pwc-archive/papers-with-abstracts huggingface dataset:
Generating Novel Glyph without Human Data by Learning to Communicate
Authors: Seung-won Park
Date: 2020-10-09 00:00:00

In this paper, we present Neural Glyph, a system that generates novel glyph without any training data. The generator and the classifier are trained to communicate via visual symbols as a medium, which enforces the generator to come up with a set of distinctive symbols. Our method results in glyphs that resemble the human-made glyphs, which may imply that the visual appearances of existing glyphs can be attributed to constraints of communication via writing. Important tricks that enable this framework are described and the code is made available.
A deep learning based interactive sketching system for fashion images design
Authors: Yao Li, Xianggang Yu, Xiaoguang Han, Nianjuan Jiang, Kui Jia, Jiangbo Lu
Date: 2020-10-09 00:00:00
Tasks: Deep Learning, Intrinsic Image Decomposition, Texture Synthesis

In this work, we propose an interactive system to design diverse high-quality garment images from fashion sketches and the texture information. The major challenge behind this system is to generate high-quality and detailed texture according to the user-provided texture information. Prior works mainly use the texture patch representation and try to map a small texture patch to a whole garment image, hence unable to generate high-quality details. In contrast, inspired by intrinsic image decomposition, we decompose this task into texture synthesis and shading enhancement. In particular, we propose a novel bi-colored edge texture representation to synthesize textured garment images and a shading enhancer to render shading based on the grayscale edges. The bi-colored edge representation provides simple but effective texture cues and color constraints, so that the details can be better reconstructed. Moreover, with the rendered shading, the synthesized garment image becomes more vivid.
Long-distance tiny face detection based on enhanced YOLOv3 for unmanned system
Authors: Jia-Yi Chang, Yan-Feng Lu, Ya-Jun Liu, Bo Zhou, Hong Qiao
Date: 2020-10-09 00:00:00
Tasks: Face Detection

Remote tiny face detection applied in unmanned system is a challeng-ing work. The detector cannot obtain sufficient context semantic information due to the relatively long distance. The received poor fine-grained features make the face detection less accurate and robust. To solve the problem of long-distance detection of tiny faces, we propose an enhanced network model (YOLOv3-C) based on the YOLOv3 algorithm for unmanned platform. In this model, we bring in multi-scale features from feature pyramid networks and make the features fu-sion to adjust prediction feature map of the output, which improves the sensitivity of the entire algorithm for tiny target faces. The enhanced model improves the accuracy of tiny face detection in the cases of long-distance and high-density crowds. The experimental evaluation results demonstrated the superior perfor-mance of the proposed YOLOv3-C in comparison with other relevant detectors in remote tiny face detection. It is worth mentioning that our proposed method achieves comparable performance with the state of the art YOLOv4[1] in the tiny face detection tasks.
Real-time Mask Detection on Google Edge TPU
Authors: Keondo Park, Wonyoung Jang, Woochul Lee, Kisung Nam, Kihong Seong, Kyuwook Chai, Wen-Syan Li
Date: 2020-10-09 00:00:00
Tasks: Quantization

After the COVID-19 outbreak, it has become important to automatically detect whether people are wearing masks in order to reduce risk of front-line workers. In addition, processing user data locally is a great way to address both privacy and network bandwidth issues. In this paper, we present a light-weighted model for detecting whether people in a particular area wear masks, which can also be deployed on Coral Dev Board, a commercially available development board containing Google Edge TPU. Our approach combines the object detecting network based on MobileNetV2 plus SSD and the quantization scheme for integer-only hardware. As a result, the lighter model in the Edge TPU has a significantly lower latency which is more appropriate for real-time execution while maintaining accuracy comparable to a floating point device.
gundapusunil at SemEval-2020 Task 8: Multimodal Memotion Analysis
Authors: Sunil Gundapu, Radhika Mamidi
Date: 2020-10-09 00:00:00
Proceeding: SEMEVAL 2020
Tasks: Sentiment Analysis

Recent technological advancements in the Internet and Social media usage have resulted in the evolution of faster and efficient platforms of communication. These platforms include visual, textual and speech mediums and have brought a unique social phenomenon called Internet memes. Internet memes are in the form of images with witty, catchy, or sarcastic text descriptions. In this paper, we present a multi-modal sentiment analysis system using deep neural networks combining Computer Vision and Natural Language Processing. Our aim is different than the normal sentiment analysis goal of predicting whether a text expresses positive or negative sentiment; instead, we aim to classify the Internet meme as a positive, negative, or neutral, identify the type of humor expressed and quantify the extent to which a particular effect is being expressed. Our system has been developed using CNN and LSTM and outperformed the baseline score.
Contralaterally Enhanced Networks for Thoracic Disease Detection
Authors: Gangming Zhao, Chaowei Fang, Guanbin Li, Licheng Jiao, Yizhou Yu
Date: 2020-10-09 00:00:00

Identifying and locating diseases in chest X-rays are very challenging, due to the low visual contrast between normal and abnormal regions, and distortions caused by other overlapping tissues. An interesting phenomenon is that there exist many similar structures in the left and right parts of the chest, such as ribs, lung fields and bronchial tubes. This kind of similarities can be used to identify diseases in chest X-rays, according to the experience of broad-certificated radiologists. Aimed at improving the performance of existing detection methods, we propose a deep end-to-end module to exploit the contralateral context information for enhancing feature representations of disease proposals. First of all, under the guidance of the spine line, the spatial transformer network is employed to extract local contralateral patches, which can provide valuable context information for disease proposals. Then, we build up a specific module, based on both additive and subtractive operations, to fuse the features of the disease proposal and the contralateral patch. Our method can be integrated into both fully and weakly supervised disease detection frameworks. It achieves 33.17 AP50 on a carefully annotated private chest X-ray dataset which contains 31,000 images. Experiments on the NIH chest X-ray dataset indicate that our method achieves state-of-the-art performance in weakly-supervised disease localization.
Background Learnable Cascade for Zero-Shot Object Detection
Authors: Ye Zheng, Ruoran Huang, Chuanqi Han, Xi Huang, Li Cui
Date: 2020-10-09 00:00:00
Tasks: Generalized Zero-Shot Object Detection, Object, object-detection, Object Detection, Region Proposal, Zero-Shot Object Detection

Zero-shot detection (ZSD) is crucial to large-scale object detection with the aim of simultaneously localizing and recognizing unseen objects. There remain several challenges for ZSD, including reducing the ambiguity between background and unseen objects as well as improving the alignment between visual and semantic concept. In this work, we propose a novel framework named Background Learnable Cascade (BLC) to improve ZSD performance. The major contributions for BLC are as follows: (i) we propose a multi-stage cascade structure named Cascade Semantic R-CNN to progressively refine the alignment between visual and semantic of ZSD; (ii) we develop the semantic information flow structure and directly add it between each stage in Cascade Semantic RCNN to further improve the semantic feature learning; (iii) we propose the background learnable region proposal network (BLRPN) to learn an appropriate word vector for background class and use this learned vector in Cascade Semantic R CNN, this design makes \Background Learnable" and reduces the confusion between background and unseen classes. Our extensive experiments show BLC obtains significantly performance improvements for MS-COCO over state-of-the-art methods.
WHO 2016 subtyping and automated segmentation of glioma using multi-task deep learning
Authors: Sebastian R. van der Voort, Fatih Incekara, Maarten M. J. Wijnenga, Georgios Kapsas, Renske Gahrmann, Joost W. Schouten, Rishi Nandoe Tewarie, Geert J. Lycklama, Philip C. De Witt Hamer, Roelant S. Eijgelaar, Pim J. French, Hendrikus J. Dubbink, Arnaud J. P. E. Vincent, Wiro J. Niessen, Martin J. van den Bent, Marion Smits, Stefan Klein
Date: 2020-10-09 00:00:00
Tasks: Decision Making, GPU

Accurate characterization of glioma is crucial for clinical decision making. A delineation of the tumor is also desirable in the initial decision stages but is a time-consuming task. Leveraging the latest GPU capabilities, we developed a single multi-task convolutional neural network that uses the full 3D, structural, pre-operative MRI scans to can predict the IDH mutation status, the 1p/19q co-deletion status, and the grade of a tumor, while simultaneously segmenting the tumor. We trained our method using the largest, most diverse patient cohort to date containing 1508 glioma patients from 16 institutes. We tested our method on an independent dataset of 240 patients from 13 different institutes, and achieved an IDH-AUC of 0.90, 1p/19q-AUC of 0.85, grade-AUC of 0.81, and a mean whole tumor DICE score of 0.84. Thus, our method non-invasively predicts multiple, clinically relevant parameters and generalizes well to the broader clinical population.
Rethinking the Extraction and Interaction of Multi-Scale Features for Vessel Segmentation
Authors: Yicheng Wu, Chengwei Pan, Shuqi Wang, Ming Zhang, Yong Xia, Yizhou Yu
Date: 2020-10-09 00:00:00
Tasks: Decoder

Analyzing the morphological attributes of blood vessels plays a critical role in the computer-aided diagnosis of many cardiovascular and ophthalmologic diseases. Although being extensively studied, segmentation of blood vessels, particularly thin vessels and capillaries, remains challenging mainly due to the lack of an effective interaction between local and global features. In this paper, we propose a novel deep learning model called PC-Net to segment retinal vessels and major arteries in 2D fundus image and 3D computed tomography angiography (CTA) scans, respectively. In PC-Net, the pyramid squeeze-and-excitation (PSE) module introduces spatial information to each convolutional block, boosting its ability to extract more effective multi-scale features, and the coarse-to-fine (CF) module replaces the conventional decoder to enhance the details of thin vessels and process hard-to-classify pixels again. We evaluated our PC-Net on the Digital Retinal Images for Vessel Extraction (DRIVE) database and an in-house 3D major artery (3MA) database against several recent methods. Our results not only demonstrate the effectiveness of the proposed PSE module and CF module, but also suggest that our proposed PC-Net sets new state of the art in the segmentation of retinal vessels (AUC: 98.31%) and major arteries (AUC: 98.35%) on both databases, respectively.
Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting
Authors: Yuan Yin, Vincent Le Guen, Jérémie Dona, Emmanuel de Bézenac, Ibrahim Ayed, Nicolas Thome, Patrick Gallinari
Date: 2020-10-09 00:00:00
Proceeding: ICLR 2021 1

Forecasting complex dynamical phenomena in settings where only partial knowledge of their dynamics is available is a prevalent problem across various scientific fields. While purely data-driven approaches are arguably insufficient in this context, standard physical modeling based approaches tend to be over-simplistic, inducing non-negligible errors. In this work, we introduce the APHYNITY framework, a principled approach for augmenting incomplete physical dynamics described by differential equations with deep data-driven models. It consists in decomposing the dynamics into two components: a physical component accounting for the dynamics for which we have some prior knowledge, and a data-driven component accounting for errors of the physical model. The learning problem is carefully formulated such that the physical model explains as much of the data as possible, while the data-driven component only describes information that cannot be captured by the physical model, no more, no less. This not only provides the existence and uniqueness for this decomposition, but also ensures interpretability and benefits generalization. Experiments made on three important use cases, each representative of a different family of phenomena, i.e. reaction-diffusion equations, wave equations and the non-linear damped pendulum, show that APHYNITY can efficiently leverage approximate physical models to accurately forecast the evolution of the system and correctly identify relevant physical parameters. Code is available at https://github.com/yuan-yin/APHYNITY .
Linear Mode Connectivity in Multitask and Continual Learning
Authors: Seyed Iman Mirzadeh, Mehrdad Farajtabar, Dilan Gorur, Razvan Pascanu, Hassan Ghasemzadeh
Date: 2020-10-09 00:00:00
Proceeding: ICLR 2021 1
Tasks: Continual Learning, Linear Mode Connectivity

Continual (sequential) training and multitask (simultaneous) training are often attempting to solve the same overall objective: to find a solution that performs well on all considered tasks. The main difference is in the training regimes, where continual learning can only have access to one task at a time, which for neural networks typically leads to catastrophic forgetting. That is, the solution found for a subsequent task does not perform well on the previous ones anymore. However, the relationship between the different minima that the two training regimes arrive at is not well understood. What sets them apart? Is there a local structure that could explain the difference in performance achieved by the two different schemes? Motivated by recent work showing that different minima of the same task are typically connected by very simple curves of low error, we investigate whether multitask and continual solutions are similarly connected. We empirically find that indeed such connectivity can be reliably achieved and, more interestingly, it can be done by a linear path, conditioned on having the same initialization for both. We thoroughly analyze this observation and discuss its significance for the continual learning process. Furthermore, we exploit this finding to propose an effective algorithm that constrains the sequentially learned minima to behave as the multitask solution. We show that our method outperforms several state of the art continual learning algorithms on various vision benchmarks.
Conditional GAN for Prediction of Glaucoma Progression with Macular Optical Coherence Tomography
Authors: Osama N. Hassan, Serhat Sahin, Vahid Mohammadzadeh, Xiaohe Yang, Navid Amini, Apoorva Mylavarapu, Jack Martinyan, Tae Hong, Golnoush Mahmoudinezhad, Daniel Rueckert, Kouros Nouri-Mahdavi, Fabien Scalzo
Date: 2020-09-28 00:00:00

The estimation of glaucoma progression is a challenging task as the rate of disease progression varies among individuals in addition to other factors such as measurement variability and the lack of standardization in defining progression. Structural tests, such as thickness measurements of the retinal nerve fiber layer or the macula with optical coherence tomography (OCT), are able to detect anatomical changes in glaucomatous eyes. Such changes may be observed before any functional damage. In this work, we built a generative deep learning model using the conditional GAN architecture to predict glaucoma progression over time. The patient's OCT scan is predicted from three or two prior measurements. The predicted images demonstrate high similarity with the ground truth images. In addition, our results suggest that OCT scans obtained from only two prior visits may actually be sufficient to predict the next OCT scan of the patient after six months.
PoinT-5: Pointer Network and T-5 based Financial NarrativeSummarisation
Authors: Abhishek Singh
Date: 2020-10-08 00:00:00
Tasks: Sentence

Companies provide annual reports to their shareholders at the end of the financial year that describes their operations and financial conditions. The average length of these reports is 80, and it may extend up to 250 pages long. In this paper, we propose our methodology PoinT-5 (the combination of Pointer Network and T-5 (Test-to-text transfer Transformer) algorithms) that we used in the Financial Narrative Summarisation (FNS) 2020 task. The proposed method uses pointer networks to extract important narrative sentences from the report, and then T-5 is used to paraphrase extracted sentences into a concise yet informative sentence. We evaluate our method using ROUGE-N (1,2), L, and SU4. The proposed method achieves the highest precision scores in all the metrics and highest F1 scores in ROUGE1, and LCS and the only solution to cross the MUSE solution baseline in ROUGE-LCS metrics.
Dual Inference for Improving Language Understanding and Generation
Authors: Shang-Yu Su, Yung-Sung Chuang, Yun-Nung Chen
Date: 2020-10-08 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Natural Language Understanding, Text Generation

Natural language understanding (NLU) and Natural language generation (NLG) tasks hold a strong dual relationship, where NLU aims at predicting semantic labels based on natural language utterances and NLG does the opposite. The prior work mainly focused on exploiting the duality in model training in order to obtain the models with better performance. However, regarding the fast-growing scale of models in the current NLP area, sometimes we may have difficulty retraining whole NLU and NLG models. To better address the issue, this paper proposes to leverage the duality in the inference stage without the need of retraining. The experiments on three benchmark datasets demonstrate the effectiveness of the proposed method in both NLU and NLG, providing the great potential of practical usage.
Evaluating the Effectiveness of Efficient Neural Architecture Search for Sentence-Pair Tasks
Authors: Ansel MacLaughlin, Jwala Dhamala, Anoop Kumar, Sriram Venkatapathy, Ragav Venkatesan, Rahul Gupta
Date: 2020-10-08 00:00:00
Proceeding: EMNLP (insights) 2020 11
Tasks: image-classification, Image Classification, Language Modeling, Language Modelling, MRPC, Natural Language Inference, Neural Architecture Search, Semantic Textual Similarity, Sentence, STS, STS-B

Neural Architecture Search (NAS) methods, which automatically learn entire neural model or individual neural cell architectures, have recently achieved competitive or state-of-the-art (SOTA) performance on variety of natural language processing and computer vision tasks, including language modeling, natural language inference, and image classification. In this work, we explore the applicability of a SOTA NAS algorithm, Efficient Neural Architecture Search (ENAS) (Pham et al., 2018) to two sentence pair tasks, paraphrase detection and semantic textual similarity. We use ENAS to perform a micro-level search and learn a task-optimized RNN cell architecture as a drop-in replacement for an LSTM. We explore the effectiveness of ENAS through experiments on three datasets (MRPC, SICK, STS-B), with two different models (ESIM, BiLSTM-Max), and two sets of embeddings (Glove, BERT). In contrast to prior work applying ENAS to NLP tasks, our results are mixed -- we find that ENAS architectures sometimes, but not always, outperform LSTMs and perform similarly to random architecture search.
Fake Reviews Detection through Analysis of Linguistic Features
Authors: Faranak Abri, Luis Felipe Gutierrez, Akbar Siami Namin, Keith S. Jones, David R. W. Sears
Date: 2020-10-08 00:00:00
Tasks: General Classification

Online reviews play an integral part for success or failure of businesses. Prior to purchasing services or goods, customers first review the online comments submitted by previous customers. However, it is possible to superficially boost or hinder some businesses through posting counterfeit and fake reviews. This paper explores a natural language processing approach to identify fake reviews. We present a detailed analysis of linguistic features for distinguishing fake and trustworthy online reviews. We study 15 linguistic features and measure their significance and importance towards the classification schemes employed in this study. Our results indicate that fake reviews tend to include more redundant terms and pauses, and generally contain longer sentences. The application of several machine learning classification algorithms revealed that we were able to discriminate fake from real reviews with high accuracy using these linguistic features.
Leveraging Unpaired Text Data for Training End-to-End Speech-to-Intent Systems
Authors: Yinghui Huang, Hong-Kwang Kuo, Samuel Thomas, Zvi Kons, Kartik Audhkhasi, Brian Kingsbury, Ron Hoory, Michael Picheny
Date: 2020-10-08 00:00:00
Tasks: Data Augmentation, intent-classification, Intent Classification, text-to-speech, Text to Speech, Transfer Learning

Training an end-to-end (E2E) neural network speech-to-intent (S2I) system that directly extracts intents from speech requires large amounts of intent-labeled speech data, which is time consuming and expensive to collect. Initializing the S2I model with an ASR model trained on copious speech data can alleviate data sparsity. In this paper, we attempt to leverage NLU text resources. We implemented a CTC-based S2I system that matches the performance of a state-of-the-art, traditional cascaded SLU system. We performed controlled experiments with varying amounts of speech and text training data. When only a tenth of the original data is available, intent classification accuracy degrades by 7.6% absolute. Assuming we have additional text-to-intent data (without speech) available, we investigated two techniques to improve the S2I system: (1) transfer learning, in which acoustic embeddings for intent classification are tied to fine-tuned BERT text embeddings; and (2) data augmentation, in which the text-to-intent data is converted into speech-to-intent data using a multi-speaker text-to-speech system. The proposed approaches recover 80% of performance lost due to using limited intent-labeled speech.
On the Role of Style in Parsing Speech with Neural Models
Authors: Trang Tran, Jiahong Yuan, Yang Liu, Mari Ostendorf
Date: 2020-10-08 00:00:00

The differences in written text and conversational speech are substantial; previous parsers trained on treebanked text have given very poor results on spontaneous speech. For spoken language, the mismatch in style also extends to prosodic cues, though it is less well understood. This paper re-examines the use of written text in parsing speech in the context of recent advances in neural language processing. We show that neural approaches facilitate using written text to improve parsing of spontaneous speech, and that prosody further improves over this state-of-the-art result. Further, we find an asymmetric degradation from read vs. spontaneous mismatch, with spontaneous speech more generally useful for training parsers.