pwc-archive/papers-with-abstracts huggingface dataset:
Towards Enhancing Fine-grained Details for Image Matting
Authors: Chang Liu, Henghui Ding, Xudong Jiang
Date: 2021-01-22 00:00:00
Tasks: Decoder, Image Matting

In recent years, deep natural image matting has been rapidly evolved by extracting high-level contextual features into the model. However, most current methods still have difficulties with handling tiny details, like hairs or furs. In this paper, we argue that recovering these microscopic details relies on low-level but high-definition texture features. However, {these features are downsampled in a very early stage in current encoder-decoder-based models, resulting in the loss of microscopic details}. To address this issue, we design a deep image matting model {to enhance fine-grained details. Our model consists of} two parallel paths: a conventional encoder-decoder Semantic Path and an independent downsampling-free Textural Compensate Path (TCP). The TCP is proposed to extract fine-grained details such as lines and edges in the original image size, which greatly enhances the fineness of prediction. Meanwhile, to leverage the benefits of high-level context, we propose a feature fusion unit(FFU) to fuse multi-scale features from the semantic path and inject them into the TCP. In addition, we have observed that poorly annotated trimaps severely affect the performance of the model. Thus we further propose a novel term in loss function and a trimap generation method to improve our model's robustness to the trimaps. The experiments show that our method outperforms previous start-of-the-art methods on the Composition-1k dataset.
Hybrid Rotation Averaging: A Fast and Robust Rotation Averaging Approach
Authors: Yu Chen, Ji Zhao, Laurent Kneip
Date: 2021-01-22 00:00:00
Proceeding: CVPR 2021 1
Tasks: 3D Reconstruction

We address rotation averaging (RA) and its application to real-world 3D reconstruction. Local optimisation based approaches are the de facto choice, though they only guarantee a local optimum. Global optimisers ensure global optimality in low noise conditions, but they are inefficient and may easily deviate under the influence of outliers or elevated noise levels. We push the envelope of rotation averaging by leveraging the advantages of a global RA method and a local RA method. Combined with a fast view graph filtering as preprocessing, the proposed hybrid approach is robust to outliers. We further apply the proposed hybrid rotation averaging approach to incremental Structure from Motion (SfM), the accuracy and robustness of SfM are both improved by adding the resulting global rotations as regularisers to bundle adjustment. Overall, we demonstrate high practicality of the proposed method as bad camera poses are effectively corrected and drift is reduced.
Solving the Same-Different Task with Convolutional Neural Networks
Authors: Nicola Messina, Giuseppe Amato, Fabio Carrara, Claudio Gennaro, Fabrizio Falchi
Date: 2021-01-22 00:00:00
Tasks: Overall - Test, Zero-shot Generalization

Deep learning demonstrated major abilities in solving many kinds of different real-world problems in computer vision literature. However, they are still strained by simple reasoning tasks that humans consider easy to solve. In this work, we probe current state-of-the-art convolutional neural networks on a difficult set of tasks known as the same-different problems. All the problems require the same prerequisite to be solved correctly: understanding if two random shapes inside the same image are the same or not. With the experiments carried out in this work, we demonstrate that residual connections, and more generally the skip connections, seem to have only a marginal impact on the learning of the proposed problems. In particular, we experiment with DenseNets, and we examine the contribution of residual and recurrent connections in already tested architectures, ResNet-18, and CorNet-S respectively. Our experiments show that older feed-forward networks, AlexNet and VGG, are almost unable to learn the proposed problems, except in some specific scenarios. We show that recently introduced architectures can converge even in the cases where the important parts of their architecture are removed. We finally carry out some zero-shot generalization tests, and we discover that in these scenarios residual and recurrent connections can have a stronger impact on the overall test accuracy. On four difficult problems from the SVRT dataset, we can reach state-of-the-art results with respect to the previous approaches, obtaining super-human performances on three of the four problems.
Dense outlier detection and open-set recognition based on training with noisy negative images
Authors: Petra Bevandić, Ivan Krešo, Marin Oršić, Siniša Šegvić
Date: 2021-01-22 00:00:00
Tasks: Open Set Learning, Outlier Detection

Deep convolutional models often produce inadequate predictions for inputs foreign to the training distribution. Consequently, the problem of detecting outlier images has recently been receiving a lot of attention. Unlike most previous work, we address this problem in the dense prediction context in order to be able to locate outlier objects in front of in-distribution background. Our approach is based on two reasonable assumptions. First, we assume that the inlier dataset is related to some narrow application field (e.g.~road driving). Second, we assume that there exists a general-purpose dataset which is much more diverse than the inlier dataset (e.g.~ImageNet-1k). We consider pixels from the general-purpose dataset as noisy negative training samples since most (but not all) of them are outliers. We encourage the model to recognize borders between known and unknown by pasting jittered negative patches over inlier training images. Our experiments target two dense open-set recognition benchmarks (WildDash 1 and Fishyscapes) and one dense open-set recognition dataset (StreetHazard). Extensive performance evaluation indicates competitive potential of the proposed approach.
A Universal Deep Learning Framework for Real-Time Denoising of Ultrasound Images
Authors: Simone Cammarasana, Paolo Nicolardi, Giuseppe Patanè
Date: 2021-01-22 00:00:00
Tasks: Deep Learning, Denoising, Medical Diagnosis

Ultrasound images are widespread in medical diagnosis for muscle-skeletal, cardiac, and obstetrical diseases, due to the efficiency and non-invasiveness of the acquisition methodology. However, ultrasound acquisition introduces noise in the signal, which corrupts the resulting image and affects further processing steps, e.g., segmentation and quantitative analysis. We define a novel deep learning framework for the real-time denoising of ultrasound images. Firstly, we compare state-of-the-art methods for denoising (e.g., spectral, low-rank methods) and select WNNM (Weighted Nuclear Norm Minimisation) as the best denoising in terms of accuracy, preservation of anatomical features, and edge enhancement. Then, we propose a tuned version of WNNM (tuned-WNNM) that improves the quality of the denoised images and extends its applicability to ultrasound images. Through a deep learning framework, the tuned-WNNM qualitatively and quantitatively replicates WNNM results in real-time. Finally, our approach is general in terms of its building blocks and parameters of the deep learning and high-performance computing framework; in fact, we can select different denoising algorithms and deep learning architectures.
Understanding Visual Saliency in Mobile User Interfaces
Authors: Luis A. Leiva, Yunfei Xue, Avya Bansal, Hamed R. Tavakoli, Tuğçe Köroğlu, Niraj R. Dayama, Antti Oulasvirta
Date: 2021-01-22 00:00:00

For graphical user interface (UI) design, it is important to understand what attracts visual attention. While previous work on saliency has focused on desktop and web-based UIs, mobile app UIs differ from these in several respects. We present findings from a controlled study with 30 participants and 193 mobile UIs. The results speak to a role of expectations in guiding where users look at. Strong bias toward the top-left corner of the display, text, and images was evident, while bottom-up features such as color or size affected saliency less. Classic, parameter-free saliency models showed a weak fit with the data, and data-driven models improved significantly when trained specifically on this dataset (e.g., NSS rose from 0.66 to 0.84). We also release the first annotated dataset for investigating visual saliency in mobile UIs.
Linear Regression with Distributed Learning: A Generalization Error Perspective
Authors: Martin Hellkvist, Ayça Özçelikkale, Anders Ahlén
Date: 2021-01-22 00:00:00
Tasks: regression

Distributed learning provides an attractive framework for scaling the learning task by sharing the computational load over multiple nodes in a network. Here, we investigate the performance of distributed learning for large-scale linear regression where the model parameters, i.e., the unknowns, are distributed over the network. We adopt a statistical learning approach. In contrast to works that focus on the performance on the training data, we focus on the generalization error, i.e., the performance on unseen data. We provide high-probability bounds on the generalization error for both isotropic and correlated Gaussian data as well as sub-gaussian data. These results reveal the dependence of the generalization performance on the partitioning of the model over the network. In particular, our results show that the generalization error of the distributed solution can be substantially higher than that of the centralized solution even when the error on the training data is at the same level for both the centralized and distributed approaches. Our numerical results illustrate the performance with both real-world image data as well as synthetic data.
Will Artificial Intelligence supersede Earth System and Climate Models?
Authors: Christopher Irrgang, Niklas Boers, Maike Sonnewald, Elizabeth A. Barnes, Christopher Kadow, Joanna Staneva, Jan Saynisch-Wagner
Date: 2021-01-22 00:00:00
Tasks: Open-Ended Question Answering

We outline a perspective of an entirely new research branch in Earth and climate sciences, where deep neural networks and Earth system models are dismantled as individual methodological approaches and reassembled as learning, self-validating, and interpretable Earth system model-network hybrids. Following this path, we coin the term "Neural Earth System Modelling" (NESYM) and highlight the necessity of a transdisciplinary discussion platform, bringing together Earth and climate scientists, big data analysts, and AI experts. We examine the concurrent potential and pitfalls of Neural Earth System Modelling and discuss the open question whether artificial intelligence will not only infuse Earth system modelling, but ultimately render them obsolete.
Sparsistent filtering of comovement networks from high-dimensional data
Authors: Arnab Chakrabarti, Anindya S. Chakrabarti
Date: 2021-01-22 00:00:00
Tasks: Dimensionality Reduction, Vocal Bursts Intensity Prediction

Network filtering is an important form of dimension reduction to isolate the core constituents of large and interconnected complex systems. We introduce a new technique to filter large dimensional networks arising out of dynamical behavior of the constituent nodes, exploiting their spectral properties. As opposed to the well known network filters that rely on preserving key topological properties of the realized network, our method treats the spectrum as the fundamental object and preserves spectral properties. Applying asymptotic theory for high dimensional data for the filter, we show that it can be tuned to interpolate between zero filtering to maximal filtering that induces sparsity and consistency while having the least spectral distance from a linear shrinkage estimator. We apply our proposed filter to covariance networks constructed from financial data, to extract the key subnetwork embedded in the full sample network.
Projected Statistical Methods for Distributional Data on the Real Line with the Wasserstein Metric
Authors: Matteo Pegoraro, Mario Beraha
Date: 2021-01-22 00:00:00
Tasks: regression

We present a novel class of projected methods, to perform statistical analysis on a data set of probability distributions on the real line, with the 2-Wasserstein metric. We focus in particular on Principal Component Analysis (PCA) and regression. To define these models, we exploit a representation of the Wasserstein space closely related to its weak Riemannian structure, by mapping the data to a suitable linear space and using a metric projection operator to constrain the results in the Wasserstein space. By carefully choosing the tangent point, we are able to derive fast empirical methods, exploiting a constrained B-spline approximation. As a byproduct of our approach, we are also able to derive faster routines for previous work on PCA for distributions. By means of simulation studies, we compare our approaches to previously proposed methods, showing that our projected PCA has similar performance for a fraction of the computational cost and that the projected regression is extremely flexible even under misspecification. Several theoretical properties of the models are investigated and asymptotic consistency is proven. Two real world applications to Covid-19 mortality in the US and wind speed forecasting are discussed.
Adversarial Laws of Large Numbers and Optimal Regret in Online Classification
Authors: Noga Alon, Omri Ben-Eliezer, Yuval Dagan, Shay Moran, Moni Naor, Eylon Yogev
Date: 2021-01-22 00:00:00
Tasks: General Classification, Open-Ended Question Answering, PAC learning

Laws of large numbers guarantee that given a large enough sample from some population, the measure of any fixed sub-population is well-estimated by its frequency in the sample. We study laws of large numbers in sampling processes that can affect the environment they are acting upon and interact with it. Specifically, we consider the sequential sampling model proposed by Ben-Eliezer and Yogev (2020), and characterize the classes which admit a uniform law of large numbers in this model: these are exactly the classes that are \emph{online learnable}. Our characterization may be interpreted as an online analogue to the equivalence between learnability and uniform convergence in statistical (PAC) learning. The sample-complexity bounds we obtain are tight for many parameter regimes, and as an application, we determine the optimal regret bounds in online learning, stated in terms of \emph{Littlestone's dimension}, thus resolving the main open question from Ben-David, P\'al, and Shalev-Shwartz (2009), which was also posed by Rakhlin, Sridharan, and Tewari (2015).
CMSAOne@Dravidian-CodeMix-FIRE2020: A Meta Embedding and Transformer model for Code-Mixed Sentiment Analysis on Social Media Text
Authors: Suman Dowlagar, Radhika Mamidi
Date: 2021-01-22 00:00:00
Tasks: Sentence, Sentiment Analysis

Code-mixing(CM) is a frequently observed phenomenon that uses multiple languages in an utterance or sentence. CM is mostly practiced on various social media platforms and in informal conversations. Sentiment analysis (SA) is a fundamental step in NLP and is well studied in the monolingual text. Code-mixing adds a challenge to sentiment analysis due to its non-standard representations. This paper proposes a meta embedding with a transformer method for sentiment analysis on the Dravidian code-mixed dataset. In our method, we used meta embeddings to capture rich text representations. We used the proposed method for the Task: "Sentiment Analysis for Dravidian Languages in Code-Mixed Text", and it achieved an F1 score of $0.58$ and $0.66$ for the given Dravidian code mixed data sets. The code is provided in the Github https://github.com/suman101112/fire-2020-Dravidian-CodeMix.
HASOCOne@FIRE-HASOC2020: Using BERT and Multilingual BERT models for Hate Speech Detection
Authors: Suman Dowlagar, Radhika Mamidi
Date: 2021-01-22 00:00:00
Tasks: Hate Speech Detection, Transfer Learning

Hateful and Toxic content has become a significant concern in today's world due to an exponential rise in social media. The increase in hate speech and harmful content motivated researchers to dedicate substantial efforts to the challenging direction of hateful content identification. In this task, we propose an approach to automatically classify hate speech and offensive content. We have used the datasets obtained from FIRE 2019 and 2020 shared tasks. We perform experiments by taking advantage of transfer learning models. We observed that the pre-trained BERT model and the multilingual-BERT model gave the best results. The code is made publically available at https://github.com/suman101112/hasoc-fire-2020.
Does a Hybrid Neural Network based Feature Selection Model Improve Text Classification?
Authors: Suman Dowlagar, Radhika Mamidi
Date: 2021-01-22 00:00:00
Proceeding: ICON 2020 12
Tasks: BIG-bench Machine Learning, Classification, Dimensionality Reduction, feature selection, General Classification, text-classification, Text Classification

Text classification is a fundamental problem in the field of natural language processing. Text classification mainly focuses on giving more importance to all the relevant features that help classify the textual data. Apart from these, the text can have redundant or highly correlated features. These features increase the complexity of the classification algorithm. Thus, many dimensionality reduction methods were proposed with the traditional machine learning classifiers. The use of dimensionality reduction methods with machine learning classifiers has achieved good results. In this paper, we propose a hybrid feature selection method for obtaining relevant features by combining various filter-based feature selection methods and fastText classifier. We then present three ways of implementing a feature selection and neural network pipeline. We observed a reduction in training time when feature selection methods are used along with neural networks. We also observed a slight increase in accuracy on some datasets.
Multilingual Pre-Trained Transformers and Convolutional NN Classification Models for Technical Domain Identification
Authors: Suman Dowlagar, Radhika Mamidi
Date: 2021-01-22 00:00:00
Proceeding: ICON 2020 12
Tasks: General Classification, text-classification, Text Classification, Transfer Learning

In this paper, we present a transfer learning system to perform technical domain identification on multilingual text data. We have submitted two runs, one uses the transformer model BERT, and the other uses XLM-ROBERTa with the CNN model for text classification. These models allowed us to identify the domain of the given sentences for the ICON 2020 shared Task, TechDOfication: Technical Domain Identification. Our system ranked the best for the subtasks 1d, 1g for the given TechDOfication dataset.
Unsupervised Technical Domain Terms Extraction using Term Extractor
Authors: Suman Dowlagar, Radhika Mamidi
Date: 2021-01-22 00:00:00
Proceeding: ICON 2020 12
Tasks: Chunking, Task 2, Term Extraction

Terminology extraction, also known as term extraction, is a subtask of information extraction. The goal of terminology extraction is to extract relevant words or phrases from a given corpus automatically. This paper focuses on the unsupervised automated domain term extraction method that considers chunking, preprocessing, and ranking domain-specific terms using relevance and cohesion functions for ICON 2020 shared task 2: TermTraction.
Enhanced word embeddings using multi-semantic representation through lexical chains
Authors: Terry Ruas, Charles Henrique Porto Ferreira, William Grosky, Fabrício Olivetti de França, Débora Maria Rossi Medeiros
Date: 2021-01-22 00:00:00
Tasks: Document Classification, Sentence, Word Embeddings

The relationship between words in a sentence often tells us more about the underlying semantic content of a document than its actual words, individually. In this work, we propose two novel algorithms, called Flexible Lexical Chain II and Fixed Lexical Chain II. These algorithms combine the semantic relations derived from lexical chains, prior knowledge from lexical databases, and the robustness of the distributional hypothesis in word embeddings as building blocks forming a single system. In short, our approach has three main contributions: (i) a set of techniques that fully integrate word embeddings and lexical chains; (ii) a more robust semantic representation that considers the latent relation between words in a document; and (iii) lightweight word embeddings models that can be extended to any natural language task. We intend to assess the knowledge of pre-trained models to evaluate their robustness in the document classification task. The proposed techniques are tested against seven word embeddings algorithms using five different machine learning classifiers over six scenarios in the document classification task. Our results show the integration between lexical chains and word embeddings representations sustain state-of-the-art results, even against more complex systems.
Lexical semantic change for Ancient Greek and Latin
Authors: Valerio Perrone, Simon Hengchen, Marco Palma, Alessandro Vatri, Jim Q. Smith, Barbara McGillivray
Date: 2021-01-22 00:00:00

Change and its precondition, variation, are inherent in languages. Over time, new words enter the lexicon, others become obsolete, and existing words acquire new senses. Associating a word's correct meaning in its historical context is a central challenge in diachronic research. Historical corpora of classical languages, such as Ancient Greek and Latin, typically come with rich metadata, and existing models are limited by their inability to exploit contextual information beyond the document timestamp. While embedding-based methods feature among the current state of the art systems, they are lacking in the interpretative power. In contrast, Bayesian models provide explicit and interpretable representations of semantic change phenomena. In this chapter we build on GASC, a recent computational approach to semantic change based on a dynamic Bayesian mixture model. In this model, the evolution of word senses over time is based not only on distributional information of lexical nature, but also on text genres. We provide a systematic comparison of dynamic Bayesian mixture models for semantic change with state-of-the-art embedding-based models. On top of providing a full description of meaning change over time, we show that Bayesian mixture models are highly competitive approaches to detect binary semantic change in both Ancient Greek and Latin.
Evaluation Discrepancy Discovery: A Sentence Compression Case-study
Authors: Yevgeniy Puzikov
Date: 2021-01-22 00:00:00
Tasks: Sentence, Sentence Compression

Reliable evaluation protocols are of utmost importance for reproducible NLP research. In this work, we show that sometimes neither metric nor conventional human evaluation is sufficient to draw conclusions about system performance. Using sentence compression as an example task, we demonstrate how a system can game a well-established dataset to achieve state-of-the-art results. In contrast with the results reported in previous work that showed correlation between human judgements and metric scores, our manual analysis of state-of-the-art system outputs demonstrates that high metric scores may only indicate a better fit to the data, but not better outputs, as perceived by humans.