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5594b0c5-c2e3-4cd2-8721-def0e9d5fda8 | automated-attribution-and-intertextual | 1405.0616 | null | http://arxiv.org/abs/1405.0616v1 | http://arxiv.org/pdf/1405.0616v1.pdf | Automated Attribution and Intertextual Analysis | In this work, we employ quantitative methods from the realm of statistics and
machine learning to develop novel methodologies for author attribution and
textual analysis. In particular, we develop techniques and software suitable
for applications to Classical study, and we illustrate the efficacy of our
approach in several interesting open questions in the field. We apply our
numerical analysis techniques to questions of authorship attribution in the
case of the Greek tragedian Euripides, to instances of intertextuality and
influence in the poetry of the Roman statesman Seneca the Younger, and to cases
of "interpolated" text with respect to the histories of Livy. | ['James Brofos', 'Ajay Kannan', 'Rui Shu'] | 2014-05-03 | null | null | null | null | ['author-attribution'] | ['natural-language-processing'] | [ 3.83682828e-03 1.94423467e-01 -1.97334036e-01 1.41735584e-01
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d0b271a7-4e52-4977-b778-393621e6b68a | dota-a-large-scale-dataset-for-object | 1711.10398 | null | https://arxiv.org/abs/1711.10398v3 | https://arxiv.org/pdf/1711.10398v3.pdf | DOTA: A Large-scale Dataset for Object Detection in Aerial Images | Object detection is an important and challenging problem in computer vision. Although the past decade has witnessed major advances in object detection in natural scenes, such successes have been slow to aerial imagery, not only because of the huge variation in the scale, orientation and shape of the object instances on the earth's surface, but also due to the scarcity of well-annotated datasets of objects in aerial scenes. To advance object detection research in Earth Vision, also known as Earth Observation and Remote Sensing, we introduce a large-scale Dataset for Object deTection in Aerial images (DOTA). To this end, we collect $2806$ aerial images from different sensors and platforms. Each image is of the size about 4000-by-4000 pixels and contains objects exhibiting a wide variety of scales, orientations, and shapes. These DOTA images are then annotated by experts in aerial image interpretation using $15$ common object categories. The fully annotated DOTA images contains $188,282$ instances, each of which is labeled by an arbitrary (8 d.o.f.) quadrilateral To build a baseline for object detection in Earth Vision, we evaluate state-of-the-art object detection algorithms on DOTA. Experiments demonstrate that DOTA well represents real Earth Vision applications and are quite challenging. | ['Jiebo Luo', 'Zhen Zhu', 'Gui-Song Xia', 'Serge Belongie', 'Mihai Datcu', 'Jian Ding', 'Marcello Pelillo', 'Liangpei Zhang', 'Xiang Bai'] | 2017-11-28 | dota-a-large-scale-dataset-for-object-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Xia_DOTA_A_Large-Scale_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Xia_DOTA_A_Large-Scale_CVPR_2018_paper.pdf | cvpr-2018-6 | ['object-detection-in-aerial-images'] | ['computer-vision'] | [ 9.53350440e-02 -4.96020436e-01 4.49861884e-01 -1.90520123e-01
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44f0c305-10d6-4d5f-8c7b-4af6821f8a03 | detection-of-malicious-android-applications | 2103.00637 | null | https://arxiv.org/abs/2103.00637v1 | https://arxiv.org/pdf/2103.00637v1.pdf | Detection of Malicious Android Applications: Classical Machine Learning vs. Deep Neural Network Integrated with Clustering | Today anti-malware community is facing challenges due to the ever-increasing sophistication and volume of malware attacks developed by adversaries. Traditional malware detection mechanisms are not able to cope-up with next-generation malware attacks. Therefore in this paper, we propose effective and efficient Android malware detection models based on machine learning and deep learning integrated with clustering. We performed a comprehensive study of different feature reduction, classification and clustering algorithms over various performance metrics to construct the Android malware detection models. Our experimental results show that malware detection models developed using Random Forest eclipsed deep neural network and other classifiers on the majority of performance metrics. The baseline Random Forest model without any feature reduction achieved the highest AUC of 99.4%. Also, the segregating of vector space using clustering integrated with Random Forest further boosted the AUC to 99.6% in one cluster and direct detection of Android malware in another cluster, thus reducing the curse of dimensionality. Additionally, we found that feature reduction in detection models does improve the model efficiency (training and testing time) many folds without much penalty on the effectiveness of the detection model. | ['Mohit Sewak', 'Shivin Thukral', 'Sanjay K. Sahay', 'Hemant Rathore'] | 2021-02-28 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [ 3.90789546e-02 -5.26746571e-01 -2.06119806e-01 -7.90205672e-02
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b711d0ea-cd0a-4348-9d81-8d61fd00508b | contrastive-multi-view-textual-visual | 2211.12926 | null | https://arxiv.org/abs/2211.12926v1 | https://arxiv.org/pdf/2211.12926v1.pdf | Contrastive Multi-View Textual-Visual Encoding: Towards One Hundred Thousand-Scale One-Shot Logo Identification | In this paper, we study the problem of identifying logos of business brands in natural scenes in an open-set one-shot setting. This problem setup is significantly more challenging than traditionally-studied 'closed-set' and 'large-scale training samples per category' logo recognition settings. We propose a novel multi-view textual-visual encoding framework that encodes text appearing in the logos as well as the graphical design of the logos to learn robust contrastive representations. These representations are jointly learned for multiple views of logos over a batch and thereby they generalize well to unseen logos. We evaluate our proposed framework for cropped logo verification, cropped logo identification, and end-to-end logo identification in natural scene tasks; and compare it against state-of-the-art methods. Further, the literature lacks a 'very-large-scale' collection of reference logo images that can facilitate the study of one-hundred thousand-scale logo identification. To fill this gap in the literature, we introduce Wikidata Reference Logo Dataset (WiRLD), containing logos for 100K business brands harvested from Wikidata. Our proposed framework that achieves an area under the ROC curve of 91.3% on the QMUL-OpenLogo dataset for the verification task, outperforms state-of-the-art methods by 9.1% and 2.6% on the one-shot logo identification task on the Toplogos-10 and the FlickrLogos32 datasets, respectively. Further, we show that our method is more stable compared to other baselines even when the number of candidate logos is on a 100K scale. | ['Anand Mishra', 'Abhirama S. Penamakuri', 'Nakul Sharma'] | 2022-11-23 | null | null | null | null | ['logo-recognition'] | ['computer-vision'] | [ 2.70649552e-01 -5.51613688e-01 -3.79272819e-01 -2.03549147e-01
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48c2e491-45fb-4e70-9ac5-c00312335977 | spsql-step-by-step-parsing-based-framework | 2305.11061 | null | https://arxiv.org/abs/2305.11061v1 | https://arxiv.org/pdf/2305.11061v1.pdf | SPSQL: Step-by-step Parsing Based Framework for Text-to-SQL Generation | Converting text into the structured query language (Text2SQL) is a research hotspot in the field of natural language processing (NLP), which has broad application prospects. In the era of big data, the use of databases has penetrated all walks of life, in which the collected data is large in scale, diverse in variety, and wide in scope, making the data query cumbersome and inefficient, and putting forward higher requirements for the Text2SQL model. In practical applications, the current mainstream end-to-end Text2SQL model is not only difficult to build due to its complex structure and high requirements for training data, but also difficult to adjust due to massive parameters. In addition, the accuracy of the model is hard to achieve the desired result. Based on this, this paper proposes a pipelined Text2SQL method: SPSQL. This method disassembles the Text2SQL task into four subtasks--table selection, column selection, SQL generation, and value filling, which can be converted into a text classification problem, a sequence labeling problem, and two text generation problems, respectively. Then, we construct data formats of different subtasks based on existing data and improve the accuracy of the overall model by improving the accuracy of each submodel. We also use the named entity recognition module and data augmentation to optimize the overall model. We construct the dataset based on the marketing business data of the State Grid Corporation of China. Experiments demonstrate our proposed method achieves the best performance compared with the end-to-end method and other pipeline methods. | ['Han Jiang', 'Liangfeng Jin', 'Yiling Li', 'Hao Shen', 'Gang Sun', 'Ran Shen'] | 2023-05-10 | null | null | null | null | ['text-to-sql', 'marketing'] | ['computer-code', 'miscellaneous'] | [-9.44228917e-02 -4.64548320e-02 -1.72476023e-02 -6.00437522e-01
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46fdd58d-4883-47d9-a0fc-dc29f987401d | a-systematic-literature-review-of-automated-2 | 2108.09646 | null | https://arxiv.org/abs/2108.09646v2 | https://arxiv.org/pdf/2108.09646v2.pdf | A Systematic Review of Automated Query Reformulations in Source Code Search | Fixing software bugs and adding new features are two of the major maintenance tasks. Software bugs and features are reported as change requests. Developers consult these requests and often choose a few keywords from them as an ad hoc query. Then they execute the query with a search engine to find the exact locations within software code that need to be changed. Unfortunately, even experienced developers often fail to choose appropriate queries, which leads to costly trials and errors during a code search. Over the years, many studies attempt to reformulate the ad hoc queries from developers to support them. In this systematic literature review, we carefully select 70 primary studies on query reformulations from 2,970 candidate studies, perform an in-depth qualitative analysis (e.g., Grounded Theory), and then answer seven research questions with major findings. First, to date, eight major methodologies (e.g., term weighting, term co-occurrence analysis, thesaurus lookup) have been adopted to reformulate queries. Second, the existing studies suffer from several major limitations (e.g., lack of generalizability, vocabulary mismatch problem, subjective bias) that might prevent their wide adoption. Finally, we discuss the best practices and future opportunities to advance the state of research in search query reformulations. | ['Chanchal K. Roy', 'Mohammad Masudur Rahman'] | 2021-08-22 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [-1.09632060e-01 -2.59966075e-01 -5.48331976e-01 -1.23588823e-01
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7a9784f7-8b3b-4d92-a1a4-c266900b5fc4 | intrinsic-decomposition-of-image-sequences | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Laffont_Intrinsic_Decomposition_of_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Laffont_Intrinsic_Decomposition_of_ICCV_2015_paper.pdf | Intrinsic Decomposition of Image Sequences From Local Temporal Variations | We present a method for intrinsic image decomposition, which aims to decompose images into reflectance and shading layers. Our input is a sequence of images with varying illumination acquired by a static camera, e.g. an indoor scene with a moving light source or an outdoor timelapse. We leverage the local color variations observed over time to infer constraints on the reflectance and solve the ill-posed image decomposition problem. In particular, we derive an adaptive local energy from the observations of each local neighborhood over time, and integrate distant pairwise constraints to enforce coherent decomposition across all surfaces with consistent shading changes. Our method is solely based on multiple observations of a Lambertian scene under varying illumination and does not require user interaction, scene geometry, or an explicit lighting model. We compare our results with several intrinsic decomposition methods on a number of synthetic and captured datasets. | ['Jean-Charles Bazin', 'Pierre-Yves Laffont'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 7.08410323e-01 -4.48156118e-01 5.18648684e-01 -4.05528784e-01
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cf1f3ccc-9b43-4c53-ba49-37ba7e27785f | editorial-introduction-to-the-issue-on-deep | 2102.06531 | null | https://arxiv.org/abs/2102.06531v1 | https://arxiv.org/pdf/2102.06531v1.pdf | Editorial: Introduction to the Issue on Deep Learning for Image/Video Restoration and Compression | Recent works have shown that learned models can achieve significant performance gains, especially in terms of perceptual quality measures, over traditional methods. Hence, the state of the art in image restoration and compression is getting redefined. This special issue covers the state of the art in learned image/video restoration and compression to promote further progress in innovative architectures and training methods for effective and efficient networks for image/video restoration and compression. | ['Chao Dong', 'Radu Timofte', 'Michele Covell', 'A. Murat Tekalp'] | 2021-02-09 | null | null | null | null | ['video-restoration'] | ['computer-vision'] | [ 6.45842791e-01 -2.17148602e-01 -4.44865465e-01 -3.53780985e-01
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6e5c922b-0bcb-47a7-a848-8e01defe0c39 | deepsdf-learning-continuous-signed-distance | 1901.05103 | null | http://arxiv.org/abs/1901.05103v1 | http://arxiv.org/pdf/1901.05103v1.pdf | DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation | Computer graphics, 3D computer vision and robotics communities have produced
multiple approaches to representing 3D geometry for rendering and
reconstruction. These provide trade-offs across fidelity, efficiency and
compression capabilities. In this work, we introduce DeepSDF, a learned
continuous Signed Distance Function (SDF) representation of a class of shapes
that enables high quality shape representation, interpolation and completion
from partial and noisy 3D input data. DeepSDF, like its classical counterpart,
represents a shape's surface by a continuous volumetric field: the magnitude of
a point in the field represents the distance to the surface boundary and the
sign indicates whether the region is inside (-) or outside (+) of the shape,
hence our representation implicitly encodes a shape's boundary as the
zero-level-set of the learned function while explicitly representing the
classification of space as being part of the shapes interior or not. While
classical SDF's both in analytical or discretized voxel form typically
represent the surface of a single shape, DeepSDF can represent an entire class
of shapes. Furthermore, we show state-of-the-art performance for learned 3D
shape representation and completion while reducing the model size by an order
of magnitude compared with previous work. | ['Steven Lovegrove', 'Richard Newcombe', 'Peter Florence', 'Jeong Joon Park', 'Julian Straub'] | 2019-01-16 | deepsdf-learning-continuous-signed-distance-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Park_DeepSDF_Learning_Continuous_Signed_Distance_Functions_for_Shape_Representation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Park_DeepSDF_Learning_Continuous_Signed_Distance_Functions_for_Shape_Representation_CVPR_2019_paper.pdf | cvpr-2019-6 | ['3d-shape-representation'] | ['computer-vision'] | [ 2.15454891e-01 4.03194457e-01 2.65169084e-01 -3.10493529e-01
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7df41fd1-66be-4be7-b2d8-1b36c161d4d3 | provable-convergence-of-variational-monte | 2303.10599 | null | https://arxiv.org/abs/2303.10599v1 | https://arxiv.org/pdf/2303.10599v1.pdf | Provable Convergence of Variational Monte Carlo Methods | The Variational Monte Carlo (VMC) is a promising approach for computing the ground state energy of many-body quantum problems and attracts more and more interests due to the development of machine learning. The recent paradigms in VMC construct neural networks as trial wave functions, sample quantum configurations using Markov chain Monte Carlo (MCMC) and train neural networks with stochastic gradient descent (SGD) method. However, the theoretical convergence of VMC is still unknown when SGD interacts with MCMC sampling given a well-designed trial wave function. Since MCMC reduces the difficulty of estimating gradients, it has inevitable bias in practice. Moreover, the local energy may be unbounded, which makes it harder to analyze the error of MCMC sampling. Therefore, we assume that the local energy is sub-exponential and use the Bernstein inequality for non-stationary Markov chains to derive error bounds of the MCMC estimator. Consequently, VMC is proven to have a first order convergence rate $O(\log K/\sqrt{n K})$ with $K$ iterations and a sample size $n$. It partially explains how MCMC influences the behavior of SGD. Furthermore, we verify the so-called correlated negative curvature condition and relate it to the zero-variance phenomena in solving eigenvalue functions. It is shown that VMC escapes from saddle points and reaches $(\epsilon,\epsilon^{1/4})$ -approximate second order stationary points or $\epsilon^{1/2}$-variance points in at least $O(\epsilon^{-11/2}\log^{2}(1/\epsilon) )$ steps with high probability. Our analysis enriches the understanding of how VMC converges efficiently and can be applied to general variational methods in physics and statistics. | ['Zaiwen Wen', 'Huajie Chen', 'Fan Chen', 'Tianyou Li'] | 2023-03-19 | null | null | null | null | ['variational-monte-carlo'] | ['miscellaneous'] | [-3.30133811e-02 -4.08844873e-02 7.98127577e-02 8.60987883e-03
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208f4081-17c2-429f-bcdd-9ffe73598ee1 | chatgpt-as-an-attack-tool-stealthy-textual | 2304.14475 | null | https://arxiv.org/abs/2304.14475v1 | https://arxiv.org/pdf/2304.14475v1.pdf | ChatGPT as an Attack Tool: Stealthy Textual Backdoor Attack via Blackbox Generative Model Trigger | Textual backdoor attacks pose a practical threat to existing systems, as they can compromise the model by inserting imperceptible triggers into inputs and manipulating labels in the training dataset. With cutting-edge generative models such as GPT-4 pushing rewriting to extraordinary levels, such attacks are becoming even harder to detect. We conduct a comprehensive investigation of the role of black-box generative models as a backdoor attack tool, highlighting the importance of researching relative defense strategies. In this paper, we reveal that the proposed generative model-based attack, BGMAttack, could effectively deceive textual classifiers. Compared with the traditional attack methods, BGMAttack makes the backdoor trigger less conspicuous by leveraging state-of-the-art generative models. Our extensive evaluation of attack effectiveness across five datasets, complemented by three distinct human cognition assessments, reveals that Figure 4 achieves comparable attack performance while maintaining superior stealthiness relative to baseline methods. | ['Chaowei Xiao', 'V. G. Vinod Vydiswaran', 'Zhuofeng Wu', 'Yijin Yang', 'Jiazhao Li'] | 2023-04-27 | null | null | null | null | ['backdoor-attack'] | ['adversarial'] | [ 2.42368832e-01 1.88269034e-01 -7.28516281e-02 9.27660912e-02
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c8bb4d5d-d265-450e-893d-28ad999ad98c | improving-the-performance-of-eeg-decoding | 2011.14694 | null | https://arxiv.org/abs/2011.14694v4 | https://arxiv.org/pdf/2011.14694v4.pdf | Anchored-STFT and GNAA: An extension of STFT in conjunction with an adversarial data augmentation technique for the decoding of neural signals | Brain-computer interfaces (BCIs) enable communication between humans and machines by translating brain activity into control commands. Electroencephalography (EEG) signals are one of the most used brain signals in non-invasive BCI applications but are often contaminated with noise. Therefore, it is possible that meaningful patterns for classifying EEG signals are deeply hidden. State-of-the-art deep-learning algorithms are successful in learning hidden, meaningful patterns. However, the quality and the quantity of the presented inputs is pivotal. Here, we propose a novel feature extraction method called anchored Short Time Fourier Transform (anchored-STFT), which is an advanced version of STFT, as it minimizes the trade-off between temporal and spectral resolution presented by STFT. In addition, we propose a novel augmentation method, called gradient norm adversarial augmentation (GNAA). GNAA is not only an augmentation method but is also used to harness adversarial inputs in EEG data, which not only improves the classification accuracy but also enhances the robustness of the classifier. In addition, we also propose a new CNN architecture, namely Skip-Net, for the classification of EEG signals. The proposed pipeline outperforms all state-of-the-art methods and yields an average classification accuracy of 90.7 % and 89.54 % on BCI competition II dataset III and BCI competition IV dataset 2b, respectively. | ['Christian Klaes', 'Ioannis Iossifidis', 'Tobias Glasmachers', 'Susanne Dyck', 'Muhammad Saif-ur-Rehman', 'Omair Ali'] | 2020-11-30 | null | null | null | null | ['eeg-decoding', 'eeg-decoding'] | ['medical', 'time-series'] | [ 3.86569679e-01 -2.30820939e-01 4.28202450e-01 -1.74478874e-01
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6c78fbcc-4fe9-4eb7-9f95-27777fedb31f | unsupervised-feature-based-algorithms-for | 2305.01429 | null | https://arxiv.org/abs/2305.01429v1 | https://arxiv.org/pdf/2305.01429v1.pdf | Unsupervised Feature Based Algorithms for Time Series Extrinsic Regression | Time Series Extrinsic Regression (TSER) involves using a set of training time series to form a predictive model of a continuous response variable that is not directly related to the regressor series. The TSER archive for comparing algorithms was released in 2022 with 19 problems. We increase the size of this archive to 63 problems and reproduce the previous comparison of baseline algorithms. We then extend the comparison to include a wider range of standard regressors and the latest versions of TSER models used in the previous study. We show that none of the previously evaluated regressors can outperform a regression adaptation of a standard classifier, rotation forest. We introduce two new TSER algorithms developed from related work in time series classification. FreshPRINCE is a pipeline estimator consisting of a transform into a wide range of summary features followed by a rotation forest regressor. DrCIF is a tree ensemble that creates features from summary statistics over random intervals. Our study demonstrates that both algorithms, along with InceptionTime, exhibit significantly better performance compared to the other 18 regressors tested. More importantly, these two proposals (DrCIF and FreshPRINCE) models are the only ones that significantly outperform the standard rotation forest regressor. | ['Anthony Bagnall', 'Diego Furtado Silva', 'Guilherme Arcencio', 'Matthew Middlehurst', 'David Guijo-Rubio'] | 2023-05-02 | null | null | null | null | ['time-series-classification'] | ['time-series'] | [ 4.47994769e-01 -3.89528126e-01 -3.99215311e-01 -3.59926969e-01
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6615c078-5e0a-4271-ae99-4ab2ef29ac96 | societal-biases-in-retrieved-contents | 2104.13640 | null | https://arxiv.org/abs/2104.13640v2 | https://arxiv.org/pdf/2104.13640v2.pdf | Societal Biases in Retrieved Contents: Measurement Framework and Adversarial Mitigation for BERT Rankers | Societal biases resonate in the retrieved contents of information retrieval (IR) systems, resulting in reinforcing existing stereotypes. Approaching this issue requires established measures of fairness in respect to the representation of various social groups in retrieval results, as well as methods to mitigate such biases, particularly in the light of the advances in deep ranking models. In this work, we first provide a novel framework to measure the fairness in the retrieved text contents of ranking models. Introducing a ranker-agnostic measurement, the framework also enables the disentanglement of the effect on fairness of collection from that of rankers. To mitigate these biases, we propose AdvBert, a ranking model achieved by adapting adversarial bias mitigation for IR, which jointly learns to predict relevance and remove protected attributes. We conduct experiments on two passage retrieval collections (MSMARCO Passage Re-ranking and TREC Deep Learning 2019 Passage Re-ranking), which we extend by fairness annotations of a selected subset of queries regarding gender attributes. Our results on the MSMARCO benchmark show that, (1) all ranking models are less fair in comparison with ranker-agnostic baselines, and (2) the fairness of Bert rankers significantly improves when using the proposed AdvBert models. Lastly, we investigate the trade-off between fairness and utility, showing that we can maintain the significant improvements in fairness without any significant loss in utility. | ['Markus Schedl', 'Simone Kopeinik', 'Navid Rekabsaz'] | 2021-04-28 | null | null | null | null | ['passage-re-ranking'] | ['natural-language-processing'] | [-2.14567363e-01 -3.11518759e-02 -3.35344404e-01 -4.71852273e-01
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aed092c5-aef8-4b32-87ec-613c55bc3691 | bayesian-optimisation-for-active-monitoring | 2202.07595 | null | https://arxiv.org/abs/2202.07595v1 | https://arxiv.org/pdf/2202.07595v1.pdf | Bayesian Optimisation for Active Monitoring of Air Pollution | Air pollution is one of the leading causes of mortality globally, resulting in millions of deaths each year. Efficient monitoring is important to measure exposure and enforce legal limits. New low-cost sensors can be deployed in greater numbers and in more varied locations, motivating the problem of efficient automated placement. Previous work suggests Bayesian optimisation is an appropriate method, but only considered a satellite data set, with data aggregated over all altitudes. It is ground-level pollution, that humans breathe, which matters most. We improve on those results using hierarchical models and evaluate our models on urban pollution data in London to show that Bayesian optimisation can be successfully applied to the problem. | ['Nigel H. Goddard', 'Christopher G. Lucas', 'Sigrid Passano Hellan'] | 2022-02-15 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 3.13731968e-01 -1.89454675e-01 3.38922068e-02 -1.48219869e-01
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f425fb4f-9325-41af-8406-27021af619f7 | end-to-end-attention-based-text-dependent | 1701.00562 | null | http://arxiv.org/abs/1701.00562v1 | http://arxiv.org/pdf/1701.00562v1.pdf | End-to-End Attention based Text-Dependent Speaker Verification | A new type of End-to-End system for text-dependent speaker verification is
presented in this paper. Previously, using the phonetically
discriminative/speaker discriminative DNNs as feature extractors for speaker
verification has shown promising results. The extracted frame-level (DNN
bottleneck, posterior or d-vector) features are equally weighted and aggregated
to compute an utterance-level speaker representation (d-vector or i-vector). In
this work we use speaker discriminative CNNs to extract the noise-robust
frame-level features. These features are smartly combined to form an
utterance-level speaker vector through an attention mechanism. The proposed
attention model takes the speaker discriminative information and the phonetic
information to learn the weights. The whole system, including the CNN and
attention model, is joint optimized using an end-to-end criterion. The training
algorithm imitates exactly the evaluation process --- directly mapping a test
utterance and a few target speaker utterances into a single verification score.
The algorithm can automatically select the most similar impostor for each
target speaker to train the network. We demonstrated the effectiveness of the
proposed end-to-end system on Windows $10$ "Hey Cortana" speaker verification
task. | ['Jinyu Li', 'Shi-Xiong Zhang', 'Zhuo Chen', 'Yifan Gong', 'Yong Zhao'] | 2017-01-03 | null | null | null | null | ['text-dependent-speaker-verification'] | ['speech'] | [ 1.30675331e-01 -2.12502539e-01 1.78593304e-02 -1.04593956e+00
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b3bb2d16-7700-4df8-8353-e6c215aead82 | video-similarity-and-alignment-learning-on | 2108.01817 | null | https://arxiv.org/abs/2108.01817v1 | https://arxiv.org/pdf/2108.01817v1.pdf | Video Similarity and Alignment Learning on Partial Video Copy Detection | Existing video copy detection methods generally measure video similarity based on spatial similarities between key frames, neglecting the latent similarity in temporal dimension, so that the video similarity is biased towards spatial information. There are methods modeling unified video similarity in an end-to-end way, but losing detailed partial alignment information, which causes the incapability of copy segments localization. To address the above issues, we propose the Video Similarity and Alignment Learning (VSAL) approach, which jointly models spatial similarity, temporal similarity and partial alignment. To mitigate the spatial similarity bias, we model the temporal similarity as the mask map predicted from frame-level spatial similarity, where each element indicates the probability of frame pair lying right on the partial alignments. To further localize partial copies, the step map is learned from the spatial similarity where the elements indicate extending directions of the current partial alignments on the spatial-temporal similarity map. Obtained from the mask map, the start points extend out into partial optimal alignments following instructions of the step map. With the similarity and alignment learning strategy, VSAL achieves the state-of-the-art F1-score on VCDB core dataset. Furthermore, we construct a new benchmark of partial video copy detection and localization by adding new segment-level annotations for FIVR-200k dataset, where VSAL also achieves the best performance, verifying its effectiveness in more challenging situations. Our project is publicly available at https://pvcd-vsal.github.io/vsal/. | ['Yiliang Lv', 'Mingqian Tang', 'Xiangteng He', 'Zhen Han'] | 2021-08-04 | null | null | null | null | ['partial-video-copy-detection', 'video-similarity'] | ['computer-vision', 'computer-vision'] | [ 2.31905356e-02 -4.57985044e-01 -6.62621915e-01 -1.58318907e-01
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b8bc39c3-ecab-4d2b-a5a3-495c9b9962de | holistic-interaction-transformer-network-for | 2210.12686 | null | https://arxiv.org/abs/2210.12686v2 | https://arxiv.org/pdf/2210.12686v2.pdf | Holistic Interaction Transformer Network for Action Detection | Actions are about how we interact with the environment, including other people, objects, and ourselves. In this paper, we propose a novel multi-modal Holistic Interaction Transformer Network (HIT) that leverages the largely ignored, but critical hand and pose information essential to most human actions. The proposed "HIT" network is a comprehensive bi-modal framework that comprises an RGB stream and a pose stream. Each of them separately models person, object, and hand interactions. Within each sub-network, an Intra-Modality Aggregation module (IMA) is introduced that selectively merges individual interaction units. The resulting features from each modality are then glued using an Attentive Fusion Mechanism (AFM). Finally, we extract cues from the temporal context to better classify the occurring actions using cached memory. Our method significantly outperforms previous approaches on the J-HMDB, UCF101-24, and MultiSports datasets. We also achieve competitive results on AVA. The code will be available at https://github.com/joslefaure/HIT. | ['Shang-Hong Lai', 'Min-Hung Chen', 'Gueter Josmy Faure'] | 2022-10-23 | null | null | null | null | ['fine-grained-action-detection'] | ['computer-vision'] | [ 4.23482098e-02 -2.79756427e-01 -2.68754065e-02 -3.40271175e-01
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0477b25e-f877-4635-89be-593b920a82b0 | fast-and-multi-aspect-mining-of-complex-time | 2303.03789 | null | https://arxiv.org/abs/2303.03789v2 | https://arxiv.org/pdf/2303.03789v2.pdf | Fast and Multi-aspect Mining of Complex Time-stamped Event Streams | Given a huge, online stream of time-evolving events with multiple attributes, such as online shopping logs: (item, price, brand, time), and local mobility activities: (pick-up and drop-off locations, time), how can we summarize large, dynamic high-order tensor streams? How can we see any hidden patterns, rules, and anomalies? Our answer is to focus on two types of patterns, i.e., ''regimes'' and ''components'', for which we present CubeScope, an efficient and effective method over high-order tensor streams. Specifically, it identifies any sudden discontinuity and recognizes distinct dynamical patterns, ''regimes'' (e.g., weekday/weekend/holiday patterns). In each regime, it also performs multi-way summarization for all attributes (e.g., item, price, brand, and time) and discovers hidden ''components'' representing latent groups (e.g., item/brand groups) and their relationship. Thanks to its concise but effective summarization, CubeScope can also detect the sudden appearance of anomalies and identify the types of anomalies that occur in practice. Our proposed method has the following properties: (a) Effective: it captures dynamical multi-aspect patterns, i.e., regimes and components, and statistically summarizes all the events; (b) General: it is practical for successful application to data compression, pattern discovery, and anomaly detection on various types of tensor streams; (c) Scalable: our algorithm does not depend on the length of the data stream and its dimensionality. Extensive experiments on real datasets demonstrate that CubeScope finds meaningful patterns and anomalies correctly, and consistently outperforms the state-of-the-art methods as regards accuracy and execution speed. | ['Yasushi Sakurai', 'Yuichiro Wada', 'Yuhei Umeda', 'Koki Kawabata', 'Yasuko Matsubara', 'Kota Nakamura'] | 2023-03-07 | null | null | null | null | ['data-compression'] | ['time-series'] | [-2.54272461e-01 -7.16270983e-01 -1.50608197e-01 9.30331089e-03
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e7be82ce-c09c-41d2-a631-1a8e92dc47b0 | flow-guided-semi-supervised-video-object | 2301.10492 | null | https://arxiv.org/abs/2301.10492v1 | https://arxiv.org/pdf/2301.10492v1.pdf | Flow-guided Semi-supervised Video Object Segmentation | We propose an optical flow-guided approach for semi-supervised video object segmentation. Optical flow is usually exploited as additional guidance information in unsupervised video object segmentation. However, its relevance in semi-supervised video object segmentation has not been fully explored. In this work, we follow an encoder-decoder approach to address the segmentation task. A model to extract the combined information from optical flow and the image is proposed, which is then used as input to the target model and the decoder network. Unlike previous methods where concatenation is used to integrate information from image data and optical flow, a simple yet effective attention mechanism is exploited in our work. Experiments on DAVIS 2017 and YouTube-VOS 2019 show that by integrating the information extracted from optical flow into the original image branch results in a strong performance gain and our method achieves state-of-the-art performance. | ['Michael Felsberg', 'Maria Magnusson', 'Andreas Robinson', 'Yushan Zhang'] | 2023-01-25 | null | null | null | null | ['semi-supervised-video-object-segmentation', 'video-object-segmentation', 'video-semantic-segmentation', 'unsupervised-video-object-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 4.11507934e-01 2.35290527e-02 -6.80338085e-01 -3.95808309e-01
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b3a5b48e-463b-4776-926d-aa1eea026f19 | continuous-sign-language-recognition-based-on | 2303.06820 | null | https://arxiv.org/abs/2303.06820v1 | https://arxiv.org/pdf/2303.06820v1.pdf | Continuous sign language recognition based on cross-resolution knowledge distillation | The goal of continuous sign language recognition(CSLR) research is to apply CSLR models as a communication tool in real life, and the real-time requirement of the models is important. In this paper, we address the model real-time problem through cross-resolution knowledge distillation. In our study, we found that keeping the frame-level feature scales consistent between the output of the student network and the teacher network is better than recovering the frame-level feature sizes for feature distillation. Based on this finding, we propose a new frame-level feature extractor that keeps the output frame-level features at the same scale as the output of by the teacher network. We further combined with the TSCM+2D hybrid convolution proposed in our previous study to form a new lightweight end-to-end CSLR network-Low resolution input net(LRINet). It is then used to combine cross-resolution knowledge distillation and traditional knowledge distillation methods to form a CSLR model based on cross-resolution knowledge distillation (CRKD). The CRKD uses high-resolution frames as input to the teacher network for training, locks the weights after training, and then uses low-resolution frames as input to the student network LRINet to perform knowledge distillation on frame-level features and classification features respectively. Experiments on two large-scale continuous sign language datasets have proved the effectiveness of CRKD. Compared with the model with high-resolution data as input, the calculation amount, parameter amount and inference time of the model have been significantly reduced under the same experimental conditions, while ensuring the accuracy of the model, and has achieved very competitive results in comparison with other advanced methods. | ['Quan Gan', 'Fei Yuan', 'Jing Li', 'Qidan Zhu'] | 2023-03-13 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [-2.76304875e-02 -4.20124233e-01 4.40012924e-02 -2.64677852e-01
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e166fd4a-444d-4b6a-9996-b093e6d3b206 | low-resource-multilingual-and-zero-shot | 2210.12223 | null | https://arxiv.org/abs/2210.12223v1 | https://arxiv.org/pdf/2210.12223v1.pdf | Low-Resource Multilingual and Zero-Shot Multispeaker TTS | While neural methods for text-to-speech (TTS) have shown great advances in modeling multiple speakers, even in zero-shot settings, the amount of data needed for those approaches is generally not feasible for the vast majority of the world's over 6,000 spoken languages. In this work, we bring together the tasks of zero-shot voice cloning and multilingual low-resource TTS. Using the language agnostic meta learning (LAML) procedure and modifications to a TTS encoder, we show that it is possible for a system to learn speaking a new language using just 5 minutes of training data while retaining the ability to infer the voice of even unseen speakers in the newly learned language. We show the success of our proposed approach in terms of intelligibility, naturalness and similarity to target speaker using objective metrics as well as human studies and provide our code and trained models open source. | ['Ngoc Thang Vu', 'Julia Koch', 'Florian Lux'] | 2022-10-21 | null | null | null | null | ['voice-cloning'] | ['speech'] | [ 1.14089474e-01 2.74816722e-01 -5.25561906e-03 -5.20323277e-01
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6957bbc1-196b-4969-a8e6-1c7724978942 | continuous-descriptor-based-control-for-deep | 2302.13542 | null | https://arxiv.org/abs/2302.13542v1 | https://arxiv.org/pdf/2302.13542v1.pdf | Continuous descriptor-based control for deep audio synthesis | Despite significant advances in deep models for music generation, the use of these techniques remains restricted to expert users. Before being democratized among musicians, generative models must first provide expressive control over the generation, as this conditions the integration of deep generative models in creative workflows. In this paper, we tackle this issue by introducing a deep generative audio model providing expressive and continuous descriptor-based control, while remaining lightweight enough to be embedded in a hardware synthesizer. We enforce the controllability of real-time generation by explicitly removing salient musical features in the latent space using an adversarial confusion criterion. User-specified features are then reintroduced as additional conditioning information, allowing for continuous control of the generation, akin to a synthesizer knob. We assess the performance of our method on a wide variety of sounds including instrumental, percussive and speech recordings while providing both timbre and attributes transfer, allowing new ways of generating sounds. | ['Philippe Esling', 'David Genova', 'Sarah Nabi', 'Nils Demerlé', 'Ninon Devis'] | 2023-02-27 | null | null | null | null | ['music-generation', 'music-generation', 'continuous-control'] | ['audio', 'music', 'playing-games'] | [ 1.89364448e-01 2.51953661e-01 4.63169575e-01 9.82465371e-02
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8feb7862-53d1-438a-ab12-7c0c02df78d0 | bayesian-approach-to-gaussian-process | 2305.11586 | null | https://arxiv.org/abs/2305.11586v2 | https://arxiv.org/pdf/2305.11586v2.pdf | Bayesian approach to Gaussian process regression with uncertain inputs | Conventional Gaussian process regression exclusively assumes the existence of noise in the output data of model observations. In many scientific and engineering applications, however, the input locations of observational data may also be compromised with uncertainties owing to modeling assumptions, measurement errors, etc. In this work, we propose a Bayesian method that integrates the variability of input data into Gaussian process regression. Considering two types of observables -- noise-corrupted outputs with fixed inputs and those with prior-distribution-defined uncertain inputs, a posterior distribution is estimated via a Bayesian framework to infer the uncertain data locations. Thereafter, such quantified uncertainties of inputs are incorporated into Gaussian process predictions by means of marginalization. The effectiveness of this new regression technique is demonstrated through several numerical examples, in which a consistently good performance of generalization is observed, while a substantial reduction in the predictive uncertainties is achieved by the Bayesian inference of uncertain inputs. | ['Mengwu Guo', 'Dongwei Ye'] | 2023-05-19 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [ 1.64281175e-01 -9.28094462e-02 2.54188210e-01 -3.81899357e-01
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72073b49-932d-4a2b-b217-aaff5383d222 | an-investigation-of-feature-selection-and | 2010.10025 | null | https://arxiv.org/abs/2010.10025v1 | https://arxiv.org/pdf/2010.10025v1.pdf | An Investigation of Feature Selection and Transfer Learning for Writer-Independent Offline Handwritten Signature Verification | SigNet is a state of the art model for feature representation used for handwritten signature verification (HSV). This representation is based on a Deep Convolutional Neural Network (DCNN) and contains 2048 dimensions. When transposed to a dissimilarity space generated by the dichotomy transformation (DT), related to the writer-independent (WI) approach, these features may include redundant information. This paper investigates the presence of overfitting when using Binary Particle Swarm Optimization (BPSO) to perform the feature selection in a wrapper mode. We proposed a method based on a global validation strategy with an external archive to control overfitting during the search for the most discriminant representation. Moreover, an investigation is also carried out to evaluate the use of the selected features in a transfer learning context. The analysis is carried out on a writer-independent approach on the CEDAR, MCYT and GPDS datasets. The experimental results showed the presence of overfitting when no validation is used during the optimization process and the improvement when the global validation strategy with an external archive is used. Also, the space generated after feature selection can be used in a transfer learning context. | ['Robert Sabourin', 'Rafael M. O. Cruz', 'Adriano L. I. Oliveira', 'Victor L. F. Souza'] | 2020-10-19 | null | null | null | null | ['2048'] | ['playing-games'] | [ 1.46620244e-01 -2.94842720e-01 2.82002330e-01 -2.35009834e-01
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7195d768-d95c-4a90-8308-03b59f6d0093 | mlprune-multi-layer-pruning-for-automated | null | null | https://openreview.net/forum?id=r1g5b2RcKm | https://openreview.net/pdf?id=r1g5b2RcKm | MLPrune: Multi-Layer Pruning for Automated Neural Network Compression | Model compression can significantly reduce the computation and memory footprint of large neural networks. To achieve a good trade-off between model size and accuracy, popular compression techniques usually rely on hand-crafted heuristics and
require manually setting the compression ratio of each layer. This process is typically costly and suboptimal. In this paper, we propose a Multi-Layer Pruning method (MLPrune), which is theoretically sound, and can automatically decide appropriate compression ratios for all layers. Towards this goal, we use an efficient approximation of the Hessian as our pruning criterion, based on a Kronecker-factored Approximate Curvature method. We demonstrate the effectiveness of our method on several datasets and architectures, outperforming previous state-of-the-art by a large margin. Our experiments show that we can compress AlexNet and VGG16 by 25x without loss in accuracy on ImageNet. Furthermore, our method has much fewer hyper-parameters and requires no expert knowledge. | ['Raquel Urtasun', 'Wenyuan Zeng'] | 2018-09-27 | null | null | null | null | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 1.00211024e-01 -2.67530471e-04 -1.99816152e-01 -3.52916151e-01
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4c469e4a-d695-4f82-8d4e-afad8f4f603d | evaluating-the-utility-of-gan-generated | 2306.13929 | null | https://arxiv.org/abs/2306.13929v1 | https://arxiv.org/pdf/2306.13929v1.pdf | Evaluating the Utility of GAN Generated Synthetic Tabular Data for Class Balancing and Low Resource Settings | The present study aimed to address the issue of imbalanced data in classification tasks and evaluated the suitability of SMOTE, ADASYN, and GAN techniques in generating synthetic data to address the class imbalance and improve the performance of classification models in low-resource settings. The study employed the Generalised Linear Model (GLM) algorithm for class balancing experiments and the Random Forest (RF) algorithm for low-resource setting experiments to assess model performance under varying training data. The recall metric was the primary evaluation metric for all classification models. The results of the class balancing experiments showed that the GLM model trained on GAN-balanced data achieved the highest recall value. Similarly, in low-resource experiments, models trained on data enhanced with GAN-synthesized data exhibited better recall values than original data. These findings demonstrate the potential of GAN-generated synthetic data for addressing the challenge of imbalanced data in classification tasks and improving model performance in low-resource settings. | ['Bharath Kumar Bolla', 'Nagarjuna Chereddy'] | 2023-06-24 | null | null | null | null | ['classification-1'] | ['methodology'] | [ 5.29521763e-01 1.61689863e-01 -5.73708236e-01 -4.98131454e-01
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dbc5ffed-44fa-4321-a5ca-d585b06ffa2a | surveying-generative-ai-s-economic | 2305.02823 | null | https://arxiv.org/abs/2305.02823v2 | https://arxiv.org/pdf/2305.02823v2.pdf | Surveying Generative AI's Economic Expectations | I introduce a survey of economic expectations formed by querying a large language model (LLM)'s expectations of various financial and macroeconomic variables based on a sample of news articles from the Wall Street Journal between 1984 and 2021. I find the resulting expectations closely match existing surveys including the Survey of Professional Forecasters (SPF), the American Association of Individual Investors, and the Duke CFO Survey. Importantly, I document that LLM based expectations match many of the deviations from full-information rational expectations exhibited in these existing survey series. The LLM's macroeconomic expectations exhibit under-reaction commonly found in consensus SPF forecasts. Additionally, its return expectations are extrapolative, disconnected from objective measures of expected returns, and negatively correlated with future realized returns. Finally, using a sample of articles outside of the LLM's training period I find that the correlation with existing survey measures persists -- indicating these results do not reflect memorization but generalization on the part of the LLM. My results provide evidence for the potential of LLMs to help us better understand human beliefs and navigate possible models of nonrational expectations. | ['Leland Bybee'] | 2023-05-04 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [-4.32815850e-01 4.15523708e-01 -5.23249388e-01 -5.17429411e-01
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358b75bc-29b2-469d-a281-095027d34411 | auto-gait-automatic-ataxia-risk-assessment | 2203.08215 | null | https://arxiv.org/abs/2203.08215v2 | https://arxiv.org/pdf/2203.08215v2.pdf | Auto-Gait: Automatic Ataxia Risk Assessment with Computer Vision on Gait Task Videos | In this paper, we investigated whether we can 1) detect participants with ataxia-specific gait characteristics (risk-prediction), and 2) assess severity of ataxia from gait (severity-assessment) using computer vision. We created a dataset of 155 videos from 89 participants, 24 controls and 65 diagnosed with (or are pre-manifest) spinocerebellar ataxias (SCAs), performing the gait task of the Scale for the Assessment and Rating of Ataxia (SARA) from 11 medical sites located in 8 different states across the United States. We develop a computer vision pipeline to detect, track, and separate out the participants from their surroundings and construct several features from their body pose coordinates to capture gait characteristics like step width, step length, swing, stability, speed, etc. Our risk-prediction model achieves 83.06% accuracy and an 80.23% F1 score. Similarly, our severity-assessment model achieves a mean absolute error (MAE) score of 0.6225 and a Pearson's correlation coefficient score of 0.7268. Our models still performed competitively when evaluated on data from sites not used during training. Furthermore, through feature importance analysis, we found that our models associate wider steps, decreased walking speed, and increased instability with greater ataxia severity, which is consistent with previously established clinical knowledge. Our models create possibilities for remote ataxia assessment in non-clinical settings in the future, which could significantly improve accessibility of ataxia care. Furthermore, our underlying dataset was assembled from a geographically diverse cohort, highlighting its potential to further increase equity. The code used in this study is open to the public, and the anonymized body pose landmark dataset is also available upon request. | ['Ehsan Hoque', 'Tetsuo Ashizawa', 'Phillip Yang', 'Abdelrahman Abdelkader', 'Jeet Thaker', 'Titilayo Olubajo', 'Md Saiful Islam', 'Masum Hasan', 'Wasifur Rahman'] | 2022-03-15 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [-2.54234344e-01 -2.07028091e-01 -3.47321965e-02 -1.77070439e-01
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e50fc29c-8f74-4f2e-aa42-c42460be5497 | shapechanger-environments-for-transfer | 1709.05070 | null | http://arxiv.org/abs/1709.05070v1 | http://arxiv.org/pdf/1709.05070v1.pdf | Shapechanger: Environments for Transfer Learning | We present Shapechanger, a library for transfer reinforcement learning
specifically designed for robotic tasks. We consider three types of knowledge
transfer---from simulation to simulation, from simulation to real, and from
real to real---and a wide range of tasks with continuous states and actions.
Shapechanger is under active development and open-sourced at:
https://github.com/seba-1511/shapechanger/. | ['Francisco J. Valero-Cuevas', 'Théo-Tim J. Denisart', 'Sébastien M. R. Arnold', 'Tsam Kiu Pun'] | 2017-09-15 | null | null | null | null | ['transfer-reinforcement-learning'] | ['methodology'] | [-5.47892272e-01 1.67792905e-02 -2.17779949e-01 -2.41641551e-01
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f5d6f9f5-660a-41f5-b5a6-89eb37f4fc99 | leveraging-unlabeled-data-to-track | 2212.04461 | null | https://arxiv.org/abs/2212.04461v1 | https://arxiv.org/pdf/2212.04461v1.pdf | Leveraging Unlabeled Data to Track Memorization | Deep neural networks may easily memorize noisy labels present in real-world data, which degrades their ability to generalize. It is therefore important to track and evaluate the robustness of models against noisy label memorization. We propose a metric, called susceptibility, to gauge such memorization for neural networks. Susceptibility is simple and easy to compute during training. Moreover, it does not require access to ground-truth labels and it only uses unlabeled data. We empirically show the effectiveness of our metric in tracking memorization on various architectures and datasets and provide theoretical insights into the design of the susceptibility metric. Finally, we show through extensive experiments on datasets with synthetic and real-world label noise that one can utilize susceptibility and the overall training accuracy to distinguish models that maintain a low memorization on the training set and generalize well to unseen clean data. | ['Patrick Thiran', 'Hanie Sedghi', 'Mahsa Forouzesh'] | 2022-12-08 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 2.79586822e-01 -1.63200572e-01 -2.46325079e-02 -7.03402936e-01
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54afdb0b-d2d6-43b3-812d-54cb27673e86 | neuralnetwork-viterbi-a-framework-for-weakly | 1805.06875 | null | http://arxiv.org/abs/1805.06875v1 | http://arxiv.org/pdf/1805.06875v1.pdf | NeuralNetwork-Viterbi: A Framework for Weakly Supervised Video Learning | Video learning is an important task in computer vision and has experienced
increasing interest over the recent years. Since even a small amount of videos
easily comprises several million frames, methods that do not rely on a
frame-level annotation are of special importance. In this work, we propose a
novel learning algorithm with a Viterbi-based loss that allows for online and
incremental learning of weakly annotated video data. We moreover show that
explicit context and length modeling leads to huge improvements in video
segmentation and labeling tasks andinclude these models into our framework. On
several action segmentation benchmarks, we obtain an improvement of up to 10%
compared to current state-of-the-art methods. | ['Hilde Kuehne', 'Ahsan Iqbal', 'Alexander Richard', 'Juergen Gall'] | 2018-05-17 | neuralnetwork-viterbi-a-framework-for-weakly-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Richard_NeuralNetwork-Viterbi_A_Framework_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Richard_NeuralNetwork-Viterbi_A_Framework_CVPR_2018_paper.pdf | cvpr-2018-6 | ['weakly-supervised-action-segmentation'] | ['computer-vision'] | [ 3.87820810e-01 -2.85591627e-03 -6.08045220e-01 -3.89274895e-01
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cfa1419f-56ae-4735-82b9-b311aa430a79 | reliability-and-sharpness-in-border-crossing | 1711.04848 | null | http://arxiv.org/abs/1711.04848v1 | http://arxiv.org/pdf/1711.04848v1.pdf | Reliability and Sharpness in Border Crossing Traffic Interval Prediction | Short-term traffic volume prediction models have been extensively studied in
the past few decades. However, most of the previous studies only focus on
single-value prediction. Considering the uncertain and chaotic nature of the
transportation system, an accurate and reliable prediction interval with upper
and lower bounds may be better than a single point value for transportation
management. In this paper, we introduce a neural network model called Extreme
Learning Machine (ELM) for interval prediction of short-term traffic volume and
improve it with the heuristic particle swarm optimization algorithm (PSO). The
hybrid PSO-ELM model can generate the prediction intervals under different
confidence levels and guarantee the quality by minimizing a multi-objective
function which considers two criteria reliability and interval sharpness. The
PSO-ELM models are built based on an hourly traffic dataset and compared with
ARMA and Kalman Filter models. The results show that ARMA models are the worst
for all confidence levels, and the PSO-ELM models are comparable with Kalman
Filter from the aspects of reliability and narrowness of the intervals,
although the parameters of PSO-ELM are fixed once the training is done while
Kalman Filter is updated in an online approach. Additionally, only the PSO-ELMs
are able to produce intervals with coverage probabilities higher than or equal
to the confidence levels. For the points outside of the prediction levels given
by PSO-ELMs, they lie very close to the bounds. | ['Adel Sadek', 'Lei Lin', 'John Handley'] | 2017-11-13 | null | null | null | null | ['value-prediction'] | ['computer-code'] | [-4.54304785e-01 -1.20548427e-01 -2.12975308e-01 -9.95104909e-02
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aca73d94-ecd5-419d-8a8a-3ff875fece3e | qa-gnn-reasoning-with-language-models-and | 2104.06378 | null | https://arxiv.org/abs/2104.06378v5 | https://arxiv.org/pdf/2104.06378v5.pdf | QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering | The problem of answering questions using knowledge from pre-trained language models (LMs) and knowledge graphs (KGs) presents two challenges: given a QA context (question and answer choice), methods need to (i) identify relevant knowledge from large KGs, and (ii) perform joint reasoning over the QA context and KG. In this work, we propose a new model, QA-GNN, which addresses the above challenges through two key innovations: (i) relevance scoring, where we use LMs to estimate the importance of KG nodes relative to the given QA context, and (ii) joint reasoning, where we connect the QA context and KG to form a joint graph, and mutually update their representations through graph neural networks. We evaluate our model on QA benchmarks in the commonsense (CommonsenseQA, OpenBookQA) and biomedical (MedQA-USMLE) domains. QA-GNN outperforms existing LM and LM+KG models, and exhibits capabilities to perform interpretable and structured reasoning, e.g., correctly handling negation in questions. | ['Jure Leskovec', 'Percy Liang', 'Antoine Bosselut', 'Hongyu Ren', 'Michihiro Yasunaga'] | 2021-04-13 | null | https://aclanthology.org/2021.naacl-main.45 | https://aclanthology.org/2021.naacl-main.45.pdf | naacl-2021-4 | ['multi-hop-question-answering', 'riddle-sense'] | ['knowledge-base', 'natural-language-processing'] | [ 2.88072795e-01 8.59596908e-01 5.58199063e-02 -3.27009171e-01
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6d0f83ae-cb50-4e6d-acb1-3c6811975d54 | storygan-a-sequential-conditional-gan-for | 1812.02784 | null | http://arxiv.org/abs/1812.02784v2 | http://arxiv.org/pdf/1812.02784v2.pdf | StoryGAN: A Sequential Conditional GAN for Story Visualization | We propose a new task, called Story Visualization. Given a multi-sentence
paragraph, the story is visualized by generating a sequence of images, one for
each sentence. In contrast to video generation, story visualization focuses
less on the continuity in generated images (frames), but more on the global
consistency across dynamic scenes and characters -- a challenge that has not
been addressed by any single-image or video generation methods. We therefore
propose a new story-to-image-sequence generation model, StoryGAN, based on the
sequential conditional GAN framework. Our model is unique in that it consists
of a deep Context Encoder that dynamically tracks the story flow, and two
discriminators at the story and image levels, to enhance the image quality and
the consistency of the generated sequences. To evaluate the model, we modified
existing datasets to create the CLEVR-SV and Pororo-SV datasets. Empirically,
StoryGAN outperforms state-of-the-art models in image quality, contextual
consistency metrics, and human evaluation. | ['Jianfeng Gao', 'Lawrence Carin', 'Yelong Shen', 'Yu Cheng', 'Jingjing Liu', 'Zhe Gan', 'Yitong Li', 'Yuexin Wu', 'David Carlson'] | 2018-12-06 | storygan-a-sequential-conditional-gan-for-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Li_StoryGAN_A_Sequential_Conditional_GAN_for_Story_Visualization_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Li_StoryGAN_A_Sequential_Conditional_GAN_for_Story_Visualization_CVPR_2019_paper.pdf | cvpr-2019-6 | ['story-visualization'] | ['computer-vision'] | [ 3.56678188e-01 1.54771328e-01 1.44108638e-01 -2.27649525e-01
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56914967-c3e6-468c-a421-b340fb99dc31 | sparsity-aware-ssaf-algorithm-with-individual | 2009.08593 | null | https://arxiv.org/abs/2009.08593v1 | https://arxiv.org/pdf/2009.08593v1.pdf | Sparsity-Aware SSAF Algorithm with Individual Weighting Factors for Acoustic Echo Cancellation | In this paper, we propose and analyze the sparsity-aware sign subband adaptive filtering with individual weighting factors (S-IWF-SSAF) algorithm, and consider its application in acoustic echo cancellation (AEC). Furthermore, we design a joint optimization scheme of the step-size and the sparsity penalty parameter to enhance the S-IWF-SSAF performance in terms of convergence rate and steady-state error. A theoretical analysis shows that the S-IWF-SSAF algorithm outperforms the previous sign subband adaptive filtering with individual weighting factors (IWF-SSAF) algorithm in sparse scenarios. In particular, compared with the existing analysis on the IWF-SSAF algorithm, the proposed analysis does not require the assumptions of large number of subbands, long adaptive filter, and paraunitary analysis filter bank, and matches well the simulated results. Simulations in both system identification and AEC situations have demonstrated our theoretical analysis and the effectiveness of the proposed algorithms. | ['Yi Yu', 'Tao Yang', 'Yingsong Li', 'Rodrigo C. de Lamare', 'Hongyang Chen'] | 2020-09-18 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 4.21961278e-01 -3.75025481e-01 5.01201153e-01 -2.14677721e-01
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945c081a-d1d1-4184-8487-910fad61682c | icdar-2023-video-text-reading-competition-for | 2304.04376 | null | https://arxiv.org/abs/2304.04376v1 | https://arxiv.org/pdf/2304.04376v1.pdf | ICDAR 2023 Video Text Reading Competition for Dense and Small Text | Recently, video text detection, tracking, and recognition in natural scenes are becoming very popular in the computer vision community. However, most existing algorithms and benchmarks focus on common text cases (e.g., normal size, density) and single scenarios, while ignoring extreme video text challenges, i.e., dense and small text in various scenarios. In this competition report, we establish a video text reading benchmark, DSText, which focuses on dense and small text reading challenges in the video with various scenarios. Compared with the previous datasets, the proposed dataset mainly include three new challenges: 1) Dense video texts, a new challenge for video text spotter. 2) High-proportioned small texts. 3) Various new scenarios, e.g., Game, sports, etc. The proposed DSText includes 100 video clips from 12 open scenarios, supporting two tasks (i.e., video text tracking (Task 1) and end-to-end video text spotting (Task 2)). During the competition period (opened on 15th February 2023 and closed on 20th March 2023), a total of 24 teams participated in the three proposed tasks with around 30 valid submissions, respectively. In this article, we describe detailed statistical information of the dataset, tasks, evaluation protocols and the results summaries of the ICDAR 2023 on DSText competition. Moreover, we hope the benchmark will promise video text research in the community. | ['Xiang Bai', 'Dimosthenis Karatzas', 'Umapada Pal', 'Mike Zheng Shou', 'Jiahong Li', 'Zhuang Li', 'Yuzhong Zhao', 'Weijia Wu'] | 2023-04-10 | null | null | null | null | ['text-spotting'] | ['computer-vision'] | [ 2.45837644e-01 -5.94189107e-01 4.32833880e-02 -6.61044866e-02
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a517a8ef-b621-42b2-8c98-fa1a23bc92a0 | pointnet-deep-hierarchical-feature-learning | 1706.02413 | null | http://arxiv.org/abs/1706.02413v1 | http://arxiv.org/pdf/1706.02413v1.pdf | PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space | Few prior works study deep learning on point sets. PointNet by Qi et al. is a
pioneer in this direction. However, by design PointNet does not capture local
structures induced by the metric space points live in, limiting its ability to
recognize fine-grained patterns and generalizability to complex scenes. In this
work, we introduce a hierarchical neural network that applies PointNet
recursively on a nested partitioning of the input point set. By exploiting
metric space distances, our network is able to learn local features with
increasing contextual scales. With further observation that point sets are
usually sampled with varying densities, which results in greatly decreased
performance for networks trained on uniform densities, we propose novel set
learning layers to adaptively combine features from multiple scales.
Experiments show that our network called PointNet++ is able to learn deep point
set features efficiently and robustly. In particular, results significantly
better than state-of-the-art have been obtained on challenging benchmarks of 3D
point clouds. | ['Leonidas J. Guibas', 'Li Yi', 'Hao Su', 'Charles R. Qi'] | 2017-06-07 | pointnet-deep-hierarchical-feature-learning-1 | http://papers.nips.cc/paper/7095-pointnet-deep-hierarchical-feature-learning-on-point-sets-in-a-metric-space | http://papers.nips.cc/paper/7095-pointnet-deep-hierarchical-feature-learning-on-point-sets-in-a-metric-space.pdf | neurips-2017-12 | ['3d-part-segmentation', 'few-shot-3d-point-cloud-classification'] | ['computer-vision', 'computer-vision'] | [-3.61962408e-01 -3.96519154e-01 -4.44357432e-02 -3.46161276e-01
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291250a1-2d2c-4b23-9026-4813bc5c38a4 | multifaceted-domain-specific-document | null | null | https://aclanthology.org/2021.naacl-demos.9 | https://aclanthology.org/2021.naacl-demos.9.pdf | Multifaceted Domain-Specific Document Embeddings | Current document embeddings require large training corpora but fail to learn high-quality representations when confronted with a small number of domain-specific documents and rare terms. Further, they transform each document into a single embedding vector, making it hard to capture different notions of document similarity or explain why two documents are considered similar. In this work, we propose our Faceted Domain Encoder, a novel approach to learn multifaceted embeddings for domain-specific documents. It is based on a Siamese neural network architecture and leverages knowledge graphs to further enhance the embeddings even if only a few training samples are available. The model identifies different types of domain knowledge and encodes them into separate dimensions of the embedding, thereby enabling multiple ways of finding and comparing related documents in the vector space. We evaluate our approach on two benchmark datasets and find that it achieves the same embedding quality as state-of-the-art models while requiring only a tiny fraction of their training data. An interactive demo, our source code, and the evaluation datasets are available online: https://hpi.de/naumann/s/multifaceted-embeddings and a screencast is available on YouTube: https://youtu.be/HHcsX2clEwg | ['Ralf Krestel', 'Philipp Hager', 'Julian Risch'] | 2021-06-01 | null | null | null | naacl-2021-4 | ['document-embedding'] | ['methodology'] | [-1.37779281e-01 -1.62312135e-01 -5.74929237e-01 -3.42986137e-01
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94f6e627-207c-4c8c-bc90-7bc28995b88b | automatic-extraction-of-parallel-speech | null | null | https://aclanthology.org/W17-2506 | https://aclanthology.org/W17-2506.pdf | Automatic Extraction of Parallel Speech Corpora from Dubbed Movies | This paper presents a methodology to extract parallel speech corpora based on any language pair from dubbed movies, together with an application framework in which some corresponding prosodic parameters are extracted. The obtained parallel corpora are especially suitable for speech-to-speech translation applications when a prosody transfer between source and target languages is desired. | ["Mireia Farr{\\'u}s", 'Alp {\\"O}ktem', 'Leo Wanner'] | 2017-08-01 | null | null | null | ws-2017-8 | ['speech-to-speech-translation'] | ['speech'] | [ 1.78157583e-01 5.16514853e-03 -2.09347069e-01 -4.71248209e-01
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b6bb40d1-50b1-48e8-a23d-73fadeaa650e | the-multilingual-amazon-reviews-corpus | 2010.02573 | null | https://arxiv.org/abs/2010.02573v1 | https://arxiv.org/pdf/2010.02573v1.pdf | The Multilingual Amazon Reviews Corpus | We present the Multilingual Amazon Reviews Corpus (MARC), a large-scale collection of Amazon reviews for multilingual text classification. The corpus contains reviews in English, Japanese, German, French, Spanish, and Chinese, which were collected between 2015 and 2019. Each record in the dataset contains the review text, the review title, the star rating, an anonymized reviewer ID, an anonymized product ID, and the coarse-grained product category (e.g., 'books', 'appliances', etc.) The corpus is balanced across the 5 possible star ratings, so each rating constitutes 20% of the reviews in each language. For each language, there are 200,000, 5,000, and 5,000 reviews in the training, development, and test sets, respectively. We report baseline results for supervised text classification and zero-shot cross-lingual transfer learning by fine-tuning a multilingual BERT model on reviews data. We propose the use of mean absolute error (MAE) instead of classification accuracy for this task, since MAE accounts for the ordinal nature of the ratings. | ['Noah A. Smith', 'György Szarvas', 'Yichao Lu', 'Phillip Keung'] | 2020-10-06 | null | https://aclanthology.org/2020.emnlp-main.369 | https://aclanthology.org/2020.emnlp-main.369.pdf | emnlp-2020-11 | ['multilingual-text-classification'] | ['miscellaneous'] | [-5.31411886e-01 -1.69922441e-01 -5.91009617e-01 -6.80528343e-01
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eb41030c-5b3d-4921-b047-95a433be6247 | multiple-granularity-analysis-for-fine | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Ni_Multiple_Granularity_Analysis_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Ni_Multiple_Granularity_Analysis_2014_CVPR_paper.pdf | Multiple Granularity Analysis for Fine-grained Action Detection | We propose to decompose the fine-grained human activity analysis problem into two sequential tasks with increasing granularity. Firstly, we infer the coarse interaction status, i.e., which object is being manipulated and where it is. Knowing that the major challenge is frequent mutual occlusions during manipulation, we propose an "interaction tracking" framework in which hand/object position and interaction status are jointly tracked by explicitly modeling the contextual information between mutual occlusion and interaction status. Secondly, the inferred hand/object position and interaction status are utilized to provide 1) more compact feature pooling by effectively pruning large number of motion features from irrelevant spatio-temporal positions and 2) discriminative action detection by a granularity fusion strategy. Comprehensive experiments on two challenging fine-grained activity datasets (i.e., cooking action) show that the proposed framework achieves high accuracy/robustness in tracking multiple mutually occluded hands/objects during manipulation as well as significant performance improvement on fine-grained action detection over state-of-the-art methods. | ['Bingbing Ni', 'Vignesh R. Paramathayalan', 'Pierre Moulin'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['fine-grained-action-detection'] | ['computer-vision'] | [ 5.32320678e-01 -4.64225352e-01 -3.73431951e-01 2.05580126e-02
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fe9053f4-28b6-44be-b2cd-1f256720d24c | adversarial-attacks-on-graph-classifiers-via | null | null | http://proceedings.neurips.cc/paper/2021/hash/38811c5285e34e2e3319ab7d9f2cfa5b-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/38811c5285e34e2e3319ab7d9f2cfa5b-Paper.pdf | Adversarial Attacks on Graph Classifiers via Bayesian Optimisation | Graph neural networks, a popular class of models effective in a wide range of graph-based learning tasks, have been shown to be vulnerable to adversarial attacks. While the majority of the literature focuses on such vulnerability in node-level classification tasks, little effort has been dedicated to analysing adversarial attacks on graph-level classification, an important problem with numerous real-life applications such as biochemistry and social network analysis. The few existing methods often require unrealistic setups, such as access to internal information of the victim models, or an impractically-large number of queries. We present a novel Bayesian optimisation-based attack method for graph classification models. Our method is black-box, query-efficient and parsimonious with respect to the perturbation applied. We empirically validate the effectiveness and flexibility of the proposed method on a wide range of graph classification tasks involving varying graph properties, constraints and modes of attack. Finally, we analyse common interpretable patterns behind the adversarial samples produced, which may shed further light on the adversarial robustness of graph classification models. | ['Xiaowen Dong', 'Michael Osborne', 'Arno Blaas', 'Robin Ru', 'Henry Kenlay', 'Xingchen Wan'] | 2021-12-01 | null | https://openreview.net/forum?id=5j_lH4OpZBl | https://openreview.net/pdf?id=5j_lH4OpZBl | neurips-2021-12 | ['bayesian-optimisation'] | ['methodology'] | [ 6.66250050e-01 1.80422977e-01 -3.87980863e-02 1.15763426e-01
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073fa75d-f83a-4b15-b1df-012d08b52e72 | enriched-robust-multi-view-kernel-subspace | 2205.10495 | null | https://arxiv.org/abs/2205.10495v1 | https://arxiv.org/pdf/2205.10495v1.pdf | Enriched Robust Multi-View Kernel Subspace Clustering | Subspace clustering is to find underlying low-dimensional subspaces and cluster the data points correctly. In this paper, we propose a novel multi-view subspace clustering method. Most existing methods suffer from two critical issues. First, they usually adopt a two-stage framework and isolate the processes of affinity learning, multi-view information fusion and clustering. Second, they assume the data lies in a linear subspace which may fail in practice as most real-world datasets may have non-linearity structures. To address the above issues, in this paper we propose a novel Enriched Robust Multi-View Kernel Subspace Clustering framework where the consensus affinity matrix is learned from both multi-view data and spectral clustering. Due to the objective and constraints which is difficult to optimize, we propose an iterative optimization method which is easy to implement and can yield closed solution in each step. Extensive experiments have validated the superiority of our method over state-of-the-art clustering methods. | ['Kai Liu', 'Mengyuan Zhang'] | 2022-05-21 | null | null | null | null | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-3.44065547e-01 -6.89614832e-01 -6.04224950e-02 -9.57318842e-02
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13ba7024-c878-4a8c-a521-37446b3de3b9 | a-study-on-cross-population-age-estimation | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Guo_A_Study_on_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Guo_A_Study_on_2014_CVPR_paper.pdf | A Study on Cross-Population Age Estimation | We study the problem of cross-population age estimation. Human aging is determined by the genes and influenced by many factors. Different populations, e.g., males and females, Caucasian and Asian, may age differently. Previous research has discovered the aging difference among different populations, and reported large errors in age estimation when crossing gender and/or ethnicity. In this paper we propose novel methods for cross-population age estimation with a good performance. The proposed methods are based on projecting the different aging patterns into a common space where the aging patterns can be correlated even though they come from different populations. The projections are also discriminative between age classes due to the integration of the classical discriminant analysis technique. Further, we study the amount of data needed in the target population to learn a cross-population age estimator. Finally, we study the feasibility of multi-source cross-population age estimation. Experiments are conducted on a large database of more than 21,000 face images selected from the MORPH. Our studies are valuable to significantly reduce the burden of training data collection for age estimation on a new population, utilizing existing aging patterns even from different populations. | ['Guodong Guo', 'Chao Zhang'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['human-aging'] | ['miscellaneous'] | [-2.10698068e-01 -3.44321102e-01 -1.17724061e-01 -5.01481235e-01
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9ad96411-3fc9-4ba9-aae0-04ff3e050743 | distracting-downpour-adversarial-weather | 2305.06716 | null | https://arxiv.org/abs/2305.06716v1 | https://arxiv.org/pdf/2305.06716v1.pdf | Distracting Downpour: Adversarial Weather Attacks for Motion Estimation | Current adversarial attacks on motion estimation, or optical flow, optimize small per-pixel perturbations, which are unlikely to appear in the real world. In contrast, adverse weather conditions constitute a much more realistic threat scenario. Hence, in this work, we present a novel attack on motion estimation that exploits adversarially optimized particles to mimic weather effects like snowflakes, rain streaks or fog clouds. At the core of our attack framework is a differentiable particle rendering system that integrates particles (i) consistently over multiple time steps (ii) into the 3D space (iii) with a photo-realistic appearance. Through optimization, we obtain adversarial weather that significantly impacts the motion estimation. Surprisingly, methods that previously showed good robustness towards small per-pixel perturbations are particularly vulnerable to adversarial weather. At the same time, augmenting the training with non-optimized weather increases a method's robustness towards weather effects and improves generalizability at almost no additional cost. | ['Andrés Bruhn', 'Lukas Mehl', 'Jenny Schmalfuss'] | 2023-05-11 | null | null | null | null | ['motion-estimation'] | ['computer-vision'] | [ 1.90136507e-01 -2.38162071e-01 4.48101401e-01 2.55911142e-01
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8aee5116-0433-464b-a13e-f2ae955cfc52 | deep-learning-of-segment-level-feature | 2302.02419 | null | https://arxiv.org/abs/2302.02419v1 | https://arxiv.org/pdf/2302.02419v1.pdf | deep learning of segment-level feature representation for speech emotion recognition in conversations | Accurately detecting emotions in conversation is a necessary yet challenging task due to the complexity of emotions and dynamics in dialogues. The emotional state of a speaker can be influenced by many different factors, such as interlocutor stimulus, dialogue scene, and topic. In this work, we propose a conversational speech emotion recognition method to deal with capturing attentive contextual dependency and speaker-sensitive interactions. First, we use a pretrained VGGish model to extract segment-based audio representation in individual utterances. Second, an attentive bi-directional gated recurrent unit (GRU) models contextual-sensitive information and explores intra- and inter-speaker dependencies jointly in a dynamic manner. The experiments conducted on the standard conversational dataset MELD demonstrate the effectiveness of the proposed method when compared against state-of the-art methods. | ['Joshua Reiss', 'Huy Phan', 'Jiachen Luo'] | 2023-02-05 | null | null | null | null | ['speech-emotion-recognition'] | ['speech'] | [ 6.93392679e-02 -1.90762877e-01 3.25206399e-01 -6.74937785e-01
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58f3c642-1985-49e5-b41f-4d85183476e8 | ictcas-ucas-tal-submission-to-the-ava | null | null | http://research.google.com/ava/2021/S1_ICTCAS-UCAS-TAL.pdf | http://research.google.com/ava/2021/S1_ICTCAS-UCAS-TAL.pdf | ICTCAS-UCAS-TAL Submission to the AVA-ActiveSpeaker Task at ActivityNet Challenge 2021 | This report presents a brief description of our method for the AVA Active Speaker Detection (ASD) task at ActivityNet
Challenge 2021. Our solution, the Extended Unified Context Network (Extended UniCon) is based on a novel Unified
Context Network (UniCon) designed for robust ASD, which combines multiple types of contextual information to optimize all candidates jointly. We propose a few changes to the original UniCon in terms of audio features, temporal modeling architecture, and loss function design. Together, our best model ensemble sets a new state-of-the-art at 93.4% mAP on the AVA-ActiveSpeaker test set without any form of pretraining, and currently ranks first on the ActivityNet challenge leaderboard. | ['Shiguang Shan', 'Zhongqin Wu', 'Xiao Liu', 'Shuang Yang', 'Susan Liang', 'Yuanhang Zhang'] | 2021-06-01 | null | null | null | the-activitynet-large-scale-activity | ['audio-visual-active-speaker-detection'] | ['computer-vision'] | [ 1.75868526e-01 2.41644308e-02 -2.41306409e-01 -4.12298411e-01
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b9171c1c-6006-4233-b8d2-c195c3963bdc | collecting-the-public-perception-of-ai-and | 2008.01339 | null | https://arxiv.org/abs/2008.01339v1 | https://arxiv.org/pdf/2008.01339v1.pdf | Collecting the Public Perception of AI and Robot Rights | Whether to give rights to artificial intelligence (AI) and robots has been a sensitive topic since the European Parliament proposed advanced robots could be granted "electronic personalities." Numerous scholars who favor or disfavor its feasibility have participated in the debate. This paper presents an experiment (N=1270) that 1) collects online users' first impressions of 11 possible rights that could be granted to autonomous electronic agents of the future and 2) examines whether debunking common misconceptions on the proposal modifies one's stance toward the issue. The results indicate that even though online users mainly disfavor AI and robot rights, they are supportive of protecting electronic agents from cruelty (i.e., favor the right against cruel treatment). Furthermore, people's perceptions became more positive when given information about rights-bearing non-human entities or myth-refuting statements. The style used to introduce AI and robot rights significantly affected how the participants perceived the proposal, similar to the way metaphors function in creating laws. For robustness, we repeated the experiment over a more representative sample of U.S. residents (N=164) and found that perceptions gathered from online users and those by the general population are similar. | ['Changyeon Kim', 'Chihyung Jeon', 'Seungho Ryu', 'Meeyoung Cha', 'Gabriel Lima'] | 2020-08-04 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [-1.99490175e-01 1.10072911e+00 -4.19223011e-01 -2.89835364e-01
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7946dea7-0c29-4164-80f9-b23a495fda02 | do-backdoors-assist-membership-inference | 2303.12589 | null | https://arxiv.org/abs/2303.12589v1 | https://arxiv.org/pdf/2303.12589v1.pdf | Do Backdoors Assist Membership Inference Attacks? | When an adversary provides poison samples to a machine learning model, privacy leakage, such as membership inference attacks that infer whether a sample was included in the training of the model, becomes effective by moving the sample to an outlier. However, the attacks can be detected because inference accuracy deteriorates due to poison samples. In this paper, we discuss a \textit{backdoor-assisted membership inference attack}, a novel membership inference attack based on backdoors that return the adversary's expected output for a triggered sample. We found three crucial insights through experiments with an academic benchmark dataset. We first demonstrate that the backdoor-assisted membership inference attack is unsuccessful. Second, when we analyzed loss distributions to understand the reason for the unsuccessful results, we found that backdoors cannot separate loss distributions of training and non-training samples. In other words, backdoors cannot affect the distribution of clean samples. Third, we also show that poison and triggered samples activate neurons of different distributions. Specifically, backdoors make any clean sample an inlier, contrary to poisoning samples. As a result, we confirm that backdoors cannot assist membership inference. | ['Naoto Yanai', 'Toshiki Shibahara', 'Nami Ashizawa', 'Yumeki Goto'] | 2023-03-22 | null | null | null | null | ['inference-attack', 'membership-inference-attack'] | ['adversarial', 'computer-vision'] | [ 1.40414461e-01 1.75292209e-01 8.30345452e-02 -2.42524177e-01
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8c37b59a-5ff8-4951-a534-c340e3a43836 | incentivizing-combinatorial-bandit | 2206.00494 | null | https://arxiv.org/abs/2206.00494v1 | https://arxiv.org/pdf/2206.00494v1.pdf | Incentivizing Combinatorial Bandit Exploration | Consider a bandit algorithm that recommends actions to self-interested users in a recommendation system. The users are free to choose other actions and need to be incentivized to follow the algorithm's recommendations. While the users prefer to exploit, the algorithm can incentivize them to explore by leveraging the information collected from the previous users. All published work on this problem, known as incentivized exploration, focuses on small, unstructured action sets and mainly targets the case when the users' beliefs are independent across actions. However, realistic exploration problems often feature large, structured action sets and highly correlated beliefs. We focus on a paradigmatic exploration problem with structure: combinatorial semi-bandits. We prove that Thompson Sampling, when applied to combinatorial semi-bandits, is incentive-compatible when initialized with a sufficient number of samples of each arm (where this number is determined in advance by the Bayesian prior). Moreover, we design incentive-compatible algorithms for collecting the initial samples. | ['Zhiwei Steven Wu', 'Aleksandrs Slivkins', 'Dung Daniel Ngo', 'Xinyan Hu'] | 2022-06-01 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 2.18271777e-01 5.48828065e-01 -1.11969256e+00 -1.80709571e-01
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c6093cdc-8ba7-4c91-935f-fcf453ed8e2d | neural-cross-lingual-entity-linking | 1712.01813 | null | http://arxiv.org/abs/1712.01813v1 | http://arxiv.org/pdf/1712.01813v1.pdf | Neural Cross-Lingual Entity Linking | A major challenge in Entity Linking (EL) is making effective use of
contextual information to disambiguate mentions to Wikipedia that might refer
to different entities in different contexts. The problem exacerbates with
cross-lingual EL which involves linking mentions written in non-English
documents to entries in the English Wikipedia: to compare textual clues across
languages we need to compute similarity between textual fragments across
languages. In this paper, we propose a neural EL model that trains fine-grained
similarities and dissimilarities between the query and candidate document from
multiple perspectives, combined with convolution and tensor networks. Further,
we show that this English-trained system can be applied, in zero-shot learning,
to other languages by making surprisingly effective use of multi-lingual
embeddings. The proposed system has strong empirical evidence yielding
state-of-the-art results in English as well as cross-lingual: Spanish and
Chinese TAC 2015 datasets. | ['Wael Hamza', 'Gourab Kundu', 'Radu Florian', 'Avirup Sil'] | 2017-12-05 | null | null | null | null | ['cross-lingual-entity-linking'] | ['natural-language-processing'] | [-7.12406039e-01 -1.29218891e-01 -2.85228997e-01 -1.17192388e-01
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f2468152-46f5-44b2-8f0d-83cfded7f63e | codified-audio-language-modeling-learns | 2107.05677 | null | https://arxiv.org/abs/2107.05677v1 | https://arxiv.org/pdf/2107.05677v1.pdf | Codified audio language modeling learns useful representations for music information retrieval | We demonstrate that language models pre-trained on codified (discretely-encoded) music audio learn representations that are useful for downstream MIR tasks. Specifically, we explore representations from Jukebox (Dhariwal et al. 2020): a music generation system containing a language model trained on codified audio from 1M songs. To determine if Jukebox's representations contain useful information for MIR, we use them as input features to train shallow models on several MIR tasks. Relative to representations from conventional MIR models which are pre-trained on tagging, we find that using representations from Jukebox as input features yields 30% stronger performance on average across four MIR tasks: tagging, genre classification, emotion recognition, and key detection. For key detection, we observe that representations from Jukebox are considerably stronger than those from models pre-trained on tagging, suggesting that pre-training via codified audio language modeling may address blind spots in conventional approaches. We interpret the strength of Jukebox's representations as evidence that modeling audio instead of tags provides richer representations for MIR. | ['Percy Liang', 'Chris Donahue', 'Rodrigo Castellon'] | 2021-07-12 | null | null | null | null | ['music-generation', 'genre-classification', 'music-generation', 'music-information-retrieval'] | ['audio', 'computer-vision', 'music', 'music'] | [ 2.71064669e-01 3.56429696e-01 -1.30163416e-01 -2.38872748e-02
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30b5c3ed-9b3c-482e-8cc2-2b8dbf7378d1 | self-supervised-prototypical-transfer | 2006.11325 | null | https://arxiv.org/abs/2006.11325v1 | https://arxiv.org/pdf/2006.11325v1.pdf | Self-Supervised Prototypical Transfer Learning for Few-Shot Classification | Most approaches in few-shot learning rely on costly annotated data related to the goal task domain during (pre-)training. Recently, unsupervised meta-learning methods have exchanged the annotation requirement for a reduction in few-shot classification performance. Simultaneously, in settings with realistic domain shift, common transfer learning has been shown to outperform supervised meta-learning. Building on these insights and on advances in self-supervised learning, we propose a transfer learning approach which constructs a metric embedding that clusters unlabeled prototypical samples and their augmentations closely together. This pre-trained embedding is a starting point for few-shot classification by summarizing class clusters and fine-tuning. We demonstrate that our self-supervised prototypical transfer learning approach ProtoTransfer outperforms state-of-the-art unsupervised meta-learning methods on few-shot tasks from the mini-ImageNet dataset. In few-shot experiments with domain shift, our approach even has comparable performance to supervised methods, but requires orders of magnitude fewer labels. | ['Arnout Devos', 'Carlos Medina', 'Matthias Grossglauser'] | 2020-06-19 | null | null | null | null | ['unsupervised-few-shot-learning', 'unsupervised-few-shot-image-classification'] | ['computer-vision', 'computer-vision'] | [ 3.82069409e-01 2.32993811e-01 -7.03700423e-01 -5.99630833e-01
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77760999-e047-4bc4-b919-8a142fabfad8 | named-entity-recognition-for-norwegian | null | null | https://aclanthology.org/W19-6123 | https://aclanthology.org/W19-6123.pdf | Named-Entity Recognition for Norwegian | NER is the task of recognizing and demarcating the segments of a document that are part of a name and which type of name it is. We use 4 different categories of names: Locations (LOC), miscellaneous (MISC), organizations (ORG), and persons (PER). Even though we employ state of the art methods—including sub-word embeddings—that work well for English, we are unable to reproduce the same success for the Norwegian written forms. However, our model performs better than any previous research on Norwegian text. The study also presents the first NER for Nynorsk. Lastly, we find that by combining Nynorsk and Bokmål into one training corpus we improve the performance of our model on both languages. | ['Bjarte Johansen'] | null | null | null | null | ws-nodalida-2019-9 | ['miscellaneous'] | ['miscellaneous'] | [-7.00797617e-01 4.49987035e-03 -5.75007834e-02 -8.46552327e-02
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a477409c-ef05-40df-a40d-32bcf8383a19 | large-scale-artificial-neural-network-1 | 1510.02709 | null | http://arxiv.org/abs/1510.02709v1 | http://arxiv.org/pdf/1510.02709v1.pdf | Large-scale Artificial Neural Network: MapReduce-based Deep Learning | Faced with continuously increasing scale of data, original back-propagation
neural network based machine learning algorithm presents two non-trivial
challenges: huge amount of data makes it difficult to maintain both efficiency
and accuracy; redundant data aggravates the system workload. This project is
mainly focused on the solution to the issues above, combining deep learning
algorithm with cloud computing platform to deal with large-scale data. A
MapReduce-based handwriting character recognizer will be designed in this
project to verify the efficiency improvement this mechanism will achieve on
training and practical large-scale data. Careful discussion and experiment will
be developed to illustrate how deep learning algorithm works to train
handwritten digits data, how MapReduce is implemented on deep learning neural
network, and why this combination accelerates computation. Besides performance,
the scalability and robustness will be mentioned in this report as well. Our
system comes with two demonstration software that visually illustrates our
handwritten digit recognition/encoding application. | ['Ruizhi Li', 'Xu Wei', 'Gengtao Jia', 'Risheng Wang', 'Kairan Sun'] | 2015-10-09 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [-3.22375327e-01 -5.63972354e-01 1.19029224e-01 -6.20855093e-01
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725fdcfb-1da2-4081-bdbe-bfabf771c137 | adversarial-unsupervised-domain-adaptation-1 | 2102.06864 | null | https://arxiv.org/abs/2102.06864v1 | https://arxiv.org/pdf/2102.06864v1.pdf | Adversarial Unsupervised Domain Adaptation Guided with Deep Clustering for Face Presentation Attack Detection | Face Presentation Attack Detection (PAD) has drawn increasing attentions to secure the face recognition systems that are widely used in many applications. Conventional face anti-spoofing methods have been proposed, assuming that testing is from the same domain used for training, and so cannot generalize well on unseen attack scenarios. The trained models tend to overfit to the acquisition sensors and attack types available in the training data. In light of this, we propose an end-to-end learning framework based on Domain Adaptation (DA) to improve PAD generalization capability. Labeled source-domain samples are used to train the feature extractor and classifier via cross-entropy loss, while unsupervised data from the target domain are utilized in adversarial DA approach causing the model to learn domain-invariant features. Using DA alone in face PAD fails to adapt well to target domain that is acquired in different conditions with different devices and attack types than the source domain. And so, in order to keep the intrinsic properties of the target domain, deep clustering of target samples is performed. Training and deep clustering are performed end-to-end, and experiments performed on several public benchmark datasets validate that our proposed Deep Clustering guided Unsupervised Domain Adaptation (DCDA) can learn more generalized information compared with the state-of-the-art classification error on the target domain. | ['Hani Mahdi', 'Mohamed N. Moustafa', 'Yomna Safaa El-Din'] | 2021-02-13 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 3.62791538e-01 -2.29826272e-01 -2.38951921e-01 -3.83262068e-01
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614549fc-9975-49c9-9306-b0721c8c7ab8 | designing-ecg-monitoring-healthcare-system | 2105.12497 | null | https://arxiv.org/abs/2105.12497v2 | https://arxiv.org/pdf/2105.12497v2.pdf | Designing ECG Monitoring Healthcare System with Federated Transfer Learning and Explainable AI | Deep learning play a vital role in classifying different arrhythmias using the electrocardiography (ECG) data. Nevertheless, training deep learning models normally requires a large amount of data and it can lead to privacy concerns. Unfortunately, a large amount of healthcare data cannot be easily collected from a single silo. Additionally, deep learning models are like black-box, with no explainability of the predicted results, which is often required in clinical healthcare. This limits the application of deep learning in real-world health systems. In this paper, we design a new explainable artificial intelligence (XAI) based deep learning framework in a federated setting for ECG-based healthcare applications. The federated setting is used to solve issues such as data availability and privacy concerns. Furthermore, the proposed framework setting effectively classifies arrhythmia's using an autoencoder and a classifier, both based on a convolutional neural network (CNN). Additionally, we propose an XAI-based module on top of the proposed classifier to explain the classification results, which help clinical practitioners make quick and reliable decisions. The proposed framework was trained and tested using the MIT-BIH Arrhythmia database. The classifier achieved accuracy up to 94% and 98% for arrhythmia detection using noisy and clean data, respectively, with five-fold cross-validation. | ['Shujun Li', 'Ludovic Koehl', 'Kim Phuc Tran', 'Ali Raza'] | 2021-05-26 | null | null | null | null | ['arrhythmia-detection', 'electrocardiography-ecg'] | ['medical', 'methodology'] | [-4.79033925e-02 3.38061452e-01 1.81120113e-01 -6.85091555e-01
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89acdc69-98f1-41b5-8a35-cb318d72c4c3 | embedding-representation-of-academic | 2210.03290 | null | https://arxiv.org/abs/2210.03290v1 | https://arxiv.org/pdf/2210.03290v1.pdf | Embedding Representation of Academic Heterogeneous Information Networks Based on Federated Learning | Academic networks in the real world can usually be portrayed as heterogeneous information networks (HINs) with multi-type, universally connected nodes and multi-relationships. Some existing studies for the representation learning of homogeneous information networks cannot be applicable to heterogeneous information networks because of the lack of ability to issue heterogeneity. At the same time, data has become a factor of production, playing an increasingly important role. Due to the closeness and blocking of businesses among different enterprises, there is a serious phenomenon of data islands. To solve the above challenges, aiming at the data information of scientific research teams closely related to science and technology, we proposed an academic heterogeneous information network embedding representation learning method based on federated learning (FedAHE), which utilizes node attention and meta path attention mechanism to learn low-dimensional, dense and real-valued vector representations while preserving the rich topological information and meta-path-based semantic information of nodes in network. Moreover, we combined federated learning with the representation learning of HINs composed of scientific research teams and put forward a federal training mechanism based on dynamic weighted aggregation of parameters (FedDWA) to optimize the node embeddings of HINs. Through sufficient experiments, the efficiency, accuracy and feasibility of our proposed framework are demonstrated. | ['Ang Li', 'Meiyu Liang', 'Yawen Li', 'Junfu Wang'] | 2022-10-07 | null | null | null | null | ['network-embedding'] | ['methodology'] | [-7.01587081e-01 1.18256360e-01 -3.49513859e-01 4.50810194e-02
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2e999653-53ea-4889-9b92-0187446a5e74 | blind-image-deconvolution-by-automatic | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Gong_Blind_Image_Deconvolution_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Gong_Blind_Image_Deconvolution_CVPR_2016_paper.pdf | Blind Image Deconvolution by Automatic Gradient Activation | Blind image deconvolution is an ill-posed inverse problem which is often addressed through the application of appropriate prior. Although some priors are informative in general, many images do not strictly conform to this, leading to degraded performance in the kernel estimation. More critically, real images may be contaminated by nonuniform noise such as saturation and outliers.Methods for removing specific image areas based on some priors have been proposed, but they operate either manually or by defining fixed criteria. We show here that a subset of the image gradients are adequate to estimate the blur kernel robustly, no matter the gradient image is sparse or not. We thus introduce a gradient activation method to automatically select a subset of gradients of the latent image in a cutting-plane-based optimization scheme for kernel estimation. No extra assumption is used in our model, which greatly improves the accuracy and flexibility. More importantly, the proposed method affords great convenience for handling noise and outliers. Experiments on both synthetic data and real-world images demonstrate the effectiveness and robustness of the proposed method in comparison with the state-of-the-art methods. | ['Anton Van Den Hengel', 'Yanning Zhang', 'Dong Gong', 'Qinfeng Shi', 'Mingkui Tan'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['image-deconvolution'] | ['computer-vision'] | [ 2.72445560e-01 -4.23934877e-01 2.73787260e-01 -3.19148868e-01
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15117032-214d-4f14-9522-b64bc7f8b783 | the-bayesian-brain-with-a-bit-less-bayes | 2111.09063 | null | https://arxiv.org/abs/2111.09063v1 | https://arxiv.org/pdf/2111.09063v1.pdf | The "Bayesian" brain, with a bit less Bayes | The idea that the brain is a probabilistic (Bayesian) inference machine, continuously trying to figure out the hidden causes of its inputs, has become very influential in cognitive (neuro)science over recent decades. Here I present a relatively straightforward generalization of this idea: the primary computational task that the brain is faced with is to track the probabilistic structure of observations themselves, without recourse to hidden states. Taking this starting point seriously turns out to have considerable explanatory power, and several key ideas are developed from it: (1) past experience, encoded in prior expectations, has an influence over the future that is analogous to regularization as known from machine learning; (2) action generation (interpreted as constraint satisfaction) is a special case of such regularization; (3) the concept of attractors in dynamical systems provides a useful lens through which prior expectations, regularization, and action induction can be viewed; these thus appear as different perspectives on the same phenomenon; (4) the phylogenetically ancient imperative of acting to ensure and thereby observe conditions beneficial for survival is likely the same as that which underlies perceptual inference. The Bayesian brain hypothesis has been touted as promising to deliver a "unified science of mind and action". In this paper, I sketch an informal step towards fulfilling that promise, while avoiding some pitfalls that other such attempts have fallen prey to. | ['Eelke Spaak'] | 2021-11-17 | null | null | null | null | ['action-generation'] | ['computer-vision'] | [ 2.81214148e-01 4.58367229e-01 -3.68445143e-02 -3.42868209e-01
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c7a04346-f021-41d4-a123-9cf3a1aeeba1 | sparse-subspace-clustering-friendly-deep | 2111.13920 | null | https://arxiv.org/abs/2111.13920v1 | https://arxiv.org/pdf/2111.13920v1.pdf | Sparse Subspace Clustering Friendly Deep Dictionary Learning for Hyperspectral Image Classification | Subspace clustering techniques have shown promise in hyperspectral image segmentation. The fundamental assumption in subspace clustering is that the samples belonging to different clusters/segments lie in separable subspaces. What if this condition does not hold? We surmise that even if the condition does not hold in the original space, the data may be nonlinearly transformed to a space where it will be separable into subspaces. In this work, we propose a transformation based on the tenets of deep dictionary learning (DDL). In particular, we incorporate the sparse subspace clustering (SSC) loss in the DDL formulation. Here DDL nonlinearly transforms the data such that the transformed representation (of the data) is separable into subspaces. We show that the proposed formulation improves over the state-of-the-art deep learning techniques in hyperspectral image clustering. | ['Angshul Majumdar', 'Anurag Goel'] | 2021-11-27 | null | null | null | null | ['hyperspectral-image-segmentation', 'image-clustering'] | ['computer-vision', 'computer-vision'] | [ 5.26059270e-01 -2.56685317e-01 3.01237870e-02 -2.99244553e-01
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68e9fb4a-d3a8-4587-be9a-7dcc34ae7c85 | gcrdn-global-context-driven-residual-dense | null | null | https://ieeexplore.ieee.org/abstract/document/10115440 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10115440&tag=1 | GCRDN: Global Context-Driven Residual Dense Network for Remote Sensing Image Superresolution | Superresolution (SR) of remote sensing images aims to restore high-quality information from low-resolution images. Recently, it has witnessed great strides with the rapid development of deep learning (DL) techniques. Despite their good performance, these DL-based models are often ineffective in balancing global and local feature extraction. Moreover, they are usually hindered by the poor image reconstruction capability of the decoder inside their SR models. To cope with this problem, this work proposes a novel global context-driven residual dense network (GCRDN) for satellite image SR based on the encoder and decoder architecture. In particular, the proposed encoder is endowed with nonlocal sparse attention modules incorporated into the residual dense network to learn robust representations from global features. Furthermore, a decoder equipped with back-sampling blocks is devised to fully exploit the feature maps extracted from the encoder. Extensive experimental comparisons based on two multisensor satellite remote sensing datasets confirm that the proposed GCRDN achieves impressive performance in terms of perceptual quality and fidelity. | ['Man-on Pun', 'Xiaokang Zhang', 'Xianping Ma', 'Jialu Sui'] | 2023-05-04 | null | null | null | jounal-2023-5 | ['image-reconstruction', 'super-resolution'] | ['computer-vision', 'computer-vision'] | [ 4.92542624e-01 -1.75938785e-01 3.38109061e-02 -4.67350125e-01
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0f95ecdd-1fd6-405e-ab48-db23cecd0f57 | submission-to-generic-event-boundary | 2206.15268 | null | https://arxiv.org/abs/2206.15268v1 | https://arxiv.org/pdf/2206.15268v1.pdf | Submission to Generic Event Boundary Detection Challenge@CVPR 2022: Local Context Modeling and Global Boundary Decoding Approach | Generic event boundary detection (GEBD) is an important yet challenging task in video understanding, which aims at detecting the moments where humans naturally perceive event boundaries. In this paper, we present a local context modeling and global boundary decoding approach for GEBD task. Local context modeling sub-network is proposed to perceive diverse patterns of generic event boundaries, and it generates powerful video representations and reliable boundary confidence. Based on them, global boundary decoding sub-network is exploited to decode event boundaries from a global view. Our proposed method achieves 85.13% F1-score on Kinetics-GEBD testing set, which achieves a more than 22% F1-score boost compared to the baseline method. The code is available at https://github.com/JackyTown/GEBD_Challenge_CVPR2022. | ['LiMin Wang', 'Wayne Wu', 'Chen Qian', 'Jing Tan', 'Zhaoyang Liu', 'Jiaqi Tang'] | 2022-06-30 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 1.72732383e-01 8.35582316e-02 -3.19861263e-01 -2.80278146e-01
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b2531e58-cfe3-46b1-aaf1-8dea287ee3bc | accurate-rgb-d-salient-object-detection-via | 2007.11782 | null | https://arxiv.org/abs/2007.11782v1 | https://arxiv.org/pdf/2007.11782v1.pdf | Accurate RGB-D Salient Object Detection via Collaborative Learning | Benefiting from the spatial cues embedded in depth images, recent progress on RGB-D saliency detection shows impressive ability on some challenge scenarios. However, there are still two limitations. One hand is that the pooling and upsampling operations in FCNs might cause blur object boundaries. On the other hand, using an additional depth-network to extract depth features might lead to high computation and storage cost. The reliance on depth inputs during testing also limits the practical applications of current RGB-D models. In this paper, we propose a novel collaborative learning framework where edge, depth and saliency are leveraged in a more efficient way, which solves those problems tactfully. The explicitly extracted edge information goes together with saliency to give more emphasis to the salient regions and object boundaries. Depth and saliency learning is innovatively integrated into the high-level feature learning process in a mutual-benefit manner. This strategy enables the network to be free of using extra depth networks and depth inputs to make inference. To this end, it makes our model more lightweight, faster and more versatile. Experiment results on seven benchmark datasets show its superior performance. | ['Yongri Piao', 'Wei Ji', 'Miao Zhang', 'Jingjing Li', 'Huchuan Lu'] | 2020-07-23 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2916_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123630052.pdf | eccv-2020-8 | ['rgb-d-salient-object-detection', 'thermal-image-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.06646055e-01 6.36989158e-03 -2.00735882e-01 -3.86748224e-01
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e3a6cbf7-48db-4f2d-984e-ecd8190f6a6d | robust-control-for-dynamical-systems-with-non | 2301.01526 | null | https://arxiv.org/abs/2301.01526v1 | https://arxiv.org/pdf/2301.01526v1.pdf | Robust Control for Dynamical Systems With Non-Gaussian Noise via Formal Abstractions | Controllers for dynamical systems that operate in safety-critical settings must account for stochastic disturbances. Such disturbances are often modeled as process noise in a dynamical system, and common assumptions are that the underlying distributions are known and/or Gaussian. In practice, however, these assumptions may be unrealistic and can lead to poor approximations of the true noise distribution. We present a novel controller synthesis method that does not rely on any explicit representation of the noise distributions. In particular, we address the problem of computing a controller that provides probabilistic guarantees on safely reaching a target, while also avoiding unsafe regions of the state space. First, we abstract the continuous control system into a finite-state model that captures noise by probabilistic transitions between discrete states. As a key contribution, we adapt tools from the scenario approach to compute probably approximately correct (PAC) bounds on these transition probabilities, based on a finite number of samples of the noise. We capture these bounds in the transition probability intervals of a so-called interval Markov decision process (iMDP). This iMDP is, with a user-specified confidence probability, robust against uncertainty in the transition probabilities, and the tightness of the probability intervals can be controlled through the number of samples. We use state-of-the-art verification techniques to provide guarantees on the iMDP and compute a controller for which these guarantees carry over to the original control system. In addition, we develop a tailored computational scheme that reduces the complexity of the synthesis of these guarantees on the iMDP. Benchmarks on realistic control systems show the practical applicability of our method, even when the iMDP has hundreds of millions of transitions. | ['Nils Jansen', 'Marielle Stoelinga', 'Hasan A. Poonawala', 'David Parker', 'Alessandro Abate', 'Licio Romao', 'Thom Badings'] | 2023-01-04 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [ 2.73230493e-01 2.48213112e-01 -1.11924887e-01 3.09912533e-01
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b2d8af69-6548-4a8f-beec-3db4c699d538 | l-spex-localized-target-speaker-extraction | 2202.09995 | null | https://arxiv.org/abs/2202.09995v1 | https://arxiv.org/pdf/2202.09995v1.pdf | L-SpEx: Localized Target Speaker Extraction | Speaker extraction aims to extract the target speaker's voice from a multi-talker speech mixture given an auxiliary reference utterance. Recent studies show that speaker extraction benefits from the location or direction of the target speaker. However, these studies assume that the target speaker's location is known in advance or detected by an extra visual cue, e.g., face image or video. In this paper, we propose an end-to-end localized target speaker extraction on pure speech cues, that is called L-SpEx. Specifically, we design a speaker localizer driven by the target speaker's embedding to extract the spatial features, including direction-of-arrival (DOA) of the target speaker and beamforming output. Then, the spatial cues and target speaker's embedding are both used to form a top-down auditory attention to the target speaker. Experiments on the multi-channel reverberant dataset called MC-Libri2Mix show that our L-SpEx approach significantly outperforms the baseline system. | ['Haizhou Li', 'Jianwu Dang', 'Eng Siong Chng', 'Longbiao Wang', 'Chenglin Xu', 'Meng Ge'] | 2022-02-21 | null | null | null | null | ['target-speaker-extraction'] | ['audio'] | [-7.57409036e-02 -2.24898070e-01 1.08919002e-01 -4.57838774e-01
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0ca16b5a-be02-4a6a-b920-3863a74c43ce | applying-transfer-learning-for-improving | 2101.02351 | null | https://arxiv.org/abs/2101.02351v1 | https://arxiv.org/pdf/2101.02351v1.pdf | Applying Transfer Learning for Improving Domain-Specific Search Experience Using Query to Question Similarity | Search is one of the most common platforms used to seek information. However, users mostly get overloaded with results whenever they use such a platform to resolve their queries. Nowadays, direct answers to queries are being provided as a part of the search experience. The question-answer (QA) retrieval process plays a significant role in enriching the search experience. Most off-the-shelf Semantic Textual Similarity models work fine for well-formed search queries, but their performances degrade when applied to a domain-specific setting having incomplete or grammatically ill-formed search queries in prevalence. In this paper, we discuss a framework for calculating similarities between a given input query and a set of predefined questions to retrieve the question which matches to it the most. We have used it for the financial domain, but the framework is generalized for any domain-specific search engine and can be used in other domains as well. We use Siamese network [6] over Long Short-Term Memory (LSTM) [3] models to train a classifier which generates unnormalized and normalized similarity scores for a given pair of questions. Moreover, for each of these question pairs, we calculate three other similarity scores: cosine similarity between their average word2vec embeddings [15], cosine similarity between their sentence embeddings [7] generated using RoBERTa [17] and their customized fuzzy-match score. Finally, we develop a metaclassifier using Support Vector Machines [19] for combining these five scores to detect if a given pair of questions is similar. We benchmark our model's performance against existing State Of The Art (SOTA) models on Quora Question Pairs (QQP) dataset as well as a dataset specific to the financial domain. | ['Sohom Ghosh', 'Shruti Agrawal', 'Ankush Chopra'] | 2021-01-07 | null | null | null | null | ['question-similarity'] | ['natural-language-processing'] | [-1.37486398e-01 -3.60385925e-01 -2.23554373e-01 -3.93500745e-01
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77d6cf64-d625-479a-9e2a-5eec297a4865 | visbert-hidden-state-visualizations-for | 2011.04507 | null | https://arxiv.org/abs/2011.04507v1 | https://arxiv.org/pdf/2011.04507v1.pdf | VisBERT: Hidden-State Visualizations for Transformers | Explainability and interpretability are two important concepts, the absence of which can and should impede the application of well-performing neural networks to real-world problems. At the same time, they are difficult to incorporate into the large, black-box models that achieve state-of-the-art results in a multitude of NLP tasks. Bidirectional Encoder Representations from Transformers (BERT) is one such black-box model. It has become a staple architecture to solve many different NLP tasks and has inspired a number of related Transformer models. Understanding how these models draw conclusions is crucial for both their improvement and application. We contribute to this challenge by presenting VisBERT, a tool for visualizing the contextual token representations within BERT for the task of (multi-hop) Question Answering. Instead of analyzing attention weights, we focus on the hidden states resulting from each encoder block within the BERT model. This way we can observe how the semantic representations are transformed throughout the layers of the model. VisBERT enables users to get insights about the model's internal state and to explore its inference steps or potential shortcomings. The tool allows us to identify distinct phases in BERT's transformations that are similar to a traditional NLP pipeline and offer insights during failed predictions. | ['Felix A. Gers', 'Alexander Löser', 'Benjamin Winter', 'Betty van Aken'] | 2020-11-09 | null | null | null | null | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 3.21342498e-01 6.97021246e-01 6.96167815e-03 -4.52296644e-01
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27e1ddb7-443b-4a4c-9c88-a9fa36dbaab4 | learning-sparse-adversarial-dictionaries-for | 1712.00640 | null | http://arxiv.org/abs/1712.00640v1 | http://arxiv.org/pdf/1712.00640v1.pdf | Learning Sparse Adversarial Dictionaries For Multi-Class Audio Classification | Audio events are quite often overlapping in nature, and more prone to noise
than visual signals. There has been increasing evidence for the superior
performance of representations learned using sparse dictionaries for
applications like audio denoising and speech enhancement. This paper
concentrates on modifying the traditional reconstructive dictionary learning
algorithms, by incorporating a discriminative term into the objective function
in order to learn class-specific adversarial dictionaries that are good at
representing samples of their own class at the same time poor at representing
samples belonging to any other class. We quantitatively demonstrate the
effectiveness of our learned dictionaries as a stand-alone solution for both
binary as well as multi-class audio classification problems. | ['Puranjoy Bhattacharya', 'Vaisakh Shaj'] | 2017-12-02 | null | null | null | null | ['audio-denoising'] | ['audio'] | [ 3.48496079e-01 -1.08512402e-01 -1.59887094e-02 -1.94960609e-01
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ba2389f7-d4eb-4099-80c8-f42a057a6a59 | mime-mimicking-emotions-for-empathetic | 2010.01454 | null | https://arxiv.org/abs/2010.01454v1 | https://arxiv.org/pdf/2010.01454v1.pdf | MIME: MIMicking Emotions for Empathetic Response Generation | Current approaches to empathetic response generation view the set of emotions expressed in the input text as a flat structure, where all the emotions are treated uniformly. We argue that empathetic responses often mimic the emotion of the user to a varying degree, depending on its positivity or negativity and content. We show that the consideration of this polarity-based emotion clusters and emotional mimicry results in improved empathy and contextual relevance of the response as compared to the state-of-the-art. Also, we introduce stochasticity into the emotion mixture that yields emotionally more varied empathetic responses than the previous work. We demonstrate the importance of these factors to empathetic response generation using both automatic- and human-based evaluations. The implementation of MIME is publicly available at https://github.com/declare-lab/MIME. | ['Soujanya Poria', 'Rada Mihalcea', 'Alexander Gelbukh', 'Deepanway Ghosal', 'Jiankun Lu', 'Shanshan Peng', 'Pengfei Hong', 'Navonil Majumder'] | 2020-10-04 | null | https://aclanthology.org/2020.emnlp-main.721 | https://aclanthology.org/2020.emnlp-main.721.pdf | emnlp-2020-11 | ['empathetic-response-generation'] | ['natural-language-processing'] | [-3.65099519e-01 2.38253132e-01 2.77221352e-02 -4.54812348e-01
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b559fd5b-9fd5-4bc2-86fc-9e964142b833 | testing-the-dark-confined-landscape-from | 2012.11614 | null | https://arxiv.org/abs/2012.11614v3 | https://arxiv.org/pdf/2012.11614v3.pdf | Testing the dark SU(N) Yang-Mills theory Confined Landscape: From the Lattice to Gravitational Waves | We pave the way for future gravitational-wave detection experiments, such as the Big Bang Observer and DECIGO, to constrain dark sectors made of SU(N) Yang-Mills confined theories. We go beyond the state-of-the-art by combining first principle lattice results and effective field theory approaches to infer essential information about the non-perturbative dark deconfinement phase transition driving the generation of gravitational-waves in the early universe, such as the order, duration and energy budget of the phase transition which are essential in establishing the strength of the resulting gravitational-wave signal. | ['Zhi-Wei Wang', 'Francesco Sannino', 'Manuel Reichert', 'Wei-Chih Huang'] | 2020-12-21 | null | null | null | null | ['gravitational-wave-detection'] | ['miscellaneous'] | [-2.49840729e-02 4.53455262e-02 -4.98336777e-02 1.32184401e-01
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55f07732-928a-4e18-bf2c-2d14deafe443 | semi-supervised-time-series-classification | 2006.11031 | null | https://arxiv.org/abs/2006.11031v1 | https://arxiv.org/pdf/2006.11031v1.pdf | Semi-supervised time series classification method for quantum computing | In this paper we develop methods to solve two problems related to time series (TS) analysis using quantum computing: reconstruction and classification. We formulate the task of reconstructing a given TS from a training set of data as an unconstrained binary optimization (QUBO) problem, which can be solved by both quantum annealers and gate-model quantum processors. We accomplish this by discretizing the TS and converting the reconstruction to a set cover problem, allowing us to perform a one-versus-all method of reconstruction. Using the solution to the reconstruction problem, we show how to extend this method to perform semi-supervised classification of TS data. We present results indicating our method is competitive with current semi- and unsupervised classification techniques, but using less data than classical techniques. | ['Andrii Kleshchonok', 'Sheir Yarkoni', 'Marc Hilbert', 'Yury Dzerin', 'Florian Neukart'] | 2020-06-19 | null | null | null | null | ['semi-supervised-time-series-classification'] | ['time-series'] | [ 1.07337475e+00 5.94598614e-03 -1.25379309e-01 -5.02299964e-01
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9c16edb4-0804-4261-9afd-8f5c160fd842 | from-image-to-imuge-immunized-image | 2110.14196 | null | https://arxiv.org/abs/2110.14196v1 | https://arxiv.org/pdf/2110.14196v1.pdf | From Image to Imuge: Immunized Image Generation | We introduce Imuge, an image tamper resilient generative scheme for image self-recovery. The traditional manner of concealing image content within the image are inflexible and fragile to diverse digital attack, i.e. image cropping and JPEG compression. To address this issue, we jointly train a U-Net backboned encoder, a tamper localization network and a decoder for image recovery. Given an original image, the encoder produces a visually indistinguishable immunized image. At the recipient's side, the verifying network localizes the malicious modifications, and the original content can be approximately recovered by the decoder, despite the presence of the attacks. Several strategies are proposed to boost the training efficiency. We demonstrate that our method can recover the details of the tampered regions with a high quality despite the presence of various kinds of attacks. Comprehensive ablation studies are conducted to validate our network designs. | ['Siyi Li', 'Xinpeng Zhang', 'Haisheng Xu', 'Hang Zhou', 'Zhenxing Qian', 'Qichao Ying'] | 2021-10-27 | null | null | null | null | ['image-cropping'] | ['computer-vision'] | [ 7.15314031e-01 5.19256704e-02 -1.90781057e-01 2.45717913e-01
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6a0fc8a7-67e6-43fa-83ee-48d0ab635ae1 | tinkering-under-the-hood-interactive-zero | 1612.04901 | null | http://arxiv.org/abs/1612.04901v1 | http://arxiv.org/pdf/1612.04901v1.pdf | Tinkering Under the Hood: Interactive Zero-Shot Learning with Net Surgery | We consider the task of visual net surgery, in which a CNN can be
reconfigured without extra data to recognize novel concepts that may be omitted
from the training set. While most prior work make use of linguistic cues for
such "zero-shot" learning, we do so by using a pictorial language
representation of the training set, implicitly learned by a CNN, to generalize
to new classes. To this end, we introduce a set of visualization techniques
that better reveal the activation patterns and relations between groups of CNN
filters. We next demonstrate that knowledge of pictorial languages can be used
to rewire certain CNN neurons into a part model, which we call a pictorial
language classifier. We demonstrate the robustness of simple PLCs by applying
them in a weakly supervised manner: labeling unlabeled concepts for visual
classes present in the training data. Specifically we show that a PLC built on
top of a CNN trained for ImageNet classification can localize humans in Graz-02
and determine the pose of birds in PASCAL-VOC without extra labeled data or
additional training. We then apply PLCs in an interactive zero-shot manner,
demonstrating that pictorial languages are expressive enough to detect a set of
visual classes in MS-COCO that never appear in the ImageNet training set. | ['Vivek Krishnan', 'Deva Ramanan'] | 2016-12-15 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 9.58626568e-02 4.03276145e-01 1.97244555e-01 -5.64491749e-01
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24b1ce6d-52a7-4b3b-bed4-63e0c8eefb08 | pseudo-supervised-metrics-evaluating | 2303.10310 | null | https://arxiv.org/abs/2303.10310v1 | https://arxiv.org/pdf/2303.10310v1.pdf | Pseudo Supervised Metrics: Evaluating Unsupervised Image to Image Translation Models In Unsupervised Cross-Domain Classification Frameworks | The ability to classify images accurately and efficiently is dependent on having access to large labeled datasets and testing on data from the same domain that the model is trained on. Classification becomes more challenging when dealing with new data from a different domain, where collecting a large labeled dataset and training a new classifier from scratch is time-consuming, expensive, and sometimes infeasible or impossible. Cross-domain classification frameworks were developed to handle this data domain shift problem by utilizing unsupervised image-to-image (UI2I) translation models to translate an input image from the unlabeled domain to the labeled domain. The problem with these unsupervised models lies in their unsupervised nature. For lack of annotations, it is not possible to use the traditional supervised metrics to evaluate these translation models to pick the best-saved checkpoint model. In this paper, we introduce a new method called Pseudo Supervised Metrics that was designed specifically to support cross-domain classification applications contrary to other typically used metrics such as the FID which was designed to evaluate the model in terms of the quality of the generated image from a human-eye perspective. We show that our metric not only outperforms unsupervised metrics such as the FID, but is also highly correlated with the true supervised metrics, robust, and explainable. Furthermore, we demonstrate that it can be used as a standard metric for future research in this field by applying it to a critical real-world problem (the boiling crisis problem). | ['Ying Sun', 'Han Hu', 'Teresa Wu', 'Md Mahfuzur Rahman Siddiquee', 'Firas Al-Hindawi'] | 2023-03-18 | null | null | null | null | ['unsupervised-image-to-image-translation'] | ['computer-vision'] | [ 6.04425848e-01 6.19500764e-02 -2.51977354e-01 -5.00452757e-01
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bee9a1fb-2dd7-40ef-b91c-584c8fb482dc | context-autoencoder-for-self-supervised | 2202.03026 | null | https://arxiv.org/abs/2202.03026v2 | https://arxiv.org/pdf/2202.03026v2.pdf | Context Autoencoder for Self-Supervised Representation Learning | We present a novel masked image modeling (MIM) approach, context autoencoder (CAE), for self-supervised representation pretraining. The goal is to pretrain an encoder by solving the pretext task: estimate the masked patches from the visible patches in an image. Our approach first feeds the visible patches into the encoder, extracting the representations. Then, we make predictions from visible patches to masked patches in the encoded representation space. We introduce an alignment constraint, encouraging that the representations for masked patches, predicted from the encoded representations of visible patches, are aligned with the masked patch presentations computed from the encoder. In other words, the predicted representations are expected to lie in the encoded representation space, which empirically shows the benefit to representation learning. Last, the predicted masked patch representations are mapped to the targets of the pretext task through a decoder. In comparison to previous MIM methods (e.g., BEiT) that couple the encoding and pretext task completion roles, our approach benefits the separation of the representation learning (encoding) role and the pretext task completion role, improving the representation learning capacity and accordingly helping more on downstream tasks. In addition, we present the explanations about why contrastive pretraining and supervised pretraining perform similarly and why MIM potentially performs better. We demonstrate the effectiveness of our CAE through superior transfer performance in downstream tasks: semantic segmentation, and object detection and instance segmentation. | ['Jingdong Wang', 'Gang Zeng', 'Ping Luo', 'Shumin Han', 'Yunhao Wang', 'Shentong Mo', 'Ying Xin', 'Xiaodi Wang', 'Mingyu Ding', 'Xiaokang Chen'] | 2022-02-07 | null | null | null | null | ['self-supervised-image-classification'] | ['computer-vision'] | [ 9.40059841e-01 9.65691030e-01 -9.32422876e-02 -4.50296193e-01
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4c5308ab-7dbe-4f56-8b84-3c93edef894c | multicenter-automatic-detection-of-invasive | 2301.06789 | null | https://arxiv.org/abs/2301.06789v1 | https://arxiv.org/pdf/2301.06789v1.pdf | Multicenter automatic detection of invasive carcinoma on breast whole slide images | Breast cancer is one of the most prevalent cancers worldwide and pathologists are closely involved in establishing a diagnosis. Tools to assist in making a diagnosis are required to manage the increasing workload. In this context, artificial intelligence (AI) and deep-learning based tools may be used in daily pathology practice. However, it is challenging to develop fast and reliable algorithms that can be trusted by practitioners, whatever the medical center. We describe a patch-based algorithm that incorporates a convolutional neural network to detect and locate invasive carcinoma on breast whole-slide images. The network was trained on a dataset extracted from a reference acquisition center. We then performed a calibration step based on transfer learning to maintain the performance when translating on a new target acquisition center by using a limited amount of additional training data. Performance was evaluated using classical binary measures (accuracy, recall, precision) for both centers (referred to as test reference dataset and test target dataset) and at two levels: patch and slide level. At patch level, accuracy, recall, and precision of the model on the reference and target test sets were 92.1\% and 96.3\%, 95\% and 87.8\%, and 73.9\% and 70.6\%, respectively. At slide level, accuracy, recall, and precision were 97.6\% and 92.0\%, 90.9\% and 100\%, and 100\% and 70.8\% for test sets 1 and 2, respectively. The high performance of the algorithm at both centers shows that the calibration process is efficient. This is performed using limited training data from the new target acquisition center and requires that the model is trained beforehand on a large database from a reference center. This methodology allows the implementation of AI diagnostic tools to help in routine pathology practice. | ['Sophie Prévot', 'Marie Sockeel', 'Elisabeth Lanteri', 'Loris Guichard', 'Thomas Depoilly', 'Christophe Bontoux', 'Yoan Ditchi', 'Claire Bocciarelli', 'Julien Adam', 'Solène-Florence Kammerer-Jacquet', 'Stéphane Sockeel', 'Nicolas Pozin', 'Rémy Peyret'] | 2023-01-17 | null | null | null | null | ['whole-slide-images'] | ['computer-vision'] | [ 2.80239105e-01 2.46095434e-01 -3.01790774e-01 -8.60928521e-02
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250a7bfd-85dc-48e2-94a4-e1cb2e467ba6 | recurrent-neural-network-for-text | 1605.05101 | null | http://arxiv.org/abs/1605.05101v1 | http://arxiv.org/pdf/1605.05101v1.pdf | Recurrent Neural Network for Text Classification with Multi-Task Learning | Neural network based methods have obtained great progress on a variety of
natural language processing tasks. However, in most previous works, the models
are learned based on single-task supervised objectives, which often suffer from
insufficient training data. In this paper, we use the multi-task learning
framework to jointly learn across multiple related tasks. Based on recurrent
neural network, we propose three different mechanisms of sharing information to
model text with task-specific and shared layers. The entire network is trained
jointly on all these tasks. Experiments on four benchmark text classification
tasks show that our proposed models can improve the performance of a task with
the help of other related tasks. | ['Xuanjing Huang', 'Xipeng Qiu', 'Pengfei Liu'] | 2016-05-17 | null | null | null | null | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 3.18732202e-01 -2.91204304e-01 -3.76591504e-01 -6.40776396e-01
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b008109a-6aec-4c8f-89fa-4629898c2fbe | from-spelling-to-grammar-a-new-framework-for | 2211.01625 | null | https://arxiv.org/abs/2211.01625v1 | https://arxiv.org/pdf/2211.01625v1.pdf | From Spelling to Grammar: A New Framework for Chinese Grammatical Error Correction | Chinese Grammatical Error Correction (CGEC) aims to generate a correct sentence from an erroneous sequence, where different kinds of errors are mixed. This paper divides the CGEC task into two steps, namely spelling error correction and grammatical error correction. Specifically, we propose a novel zero-shot approach for spelling error correction, which is simple but effective, obtaining a high precision to avoid error accumulation of the pipeline structure. To handle grammatical error correction, we design part-of-speech (POS) features and semantic class features to enhance the neural network model, and propose an auxiliary task to predict the POS sequence of the target sentence. Our proposed framework achieves a 42.11 F0.5 score on CGEC dataset without using any synthetic data or data augmentation methods, which outperforms the previous state-of-the-art by a wide margin of 1.30 points. Moreover, our model produces meaningful POS representations that capture different POS words and convey reasonable POS transition rules. | ['Yunfang Wu', 'Xiuyu Wu'] | 2022-11-03 | null | null | null | null | ['grammatical-error-correction'] | ['natural-language-processing'] | [ 4.79775906e-01 9.21185613e-02 3.86952788e-01 -5.70159853e-01
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91480f83-9f50-4329-964e-a7984e6dbe78 | pairwise-symmetry-reasoning-for-multi-agent | 2103.07116 | null | https://arxiv.org/abs/2103.07116v1 | https://arxiv.org/pdf/2103.07116v1.pdf | Pairwise Symmetry Reasoning for Multi-Agent Path Finding Search | Multi-Agent Path Finding (MAPF) is a challenging combinatorial problem that asks us to plan collision-free paths for a team of cooperative agents. In this work, we show that one of the reasons why MAPF is so hard to solve is due to a phenomenon called pairwise symmetry, which occurs when two agents have many different paths to their target locations, all of which appear promising, but every combination of them results in a collision. We identify several classes of pairwise symmetries and show that each one arises commonly in practice and can produce an exponential explosion in the space of possible collision resolutions, leading to unacceptable runtimes for current state-of-the-art (bounded-sub)optimal MAPF algorithms. We propose a variety of reasoning techniques that detect the symmetries efficiently as they arise and resolve them by using specialized constraints to eliminate all permutations of pairwise colliding paths in a single branching step. We implement these ideas in the context of the leading optimal MAPF algorithm CBS and show that the addition of the symmetry reasoning techniques can have a dramatic positive effect on its performance - we report a reduction in the number of node expansions by up to four orders of magnitude and an increase in scalability by up to thirty times. These gains allow us to solve to optimality a variety of challenging MAPF instances previously considered out of reach for CBS. | ['Sven Koenig', 'Peter J. Stuckey', 'Daniel Harabor', 'Jiaoyang Li'] | 2021-03-12 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 1.02400593e-01 3.90113175e-01 1.27840608e-01 1.17142670e-01
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fc238214-8d1e-4fd8-86c5-03e82a2d01c5 | a-similarity-preserving-neural-network | 2102.05503 | null | https://arxiv.org/abs/2102.05503v1 | https://arxiv.org/pdf/2102.05503v1.pdf | A Similarity-preserving Neural Network Trained on Transformed Images Recapitulates Salient Features of the Fly Motion Detection Circuit | Learning to detect content-independent transformations from data is one of the central problems in biological and artificial intelligence. An example of such problem is unsupervised learning of a visual motion detector from pairs of consecutive video frames. Rao and Ruderman formulated this problem in terms of learning infinitesimal transformation operators (Lie group generators) via minimizing image reconstruction error. Unfortunately, it is difficult to map their model onto a biologically plausible neural network (NN) with local learning rules. Here we propose a biologically plausible model of motion detection. We also adopt the transformation-operator approach but, instead of reconstruction-error minimization, start with a similarity-preserving objective function. An online algorithm that optimizes such an objective function naturally maps onto an NN with biologically plausible learning rules. The trained NN recapitulates major features of the well-studied motion detector in the fly. In particular, it is consistent with the experimental observation that local motion detectors combine information from at least three adjacent pixels, something that contradicts the celebrated Hassenstein-Reichardt model. | ['Dmitri B. Chklovskii', 'Anirvan M. Sengupta', 'Yanis Bahroun'] | 2021-02-10 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 5.25224626e-01 1.05514526e-01 -1.17913134e-01 -2.51519054e-01
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2d506c98-eef7-46e3-8eda-fc825254235c | renoir-a-dataset-for-real-low-light-image | 1409.8230 | null | http://arxiv.org/abs/1409.8230v9 | http://arxiv.org/pdf/1409.8230v9.pdf | RENOIR - A Dataset for Real Low-Light Image Noise Reduction | Image denoising algorithms are evaluated using images corrupted by artificial
noise, which may lead to incorrect conclusions about their performances on real
noise. In this paper we introduce a dataset of color images corrupted by
natural noise due to low-light conditions, together with spatially and
intensity-aligned low noise images of the same scenes. We also introduce a
method for estimating the true noise level in our images, since even the low
noise images contain small amounts of noise. We evaluate the accuracy of our
noise estimation method on real and artificial noise, and investigate the
Poisson-Gaussian noise model. Finally, we use our dataset to evaluate six
denoising algorithms: Active Random Field, BM3D, Bilevel-MRF, Multi-Layer
Perceptron, and two versions of NL-means. We show that while the Multi-Layer
Perceptron, Bilevel-MRF, and NL-means with soft threshold outperform BM3D on
gray images with synthetic noise, they lag behind on our dataset. | ['Adrian Barbu', 'Josue Anaya'] | 2014-09-29 | null | null | null | null | ['color-image-denoising', 'noise-estimation'] | ['computer-vision', 'medical'] | [ 4.72476244e-01 -5.95768273e-01 6.55345976e-01 -2.49205843e-01
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ca56bde9-12ba-431b-a82f-531e7c72a68b | a-tale-of-two-laws-of-semantic-change | 2305.19143 | null | https://arxiv.org/abs/2305.19143v1 | https://arxiv.org/pdf/2305.19143v1.pdf | A Tale of Two Laws of Semantic Change: Predicting Synonym Changes with Distributional Semantic Models | Lexical Semantic Change is the study of how the meaning of words evolves through time. Another related question is whether and how lexical relations over pairs of words, such as synonymy, change over time. There are currently two competing, apparently opposite hypotheses in the historical linguistic literature regarding how synonymous words evolve: the Law of Differentiation (LD) argues that synonyms tend to take on different meanings over time, whereas the Law of Parallel Change (LPC) claims that synonyms tend to undergo the same semantic change and therefore remain synonyms. So far, there has been little research using distributional models to assess to what extent these laws apply on historical corpora. In this work, we take a first step toward detecting whether LD or LPC operates for given word pairs. After recasting the problem into a more tractable task, we combine two linguistic resources to propose the first complete evaluation framework on this problem and provide empirical evidence in favor of a dominance of LD. We then propose various computational approaches to the problem using Distributional Semantic Models and grounded in recent literature on Lexical Semantic Change detection. Our best approaches achieve a balanced accuracy above 0.6 on our dataset. We discuss challenges still faced by these approaches, such as polysemy or the potential confusion between synonymy and hypernymy. | ['Pascal Denis', 'Mikaela Keller', 'Bastien Liétard'] | 2023-05-30 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 1.15743965e-01 -2.85616815e-01 -4.36491519e-01 -2.97052413e-01
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f61f5a81-e8f2-4dc9-a1f5-cd092d81fae1 | wild-patterns-ten-years-after-the-rise-of | 1712.03141 | null | http://arxiv.org/abs/1712.03141v2 | http://arxiv.org/pdf/1712.03141v2.pdf | Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning | Learning-based pattern classifiers, including deep networks, have shown
impressive performance in several application domains, ranging from computer
vision to cybersecurity. However, it has also been shown that adversarial input
perturbations carefully crafted either at training or at test time can easily
subvert their predictions. The vulnerability of machine learning to such wild
patterns (also referred to as adversarial examples), along with the design of
suitable countermeasures, have been investigated in the research field of
adversarial machine learning. In this work, we provide a thorough overview of
the evolution of this research area over the last ten years and beyond,
starting from pioneering, earlier work on the security of non-deep learning
algorithms up to more recent work aimed to understand the security properties
of deep learning algorithms, in the context of computer vision and
cybersecurity tasks. We report interesting connections between these
apparently-different lines of work, highlighting common misconceptions related
to the security evaluation of machine-learning algorithms. We review the main
threat models and attacks defined to this end, and discuss the main limitations
of current work, along with the corresponding future challenges towards the
design of more secure learning algorithms. | ['Battista Biggio', 'Fabio Roli'] | 2017-12-08 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [ 5.91831982e-01 3.25900197e-01 2.32541487e-01 -2.35378012e-01
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d089a3f8-f357-4fc7-b521-2cc6fdc81fc4 | a-comprehensive-modeling-approach-for-crop | 2306.10121 | null | https://arxiv.org/abs/2306.10121v1 | https://arxiv.org/pdf/2306.10121v1.pdf | A Comprehensive Modeling Approach for Crop Yield Forecasts using AI-based Methods and Crop Simulation Models | Numerous solutions for yield estimation are either based on data-driven models, or on crop-simulation models (CSMs). Researchers tend to build data-driven models using nationwide crop information databases provided by agencies such as the USDA. On the opposite side of the spectrum, CSMs require fine data that may be hard to generalize from a handful of fields. In this paper, we propose a comprehensive approach for yield forecasting that combines data-driven solutions, crop simulation models, and model surrogates to support multiple user-profiles and needs when dealing with crop management decision-making. To achieve this goal, we have developed a solution to calibrate CSMs at scale, a surrogate model of a CSM assuring faster execution, and a neural network-based approach that performs efficient risk assessment in such settings. Our data-driven modeling approach outperforms previous works with yield correlation predictions close to 91\%. The crop simulation modeling architecture achieved 6% error; the proposed crop simulation model surrogate performs predictions almost 100 times faster than the adopted crop simulator with similar accuracy levels. | ['Priscilla Barreira Avegliano', 'Bruno Silva', 'Renato Luiz de Freitas Cunha'] | 2023-06-16 | null | null | null | null | ['management'] | ['miscellaneous'] | [-2.71434244e-02 -1.31311081e-03 -3.45285416e-01 -1.62743106e-01
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8477cc7e-37ce-4b73-b6fc-475911b6c98a | cross-modal-fine-tuning-align-then-refine | 2302.05738 | null | https://arxiv.org/abs/2302.05738v2 | https://arxiv.org/pdf/2302.05738v2.pdf | Cross-Modal Fine-Tuning: Align then Refine | Fine-tuning large-scale pretrained models has led to tremendous progress in well-studied modalities such as vision and NLP. However, similar gains have not been observed in many other modalities due to a lack of relevant pretrained models. In this work, we propose ORCA, a general cross-modal fine-tuning framework that extends the applicability of a single large-scale pretrained model to diverse modalities. ORCA adapts to a target task via an align-then-refine workflow: given the target input, ORCA first learns an embedding network that aligns the embedded feature distribution with the pretraining modality. The pretrained model is then fine-tuned on the embedded data to exploit the knowledge shared across modalities. Through extensive experiments, we show that ORCA obtains state-of-the-art results on 3 benchmarks containing over 60 datasets from 12 modalities, outperforming a wide range of hand-designed, AutoML, general-purpose, and task-specific methods. We highlight the importance of data alignment via a series of ablation studies and demonstrate ORCA's utility in data-limited regimes. | ['Ameet Talwalkar', 'Graham Neubig', 'Mikhail Khodak', 'Corey Staten', 'Lucio M. Dery', 'Liam Li', 'Junhong Shen'] | 2023-02-11 | null | null | null | null | ['automl'] | ['methodology'] | [ 5.19335568e-01 -5.38331605e-02 -3.73398632e-01 -5.07059157e-01
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07bd73e8-e140-424e-b9fc-6ab1a1386e4f | motion-compensation-via-epipolar-consistency | 2303.00449 | null | https://arxiv.org/abs/2303.00449v1 | https://arxiv.org/pdf/2303.00449v1.pdf | Motion Compensation via Epipolar Consistency for In-Vivo X-Ray Microscopy | Intravital X-ray microscopy (XRM) in preclinical mouse models is of vital importance for the identification of microscopic structural pathological changes in the bone which are characteristic of osteoporosis. The complexity of this method stems from the requirement for high-quality 3D reconstructions of the murine bones. However, respiratory motion and muscle relaxation lead to inconsistencies in the projection data which result in artifacts in uncompensated reconstructions. Motion compensation using epipolar consistency conditions (ECC) has previously shown good performance in clinical CT settings. Here, we explore whether such algorithms are suitable for correcting motion-corrupted XRM data. Different rigid motion patterns are simulated and the quality of the motion-compensated reconstructions is assessed. The method is able to restore microscopic features for out-of-plane motion, but artifacts remain for more realistic motion patterns including all six degrees of freedom of rigid motion. Therefore, ECC is valuable for the initial alignment of the projection data followed by further fine-tuning of motion parameters using a reconstruction-based method | ['Andreas Maier', 'Silke Christiansen', 'Georg Schett', 'Stefan Uderhardt', 'Georgiana Neag', 'Daniela Weidner', 'Oliver Aust', 'Sabrina Pechmann', 'Yixing Huang', 'Mingxuan Gu', 'Fabian Wagner', 'Mareike Thies'] | 2023-03-01 | null | null | null | null | ['motion-compensation'] | ['computer-vision'] | [ 2.08350256e-01 -3.08557242e-01 2.90594429e-01 -1.46582380e-01
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2f3e2e51-c5db-4a58-97c3-20b78292d89a | change-point-detection-in-time-series-data-by | 1203.0453 | null | https://arxiv.org/abs/1203.0453v2 | https://arxiv.org/pdf/1203.0453v2.pdf | Change-Point Detection in Time-Series Data by Relative Density-Ratio Estimation | The objective of change-point detection is to discover abrupt property changes lying behind time-series data. In this paper, we present a novel statistical change-point detection algorithm based on non-parametric divergence estimation between time-series samples from two retrospective segments. Our method uses the relative Pearson divergence as a divergence measure, and it is accurately and efficiently estimated by a method of direct density-ratio estimation. Through experiments on artificial and real-world datasets including human-activity sensing, speech, and Twitter messages, we demonstrate the usefulness of the proposed method. | ['Masashi Sugiyama', 'Nigel Collier', 'Makoto Yamada', 'Song Liu'] | 2012-03-02 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [ 1.59780070e-01 -4.10229623e-01 -2.08380714e-01 -3.47024977e-01
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025add83-862e-43ac-aace-1e43b58d0b4f | synthetic-to-real-unsupervised-domain-1 | 2009.01766 | null | https://arxiv.org/abs/2009.01766v1 | https://arxiv.org/pdf/2009.01766v1.pdf | Synthetic-to-Real Unsupervised Domain Adaptation for Scene Text Detection in the Wild | Deep learning-based scene text detection can achieve preferable performance, powered with sufficient labeled training data. However, manual labeling is time consuming and laborious. At the extreme, the corresponding annotated data are unavailable. Exploiting synthetic data is a very promising solution except for domain distribution mismatches between synthetic datasets and real datasets. To address the severe domain distribution mismatch, we propose a synthetic-to-real domain adaptation method for scene text detection, which transfers knowledge from synthetic data (source domain) to real data (target domain). In this paper, a text self-training (TST) method and adversarial text instance alignment (ATA) for domain adaptive scene text detection are introduced. ATA helps the network learn domain-invariant features by training a domain classifier in an adversarial manner. TST diminishes the adverse effects of false positives~(FPs) and false negatives~(FNs) from inaccurate pseudo-labels. Two components have positive effects on improving the performance of scene text detectors when adapting from synthetic-to-real scenes. We evaluate the proposed method by transferring from SynthText, VISD to ICDAR2015, ICDAR2013. The results demonstrate the effectiveness of the proposed method with up to 10% improvement, which has important exploration significance for domain adaptive scene text detection. Code is available at https://github.com/weijiawu/SyntoReal_STD | ['Enze Xie', 'Weijia Wu', 'Ning Lu'] | 2020-09-03 | null | null | null | null | ['adversarial-text', 'scene-text-detection'] | ['adversarial', 'computer-vision'] | [ 5.71533442e-01 -1.79907337e-01 1.14171140e-01 -4.24262494e-01
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87ea92a9-0e58-4390-b386-59ee069230a5 | query-performance-prediction-from-ad-hoc-to | 2305.10923 | null | https://arxiv.org/abs/2305.10923v1 | https://arxiv.org/pdf/2305.10923v1.pdf | Query Performance Prediction: From Ad-hoc to Conversational Search | Query performance prediction (QPP) is a core task in information retrieval. The QPP task is to predict the retrieval quality of a search system for a query without relevance judgments. Research has shown the effectiveness and usefulness of QPP for ad-hoc search. Recent years have witnessed considerable progress in conversational search (CS). Effective QPP could help a CS system to decide an appropriate action to be taken at the next turn. Despite its potential, QPP for CS has been little studied. We address this research gap by reproducing and studying the effectiveness of existing QPP methods in the context of CS. While the task of passage retrieval remains the same in the two settings, a user query in CS depends on the conversational history, introducing novel QPP challenges. In particular, we seek to explore to what extent findings from QPP methods for ad-hoc search generalize to three CS settings: (i) estimating the retrieval quality of different query rewriting-based retrieval methods, (ii) estimating the retrieval quality of a conversational dense retrieval method, and (iii) estimating the retrieval quality for top ranks vs. deeper-ranked lists. Our findings can be summarized as follows: (i) supervised QPP methods distinctly outperform unsupervised counterparts only when a large-scale training set is available; (ii) point-wise supervised QPP methods outperform their list-wise counterparts in most cases; and (iii) retrieval score-based unsupervised QPP methods show high effectiveness in assessing the conversational dense retrieval method, ConvDR. | ['Maarten de Rijke', 'Mohammad Aliannejadi', 'Negar Arabzadeh', 'Chuan Meng'] | 2023-05-18 | null | null | null | null | ['passage-retrieval', 'conversational-search'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.15201989e-01 -2.37106025e-01 -5.49501419e-01 -1.01933032e-01
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457cde9b-0d5e-4313-8f36-5e6120248c86 | stochastic-bandit-models-for-delayed | 1706.09186 | null | http://arxiv.org/abs/1706.09186v3 | http://arxiv.org/pdf/1706.09186v3.pdf | Stochastic Bandit Models for Delayed Conversions | Online advertising and product recommendation are important domains of
applications for multi-armed bandit methods. In these fields, the reward that
is immediately available is most often only a proxy for the actual outcome of
interest, which we refer to as a conversion. For instance, in web advertising,
clicks can be observed within a few seconds after an ad display but the
corresponding sale --if any-- will take hours, if not days to happen. This
paper proposes and investigates a new stochas-tic multi-armed bandit model in
the framework proposed by Chapelle (2014) --based on empirical studies in the
field of web advertising-- in which each action may trigger a future reward
that will then happen with a stochas-tic delay. We assume that the probability
of conversion associated with each action is unknown while the distribution of
the conversion delay is known, distinguishing between the (idealized) case
where the conversion events may be observed whatever their delay and the more
realistic setting in which late conversions are censored. We provide
performance lower bounds as well as two simple but efficient algorithms based
on the UCB and KLUCB frameworks. The latter algorithm, which is preferable when
conversion rates are low, is based on a Poissonization argument, of independent
interest in other settings where aggregation of Bernoulli observations with
different success probabilities is required. | ['Olivier Cappé', 'Vianney Perchet', 'Claire Vernade'] | 2017-06-28 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [ 8.81263092e-02 2.27233469e-02 -5.74202180e-01 -1.66788608e-01
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e06db993-e4f2-4f22-bcae-e58ce92026ba | generating-classical-chinese-poems-from | 1909.00279 | null | https://arxiv.org/abs/1909.00279v1 | https://arxiv.org/pdf/1909.00279v1.pdf | Generating Classical Chinese Poems from Vernacular Chinese | Classical Chinese poetry is a jewel in the treasure house of Chinese culture. Previous poem generation models only allow users to employ keywords to interfere the meaning of generated poems, leaving the dominion of generation to the model. In this paper, we propose a novel task of generating classical Chinese poems from vernacular, which allows users to have more control over the semantic of generated poems. We adapt the approach of unsupervised machine translation (UMT) to our task. We use segmentation-based padding and reinforcement learning to address under-translation and over-translation respectively. According to experiments, our approach significantly improve the perplexity and BLEU compared with typical UMT models. Furthermore, we explored guidelines on how to write the input vernacular to generate better poems. Human evaluation showed our approach can generate high-quality poems which are comparable to amateur poems. | ['Elena Suet-Ying Chiu', 'Zhichao Yang', 'Weijiang Feng', 'Yansong Feng', 'Pengshan Cai', 'Hong Yu', 'Fei Li'] | 2019-08-31 | generating-classical-chinese-poems-from-1 | https://aclanthology.org/D19-1637 | https://aclanthology.org/D19-1637.pdf | ijcnlp-2019-11 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 3.01543444e-01 3.51148456e-01 9.65069234e-02 1.41803445e-02
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948e9115-f249-43da-bc17-73923b9a9770 | automatic-segmentation-of-left-ventricle-in | 2201.12805 | null | https://arxiv.org/abs/2201.12805v1 | https://arxiv.org/pdf/2201.12805v1.pdf | Automatic Segmentation of Left Ventricle in Cardiac Magnetic Resonance Images | Segmentation of the left ventricle in cardiac magnetic resonance imaging MRI scans enables cardiologists to calculate the volume of the left ventricle and subsequently its ejection fraction. The ejection fraction is a measurement that expresses the percentage of blood leaving the heart with each contraction. Cardiologists often use ejection fraction to determine one's cardiac function. We propose multiscale template matching technique for detection and an elliptical active disc for automated segmentation of the left ventricle in MR images. The elliptical active disc optimizes the local energy function with respect to its five free parameters which define the disc. Gradient descent is used to minimize the energy function along with Green's theorem to optimize the computation expenses. We report validations on 320 scans containing 5,273 annotated slices which are publicly available through the Multi-Centre, Multi-Vendor, and Multi-Disease Cardiac Segmentation (M&Ms) Challenge. We achieved successful localization of the left ventricle in 89.63% of the cases and a Dice coefficient of 0.873 on diastole slices and 0.770 on systole slices. The proposed technique is based on traditional image processing techniques with a performance on par with the deep learning techniques. | ['J. R. Harish Kumar', 'J. H. Gagan', 'Garvit Chhabra'] | 2022-01-30 | null | null | null | null | ['template-matching', 'cardiac-segmentation'] | ['computer-vision', 'medical'] | [-1.80645883e-01 8.71696323e-02 1.13964483e-01 -4.17172700e-01
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720a8e0c-33c8-4749-9f48-06a23b6e815c | puzzling-machines-a-challenge-on-learning | 2004.13161 | null | https://arxiv.org/abs/2004.13161v1 | https://arxiv.org/pdf/2004.13161v1.pdf | PuzzLing Machines: A Challenge on Learning From Small Data | Deep neural models have repeatedly proved excellent at memorizing surface patterns from large datasets for various ML and NLP benchmarks. They struggle to achieve human-like thinking, however, because they lack the skill of iterative reasoning upon knowledge. To expose this problem in a new light, we introduce a challenge on learning from small data, PuzzLing Machines, which consists of Rosetta Stone puzzles from Linguistic Olympiads for high school students. These puzzles are carefully designed to contain only the minimal amount of parallel text necessary to deduce the form of unseen expressions. Solving them does not require external information (e.g., knowledge bases, visual signals) or linguistic expertise, but meta-linguistic awareness and deductive skills. Our challenge contains around 100 puzzles covering a wide range of linguistic phenomena from 81 languages. We show that both simple statistical algorithms and state-of-the-art deep neural models perform inadequately on this challenge, as expected. We hope that this benchmark, available at https://ukplab.github.io/PuzzLing-Machines/, inspires further efforts towards a new paradigm in NLP---one that is grounded in human-like reasoning and understanding. | ['Gözde Gül Şahin', 'Iryna Gurevych', 'Phillip Rust', 'Yova Kementchedjhieva'] | 2020-04-27 | puzzling-machines-a-challenge-on-learning-1 | https://aclanthology.org/2020.acl-main.115 | https://aclanthology.org/2020.acl-main.115.pdf | acl-2020-6 | ['small-data'] | ['computer-vision'] | [ 1.40478825e-02 4.15836424e-01 -1.37380630e-01 -2.40207016e-01
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3591a1fa-a4ee-4016-a0a6-f61ee9aeda9e | a-regularization-method-to-improve | 2110.09759 | null | https://arxiv.org/abs/2110.09759v2 | https://arxiv.org/pdf/2110.09759v2.pdf | A Regularization Method to Improve Adversarial Robustness of Neural Networks for ECG Signal Classification | Electrocardiogram (ECG) is the most widely used diagnostic tool to monitor the condition of the human heart. By using deep neural networks (DNNs), interpretation of ECG signals can be fully automated for the identification of potential abnormalities in a patient's heart in a fraction of a second. Studies have shown that given a sufficiently large amount of training data, DNN accuracy for ECG classification could reach human-expert cardiologist level. However, despite of the excellent performance in classification accuracy, DNNs are highly vulnerable to adversarial noises that are subtle changes in the input of a DNN and may lead to a wrong class-label prediction. It is challenging and essential to improve robustness of DNNs against adversarial noises, which are a threat to life-critical applications. In this work, we proposed a regularization method to improve DNN robustness from the perspective of noise-to-signal ratio (NSR) for the application of ECG signal classification. We evaluated our method on PhysioNet MIT-BIH dataset and CPSC2018 ECG dataset, and the results show that our method can substantially enhance DNN robustness against adversarial noises generated from adversarial attacks, with a minimal change in accuracy on clean data. | ['Liang Liang', 'Linhai Ma'] | 2021-10-19 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 4.93917882e-01 -1.75739914e-01 4.76632684e-01 -2.75327832e-01
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ba11c51f-8bc5-4d12-8926-7f4f47ffcd5f | fastlts-non-autoregressive-end-to-end | 2207.03800 | null | https://arxiv.org/abs/2207.03800v2 | https://arxiv.org/pdf/2207.03800v2.pdf | FastLTS: Non-Autoregressive End-to-End Unconstrained Lip-to-Speech Synthesis | Unconstrained lip-to-speech synthesis aims to generate corresponding speeches from silent videos of talking faces with no restriction on head poses or vocabulary. Current works mainly use sequence-to-sequence models to solve this problem, either in an autoregressive architecture or a flow-based non-autoregressive architecture. However, these models suffer from several drawbacks: 1) Instead of directly generating audios, they use a two-stage pipeline that first generates mel-spectrograms and then reconstructs audios from the spectrograms. This causes cumbersome deployment and degradation of speech quality due to error propagation; 2) The audio reconstruction algorithm used by these models limits the inference speed and audio quality, while neural vocoders are not available for these models since their output spectrograms are not accurate enough; 3) The autoregressive model suffers from high inference latency, while the flow-based model has high memory occupancy: neither of them is efficient enough in both time and memory usage. To tackle these problems, we propose FastLTS, a non-autoregressive end-to-end model which can directly synthesize high-quality speech audios from unconstrained talking videos with low latency, and has a relatively small model size. Besides, different from the widely used 3D-CNN visual frontend for lip movement encoding, we for the first time propose a transformer-based visual frontend for this task. Experiments show that our model achieves $19.76\times$ speedup for audio waveform generation compared with the current autoregressive model on input sequences of 3 seconds, and obtains superior audio quality. | ['Zhou Zhao', 'Yongqi Wang'] | 2022-07-08 | null | null | null | null | ['lip-to-speech-synthesis'] | ['computer-vision'] | [ 6.13889471e-02 -5.00490405e-02 -9.22568142e-02 -9.44017991e-02
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4a6bf798-3552-476e-9c22-fc31aa7113f4 | imposing-connectome-derived-topology-on-an | 2201.09359 | null | https://arxiv.org/abs/2201.09359v1 | https://arxiv.org/pdf/2201.09359v1.pdf | Imposing Connectome-Derived Topology on an Echo State Network | Can connectome-derived constraints inform computation? In this paper we investigate the contribution of a fruit fly connectome's topology on the performance of an Echo State Network (ESN) -- a subset of Reservoir Computing which is state of the art in chaotic time series prediction. Specifically, we replace the reservoir layer of a classical ESN -- normally a fixed, random graph represented as a 2-d matrix -- with a particular (female) fruit fly connectome-derived connectivity matrix. We refer to this experimental class of models (with connectome-derived reservoirs) as "Fruit Fly ESNs" (FFESNs). We train and validate the FFESN on a chaotic time series prediction task; here we consider four sets of trials with different training input sizes (small, large) and train-validate splits (two variants). We compare the validation performance (Mean-Squared Error) of all of the best FFESN models to a class of control model ESNs (simply referred to as "ESNs"). Overall, for all four sets of trials we find that the FFESN either significantly outperforms (and has lower variance than) the ESN; or simply has lower variance than the ESN. | ['Mark Daley', 'Jacob Morra'] | 2022-01-23 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [-1.14709269e-02 -7.78460652e-02 2.05513328e-01 6.00728355e-02
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11815499-af09-4ebf-9f5f-9d0bd66600ec | gretel-a-unified-framework-for-graph | 2206.02957 | null | https://arxiv.org/abs/2206.02957v1 | https://arxiv.org/pdf/2206.02957v1.pdf | GRETEL: A unified framework for Graph Counterfactual Explanation Evaluation | Machine Learning (ML) systems are a building part of the modern tools which impact our daily life in several application domains. Due to their black-box nature, those systems are hardly adopted in application domains (e.g. health, finance) where understanding the decision process is of paramount importance. Explanation methods were developed to explain how the ML model has taken a specific decision for a given case/instance. Graph Counterfactual Explanations (GCE) is one of the explanation techniques adopted in the Graph Learning domain. The existing works of Graph Counterfactual Explanations diverge mostly in the problem definition, application domain, test data, and evaluation metrics, and most existing works do not compare exhaustively against other counterfactual explanation techniques present in the literature. We present GRETEL, a unified framework to develop and test GCE methods in several settings. GRETEL is a highly extensible evaluation framework which promotes the Open Science and the evaluations reproducibility by providing a set of well-defined mechanisms to integrate and manage easily: both real and synthetic datasets, ML models, state-of-the-art explanation techniques, and evaluation measures. To present GRETEL, we show the experiments conducted to integrate and test several synthetic and real datasets with several existing explanation techniques and base ML models. | ['Giovanni Stilo', 'Mario Alfonso Prado-Romero'] | 2022-06-07 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 2.33530357e-01 7.43296683e-01 -6.60526991e-01 -2.53224730e-01
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322b2da6-c53f-4e82-a810-88f83a9783c3 | analyse-automatique-de-lancien-armenien | null | null | https://aclanthology.org/2022.digitam-1.3 | https://aclanthology.org/2022.digitam-1.3.pdf | Analyse Automatique de l’Ancien Arménien. Évaluation d’une méthode hybride « dictionnaire » et « réseau de neurones » sur un Extrait de l’Adversus Haereses d’Irénée de Lyon | The aim of this paper is to evaluate a lexical analysis (mainly lemmatization and POS-tagging) of a sample of the Ancient Armenian version of the Adversus Haereses by Irenaeus of Lyons (2nd c.) by using hybrid approach based on digital dictionaries on the one hand, and on Recurrent Neural Network (RNN) on the other hand. The quality of the results is checked by comparing data obtained by implementing these two methods with data manually checked. In the present case, 98,37% of the results are correct by using the first (lexical) approach, and 74,64% by using the second (RNN). But, in fact, both methods present advantages and disadvantages and argue for the hybrid method. The linguistic resources implemented here are jointly developed and tested by GREgORI and Calfa. | ['Gabriel Kepeklian', 'Bastien Kindt'] | null | null | null | null | digitam-lrec-2022-6 | ['lemmatization', 'lexical-analysis'] | ['natural-language-processing', 'natural-language-processing'] | [-9.07356963e-02 3.02393109e-01 5.04913442e-02 6.18033856e-02
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-2.58837044e-01 -6.44344568e-01 -4.93951738e-01 -8.12183842e-02] | [10.337142944335938, 10.259321212768555] |
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