pwc-archive/papers-with-abstracts huggingface dataset:
Pooling Regularized Graph Neural Network for fMRI Biomarker Analysis
Authors: Xiaoxiao Li, Yuan Zhou, Nicha C. Dvornek, Muhan Zhang, Juntang Zhuang, Pamela Ventola, James S. Duncan
Date: 2020-07-29 00:00:00
Tasks: Graph Neural Network

Understanding how certain brain regions relate to a specific neurological disorder has been an important area of neuroimaging research. A promising approach to identify the salient regions is using Graph Neural Networks (GNNs), which can be used to analyze graph structured data, e.g. brain networks constructed by functional magnetic resonance imaging (fMRI). We propose an interpretable GNN framework with a novel salient region selection mechanism to determine neurological brain biomarkers associated with disorders. Specifically, we design novel regularized pooling layers that highlight salient regions of interests (ROIs) so that we can infer which ROIs are important to identify a certain disease based on the node pooling scores calculated by the pooling layers. Our proposed framework, Pooling Regularized-GNN (PR-GNN), encourages reasonable ROI-selection and provides flexibility to preserve either individual- or group-level patterns. We apply the PR-GNN framework on a Biopoint Autism Spectral Disorder (ASD) fMRI dataset. We investigate different choices of the hyperparameters and show that PR-GNN outperforms baseline methods in terms of classification accuracy. The salient ROI detection results show high correspondence with the previous neuroimaging-derived biomarkers for ASD.
Composer Style Classification of Piano Sheet Music Images Using Language Model Pretraining
Authors: TJ Tsai, Kevin Ji
Date: 2020-07-29 00:00:00
Tasks: Classification, General Classification, Language Modeling, Language Modelling

This paper studies composer style classification of piano sheet music images. Previous approaches to the composer classification task have been limited by a scarcity of data. We address this issue in two ways: (1) we recast the problem to be based on raw sheet music images rather than a symbolic music format, and (2) we propose an approach that can be trained on unlabeled data. Our approach first converts the sheet music image into a sequence of musical "words" based on the bootleg feature representation, and then feeds the sequence into a text classifier. We show that it is possible to significantly improve classifier performance by first training a language model on a set of unlabeled data, initializing the classifier with the pretrained language model weights, and then finetuning the classifier on a small amount of labeled data. We train AWD-LSTM, GPT-2, and RoBERTa language models on all piano sheet music images in IMSLP. We find that transformer-based architectures outperform CNN and LSTM models, and pretraining boosts classification accuracy for the GPT-2 model from 46\% to 70\% on a 9-way classification task. The trained model can also be used as a feature extractor that projects piano sheet music into a feature space that characterizes compositional style.
A regularized deep matrix factorized model of matrix completion for image restoration
Authors: Zhemin Li, Zhi-Qin John Xu, Tao Luo, Hongxia Wang
Date: 2020-07-29 00:00:00
Tasks: Image Restoration, Matrix Completion

It has been an important approach of using matrix completion to perform image restoration. Most previous works on matrix completion focus on the low-rank property by imposing explicit constraints on the recovered matrix, such as the constraint of the nuclear norm or limiting the dimension of the matrix factorization component. Recently, theoretical works suggest that deep linear neural network has an implicit bias towards low rank on matrix completion. However, low rank is not adequate to reflect the intrinsic characteristics of a natural image. Thus, algorithms with only the constraint of low rank are insufficient to perform image restoration well. In this work, we propose a Regularized Deep Matrix Factorized (RDMF) model for image restoration, which utilizes the implicit bias of the low rank of deep neural networks and the explicit bias of total variation. We demonstrate the effectiveness of our RDMF model with extensive experiments, in which our method surpasses the state of art models in common examples, especially for the restoration from very few observations. Our work sheds light on a more general framework for solving other inverse problems by combining the implicit bias of deep learning with explicit regularization.
Camera-Based Piano Sheet Music Identification
Authors: Daniel Yang, TJ Tsai
Date: 2020-07-29 00:00:00
Tasks: Retrieval

This paper presents a method for large-scale retrieval of piano sheet music images. Our work differs from previous studies on sheet music retrieval in two ways. First, we investigate the problem at a much larger scale than previous studies, using all solo piano sheet music images in the entire IMSLP dataset as a searchable database. Second, we use cell phone images of sheet music as our input queries, which lends itself to a practical, user-facing application. We show that a previously proposed fingerprinting method for sheet music retrieval is far too slow for a real-time application, and we diagnose its shortcomings. We propose a novel hashing scheme called dynamic n-gram fingerprinting that significantly reduces runtime while simultaneously boosting retrieval accuracy. In experiments on IMSLP data, our proposed method achieves a mean reciprocal rank of 0.85 and an average runtime of 0.98 seconds per query.
Development of POS tagger for English-Bengali Code-Mixed data
Authors: Tathagata Raha, Sainik Kumar Mahata, Dipankar Das, Sivaji Bandyopadhyay
Date: 2020-07-29 00:00:00
Proceeding: ICON 2019 12
Tasks: POS, Sentence, TAG

Code-mixed texts are widespread nowadays due to the advent of social media. Since these texts combine two languages to formulate a sentence, it gives rise to various research problems related to Natural Language Processing. In this paper, we try to excavate one such problem, namely, Parts of Speech tagging of code-mixed texts. We have built a system that can POS tag English-Bengali code-mixed data where the Bengali words were written in Roman script. Our approach initially involves the collection and cleaning of English-Bengali code-mixed tweets. These tweets were used as a development dataset for building our system. The proposed system is a modular approach that starts by tagging individual tokens with their respective languages and then passes them to different POS taggers, designed for different languages (English and Bengali, in our case). Tags given by the two systems are later joined together and the final result is then mapped to a universal POS tag set. Our system was checked using 100 manually POS tagged code-mixed sentences and it returned an accuracy of 75.29%
FIVES: Feature Interaction Via Edge Search for Large-Scale Tabular Data
Authors: Yuexiang Xie, Zhen Wang, Yaliang Li, Bolin Ding, Nezihe Merve Gürel, Ce Zhang, Minlie Huang, Wei. Lin, Jingren Zhou
Date: 2020-07-29 00:00:00
Tasks: Graph Neural Network, Recommendation Systems

High-order interactive features capture the correlation between different columns and thus are promising to enhance various learning tasks on ubiquitous tabular data. To automate the generation of interactive features, existing works either explicitly traverse the feature space or implicitly express the interactions via intermediate activations of some designed models. These two kinds of methods show that there is essentially a trade-off between feature interpretability and search efficiency. To possess both of their merits, we propose a novel method named Feature Interaction Via Edge Search (FIVES), which formulates the task of interactive feature generation as searching for edges on the defined feature graph. Specifically, we first present our theoretical evidence that motivates us to search for useful interactive features with increasing order. Then we instantiate this search strategy by optimizing both a dedicated graph neural network (GNN) and the adjacency tensor associated with the defined feature graph. In this way, the proposed FIVES method simplifies the time-consuming traversal as a typical training course of GNN and enables explicit feature generation according to the learned adjacency tensor. Experimental results on both benchmark and real-world datasets show the advantages of FIVES over several state-of-the-art methods. Moreover, the interactive features identified by FIVES are deployed on the recommender system of Taobao, a worldwide leading e-commerce platform. Results of an online A/B testing further verify the effectiveness of the proposed method FIVES, and we further provide FIVES as AI utilities for the customers of Alibaba Cloud.
Realistic Video Summarization through VISIOCITY: A New Benchmark and Evaluation Framework
Authors: Vishal Kaushal, Suraj Kothawade, Rishabh Iyer, Ganesh Ramakrishnan
Date: 2020-07-29 00:00:00
Tasks: Benchmarking, Video Summarization

Automatic video summarization is still an unsolved problem due to several challenges. We take steps towards making automatic video summarization more realistic by addressing them. Firstly, the currently available datasets either have very short videos or have few long videos of only a particular type. We introduce a new benchmarking dataset VISIOCITY which comprises of longer videos across six different categories with dense concept annotations capable of supporting different flavors of video summarization and can be used for other vision problems. Secondly, for long videos, human reference summaries are difficult to obtain. We present a novel recipe based on pareto optimality to automatically generate multiple reference summaries from indirect ground truth present in VISIOCITY. We show that these summaries are at par with human summaries. Thirdly, we demonstrate that in the presence of multiple ground truth summaries (due to the highly subjective nature of the task), learning from a single combined ground truth summary using a single loss function is not a good idea. We propose a simple recipe VISIOCITY-SUM to enhance an existing model using a combination of losses and demonstrate that it beats the current state of the art techniques when tested on VISIOCITY. We also show that a single measure to evaluate a summary, as is the current typical practice, falls short. We propose a framework for better quantitative assessment of summary quality which is closer to human judgment than a single measure, say F1. We report the performance of a few representative techniques of video summarization on VISIOCITY assessed using various measures and bring out the limitation of the techniques and/or the assessment mechanism in modeling human judgment and demonstrate the effectiveness of our evaluation framework in doing so.
BiTraP: Bi-directional Pedestrian Trajectory Prediction with Multi-modal Goal Estimation
Authors: Yu Yao, Ella Atkins, Matthew Johnson-Roberson, Ram Vasudevan, Xiaoxiao Du
Date: 2020-07-29 00:00:00
Tasks: Autonomous Driving, Collision Avoidance, Decoder, Multi-future Trajectory Prediction, Pedestrian Trajectory Prediction, Prediction, Robot Navigation, Trajectory Prediction

Pedestrian trajectory prediction is an essential task in robotic applications such as autonomous driving and robot navigation. State-of-the-art trajectory predictors use a conditional variational autoencoder (CVAE) with recurrent neural networks (RNNs) to encode observed trajectories and decode multi-modal future trajectories. This process can suffer from accumulated errors over long prediction horizons (>=2 seconds). This paper presents BiTraP, a goal-conditioned bi-directional multi-modal trajectory prediction method based on the CVAE. BiTraP estimates the goal (end-point) of trajectories and introduces a novel bi-directional decoder to improve longer-term trajectory prediction accuracy. Extensive experiments show that BiTraP generalizes to both first-person view (FPV) and bird's-eye view (BEV) scenarios and outperforms state-of-the-art results by ~10-50%. We also show that different choices of non-parametric versus parametric target models in the CVAE directly influence the predicted multi-modal trajectory distributions. These results provide guidance on trajectory predictor design for robotic applications such as collision avoidance and navigation systems.
Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking
Authors: Jinlong Peng, Changan Wang, Fangbin Wan, Yang Wu, Yabiao Wang, Ying Tai, Chengjie Wang, Jilin Li, Feiyue Huang, Yanwei Fu
Date: 2020-07-29 00:00:00
Proceeding: ECCV 2020 8
Tasks: Multiple Object Tracking, Object, object-detection, Object Detection, Object Tracking, regression

Existing Multiple-Object Tracking (MOT) methods either follow the tracking-by-detection paradigm to conduct object detection, feature extraction and data association separately, or have two of the three subtasks integrated to form a partially end-to-end solution. Going beyond these sub-optimal frameworks, we propose a simple online model named Chained-Tracker (CTracker), which naturally integrates all the three subtasks into an end-to-end solution (the first as far as we know). It chains paired bounding boxes regression results estimated from overlapping nodes, of which each node covers two adjacent frames. The paired regression is made attentive by object-attention (brought by a detection module) and identity-attention (ensured by an ID verification module). The two major novelties: chained structure and paired attentive regression, make CTracker simple, fast and effective, setting new MOTA records on MOT16 and MOT17 challenge datasets (67.6 and 66.6, respectively), without relying on any extra training data. The source code of CTracker can be found at: github.com/pjl1995/CTracker.
Accurate Lung Nodules Segmentation with Detailed Representation Transfer and Soft Mask Supervision
Authors: Changwei Wang, Rongtao Xu, Shibiao Xu, Weiliang Meng, Jun Xiao, Xiaopeng Zhang
Date: 2020-07-29 00:00:00
Tasks: Computed Tomography (CT), Lesion Segmentation, Lung Nodule Segmentation, Segmentation, Super-Resolution

Accurate lung lesion segmentation from Computed Tomography (CT) images is crucial to the analysis and diagnosis of lung diseases such as COVID-19 and lung cancer. However, the smallness and variety of lung nodules and the lack of high-quality labeling make the accurate lung nodule segmentation difficult. To address these issues, we first introduce a novel segmentation mask named Soft Mask which has richer and more accurate edge details description and better visualization and develop a universal automatic Soft Mask annotation pipeline to deal with different datasets correspondingly. Then, a novel Network with detailed representation transfer and Soft Mask supervision (DSNet) is proposed to process the input low-resolution images of lung nodules into high-quality segmentation results. Our DSNet contains a special Detail Representation Transfer Module (DRTM) for reconstructing the detailed representation to alleviate the small size of lung nodules images, and an adversarial training framework with Soft Mask for further improving the accuracy of segmentation. Extensive experiments validate that our DSNet outperforms other state-of-the-art methods for accurate lung nodule segmentation and has strong generalization ability in other accurate medical segmentation tasks with competitive results. Besides, we provide a new challenging lung nodules segmentation dataset for further studies.
Compare and Select: Video Summarization with Multi-Agent Reinforcement Learning
Authors: Tianyu Liu
Date: 2020-07-29 00:00:00
Tasks: Decision Making, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making, Video Summarization

Video summarization aims at generating concise video summaries from the lengthy videos, to achieve better user watching experience. Due to the subjectivity, purely supervised methods for video summarization may bring the inherent errors from the annotations. To solve the subjectivity problem, we study the general user summarization process. General users usually watch the whole video, compare interesting clips and select some clips to form a final summary. Inspired by the general user behaviours, we formulate the summarization process as multiple sequential decision-making processes, and propose Comparison-Selection Network (CoSNet) based on multi-agent reinforcement learning. Each agent focuses on a video clip and constantly changes its focus during the iterations, and the final focus clips of all agents form the summary. The comparison network provides the agent with the visual feature from clips and the chronological feature from the past round, while the selection network of the agent makes decisions on the change of its focus clip. The specially designed unsupervised reward and supervised reward together contribute to the policy advancement, each containing local and global parts. Extensive experiments on two benchmark datasets show that CoSNet outperforms state-of-the-art unsupervised methods with the unsupervised reward and surpasses most supervised methods with the complete reward.
An Index-based Deterministic Asymptotically Optimal Algorithm for Constrained Multi-armed Bandit Problems
Authors: Hyeong Soo Chang
Date: 2020-07-29 00:00:00

For the model of constrained multi-armed bandit, we show that by construction there exists an index-based deterministic asymptotically optimal algorithm. The optimality is achieved by the convergence of the probability of choosing an optimal feasible arm to one over infinite horizon. The algorithm is built upon Locatelli et al.'s "anytime parameter-free thresholding" algorithm under the assumption that the optimal value is known. We provide a finite-time bound to the probability of the asymptotic optimality given as 1-O(|A|Te^{-T}) where T is the horizon size and A is the set of the arms in the bandit. We then study a relaxed-version of the algorithm in a general form that estimates the optimal value and discuss the asymptotic optimality of the algorithm after a sufficiently large T with examples.
Online Visual Place Recognition via Saliency Re-identification
Authors: Han Wang, Chen Wang, Lihua Xie
Date: 2020-07-29 00:00:00
Tasks: Autonomous Driving, Robot Navigation, Saliency Detection, Simultaneous Localization and Mapping, Visual Place Recognition

As an essential component of visual simultaneous localization and mapping (SLAM), place recognition is crucial for robot navigation and autonomous driving. Existing methods often formulate visual place recognition as feature matching, which is computationally expensive for many robotic applications with limited computing power, e.g., autonomous driving and cleaning robot. Inspired by the fact that human beings always recognize a place by remembering salient regions or landmarks that are more attractive or interesting than others, we formulate visual place recognition as saliency re-identification. In the meanwhile, we propose to perform both saliency detection and re-identification in frequency domain, in which all operations become element-wise. The experiments show that our proposed method achieves competitive accuracy and much higher speed than the state-of-the-art feature-based methods. The proposed method is open-sourced and available at https://github.com/wh200720041/SRLCD.git.
MLR-SNet: Transferable LR Schedules for Heterogeneous Tasks
Authors: Jun Shu, Yanwen Zhu, Qian Zhao, Zongben Xu, Deyu Meng
Date: 2020-07-29 00:00:00
Tasks: text-classification, Text Classification

The learning rate (LR) is one of the most important hyper-parameters in stochastic gradient descent (SGD) algorithm for training deep neural networks (DNN). However, current hand-designed LR schedules need to manually pre-specify a fixed form, which limits their ability to adapt practical non-convex optimization problems due to the significant diversification of training dynamics. Meanwhile, it always needs to search proper LR schedules from scratch for new tasks, which, however, are often largely different with task variations, like data modalities, network architectures, or training data capacities. To address this learning-rate-schedule setting issues, we propose to parameterize LR schedules with an explicit mapping formulation, called \textit{MLR-SNet}. The learnable parameterized structure brings more flexibility for MLR-SNet to learn a proper LR schedule to comply with the training dynamics of DNN. Image and text classification benchmark experiments substantiate the capability of our method for achieving proper LR schedules. Moreover, the explicit parameterized structure makes the meta-learned LR schedules capable of being transferable and plug-and-play, which can be easily generalized to new heterogeneous tasks. We transfer our meta-learned MLR-SNet to query tasks like different training epochs, network architectures, data modalities, dataset sizes from the training ones, and achieve comparable or even better performance compared with hand-designed LR schedules specifically designed for the query tasks. The robustness of MLR-SNet is also substantiated when the training data are biased with corrupted noise. We further prove the convergence of the SGD algorithm equipped with LR schedule produced by our MLR-Net, with the convergence rate comparable to the best-known ones of the algorithm for solving the problem.
Truncated Linear Regression in High Dimensions
Authors: Constantinos Daskalakis, Dhruv Rohatgi, Manolis Zampetakis
Date: 2020-07-29 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: compressed sensing, regression, Vocal Bursts Intensity Prediction

As in standard linear regression, in truncated linear regression, we are given access to observations $(A_i, y_i)_i$ whose dependent variable equals $y_i= A_i^{\rm T} \cdot x^* + \eta_i$, where $x^*$ is some fixed unknown vector of interest and $\eta_i$ is independent noise; except we are only given an observation if its dependent variable $y_i$ lies in some "truncation set" $S \subset \mathbb{R}$. The goal is to recover $x^*$ under some favorable conditions on the $A_i$'s and the noise distribution. We prove that there exists a computationally and statistically efficient method for recovering $k$-sparse $n$-dimensional vectors $x^*$ from $m$ truncated samples, which attains an optimal $\ell_2$ reconstruction error of $O(\sqrt{(k \log n)/m})$. As a corollary, our guarantees imply a computationally efficient and information-theoretically optimal algorithm for compressed sensing with truncation, which may arise from measurement saturation effects. Our result follows from a statistical and computational analysis of the Stochastic Gradient Descent (SGD) algorithm for solving a natural adaptation of the LASSO optimization problem that accommodates truncation. This generalizes the works of both: (1) [Daskalakis et al. 2018], where no regularization is needed due to the low-dimensionality of the data, and (2) [Wainright 2009], where the objective function is simple due to the absence of truncation. In order to deal with both truncation and high-dimensionality at the same time, we develop new techniques that not only generalize the existing ones but we believe are of independent interest.