pwc-archive/papers-with-abstracts huggingface dataset:
Confidence Score for Unsupervised Foreground Background Separation of Document Images
Authors: Soumyadeep Dey, Pratik Jawanpuria
Date: 2022-04-03 00:00:00
Tasks: Binarization

Foreground-background separation is an important problem in document image analysis. Popular unsupervised binarization methods (such as the Sauvola's algorithm) employ adaptive thresholding to classify pixels as foreground or background. In this work, we propose a novel approach for computing confidence scores of the classification in such algorithms. This score provides an insight of the confidence level of the prediction. The computational complexity of the proposed approach is the same as the underlying binarization algorithm. Our experiments illustrate the utility of the proposed scores in various applications like document binarization, document image cleanup, and texture addition.
Multi-Depth Fusion Network for Whole-Heart CT Image Segmentation
Authors: Chengqin Ye, Wei Wang, Shanzhuo Zhang, Kuanquan Wang
Date: 2019-02-15 00:00:00
Proceeding: IEEE Access 2019 2
Tasks: Computed Tomography (CT), Heart Segmentation, Image Segmentation, Segmentation, Semantic Segmentation

Obtaining precise whole-heart segmentation from computed tomography (CT) or other imaging
techniques is prerequisite to clinically analyze the cardiac status, which plays an important role in the
treatment of cardiovascular diseases. However, the whole-heart segmentation is still a challenging task due
to the characteristic of medical images, such as far more background voxels than foreground voxels and
the indistinct boundaries of adjacent tissues. In this paper, we first present a new deeply supervised 3D
UNET which applies multi-depth fusion to the original network for a better extract context information.
Then, we apply focal loss to the field of image segmentation and expand its application to multi-category
tasks. Finally, the focal loss is incorporated into the Dice loss function (which can be used to solve category
imbalance problem) to form a new loss function, which we call hybrid loss. We evaluate our new pipeline
on the MICCAI 2017 whole-heart CT dataset, and it obtains a Dice score of 90.73%, which is better than
most of the state-of-the-art methods.
Fast-Spherical-Projection-Based Point Cloud Clustering Algorithm
Authors: Zhihui Chen, Hao Xu, Junxuan Zhao, and Hongchao Liu
Date: 2022-02-05 00:00:00
Proceeding: 02/05 2022 2
Tasks: Autonomous Driving, Clustering, vehicle detection

Abstract
Roadside LiDAR (light detection and ranging) is a solution to fill in the gaps for connected vehicles (CV) by detecting the sta-
tus of global road users at transportation facilities. It relies greatly on the clustering algorithm for accurate and rapid data pro-
cessing so as to ensure effectiveness and reliability. To contribute to better roadside LiDAR-based transportation facilities,
this paper presents a fast-spherical-projection-based clustering algorithm (FSPC) for real-time LiDAR data processing with
higher clustering accuracy and noise handling. The FSPC is designed to work on a spherical map which could be directly
derived from the instant returns of a LiDAR sensor. A 2D-window searching strategy is specifically designed to accelerate the
computation and alleviate the density variation impact in the LiDAR point cloud. The test results show the proposed algo-
rithm can achieve a high processing efficiency with 24.4ms per frame, satisfying the real-time requirement for most common
LiDAR applications (100 ms per frame), and it also ensures a high accuracy in object clustering, with 96%. Additionally, it is
observed that the proposed FSPC allows a wider detection range and is more stable, tackling the surge in foreground points
that frequently occurs in roadside LiDAR applications. Finally, the generality of the proposed FSPC indicates the proposed
algorithm could also be implemented in other areas such as autonomous driving and remote sensing.
Keywords
data and data science, automatic vehicle detection and identification systems, computer vision, data science, remote sensing,
vehicle detection
Connected-vehicle (CV) technology is an emerging tech-
nology to reduce crashes and increase energy efficiency
in the transportation system, one which enables bi-
directional communications between infrastructure and
ELECTRIcity: An Efficient Transformer for Non-Intrusive Load Monitoring
Authors: Stavros Sykiotis, Maria Kaselimi, Anastasios Doulamis, Nikolaos Doulamis
Date: 2022-04-11 00:00:00
Proceeding: MDPI Sensors 2022 4
Tasks: Non-Intrusive Load Monitoring, Unsupervised Pre-training

Non-Intrusive Load Monitoring (NILM) describes the process of inferring the consumption pattern of appliances by only having access to the aggregated household signal. Sequence-to-sequence deep learning models have been firmly established as state-of-the-art approaches for NILM, in an attempt to identify the pattern of the appliance power consumption signal into the aggregated power signal. Exceeding the limitations of recurrent models that have been widely used in sequential modeling, this paper proposes a transformer-based architecture for NILM. Our approach, called ELECTRIcity, utilizes transformer layers to accurately estimate the power signal of domestic appliances by relying entirely on attention mechanisms to extract global dependencies between the aggregate and the domestic appliance signals. Another additive value of the proposed model is that ELECTRIcity works with minimal dataset pre-processing and without requiring data balancing. Furthermore, ELECTRIcity introduces an efficient training routine compared to other traditional transformer-based architectures. According to this routine, ELECTRIcity splits model training into unsupervised pre-training and downstream task fine-tuning, which yields performance increases in both predictive accuracy and training time decrease. Experimental results indicate ELECTRIcity’s superiority compared to several state-of-the-art methods.
FOSTER: Feature Boosting and Compression for Class-Incremental Learning
Authors: Fu-Yun Wang, Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan
Date: 2022-04-10 00:00:00
Tasks: class-incremental learning, Class Incremental Learning, Incremental Learning

The ability to learn new concepts continually is necessary in this ever-changing world. However, deep neural networks suffer from catastrophic forgetting when learning new categories. Many works have been proposed to alleviate this phenomenon, whereas most of them either fall into the stability-plasticity dilemma or take too much computation or storage overhead. Inspired by the gradient boosting algorithm to gradually fit the residuals between the target model and the previous ensemble model, we propose a novel two-stage learning paradigm FOSTER, empowering the model to learn new categories adaptively. Specifically, we first dynamically expand new modules to fit the residuals between the target and the output of the original model. Next, we remove redundant parameters and feature dimensions through an effective distillation strategy to maintain the single backbone model. We validate our method FOSTER on CIFAR-100 and ImageNet-100/1000 under different settings. Experimental results show that our method achieves state-of-the-art performance. Code is available at: https://github.com/G-U-N/ECCV22-FOSTER.
Neuronal diversity can improve machine learning for physics and beyond
Authors: Anshul Choudhary, Anil Radhakrishnan, John F. Lindner, Sudeshna Sinha, William L. Ditto
Date: 2022-04-09 00:00:00
Tasks: Diversity, image-classification, Image Classification, Meta-Learning, regression

Diversity conveys advantages in nature, yet homogeneous neurons typically comprise the layers of artificial neural networks. Here we construct neural networks from neurons that learn their own activation functions, quickly diversify, and subsequently outperform their homogeneous counterparts on image classification and nonlinear regression tasks. Sub-networks instantiate the neurons, which meta-learn especially efficient sets of nonlinear responses. Examples include conventional neural networks classifying digits and forecasting a van der Pol oscillator and physics-informed Hamiltonian neural networks learning H\'enon-Heiles stellar orbits and the swing of a video recorded pendulum clock. Such \textit{learned diversity} provides examples of dynamical systems selecting diversity over uniformity and elucidates the role of diversity in natural and artificial systems.
JORLDY: a fully customizable open source framework for reinforcement learning
Authors: Kyushik Min, Hyunho Lee, Kwansu Shin, Taehak Lee, Hojoon Lee, Jinwon Choi, Sungho Son
Date: 2022-04-11 00:00:00
Tasks: MuJoCo, OpenAI Gym, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Unity

Recently, Reinforcement Learning (RL) has been actively researched in both academic and industrial fields. However, there exist only a few RL frameworks which are developed for researchers or students who want to study RL. In response, we propose an open-source RL framework "Join Our Reinforcement Learning framework for Developing Yours" (JORLDY). JORLDY provides more than 20 widely used RL algorithms which are implemented with Pytorch. Also, JORLDY supports multiple RL environments which include OpenAI gym, Unity ML-Agents, Mujoco, Super Mario Bros and Procgen. Moreover, the algorithmic components such as agent, network, environment can be freely customized, so that the users can easily modify and append algorithmic components. We expect that JORLDY will support various RL research and contribute further advance the field of RL. The source code of JORLDY is provided on the following Github: https://github.com/kakaoenterprise/JORLDY
Machine Learning of Generic and User-Focused Summarization
Authors: Inderjeet Mani, Eric Bloedorn
Date: 1998-11-02 00:00:00
Tasks: BIG-bench Machine Learning, Text Summarization

A key problem in text summarization is finding a salience function which determines what information in the source should be included in the summary. This paper describes the use of machine learning on a training corpus of documents and their abstracts to discover salience functions which describe what combination of features is optimal for a given summarization task. The method addresses both "generic" and user-focused summaries.
Investigating Deep Learning Benchmarks for Electrocardiography Signal Processing
Authors: Wen Hao, Kang Jingsu
Date: 2022-04-09 00:00:00
Tasks: Atrial Fibrillation Detection, Deep Learning, ECG Classification, ECG QRS Detection, ECG Wave Delineation, Electrocardiography (ECG)

In recent years, deep learning has witnessed its blossom in the field of Electrocardiography (ECG) processing, outperforming traditional signal processing methods in various tasks, for example, classification, QRS detection, wave delineation. Although many neural architectures have been proposed in the literature, there is a lack of systematic studies and open-source libraries for ECG deep learning. In this paper, we propose a deep learning framework, named \texttt{torch\_ecg}, which gathers a large number of neural networks, both from literature and novel, for various ECG processing tasks. It establishes a convenient and modular way for automatic building and flexible scaling of the networks, as well as a neat and uniform way of organizing the preprocessing procedures and augmentation techniques for preparing the input data for the models. Besides, \texttt{torch\_ecg} provides benchmark studies using the latest databases, illustrating the principles and pipelines for solving ECG processing tasks and reproducing results from the literature. \texttt{torch\_ecg} offers the ECG research community a powerful tool meeting the growing demand for the application of deep learning techniques.
Learning Trajectory-Aware Transformer for Video Super-Resolution
Authors: Chengxu Liu, Huan Yang, Jianlong Fu, Xueming Qian
Date: 2022-04-08 00:00:00
Proceeding: CVPR 2022 1
Tasks: Super-Resolution, Video deraining, Video Super-Resolution

Video super-resolution (VSR) aims to restore a sequence of high-resolution (HR) frames from their low-resolution (LR) counterparts. Although some progress has been made, there are grand challenges to effectively utilize temporal dependency in entire video sequences. Existing approaches usually align and aggregate video frames from limited adjacent frames (e.g., 5 or 7 frames), which prevents these approaches from satisfactory results. In this paper, we take one step further to enable effective spatio-temporal learning in videos. We propose a novel Trajectory-aware Transformer for Video Super-Resolution (TTVSR). In particular, we formulate video frames into several pre-aligned trajectories which consist of continuous visual tokens. For a query token, self-attention is only learned on relevant visual tokens along spatio-temporal trajectories. Compared with vanilla vision Transformers, such a design significantly reduces the computational cost and enables Transformers to model long-range features. We further propose a cross-scale feature tokenization module to overcome scale-changing problems that often occur in long-range videos. Experimental results demonstrate the superiority of the proposed TTVSR over state-of-the-art models, by extensive quantitative and qualitative evaluations in four widely-used video super-resolution benchmarks. Both code and pre-trained models can be downloaded at https://github.com/researchmm/TTVSR.
Word Embeddings Are Capable of Capturing Rhythmic Similarity of Words
Authors: Hosein Rezaei
Date: 2022-04-11 00:00:00
Tasks: Word Embeddings

Word embedding systems such as Word2Vec and GloVe are well-known in deep learning approaches to NLP. This is largely due to their ability to capture semantic relationships between words. In this work we investigated their usefulness in capturing rhythmic similarity of words instead. The results show that vectors these embeddings assign to rhyming words are more similar to each other, compared to the other words. It is also revealed that GloVe performs relatively better than Word2Vec in this regard. We also proposed a first of its kind metric for quantifying rhythmic similarity of a pair of words.
Domain-Oriented Prefix-Tuning: Towards Efficient and Generalizable Fine-tuning for Zero-Shot Dialogue Summarization
Authors: Lulu Zhao, Fujia Zheng, Weihao Zeng, Keqing He, Weiran Xu, Huixing Jiang, Wei Wu, Yanan Wu
Date: 2022-04-09 00:00:00
Proceeding: NAACL 2022 7
Tasks: Domain Adaptation

The most advanced abstractive dialogue summarizers lack generalization ability on new domains and the existing researches for domain adaptation in summarization generally rely on large-scale pre-trainings. To explore the lightweight fine-tuning methods for domain adaptation of dialogue summarization, in this paper, we propose an efficient and generalizable Domain-Oriented Prefix-tuning model, which utilizes a domain word initialized prefix module to alleviate domain entanglement and adopts discrete prompts to guide the model to focus on key contents of dialogues and enhance model generalization. We conduct zero-shot experiments and build domain adaptation benchmarks on two multi-domain dialogue summarization datasets, TODSum and QMSum. Adequate experiments and qualitative analysis prove the effectiveness of our methods.
Interior Attention-Aware Network for Infrared Small Target Detection
Authors: Kewei Wang, Shuaiyuan Du, Chengxin Liu, Zhiguo Cao
Date: 2022-03-30 00:00:00
Proceeding: IEEE Transactions on Geoscience and Remote Sensing 2022 3
Tasks: 2D Object Detection, 2D Semantic Segmentation

Infrared small target detection plays an important role in target warning, ground monitoring, and flight guidance. Existing methods typically utilize local-contrast information of each pixel to detect infrared small targets, neglecting the interior relation between target pixels or background pixels. The mere use of the local information of one pixel, however, is not sufficient for accurate detection, which may lead to missing detection and false alarms. As a harmonious whole, information between pixels are necessary to determine if a pixel belongs to the target or the background.  

Motivated by the fact that pixels from targets or backgrounds are correlated to each other, we propose a coarse-to-fine interior attention-aware network (IAANet) for infrared small target detection. Specifically, a region proposal network (RPN) is first applied to obtain coarse target regions and filter out backgrounds. Then, we leverage a transformer encoder to model the attention between pixels in coarse target regions, outputting attention-aware features. Finally, predictions are obtained by feeding attention-aware features to a classification head. Extensive experiments show that our approach is capable of detecting targets precisely, of suppressing a variety of false alarm sources, and works effectively in various background environments and target appearances. We show that our IAANet outperforms the state-of-the-art methods by a large margin. Code will be made available at: https://github.com/kwwcv/iaanet.
Generative Biomedical Entity Linking via Knowledge Base-Guided Pre-training and Synonyms-Aware Fine-tuning
Authors: Hongyi Yuan, Zheng Yuan, Sheng Yu
Date: 2022-04-11 00:00:00
Proceeding: NAACL 2022 7
Tasks: Decoder, Entity Linking, Natural Language Understanding

Entities lie in the heart of biomedical natural language understanding, and the biomedical entity linking (EL) task remains challenging due to the fine-grained and diversiform concept names. Generative methods achieve remarkable performances in general domain EL with less memory usage while requiring expensive pre-training. Previous biomedical EL methods leverage synonyms from knowledge bases (KB) which is not trivial to inject into a generative method. In this work, we use a generative approach to model biomedical EL and propose to inject synonyms knowledge in it. We propose KB-guided pre-training by constructing synthetic samples with synonyms and definitions from KB and require the model to recover concept names. We also propose synonyms-aware fine-tuning to select concept names for training, and propose decoder prompt and multi-synonyms constrained prefix tree for inference. Our method achieves state-of-the-art results on several biomedical EL tasks without candidate selection which displays the effectiveness of proposed pre-training and fine-tuning strategies.
Full-Spectrum Out-of-Distribution Detection
Authors: Jingkang Yang, Kaiyang Zhou, Ziwei Liu
Date: 2022-04-11 00:00:00
Tasks: Out-of-Distribution Detection, Out of Distribution (OOD) Detection

Existing out-of-distribution (OOD) detection literature clearly defines semantic shift as a sign of OOD but does not have a consensus over covariate shift. Samples experiencing covariate shift but not semantic shift are either excluded from the test set or treated as OOD, which contradicts the primary goal in machine learning -- being able to generalize beyond the training distribution. In this paper, we take into account both shift types and introduce full-spectrum OOD (FS-OOD) detection, a more realistic problem setting that considers both detecting semantic shift and being tolerant to covariate shift; and designs three benchmarks. These new benchmarks have a more fine-grained categorization of distributions (i.e., training ID, covariate-shifted ID, near-OOD, and far-OOD) for the purpose of more comprehensively evaluating the pros and cons of algorithms. To address the FS-OOD detection problem, we propose SEM, a simple feature-based semantics score function. SEM is mainly composed of two probability measures: one is based on high-level features containing both semantic and non-semantic information, while the other is based on low-level feature statistics only capturing non-semantic image styles. With a simple combination, the non-semantic part is cancelled out, which leaves only semantic information in SEM that can better handle FS-OOD detection. Extensive experiments on the three new benchmarks show that SEM significantly outperforms current state-of-the-art methods. Our code and benchmarks are released in https://github.com/Jingkang50/OpenOOD.
Survey of Aspect-based Sentiment Analysis Datasets
Authors: Siva Uday Sampreeth Chebolu, Franck Dernoncourt, Nedim Lipka, Thamar Solorio
Date: 2022-04-11 00:00:00
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sentiment Analysis, Survey

Aspect-based sentiment analysis (ABSA) is a natural language processing problem that requires analyzing user-generated reviews to determine: a) The target entity being reviewed, b) The high-level aspect to which it belongs, and c) The sentiment expressed toward the targets and the aspects. Numerous yet scattered corpora for ABSA make it difficult for researchers to identify corpora best suited for a specific ABSA subtask quickly. This study aims to present a database of corpora that can be used to train and assess autonomous ABSA systems. Additionally, we provide an overview of the major corpora for ABSA and its subtasks and highlight several features that researchers should consider when selecting a corpus. Finally, we discuss the advantages and disadvantages of current collection approaches and make recommendations for future corpora creation. This survey examines 65 publicly available ABSA datasets covering over 25 domains, including 45 English and 20 other languages datasets.
Towards Generalizable Semantic Product Search by Text Similarity Pre-training on Search Click Logs
Authors: Zheng Liu, Wei zhang, Yan Chen, Weiyi Sun, Tianchuan Du, Benjamin Schroeder
Date: 2022-04-11 00:00:00
Tasks: text similarity

Recently, semantic search has been successfully applied to e-commerce product search and the learned semantic space(s) for query and product encoding are expected to generalize to unseen queries or products. Yet, whether generalization can conveniently emerge has not been thoroughly studied in the domain thus far. In this paper, we examine several general-domain and domain-specific pre-trained Roberta variants and discover that general-domain fine-tuning does not help generalization, which aligns with the discovery of prior art. Proper domain-specific fine-tuning with clickstream data can lead to better model generalization, based on a bucketed analysis of a publicly available manual annotated query-product pair da
Persona-driven Dominant/Submissive Map (PDSM) Generation for Tutorials
Authors: Michael Cerny Green, Ahmed Khalifa, M Charity, Julian Togelius
Date: 2022-04-11 00:00:00
Tasks: Diversity

In this paper, we present a method for automated persona-driven video game tutorial level generation. Tutorial levels are scenarios in which the player can explore and discover different rules and game mechanics. Procedural personas can guide generators to create content which encourages or discourages certain playstyle behaviors. In this system, we use procedural personas to calculate the behavioral characteristics of levels which are evolved using the quality-diversity algorithm known as Constrained MAP-Elites. An evolved map's quality is determined by its simplicity: the simpler it is, the better it is. Within this work, we show that the generated maps can strongly encourage or discourage different persona-like behaviors and range from simple solutions to complex puzzle-levels, making them perfect candidates for a tutorial generative system.