Robotic Scene Segmentation Challenge
Authors: Max Allan, Satoshi Kondo, Sebastian Bodenstedt, Stefan Leger, Rahim Kadkhodamohammadi, Imanol Luengo, Felix Fuentes, Evangello Flouty, Ahmed Mohammed, Marius Pedersen, Avinash Kori, Varghese Alex, Ganapathy Krishnamurthi, David Rauber, Robert Mendel, Christoph Palm, Sophia Bano, Guinther Saibro, Chi-Sheng Shih, Hsun-An Chiang, Juntang Zhuang, Junlin Yang, Vladimir Iglovikov, Anton Dobrenkii, Madhu Reddiboina, Anubhav Reddy, Xingtong Liu, Cong Gao, Mathias Unberath, Myeonghyeon Kim, Chanho Kim, Chaewon Kim, Hye-Jin Kim, Gyeongmin Lee, Ihsan Ullah, Miguel Luna, Sang Hyun Park, Mahdi Azizian, Danail Stoyanov, Lena Maier-Hein, Stefanie Speidel
Date: 2020-01-30 00:00:00
Tasks: Scene Segmentation, Segmentation

In 2015 we began a sub-challenge at the EndoVis workshop at MICCAI in Munich using endoscope images of ex-vivo tissue with automatically generated annotations from robot forward kinematics and instrument CAD models. However, the limited background variation and simple motion rendered the dataset uninformative in learning about which techniques would be suitable for segmentation in real surgery. In 2017, at the same workshop in Quebec we introduced the robotic instrument segmentation dataset with 10 teams participating in the challenge to perform binary, articulating parts and type segmentation of da Vinci instruments. This challenge included realistic instrument motion and more complex porcine tissue as background and was widely addressed with modifications on U-Nets and other popular CNN architectures. In 2018 we added to the complexity by introducing a set of anatomical objects and medical devices to the segmented classes. To avoid over-complicating the challenge, we continued with porcine data which is dramatically simpler than human tissue due to the lack of fatty tissue occluding many organs.
How Much and When Do We Need Higher-order Information in Hypergraphs? A Case Study on Hyperedge Prediction
Authors: Se-eun Yoon, HyungSeok Song, Kijung Shin, Yung Yi
Date: 2020-01-30 00:00:00
Tasks: Hyperedge Prediction, Link Prediction

Hypergraphs provide a natural way of representing group relations, whose complexity motivates an extensive array of prior work to adopt some form of abstraction and simplification of higher-order interactions. However, the following question has yet to be addressed: How much abstraction of group interactions is sufficient in solving a hypergraph task, and how different such results become across datasets? This question, if properly answered, provides a useful engineering guideline on how to trade off between complexity and accuracy of solving a downstream task. To this end, we propose a method of incrementally representing group interactions using a notion of n-projected graph whose accumulation contains information on up to n-way interactions, and quantify the accuracy of solving a task as n grows for various datasets. As a downstream task, we consider hyperedge prediction, an extension of link prediction, which is a canonical task for evaluating graph models. Through experiments on 15 real-world datasets, we draw the following messages: (a) Diminishing returns: small n is enough to achieve accuracy comparable with near-perfect approximations, (b) Troubleshooter: as the task becomes more challenging, larger n brings more benefit, and (c) Irreducibility: datasets whose pairwise interactions do not tell much about higher-order interactions lose much accuracy when reduced to pairwise abstractions.
Multiple Object Tracking by Flowing and Fusing
Authors: Jimuyang Zhang, Sanping Zhou, Xin Chang, Fangbin Wan, Jinjun Wang, Yang Wu, Dong Huang
Date: 2020-01-30 00:00:00
Tasks: Multiple Object Tracking, Object, object-detection, Object Detection, Object Tracking

Most of Multiple Object Tracking (MOT) approaches compute individual target features for two subtasks: estimating target-wise motions and conducting pair-wise Re-Identification (Re-ID). Because of the indefinite number of targets among video frames, both subtasks are very difficult to scale up efficiently in end-to-end Deep Neural Networks (DNNs). In this paper, we design an end-to-end DNN tracking approach, Flow-Fuse-Tracker (FFT), that addresses the above issues with two efficient techniques: target flowing and target fusing. Specifically, in target flowing, a FlowTracker DNN module learns the indefinite number of target-wise motions jointly from pixel-level optical flows. In target fusing, a FuseTracker DNN module refines and fuses targets proposed by FlowTracker and frame-wise object detection, instead of trusting either of the two inaccurate sources of target proposal. Because FlowTracker can explore complex target-wise motion patterns and FuseTracker can refine and fuse targets from FlowTracker and detectors, our approach can achieve the state-of-the-art results on several MOT benchmarks. As an online MOT approach, FFT produced the top MOTA of 46.3 on the 2DMOT15, 56.5 on the MOT16, and 56.5 on the MOT17 tracking benchmarks, surpassing all the online and offline methods in existing publications.
A Hybrid Two-layer Feature Selection Method Using GeneticAlgorithm and Elastic Net
Authors: Fatemeh Amini, Guiping Hu
Date: 2020-01-30 00:00:00
Tasks: Computational Efficiency, feature selection, Prediction

Feature selection, as a critical pre-processing step for machine learning, aims at determining representative predictors from a high-dimensional feature space dataset to improve the prediction accuracy. However, the increase in feature space dimensionality, comparing to the number of observations, poses a severe challenge to many existing feature selection methods with respect to computational efficiency and prediction performance. This paper presents a new hybrid two-layer feature selection approach that combines a wrapper and an embedded method in constructing an appropriate subset of predictors. In the first layer of the proposed method, the Genetic Algorithm(GA) has been adopted as a wrapper to search for the optimal subset of predictors, which aims to reduce the number of predictors and the prediction error. As one of the meta-heuristic approaches, GA is selected due to its computational efficiency; however, GAs do not guarantee the optimality. To address this issue, a second layer is added to the proposed method to eliminate any remaining redundant/irrelevant predictors to improve the prediction accuracy. Elastic Net(EN) has been selected as the embedded method in the second layer because of its flexibility in adjusting the penalty terms in regularization process and time efficiency. This hybrid two-layer approach has been applied on a Maize genetic dataset from NAM population, which consists of multiple subsets of datasets with different ratio of the number of predictors to the number of observations. The numerical results confirm the superiority of the proposed model.
Unsupervised Pixel-level Road Defect Detection via Adversarial Image-to-Frequency Transform
Authors: Jongmin Yu, Duyong Kim, Younkwan Lee, Moongu Jeon
Date: 2020-01-30 00:00:00
Tasks: Defect Detection

In the past few years, the performance of road defect detection has been remarkably improved thanks to advancements on various studies on computer vision and deep learning. Although a large-scale and well-annotated datasets enhance the performance of detecting road pavement defects to some extent, it is still challengeable to derive a model which can perform reliably for various road conditions in practice, because it is intractable to construct a dataset considering diverse road conditions and defect patterns. To end this, we propose an unsupervised approach to detecting road defects, using Adversarial Image-to-Frequency Transform (AIFT). AIFT adopts the unsupervised manner and adversarial learning in deriving the defect detection model, so AIFT does not need annotations for road pavement defects. We evaluate the efficiency of AIFT using GAPs384 dataset, Cracktree200 dataset, CRACK500 dataset, and CFD dataset. The experimental results demonstrate that the proposed approach detects various road detects, and it outperforms existing state-of-the-art approaches.