pwc-archive/papers-with-abstracts huggingface dataset:
Self-supervised Learning of Point Clouds via Orientation Estimation
Authors: Omid Poursaeed, Tianxing Jiang, Han Qiao, Nayun Xu, Vladimir G. Kim
Date: 2020-08-01 00:00:00
Tasks: 3D Point Cloud Linear Classification, Self-Supervised Learning, Unsupervised 3D Point Cloud Linear Evaluation

Point clouds provide a compact and efficient representation of 3D shapes. While deep neural networks have achieved impressive results on point cloud learning tasks, they require massive amounts of manually labeled data, which can be costly and time-consuming to collect. In this paper, we leverage 3D self-supervision for learning downstream tasks on point clouds with fewer labels. A point cloud can be rotated in infinitely many ways, which provides a rich label-free source for self-supervision. We consider the auxiliary task of predicting rotations that in turn leads to useful features for other tasks such as shape classification and 3D keypoint prediction. Using experiments on ShapeNet and ModelNet, we demonstrate that our approach outperforms the state-of-the-art. Moreover, features learned by our model are complementary to other self-supervised methods and combining them leads to further performance improvement.
SemEval-2020 Task 7: Assessing Humor in Edited News Headlines
Authors: Nabil Hossain, John Krumm, Michael Gamon, Henry Kautz
Date: 2020-08-01 00:00:00
Proceeding: SEMEVAL 2020

This paper describes the SemEval-2020 shared task "Assessing Humor in Edited News Headlines." The task's dataset contains news headlines in which short edits were applied to make them funny, and the funniness of these edited headlines was rated using crowdsourcing. This task includes two subtasks, the first of which is to estimate the funniness of headlines on a humor scale in the interval 0-3. The second subtask is to predict, for a pair of edited versions of the same original headline, which is the funnier version. To date, this task is the most popular shared computational humor task, attracting 48 teams for the first subtask and 31 teams for the second.
Accurate and Efficient Intracranial Hemorrhage Detection and Subtype Classification in 3D CT Scans with Convolutional and Long Short-Term Memory Neural Networks
Authors: Mihail Burduja, Radu Tudor Ionescu, Nicolae Verga
Date: 2020-08-01 00:00:00
Tasks: feature selection

In this paper, we present our system for the RSNA Intracranial Hemorrhage Detection challenge. The proposed system is based on a lightweight deep neural network architecture composed of a convolutional neural network (CNN) that takes as input individual CT slices, and a Long Short-Term Memory (LSTM) network that takes as input feature embeddings provided by the CNN. For efficient processing, we consider various feature selection methods to produce a subset of useful CNN features for the LSTM. Furthermore, we reduce the CT slices by a factor of 2x, allowing ourselves to train the model faster. Even if our model is designed to balance speed and accuracy, we report a weighted mean log loss of 0.04989 on the final test set, which places us in the top 30 ranking (2%) from a total of 1345 participants. Although our computing infrastructure does not allow it, processing CT slices at their original scale is likely to improve performance. In order to enable others to reproduce our results, we provide our code as open source at https://github.com/warchildmd/ihd. After the challenge, we conducted a subjective intracranial hemorrhage detection assessment by radiologists, indicating that the performance of our deep model is on par with that of doctors specialized in reading CT scans. Another contribution of our work is to integrate Grad-CAM visualizations in our system, providing useful explanations for its predictions. We therefore consider our system as a viable option when a fast diagnosis or a second opinion on intracranial hemorrhage detection are needed.
Eigen-CAM: Class Activation Map using Principal Components
Authors: Mohammed Bany Muhammad, Mohammed Yeasin
Date: 2020-08-01 00:00:00
Tasks: Object Localization, Weakly-Supervised Object Localization

Deep neural networks are ubiquitous due to the ease of developing models and their influence on other domains. At the heart of this progress is convolutional neural networks (CNNs) that are capable of learning representations or features given a set of data. Making sense of such complex models (i.e., millions of parameters and hundreds of layers) remains challenging for developers as well as the end-users. This is partially due to the lack of tools or interfaces capable of providing interpretability and transparency. A growing body of literature, for example, class activation map (CAM), focuses on making sense of what a model learns from the data or why it behaves poorly in a given task. This paper builds on previous ideas to cope with the increasing demand for interpretable, robust, and transparent models. Our approach provides a simpler and intuitive (or familiar) way of generating CAM. The proposed Eigen-CAM computes and visualizes the principle components of the learned features/representations from the convolutional layers. Empirical studies were performed to compare the Eigen-CAM with the state-of-the-art methods (such as Grad-CAM, Grad-CAM++, CNN-fixations) by evaluating on benchmark datasets such as weakly-supervised localization and localizing objects in the presence of adversarial noise. Eigen-CAM was found to be robust against classification errors made by fully connected layers in CNNs, does not rely on the backpropagation of gradients, class relevance score, maximum activation locations, or any other form of weighting features. In addition, it works with all CNN models without the need to modify layers or retrain models. Empirical results show up to 12% improvement over the best method among the methods compared on weakly supervised object localization.
The Price of Tailoring the Index to Your Data: Poisoning Attacks on Learned Index Structures
Authors: Evgenios M. Kornaropoulos, Silei Ren, Roberto Tamassia
Date: 2020-08-01 00:00:00
Tasks: BIG-bench Machine Learning, Data Poisoning

The concept of learned index structures relies on the idea that the input-output functionality of a database index can be viewed as a prediction task and, thus, be implemented using a machine learning model instead of traditional algorithmic techniques. This novel angle for a decades-old problem has inspired numerous exciting results in the intersection of machine learning and data structures. However, the main advantage of learned index structures, i.e., the ability to adjust to the data at hand via the underlying ML-model, can become a disadvantage from a security perspective as it could be exploited. In this work, we present the first study of poisoning attacks on learned index structures. The required poisoning approach is different from all previous works since the model under attack is trained on a cumulative distribution function (CDF) and, thus, every injection on the training set has a cascading impact on multiple data values. We formulate the first poisoning attacks on linear regression models trained on the CDF, which is a basic building block of the proposed learned index structures. We generalize our poisoning techniques to attack a more advanced two-stage design of learned index structures called recursive model index (RMI), which has been shown to outperform traditional B-Trees. We evaluate our attacks on real-world and synthetic datasets under a wide variety of parameterizations of the model and show that the error of the RMI increases up to $300\times$ and the error of its second-stage models increases up to $3000\times$.
The test set for the TransCoder system
Authors: Ernest Davis
Date: 2020-08-01 00:00:00

The TransCoder system translates source code between Java, C++, and Python 3. The test set that was used to evaluate its quality is missing important features of Java, including the ability to define and use classes and the ability to call user-defined functions other than recursively. Therefore, the accuracy of TransCoder over programs with those features remains unknown.
An Empirical Study of Clarifying Question-Based Systems
Authors: Jie Zou, Evangelos Kanoulas, Yiqun Liu
Date: 2020-08-01 00:00:00
Tasks: Recommendation Systems

Search and recommender systems that take the initiative to ask clarifying questions to better understand users' information needs are receiving increasing attention from the research community. However, to the best of our knowledge, there is no empirical study to quantify whether and to what extent users are willing or able to answer these questions. In this work, we conduct an online experiment by deploying an experimental system, which interacts with users by asking clarifying questions against a product repository. We collect both implicit interaction behavior data and explicit feedback from users showing that: (a) users are willing to answer a good number of clarifying questions (11-21 on average), but not many more than that; (b) most users answer questions until they reach the target product, but also a fraction of them stops due to fatigue or due to receiving irrelevant questions; (c) part of the users' answers (12-17%) are actually opposite to the description of the target product; while (d) most of the users (66-84%) find the question-based system helpful towards completing their tasks. Some of the findings of the study contradict current assumptions on simulated evaluations in the field, while they point towards improvements in the evaluation framework and can inspire future interactive search/recommender system designs.
From Shadow Segmentation to Shadow Removal
Authors: Hieu Le, Dimitris Samaras
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Diversity, Segmentation, Shadow Removal

The requirement for paired shadow and shadow-free images limits the size and diversity of shadow removal datasets and hinders the possibility of training large-scale, robust shadow removal algorithms. We propose a shadow removal method that can be trained using only shadow and non-shadow patches cropped from the shadow images themselves. Our method is trained via an adversarial framework, following a physical model of shadow formation. Our central contribution is a set of physics-based constraints that enables this adversarial training. Our method achieves competitive shadow removal results compared to state-of-the-art methods that are trained with fully paired shadow and shadow-free images. The advantages of our training regime are even more pronounced in shadow removal for videos. Our method can be fine-tuned on a testing video with only the shadow masks generated by a pre-trained shadow detector and outperforms state-of-the-art methods on this challenging test. We illustrate the advantages of our method on our proposed video shadow removal dataset.
Distilling Visual Priors from Self-Supervised Learning
Authors: Bingchen Zhao, Xin Wen
Date: 2020-08-01 00:00:00
Tasks: Classification, Contrastive Learning, General Classification, image-classification, Image Classification, Knowledge Distillation, Self-Supervised Learning

Convolutional Neural Networks (CNNs) are prone to overfit small training datasets. We present a novel two-phase pipeline that leverages self-supervised learning and knowledge distillation to improve the generalization ability of CNN models for image classification under the data-deficient setting. The first phase is to learn a teacher model which possesses rich and generalizable visual representations via self-supervised learning, and the second phase is to distill the representations into a student model in a self-distillation manner, and meanwhile fine-tune the student model for the image classification task. We also propose a novel margin loss for the self-supervised contrastive learning proxy task to better learn the representation under the data-deficient scenario. Together with other tricks, we achieve competitive performance in the VIPriors image classification challenge.
Deep Reinforcement Learning Based Mobile Edge Computing for Intelligent Internet of Things
Authors: Rui Zhao, Xinjie Wang, Junjuan Xia, Liseng Fan
Date: 2020-08-01 00:00:00
Tasks: Deep Reinforcement Learning, Edge-computing, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we investigate mobile edge computing (MEC) networks for intelligent internet of things (IoT), where multiple users have some computational tasks assisted by multiple computational access points (CAPs). By offloading some tasks to the CAPs, the system performance can be improved through reducing the latency and energy consumption, which are the two important metrics of interest in the MEC networks. We devise the system by proposing the offloading strategy intelligently through the deep reinforcement learning algorithm. In this algorithm, Deep Q-Network is used to automatically learn the offloading decision in order to optimize the system performance, and a neural network (NN) is trained to predict the offloading action, where the training data is generated from the environmental system. Moreover, we employ the bandwidth allocation in order to optimize the wireless spectrum for the links between the users and CAPs, where several bandwidth allocation schemes are proposed. In further, we use the CAP selection in order to choose one best CAP to assist the computational tasks from the users. Simulation results are finally presented to show the effectiveness of the proposed reinforcement learning offloading strategy. In particular, the system cost of latency and energy consumption can be reduced significantly by the proposed deep reinforcement learning based algorithm.
Meta-DRN: Meta-Learning for 1-Shot Image Segmentation
Authors: Atmadeep Banerjee
Date: 2020-08-01 00:00:00
Tasks: Few-Shot Learning, image-classification, Image Classification, Image Segmentation, Meta-Learning, Segmentation, Semantic Segmentation

Modern deep learning models have revolutionized the field of computer vision. But, a significant drawback of most of these models is that they require a large number of labelled examples to generalize properly. Recent developments in few-shot learning aim to alleviate this requirement. In this paper, we propose a novel lightweight CNN architecture for 1-shot image segmentation. The proposed model is created by taking inspiration from well-performing architectures for semantic segmentation and adapting it to the 1-shot domain. We train our model using 4 meta-learning algorithms that have worked well for image classification and compare the results. For the chosen dataset, our proposed model has a 70% lower parameter count than the benchmark, while having better or comparable mean IoU scores using all 4 of the meta-learning algorithms.
Exploring Multi-Scale Feature Propagation and Communication for Image Super Resolution
Authors: Ruicheng Feng, Weipeng Guan, Yu Qiao, Chao Dong
Date: 2020-08-01 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Multi-scale techniques have achieved great success in a wide range of computer vision tasks. However, while this technique is incorporated in existing works, there still lacks a comprehensive investigation on variants of multi-scale convolution in image super resolution. In this work, we present a unified formulation over widely-used multi-scale structures. With this framework, we systematically explore the two factors of multi-scale convolution -- feature propagation and cross-scale communication. Based on the investigation, we propose a generic and efficient multi-scale convolution unit -- Multi-Scale cross-Scale Share-weights convolution (MS$^3$-Conv). Extensive experiments demonstrate that the proposed MS$^3$-Conv can achieve better SR performance than the standard convolution with less parameters and computational cost. Beyond quantitative analysis, we comprehensively study the visual quality, which shows that MS$^3$-Conv behave better to recover high-frequency details.
An Explainable Machine Learning Model for Early Detection of Parkinson's Disease using LIME on DaTscan Imagery
Authors: Pavan Rajkumar Magesh, Richard Delwin Myloth, Rijo Jackson Tom
Date: 2020-08-01 00:00:00
Tasks: BIG-bench Machine Learning, Specificity, Superpixels, Transfer Learning

Parkinson's disease (PD) is a degenerative and progressive neurological condition. Early diagnosis can improve treatment for patients and is performed through dopaminergic imaging techniques like the SPECT DaTscan. In this study, we propose a machine learning model that accurately classifies any given DaTscan as having Parkinson's disease or not, in addition to providing a plausible reason for the prediction. This is kind of reasoning is done through the use of visual indicators generated using Local Interpretable Model-Agnostic Explainer (LIME) methods. DaTscans were drawn from the Parkinson's Progression Markers Initiative database and trained on a CNN (VGG16) using transfer learning, yielding an accuracy of 95.2%, a sensitivity of 97.5%, and a specificity of 90.9%. Keeping model interpretability of paramount importance, especially in the healthcare field, this study utilises LIME explanations to distinguish PD from non-PD, using visual superpixels on the DaTscans. It could be concluded that the proposed system, in union with its measured interpretability and accuracy may effectively aid medical workers in the early diagnosis of Parkinson's Disease.
Two-step penalised logistic regression for multi-omic data with an application to cardiometabolic syndrome
Authors: Alessandra Cabassi, Denis Seyres, Mattia Frontini, Paul D. W. Kirk
Date: 2020-08-01 00:00:00
Tasks: regression, Variable Selection

Building classification models that predict a binary class label on the basis of high dimensional multi-omics datasets poses several challenges, due to the typically widely differing characteristics of the data layers in terms of number of predictors, type of data, and levels of noise. Previous research has shown that applying classical logistic regression with elastic-net penalty to these datasets can lead to poor results (Liu et al., 2018). We implement a two-step approach to multi-omic logistic regression in which variable selection is performed on each layer separately and a predictive model is then built using the variables selected in the first step. Here, our approach is compared to other methods that have been developed for the same purpose, and we adapt existing software for multi-omic linear regression (Zhao and Zucknick, 2020) to the logistic regression setting. Extensive simulation studies show that our approach should be preferred if the goal is to select as many relevant predictors as possible, as well as achieving prediction performances comparable to those of the best competitors. Our motivating example is a cardiometabolic syndrome dataset comprising eight 'omic data types for 2 extreme phenotype groups (10 obese and 10 lipodystrophy individuals) and 185 blood donors. Our proposed approach allows us to identify features that characterise cardiometabolic syndrome at the molecular level. R code is available at https://github.com/acabassi/logistic-regression-for-multi-omic-data.
Ergodic Annealing
Authors: Carlo Baldassi, Fabio Maccheroni, Massimo Marinacci, Marco Pirazzini
Date: 2020-08-01 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Simulated Annealing is the crowning glory of Markov Chain Monte Carlo Methods for the solution of NP-hard optimization problems in which the cost function is known. Here, by replacing the Metropolis engine of Simulated Annealing with a reinforcement learning variation -- that we call Macau Algorithm -- we show that the Simulated Annealing heuristic can be very effective also when the cost function is unknown and has to be learned by an artificial agent.
RGB-D Salient Object Detection: A Survey
Authors: Tao Zhou, Deng-Ping Fan, Ming-Ming Cheng, Jianbing Shen, Ling Shao
Date: 2020-08-01 00:00:00
Tasks: Attribute, Object, object-detection, Object Detection, RGB-D Salient Object Detection, RGB Salient Object Detection, Salient Object Detection, Survey

Salient object detection (SOD), which simulates the human visual perception system to locate the most attractive object(s) in a scene, has been widely applied to various computer vision tasks. Now, with the advent of depth sensors, depth maps with affluent spatial information that can be beneficial in boosting the performance of SOD, can easily be captured. Although various RGB-D based SOD models with promising performance have been proposed over the past several years, an in-depth understanding of these models and challenges in this topic remains lacking. In this paper, we provide a comprehensive survey of RGB-D based SOD models from various perspectives, and review related benchmark datasets in detail. Further, considering that the light field can also provide depth maps, we review SOD models and popular benchmark datasets from this domain as well. Moreover, to investigate the SOD ability of existing models, we carry out a comprehensive evaluation, as well as attribute-based evaluation of several representative RGB-D based SOD models. Finally, we discuss several challenges and open directions of RGB-D based SOD for future research. All collected models, benchmark datasets, source code links, datasets constructed for attribute-based evaluation, and codes for evaluation will be made publicly available at https://github.com/taozh2017/RGBDSODsurvey
Regularization by Denoising via Fixed-Point Projection (RED-PRO)
Authors: Regev Cohen, Michael Elad, Peyman Milanfar
Date: 2020-08-01 00:00:00
Tasks: Deblurring, Denoising, Image Deblurring, Super-Resolution

Inverse problems in image processing are typically cast as optimization tasks, consisting of data-fidelity and stabilizing regularization terms. A recent regularization strategy of great interest utilizes the power of denoising engines. Two such methods are the Plug-and-Play Prior (PnP) and Regularization by Denoising (RED). While both have shown state-of-the-art results in various recovery tasks, their theoretical justification is incomplete. In this paper, we aim to bridge between RED and PnP, enriching the understanding of both frameworks. Towards that end, we reformulate RED as a convex optimization problem utilizing a projection (RED-PRO) onto the fixed-point set of demicontractive denoisers. We offer a simple iterative solution to this problem, by which we show that PnP proximal gradient method is a special case of RED-PRO, while providing guarantees for the convergence of both frameworks to globally optimal solutions. In addition, we present relaxations of RED-PRO that allow for handling denoisers with limited fixed-point sets. Finally, we demonstrate RED-PRO for the tasks of image deblurring and super-resolution, showing improved results with respect to the original RED framework.
Unsupervised Deep Cross-modality Spectral Hashing
Authors: Tuan Hoang, Thanh-Toan Do, Tam V. Nguyen, Ngai-Man Cheung
Date: 2020-08-01 00:00:00
Tasks: Cross-Modal Retrieval, Retrieval, Word Embeddings

This paper presents a novel framework, namely Deep Cross-modality Spectral Hashing (DCSH), to tackle the unsupervised learning problem of binary hash codes for efficient cross-modal retrieval. The framework is a two-step hashing approach which decouples the optimization into (1) binary optimization and (2) hashing function learning. In the first step, we propose a novel spectral embedding-based algorithm to simultaneously learn single-modality and binary cross-modality representations. While the former is capable of well preserving the local structure of each modality, the latter reveals the hidden patterns from all modalities. In the second step, to learn mapping functions from informative data inputs (images and word embeddings) to binary codes obtained from the first step, we leverage the powerful CNN for images and propose a CNN-based deep architecture to learn text modality. Quantitative evaluations on three standard benchmark datasets demonstrate that the proposed DCSH method consistently outperforms other state-of-the-art methods.