pwc-archive/papers-with-abstracts huggingface dataset:
GreyReID: A Two-stream Deep Framework with RGB-grey Information for Person Re-identification
Authors: Lei Qi, Lei Wang, Jing Huo, Yinghuan Shi, Yang Gao
Date: 2019-08-14 00:00:00
Tasks: Person Re-Identification

In this paper, we observe that most false positive images (i.e., different identities with query images) in the top ranking list usually have the similar color information with the query image in person re-identification (Re-ID). Meanwhile, when we use the greyscale images generated from RGB images to conduct the person Re-ID task, some hard query images can obtain better performance compared with using RGB images. Therefore, RGB and greyscale images seem to be complementary to each other for person Re-ID. In this paper, we aim to utilize both RGB and greyscale images to improve the person Re-ID performance. To this end, we propose a novel two-stream deep neural network with RGB-grey information, which can effectively fuse RGB and greyscale feature representations to enhance the generalization ability of Re-ID. Firstly, we convert RGB images to greyscale images in each training batch. Based on these RGB and greyscale images, we train the RGB and greyscale branches, respectively. Secondly, to build up connections between RGB and greyscale branches, we merge the RGB and greyscale branches into a new joint branch. Finally, we concatenate the features of all three branches as the final feature representation for Re-ID. Moreover, in the training process, we adopt the joint learning scheme to simultaneously train each branch by the independent loss function, which can enhance the generalization ability of each branch. Besides, a global loss function is utilized to further fine-tune the final concatenated feature. The extensive experiments on multiple benchmark datasets fully show that the proposed method can outperform the state-of-the-art person Re-ID methods. Furthermore, using greyscale images can indeed improve the person Re-ID performance.
MemeFaceGenerator: Adversarial Synthesis of Chinese Meme-face from Natural Sentences
Authors: Yifu Chen, Zongsheng Wang, Bowen Wu, Mengyuan Li, huan zhang, Lin Ma, Feng Liu, Qihang Feng, Baoxun Wang
Date: 2019-08-14 00:00:00
Tasks: Face Generation, Generative Adversarial Network

Chinese meme-face is a special kind of internet subculture widely spread in Chinese Social Community Networks. It usually consists of a template image modified by some amusing details and a text caption. In this paper, we present MemeFaceGenerator, a Generative Adversarial Network with the attention module and template information as supplementary signals, to automatically generate meme-faces from text inputs. We also develop a web service as system demonstration of meme-face synthesis. MemeFaceGenerator has been shown to be capable of generating high-quality meme-faces from random text inputs.
Mastering emergent language: learning to guide in simulated navigation
Authors: Mathijs Mul, Diane Bouchacourt, Elia Bruni
Date: 2019-08-14 00:00:00
Tasks: Navigate

To cooperate with humans effectively, virtual agents need to be able to understand and execute language instructions. A typical setup to achieve this is with a scripted teacher which guides a virtual agent using language instructions. However, such setup has clear limitations in scalability and, more importantly, it is not interactive. Here, we introduce an autonomous agent that uses discrete communication to interactively guide other agents to navigate and act on a simulated environment. The developed communication protocol is trainable, emergent and requires no additional supervision. The emergent language speeds up learning of new agents, it generalizes across incrementally more difficult tasks and, contrary to most other emergent languages, it is highly interpretable. We demonstrate how the emitted messages correlate with particular actions and observations, and how new agents become less dependent on this guidance as training progresses. By exploiting the correlations identified in our analysis, we manage to successfully address the agents in their own language.
FlowDelta: Modeling Flow Information Gain in Reasoning for Conversational Machine Comprehension
Authors: Yi-Ting Yeh, Yun-Nung Chen
Date: 2019-08-14 00:00:00
Proceeding: WS 2019 11
Tasks: Reading Comprehension

Conversational machine comprehension requires deep understanding of the dialogue flow, and the prior work proposed FlowQA to implicitly model the context representations in reasoning for better understanding. This paper proposes to explicitly model the information gain through dialogue reasoning in order to allow the model to focus on more informative cues. The proposed model achieves state-of-the-art performance in a conversational QA dataset QuAC and sequential instruction understanding dataset SCONE, which shows the effectiveness of the proposed mechanism and demonstrates its capability of generalization to different QA models and tasks.
X-WikiRE: A Large, Multilingual Resource for Relation Extraction as Machine Comprehension
Authors: Mostafa Abdou, Cezar Sas, Rahul Aralikatte, Isabelle Augenstein, Anders Søgaard
Date: 2019-08-14 00:00:00
Proceeding: WS 2019 11
Tasks: Reading Comprehension, Relation, Relation Extraction

Although the vast majority of knowledge bases KBs are heavily biased towards English, Wikipedias do cover very different topics in different languages. Exploiting this, we introduce a new multilingual dataset (X-WikiRE), framing relation extraction as a multilingual machine reading problem. We show that by leveraging this resource it is possible to robustly transfer models cross-lingually and that multilingual support significantly improves (zero-shot) relation extraction, enabling the population of low-resourced KBs from their well-populated counterparts.
D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation
Authors: Yongjin Zhou, Weijian Huang, Pei Dong, Yong Xia, Shan-Shan Wang
Date: 2019-08-14 00:00:00
Tasks: Decoder, Image Segmentation, Lesion Segmentation, Medical Diagnosis, Medical Image Segmentation, Prognosis, Semantic Segmentation

Assessing the location and extent of lesions caused by chronic stroke is critical for medical diagnosis, surgical planning, and prognosis. In recent years, with the rapid development of 2D and 3D convolutional neural networks (CNN), the encoder-decoder structure has shown great potential in the field of medical image segmentation. However, the 2D CNN ignores the 3D information of medical images, while the 3D CNN suffers from high computational resource demands. This paper proposes a new architecture called dimension-fusion-UNet (D-UNet), which combines 2D and 3D convolution innovatively in the encoding stage. The proposed architecture achieves a better segmentation performance than 2D networks, while requiring significantly less computation time in comparison to 3D networks. Furthermore, to alleviate the data imbalance issue between positive and negative samples for the network training, we propose a new loss function called Enhance Mixing Loss (EML). This function adds a weighted focal coefficient and combines two traditional loss functions. The proposed method has been tested on the ATLAS dataset and compared to three state-of-the-art methods. The results demonstrate that the proposed method achieves the best quality performance in terms of DSC = 0.5349+0.2763 and precision = 0.6331+0.295).
Unsupervised Behavior Change Detection in Multidimensional Data Streams for Maritime Traffic Monitoring
Authors: Lucas May Petry, Amilcar Soares, Vania Bogorny, Stan Matwin
Date: 2019-08-14 00:00:00
Tasks: BIG-bench Machine Learning, Change Detection, Event Detection, Semi-supervised Change Detection

The worldwide growth of maritime traffic and the development of the Automatic Identification System (AIS) has led to advances in monitoring systems for preventing vessel accidents and detecting illegal activities. In this work, we describe research gaps and challenges in machine learning for vessel behavior change and event detection, considering several constraints imposed by real-time data streams and the maritime monitoring domain. As a starting point, we investigate how unsupervised and semi-supervised change detection methods may be employed for identifying shifts in vessel behavior, aiming to detect and label unusual events.
Shape-Aware Complementary-Task Learning for Multi-Organ Segmentation
Authors: Fernando Navarro, Suprosanna Shit, Ivan Ezhov, Johannes Paetzold, Andrei Gafita, Jan Peeken, Stephanie Combs, Bjoern Menze
Date: 2019-08-14 00:00:00
Tasks: Computed Tomography (CT), Image Retrieval, Organ Segmentation, regression, Retrieval

Multi-organ segmentation in whole-body computed tomography (CT) is a constant pre-processing step which finds its application in organ-specific image retrieval, radiotherapy planning, and interventional image analysis. We address this problem from an organ-specific shape-prior learning perspective. We introduce the idea of complementary-task learning to enforce shape-prior leveraging the existing target labels. We propose two complementary-tasks namely i) distance map regression and ii) contour map detection to explicitly encode the geometric properties of each organ. We evaluate the proposed solution on the public VISCERAL dataset containing CT scans of multiple organs. We report a significant improvement of overall dice score from 0.8849 to 0.9018 due to the incorporation of complementary-task learning.
Towards Optimisation of Collaborative Question Answering over Knowledge Graphs
Authors: Kuldeep Singh, Mohamad Yaser Jaradeh, Saeedeh Shekarpour, Akash Kulkarni, Arun Sethupat Radhakrishna, Ioanna Lytra, Maria-Esther Vidal, Jens Lehmann
Date: 2019-08-14 00:00:00
Tasks: feature selection, Knowledge Graphs, Question Answering

Collaborative Question Answering (CQA) frameworks for knowledge graphs aim at integrating existing question answering (QA) components for implementing sequences of QA tasks (i.e. QA pipelines). The research community has paid substantial attention to CQAs since they support reusability and scalability of the available components in addition to the flexibility of pipelines. CQA frameworks attempt to build such pipelines automatically by solving two optimisation problems: 1) local collective performance of QA components per QA task and 2) global performance of QA pipelines. In spite offering several advantages over monolithic QA systems, the effectiveness and efficiency of CQA frameworks in answering questions is limited. In this paper, we tackle the problem of local optimisation of CQA frameworks and propose a three fold approach, which applies feature selection techniques with supervised machine learning approaches in order to identify the best performing components efficiently. We have empirically evaluated our approach over existing benchmarks and compared to existing automatic CQA frameworks. The observed results provide evidence that our approach answers a higher number of questions than the state of the art while reducing: i) the number of used features by 50% and ii) the number of components used by 76%.
Segmentation of Multimodal Myocardial Images Using Shape-Transfer GAN
Authors: Xumin Tao, Hongrong Wei, Wufeng Xue, Dong Ni
Date: 2019-08-14 00:00:00
Tasks: Myocardium Segmentation, Segmentation

Myocardium segmentation of late gadolinium enhancement (LGE) Cardiac MR images is important for evaluation of infarction regions in clinical practice. The pathological myocardium in LGE images presents distinctive brightness and textures compared with the healthy tissues, making it much more challenging to be segment. Instead, the balanced-Steady State Free Precession (bSSFP) cine images show clearly boundaries and can be easily segmented. Given this fact, we propose a novel shape-transfer GAN for LGE images, which can 1) learn to generate realistic LGE images from bSSFP with the anatomical shape preserved, and 2) learn to segment the myocardium of LGE images from these generated images. It's worth to note that no segmentation label of the LGE images is used during this procedure. We test our model on dataset from the Multi-sequence Cardiac MR Segmentation Challenge. The results show that the proposed Shape-Transfer GAN can achieve accurate myocardium masks of LGE images.
A Reproducible Comparison of RSSI Fingerprinting Localization Methods Using LoRaWAN
Authors: Grigorios G. Anagnostopoulos, Alexandros Kalousis
Date: 2019-08-14 00:00:00

The use of fingerprinting localization techniques in outdoor IoT settings has started to gain popularity over the recent years. Communication signals of Low Power Wide Area Networks (LPWAN), such as LoRaWAN, are used to estimate the location of low power mobile devices. In this study, a publicly available dataset of LoRaWAN RSSI measurements is utilized to compare different machine learning methods and their accuracy in producing location estimates. The tested methods are: the k Nearest Neighbours method, the Extra Trees method and a neural network approach using a Multilayer Perceptron. To facilitate the reproducibility of tests and the comparability of results, the code and the train/validation/test split of the dataset used in this study have become available. The neural network approach was the method with the highest accuracy, achieving a mean error of 358 meters and a median error of 204 meters.
AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models
Authors: Ke Sun, Zhanxing Zhu, Zhouchen Lin
Date: 2019-08-14 00:00:00
Proceeding: ICLR 2021 1
Tasks: Graph Neural Network, Node Classification

The design of deep graph models still remains to be investigated and the crucial part is how to explore and exploit the knowledge from different hops of neighbors in an efficient way. In this paper, we propose a novel RNN-like deep graph neural network architecture by incorporating AdaBoost into the computation of network; and the proposed graph convolutional network called AdaGCN~(Adaboosting Graph Convolutional Network) has the ability to efficiently extract knowledge from high-order neighbors of current nodes and then integrates knowledge from different hops of neighbors into the network in an Adaboost way. Different from other graph neural networks that directly stack many graph convolution layers, AdaGCN shares the same base neural network architecture among all ``layers'' and is recursively optimized, which is similar to an RNN. Besides, We also theoretically established the connection between AdaGCN and existing graph convolutional methods, presenting the benefits of our proposal. Finally, extensive experiments demonstrate the consistent state-of-the-art prediction performance on graphs across different label rates and the computational advantage of our approach AdaGCN~\footnote{Code is available at \url{https://github.com/datake/AdaGCN}.}
Reactive Multi-Stage Feature Fusion for Multimodal Dialogue Modeling
Authors: Yi-Ting Yeh, Tzu-Chuan Lin, Hsiao-Hua Cheng, Yu-Hsuan Deng, Shang-Yu Su, Yun-Nung Chen
Date: 2019-08-14 00:00:00
Tasks: Question Answering, Scene-Aware Dialogue, Visual Dialog, Visual Question Answering, Visual Question Answering (VQA)

Visual question answering and visual dialogue tasks have been increasingly studied in the multimodal field towards more practical real-world scenarios. A more challenging task, audio visual scene-aware dialogue (AVSD), is proposed to further advance the technologies that connect audio, vision, and language, which introduces temporal video information and dialogue interactions between a questioner and an answerer. This paper proposes an intuitive mechanism that fuses features and attention in multiple stages in order to well integrate multimodal features, and the results demonstrate its capability in the experiments. Also, we apply several state-of-the-art models in other tasks to the AVSD task, and further analyze their generalization across different tasks.
Mask Mining for Improved Liver Lesion Segmentation
Authors: Karsten Roth, Jürgen Hesser, Tomasz Konopczyński
Date: 2019-08-14 00:00:00
Tasks: Lesion Segmentation, Segmentation, Tumor Segmentation

We propose a novel procedure to improve liver and lesion segmentation from CT scans for U-Net based models. Our method extends standard segmentation pipelines to focus on higher target recall or reduction of noisy false-positive predictions, boosting overall segmentation performance. To achieve this, we include segmentation errors into a new learning process appended to the main training setup, allowing the model to find features which explain away previous errors. We evaluate this on semantically distinct architectures: cascaded two- and three-dimensional as well as combined learning setups for multitask segmentation. Liver and lesion segmentation data are provided by the Liver Tumor Segmentation challenge (LiTS), with an increase in dice score of up to 2 points.
Towards Explainable AI Planning as a Service
Authors: Michael Cashmore, Anna Collins, Benjamin Krarup, Senka Krivic, Daniele Magazzeni, David Smith
Date: 2019-08-14 00:00:00

Explainable AI is an important area of research within which Explainable Planning is an emerging topic. In this paper, we argue that Explainable Planning can be designed as a service -- that is, as a wrapper around an existing planning system that utilises the existing planner to assist in answering contrastive questions. We introduce a prototype framework to facilitate this, along with some examples of how a planner can be used to address certain types of contrastive questions. We discuss the main advantages and limitations of such an approach and we identify open questions for Explainable Planning as a service that identify several possible research directions.
Fusion of Detected Objects in Text for Visual Question Answering
Authors: Chris Alberti, Jeffrey Ling, Michael Collins, David Reitter
Date: 2019-08-14 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Question Answering, Visual Commonsense Reasoning, Visual Question Answering, Visual Question Answering (VQA)

To advance models of multimodal context, we introduce a simple yet powerful neural architecture for data that combines vision and natural language. The "Bounding Boxes in Text Transformer" (B2T2) also leverages referential information binding words to portions of the image in a single unified architecture. B2T2 is highly effective on the Visual Commonsense Reasoning benchmark (https://visualcommonsense.com), achieving a new state-of-the-art with a 25% relative reduction in error rate compared to published baselines and obtaining the best performance to date on the public leaderboard (as of May 22, 2019). A detailed ablation analysis shows that the early integration of the visual features into the text analysis is key to the effectiveness of the new architecture. A reference implementation of our models is provided (https://github.com/google-research/language/tree/master/language/question_answering/b2t2).
Deep Generalized Max Pooling
Authors: Vincent Christlein, Lukas Spranger, Mathias Seuret, Anguelos Nicolaou, Pavel Král, Andreas Maier
Date: 2019-08-14 00:00:00

Global pooling layers are an essential part of Convolutional Neural Networks (CNN). They are used to aggregate activations of spatial locations to produce a fixed-size vector in several state-of-the-art CNNs. Global average pooling or global max pooling are commonly used for converting convolutional features of variable size images to a fix-sized embedding. However, both pooling layer types are computed spatially independent: each individual activation map is pooled and thus activations of different locations are pooled together. In contrast, we propose Deep Generalized Max Pooling that balances the contribution of all activations of a spatially coherent region by re-weighting all descriptors so that the impact of frequent and rare ones is equalized. We show that this layer is superior to both average and max pooling on the classification of Latin medieval manuscripts (CLAMM'16, CLAMM'17), as well as writer identification (Historical-WI'17).
Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks
Authors: Ruihao Gong, Xianglong Liu, Shenghu Jiang, Tianxiang Li, Peng Hu, Jiazhen Lin, Fengwei Yu, Junjie Yan
Date: 2019-08-14 00:00:00
Proceeding: ICCV 2019 10
Tasks: Quantization

Hardware-friendly network quantization (e.g., binary/uniform quantization) can efficiently accelerate the inference and meanwhile reduce memory consumption of the deep neural networks, which is crucial for model deployment on resource-limited devices like mobile phones. However, due to the discreteness of low-bit quantization, existing quantization methods often face the unstable training process and severe performance degradation. To address this problem, in this paper we propose Differentiable Soft Quantization (DSQ) to bridge the gap between the full-precision and low-bit networks. DSQ can automatically evolve during training to gradually approximate the standard quantization. Owing to its differentiable property, DSQ can help pursue the accurate gradients in backward propagation, and reduce the quantization loss in forward process with an appropriate clipping range. Extensive experiments over several popular network structures show that training low-bit neural networks with DSQ can consistently outperform state-of-the-art quantization methods. Besides, our first efficient implementation for deploying 2 to 4-bit DSQ on devices with ARM architecture achieves up to 1.7$\times$ speed up, compared with the open-source 8-bit high-performance inference framework NCNN. [31]
Person Re-identification in Aerial Imagery
Authors: Shizhou Zhang, Qi Zhang, Yifei Yang, Xing Wei, Peng Wang, Bingliang Jiao, Yanning Zhang
Date: 2019-08-14 00:00:00
Tasks: object-detection, Object Detection, Person Re-Identification

Nowadays, with the rapid development of consumer Unmanned Aerial Vehicles (UAVs), visual surveillance by utilizing the UAV platform has been very attractive. Most of the research works for UAV captured visual data are mainly focused on the tasks of object detection and tracking. However, limited attention has been paid to the task of person Re-identification (ReID) which has been widely studied in ordinary surveillance cameras with fixed emplacements. In this paper, to facilitate the research of person ReID in aerial imagery, we collect a large scale airborne person ReID dataset named as Person ReID for Aerial Imagery (PRAI-1581), which consists of 39,461 images of 1581 person identities. The images of the dataset are shot by two DJI consumer UAVs flying at an altitude ranging from 20 to 60 meters above the ground, which covers most of the real UAV surveillance scenarios. In addition, we propose to utilize subspace pooling of convolution feature maps to represent the input person images. Our method can learn a discriminative and compact feature representation for ReID in aerial imagery and can be trained in an end-to-end fashion efficiently. We conduct extensive experiments on the proposed dataset and the experimental results demonstrate that re-identify persons in aerial imagery is a challenging problem, where our method performs favorably against state of the arts. Our dataset can be accessed via \url{https://github.com/stormyoung/PRAI-1581}.