pwc-archive/papers-with-abstracts huggingface dataset:
Learning Visually Consistent Label Embeddings for Zero-Shot Learning
Authors: Berkan Demirel, Ramazan Gokberk Cinbis, Nazli Ikizler-Cinbis
Date: 2019-05-16 00:00:00
Tasks: Transfer Learning, Zero-Shot Learning

In this work, we propose a zero-shot learning method to effectively model knowledge transfer between classes via jointly learning visually consistent word vectors and label embedding model in an end-to-end manner. The main idea is to project the vector space word vectors of attributes and classes into the visual space such that word representations of semantically related classes become more closer, and use the projected vectors in the proposed embedding model to identify unseen classes. We evaluate the proposed approach on two benchmark datasets and the experimental results show that our method yields significant improvements in recognition accuracy.
Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation
Authors: Ruohan Wang, Carlo Ciliberto, Pierluigi Amadori, Yiannis Demiris
Date: 2019-05-16 00:00:00
Tasks: Imitation Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We consider the problem of imitation learning from a finite set of expert trajectories, without access to reinforcement signals. The classical approach of extracting the expert's reward function via inverse reinforcement learning, followed by reinforcement learning is indirect and may be computationally expensive. Recent generative adversarial methods based on matching the policy distribution between the expert and the agent could be unstable during training. We propose a new framework for imitation learning by estimating the support of the expert policy to compute a fixed reward function, which allows us to re-frame imitation learning within the standard reinforcement learning setting. We demonstrate the efficacy of our reward function on both discrete and continuous domains, achieving comparable or better performance than the state of the art under different reinforcement learning algorithms.
Stroke extraction for offline handwritten mathematical expression recognition
Authors: Chungkwong Chan
Date: 2019-05-16 00:00:00
Tasks: Optical Character Recognition, Optical Character Recognition (OCR)

Offline handwritten mathematical expression recognition is often considered much harder than its online counterpart due to the absence of temporal information. In order to take advantage of the more mature methods for online recognition and save resources, an oversegmentation approach is proposed to recover strokes from textual bitmap images automatically. The proposed algorithm first breaks down the skeleton of a binarized image into junctions and segments, then segments are merged to form strokes, finally stroke order is normalized by using recursive projection and topological sort. Good offline accuracy was obtained in combination with ordinary online recognizers, which are not specially designed for extracted strokes. Given a ready-made state-of-the-art online handwritten mathematical expression recognizer, the proposed procedure correctly recognized 58.22%, 65.65%, and 65.22% of the offline formulas rendered from the datasets of the Competitions on Recognition of Online Handwritten Mathematical Expressions(CROHME) in 2014, 2016, and 2019 respectively. Furthermore, given a trainable online recognition system, retraining it with extracted strokes resulted in an offline recognizer with the same level of accuracy. On the other hand, the speed of the entire pipeline was fast enough to facilitate on-device recognition on mobile phones with limited resources. To conclude, stroke extraction provides an attractive way to build optical character recognition software.
Inductive Guided Filter: Real-time Deep Image Matting with Weakly Annotated Masks on Mobile Devices
Authors: Yaoyi Li, Jianfu Zhang, Weijie Zhao, Hongtao Lu
Date: 2019-05-16 00:00:00
Tasks: Image Matting

Recently, significant progress has been achieved in deep image matting. Most of the classical image matting methods are time-consuming and require an ideal trimap which is difficult to attain in practice. A high efficient image matting method based on a weakly annotated mask is in demand for mobile applications. In this paper, we propose a novel method based on Deep Learning and Guided Filter, called Inductive Guided Filter, which can tackle the real-time general image matting task on mobile devices. We design a lightweight hourglass network to parameterize the original Guided Filter method that takes an image and a weakly annotated mask as input. Further, the use of Gabor loss is proposed for training networks for complicated textures in image matting. Moreover, we create an image matting dataset MAT-2793 with a variety of foreground objects. Experimental results demonstrate that our proposed method massively reduces running time with robust accuracy.
RGB-T Image Saliency Detection via Collaborative Graph Learning
Authors: Zhengzheng Tu, Tian Xia, Chenglong Li, Xiaoxiao Wang, Yan Ma, Jin Tang
Date: 2019-05-16 00:00:00
Tasks: Graph Learning, Saliency Detection, Superpixels

Image saliency detection is an active research topic in the community of computer vision and multimedia. Fusing complementary RGB and thermal infrared data has been proven to be effective for image saliency detection. In this paper, we propose an effective approach for RGB-T image saliency detection. Our approach relies on a novel collaborative graph learning algorithm. In particular, we take superpixels as graph nodes, and collaboratively use hierarchical deep features to jointly learn graph affinity and node saliency in a unified optimization framework. Moreover, we contribute a more challenging dataset for the purpose of RGB-T image saliency detection, which contains 1000 spatially aligned RGB-T image pairs and their ground truth annotations. Extensive experiments on the public dataset and the newly created dataset suggest that the proposed approach performs favorably against the state-of-the-art RGB-T saliency detection methods.
BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning
Authors: Abhijit Guha Roy, Shayan Siddiqui, Sebastian Pölsterl, Nassir Navab, Christian Wachinger
Date: 2019-05-16 00:00:00
Tasks: Brain Segmentation, Federated Learning

Access to sufficient annotated data is a common challenge in training deep neural networks on medical images. As annotating data is expensive and time-consuming, it is difficult for an individual medical center to reach large enough sample sizes to build their own, personalized models. As an alternative, data from all centers could be pooled to train a centralized model that everyone can use. However, such a strategy is often infeasible due to the privacy-sensitive nature of medical data. Recently, federated learning (FL) has been introduced to collaboratively learn a shared prediction model across centers without the need for sharing data. In FL, clients are locally training models on site-specific datasets for a few epochs and then sharing their model weights with a central server, which orchestrates the overall training process. Importantly, the sharing of models does not compromise patient privacy. A disadvantage of FL is the dependence on a central server, which requires all clients to agree on one trusted central body, and whose failure would disrupt the training process of all clients. In this paper, we introduce BrainTorrent, a new FL framework without a central server, particularly targeted towards medical applications. BrainTorrent presents a highly dynamic peer-to-peer environment, where all centers directly interact with each other without depending on a central body. We demonstrate the overall effectiveness of FL for the challenging task of whole brain segmentation and observe that the proposed server-less BrainTorrent approach does not only outperform the traditional server-based one but reaches a similar performance to a model trained on pooled data.
Deep Compressed Sensing
Authors: Yan Wu, Mihaela Rosca, Timothy Lillicrap
Date: 2019-05-16 00:00:00
Tasks: compressed sensing, Meta-Learning

Compressed sensing (CS) provides an elegant framework for recovering sparse signals from compressed measurements. For example, CS can exploit the structure of natural images and recover an image from only a few random measurements. CS is flexible and data efficient, but its application has been restricted by the strong assumption of sparsity and costly reconstruction process. A recent approach that combines CS with neural network generators has removed the constraint of sparsity, but reconstruction remains slow. Here we propose a novel framework that significantly improves both the performance and speed of signal recovery by jointly training a generator and the optimisation process for reconstruction via meta-learning. We explore training the measurements with different objectives, and derive a family of models based on minimising measurement errors. We show that Generative Adversarial Nets (GANs) can be viewed as a special case in this family of models. Borrowing insights from the CS perspective, we develop a novel way of improving GANs using gradient information from the discriminator.
Autonomous Vehicle Control: End-to-end Learning in Simulated Urban Environments
Authors: Hege Haavaldsen, Max Aasboe, Frank Lindseth
Date: 2019-05-16 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles

In recent years, considerable progress has been made towards a vehicle's ability to operate autonomously. An end-to-end approach attempts to achieve autonomous driving using a single, comprehensive software component. Recent breakthroughs in deep learning have significantly increased end-to-end systems' capabilities, and such systems are now considered a possible alternative to the current state-of-the-art solutions. This paper examines end-to-end learning for autonomous vehicles in simulated urban environments containing other vehicles, traffic lights, and speed limits. Furthermore, the paper explores end-to-end systems' ability to execute navigational commands and examines whether improved performance can be achieved by utilizing temporal dependencies between subsequent visual cues. Two end-to-end architectures are proposed: a traditional Convolutional Neural Network and an extended design combining a Convolutional Neural Network with a recurrent layer. The models are trained using expert driving data from a simulated urban setting, and are evaluated by their driving performance in an unseen simulated environment. The results of this paper indicate that end-to-end systems can operate autonomously in simple urban environments. Moreover, it is found that the exploitation of temporal information in subsequent images enhances a system's ability to judge movement and distance.
Inferring Javascript types using Graph Neural Networks
Authors: Jessica Schrouff, Kai Wohlfahrt, Bruno Marnette, Liam Atkinson
Date: 2019-05-16 00:00:00
Tasks: Code Repair, Graph Neural Network

The recent use of `Big Code' with state-of-the-art deep learning methods offers promising avenues to ease program source code writing and correction. As a first step towards automatic code repair, we implemented a graph neural network model that predicts token types for Javascript programs. The predictions achieve an accuracy above $90\%$, which improves on previous similar work.
SWAT: A System for Detecting Salient Wikipedia Entities in Texts
Authors: Marco Ponza, Paolo Ferragina, Francesco Piccinno
Date: 2018-04-10 00:00:00

We study the problem of entity salience by proposing the design and implementation of SWAT, a system that identifies the salient Wikipedia entities occurring in an input document. SWAT consists of several modules that are able to detect and classify on-the-fly Wikipedia entities as salient or not, based on a large number of syntactic, semantic and latent features properly extracted via a supervised process which has been trained over millions of examples drawn from the New York Times corpus. The validation process is performed through a large experimental assessment, eventually showing that SWAT improves known solutions over all publicly available datasets. We release SWAT via an API that we describe and comment in the paper in order to ease its use in other software.
Formal derivation of Mesh Neural Networks with their Forward-Only gradient Propagation
Authors: Federico A. Galatolo, Mario G. C. A. Cimino, Gigliola Vaglini
Date: 2019-05-16 00:00:00
Tasks: tensor algebra

This paper proposes the Mesh Neural Network (MNN), a novel architecture which allows neurons to be connected in any topology, to efficiently route information. In MNNs, information is propagated between neurons throughout a state transition function. State and error gradients are then directly computed from state updates without backward computation. The MNN architecture and the error propagation schema is formalized and derived in tensor algebra. The proposed computational model can fully supply a gradient descent process, and is potentially suitable for very large scale sparse NNs, due to its expressivity and training efficiency, with respect to NNs based on back-propagation and computational graphs.
Uneven illumination surface defects inspection based on convolutional neural network
Authors: Hao Wu, Yulong Liu, Wenbin Gao, Xiangrong Xu
Date: 2019-05-16 00:00:00

Surface defect inspection based on machine vision is often affected by uneven illumination. In order to improve the inspection rate of surface defects inspection under uneven illumination condition, this paper proposes a method for detecting surface image defects based on convolutional neural network, which is based on the adjustment of convolutional neural networks, training parameters, changing the structure of the network, to achieve the purpose of accurately identifying various defects. Experimental on defect inspection of copper strip and steel images shows that the convolutional neural network can automatically learn features without preprocessing the image, and correct identification of various types of image defects affected by uneven illumination, thus overcoming the drawbacks of traditional machine vision inspection methods under uneven illumination.
Towards Unsupervised Single-Channel Blind Source Separation using Adversarial Pair Unmix-and-Remix
Authors: Yedid Hoshen
Date: 2018-12-14 00:00:00
Tasks: blind source separation, regression

Blind single-channel source separation is a long standing signal processing challenge. Many methods were proposed to solve this task utilizing multiple signal priors such as low rank, sparsity, temporal continuity etc. The recent advance of generative adversarial models presented new opportunities in signal regression tasks. The power of adversarial training however has not yet been realized for blind source separation tasks. In this work, we propose a novel method for blind source separation (BSS) using adversarial methods. We rely on the independence of sources for creating adversarial constraints on pairs of approximately separated sources, which ensure good separation. Experiments are carried out on image sources validating the good performance of our approach, and presenting our method as a promising approach for solving BSS for general signals.
Vision-based Robotic Grasping From Object Localization, Object Pose Estimation to Grasp Estimation for Parallel Grippers: A Review
Authors: Guoguang Du, Kai Wang, Shiguo Lian, Kaiyong Zhao
Date: 2019-05-16 00:00:00
Tasks: 6D Pose Estimation, 6D Pose Estimation using RGB, Instance Segmentation, Motion Planning, Object, object-detection, Object Detection, Object Localization, Pose Estimation, Reinforcement Learning, Robotic Grasping, Semantic Segmentation, Survey

This paper presents a comprehensive survey on vision-based robotic grasping. We conclude three key tasks during vision-based robotic grasping, which are object localization, object pose estimation and grasp estimation. In detail, the object localization task contains object localization without classification, object detection and object instance segmentation. This task provides the regions of the target object in the input data. The object pose estimation task mainly refers to estimating the 6D object pose and includes correspondence-based methods, template-based methods and voting-based methods, which affords the generation of grasp poses for known objects. The grasp estimation task includes 2D planar grasp methods and 6DoF grasp methods, where the former is constrained to grasp from one direction. These three tasks could accomplish the robotic grasping with different combinations. Lots of object pose estimation methods need not object localization, and they conduct object localization and object pose estimation jointly. Lots of grasp estimation methods need not object localization and object pose estimation, and they conduct grasp estimation in an end-to-end manner. Both traditional methods and latest deep learning-based methods based on the RGB-D image inputs are reviewed elaborately in this survey. Related datasets and comparisons between state-of-the-art methods are summarized as well. In addition, challenges about vision-based robotic grasping and future directions in addressing these challenges are also pointed out.
Effective Sentence Scoring Method using Bidirectional Language Model for Speech Recognition
Authors: Joongbo Shin, Yoonhyung Lee, Kyomin Jung
Date: 2019-05-16 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, Sentence, speech-recognition, Speech Recognition

In automatic speech recognition, many studies have shown performance improvements using language models (LMs). Recent studies have tried to use bidirectional LMs (biLMs) instead of conventional unidirectional LMs (uniLMs) for rescoring the $N$-best list decoded from the acoustic model. In spite of their theoretical benefits, the biLMs have not given notable improvements compared to the uniLMs in their experiments. This is because their biLMs do not consider the interaction between the two directions. In this paper, we propose a novel sentence scoring method considering the interaction between the past and the future words on the biLM. Our experimental results on the LibriSpeech corpus show that the biLM with the proposed sentence scoring outperforms the uniLM for the $N$-best list rescoring, consistently and significantly in all experimental conditions. The analysis of WERs by word position demonstrates that the biLM is more robust than the uniLM especially when a recognized sentence is short or a misrecognized word is at the beginning of the sentence.
One-Shot Texture Retrieval with Global Context Metric
Authors: Kai Zhu, Wei Zhai, Zheng-Jun Zha, Yang Cao
Date: 2019-05-16 00:00:00
Tasks: Relation, Relation Network, Retrieval, Segmentation

In this paper, we tackle one-shot texture retrieval: given an example of a new reference texture, detect and segment all the pixels of the same texture category within an arbitrary image. To address this problem, we present an OS-TR network to encode both reference and query image, leading to achieve texture segmentation towards the reference category. Unlike the existing texture encoding methods that integrate CNN with orderless pooling, we propose a directionality-aware module to capture the texture variations at each direction, resulting in spatially invariant representation. To segment new categories given only few examples, we incorporate a self-gating mechanism into relation network to exploit global context information for adjusting per-channel modulation weights of local relation features. Extensive experiments on benchmark texture datasets and real scenarios demonstrate the above-par segmentation performance and robust generalization across domains of our proposed method.
Robust Real-time Pedestrian Detection in Aerial Imagery on Jetson TX2
Authors: Mohamed Afifi, Yara Ali, Karim Amer, Mahmoud Shaker, Mohamed El-Helw
Date: 2019-05-16 00:00:00
Tasks: object-detection, Object Detection, Pedestrian Detection

Detection of pedestrians in aerial imagery captured by drones has many applications including intersection monitoring, patrolling, and surveillance, to name a few. However, the problem is involved due to continuouslychanging camera viewpoint and object appearance as well as the need for lightweight algorithms to run on on-board embedded systems. To address this issue, the paper proposes a framework for pedestrian detection in videos based on the YOLO object detection network [6] while having a high throughput of more than 5 FPS on the Jetson TX2 embedded board. The framework exploits deep learning for robust operation and uses a pre-trained model without the need for any additional training which makes it flexible to apply on different setups with minimum amount of tuning. The method achieves ~81 mAP when applied on a sample video from the Embedded Real-Time Inference (ERTI) Challenge where pedestrians are monitored by a UAV.
What do Entity-Centric Models Learn? Insights from Entity Linking in Multi-Party Dialogue
Authors: Laura Aina, Carina Silberer, Matthijs Westera, Ionut-Teodor Sorodoc, Gemma Boleda
Date: 2019-05-16 00:00:00
Proceeding: NAACL 2019 6
Tasks: Entity Linking

Humans use language to refer to entities in the external world. Motivated by this, in recent years several models that incorporate a bias towards learning entity representations have been proposed. Such entity-centric models have shown empirical success, but we still know little about why. In this paper we analyze the behavior of two recently proposed entity-centric models in a referential task, Entity Linking in Multi-party Dialogue (SemEval 2018 Task 4). We show that these models outperform the state of the art on this task, and that they do better on lower frequency entities than a counterpart model that is not entity-centric, with the same model size. We argue that making models entity-centric naturally fosters good architectural decisions. However, we also show that these models do not really build entity representations and that they make poor use of linguistic context. These negative results underscore the need for model analysis, to test whether the motivations for particular architectures are borne out in how models behave when deployed.
Using Entity Relations for Opinion Mining of Vietnamese Comments
Authors: P. T. Nguyen, L. T. Le, V. M. Ngo, P. M. Nguyen
Date: 2019-05-16 00:00:00
Tasks: Opinion Mining

In this paper, we propose several novel techniques to extract and mining opinions of Vietnamese reviews of customers about a number of products traded on e-commerce in Vietnam. The assessment is based on the emotional level of customers on a specific product such as mobile and laptop. We exploit the features of the products because they are much interested by customers and have many products in the Vietnam e-commerce market. Thence, it can be known the favorites and dislikes of customers about exploited products.
Machine Learning based English Sentiment Analysis
Authors: T. N. T. Tran, L. K. N. Nguyen, V. M. Ngo
Date: 2019-05-16 00:00:00
Tasks: BIG-bench Machine Learning, Opinion Mining, Sentiment Analysis

Sentiment analysis or opinion mining aims to determine attitudes, judgments and opinions of customers for a product or a service. This is a great system to help manufacturers or servicers know the satisfaction level of customers about their products or services. From that, they can have appropriate adjustments. We use a popular machine learning method, being Support Vector Machine, combine with the library in Waikato Environment for Knowledge Analysis (WEKA) to build Java web program which analyzes the sentiment of English comments belongs one in four types of woman products. That are dresses, handbags, shoes and rings. We have developed and test our system with a training set having 300 comments and a test set having 400 comments. The experimental results of the system about precision, recall and F measures for positive comments are 89.3%, 95.0% and 92,.1%; for negative comments are 97.1%, 78.5% and 86.8%; and for neutral comments are 76.7%, 86.2% and 81.2%.