pwc-archive/papers-with-abstracts huggingface dataset:
Encoders Help You Disambiguate Word Senses in Neural Machine Translation
Authors: Gongbo Tang, Rico Sennrich, Joakim Nivre
Date: 2019-08-30 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Decoder, Machine Translation, NMT, Translation, Word Embeddings

Neural machine translation (NMT) has achieved new state-of-the-art performance in translating ambiguous words. However, it is still unclear which component dominates the process of disambiguation. In this paper, we explore the ability of NMT encoders and decoders to disambiguate word senses by evaluating hidden states and investigating the distributions of self-attention. We train a classifier to predict whether a translation is correct given the representation of an ambiguous noun. We find that encoder hidden states outperform word embeddings significantly which indicates that encoders adequately encode relevant information for disambiguation into hidden states. Decoders could provide further relevant information for disambiguation. Moreover, the attention weights and attention entropy show that self-attention can detect ambiguous nouns and distribute more attention to the context. Note that this is a revised version. The content related to decoder hidden states has been updated.
Classifying single-qubit noise using machine learning
Authors: Travis L. Scholten, Yi-Kai Liu, Kevin Young, Robin Blume-Kohout
Date: 2019-08-30 00:00:00
Tasks: BIG-bench Machine Learning, Feature Engineering

Quantum characterization, validation, and verification (QCVV) techniques are used to probe, characterize, diagnose, and detect errors in quantum information processors (QIPs). An important component of any QCVV protocol is a mapping from experimental data to an estimate of a property of a QIP. Machine learning (ML) algorithms can help automate the development of QCVV protocols, creating such maps by learning them from training data. We identify the critical components of "machine-learned" QCVV techniques, and present a rubric for developing them. To demonstrate this approach, we focus on the problem of determining whether noise affecting a single qubit is coherent or stochastic (incoherent) using the data sets originally proposed for gate set tomography. We leverage known ML algorithms to train a classifier distinguishing these two kinds of noise. The accuracy of the classifier depends on how well it can approximate the "natural" geometry of the training data. We find GST data sets generated by a noisy qubit can reliably be separated by linear surfaces, although feature engineering can be necessary. We also show the classifier learned by a support vector machine (SVM) is robust under finite-sample noise.
Temporal Coherence for Active Learning in Videos
Authors: Javad Zolfaghari Bengar, Abel Gonzalez-Garcia, Gabriel Villalonga, Bogdan Raducanu, Hamed H. Aghdam, Mikhail Mozerov, Antonio M. Lopez, Joost Van de Weijer
Date: 2019-08-30 00:00:00
Tasks: Active Learning, Autonomous Driving, Object, object-detection, Object Detection, Video Object Detection

Autonomous driving systems require huge amounts of data to train. Manual annotation of this data is time-consuming and prohibitively expensive since it involves human resources. Therefore, active learning emerged as an alternative to ease this effort and to make data annotation more manageable. In this paper, we introduce a novel active learning approach for object detection in videos by exploiting temporal coherence. Our active learning criterion is based on the estimated number of errors in terms of false positives and false negatives. The detections obtained by the object detector are used to define the nodes of a graph and tracked forward and backward to temporally link the nodes. Minimizing an energy function defined on this graphical model provides estimates of both false positives and false negatives. Additionally, we introduce a synthetic video dataset, called SYNTHIA-AL, specially designed to evaluate active learning for video object detection in road scenes. Finally, we show that our approach outperforms active learning baselines tested on two datasets.
Fashion Retrieval via Graph Reasoning Networks on a Similarity Pyramid
Authors: Zhanghui Kuang, Yiming Gao, Guanbin Li, Ping Luo, Yimin Chen, Liang Lin, Wayne Zhang
Date: 2019-08-30 00:00:00
Proceeding: ICCV 2019 10
Tasks: Image Retrieval, Retrieval

Matching clothing images from customers and online shopping stores has rich applications in E-commerce. Existing algorithms encoded an image as a global feature vector and performed retrieval with the global representation. However, discriminative local information on clothes are submerged in this global representation, resulting in sub-optimal performance. To address this issue, we propose a novel Graph Reasoning Network (GRNet) on a Similarity Pyramid, which learns similarities between a query and a gallery cloth by using both global and local representations in multiple scales. The similarity pyramid is represented by a Graph of similarity, where nodes represent similarities between clothing components at different scales, and the final matching score is obtained by message passing along edges. In GRNet, graph reasoning is solved by training a graph convolutional network, enabling to align salient clothing components to improve clothing retrieval. To facilitate future researches, we introduce a new benchmark FindFashion, containing rich annotations of bounding boxes, views, occlusions, and cropping. Extensive experiments show that GRNet obtains new state-of-the-art results on two challenging benchmarks, e.g., pushing the top-1, top-20, and top-50 accuracies on DeepFashion to 26%, 64%, and 75% (i.e., 4%, 10%, and 10% absolute improvements), outperforming competitors with large margins. On FindFashion, GRNet achieves considerable improvements on all empirical settings.
Learning to Ask: Question-based Sequential Bayesian Product Search
Authors: Jie Zou, Evangelos Kanoulas
Date: 2019-08-30 00:00:00
Tasks: Retrieval

Product search is generally recognized as the first and foremost stage of online shopping and thus significant for users and retailers of e-commerce. Most of the traditional retrieval methods use some similarity functions to match the user's query and the document that describes a product, either directly or in a latent vector space. However, user queries are often too general to capture the minute details of the specific product that a user is looking for. In this paper, we propose a novel interactive method to effectively locate the best matching product. The method is based on the assumption that there is a set of candidate questions for each product to be asked. In this work, we instantiate this candidate set by making the hypothesis that products can be discriminated by the entities that appear in the documents associated with them. We propose a Question-based Sequential Bayesian Product Search method, QSBPS, which directly queries users on the expected presence of entities in the relevant product documents. The method learns the product relevance as well as the reward of the potential questions to be asked to the user by being trained on the search history and purchase behavior of a specific user together with that of other users. The experimental results show that the proposed method can greatly improve the performance of product search compared to the state-of-the-art baselines.
Earlier Isn't Always Better: Sub-aspect Analysis on Corpus and System Biases in Summarization
Authors: Taehee Jung, Dongyeop Kang, Lucas Mentch, Eduard Hovy
Date: 2019-08-30 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Articles, Diversity, News Summarization, Position

Despite the recent developments on neural summarization systems, the underlying logic behind the improvements from the systems and its corpus-dependency remains largely unexplored. Position of sentences in the original text, for example, is a well known bias for news summarization. Following in the spirit of the claim that summarization is a combination of sub-functions, we define three sub-aspects of summarization: position, importance, and diversity and conduct an extensive analysis of the biases of each sub-aspect with respect to the domain of nine different summarization corpora (e.g., news, academic papers, meeting minutes, movie script, books, posts). We find that while position exhibits substantial bias in news articles, this is not the case, for example, with academic papers and meeting minutes. Furthermore, our empirical study shows that different types of summarization systems (e.g., neural-based) are composed of different degrees of the sub-aspects. Our study provides useful lessons regarding consideration of underlying sub-aspects when collecting a new summarization dataset or developing a new system.
Fact-Checking Meets Fauxtography: Verifying Claims About Images
Authors: Dimitrina Zlatkova, Preslav Nakov, Ivan Koychev
Date: 2019-08-30 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Fact Checking

The recent explosion of false claims in social media and on the Web in general has given rise to a lot of manual fact-checking initiatives. Unfortunately, the number of claims that need to be fact-checked is several orders of magnitude larger than what humans can handle manually. Thus, there has been a lot of research aiming at automating the process. Interestingly, previous work has largely ignored the growing number of claims about images. This is despite the fact that visual imagery is more influential than text and naturally appears alongside fake news. Here we aim at bridging this gap. In particular, we create a new dataset for this problem, and we explore a variety of features modeling the claim, the image, and the relationship between the claim and the image. The evaluation results show sizable improvements over the baseline. We release our dataset, hoping to enable further research on fact-checking claims about images.
Multi-Modal Fusion for End-to-End RGB-T Tracking
Authors: Lichao Zhang, Martin Danelljan, Abel Gonzalez-Garcia, Joost Van de Weijer, Fahad Shahbaz Khan
Date: 2019-08-30 00:00:00
Tasks: Image-to-Image Translation, Rgb-T Tracking

We propose an end-to-end tracking framework for fusing the RGB and TIR modalities in RGB-T tracking. Our baseline tracker is DiMP (Discriminative Model Prediction), which employs a carefully designed target prediction network trained end-to-end using a discriminative loss. We analyze the effectiveness of modality fusion in each of the main components in DiMP, i.e. feature extractor, target estimation network, and classifier. We consider several fusion mechanisms acting at different levels of the framework, including pixel-level, feature-level and response-level. Our tracker is trained in an end-to-end manner, enabling the components to learn how to fuse the information from both modalities. As data to train our model, we generate a large-scale RGB-T dataset by considering an annotated RGB tracking dataset (GOT-10k) and synthesizing paired TIR images using an image-to-image translation approach. We perform extensive experiments on VOT-RGBT2019 dataset and RGBT210 dataset, evaluating each type of modality fusing on each model component. The results show that the proposed fusion mechanisms improve the performance of the single modality counterparts. We obtain our best results when fusing at the feature-level on both the IoU-Net and the model predictor, obtaining an EAO score of 0.391 on VOT-RGBT2019 dataset. With this fusion mechanism we achieve the state-of-the-art performance on RGBT210 dataset.
The OMG-Empathy Dataset: Evaluating the Impact of Affective Behavior in Storytelling
Authors: Pablo Barros, Nikhil Churamani, Angelica Lim, Stefan Wermter
Date: 2019-08-30 00:00:00

Processing human affective behavior is important for developing intelligent agents that interact with humans in complex interaction scenarios. A large number of current approaches that address this problem focus on classifying emotion expressions by grouping them into known categories. Such strategies neglect, among other aspects, the impact of the affective responses from an individual on their interaction partner thus ignoring how people empathize towards each other. This is also reflected in the datasets used to train models for affective processing tasks. Most of the recent datasets, in particular, the ones which capture natural interactions ("in-the-wild" datasets), are designed, collected, and annotated based on the recognition of displayed affective reactions, ignoring how these displayed or expressed emotions are perceived. In this paper, we propose a novel dataset composed of dyadic interactions designed, collected and annotated with a focus on measuring the affective impact that eight different stories have on the listener. Each video of the dataset contains around 5 minutes of interaction where a speaker tells a story to a listener. After each interaction, the listener annotated, using a valence scale, how the story impacted their affective state, reflecting how they empathized with the speaker as well as the story. We also propose different evaluation protocols and a baseline that encourages participation in the advancement of the field of artificial empathy and emotion contagion.
Discovering Reliable Correlations in Categorical Data
Authors: Panagiotis Mandros, Mario Boley, Jilles Vreeken
Date: 2019-08-30 00:00:00
Tasks: Attribute, Heuristic Search

In many scientific tasks we are interested in discovering whether there exist any correlations in our data. This raises many questions, such as how to reliably and interpretably measure correlation between a multivariate set of attributes, how to do so without having to make assumptions on distribution of the data or the type of correlation, and, how to efficiently discover the top-most reliably correlated attribute sets from data. In this paper we answer these questions for discovery tasks in categorical data. In particular, we propose a corrected-for-chance, consistent, and efficient estimator for normalized total correlation, by which we obtain a reliable, naturally interpretable, non-parametric measure for correlation over multivariate sets. For the discovery of the top-k correlated sets, we derive an effective algorithmic framework based on a tight bounding function. This framework offers exact, approximate, and heuristic search. Empirical evaluation shows that already for small sample sizes the estimator leads to low-regret optimization outcomes, while the algorithms are shown to be highly effective for both large and high-dimensional data. Through two case studies we confirm that our discovery framework identifies interesting and meaningful correlations.
Motion Capture from Pan-Tilt Cameras with Unknown Orientation
Authors: Roman Bachmann, Jörg Spörri, Pascal Fua, Helge Rhodin
Date: 2019-08-30 00:00:00
Tasks: Markerless Motion Capture

In sports, such as alpine skiing, coaches would like to know the speed and various biomechanical variables of their athletes and competitors. Existing methods use either body-worn sensors, which are cumbersome to setup, or manual image annotation, which is time consuming. We propose a method for estimating an athlete's global 3D position and articulated pose using multiple cameras. By contrast to classical markerless motion capture solutions, we allow cameras to rotate freely so that large capture volumes can be covered. In a first step, tight crops around the skier are predicted and fed to a 2D pose estimator network. The 3D pose is then reconstructed using a bundle adjustment method. Key to our solution is the rotation estimation of Pan-Tilt cameras in a joint optimization with the athlete pose and conditioning on relative background motion computed with feature tracking. Furthermore, we created a new alpine skiing dataset and annotated it with 2D pose labels, to overcome shortcomings of existing ones. Our method estimates accurate global 3D poses from images only and provides coaches with an automatic and fast tool for measuring and improving an athlete's performance.
Small Obstacle Avoidance Based on RGB-D Semantic Segmentation
Authors: Minjie Hua, Yibing Nan, Shiguo Lian
Date: 2019-08-30 00:00:00
Tasks: Optical Flow Estimation, Semantic Segmentation

This paper presents a novel obstacle avoidance system for road robots equipped with RGB-D sensor that captures scenes of its way forward. The purpose of the system is to have road robots move around autonomously and constantly without any collision even with small obstacles, which are often missed by existing solutions. For each input RGB-D image, the system uses a new two-stage semantic segmentation network followed by the morphological processing to generate the accurate semantic map containing road and obstacles. Based on the map, the local path planning is applied to avoid possible collision. Additionally, optical flow supervision and motion blurring augmented training scheme is applied to improve temporal consistency between adjacent frames and overcome the disturbance caused by camera shake. Various experiments are conducted to show that the proposed architecture obtains high performance both in indoor and outdoor scenarios.
Exploring Domain Shift in Extractive Text Summarization
Authors: Danqing Wang, PengFei Liu, Ming Zhong, Jie Fu, Xipeng Qiu, Xuanjing Huang
Date: 2019-08-30 00:00:00
Tasks: Extractive Text Summarization, Meta-Learning, Text Summarization

Although domain shift has been well explored in many NLP applications, it still has received little attention in the domain of extractive text summarization. As a result, the model is under-utilizing the nature of the training data due to ignoring the difference in the distribution of training sets and shows poor generalization on the unseen domain. With the above limitation in mind, in this paper, we first extend the conventional definition of the domain from categories into data sources for the text summarization task. Then we re-purpose a multi-domain summarization dataset and verify how the gap between different domains influences the performance of neural summarization models. Furthermore, we investigate four learning strategies and examine their abilities to deal with the domain shift problem. Experimental results on three different settings show their different characteristics in our new testbed. Our source code including \textit{BERT-based}, \textit{meta-learning} methods for multi-domain summarization learning and the re-purposed dataset \textsc{Multi-SUM} will be available on our project: \url{http://pfliu.com/TransferSum/}.
Autoregressive Text Generation Beyond Feedback Loops
Authors: Florian Schmidt, Stephan Mandt, Thomas Hofmann
Date: 2019-08-30 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Sentence, Text Generation

Autoregressive state transitions, where predictions are conditioned on past predictions, are the predominant choice for both deterministic and stochastic sequential models. However, autoregressive feedback exposes the evolution of the hidden state trajectory to potential biases from well-known train-test discrepancies. In this paper, we combine a latent state space model with a CRF observation model. We argue that such autoregressive observation models form an interesting middle ground that expresses local correlations on the word level but keeps the state evolution non-autoregressive. On unconditional sentence generation we show performance improvements compared to RNN and GAN baselines while avoiding some prototypical failure modes of autoregressive models.
EBPC: Extended Bit-Plane Compression for Deep Neural Network Inference and Training Accelerators
Authors: Lukas Cavigelli, Georg Rutishauser, Luca Benini
Date: 2019-08-30 00:00:00
Tasks: image-classification, Image Classification, Object Recognition, speech-recognition, Speech Recognition

In the wake of the success of convolutional neural networks in image classification, object recognition, speech recognition, etc., the demand for deploying these compute-intensive ML models on embedded and mobile systems with tight power and energy constraints at low cost, as well as for boosting throughput in data centers, is growing rapidly. This has sparked a surge of research into specialized hardware accelerators. Their performance is typically limited by I/O bandwidth, power consumption is dominated by I/O transfers to off-chip memory, and on-chip memories occupy a large part of the silicon area. We introduce and evaluate a novel, hardware-friendly, and lossless compression scheme for the feature maps present within convolutional neural networks. We present hardware architectures and synthesis results for the compressor and decompressor in 65nm. With a throughput of one 8-bit word/cycle at 600MHz, they fit into 2.8kGE and 3.0kGE of silicon area, respectively - together the size of less than seven 8-bit multiply-add units at the same throughput. We show that an average compression ratio of 5.1x for AlexNet, 4x for VGG-16, 2.4x for ResNet-34 and 2.2x for MobileNetV2 can be achieved - a gain of 45-70% over existing methods. Our approach also works effectively for various number formats, has a low frame-to-frame variance on the compression ratio, and achieves compression factors for gradient map compression during training that are even better than for inference.
Domain Intersection and Domain Difference
Authors: Sagie Benaim, Michael Khaitov, Tomer Galanti, Lior Wolf
Date: 2019-08-30 00:00:00
Proceeding: ICCV 2019 10

We present a method for recovering the shared content between two visual domains as well as the content that is unique to each domain. This allows us to map from one domain to the other, in a way in which the content that is specific for the first domain is removed and the content that is specific for the second is imported from any image in the second domain. In addition, our method enables generation of images from the intersection of the two domains as well as their union, despite having no such samples during training. The method is shown analytically to contain all the sufficient and necessary constraints. It also outperforms the literature methods in an extensive set of experiments. Our code is available at https://github.com/sagiebenaim/DomainIntersectionDifference.
Semi-supervised Learning of Fetal Anatomy from Ultrasound
Authors: Jeremy Tan, Anselm Au, Qingjie Meng, Bernhard Kainz
Date: 2019-08-30 00:00:00
Tasks: Anatomy, General Classification

Semi-supervised learning methods have achieved excellent performance on standard benchmark datasets using very few labelled images. Anatomy classification in fetal 2D ultrasound is an ideal problem setting to test whether these results translate to non-ideal data. Our results indicate that inclusion of a challenging background class can be detrimental and that semi-supervised learning mostly benefits classes that are already distinct, sometimes at the expense of more similar classes.