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5f75a59c-d14d-4dd1-8250-becff89bc293 | fsuie-a-novel-fuzzy-span-mechanism-for | 2306.14913 | null | https://arxiv.org/abs/2306.14913v1 | https://arxiv.org/pdf/2306.14913v1.pdf | FSUIE: A Novel Fuzzy Span Mechanism for Universal Information Extraction | Universal Information Extraction (UIE) has been introduced as a unified framework for various Information Extraction (IE) tasks and has achieved widespread success. Despite this, UIE models have limitations. For example, they rely heavily on span boundaries in the data during training, which does not reflect the reality of span annotation challenges. Slight adjustments to positions can also meet requirements. Additionally, UIE models lack attention to the limited span length feature in IE. To address these deficiencies, we propose the Fuzzy Span Universal Information Extraction (FSUIE) framework. Specifically, our contribution consists of two concepts: fuzzy span loss and fuzzy span attention. Our experimental results on a series of main IE tasks show significant improvement compared to the baseline, especially in terms of fast convergence and strong performance with small amounts of data and training epochs. These results demonstrate the effectiveness and generalization of FSUIE in different tasks, settings, and scenarios. | ['Hai Zhao', 'Bo Du', 'Lefei Zhang', 'Zuchao Li', 'Tianshuo Peng'] | 2023-06-19 | null | null | null | null | ['uie'] | ['computer-vision'] | [ 1.13807231e-01 -1.59819514e-01 -4.23544973e-01 -2.41517365e-01
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1a89ab01-ce0b-434e-a564-419ec0ca2a2d | 3d-object-detection-with-pointformer | 2012.11409 | null | https://arxiv.org/abs/2012.11409v3 | https://arxiv.org/pdf/2012.11409v3.pdf | 3D Object Detection with Pointformer | Feature learning for 3D object detection from point clouds is very challenging due to the irregularity of 3D point cloud data. In this paper, we propose Pointformer, a Transformer backbone designed for 3D point clouds to learn features effectively. Specifically, a Local Transformer module is employed to model interactions among points in a local region, which learns context-dependent region features at an object level. A Global Transformer is designed to learn context-aware representations at the scene level. To further capture the dependencies among multi-scale representations, we propose Local-Global Transformer to integrate local features with global features from higher resolution. In addition, we introduce an efficient coordinate refinement module to shift down-sampled points closer to object centroids, which improves object proposal generation. We use Pointformer as the backbone for state-of-the-art object detection models and demonstrate significant improvements over original models on both indoor and outdoor datasets. | ['Gao Huang', 'Li Erran Li', 'Shiji Song', 'Zhuofan Xia', 'Xuran Pan'] | 2020-12-21 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Pan_3D_Object_Detection_With_Pointformer_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Pan_3D_Object_Detection_With_Pointformer_CVPR_2021_paper.pdf | cvpr-2021-1 | ['object-proposal-generation'] | ['computer-vision'] | [-2.05535904e-01 -3.67699087e-01 2.16240212e-01 -4.11658406e-01
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39327e6e-dd2c-4cf6-af15-576967ee8454 | title2event-benchmarking-open-event | 2211.00869 | null | https://arxiv.org/abs/2211.00869v1 | https://arxiv.org/pdf/2211.00869v1.pdf | Title2Event: Benchmarking Open Event Extraction with a Large-scale Chinese Title Dataset | Event extraction (EE) is crucial to downstream tasks such as new aggregation and event knowledge graph construction. Most existing EE datasets manually define fixed event types and design specific schema for each of them, failing to cover diverse events emerging from the online text. Moreover, news titles, an important source of event mentions, have not gained enough attention in current EE research. In this paper, We present Title2Event, a large-scale sentence-level dataset benchmarking Open Event Extraction without restricting event types. Title2Event contains more than 42,000 news titles in 34 topics collected from Chinese web pages. To the best of our knowledge, it is currently the largest manually-annotated Chinese dataset for open event extraction. We further conduct experiments on Title2Event with different models and show that the characteristics of titles make it challenging for event extraction, addressing the significance of advanced study on this problem. The dataset and baseline codes are available at https://open-event-hub.github.io/title2event. | ['Tianhua Zhou', 'Xiang Chen', 'Jin Ma', 'Nan Yang', 'Xiaoling Bai', 'Wei Wang', 'Jun Gao', 'Changlong Yu', 'Wangyang Ying', 'Yangfan Zhang', 'Yanan Zhang', 'Haolin Deng'] | 2022-11-02 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [-1.86128750e-01 2.71124154e-01 -4.83017772e-01 -2.15104446e-01
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f33192d2-3737-427b-8bd6-b475b43a3616 | machine-fault-classification-using | 2301.02243 | null | https://arxiv.org/abs/2301.02243v1 | https://arxiv.org/pdf/2301.02243v1.pdf | Machine Fault Classification using Hamiltonian Neural Networks | A new approach is introduced to classify faults in rotating machinery based on the total energy signature estimated from sensor measurements. The overall goal is to go beyond using black-box models and incorporate additional physical constraints that govern the behavior of mechanical systems. Observational data is used to train Hamiltonian neural networks that describe the conserved energy of the system for normal and various abnormal regimes. The estimated total energy function, in the form of the weights of the Hamiltonian neural network, serves as the new feature vector to discriminate between the faults using off-the-shelf classification models. The experimental results are obtained using the MaFaulDa database, where the proposed model yields a promising area under the curve (AUC) of $0.78$ for the binary classification (normal vs abnormal) and $0.84$ for the multi-class problem (normal, and $5$ different abnormal regimes). | ['Gabriel Terejanu', 'Sourav Banerjee', 'Jawad Chowdhury', 'Jeremy Shen'] | 2023-01-04 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [-5.12322634e-02 7.67464787e-02 -1.06282108e-01 -1.45425692e-01
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d13300ca-6c25-4ae0-82c6-8808fdfb64c6 | an-internal-validity-index-based-on-density | null | null | https://ieeexplore.ieee.org/document/8672850 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8672850 | An Internal Validity Index Based on Density-Involved Distance | It is crucial to evaluate the quality of clustering results in cluster analysis. Although many cluster validity indices (CVIs) have been proposed in the literature, they have some limitations when dealing with non-spherical datasets. One reason is that the measure of cluster separation does not consider the impact of outliers and neighborhood clusters. In this paper, a new robust distance measure, one into which density is incorporated, is designed to solve the problem, and an internal validity index based on this separation measure is then proposed. This index can cope with both the spherical and non-spherical structure of clusters. The experimental results indicate that the proposed index outperforms some classical CVIs. The MATLAB code and experimental data are available at https://github.com/hulianyu/CVDD | ['Caiming Zhong', 'Lianyu Hu'] | 2019-03-22 | null | null | null | null | ['face-clustering', 'imagedocument-clustering', 'spectral-graph-clustering', 'clustering-ensemble', 'clustering-algorithms-evaluation'] | ['computer-vision', 'computer-vision', 'graphs', 'graphs', 'methodology'] | [-5.55228531e-01 -3.96365404e-01 -8.69618431e-02 -2.34723657e-01
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c0e71bb1-2a65-4d5e-b26d-b3cf96cbd522 | deception-detection-in-news-reports-in-the | null | null | https://aclanthology.org/W17-4213 | https://aclanthology.org/W17-4213.pdf | Deception Detection in News Reports in the Russian Language: Lexics and Discourse | News verification and automated fact checking tend to be very important issues in our world. The research is initial. We collected a corpus for Russian (174 news reports, truthful and fake ones). We held two experiments, for both we applied SVMs algorithm (linear/rbf kernel) and Random Forest to classify the news reports into 2 classes: truthful/deceptive. In the first experiment, we used 18 markers on lexics level, mostly frequencies of POS tags in texts. In the second experiment, on discourse level we used frequencies of rhetorical relations types in texts. The classification task in the first experiment is solved better by SVMs (rbf kernel) (f-measure 0.65). The model based on RST features shows best results with Random Forest Classifier (f-measure 0.54) and should be modified. In the next research, the combination of different deception detection markers for the Russian language should be taken in order to make a better predictive model. | ['Dina Pisarevskaya'] | 2017-09-01 | null | null | null | ws-2017-9 | ['deception-detection', 'rumour-detection'] | ['miscellaneous', 'natural-language-processing'] | [-3.43056500e-01 5.19638538e-01 -5.49312353e-01 -4.48532701e-01
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70849e14-944f-4378-95ee-89080add2cf8 | learning-implicit-probability-distribution | 2211.11394 | null | https://arxiv.org/abs/2211.11394v1 | https://arxiv.org/pdf/2211.11394v1.pdf | Learning Implicit Probability Distribution Functions for Symmetric Orientation Estimation from RGB Images Without Pose Labels | Object pose estimation is a necessary prerequisite for autonomous robotic manipulation, but the presence of symmetry increases the complexity of the pose estimation task. Existing methods for object pose estimation output a single 6D pose. Thus, they lack the ability to reason about symmetries. Lately, modeling object orientation as a non-parametric probability distribution on the SO(3) manifold by neural networks has shown impressive results. However, acquiring large-scale datasets to train pose estimation models remains a bottleneck. To address this limitation, we introduce an automatic pose labeling scheme. Given RGB-D images without object pose annotations and 3D object models, we design a two-stage pipeline consisting of point cloud registration and render-and-compare validation to generate multiple symmetrical pseudo-ground-truth pose labels for each image. Using the generated pose labels, we train an ImplicitPDF model to estimate the likelihood of an orientation hypothesis given an RGB image. An efficient hierarchical sampling of the SO(3) manifold enables tractable generation of the complete set of symmetries at multiple resolutions. During inference, the most likely orientation of the target object is estimated using gradient ascent. We evaluate the proposed automatic pose labeling scheme and the ImplicitPDF model on a photorealistic dataset and the T-Less dataset, demonstrating the advantages of the proposed method. | ['Sven Behnke', 'Luis Denninger', 'Arul Selvam Periyasamy'] | 2022-11-21 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [ 2.52598047e-01 2.68899381e-01 -1.07261255e-01 -5.71915746e-01
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d013a1a7-e2f5-4bb0-a33c-87e68ae54b1c | automatic-diagnosis-of-short-duration-12-lead | 1811.12194 | null | http://arxiv.org/abs/1811.12194v2 | http://arxiv.org/pdf/1811.12194v2.pdf | Automatic Diagnosis of Short-Duration 12-Lead ECG using a Deep Convolutional Network | We present a model for predicting electrocardiogram (ECG) abnormalities in
short-duration 12-lead ECG signals which outperformed medical doctors on the
4th year of their cardiology residency. Such exams can provide a full
evaluation of heart activity and have not been studied in previous end-to-end
machine learning papers. Using the database of a large telehealth network, we
built a novel dataset with more than 2 million ECG tracings, orders of
magnitude larger than those used in previous studies. Moreover, our dataset is
more realistic, as it consist of 12-lead ECGs recorded during standard
in-clinics exams. Using this data, we trained a residual neural network with 9
convolutional layers to map 7 to 10 second ECG signals to 6 classes of ECG
abnormalities. Future work should extend these results to cover a large range
of ECG abnormalities, which could improve the accessibility of this diagnostic
tool and avoid wrong diagnosis from medical doctors. | ['Thomas B. Schön', 'Wagner Meira Jr.', 'Jéssica A. Canazart', 'Paulo R. Gomes', 'Gabriela Paixão', 'Antônio H. Ribeiro', 'Milton Pifano', 'Antonio Luiz Ribeiro', 'Derick Oliveira', 'Manoel Horta Ribeiro'] | 2018-11-28 | null | null | null | null | ['electrocardiography-ecg'] | ['methodology'] | [ 4.28847790e-01 1.94040179e-01 1.36307195e-01 -6.30725741e-01
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4f960617-5996-4d69-b6dc-e83dd142f226 | a-multi-turn-machine-reading-comprehension | 2209.07972 | null | https://arxiv.org/abs/2209.07972v1 | https://arxiv.org/pdf/2209.07972v1.pdf | A Multi-turn Machine Reading Comprehension Framework with Rethink Mechanism for Emotion-Cause Pair Extraction | Emotion-cause pair extraction (ECPE) is an emerging task in emotion cause analysis, which extracts potential emotion-cause pairs from an emotional document. Most recent studies use end-to-end methods to tackle the ECPE task. However, these methods either suffer from a label sparsity problem or fail to model complicated relations between emotions and causes. Furthermore, they all do not consider explicit semantic information of clauses. To this end, we transform the ECPE task into a document-level machine reading comprehension (MRC) task and propose a Multi-turn MRC framework with Rethink mechanism (MM-R). Our framework can model complicated relations between emotions and causes while avoiding generating the pairing matrix (the leading cause of the label sparsity problem). Besides, the multi-turn structure can fuse explicit semantic information flow between emotions and causes. Extensive experiments on the benchmark emotion cause corpus demonstrate the effectiveness of our proposed framework, which outperforms existing state-of-the-art methods. | ['Zhijing Wu', 'Jing Xu', 'Dandan song', 'Changzhi Zhou'] | 2022-09-16 | null | https://aclanthology.org/2022.coling-1.584 | https://aclanthology.org/2022.coling-1.584.pdf | coling-2022-10 | ['emotion-cause-pair-extraction', 'machine-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.82475138e-01 1.58887044e-01 -1.17744640e-01 -6.61034703e-01
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14aaeacb-70ea-4adf-bcac-53e9e544f4c4 | a-bert-based-one-pass-multi-task-model-for | null | null | https://aclanthology.org/2020.bionlp-1.7 | https://aclanthology.org/2020.bionlp-1.7.pdf | A BERT-based One-Pass Multi-Task Model for Clinical Temporal Relation Extraction | Recently BERT has achieved a state-of-the-art performance in temporal relation extraction from clinical Electronic Medical Records text. However, the current approach is inefficient as it requires multiple passes through each input sequence. We extend a recently-proposed one-pass model for relation classification to a one-pass model for relation extraction. We augment this framework by introducing global embeddings to help with long-distance relation inference, and by multi-task learning to increase model performance and generalizability. Our proposed model produces results on par with the state-of-the-art in temporal relation extraction on the THYME corpus and is much {``}greener{''} in computational cost. | ['Farig Sadeque', 'Dmitriy Dligach', 'Guergana Savova', 'Chen Lin', 'Timothy Miller', 'Steven Bethard'] | 2020-07-01 | null | null | null | ws-2020-7 | ['temporal-relation-extraction'] | ['natural-language-processing'] | [ 3.32331270e-01 5.09117186e-01 -6.49838209e-01 -2.38622814e-01
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07c595eb-a5cb-4479-91ae-991070b7fd38 | pmi-sampler-patch-similarity-guided-frame | 2304.06866 | null | https://arxiv.org/abs/2304.06866v1 | https://arxiv.org/pdf/2304.06866v1.pdf | PMI Sampler: Patch similarity guided frame selection for Aerial Action Recognition | We present a new algorithm for selection of informative frames in video action recognition. Our approach is designed for aerial videos captured using a moving camera where human actors occupy a small spatial resolution of video frames. Our algorithm utilizes the motion bias within aerial videos, which enables the selection of motion-salient frames. We introduce the concept of patch mutual information (PMI) score to quantify the motion bias between adjacent frames, by measuring the similarity of patches. We use this score to assess the amount of discriminative motion information contained in one frame relative to another. We present an adaptive frame selection strategy using shifted leaky ReLu and cumulative distribution function, which ensures that the sampled frames comprehensively cover all the essential segments with high motion salience. Our approach can be integrated with any action recognition model to enhance its accuracy. In practice, our method achieves a relative improvement of 2.2 - 13.8% in top-1 accuracy on UAV-Human, 6.8% on NEC Drone, and 9.0% on Diving48 datasets. | ['Dinesh Manocha', 'Divya Kothandaraman', 'Xijun Wang', 'Ruiqi Xian'] | 2023-04-14 | null | null | null | null | ['action-recognition-in-videos'] | ['computer-vision'] | [ 5.49169660e-01 -4.23157841e-01 -3.29906225e-01 -1.28441408e-01
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4bf42f90-4bdf-4e70-bd7c-b8659afe6430 | m-2-sgd-stable-stochastic-optimization-via-a | 2304.04172 | null | https://arxiv.org/abs/2304.04172v1 | https://arxiv.org/pdf/2304.04172v1.pdf | $μ^2$-SGD: Stable Stochastic Optimization via a Double Momentum Mechanism | We consider stochastic convex optimization problems where the objective is an expectation over smooth functions. For this setting we suggest a novel gradient estimate that combines two recent mechanism that are related to notion of momentum. Then, we design an SGD-style algorithm as well as an accelerated version that make use of this new estimator, and demonstrate the robustness of these new approaches to the choice of the learning rate. Concretely, we show that these approaches obtain the optimal convergence rates for both noiseless and noisy case with the same choice of fixed learning rate. Moreover, for the noisy case we show that these approaches achieve the same optimal bound for a very wide range of learning rates. | ['Kfir Y. Levy'] | 2023-04-09 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [-2.30454922e-01 -5.97335882e-02 -2.10911017e-02 -1.79595947e-01
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2caf7854-d687-4126-9d88-44f3a301068d | representation-learning-for-aspect-category | null | null | https://ojs.aaai.org/index.php/AAAI/article/view/9194 | https://ojs.aaai.org/index.php/AAAI/article/view/9194/9053 | Representation Learning for Aspect Category Detection in Online Reviews | User-generated reviews are valuable resources for decision making. Identifying the aspect categories discussed in a given review sentence (e.g., “food” and “service” in restaurant reviews) is an important task of sentiment analysis and opinion mining. Given a predefined aspect category set, most previous researches leverage hand-crafted features and a classification algorithm to accomplish the task. The crucial step to achieve better performance is feature engineering which consumes much human effort and may be unstable when the product domain changes. In this paper, we propose a representation learning approach to automatically learn useful features for aspect category detection. Specifically, a semi-supervised word embedding algorithm is first proposed to obtain continuous word representations on a large set of reviews with noisy labels. Afterwards, we propose to generate deeper and hybrid features through neural networks stacked on the word vectors. A logistic regression classifier is finally trained with the hybrid features to predict the aspect category. The experiments are carried out on a benchmark dataset released by SemEval-2014. Our approach achieves the state-of-the-art performance and outperforms the best participating team as well as a few strong baselines. | ['Jianguo Xiao', 'Xiaojun Wan', 'Xinjie Zhou'] | 2015-02-09 | null | null | null | aaai-2015-2 | ['feature-engineering', 'aspect-category-detection'] | ['methodology', 'natural-language-processing'] | [ 2.01620921e-01 -1.60211906e-01 -5.02366841e-01 -7.87877440e-01
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6e45839a-e730-4049-8048-7ddcec7cf5e1 | an-iterative-emotion-interaction-network-for | null | null | https://aclanthology.org/2020.coling-main.360 | https://aclanthology.org/2020.coling-main.360.pdf | An Iterative Emotion Interaction Network for Emotion Recognition in Conversations | Emotion recognition in conversations (ERC) has received much attention recently in the natural language processing community. Considering that the emotions of the utterances in conversations are interactive, previous works usually implicitly model the emotion interaction between utterances by modeling dialogue context, but the misleading emotion information from context often interferes with the emotion interaction. We noticed that the gold emotion labels of the context utterances can provide explicit and accurate emotion interaction, but it is impossible to input gold labels at inference time. To address this problem, we propose an iterative emotion interaction network, which uses iteratively predicted emotion labels instead of gold emotion labels to explicitly model the emotion interaction. This approach solves the above problem, and can effectively retain the performance advantages of explicit modeling. We conduct experiments on two datasets, and our approach achieves state-of-the-art performance. | ['Bing Qin', 'Huipeng Chen', 'Yijian Tian', 'Yang Wu', 'Yanyan Zhao', 'Xin Lu'] | 2020-12-01 | null | null | null | coling-2020-8 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 4.44159210e-02 2.49234170e-01 9.31749493e-02 -8.66326511e-01
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5cbc166a-4f17-4c8e-8454-2292ba9618e8 | openndd-open-set-recognition-for | 2306.16045 | null | https://arxiv.org/abs/2306.16045v1 | https://arxiv.org/pdf/2306.16045v1.pdf | OpenNDD: Open Set Recognition for Neurodevelopmental Disorders Detection | Neurodevelopmental disorders (NDDs) are a highly prevalent group of disorders and represent strong clinical behavioral similarities, and that make it very challenging for accurate identification of different NDDs such as autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD). Moreover, there is no reliable physiological markers for NDDs diagnosis and it solely relies on psychological evaluation criteria. However, it is crucial to prevent misdiagnosis and underdiagnosis by intelligent assisted diagnosis, which is closely related to the follow-up corresponding treatment. In order to relieve these issues, we propose a novel open set recognition framework for NDDs screening and detection, which is the first application of open set recognition in this field. It combines auto encoder and adversarial reciprocal points open set recognition to accurately identify known classes as well as recognize classes never encountered. And considering the strong similarities between different subjects, we present a joint scaling method called MMS to distinguish unknown disorders. To validate the feasibility of our presented method, we design a reciprocal opposition experiment protocol on the hybrid datasets from Autism Brain Imaging Data Exchange I (ABIDE I) and THE ADHD-200 SAMPLE (ADHD-200) with 791 samples from four sites and the results demonstrate the superiority on various metrics. Our OpenNDD has achieved promising performance, where the accuracy is 77.38%, AUROC is 75.53% and the open set classification rate is as high as 59.43%. | ['Lifang Wei', 'Lanyan Xue', 'Riqing Chen', 'Changcai Yang', 'Zhenshan Shi', 'Xiumei Liu', 'Xinyue Chang', 'Zihao Guan', 'Jiaming Yu'] | 2023-06-28 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 1.36809930e-01 1.09660946e-01 4.51302156e-02 -2.89147913e-01
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14ce419d-1709-414f-b8c4-b451e2b00ba9 | a-large-scale-varying-view-rgb-d-action | 1904.10681 | null | http://arxiv.org/abs/1904.10681v1 | http://arxiv.org/pdf/1904.10681v1.pdf | A Large-scale Varying-view RGB-D Action Dataset for Arbitrary-view Human Action Recognition | Current researches of action recognition mainly focus on single-view and
multi-view recognition, which can hardly satisfies the requirements of
human-robot interaction (HRI) applications to recognize actions from arbitrary
views. The lack of datasets also sets up barriers. To provide data for
arbitrary-view action recognition, we newly collect a large-scale RGB-D action
dataset for arbitrary-view action analysis, including RGB videos, depth and
skeleton sequences. The dataset includes action samples captured in 8 fixed
viewpoints and varying-view sequences which covers the entire 360 degree view
angles. In total, 118 persons are invited to act 40 action categories, and
25,600 video samples are collected. Our dataset involves more participants,
more viewpoints and a large number of samples. More importantly, it is the
first dataset containing the entire 360 degree varying-view sequences. The
dataset provides sufficient data for multi-view, cross-view and arbitrary-view
action analysis. Besides, we propose a View-guided Skeleton CNN (VS-CNN) to
tackle the problem of arbitrary-view action recognition. Experiment results
show that the VS-CNN achieves superior performance. | ['Wei-Shi Zheng', 'Yang Yang', 'Yanli Ji', 'Feixiang Xu', 'Fumin Shen', 'Heng Tao Shen'] | 2019-04-24 | null | null | null | null | ['action-analysis'] | ['computer-vision'] | [ 1.48215532e-01 -4.30176049e-01 -3.64319354e-01 -4.04260814e-01
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edc8d3f5-c3e2-43ff-98ae-311b543c9a1c | behavioral-estimates-of-conceptual-structure | 2304.02754 | null | https://arxiv.org/abs/2304.02754v1 | https://arxiv.org/pdf/2304.02754v1.pdf | Behavioral estimates of conceptual structure are robust across tasks in humans but not large language models | Neural network models of language have long been used as a tool for developing hypotheses about conceptual representation in the mind and brain. For many years, such use involved extracting vector-space representations of words and using distances among these to predict or understand human behavior in various semantic tasks. In contemporary language AIs, however, it is possible to interrogate the latent structure of conceptual representations using methods nearly identical to those commonly used with human participants. The current work uses two common techniques borrowed from cognitive psychology to estimate and compare lexical-semantic structure in both humans and a well-known AI, the DaVinci variant of GPT-3. In humans, we show that conceptual structure is robust to differences in culture, language, and method of estimation. Structures estimated from AI behavior, while individually fairly consistent with those estimated from human behavior, depend much more upon the particular task used to generate behavior responses--responses generated by the very same model in the two tasks yield estimates of conceptual structure that cohere less with one another than do human structure estimates. The results suggest one important way that knowledge inhering in contemporary AIs can differ from human cognition. | ['Timothy T Rogers', 'Kushin Mukherjee', 'Lisa Padua', 'Siddharth Suresh'] | 2023-04-05 | null | null | null | null | ['culture'] | ['speech'] | [ 1.18974934e-03 -9.66761261e-02 2.15084478e-02 -5.39074957e-01
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0f91f672-509d-47f5-aa28-567e4911cb58 | iterative-frame-level-representation-learning | 2112.01402 | null | https://arxiv.org/abs/2112.01402v2 | https://arxiv.org/pdf/2112.01402v2.pdf | Iterative Contrast-Classify For Semi-supervised Temporal Action Segmentation | Temporal action segmentation classifies the action of each frame in (long) video sequences. Due to the high cost of frame-wise labeling, we propose the first semi-supervised method for temporal action segmentation. Our method hinges on unsupervised representation learning, which, for temporal action segmentation, poses unique challenges. Actions in untrimmed videos vary in length and have unknown labels and start/end times. Ordering of actions across videos may also vary. We propose a novel way to learn frame-wise representations from temporal convolutional networks (TCNs) by clustering input features with added time-proximity condition and multi-resolution similarity. By merging representation learning with conventional supervised learning, we develop an "Iterative-Contrast-Classify (ICC)" semi-supervised learning scheme. With more labelled data, ICC progressively improves in performance; ICC semi-supervised learning, with 40% labelled videos, performs similar to fully-supervised counterparts. Our ICC improves MoF by {+1.8, +5.6, +2.5}% on Breakfast, 50Salads and GTEA respectively for 100% labelled videos. | ['Angela Yao', 'Rahul Rahaman', 'Dipika Singhania'] | 2021-12-02 | null | null | null | null | ['semi-supervised-video-classification'] | ['computer-vision'] | [ 6.48387671e-01 -1.87300965e-02 -5.34983218e-01 -3.80954653e-01
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418cf33b-23f9-4aa4-8eea-25c85f3b15e2 | api-boosting-multi-agent-reinforcement | 2203.05285 | null | https://arxiv.org/abs/2203.05285v2 | https://arxiv.org/pdf/2203.05285v2.pdf | Breaking the Curse of Dimensionality in Multiagent State Space: A Unified Agent Permutation Framework | The state space in Multiagent Reinforcement Learning (MARL) grows exponentially with the agent number. Such a curse of dimensionality results in poor scalability and low sample efficiency, inhibiting MARL for decades. To break this curse, we propose a unified agent permutation framework that exploits the permutation invariance (PI) and permutation equivariance (PE) inductive biases to reduce the multiagent state space. Our insight is that permuting the order of entities in the factored multiagent state space does not change the information. Specifically, we propose two novel implementations: a Dynamic Permutation Network (DPN) and a Hyper Policy Network (HPN). The core idea is to build separate entity-wise PI input and PE output network modules to connect the entity-factored state space and action space in an end-to-end way. DPN achieves such connections by two separate module selection networks, which consistently assign the same input module to the same input entity (guarantee PI) and assign the same output module to the same entity-related output (guarantee PE). To enhance the representation capability, HPN replaces the module selection networks of DPN with hypernetworks to directly generate the corresponding module weights. Extensive experiments in SMAC, Google Research Football and MPE validate that the proposed methods significantly boost the performance and the learning efficiency of existing MARL algorithms. Remarkably, in SMAC, we achieve 100% win rates in almost all hard and super-hard scenarios (never achieved before). | ['Weixun Wang', 'Jianye Hao', 'Zhen Wang', 'Yan Zheng', 'Dong Li', 'Yaodong Yang', 'Hangyu Mao', 'Xiaotian Hao'] | 2022-03-10 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-2.19027456e-02 1.17480800e-01 -3.74181539e-01 1.62646487e-01
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2205633f-34ef-460e-8028-7167f691159b | self-supervised-pretraining-of-visual | 2103.01988 | null | https://arxiv.org/abs/2103.01988v2 | https://arxiv.org/pdf/2103.01988v2.pdf | Self-supervised Pretraining of Visual Features in the Wild | Recently, self-supervised learning methods like MoCo, SimCLR, BYOL and SwAV have reduced the gap with supervised methods. These results have been achieved in a control environment, that is the highly curated ImageNet dataset. However, the premise of self-supervised learning is that it can learn from any random image and from any unbounded dataset. In this work, we explore if self-supervision lives to its expectation by training large models on random, uncurated images with no supervision. Our final SElf-supERvised (SEER) model, a RegNetY with 1.3B parameters trained on 1B random images with 512 GPUs achieves 84.2% top-1 accuracy, surpassing the best self-supervised pretrained model by 1% and confirming that self-supervised learning works in a real world setting. Interestingly, we also observe that self-supervised models are good few-shot learners achieving 77.9% top-1 with access to only 10% of ImageNet. Code: https://github.com/facebookresearch/vissl | ['Piotr Bojanowski', 'Armand Joulin', 'Ishan Misra', 'Vitaliy Liptchinsky', 'Mannat Singh', 'Vivek Pai', 'Pengchao Wang', 'Min Xu', 'Benjamin Lefaudeux', 'Mathilde Caron', 'Priya Goyal'] | 2021-03-02 | null | null | null | null | ['self-supervised-image-classification'] | ['computer-vision'] | [ 1.08175978e-01 3.46485287e-01 -3.92541319e-01 -2.71832347e-01
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b0c0473b-cc8a-4828-9e25-22c21b4c7e94 | in-defense-of-online-models-for-video | 2207.10661 | null | https://arxiv.org/abs/2207.10661v1 | https://arxiv.org/pdf/2207.10661v1.pdf | In Defense of Online Models for Video Instance Segmentation | In recent years, video instance segmentation (VIS) has been largely advanced by offline models, while online models gradually attracted less attention possibly due to their inferior performance. However, online methods have their inherent advantage in handling long video sequences and ongoing videos while offline models fail due to the limit of computational resources. Therefore, it would be highly desirable if online models can achieve comparable or even better performance than offline models. By dissecting current online models and offline models, we demonstrate that the main cause of the performance gap is the error-prone association between frames caused by the similar appearance among different instances in the feature space. Observing this, we propose an online framework based on contrastive learning that is able to learn more discriminative instance embeddings for association and fully exploit history information for stability. Despite its simplicity, our method outperforms all online and offline methods on three benchmarks. Specifically, we achieve 49.5 AP on YouTube-VIS 2019, a significant improvement of 13.2 AP and 2.1 AP over the prior online and offline art, respectively. Moreover, we achieve 30.2 AP on OVIS, a more challenging dataset with significant crowding and occlusions, surpassing the prior art by 14.8 AP. The proposed method won first place in the video instance segmentation track of the 4th Large-scale Video Object Segmentation Challenge (CVPR2022). We hope the simplicity and effectiveness of our method, as well as our insight into current methods, could shed light on the exploration of VIS models. | ['Xiang Bai', 'Alan Yuille', 'Song Bai', 'Yi Jiang', 'Qihao Liu', 'Junfeng Wu'] | 2022-07-21 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 6.69996440e-02 -1.14186570e-01 -4.33512479e-01 2.81184763e-02
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b50a09dd-e638-4e2e-86f2-5c34e927ed18 | synthetic-dataset-generation-of-driver | 2102.00252 | null | https://arxiv.org/abs/2102.00252v1 | https://arxiv.org/pdf/2102.00252v1.pdf | Synthetic Dataset Generation of Driver Telematics | This article describes techniques employed in the production of a synthetic dataset of driver telematics emulated from a similar real insurance dataset. The synthetic dataset generated has 100,000 policies that included observations about driver's claims experience together with associated classical risk variables and telematics-related variables. This work is aimed to produce a resource that can be used to advance models to assess risks for usage-based insurance. It follows a three-stage process using machine learning algorithms. The first stage is simulating values for the number of claims as multiple binary classifications applying feedforward neural networks. The second stage is simulating values for aggregated amount of claims as regression using feedforward neural networks, with number of claims included in the set of feature variables. In the final stage, a synthetic portfolio of the space of feature variables is generated applying an extended $\texttt{SMOTE}$ algorithm. The resulting dataset is evaluated by comparing the synthetic and real datasets when Poisson and gamma regression models are fitted to the respective data. Other visualization and data summarization produce remarkable similar statistics between the two datasets. We hope that researchers interested in obtaining telematics datasets to calibrate models or learning algorithms will find our work valuable. | ['Emiliano A. Valdez', 'Jean-Philippe Boucher', 'Banghee So'] | 2021-01-30 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [ 1.81449398e-01 3.80556911e-01 -2.77195990e-01 -7.68100858e-01
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4979600e-c1de-4216-892f-257a3e116a6b | predictive-linguistic-cues-for-fake-news-a | 2211.14505 | null | https://arxiv.org/abs/2211.14505v1 | https://arxiv.org/pdf/2211.14505v1.pdf | Predictive linguistic cues for fake news: a societal artificial intelligence problem | Media news are making a large part of public opinion and, therefore, must not be fake. News on web sites, blogs, and social media must be analyzed before being published. In this paper, we present linguistic characteristics of media news items to differentiate between fake news and real news using machine learning algorithms. Neural fake news generation, headlines created by machines, semantic incongruities in text and image captions generated by machine are other types of fake news problems. These problems use neural networks which mainly control distributional features rather than evidence. We propose applying correlation between features set and class, and correlation among the features to compute correlation attribute evaluation metric and covariance metric to compute variance of attributes over the news items. Features unique, negative, positive, and cardinal numbers with high values on the metrics are observed to provide a high area under the curve (AUC) and F1-score. | ['Ponnurangam Kumaraguru', 'Nagender Aneja', 'Sandhya Aneja'] | 2022-11-26 | null | null | null | null | ['news-generation'] | ['natural-language-processing'] | [-2.11224079e-01 9.91143063e-02 -3.99703562e-01 -4.85204577e-01
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258c04cf-e7f3-4881-9c44-2cd2fd18f2ef | transformers-and-cnns-both-beat-humans-on | 2209.06629 | null | https://arxiv.org/abs/2209.06629v1 | https://arxiv.org/pdf/2209.06629v1.pdf | Transformers and CNNs both Beat Humans on SBIR | Sketch-based image retrieval (SBIR) is the task of retrieving natural images (photos) that match the semantics and the spatial configuration of hand-drawn sketch queries. The universality of sketches extends the scope of possible applications and increases the demand for efficient SBIR solutions. In this paper, we study classic triplet-based SBIR solutions and show that a persistent invariance to horizontal flip (even after model finetuning) is harming performance. To overcome this limitation, we propose several approaches and evaluate in depth each of them to check their effectiveness. Our main contributions are twofold: We propose and evaluate several intuitive modifications to build SBIR solutions with better flip equivariance. We show that vision transformers are more suited for the SBIR task, and that they outperform CNNs with a large margin. We carried out numerous experiments and introduce the first models to outperform human performance on a large-scale SBIR benchmark (Sketchy). Our best model achieves a recall of 62.25% (at k = 1) on the sketchy benchmark compared to previous state-of-the-art methods 46.2%. | ['Thierry Dutoit', 'Saïd Mahmoudi', 'Stéphane Dupont', 'Omar Seddati'] | 2022-09-14 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 8.09002519e-02 -4.01506573e-01 -1.98421761e-01 -1.14575379e-01
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4259ec41-fef7-4bdc-8cc2-42b18c976a86 | gaussian-constrained-attention-network-for | 2010.09169 | null | https://arxiv.org/abs/2010.09169v1 | https://arxiv.org/pdf/2010.09169v1.pdf | Gaussian Constrained Attention Network for Scene Text Recognition | Scene text recognition has been a hot topic in computer vision. Recent methods adopt the attention mechanism for sequence prediction which achieve convincing results. However, we argue that the existing attention mechanism faces the problem of attention diffusion, in which the model may not focus on a certain character area. In this paper, we propose Gaussian Constrained Attention Network to deal with this problem. It is a 2D attention-based method integrated with a novel Gaussian Constrained Refinement Module, which predicts an additional Gaussian mask to refine the attention weights. Different from adopting an additional supervision on the attention weights simply, our proposed method introduces an explicit refinement. In this way, the attention weights will be more concentrated and the attention-based recognition network achieves better performance. The proposed Gaussian Constrained Refinement Module is flexible and can be applied to existing attention-based methods directly. The experiments on several benchmark datasets demonstrate the effectiveness of our proposed method. Our code has been available at https://github.com/Pay20Y/GCAN. | ['Weiping Wang', 'Fei Yang', 'Yu Zhou', 'Xugong Qin', 'Zhi Qiao'] | 2020-10-19 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 1.44373149e-01 -1.12513743e-01 -1.99598018e-02 -1.99421197e-01
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7ff330ba-7ecc-41de-aeb3-e7ca873d8530 | probabilistic-neural-programs | 1612.00712 | null | http://arxiv.org/abs/1612.00712v1 | http://arxiv.org/pdf/1612.00712v1.pdf | Probabilistic Neural Programs | We present probabilistic neural programs, a framework for program induction
that permits flexible specification of both a computational model and inference
algorithm while simultaneously enabling the use of deep neural networks.
Probabilistic neural programs combine a computation graph for specifying a
neural network with an operator for weighted nondeterministic choice. Thus, a
program describes both a collection of decisions as well as the neural network
architecture used to make each one. We evaluate our approach on a challenging
diagram question answering task where probabilistic neural programs correctly
execute nearly twice as many programs as a baseline model. | ['Kenton W. Murray', 'Jayant Krishnamurthy'] | 2016-12-02 | null | null | null | null | ['program-induction'] | ['computer-code'] | [ 3.00291747e-01 4.29209083e-01 -4.77959633e-01 -6.70065999e-01
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a256ebc9-a5f7-47f6-954f-f4bcc1a415e7 | multi-model-ensemble-analysis-with-neural | 2202.04152 | null | https://arxiv.org/abs/2202.04152v4 | https://arxiv.org/pdf/2202.04152v4.pdf | Multi-model Ensemble Analysis with Neural Network Gaussian Processes | Multi-model ensemble analysis integrates information from multiple climate models into a unified projection. However, existing integration approaches based on model averaging can dilute fine-scale spatial information and incur bias from rescaling low-resolution climate models. We propose a statistical approach, called NN-GPR, using Gaussian process regression (GPR) with an infinitely wide deep neural network based covariance function. NN-GPR requires no assumptions about the relationships between models, no interpolation to a common grid, no stationarity assumptions, and automatically downscales as part of its prediction algorithm. Model experiments show that NN-GPR can be highly skillful at surface temperature and precipitation forecasting by preserving geospatial signals at multiple scales and capturing inter-annual variability. Our projections particularly show improved accuracy and uncertainty quantification skill in regions of high variability, which allows us to cheaply assess tail behavior at a 0.44$^\circ$/50 km spatial resolution without a regional climate model (RCM). Evaluations on reanalysis data and SSP245 forced climate models show that NN-GPR produces similar, overall climatologies to the model ensemble while better capturing fine scale spatial patterns. Finally, we compare NN-GPR's regional predictions against two RCMs and show that NN-GPR can rival the performance of RCMs using only global model data as input. | ['Ryan Sriver', 'Bo Li', 'Trevor Harris'] | 2022-02-08 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [-2.49068618e-01 -3.83682936e-01 3.09547246e-01 -2.17109442e-01
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1fefa5a2-83d4-4ad1-92a3-6c4a28748809 | meta-gating-framework-for-fast-and-continuous | 2306.13277 | null | https://arxiv.org/abs/2306.13277v1 | https://arxiv.org/pdf/2306.13277v1.pdf | Meta-Gating Framework for Fast and Continuous Resource Optimization in Dynamic Wireless Environments | With the great success of deep learning (DL) in image classification, speech recognition, and other fields, more and more studies have applied various neural networks (NNs) to wireless resource allocation. Generally speaking, these artificial intelligent (AI) models are trained under some special learning hypotheses, especially that the statistics of the training data are static during the training stage. However, the distribution of channel state information (CSI) is constantly changing in the real-world wireless communication environment. Therefore, it is essential to study effective dynamic DL technologies to solve wireless resource allocation problems. In this paper, we propose a novel framework, named meta-gating, for solving resource allocation problems in an episodically dynamic wireless environment, where the CSI distribution changes over periods and remains constant within each period. The proposed framework, consisting of an inner network and an outer network, aims to adapt to the dynamic wireless environment by achieving three important goals, i.e., seamlessness, quickness and continuity. Specifically, for the former two goals, we propose a training method by combining a model-agnostic meta-learning (MAML) algorithm with an unsupervised learning mechanism. With this training method, the inner network is able to fast adapt to different channel distributions because of the good initialization. As for the goal of continuity, the outer network can learn to evaluate the importance of inner network's parameters under different CSI distributions, and then decide which subset of the inner network should be activated through the gating operation. Additionally, we theoretically analyze the performance of the proposed meta-gating framework. | ['Yunlong Cai', 'Guanding Yu', 'Mengyuan Lee', 'Qiushuo Hou'] | 2023-06-23 | null | null | null | null | ['meta-learning'] | ['methodology'] | [ 1.68037802e-01 -3.54309589e-01 -3.51490229e-01 -2.10239574e-01
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57c5c71a-7334-479e-bafa-af7c8780321c | probabilistic-soft-logic-for-semantic-textual | null | null | https://aclanthology.org/P14-1114 | https://aclanthology.org/P14-1114.pdf | Probabilistic Soft Logic for Semantic Textual Similarity | null | ['Katrin Erk', 'Islam Beltagy', 'Raymond Mooney'] | 2014-06-01 | null | null | null | acl-2014-6 | ['video-description'] | ['computer-vision'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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d00e8b32-bd3b-4b2b-bc78-8e5974fc9bf9 | walkingtime-dynamic-graph-embedding-using | 2111.10928 | null | https://arxiv.org/abs/2111.10928v1 | https://arxiv.org/pdf/2111.10928v1.pdf | WalkingTime: Dynamic Graph Embedding Using Temporal-Topological Flows | Increased attention has been paid over the last four years to dynamic network embedding. Existing dynamic embedding methods, however, consider the problem as limited to the evolution of a topology over a sequence of global, discrete states. We propose a novel embedding algorithm, WalkingTime, based on a fundamentally different handling of time, allowing for the local consideration of continuously occurring phenomena; while others consider global time-steps to be first-order citizens of the dynamic environment, we hold flows comprised of temporally and topologically local interactions as our primitives, without any discretization or alignment of time-related attributes being necessary. Keywords: dynamic networks , representation learning , dynamic graph embedding , time-respecting paths , temporal-topological flows , temporal random walks , temporal networks , real-attributed knowledge graphs , streaming graphs , online networks , asynchronous graphs , asynchronous networks , graph algorithms , deep learning , network analysis , datamining , network science | ['David Bayani'] | 2021-11-22 | null | null | null | null | ['dynamic-graph-embedding', 'network-embedding'] | ['graphs', 'methodology'] | [-4.79089804e-02 1.43072098e-01 -3.46505046e-01 -1.02373064e-01
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9691c13e-1589-4e85-bccc-b6b5ab825452 | the-devil-is-in-the-labels-noisy-label-1 | 2206.03014 | null | https://arxiv.org/abs/2206.03014v1 | https://arxiv.org/pdf/2206.03014v1.pdf | The Devil is in the Labels: Noisy Label Correction for Robust Scene Graph Generation | Unbiased SGG has achieved significant progress over recent years. However, almost all existing SGG models have overlooked the ground-truth annotation qualities of prevailing SGG datasets, i.e., they always assume: 1) all the manually annotated positive samples are equally correct; 2) all the un-annotated negative samples are absolutely background. In this paper, we argue that both assumptions are inapplicable to SGG: there are numerous "noisy" groundtruth predicate labels that break these two assumptions, and these noisy samples actually harm the training of unbiased SGG models. To this end, we propose a novel model-agnostic NoIsy label CorrEction strategy for SGG: NICE. NICE can not only detect noisy samples but also reassign more high-quality predicate labels to them. After the NICE training, we can obtain a cleaner version of SGG dataset for model training. Specifically, NICE consists of three components: negative Noisy Sample Detection (Neg-NSD), positive NSD (Pos-NSD), and Noisy Sample Correction (NSC). Firstly, in Neg-NSD, we formulate this task as an out-of-distribution detection problem, and assign pseudo labels to all detected noisy negative samples. Then, in Pos-NSD, we use a clustering-based algorithm to divide all positive samples into multiple sets, and treat the samples in the noisiest set as noisy positive samples. Lastly, in NSC, we use a simple but effective weighted KNN to reassign new predicate labels to noisy positive samples. Extensive results on different backbones and tasks have attested to the effectiveness and generalization abilities of each component of NICE. | ['Jun Xiao', 'Songyang Zhang', 'Zhimeng Zhang', 'Yifeng Huang', 'Long Chen', 'Lin Li'] | 2022-06-07 | the-devil-is-in-the-labels-noisy-label | http://openaccess.thecvf.com//content/CVPR2022/html/Li_The_Devil_Is_in_the_Labels_Noisy_Label_Correction_for_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Li_The_Devil_Is_in_the_Labels_Noisy_Label_Correction_for_CVPR_2022_paper.pdf | cvpr-2022-1 | ['scene-graph-generation'] | ['computer-vision'] | [ 4.62968320e-01 3.34479690e-01 -2.94087797e-01 -4.63906586e-01
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d69d4a62-c39f-4ffb-ab81-a650b0322530 | towards-unconstrained-end-to-end-text | 1908.09231 | null | https://arxiv.org/abs/1908.09231v1 | https://arxiv.org/pdf/1908.09231v1.pdf | Towards Unconstrained End-to-End Text Spotting | We propose an end-to-end trainable network that can simultaneously detect and recognize text of arbitrary shape, making substantial progress on the open problem of reading scene text of irregular shape. We formulate arbitrary shape text detection as an instance segmentation problem; an attention model is then used to decode the textual content of each irregularly shaped text region without rectification. To extract useful irregularly shaped text instance features from image scale features, we propose a simple yet effective RoI masking step. Additionally, we show that predictions from an existing multi-step OCR engine can be leveraged as partially labeled training data, which leads to significant improvements in both the detection and recognition accuracy of our model. Our method surpasses the state-of-the-art for end-to-end recognition tasks on the ICDAR15 (straight) benchmark by 4.6%, and on the Total-Text (curved) benchmark by more than 16%. | ['Ying Xiao', 'Michalis Raptis', 'Yasuhisa Fujii', 'Siyang Qin', 'Alessandro Bissacco'] | 2019-08-24 | towards-unconstrained-end-to-end-text-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Qin_Towards_Unconstrained_End-to-End_Text_Spotting_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Qin_Towards_Unconstrained_End-to-End_Text_Spotting_ICCV_2019_paper.pdf | iccv-2019-10 | ['text-spotting'] | ['computer-vision'] | [ 8.60530555e-01 3.02084833e-01 2.47984789e-02 -3.23608994e-01
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e51d7ee8-04f4-4158-a44e-388ca8698908 | system-iii-learning-with-domain-knowledge-for | 2304.11593 | null | https://arxiv.org/abs/2304.11593v1 | https://arxiv.org/pdf/2304.11593v1.pdf | System III: Learning with Domain Knowledge for Safety Constraints | Reinforcement learning agents naturally learn from extensive exploration. Exploration is costly and can be unsafe in $\textit{safety-critical}$ domains. This paper proposes a novel framework for incorporating domain knowledge to help guide safe exploration and boost sample efficiency. Previous approaches impose constraints, such as regularisation parameters in neural networks, that rely on large sample sets and often are not suitable for safety-critical domains where agents should almost always avoid unsafe actions. In our approach, called $\textit{System III}$, which is inspired by psychologists' notions of the brain's $\textit{System I}$ and $\textit{System II}$, we represent domain expert knowledge of safety in form of first-order logic. We evaluate the satisfaction of these constraints via p-norms in state vector space. In our formulation, constraints are analogous to hazards, objects, and regions of state that have to be avoided during exploration. We evaluated the effectiveness of the proposed method on OpenAI's Gym and Safety-Gym environments. In all tasks, including classic Control and Safety Games, we show that our approach results in safer exploration and sample efficiency. | ['Alesandro Abbate', 'Hosien Hasanbieg', 'Fazl Barez'] | 2023-04-23 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 2.20613822e-01 4.34510589e-01 -1.61642507e-01 -2.80242950e-01
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ee8e36c1-89a0-4511-bd8d-fdcc2df33b17 | a-semantically-consistent-and-syntactically | null | null | https://aclanthology.org/2020.coling-main.102 | https://aclanthology.org/2020.coling-main.102.pdf | A Semantically Consistent and Syntactically Variational Encoder-Decoder Framework for Paraphrase Generation | Paraphrase generation aims to generate semantically consistent sentences with different syntactic realizations. Most of the recent studies rely on the typical encoder-decoder framework where the generation process is deterministic. However, in practice, the ability to generate multiple syntactically different paraphrases is important. Recent work proposed to cooperate variational inference on a target-related latent variable to introduce the diversity. But the latent variable may be contaminated by the semantic information of other unrelated sentences, and in turn, change the conveyed meaning of generated paraphrases. In this paper, we propose a semantically consistent and syntactically variational encoder-decoder framework, which uses adversarial learning to ensure the syntactic latent variable be semantic-free. Moreover, we adopt another discriminator to improve the word-level and sentence-level semantic consistency. So the proposed framework can generate multiple semantically consistent and syntactically different paraphrases. The experiments show that our model outperforms the baseline models on the metrics based on both n-gram matching and semantic similarity, and our model can generate multiple different paraphrases by assembling different syntactic variables. | ['Yaohui Jin', 'Hao He', 'Liqiang Xiao', 'Jidong Tian', 'Wenqing Chen'] | 2020-12-01 | null | null | null | coling-2020-8 | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 1.82050422e-01 2.18329336e-02 -1.14416040e-01 -6.06424749e-01
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3d3a1deb-1da9-4efb-a46c-4945184f3b64 | fair-and-skill-diverse-student-group | 2301.09984 | null | https://arxiv.org/abs/2301.09984v1 | https://arxiv.org/pdf/2301.09984v1.pdf | Fair and skill-diverse student group formation via constrained k-way graph partitioning | Forming the right combination of students in a group promises to enable a powerful and effective environment for learning and collaboration. However, defining a group of students is a complex task which has to satisfy multiple constraints. This work introduces an unsupervised algorithm for fair and skill-diverse student group formation. This is achieved by taking account of student course marks and sensitive attributes provided by the education office. The skill sets of students are determined using unsupervised dimensionality reduction of course mark data via the Laplacian eigenmap. The problem is formulated as a constrained graph partitioning problem, whereby the diversity of skill sets in each group are maximised, group sizes are upper and lower bounded according to available resources, and `balance' of a sensitive attribute is lower bounded to enforce fairness in group formation. This optimisation problem is solved using integer programming and its effectiveness is demonstrated on a dataset of student course marks from Imperial College London. | ['Danilo Mandic', 'Ljubisa Stankovic', 'Imad Jaimoukha', 'Alexander Jenkins'] | 2023-01-12 | null | null | null | null | ['graph-partitioning'] | ['graphs'] | [ 2.55754799e-01 4.49982852e-01 -2.56639898e-01 -4.06774998e-01
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a7466225-ae9a-4252-be49-5d97feaa5e5f | chimst-a-chinese-medical-corpus-for-word | null | null | https://aclanthology.org/2022.lrec-1.607 | https://aclanthology.org/2022.lrec-1.607.pdf | ChiMST: A Chinese Medical Corpus for Word Segmentation and Medical Term Recognition | Chinese word segmentation (CWS) and named entity recognition (NER) are two important tasks in Chinese natural language processing. To achieve good model performance on these tasks, existing neural approaches normally require a large amount of labeled training data, which is often unavailable for specific domains such as the Chinese medical domain due to privacy and legal issues. To address this problem, we have developed a Chinese medical corpus named ChiMST which consists of question-answer pairs collected from an online medical healthcare platform and is annotated with word boundary and medical term information. For word boundary, we mainly follow the word segmentation guidelines for the Penn Chinese Treebank (Xia, 2000); for medical terms, we define 9 categories and 18 sub-categories after consulting medical experts. To provide baselines on this corpus, we train existing state-of-the-art models on it and achieve good performance. We believe that the corpus and the baseline systems will be a valuable resource for CWS and NER research on the medical domain. | ['Yan Song', 'Fei Xia', 'Han Qin', 'Yuanhe Tian'] | null | null | null | null | lrec-2022-6 | ['chinese-word-segmentation'] | ['natural-language-processing'] | [ 2.08855063e-01 2.05909163e-01 -3.03283572e-01 -6.55000985e-01
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89650a79-a5b4-4c5e-9141-7b1f522cc91c | multilingual-end-to-end-entity-linking | 2306.08896 | null | https://arxiv.org/abs/2306.08896v1 | https://arxiv.org/pdf/2306.08896v1.pdf | Multilingual End to End Entity Linking | Entity Linking is one of the most common Natural Language Processing tasks in practical applications, but so far efficient end-to-end solutions with multilingual coverage have been lacking, leading to complex model stacks. To fill this gap, we release and open source BELA, the first fully end-to-end multilingual entity linking model that efficiently detects and links entities in texts in any of 97 languages. We provide here a detailed description of the model and report BELA's performance on four entity linking datasets covering high- and low-resource languages. | ['Nicola Cancedda', 'Frédéric A. Dreyer', 'Borislav Kozlovskii', 'Simone Merello', 'Louis Martin', 'Kashyap Popat', 'Nora Kassner', 'Mikhail Plekhanov'] | 2023-06-15 | null | null | null | null | ['entity-linking'] | ['natural-language-processing'] | [-7.83460796e-01 3.10063571e-01 -4.97022390e-01 -1.54196739e-01
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1cc2da6b-4006-4b6d-8a21-4655745f1db5 | modelling-low-resource-accents-without-accent | 2301.04606 | null | https://arxiv.org/abs/2301.04606v1 | https://arxiv.org/pdf/2301.04606v1.pdf | Modelling low-resource accents without accent-specific TTS frontend | This work focuses on modelling a speaker's accent that does not have a dedicated text-to-speech (TTS) frontend, including a grapheme-to-phoneme (G2P) module. Prior work on modelling accents assumes a phonetic transcription is available for the target accent, which might not be the case for low-resource, regional accents. In our work, we propose an approach whereby we first augment the target accent data to sound like the donor voice via voice conversion, then train a multi-speaker multi-accent TTS model on the combination of recordings and synthetic data, to generate the donor's voice speaking in the target accent. Throughout the procedure, we use a TTS frontend developed for the same language but a different accent. We show qualitative and quantitative analysis where the proposed strategy achieves state-of-the-art results compared to other generative models. Our work demonstrates that low resource accents can be modelled with relatively little data and without developing an accent-specific TTS frontend. Audio samples of our model converting to multiple accents are available on our web page. | ['Marius Cotescu', 'Kayoko Yanagisawa', 'Kamil Deja', 'Marta Czarnowska', 'Georgi Tinchev'] | 2023-01-11 | null | null | null | null | ['voice-conversion', 'voice-conversion'] | ['audio', 'speech'] | [ 1.34868801e-01 3.56771559e-01 3.72467011e-01 -4.55446541e-01
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40369488-61f3-421c-9db5-eb386c4ef70d | traffic-sign-classification-using-deep-and | 2209.15251 | null | https://arxiv.org/abs/2209.15251v1 | https://arxiv.org/pdf/2209.15251v1.pdf | Traffic Sign Classification Using Deep and Quantum Neural Networks | Quantum Neural Networks (QNNs) are an emerging technology that can be used in many applications including computer vision. In this paper, we presented a traffic sign classification system implemented using a hybrid quantum-classical convolutional neural network. Experiments on the German Traffic Sign Recognition Benchmark dataset indicate that currently QNN do not outperform classical DCNN (Deep Convolutuional Neural Networks), yet still provide an accuracy of over 90% and are a definitely promising solution for advanced computer vision. | ['Tomasz Kryjak', 'Sylwia Kuros'] | 2022-09-30 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 2.06916049e-01 -3.61944586e-01 -2.54883617e-01 -3.26932818e-01
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6d1be044-376d-4a16-b65e-056ad8fca5d3 | recovering-3d-human-mesh-from-monocular | 2203.01923 | null | https://arxiv.org/abs/2203.01923v3 | https://arxiv.org/pdf/2203.01923v3.pdf | Recovering 3D Human Mesh from Monocular Images: A Survey | Estimating human pose and shape from monocular images is a long-standing problem in computer vision. Since the release of statistical body models, 3D human mesh recovery has been drawing broader attention. With the same goal of obtaining well-aligned and physically plausible mesh results, two paradigms have been developed to overcome challenges in the 2D-to-3D lifting process: i) an optimization-based paradigm, where different data terms and regularization terms are exploited as optimization objectives; and ii) a regression-based paradigm, where deep learning techniques are embraced to solve the problem in an end-to-end fashion. Meanwhile, continuous efforts are devoted to improving the quality of 3D mesh labels for a wide range of datasets. Though remarkable progress has been achieved in the past decade, the task is still challenging due to flexible body motions, diverse appearances, complex environments, and insufficient in-the-wild annotations. To the best of our knowledge, this is the first survey that focuses on the task of monocular 3D human mesh recovery. We start with the introduction of body models and then elaborate recovery frameworks and training objectives by providing in-depth analyses of their strengths and weaknesses. We also summarize datasets, evaluation metrics, and benchmark results. Open issues and future directions are discussed in the end, hoping to motivate researchers and facilitate their research in this area. A regularly updated project page can be found at https://github.com/tinatiansjz/hmr-survey. | ['LiMin Wang', 'Yebin Liu', 'Hongwen Zhang', 'Yating Tian'] | 2022-03-03 | null | null | null | null | ['3d-human-pose-and-shape-estimation', 'human-mesh-recovery'] | ['computer-vision', 'computer-vision'] | [ 4.55726646e-02 -4.74995524e-02 -3.53675455e-01 -2.77585447e-01
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29e6aab6-a3f3-4d2a-a63f-303cf4c49e8f | enhancing-black-box-few-shot-text | 2305.13785 | null | https://arxiv.org/abs/2305.13785v1 | https://arxiv.org/pdf/2305.13785v1.pdf | Enhancing Black-Box Few-Shot Text Classification with Prompt-Based Data Augmentation | Training or finetuning large-scale language models (LLMs) such as GPT-3 requires substantial computation resources, motivating recent efforts to explore parameter-efficient adaptation to downstream tasks. One practical area of research is to treat these models as black boxes and interact with them through their inference APIs. In this paper, we investigate how to optimize few-shot text classification without accessing the gradients of the LLMs. To achieve this, we treat the black-box model as a feature extractor and train a classifier with the augmented text data. Data augmentation is performed using prompt-based finetuning on an auxiliary language model with a much smaller parameter size than the black-box model. Through extensive experiments on eight text classification datasets, we show that our approach, dubbed BT-Classifier, significantly outperforms state-of-the-art black-box few-shot learners and performs on par with methods that rely on full-model tuning. | ['Haizhou Li', 'Yan Zhang', 'Yiming Chen', 'Bin Wang', 'Jiahui Xu', 'Chen Zhang', 'Danqing Luo'] | 2023-05-23 | null | null | null | null | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 2.75192112e-01 3.65303993e-01 -6.30763352e-01 -5.14250040e-01
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13fec705-bb28-45c8-b6d1-70386d7cca8d | cmdfusion-bidirectional-fusion-network-with | 2307.04091 | null | https://arxiv.org/abs/2307.04091v1 | https://arxiv.org/pdf/2307.04091v1.pdf | CMDFusion: Bidirectional Fusion Network with Cross-modality Knowledge Distillation for LIDAR Semantic Segmentation | 2D RGB images and 3D LIDAR point clouds provide complementary knowledge for the perception system of autonomous vehicles. Several 2D and 3D fusion methods have been explored for the LIDAR semantic segmentation task, but they suffer from different problems. 2D-to-3D fusion methods require strictly paired data during inference, which may not be available in real-world scenarios, while 3D-to-2D fusion methods cannot explicitly make full use of the 2D information. Therefore, we propose a Bidirectional Fusion Network with Cross-Modality Knowledge Distillation (CMDFusion) in this work. Our method has two contributions. First, our bidirectional fusion scheme explicitly and implicitly enhances the 3D feature via 2D-to-3D fusion and 3D-to-2D fusion, respectively, which surpasses either one of the single fusion schemes. Second, we distillate the 2D knowledge from a 2D network (Camera branch) to a 3D network (2D knowledge branch) so that the 3D network can generate 2D information even for those points not in the FOV (field of view) of the camera. In this way, RGB images are not required during inference anymore since the 2D knowledge branch provides 2D information according to the 3D LIDAR input. We show that our CMDFusion achieves the best performance among all fusion-based methods on SemanticKITTI and nuScenes datasets. The code will be released at https://github.com/Jun-CEN/CMDFusion. | ['Qifeng Chen', 'Yingya Zhang', 'Maochun Luo', 'Hang Zheng', 'Kun Li', 'Yixuan Pei', 'Shiwei Zhang', 'Jun Cen'] | 2023-07-09 | null | null | null | null | ['semantic-segmentation', 'autonomous-vehicles', 'lidar-semantic-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.67680174e-01 7.20654940e-03 -3.07427526e-01 -5.45897841e-01
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8532b7f0-cbb1-46f4-be0f-24440a44781a | dressing-3d-humans-using-a-conditional-mesh | 1907.13615 | null | https://arxiv.org/abs/1907.13615v3 | https://arxiv.org/pdf/1907.13615v3.pdf | Learning to Dress 3D People in Generative Clothing | Three-dimensional human body models are widely used in the analysis of human pose and motion. Existing models, however, are learned from minimally-clothed 3D scans and thus do not generalize to the complexity of dressed people in common images and videos. Additionally, current models lack the expressive power needed to represent the complex non-linear geometry of pose-dependent clothing shapes. To address this, we learn a generative 3D mesh model of clothed people from 3D scans with varying pose and clothing. Specifically, we train a conditional Mesh-VAE-GAN to learn the clothing deformation from the SMPL body model, making clothing an additional term in SMPL. Our model is conditioned on both pose and clothing type, giving the ability to draw samples of clothing to dress different body shapes in a variety of styles and poses. To preserve wrinkle detail, our Mesh-VAE-GAN extends patchwise discriminators to 3D meshes. Our model, named CAPE, represents global shape and fine local structure, effectively extending the SMPL body model to clothing. To our knowledge, this is the first generative model that directly dresses 3D human body meshes and generalizes to different poses. The model, code and data are available for research purposes at https://cape.is.tue.mpg.de. | ['Gerard Pons-Moll', 'Sergi Pujades', 'Qianli Ma', 'Michael J. Black', 'Anurag Ranjan', 'Jinlong Yang', 'Siyu Tang'] | 2019-07-31 | learning-to-dress-3d-people-in-generative | http://openaccess.thecvf.com/content_CVPR_2020/html/Ma_Learning_to_Dress_3D_People_in_Generative_Clothing_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Ma_Learning_to_Dress_3D_People_in_Generative_Clothing_CVPR_2020_paper.pdf | cvpr-2020-6 | ['3d-shape-modeling', '3d-shape-generation', '3d-human-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.11197357e-03 -9.18707624e-02 1.24869764e-01 -3.65585089e-01
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4d7ad9fb-9205-483e-97ed-2c4cb6b199ca | counter-twit-an-italian-corpus-for-online | null | null | https://aclanthology.org/2022.woah-1.6 | https://aclanthology.org/2022.woah-1.6.pdf | Counter-TWIT: An Italian Corpus for Online Counterspeech in Ecological Contexts | This work describes the process of creating a corpus of Twitter conversations annotated for the presence of counterspeech in response to toxic speech related to axes of discrimination linked to sexism, racism and homophobia. The main novelty is an annotated dataset comprising relevant tweets in their context of occurrence. The corpus is made up of tweets and responses captured by different profiles replying to discriminatory content or objectionably couched news. An annotation scheme was created to make explicit the knowledge on the dimensions of toxic speech and counterspeech.An analysis of the collected and annotated data and of the IAA that emerged during the annotation process is included. Moreover, we report about preliminary experiments on automatic counterspeech detection, based on supervised automatic learning models trained on the new dataset. The results highlight the fundamental role played by the context in this detection task, confirming our intuitions about the importance to collect tweets in their context of occurrence. | ['Viviana Patti', 'Biancamaria Cepollaro', 'Valerio Basile', 'Pierpaolo Goffredo'] | null | null | null | null | naacl-woah-2022-7 | ['counterspeech-detection'] | ['natural-language-processing'] | [ 4.58378345e-01 5.09051859e-01 -3.03781718e-01 -4.77762103e-01
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f70c1366-e015-4734-a566-aa0e9a190f2e | an-explainable-cnn-approach-for-medical-codes | 2101.11430 | null | https://arxiv.org/abs/2101.11430v1 | https://arxiv.org/pdf/2101.11430v1.pdf | An Explainable CNN Approach for Medical Codes Prediction from Clinical Text | Method: We develop CNN-based methods for automatic ICD coding based on clinical text from intensive care unit (ICU) stays. We come up with the Shallow and Wide Attention convolutional Mechanism (SWAM), which allows our model to learn local and low-level features for each label. The key idea behind our model design is to look for the presence of informative snippets in the clinical text that correlated with each code, and we infer that there exists a correspondence between "informative snippet" and convolution filter. Results: We evaluate our approach on MIMIC-III, an open-access dataset of ICU medical records. Our approach substantially outperforms previous results on top-50 medical code prediction on MIMIC-III dataset. We attribute this improvement to SWAM, by which the wide architecture gives the model ability to more extensively learn the unique features of different codes, and we prove it by ablation experiment. Besides, we perform manual analysis of the performance imbalance between different codes, and preliminary conclude the characteristics that determine the difficulty of learning specific codes. Conclusions: We present SWAM, an explainable CNN approach for multi-label document classification, which employs a wide convolution layer to learn local and low-level features for each label, yields strong improvements over previous metrics on the ICD-9 code prediction task, while providing satisfactory explanations for its internal mechanics. | ['Fei Teng', 'Shu Yuan Hu'] | 2021-01-14 | null | null | null | null | ['medical-code-prediction'] | ['medical'] | [ 1.29540384e-01 2.60556996e-01 -2.16582105e-01 -6.77286088e-01
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199213bd-e536-47a0-92c1-5c644f2df036 | learning-to-substitute-spans-towards | 2306.02840 | null | https://arxiv.org/abs/2306.02840v1 | https://arxiv.org/pdf/2306.02840v1.pdf | Learning to Substitute Spans towards Improving Compositional Generalization | Despite the rising prevalence of neural sequence models, recent empirical evidences suggest their deficiency in compositional generalization. One of the current de-facto solutions to this problem is compositional data augmentation, aiming to incur additional compositional inductive bias. Nonetheless, the improvement offered by existing handcrafted augmentation strategies is limited when successful systematic generalization of neural sequence models requires multi-grained compositional bias (i.e., not limited to either lexical or structural biases only) or differentiation of training sequences in an imbalanced difficulty distribution. To address the two challenges, we first propose a novel compositional augmentation strategy dubbed \textbf{Span} \textbf{Sub}stitution (SpanSub) that enables multi-grained composition of substantial substructures in the whole training set. Over and above that, we introduce the \textbf{L}earning \textbf{to} \textbf{S}ubstitute \textbf{S}pan (L2S2) framework which empowers the learning of span substitution probabilities in SpanSub in an end-to-end manner by maximizing the loss of neural sequence models, so as to outweigh those challenging compositions with elusive concepts and novel surroundings. Our empirical results on three standard compositional generalization benchmarks, including SCAN, COGS and GeoQuery (with an improvement of at most 66.5\%, 10.3\%, 1.2\%, respectively), demonstrate the superiority of SpanSub, %the learning framework L2S2 and their combination. | ['Defu Lian', 'Ying WEI', 'Zhaoyi Li'] | 2023-06-05 | null | null | null | null | ['systematic-generalization'] | ['reasoning'] | [ 8.73681307e-01 -3.51399891e-02 -3.21456224e-01 -2.58538246e-01
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2108cd23-de01-4556-b602-1bc0ec15f57d | music-data-analysis-a-state-of-the-art-survey | 1411.5014 | null | http://arxiv.org/abs/1411.5014v1 | http://arxiv.org/pdf/1411.5014v1.pdf | Music Data Analysis: A State-of-the-art Survey | Music accounts for a significant chunk of interest among various online
activities. This is reflected by wide array of alternatives offered in music
related web/mobile apps, information portals, featuring millions of artists,
songs and events attracting user activity at similar scale. Availability of
large scale structured and unstructured data has attracted similar level of
attention by data science community. This paper attempts to offer current
state-of-the-art in music related analysis. Various approaches involving
machine learning, information theory, social network analysis, semantic web and
linked open data are represented in the form of taxonomy along with data
sources and use cases addressed by the research community. | ['Shubhanshu Gupta'] | 2014-11-18 | null | null | null | null | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [-8.39323476e-02 4.89045307e-02 -5.91759920e-01 2.11998448e-01
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c72d5b91-6d0c-4361-a429-f73fcefa59d2 | attribute-aware-pooling-for-pedestrian | 1907.11837 | null | https://arxiv.org/abs/1907.11837v1 | https://arxiv.org/pdf/1907.11837v1.pdf | Attribute Aware Pooling for Pedestrian Attribute Recognition | This paper expands the strength of deep convolutional neural networks (CNNs) to the pedestrian attribute recognition problem by devising a novel attribute aware pooling algorithm. Existing vanilla CNNs cannot be straightforwardly applied to handle multi-attribute data because of the larger label space as well as the attribute entanglement and correlations. We tackle these challenges that hampers the development of CNNs for multi-attribute classification by fully exploiting the correlation between different attributes. The multi-branch architecture is adopted for fucusing on attributes at different regions. Besides the prediction based on each branch itself, context information of each branch are employed for decision as well. The attribute aware pooling is developed to integrate both kinds of information. Therefore, attributes which are indistinct or tangled with others can be accurately recognized by exploiting the context information. Experiments on benchmark datasets demonstrate that the proposed pooling method appropriately explores and exploits the correlations between attributes for the pedestrian attribute recognition. | ['Yunhe Wang', 'Kai Han', 'Chuanjian Liu', 'Chunjing Xu', 'Han Shu', 'Chang Xu'] | 2019-07-27 | null | null | null | null | ['pedestrian-attribute-recognition'] | ['computer-vision'] | [ 1.41872615e-01 -1.28980070e-01 -2.68433660e-01 -5.67777753e-01
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60845098-2e1e-4baa-8995-54217bddf825 | multiscene-a-large-scale-dataset-and | 2104.02846 | null | https://arxiv.org/abs/2104.02846v3 | https://arxiv.org/pdf/2104.02846v3.pdf | MultiScene: A Large-scale Dataset and Benchmark for Multi-scene Recognition in Single Aerial Images | Aerial scene recognition is a fundamental research problem in interpreting high-resolution aerial imagery. Over the past few years, most studies focus on classifying an image into one scene category, while in real-world scenarios, it is more often that a single image contains multiple scenes. Therefore, in this paper, we investigate a more practical yet underexplored task -- multi-scene recognition in single images. To this end, we create a large-scale dataset, called MultiScene, composed of 100,000 unconstrained high-resolution aerial images. Considering that manually labeling such images is extremely arduous, we resort to low-cost annotations from crowdsourcing platforms, e.g., OpenStreetMap (OSM). However, OSM data might suffer from incompleteness and incorrectness, which introduce noise into image labels. To address this issue, we visually inspect 14,000 images and correct their scene labels, yielding a subset of cleanly-annotated images, named MultiScene-Clean. With it, we can develop and evaluate deep networks for multi-scene recognition using clean data. Moreover, we provide crowdsourced annotations of all images for the purpose of studying network learning with noisy labels. We conduct experiments with extensive baseline models on both MultiScene-Clean and MultiScene to offer benchmarks for multi-scene recognition in single images and learning from noisy labels for this task, respectively. To facilitate progress, we make our dataset and trained models available on https://gitlab.lrz.de/ai4eo/reasoning/multiscene. | ['Xiao Xiang Zhu', 'Pu Jin', 'Lichao Mou', 'Yuansheng Hua'] | 2021-04-07 | null | null | null | null | ['scene-recognition'] | ['computer-vision'] | [ 4.59897488e-01 -3.87282670e-01 2.98590481e-01 -5.46844304e-01
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23c6dc96-0007-43c2-b501-a4e0a0843692 | rfid-towards-rational-fusion-in-decoder-for | 2305.17041 | null | https://arxiv.org/abs/2305.17041v1 | https://arxiv.org/pdf/2305.17041v1.pdf | RFiD: Towards Rational Fusion-in-Decoder for Open-Domain Question Answering | Open-Domain Question Answering (ODQA) systems necessitate a reader model capable of generating answers by simultaneously referring to multiple passages. Although representative models like Fusion-in-Decoder (FiD) have been proposed to address this challenge, these systems can inadvertently rely on spurious features instead of genuine causal relationships between the question and the passages to generate answers. To counter this problem, we introduce the Rational Fusion-in-Decoder (RFiD) model. Our model leverages the encoders of FiD to differentiate between causal relationships and spurious features, subsequently guiding the decoder to generate answers informed by this discernment. Experimental results on two ODQA datasets, Natural Questions (NQ) and TriviaQA (TQ), demonstrate that our model surpasses previous methods, achieving improvements of up to 1.5 and 0.7 in Exact Match scores on NQ, and exhibits an enhanced ability to identify causal relationships. | ['Yue Zhang', 'Haofei Yu', 'Cunxiang Wang'] | 2023-05-26 | null | null | null | null | ['natural-questions', 'triviaqa', 'open-domain-question-answering'] | ['miscellaneous', 'miscellaneous', 'natural-language-processing'] | [ 2.51545936e-01 5.97724438e-01 2.46626973e-01 -4.29214507e-01
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cad5c997-ae8d-4d6d-b68e-b1868dfc50c1 | intelligent-grimm-open-ended-visual | 2306.00973 | null | https://arxiv.org/abs/2306.00973v1 | https://arxiv.org/pdf/2306.00973v1.pdf | Intelligent Grimm -- Open-ended Visual Storytelling via Latent Diffusion Models | Generative models have recently exhibited exceptional capabilities in various scenarios, for example, image generation based on text description. In this work, we focus on the task of generating a series of coherent image sequence based on a given storyline, denoted as open-ended visual storytelling. We make the following three contributions: (i) to fulfill the task of visual storytelling, we introduce two modules into a pre-trained stable diffusion model, and construct an auto-regressive image generator, termed as StoryGen, that enables to generate the current frame by conditioning on both a text prompt and a preceding frame; (ii) to train our proposed model, we collect paired image and text samples by sourcing from various online sources, such as videos, E-books, and establish a data processing pipeline for constructing a diverse dataset, named StorySalon, with a far larger vocabulary than existing animation-specific datasets; (iii) we adopt a three-stage curriculum training strategy, that enables style transfer, visual context conditioning, and human feedback alignment, respectively. Quantitative experiments and human evaluation have validated the superiority of our proposed model, in terms of image quality, style consistency, content consistency, and visual-language alignment. We will make the code, model, and dataset publicly available to the research community. | ['Weidi Xie', 'Xiaoyun Zhang', 'Yujie Zhong', 'HaoNing Wu', 'Chang Liu'] | 2023-06-01 | null | null | null | null | ['style-transfer', 'visual-storytelling'] | ['computer-vision', 'natural-language-processing'] | [ 3.20177615e-01 -1.48958907e-01 1.77977756e-01 -2.15718746e-01
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314657c8-5bbb-4549-bbca-722e1c253941 | real-time-eeg-classification-via-coresets-for | 1901.00512 | null | http://arxiv.org/abs/1901.00512v1 | http://arxiv.org/pdf/1901.00512v1.pdf | Real-Time EEG Classification via Coresets for BCI Applications | A brain-computer interface (BCI) based on the motor imagery (MI) paradigm
translates one's motor intention into a control signal by classifying the
Electroencephalogram (EEG) signal of different tasks. However, most existing
systems either (i) use a high-quality algorithm to train the data off-line and
run only classification in real-time, since the off-line algorithm is too slow,
or (ii) use low-quality heuristics that are sufficiently fast for real-time
training but introduces relatively large classification error. In this work, we
propose a novel processing pipeline that allows real-time and parallel learning
of EEG signals using high-quality but possibly inefficient algorithms. This is
done by forging a link between BCI and core-sets, a technique that originated
in computational geometry for handling streaming data via data summarization.
We suggest an algorithm that maintains the representation such coreset
tailored to handle the EEG signal which enables: (i) real time and continuous
computation of the Common Spatial Pattern (CSP) feature extraction method on a
coreset representation of the signal (instead on the signal itself) , (ii)
improvement of the CSP algorithm efficiency with provable guarantees by
applying CSP algorithm on the coreset, and (iii) real time addition of the data
trials (EEG data windows) to the coreset.
For simplicity, we focus on the CSP algorithm, which is a classic algorithm.
Nevertheless, we expect that our coreset will be extended to other algorithms
in future papers. In the experimental results we show that our system can
indeed learn EEG signals in real-time for example a 64 channels setup with
hundreds of time samples per second. Full open source is provided to reproduce
the experiment and in the hope that it will be used and extended to more
coresets and BCI applications in the future. | ['Dan Feldman', 'Alex Frid', 'Eitan Netzer'] | 2019-01-02 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [ 6.74973369e-01 -1.02794521e-01 4.00427461e-01 -2.86904305e-01
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0ae2bf74-a3e6-46f7-87e9-5e96079de369 | europarl-st-a-multilingual-corpus-for-speech | 1911.03167 | null | https://arxiv.org/abs/1911.03167v3 | https://arxiv.org/pdf/1911.03167v3.pdf | Europarl-ST: A Multilingual Corpus For Speech Translation Of Parliamentary Debates | Current research into spoken language translation (SLT),or speech-to-text translation, is often hampered by the lack of specific data resources for this task, as currently available SLT datasets are restricted to a limited set of language pairs. In this paper we present Europarl-ST, a novel multilingual SLT corpus containing paired audio-text samples for SLT from and into 6 European languages, for a total of 30 different translation directions. This corpus has been compiled using the debates held in the European Parliament in the period between 2008 and 2012. This paper describes the corpus creation process and presents a series of automatic speech recognition, machine translation and spoken language translation experiments that highlight the potential of this new resource. The corpus is released under a Creative Commons license and is freely accessible and downloadable. | ['Adrià Giménez', 'Nahuel Roselló', 'Joan Albert Silvestre-Cerdà', 'Javier Iranzo-Sánchez', 'Jorge Civera', 'Alfons Juan', 'Albert Sanchis', 'Javier Jorge'] | 2019-11-08 | null | null | null | null | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 2.40024537e-01 -1.42655671e-01 -2.85670370e-01 -2.83395469e-01
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eabb2dd6-74c1-408e-ab12-a89f5eeefcc8 | how-to-train-your-hippo-state-space-models | 2206.12037 | null | https://arxiv.org/abs/2206.12037v2 | https://arxiv.org/pdf/2206.12037v2.pdf | How to Train Your HiPPO: State Space Models with Generalized Orthogonal Basis Projections | Linear time-invariant state space models (SSM) are a classical model from engineering and statistics, that have recently been shown to be very promising in machine learning through the Structured State Space sequence model (S4). A core component of S4 involves initializing the SSM state matrix to a particular matrix called a HiPPO matrix, which was empirically important for S4's ability to handle long sequences. However, the specific matrix that S4 uses was actually derived in previous work for a particular time-varying dynamical system, and the use of this matrix as a time-invariant SSM had no known mathematical interpretation. Consequently, the theoretical mechanism by which S4 models long-range dependencies actually remains unexplained. We derive a more general and intuitive formulation of the HiPPO framework, which provides a simple mathematical interpretation of S4 as a decomposition onto exponentially-warped Legendre polynomials, explaining its ability to capture long dependencies. Our generalization introduces a theoretically rich class of SSMs that also lets us derive more intuitive S4 variants for other bases such as the Fourier basis, and explains other aspects of training S4, such as how to initialize the important timescale parameter. These insights improve S4's performance to 86% on the Long Range Arena benchmark, with 96% on the most difficult Path-X task. | ['Christopher Ré', 'Atri Rudra', 'Aman Timalsina', 'Isys Johnson', 'Albert Gu'] | 2022-06-24 | null | null | null | null | ['long-range-modeling'] | ['natural-language-processing'] | [-8.40421394e-03 -3.10095549e-01 -3.39336038e-01 -4.24608625e-02
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36982849-3b2e-4c52-a973-cfdbfff060d1 | sheaf-neural-networks-for-graph-based | 2304.09097 | null | https://arxiv.org/abs/2304.09097v1 | https://arxiv.org/pdf/2304.09097v1.pdf | Sheaf Neural Networks for Graph-based Recommender Systems | Recent progress in Graph Neural Networks has resulted in wide adoption by many applications, including recommendation systems. The reason for Graph Neural Networks' superiority over other approaches is that many problems in recommendation systems can be naturally modeled as graphs, where nodes can be either users or items and edges represent preference relationships. In current Graph Neural Network approaches, nodes are represented with a static vector learned at training time. This static vector might only be suitable to capture some of the nuances of users or items they define. To overcome this limitation, we propose using a recently proposed model inspired by category theory: Sheaf Neural Networks. Sheaf Neural Networks, and its connected Laplacian, can address the previous problem by associating every node (and edge) with a vector space instead than a single vector. The vector space representation is richer and allows picking the proper representation at inference time. This approach can be generalized for different related tasks on graphs and achieves state-of-the-art performance in terms of F1-Score@N in collaborative filtering and Hits@20 in link prediction. For collaborative filtering, the approach is evaluated on the MovieLens 100K with a 5.1% improvement, on MovieLens 1M with a 5.4% improvement and on Book-Crossing with a 2.8% improvement, while for link prediction on the ogbl-ddi dataset with a 1.6% refinement with respect to the respective baselines. | ['Fabrizio Silvestri', 'Pietro Liò', 'Giulia Cassarà', 'Antonio Purificato'] | 2023-04-07 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [ 4.92015816e-02 1.31111547e-01 -5.90418696e-01 -2.73637205e-01
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bb4ae463-42a7-4c0d-9002-59fea6f5a893 | neural-rgbrd-sensing-depth-and-uncertainty | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Liu_Neural_RGBrD_Sensing_Depth_and_Uncertainty_From_a_Video_Camera_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_Neural_RGBrD_Sensing_Depth_and_Uncertainty_From_a_Video_Camera_CVPR_2019_paper.pdf | Neural RGB(r)D Sensing: Depth and Uncertainty From a Video Camera | Depth sensing is crucial for 3D reconstruction and scene understanding. Active depth sensors provide dense metric measurements, but often suffer from limitations such as restricted operating ranges, low spatial resolution, sensor interference, and high power consumption. In this paper, we propose a deep learning (DL) method to estimate per-pixel depth and its uncertainty continuously from a monocular video stream, with the goal of effectively turning an RGB camera into an RGB-D camera. Unlike prior DL-based methods, we estimate a depth probability distribution for each pixel rather than a single depth value, leading to an estimate of a 3D depth probability volume for each input frame. These depth probability volumes are accumulated over time under a Bayesian filtering framework as more incoming frames are processed sequentially, which effectively reduces depth uncertainty and improves accuracy, robustness, and temporal stability. Compared to prior work, the proposed approach achieves more accurate and stable results, and generalizes better to new datasets. Experimental results also show the output of our approach can be directly fed into classical RGB-D based 3D scanning methods for 3D scene reconstruction.
| [' Jan Kautz', ' Srinivasa G. Narasimhan', ' Kihwan Kim', ' Jinwei Gu', 'Chao Liu'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['3d-scene-reconstruction'] | ['computer-vision'] | [ 4.74203467e-01 -2.00251658e-02 -9.02076624e-03 -5.54722309e-01
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fa4e3307-1509-4981-9936-5ee201975f29 | bayesian-detection-of-a-sinusoidal-signal | 2211.05977 | null | https://arxiv.org/abs/2211.05977v1 | https://arxiv.org/pdf/2211.05977v1.pdf | Bayesian Detection of a Sinusoidal Signal with Randomly Varying Frequency | The problem of detecting a sinusoidal signal with randomly varying frequency has a long history. It is one of the core problems in signal processing, arising in many applications including, for example, underwater acoustic frequency line tracking, demodulation of FM radio communications, laser phase drift in optical communications and, recently, continuous gravitational wave astronomy. In this paper we describe a Markov Chain Monte Carlo based procedure to compute a specific detection posterior density. We demonstrate via simulation that our approach results in an up to $25$ percent higher detection rate than Hidden Markov Model based solutions, which are generally considered to be the leading techniques for these problems. | ['A. Melatos', 'B. Moran', 'R. J. Evans', 'S. Suvorova', 'Changrong Liu'] | 2022-11-11 | null | null | null | null | ['astronomy'] | ['miscellaneous'] | [ 2.88833857e-01 -2.72990048e-01 3.66012812e-01 -5.06738424e-02
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056308b4-2d82-417b-a6eb-8c9528c11499 | sequence-to-sequence-natural-language-to | 1907.04198 | null | https://arxiv.org/abs/1907.04198v1 | https://arxiv.org/pdf/1907.04198v1.pdf | Sequence-to-Sequence Natural Language to Humanoid Robot Sign Language | This paper presents a study on natural language to sign language translation with human-robot interaction application purposes. By means of the presented methodology, the humanoid robot TEO is expected to represent Spanish sign language automatically by converting text into movements, thanks to the performance of neural networks. Natural language to sign language translation presents several challenges to developers, such as the discordance between the length of input and output data and the use of non-manual markers. Therefore, neural networks and, consequently, sequence-to-sequence models, are selected as a data-driven system to avoid traditional expert system approaches or temporal dependencies limitations that lead to limited or too complex translation systems. To achieve these objectives, it is necessary to find a way to perform human skeleton acquisition in order to collect the signing input data. OpenPose and skeletonRetriever are proposed for this purpose and a 3D sensor specification study is developed to select the best acquisition hardware. | ['Bartek Łukawski', 'Jennifer J. Gago', 'Valentina Vasco', 'Juan G. Victores', 'Ugo Pattacini', 'Vadim Tikhanoff', 'Carlos Balaguer'] | 2019-07-09 | null | null | null | null | ['sign-language-translation'] | ['computer-vision'] | [ 3.84317875e-01 2.13937566e-01 -5.09911031e-02 -3.66079837e-01
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611e83bf-2052-40e4-b3d5-3bbed10b88cf | a-provable-splitting-approach-for-symmetric | 2301.10499 | null | https://arxiv.org/abs/2301.10499v1 | https://arxiv.org/pdf/2301.10499v1.pdf | A Provable Splitting Approach for Symmetric Nonnegative Matrix Factorization | The symmetric Nonnegative Matrix Factorization (NMF), a special but important class of the general NMF, has found numerous applications in data analysis such as various clustering tasks. Unfortunately, designing fast algorithms for the symmetric NMF is not as easy as for its nonsymmetric counterpart, since the latter admits the splitting property that allows state-of-the-art alternating-type algorithms. To overcome this issue, we first split the decision variable and transform the symmetric NMF to a penalized nonsymmetric one, paving the way for designing efficient alternating-type algorithms. We then show that solving the penalized nonsymmetric reformulation returns a solution to the original symmetric NMF. Moreover, we design a family of alternating-type algorithms and show that they all admit strong convergence guarantee: the generated sequence of iterates is convergent and converges at least sublinearly to a critical point of the original symmetric NMF. Finally, we conduct experiments on both synthetic data and real image clustering to support our theoretical results and demonstrate the performance of the alternating-type algorithms. | ['Kai Liu', 'Qiuwei Li', 'Zhihui Zhu', 'Xiao Li'] | 2023-01-25 | null | null | null | null | ['image-clustering', 'type'] | ['computer-vision', 'speech'] | [ 3.12478870e-01 2.42541078e-02 -1.96225986e-01 -1.89126417e-01
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00ea0610-a5d4-46aa-a0b1-fe9a89219171 | precise-zero-shot-dense-retrieval-without | 2212.10496 | null | https://arxiv.org/abs/2212.10496v1 | https://arxiv.org/pdf/2212.10496v1.pdf | Precise Zero-Shot Dense Retrieval without Relevance Labels | While dense retrieval has been shown effective and efficient across tasks and languages, it remains difficult to create effective fully zero-shot dense retrieval systems when no relevance label is available. In this paper, we recognize the difficulty of zero-shot learning and encoding relevance. Instead, we propose to pivot through Hypothetical Document Embeddings~(HyDE). Given a query, HyDE first zero-shot instructs an instruction-following language model (e.g. InstructGPT) to generate a hypothetical document. The document captures relevance patterns but is unreal and may contain false details. Then, an unsupervised contrastively learned encoder~(e.g. Contriever) encodes the document into an embedding vector. This vector identifies a neighborhood in the corpus embedding space, where similar real documents are retrieved based on vector similarity. This second step ground the generated document to the actual corpus, with the encoder's dense bottleneck filtering out the incorrect details. Our experiments show that HyDE significantly outperforms the state-of-the-art unsupervised dense retriever Contriever and shows strong performance comparable to fine-tuned retrievers, across various tasks (e.g. web search, QA, fact verification) and languages~(e.g. sw, ko, ja). | ['Jamie Callan', 'Jimmy Lin', 'Xueguang Ma', 'Luyu Gao'] | 2022-12-20 | null | null | null | null | ['fact-verification'] | ['natural-language-processing'] | [ 1.47772223e-01 -1.84499100e-02 -2.63219327e-01 -1.78368345e-01
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1711ff3a-bc9d-428b-859b-1af897fd1dd3 | variational-bayesian-framework-for-advanced | 2305.13872 | null | https://arxiv.org/abs/2305.13872v1 | https://arxiv.org/pdf/2305.13872v1.pdf | Variational Bayesian Framework for Advanced Image Generation with Domain-Related Variables | Deep generative models (DGMs) and their conditional counterparts provide a powerful ability for general-purpose generative modeling of data distributions. However, it remains challenging for existing methods to address advanced conditional generative problems without annotations, which can enable multiple applications like image-to-image translation and image editing. We present a unified Bayesian framework for such problems, which introduces an inference stage on latent variables within the learning process. In particular, we propose a variational Bayesian image translation network (VBITN) that enables multiple image translation and editing tasks. Comprehensive experiments show the effectiveness of our method on unsupervised image-to-image translation, and demonstrate the novel advanced capabilities for semantic editing and mixed domain translation. | ['Yuan Shen', 'Santiago Mazuelas', 'Yuxiao Li'] | 2023-05-23 | null | null | null | null | ['unsupervised-image-to-image-translation', 'image-to-image-translation', 'image-to-image-translation'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [ 4.17661458e-01 2.28205711e-01 -5.36485687e-02 -5.77638090e-01
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c8a81c3d-40e3-4b1e-9374-6bff73998420 | style-transfer-from-non-parallel-text-by | 1705.09655 | null | http://arxiv.org/abs/1705.09655v2 | http://arxiv.org/pdf/1705.09655v2.pdf | Style Transfer from Non-Parallel Text by Cross-Alignment | This paper focuses on style transfer on the basis of non-parallel text. This
is an instance of a broad family of problems including machine translation,
decipherment, and sentiment modification. The key challenge is to separate the
content from other aspects such as style. We assume a shared latent content
distribution across different text corpora, and propose a method that leverages
refined alignment of latent representations to perform style transfer. The
transferred sentences from one style should match example sentences from the
other style as a population. We demonstrate the effectiveness of this
cross-alignment method on three tasks: sentiment modification, decipherment of
word substitution ciphers, and recovery of word order. | ['Regina Barzilay', 'Tianxiao Shen', 'Tao Lei', 'Tommi Jaakkola'] | 2017-05-26 | style-transfer-from-non-parallel-text-by-1 | http://papers.nips.cc/paper/7259-style-transfer-from-non-parallel-text-by-cross-alignment | http://papers.nips.cc/paper/7259-style-transfer-from-non-parallel-text-by-cross-alignment.pdf | neurips-2017-12 | ['decipherment'] | ['natural-language-processing'] | [ 1.02647161e+00 -1.62191793e-01 -1.13554344e-01 -4.71122622e-01
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7e51cab5-82c9-4cad-9ae8-e8af91660c24 | real-time-optimization-for-wind-to-h2-driven | 2306.17395 | null | https://arxiv.org/abs/2306.17395v1 | https://arxiv.org/pdf/2306.17395v1.pdf | Real-time Optimization for Wind-to-H2 Driven Critical Infrastructures: High-fidelity Active Constraints and Integer Variables Prediction Enhanced by Feature Space Expansion | This paper focuses on developing a real-time optimal operation model for a new engineering system, wind-to-hydrogen-driven low-carbon critical infrastructure (W2H-LCCI), that utilizes wind power to generate hydrogen through electrolysis and combines it with carbon capture to reduce carbon emissions from the power sector. First, a convex mathematical model for W2H-LCCI is proposed, and then optimization models for its real-time decision-making are developed, which are mixed-integer convex programs (MICPs). Furthermore, since this large-scale MICP problem must be solved in real-time, a fast solution method based on active constraint and integer variable prediction (ACIVP) is presented. ACIVP method predicts the binary variable values and the set of limited-number constraints, which most likely contain all of the active constraints, based on historical optimization data. It results in only a small-scale continuous convex optimization problem needing to be solved by optimization solvers for W2H-LCCI real-time optimal operation. To increase the accuracy of the ACIVP method, feature space expansion (FSE) is employed, and a multi-stage ACIVP-FSE method is proposed. The effects of stage design and stage ordering on ACIVP-FSE performance are also discussed. We validate the effectiveness of the developed system and solution method using two water-energy nexus case studies. | ['QiFeng Li', 'Mostafa Goodarzi'] | 2023-06-30 | null | null | null | null | ['decision-making'] | ['reasoning'] | [-7.25369453e-02 1.11931033e-01 -3.56460690e-01 3.17980886e-01
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3f372e91-ea28-424c-abf2-31f4f3b46a7f | boosting-image-forgery-detection-using | 1802.03154 | null | http://arxiv.org/abs/1802.03154v2 | http://arxiv.org/pdf/1802.03154v2.pdf | Boosting Image Forgery Detection using Resampling Features and Copy-move analysis | Realistic image forgeries involve a combination of splicing, resampling,
cloning, region removal and other methods. While resampling detection
algorithms are effective in detecting splicing and resampling, copy-move
detection algorithms excel in detecting cloning and region removal. In this
paper, we combine these complementary approaches in a way that boosts the
overall accuracy of image manipulation detection. We use the copy-move
detection method as a pre-filtering step and pass those images that are
classified as untampered to a deep learning based resampling detection
framework. Experimental results on various datasets including the 2017 NIST
Nimble Challenge Evaluation dataset comprising nearly 10,000 pristine and
tampered images shows that there is a consistent increase of 8%-10% in
detection rates, when copy-move algorithm is combined with different resampling
detection algorithms. | ['Lawrence Peterson', 'Amit K. Roy-Chowdhury', 'Tajuddin Manhar Mohammed', 'Lakshmanan Nataraj', 'Jawadul H. Bappy', 'Jason Bunk', 'B. S. Manjunath', 'Shivkumar Chandrasekaran', 'Arjuna Flenner'] | 2018-02-09 | null | null | null | null | ['image-manipulation-detection'] | ['computer-vision'] | [ 8.77916098e-01 -5.55563569e-01 1.21091574e-01 -2.18354668e-02
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1d6c622a-9994-4b58-ac4f-870a929d3da9 | streamlining-social-media-information | 2306.16001 | null | https://arxiv.org/abs/2306.16001v1 | https://arxiv.org/pdf/2306.16001v1.pdf | Streamlining Social Media Information Retrieval for Public Health Research with Deep Learning | The utilization of social media in epidemic surveillance has been well established. Nonetheless, bias is often introduced when pre-defined lexicons are used to retrieve relevant corpus. This study introduces a framework aimed at curating extensive dictionaries of medical colloquialisms and Unified Medical Language System (UMLS) concepts. The framework comprises three modules: a BERT-based Named Entity Recognition (NER) model that identifies medical entities from social media content, a deep-learning powered normalization module that standardizes the extracted entities, and a semi-supervised clustering module that assigns the most probable UMLS concept to each standardized entity. We applied this framework to COVID-19-related tweets from February 1, 2020, to April 30, 2022, generating a symptom dictionary (available at https://github.com/ningkko/UMLS_colloquialism/) composed of 9,249 standardized entities mapped to 876 UMLS concepts and 38,175 colloquial expressions. This framework demonstrates encouraging potential in addressing the constraints of keyword matching information retrieval in social media-based public health research. | ['Jie Yang', 'Li Zhou', 'Ying-Chih Lo', 'Peilin Zhou', 'Yujie Zhang', 'Minghui Li', 'Shixu Lin', 'Yining Hua'] | 2023-06-28 | null | null | null | null | ['retrieval', 'named-entity-recognition-ner', 'information-retrieval', 'cg'] | ['methodology', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-1.79749336e-02 1.30133629e-01 -3.52786928e-01 -1.49109796e-01
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36c77fea-4fda-4003-b782-8adc2bd30f34 | estimating-generic-3d-room-structures-from-2d | 2306.09077 | null | https://arxiv.org/abs/2306.09077v1 | https://arxiv.org/pdf/2306.09077v1.pdf | Estimating Generic 3D Room Structures from 2D Annotations | Indoor rooms are among the most common use cases in 3D scene understanding. Current state-of-the-art methods for this task are driven by large annotated datasets. Room layouts are especially important, consisting of structural elements in 3D, such as wall, floor, and ceiling. However, they are difficult to annotate, especially on pure RGB video. We propose a novel method to produce generic 3D room layouts just from 2D segmentation masks, which are easy to annotate for humans. Based on these 2D annotations, we automatically reconstruct 3D plane equations for the structural elements and their spatial extent in the scene, and connect adjacent elements at the appropriate contact edges. We annotate and publicly release 2266 3D room layouts on the RealEstate10k dataset, containing YouTube videos. We demonstrate the high quality of these 3D layouts annotations with extensive experiments. | ['Vittorio Ferrari', 'Matthias Nießner', 'Kevis-Kokitsi Maninis', 'Stefan Popov', 'Denys Rozumnyi'] | 2023-06-15 | null | null | null | null | ['scene-understanding'] | ['computer-vision'] | [-2.40746532e-02 9.85226855e-02 4.95052159e-01 -5.29771090e-01
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b7cd326e-bae3-4180-999f-c5a703c34134 | jarvis-a-neuro-symbolic-commonsense-reasoning | 2208.13266 | null | https://arxiv.org/abs/2208.13266v3 | https://arxiv.org/pdf/2208.13266v3.pdf | JARVIS: A Neuro-Symbolic Commonsense Reasoning Framework for Conversational Embodied Agents | Building a conversational embodied agent to execute real-life tasks has been a long-standing yet quite challenging research goal, as it requires effective human-agent communication, multi-modal understanding, long-range sequential decision making, etc. Traditional symbolic methods have scaling and generalization issues, while end-to-end deep learning models suffer from data scarcity and high task complexity, and are often hard to explain. To benefit from both worlds, we propose JARVIS, a neuro-symbolic commonsense reasoning framework for modular, generalizable, and interpretable conversational embodied agents. First, it acquires symbolic representations by prompting large language models (LLMs) for language understanding and sub-goal planning, and by constructing semantic maps from visual observations. Then the symbolic module reasons for sub-goal planning and action generation based on task- and action-level common sense. Extensive experiments on the TEACh dataset validate the efficacy and efficiency of our JARVIS framework, which achieves state-of-the-art (SOTA) results on all three dialog-based embodied tasks, including Execution from Dialog History (EDH), Trajectory from Dialog (TfD), and Two-Agent Task Completion (TATC) (e.g., our method boosts the unseen Success Rate on EDH from 6.1\% to 15.8\%). Moreover, we systematically analyze the essential factors that affect the task performance and also demonstrate the superiority of our method in few-shot settings. Our JARVIS model ranks first in the Alexa Prize SimBot Public Benchmark Challenge. | ['Zonglin Di', 'Xin Eric Wang', 'Xuehai He', 'Jialu Wang', 'Yue Fan', 'Jing Gu', 'Kaiwen Zhou', 'Kaizhi Zheng'] | 2022-08-28 | null | null | null | null | ['action-generation'] | ['computer-vision'] | [-4.14334051e-02 4.10560906e-01 6.70607314e-02 -3.38644385e-01
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789581fa-d0d8-4d32-9f1a-0475d1450561 | shape-preserving-facial-landmarks-with-graph | 2210.07233 | null | https://arxiv.org/abs/2210.07233v1 | https://arxiv.org/pdf/2210.07233v1.pdf | Shape Preserving Facial Landmarks with Graph Attention Networks | Top-performing landmark estimation algorithms are based on exploiting the excellent ability of large convolutional neural networks (CNNs) to represent local appearance. However, it is well known that they can only learn weak spatial relationships. To address this problem, we propose a model based on the combination of a CNN with a cascade of Graph Attention Network regressors. To this end, we introduce an encoding that jointly represents the appearance and location of facial landmarks and an attention mechanism to weigh the information according to its reliability. This is combined with a multi-task approach to initialize the location of graph nodes and a coarse-to-fine landmark description scheme. Our experiments confirm that the proposed model learns a global representation of the structure of the face, achieving top performance in popular benchmarks on head pose and landmark estimation. The improvement provided by our model is most significant in situations involving large changes in the local appearance of landmarks. | ['Luis Baumela', 'José M. Buenaposada', 'Andrés Prados-Torreblanca'] | 2022-10-13 | null | null | null | null | ['head-pose-estimation', 'face-alignment', 'facial-landmark-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-4.11487371e-01 3.22115481e-01 -1.37978807e-01 -7.06979215e-01
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8d708d4f-ef8c-4e54-8a50-15e70b9c529b | casia-at-semeval-2022-task-11-chinese-named | null | null | https://aclanthology.org/2022.semeval-1.208 | https://aclanthology.org/2022.semeval-1.208.pdf | CASIA at SemEval-2022 Task 11: Chinese Named Entity Recognition for Complex and Ambiguous Entities | This paper describes our approach to develop a complex named entity recognition system in SemEval 2022 Task 11: MultiCoNER Multilingual Complex Named Entity Recognition,Track 9 - Chinese. In this task, we need to identify the entity boundaries and categorylabels for the six identified categories of CW,LOC, PER, GRP, CORP, and PORD.The task focuses on detecting semantically ambiguous and complex entities in short and low-context settings. We constructed a hybrid system based on Roberta-large model with three training mechanisms and a series of data gugmentation.Three training mechanisms include adversarial training, Child-Tuning training, and continued pre-training. The core idea of the hybrid system is to improve the performance of the model in complex environments by introducing more domain knowledge through data augmentation and continuing pre-training domain adaptation of the model. Our proposed method in this paper achieves a macro-F1 of 0.797 on the final test set, ranking second. | ['Jun Zhao', 'Kang Liu', 'Yubo Chen', 'Dianbo Sui', 'Sirui Li', 'Zhucong Li', 'Zhen Gan', 'Jia Fu'] | null | null | null | null | semeval-naacl-2022-7 | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [-1.97192878e-01 2.49338984e-01 8.92403796e-02 -5.80513775e-01
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9d581abe-5b63-42d2-a5ff-e11903c7730c | self-supervised-pretraining-for-2d-medical | 2209.00314 | null | https://arxiv.org/abs/2209.00314v1 | https://arxiv.org/pdf/2209.00314v1.pdf | Self-Supervised Pretraining for 2D Medical Image Segmentation | Supervised machine learning provides state-of-the-art solutions to a wide range of computer vision problems. However, the need for copious labelled training data limits the capabilities of these algorithms in scenarios where such input is scarce or expensive. Self-supervised learning offers a way to lower the need for manually annotated data by pretraining models for a specific domain on unlabelled data. In this approach, labelled data are solely required to fine-tune models for downstream tasks. Medical image segmentation is a field where labelling data requires expert knowledge and collecting large labelled datasets is challenging; therefore, self-supervised learning algorithms promise substantial improvements in this field. Despite this, self-supervised learning algorithms are used rarely to pretrain medical image segmentation networks. In this paper, we elaborate and analyse the effectiveness of supervised and self-supervised pretraining approaches on downstream medical image segmentation, focusing on convergence and data efficiency. We find that self-supervised pretraining on natural images and target-domain-specific images leads to the fastest and most stable downstream convergence. In our experiments on the ACDC cardiac segmentation dataset, this pretraining approach achieves 4-5 times faster fine-tuning convergence compared to an ImageNet pretrained model. We also show that this approach requires less than five epochs of pretraining on domain-specific data to achieve such improvement in the downstream convergence time. Finally, we find that, in low-data scenarios, supervised ImageNet pretraining achieves the best accuracy, requiring less than 100 annotated samples to realise close to minimal error. | ['Bálint Gyires-Tóth', 'András Kalapos'] | 2022-09-01 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 5.82434773e-01 5.21530926e-01 -2.46068925e-01 -7.11768448e-01
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2004cdbf-47a4-4915-9c84-2e20456f6dd0 | weakly-supervised-classification-and-1 | null | null | https://arxiv.org/abs/2107.04878 | https://arxiv.org/pdf/2107.04878.pdf | Weakly-Supervised Classification and Detection of Bird Sounds in the Wild. | It is easier to hear birds than see them, however, they still play an essential role in nature and they are excellent indicators of deteriorating environmental quality and pollution. Recent advances in Machine Learning and Convolutional Neural Networks allow us to detect and classify bird sounds, by doing this, we can assist researchers in monitoring the status and trends of bird populations and biodiversity in ecosystems. We propose a sound detection and classification pipeline for analyzing complex soundscape recordings and identify birdcalls in the background. Our pipeline learns from weak labels, classifies fine-grained bird vocalizations in the wild, and is robust against background sounds (e.g., airplanes, rain, etc). Our solution achieved 10th place of 816 teams at the BirdCLEF 2021 Challenge hosted on Kaggle. | ['Szilard Bessenyei', 'Nitin D. Movva', 'Prateek Agnihotri', 'Kumar Shubham', 'Marcos V. Conde'] | 2021-07-10 | null | null | null | clef-2021-7 | ['audio-tagging', 'bird-audio-detection'] | ['audio', 'audio'] | [-1.40906200e-01 -7.74305105e-01 4.76103306e-01 -2.89925635e-02
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ccc9ce33-9c2d-4a36-9d1a-ea393bb77426 | reflection-based-word-attribute-transfer-2 | 2007.02598 | null | https://arxiv.org/abs/2007.02598v2 | https://arxiv.org/pdf/2007.02598v2.pdf | Reflection-based Word Attribute Transfer | Word embeddings, which often represent such analogic relations as king - man + woman = queen, can be used to change a word's attribute, including its gender. For transferring king into queen in this analogy-based manner, we subtract a difference vector man - woman based on the knowledge that king is male. However, developing such knowledge is very costly for words and attributes. In this work, we propose a novel method for word attribute transfer based on reflection mappings without such an analogy operation. Experimental results show that our proposed method can transfer the word attributes of the given words without changing the words that do not have the target attributes. | ['Katsuhito Sudoh', 'Yoichi Ishibashi', 'Koichiro Yoshino', 'Satoshi Nakamura'] | 2020-07-06 | reflection-based-word-attribute-transfer-1 | https://aclanthology.org/2020.acl-srw.8 | https://aclanthology.org/2020.acl-srw.8.pdf | acl-2020-6 | ['word-attribute-transfer'] | ['natural-language-processing'] | [ 1.68890692e-02 2.26711571e-01 -2.95824166e-02 -6.52904689e-01
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042bacab-890e-4114-8cc7-bf22cff12844 | differentially-private-synthetic-data | 2304.11336 | null | https://arxiv.org/abs/2304.11336v1 | https://arxiv.org/pdf/2304.11336v1.pdf | Differentially Private Synthetic Data Generation via Lipschitz-Regularised Variational Autoencoders | Synthetic data has been hailed as the silver bullet for privacy preserving data analysis. If a record is not real, then how could it violate a person's privacy? In addition, deep-learning based generative models are employed successfully to approximate complex high-dimensional distributions from data and draw realistic samples from this learned distribution. It is often overlooked though that generative models are prone to memorising many details of individual training records and often generate synthetic data that too closely resembles the underlying sensitive training data, hence violating strong privacy regulations as, e.g., encountered in health care. Differential privacy is the well-known state-of-the-art framework for guaranteeing protection of sensitive individuals' data, allowing aggregate statistics and even machine learning models to be released publicly without compromising privacy. The training mechanisms however often add too much noise during the training process, and thus severely compromise the utility of these private models. Even worse, the tight privacy budgets do not allow for many training epochs so that model quality cannot be properly controlled in practice. In this paper we explore an alternative approach for privately generating data that makes direct use of the inherent stochasticity in generative models, e.g., variational autoencoders. The main idea is to appropriately constrain the continuity modulus of the deep models instead of adding another noise mechanism on top. For this approach, we derive mathematically rigorous privacy guarantees and illustrate its effectiveness with practical experiments. | ['Gerhard Wunder', 'Benedikt Groß'] | 2023-04-22 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 2.25505024e-01 4.98857617e-01 1.84623823e-01 -5.18470764e-01
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22e002b0-01f1-474e-8c66-a83b25e278a2 | collective-named-entity-disambiguation-using | null | null | https://aclanthology.org/C14-1147 | https://aclanthology.org/C14-1147.pdf | Collective Named Entity Disambiguation using Graph Ranking and Clique Partitioning Approaches | null | ['Robert Gaizauskas', 'Ayman Alhelbawy'] | 2014-08-01 | collective-named-entity-disambiguation-using-1 | https://aclanthology.org/C14-1147 | https://aclanthology.org/C14-1147.pdf | coling-2014-8 | ['graph-ranking'] | ['graphs'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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ec9568dd-4e0f-49dc-b96e-3b938d86f0d9 | a-public-ground-truth-dataset-for-handwritten | 2107.10373 | null | https://arxiv.org/abs/2107.10373v1 | https://arxiv.org/pdf/2107.10373v1.pdf | A Public Ground-Truth Dataset for Handwritten Circuit Diagram Images | The development of digitization methods for line drawings (especially in the area of electrical engineering) relies on the availability of publicly available training and evaluation data. This paper presents such an image set along with annotations. The dataset consists of 1152 images of 144 circuits by 12 drafters and 48 563 annotations. Each of these images depicts an electrical circuit diagram, taken by consumer grade cameras under varying lighting conditions and perspectives. A variety of different pencil types and surface materials has been used. For each image, all individual electrical components are annotated with bounding boxes and one out of 45 class labels. In order to simplify a graph extraction process, different helper symbols like junction points and crossovers are introduced, while texts are annotated as well. The geometric and taxonomic problems arising from this task as well as the classes themselves and statistics of their appearances are stated. The performance of a standard Faster RCNN on the dataset is provided as an object detection baseline. | ['Yakun Li', 'Johannes Bayer', 'Felix Thoma'] | 2021-07-21 | null | null | null | null | ['electrical-engineering'] | ['miscellaneous'] | [ 6.30361080e-01 8.59246850e-02 4.32847403e-02 -3.61465931e-01
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b6c2519b-a5fc-4d38-a286-22d6c22b267f | a-parameter-efficient-learning-approach-to | 2305.11244 | null | https://arxiv.org/abs/2305.11244v1 | https://arxiv.org/pdf/2305.11244v1.pdf | A Parameter-Efficient Learning Approach to Arabic Dialect Identification with Pre-Trained General-Purpose Speech Model | In this work, we explore Parameter-Efficient-Learning (PEL) techniques to repurpose a General-Purpose-Speech (GSM) model for Arabic dialect identification (ADI). Specifically, we investigate different setups to incorporate trainable features into a multi-layer encoder-decoder GSM formulation under frozen pre-trained settings. Our architecture includes residual adapter and model reprogramming (input-prompting). We design a token-level label mapping to condition the GSM for Arabic Dialect Identification (ADI). This is challenging due to the high variation in vocabulary and pronunciation among the numerous regional dialects. We achieve new state-of-the-art accuracy on the ADI-17 dataset by vanilla fine-tuning. We further reduce the training budgets with the PEL method, which performs within 1.86% accuracy to fine-tuning using only 2.5% of (extra) network trainable parameters. Our study demonstrates how to identify Arabic dialects using a small dataset and limited computation with open source code and pre-trained models. | ['Jesper N. Tegner', 'David Gomez-Cabrero', 'Narsis A. Kiani', 'Sumeer Ahmad Khan', 'Chao-Han Huck Yang', 'Srijith Radhakrishnan'] | 2023-05-18 | null | null | null | null | ['dialect-identification'] | ['natural-language-processing'] | [ 9.48506445e-02 3.62636149e-02 1.83656409e-01 -8.55317235e-01
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e96b1fa0-7133-4792-9fc2-7023adea644f | training-free-synthesized-face-sketch | 1603.07823 | null | http://arxiv.org/abs/1603.07823v1 | http://arxiv.org/pdf/1603.07823v1.pdf | Training-Free Synthesized Face Sketch Recognition Using Image Quality Assessment Metrics | Face sketch synthesis has wide applications ranging from digital
entertainments to law enforcements. Objective image quality assessment scores
and face recognition accuracy are two mainly used tools to evaluate the
synthesis performance. In this paper, we proposed a synthesized face sketch
recognition framework based on full-reference image quality assessment metrics.
Synthesized sketches generated from four state-of-the-art methods are utilized
to test the performance of the proposed recognition framework. For the image
quality assessment metrics, we employed the classical structured similarity
index metric and other three prevalent metrics: visual information fidelity,
feature similarity index metric and gradient magnitude similarity deviation.
Extensive experiments compared with baseline methods illustrate the
effectiveness of the proposed synthesized face sketch recognition framework.
Data and implementation code in this paper are available online at
www.ihitworld.com/WNN/IQA_Sketch.zip. | ['Nannan Wang', 'Leiyu Sun', 'Jie Li', 'Xinbo Gao', 'Bin Song'] | 2016-03-25 | null | null | null | null | ['sketch-recognition', 'face-sketch-synthesis'] | ['computer-vision', 'computer-vision'] | [ 2.78374523e-01 -3.18973690e-01 -2.90777296e-01 -2.95931578e-01
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4701115c-849c-4b1e-b1e8-2172595dfd88 | learning-based-dimensionality-reduction-for | 2209.13586 | null | https://arxiv.org/abs/2209.13586v1 | https://arxiv.org/pdf/2209.13586v1.pdf | Learning-Based Dimensionality Reduction for Computing Compact and Effective Local Feature Descriptors | A distinctive representation of image patches in form of features is a key component of many computer vision and robotics tasks, such as image matching, image retrieval, and visual localization. State-of-the-art descriptors, from hand-crafted descriptors such as SIFT to learned ones such as HardNet, are usually high dimensional; 128 dimensions or even more. The higher the dimensionality, the larger the memory consumption and computational time for approaches using such descriptors. In this paper, we investigate multi-layer perceptrons (MLPs) to extract low-dimensional but high-quality descriptors. We thoroughly analyze our method in unsupervised, self-supervised, and supervised settings, and evaluate the dimensionality reduction results on four representative descriptors. We consider different applications, including visual localization, patch verification, image matching and retrieval. The experiments show that our lightweight MLPs achieve better dimensionality reduction than PCA. The lower-dimensional descriptors generated by our approach outperform the original higher-dimensional descriptors in downstream tasks, especially for the hand-crafted ones. The code will be available at https://github.com/PRBonn/descriptor-dr. | ['Cyrill Stachniss', 'Marc Pollefeys', 'Viktor Larsson', 'Mihai Dusmanu', 'Xieyuanli Chen', 'Hao Dong'] | 2022-09-27 | null | null | null | null | ['visual-localization'] | ['computer-vision'] | [-4.43226956e-02 -5.00634134e-01 -4.53104943e-01 -3.86231482e-01
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e5ecb44d-70a9-4777-bb79-42243a1a87f3 | tassy-a-text-annotation-survey-system | 2112.07391 | null | https://arxiv.org/abs/2112.07391v2 | https://arxiv.org/pdf/2112.07391v2.pdf | TASSY -- A Text Annotation Survey System | We present a free and open-source tool for creating web-based surveys that include text annotation tasks. Existing tools offer either text annotation or survey functionality but not both. Combining the two input types is particularly relevant for investigating a reader's perception of a text which also depends on the reader's background, such as age, gender, and education. Our tool caters primarily to the needs of researchers in the Library and Information Sciences, the Social Sciences, and the Humanities who apply Content Analysis to investigate, e.g., media bias, political communication, or fake news. | ['Bela Gipp', 'Norman Meuschke', 'Kanishka Sinha', 'Timo Spinde'] | 2021-12-14 | null | null | null | null | ['text-annotation'] | ['natural-language-processing'] | [ 2.23115236e-02 3.67289335e-01 -5.09818792e-01 -5.61503023e-02
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d737559b-2c80-4ba8-8a60-7c8811d191d3 | frames-a-corpus-for-adding-memory-to-goal | 1704.00057 | null | http://arxiv.org/abs/1704.00057v2 | http://arxiv.org/pdf/1704.00057v2.pdf | Frames: A Corpus for Adding Memory to Goal-Oriented Dialogue Systems | This paper presents the Frames dataset (Frames is available at
http://datasets.maluuba.com/Frames), a corpus of 1369 human-human dialogues
with an average of 15 turns per dialogue. We developed this dataset to study
the role of memory in goal-oriented dialogue systems. Based on Frames, we
introduce a task called frame tracking, which extends state tracking to a
setting where several states are tracked simultaneously. We propose a baseline
model for this task. We show that Frames can also be used to study memory in
dialogue management and information presentation through natural language
generation. | ['Jeremie Zumer', 'Shikhar Sharma', 'Rahul Mehrotra', 'Kaheer Suleman', 'Layla El Asri', 'Justin Harris', 'Hannes Schulz', 'Emery Fine'] | 2017-03-31 | frames-a-corpus-for-adding-memory-to-goal-1 | https://aclanthology.org/W17-5526 | https://aclanthology.org/W17-5526.pdf | ws-2017-8 | ['goal-oriented-dialogue-systems'] | ['natural-language-processing'] | [-4.85119671e-02 6.40074909e-01 -1.74174979e-01 -3.40107411e-01
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f04bdc6f-a5a8-4a38-a669-c39845f028c6 | cross-lingual-sentiment-classification-with | null | null | https://aclanthology.org/P16-1133 | https://aclanthology.org/P16-1133.pdf | Cross-Lingual Sentiment Classification with Bilingual Document Representation Learning | null | ['Xinjie Zhou', 'Xiaojun Wan', 'Jianguo Xiao'] | 2016-08-01 | null | null | null | acl-2016-8 | ['stock-market-prediction'] | ['time-series'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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cae3a043-d17d-4665-a091-3b2d8e1e66ea | coarse-to-fine-domain-adaptive-semantic | 2103.13041 | null | https://arxiv.org/abs/2103.13041v1 | https://arxiv.org/pdf/2103.13041v1.pdf | Coarse-to-Fine Domain Adaptive Semantic Segmentation with Photometric Alignment and Category-Center Regularization | Unsupervised domain adaptation (UDA) in semantic segmentation is a fundamental yet promising task relieving the need for laborious annotation works. However, the domain shifts/discrepancies problem in this task compromise the final segmentation performance. Based on our observation, the main causes of the domain shifts are differences in imaging conditions, called image-level domain shifts, and differences in object category configurations called category-level domain shifts. In this paper, we propose a novel UDA pipeline that unifies image-level alignment and category-level feature distribution regularization in a coarse-to-fine manner. Specifically, on the coarse side, we propose a photometric alignment module that aligns an image in the source domain with a reference image from the target domain using a set of image-level operators; on the fine side, we propose a category-oriented triplet loss that imposes a soft constraint to regularize category centers in the source domain and a self-supervised consistency regularization method in the target domain. Experimental results show that our proposed pipeline improves the generalization capability of the final segmentation model and significantly outperforms all previous state-of-the-arts. | ['Yizhou Yu', 'Zifeng Wu', 'Xiangru Lin', 'Haoyu Ma'] | 2021-03-24 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Ma_Coarse-To-Fine_Domain_Adaptive_Semantic_Segmentation_With_Photometric_Alignment_and_Category-Center_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Ma_Coarse-To-Fine_Domain_Adaptive_Semantic_Segmentation_With_Photometric_Alignment_and_Category-Center_CVPR_2021_paper.pdf | cvpr-2021-1 | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 3.82299632e-01 1.31863328e-02 -3.98554057e-02 -7.16881156e-01
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801b73f9-91fa-4498-b6bd-0891111aae70 | uctrl-unbiased-contrastive-representation | 2305.12768 | null | https://arxiv.org/abs/2305.12768v1 | https://arxiv.org/pdf/2305.12768v1.pdf | uCTRL: Unbiased Contrastive Representation Learning via Alignment and Uniformity for Collaborative Filtering | Because implicit user feedback for the collaborative filtering (CF) models is biased toward popular items, CF models tend to yield recommendation lists with popularity bias. Previous studies have utilized inverse propensity weighting (IPW) or causal inference to mitigate this problem. However, they solely employ pointwise or pairwise loss functions and neglect to adopt a contrastive loss function for learning meaningful user and item representations. In this paper, we propose Unbiased ConTrastive Representation Learning (uCTRL), optimizing alignment and uniformity functions derived from the InfoNCE loss function for CF models. Specifically, we formulate an unbiased alignment function used in uCTRL. We also devise a novel IPW estimation method that removes the bias of both users and items. Despite its simplicity, uCTRL equipped with existing CF models consistently outperforms state-of-the-art unbiased recommender models, up to 12.22% for Recall@20 and 16.33% for NDCG@20 gains, on four benchmark datasets. | ['Jongwuk Lee', 'Mincheol Yoon', 'Seongmin Park', 'Jae-woong Lee'] | 2023-05-22 | null | null | null | null | ['causal-inference', 'collaborative-filtering', 'causal-inference'] | ['knowledge-base', 'miscellaneous', 'miscellaneous'] | [ 7.81302229e-02 -1.04280010e-01 -7.40911484e-01 -5.87434471e-01
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9156ea15-489d-46e3-9ef4-9bc15cc1635a | piml-toolbox-for-interpretable-machine | 2305.04214 | null | https://arxiv.org/abs/2305.04214v2 | https://arxiv.org/pdf/2305.04214v2.pdf | PiML Toolbox for Interpretable Machine Learning Model Development and Validation | PiML (read $\pi$-ML, /`pai.`em.`el/) is an integrated and open-access Python toolbox for interpretable machine learning model development and model diagnostics. It is designed with machine learning workflows in both low-code and high-code modes, including data pipeline, model training, model interpretation and explanation, and model diagnostics and comparison. The toolbox supports a growing list of interpretable models (e.g. GAM, GAMI-Net, XGB2) with inherent local and/or global interpretability. It also supports model-agnostic explainability tools (e.g. PFI, PDP, LIME, SHAP) and a powerful suite of model-agnostic diagnostics (e.g. weakness, uncertainty, robustness, fairness). Integration of PiML models and tests to existing MLOps platforms for quality assurance are enabled by flexible high-code APIs. Furthermore, PiML toolbox comes with a comprehensive user guide and hands-on examples, including the applications for model development and validation in banking. The project is available at https://github.com/SelfExplainML/PiML-Toolbox. | ['Ningzhou Zeng', 'Yu Su', 'Zebin Yang', 'Aijun Zhang', 'Agus Sudjianto'] | 2023-05-07 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [-5.68366647e-01 5.04681885e-01 -3.22781712e-01 -6.27685130e-01
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6f5e8014-49fb-42de-bee4-68e36a775189 | exploring-state-change-capture-of | 2211.08728 | null | https://arxiv.org/abs/2211.08728v1 | https://arxiv.org/pdf/2211.08728v1.pdf | Exploring State Change Capture of Heterogeneous Backbones @ Ego4D Hands and Objects Challenge 2022 | Capturing the state changes of interacting objects is a key technology for understanding human-object interactions. This technical report describes our method using heterogeneous backbones for the Ego4D Object State Change Classification and PNR Temporal Localization Challenge. In the challenge, we used the heterogeneous video understanding backbones, namely CSN with 3D convolution as operator and VideoMAE with Transformer as operator. Our method achieves an accuracy of 0.796 on OSCC while achieving an absolute temporal localization error of 0.516 on PNR. These excellent results rank 1st on the leaderboard of Ego4D OSCC & PNR-TL Challenge 2022. | ['LiMin Wang', 'Tong Lu', 'Jiahao Wang', 'Guo Chen', 'Yin-Dong Zheng'] | 2022-11-16 | null | null | null | null | ['human-object-interaction-detection'] | ['computer-vision'] | [-5.82666159e-01 -5.09085476e-01 -1.86009273e-01 -9.65025797e-02
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eea87304-b23b-4275-96df-523c91305f88 | from-news-to-medical-cross-domain-discourse | 1904.06682 | null | http://arxiv.org/abs/1904.06682v1 | http://arxiv.org/pdf/1904.06682v1.pdf | From News to Medical: Cross-domain Discourse Segmentation | The first step in discourse analysis involves dividing a text into segments.
We annotate the first high-quality small-scale medical corpus in English with
discourse segments and analyze how well news-trained segmenters perform on this
domain. While we expectedly find a drop in performance, the nature of the
segmentation errors suggests some problems can be addressed earlier in the
pipeline, while others would require expanding the corpus to a trainable size
to learn the nuances of the medical domain. | ['Katrin Erk', 'Junyi Jessy Li', 'Titan Page', 'Elisa Ferracane'] | 2019-04-14 | from-news-to-medical-cross-domain-discourse-1 | https://aclanthology.org/W19-2704 | https://aclanthology.org/W19-2704.pdf | ws-2019-6 | ['discourse-segmentation'] | ['natural-language-processing'] | [ 5.55510998e-01 1.22487056e+00 -4.97727960e-01 -4.71189171e-01
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899303b3-6376-4f39-888e-7e35eddc1e06 | planning-as-theorem-proving-with-heuristics | 2303.13638 | null | https://arxiv.org/abs/2303.13638v3 | https://arxiv.org/pdf/2303.13638v3.pdf | Planning as Theorem Proving with Heuristics | Planning as theorem proving in situation calculus was abandoned 50 years ago as an impossible project. But we have developed a Theorem Proving Lifted Heuristic (TPLH) planner that searches for a plan in a tree of situations using the A* search algorithm. It is controlled by a delete relaxation-based domain independent heuristic. We compare TPLH with Fast Downward (FD) and Best First Width Search (BFWS) planners over several standard benchmarks. Since our implementation of the heuristic function is not optimized, TPLH is slower than FD and BFWS. But it computes shorter plans, and it explores fewer states. We discuss previous research on planning within KR\&R and identify related directions. Thus, we show that deductive lifted heuristic planning in situation calculus is actually doable. | ['Ryan Young', 'Mikhail Soutchanski'] | 2023-03-23 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 5.44433713e-01 1.20998085e+00 -5.07411778e-01 -1.52934998e-01
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06d8546b-afe3-4fb3-8a7e-b358e67856a7 | inducing-temporal-relations-from-time-anchor | null | null | https://aclanthology.org/N18-1166 | https://aclanthology.org/N18-1166.pdf | Inducing Temporal Relations from Time Anchor Annotation | Recognizing temporal relations among events and time expressions has been an essential but challenging task in natural language processing. Conventional annotation of judging temporal relations puts a heavy load on annotators. In reality, the existing annotated corpora include annotations on only {``}salient{''} event pairs, or on pairs in a fixed window of sentences. In this paper, we propose a new approach to obtain temporal relations from absolute time value (a.k.a. time anchors), which is suitable for texts containing rich temporal information such as news articles. We start from time anchors for events and time expressions, and temporal relation annotations are induced automatically by computing relative order of two time anchors. This proposal shows several advantages over the current methods for temporal relation annotation: it requires less annotation effort, can induce inter-sentence relations easily, and increases informativeness of temporal relations. We compare the empirical statistics and automatic recognition results with our data against a previous temporal relation corpus. We also reveal that our data contributes to a significant improvement of the downstream time anchor prediction task, demonstrating 14.1 point increase in overall accuracy. | ['Yusuke Miyao', 'Fei Cheng'] | 2018-06-01 | null | null | null | naacl-2018-6 | ['temporal-information-extraction'] | ['natural-language-processing'] | [ 3.00828218e-01 1.21269301e-01 -5.41419327e-01 -5.68835616e-01
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1f0af272-9e42-4d7d-be61-e10d73f59a7f | depth-image-upsampling-based-on-guided-filter | 1811.04620 | null | http://arxiv.org/abs/1811.04620v1 | http://arxiv.org/pdf/1811.04620v1.pdf | Depth Image Upsampling based on Guided Filter with Low Gradient Minimization | In this paper, we present a novel upsampling framework to enhance the spatial
resolution of the depth image. In our framework, the upscaling of a
low-resolution depth image is guided by a corresponding intensity images, we
formulate it as a cost aggregation problem with the guided filter. However, the
guided filter does not make full use of the properties of the depth image.
Since depth images have quite sparse gradients, it inspires us to regularize
the gradients for improving depth upscaling results. Statistics show a special
property of depth images, that is, there is a non-ignorable part of pixels
whose horizontal or vertical derivatives are equal to $\pm 1$. Considering this
special property, we propose a low gradient regularization method which reduces
the penalty for horizontal or vertical derivative $\pm1$. The proposed low
gradient regularization is integrated with the guided filter into the depth
image upsampling method. Experimental results demonstrate the effectiveness of
our proposed approach both qualitatively and quantitatively compared with the
state-of-the-art methods. | ['Hang Yang', 'Zhongbo Zhang'] | 2018-11-12 | null | null | null | null | ['depth-image-upsampling'] | ['computer-vision'] | [ 3.38114053e-01 4.31813523e-02 1.05895296e-01 -4.84272391e-01
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d285d449-45a0-434d-95d8-14f0ac1022e3 | you-don-t-only-look-once-constructing-spatial | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Sun_You_Dont_Only_Look_Once_Constructing_Spatial-Temporal_Memory_for_Integrated_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Sun_You_Dont_Only_Look_Once_Constructing_Spatial-Temporal_Memory_for_Integrated_ICCV_2021_paper.pdf | You Don't Only Look Once: Constructing Spatial-Temporal Memory for Integrated 3D Object Detection and Tracking | Humans are able to continuously detect and track surrounding objects by constructing a spatial-temporal memory of the objects when looking around. In contrast, 3D object detectors in existing tracking-by-detection systems often search for objects in every new video frame from scratch, without fully leveraging memory from previous detection results. In this work, we propose a novel system for integrated 3D object detection and tracking, which uses a dynamic object occupancy map and previous object states as spatial-temporal memory to assist object detection in future frames. This memory, together with the ego-motion from back-end odometry, guides the detector to achieve more efficient object proposal generation and more accurate object state estimation. The experiments demonstrate the effectiveness of the proposed system and its performance on the ScanNet and KITTI datasets. Moreover, the proposed system produces stable bounding boxes and pose trajectories over time, while being able to handle occluded and truncated objects. Code is available at the project page: https://zju3dv.github.io/UDOLO. | ['Xiaowei Zhou', 'Hujun Bao', 'Guofeng Zhang', 'Linghao Chen', 'Siyu Zhang', 'Yiming Xie', 'Jiaming Sun'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['object-proposal-generation'] | ['computer-vision'] | [-5.17366707e-01 -3.97773415e-01 -1.35098204e-01 -1.10785529e-01
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d9642014-e578-441c-a2c2-d77f2d3b3178 | multinews-a-web-collection-of-an-aligned | null | null | https://aclanthology.org/W17-5602 | https://aclanthology.org/W17-5602.pdf | MultiNews: A Web collection of an Aligned Multimodal and Multilingual Corpus | Integrating Natural Language Processing (NLP) and computer vision is a promising effort. However, the applicability of these methods directly depends on the availability of a specific multimodal data that includes images and texts. In this paper, we present a collection of a Multimodal corpus of comparable texts and their images in 9 languages from the web news articles of Euronews website. This corpus has found widespread use in the NLP community in Multilingual and multimodal tasks. Here, we focus on its acquisition of the images and text data and their multilingual alignment. | ['Pintu Lohar', 'Haithem Afli', 'Andy Way'] | 2017-11-01 | null | null | null | ws-2017-11 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [-6.55972213e-02 -1.96823865e-01 -2.06799492e-01 -2.20341325e-01
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e8126213-5cb3-48bf-a053-5c8e8b057937 | predicting-grokking-long-before-it-happens-a | 2306.13253 | null | https://arxiv.org/abs/2306.13253v1 | https://arxiv.org/pdf/2306.13253v1.pdf | Predicting Grokking Long Before it Happens: A look into the loss landscape of models which grok | This paper focuses on predicting the occurrence of grokking in neural networks, a phenomenon in which perfect generalization emerges long after signs of overfitting or memorization are observed. It has been reported that grokking can only be observed with certain hyper-parameters. This makes it critical to identify the parameters that lead to grokking. However, since grokking occurs after a large number of epochs, searching for the hyper-parameters that lead to it is time-consuming. In this paper, we propose a low-cost method to predict grokking without training for a large number of epochs. In essence, by studying the learning curve of the first few epochs, we show that one can predict whether grokking will occur later on. Specifically, if certain oscillations occur in the early epochs, one can expect grokking to occur if the model is trained for a much longer period of time. We propose using the spectral signature of a learning curve derived by applying the Fourier transform to quantify the amplitude of low-frequency components to detect the presence of such oscillations. We also present additional experiments aimed at explaining the cause of these oscillations and characterizing the loss landscape. | ['Guillaume Dumas', 'Irina Rish', 'Mohammad Pezeshki', 'Hattie Zhou', 'Pascal Jr. Tikeng Notsawo'] | 2023-06-23 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 3.45910341e-01 -7.04149008e-02 6.35494962e-02 6.35832474e-02
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c009b560-a277-4beb-bfe9-62536fbed194 | dynamical-graph-echo-state-networks-with | 2307.01237 | null | https://arxiv.org/abs/2307.01237v1 | https://arxiv.org/pdf/2307.01237v1.pdf | Dynamical Graph Echo State Networks with Snapshot Merging for Dissemination Process Classification | The Dissemination Process Classification (DPC) is a popular application of temporal graph classification. The aim of DPC is to classify different spreading patterns of information or pestilence within a community represented by discrete-time temporal graphs. Recently, a reservoir computing-based model named Dynamical Graph Echo State Network (DynGESN) has been proposed for processing temporal graphs with relatively high effectiveness and low computational costs. In this study, we propose a novel model which combines a novel data augmentation strategy called snapshot merging with the DynGESN for dealing with DPC tasks. In our model, the snapshot merging strategy is designed for forming new snapshots by merging neighboring snapshots over time, and then multiple reservoir encoders are set for capturing spatiotemporal features from merged snapshots. After those, the logistic regression is adopted for decoding the sum-pooled embeddings into the classification results. Experimental results on six benchmark DPC datasets show that our proposed model has better classification performances than the DynGESN and several kernel-based models. | ['Gouhei Tanaka', 'Kantaro Fujiwara', 'Ziqiang Li'] | 2023-07-03 | null | null | null | null | ['graph-classification', 'classification-1'] | ['graphs', 'methodology'] | [ 2.54136720e-03 -4.56047088e-01 -1.69458359e-01 1.19515426e-01
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71097244-38ec-479a-899c-39a358385f97 | facial-action-unit-detection-with | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Jacob_Facial_Action_Unit_Detection_With_Transformers_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Jacob_Facial_Action_Unit_Detection_With_Transformers_CVPR_2021_paper.pdf | Facial Action Unit Detection With Transformers | The Facial Action Coding System is a taxonomy for fine-grained facial expression analysis. This paper proposes a method for detecting Facial Action Units (FAU), which define particular face muscle activity, from an input image. FAU detection is formulated as a multi-task learning problem, where image features and attention maps are input to a branch for each action unit to extract discriminative feature embeddings, using a new loss function, the Center Contrastive (CC) loss. We employ a new FAU correlation network, based on a transformer encoder architecture, to capture the relationships between different action units for the wide range of expressions in the training data. The resulting features are shown to yield high classification performance. We validate our design choices, including the use of CC loss and Tversky loss functions, in ablative experiments. We show that the proposed method outperforms state-of-theart techniques on two public datasets, BP4D and DISFA, with an absolute improvement of the F1-score of over 2% on each. | ['Bjorn Stenger', 'Geethu Miriam Jacob'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 2.76445329e-01 -5.20766787e-02 -3.93795818e-01 -6.07134819e-01
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37a7746d-d4c3-4cb3-9c95-6225aecd4ad9 | a-robots-sense-making-of-fallacies-and | 1906.09689 | null | https://arxiv.org/abs/1906.09689v2 | https://arxiv.org/pdf/1906.09689v2.pdf | A robot's sense-making of fallacies and rhetorical tropes. Creating ontologies of what humans try to say | In the design of user-friendly robots, human communication should be understood by the system beyond mere logics and literal meaning. Robot communication-design has long ignored the importance of communication and politeness rules that are 'forgiving' and 'suspending disbelief' and cannot handle the basically metaphorical way humans design their utterances. Through analysis of the psychological causes of illogical and non-literal statements, signal detection, fundamental attribution errors, and anthropomorphism, we developed a fail-safe protocol for fallacies and tropes that makes use of Frege's distinction between reference and sense, Beth's tableau analytics, Grice's maxim of quality, and epistemic considerations to have the robot politely make sense of a user's sometimes unintelligible demands. Keywords: social robots, logical fallacies, metaphors, reference, sense, maxim of quality, tableau reasoning, epistemics of the virtual | ['Johan F. Hoorn', 'Denice J. Tuinhof'] | 2019-06-24 | null | null | null | null | ['logical-fallacies'] | ['miscellaneous'] | [-1.45798549e-01 1.09504151e+00 1.06489472e-01 -3.20657581e-01
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60268e05-e10f-4bff-90a6-f7a2601776c4 | video-understanding-based-on-human-action-and | 2010.12968 | null | https://arxiv.org/abs/2010.12968v2 | https://arxiv.org/pdf/2010.12968v2.pdf | Improved Actor Relation Graph based Group Activity Recognition | Video understanding is to recognize and classify different actions or activities appearing in the video. A lot of previous work, such as video captioning, has shown promising performance in producing general video understanding. However, it is still challenging to generate a fine-grained description of human actions and their interactions using state-of-the-art video captioning techniques. The detailed description of human actions and group activities is essential information, which can be used in real-time CCTV video surveillance, health care, sports video analysis, etc. This study proposes a video understanding method that mainly focused on group activity recognition by learning the pair-wise actor appearance similarity and actor positions. We propose to use Normalized cross-correlation (NCC) and the sum of absolute differences (SAD) to calculate the pair-wise appearance similarity and build the actor relationship graph to allow the graph convolution network to learn how to classify group activities. We also propose to use MobileNet as the backbone to extract features from each video frame. A visualization model is further introduced to visualize each input video frame with predicted bounding boxes on each human object and predict individual action and collective activity. | ['Xinran Tie', 'Zijian Kuang'] | 2020-10-24 | null | null | null | null | ['group-activity-recognition'] | ['computer-vision'] | [ 4.29436475e-01 -8.72932896e-02 -2.03216076e-01 -5.17354965e-01
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1b03646e-070e-49ae-b241-cc71d6e5f3e5 | grass-contrastive-learning-with-gradient | 2306.15868 | null | https://arxiv.org/abs/2306.15868v2 | https://arxiv.org/pdf/2306.15868v2.pdf | GraSS: Contrastive Learning with Gradient Guided Sampling Strategy for Remote Sensing Image Semantic Segmentation | Self-supervised contrastive learning (SSCL) has achieved significant milestones in remote sensing image (RSI) understanding. Its essence lies in designing an unsupervised instance discrimination pretext task to extract image features from a large number of unlabeled images that are beneficial for downstream tasks. However, existing instance discrimination based SSCL suffer from two limitations when applied to the RSI semantic segmentation task: 1) Positive sample confounding issue; 2) Feature adaptation bias. It introduces a feature adaptation bias when applied to semantic segmentation tasks that require pixel-level or object-level features. In this study, We observed that the discrimination information can be mapped to specific regions in RSI through the gradient of unsupervised contrastive loss, these specific regions tend to contain singular ground objects. Based on this, we propose contrastive learning with Gradient guided Sampling Strategy (GraSS) for RSI semantic segmentation. GraSS consists of two stages: Instance Discrimination warm-up (ID warm-up) and Gradient guided Sampling contrastive training (GS training). The ID warm-up aims to provide initial discrimination information to the contrastive loss gradients. The GS training stage aims to utilize the discrimination information contained in the contrastive loss gradients and adaptively select regions in RSI patches that contain more singular ground objects, in order to construct new positive and negative samples. Experimental results on three open datasets demonstrate that GraSS effectively enhances the performance of SSCL in high-resolution RSI semantic segmentation. Compared to seven baseline methods from five different types of SSCL, GraSS achieves an average improvement of 1.57\% and a maximum improvement of 3.58\% in terms of mean intersection over the union. The source code is available at https://github.com/GeoX-Lab/GraSS | ['Haifeng Li', 'Chengli Peng', 'Yunsheng Zhang', 'Chao Tao', 'Zhen Ren', 'Zhaoyang Zhang'] | 2023-06-28 | null | null | null | null | ['contrastive-learning', 'contrastive-learning'] | ['computer-vision', 'methodology'] | [ 7.64639676e-01 -7.16651157e-02 -2.18428627e-01 -4.93308753e-01
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51939067-89a4-45a8-9abc-68970d793de1 | ntua-islab-at-semeval-2019-task-9-mining | null | null | https://aclanthology.org/S19-2215 | https://aclanthology.org/S19-2215.pdf | NTUA-ISLab at SemEval-2019 Task 9: Mining Suggestions in the wild | As online customer forums and product comparison sites increase their societal influence, users are actively expressing their opinions and posting their recommendations on their fellow customers online. However, systems capable of recognizing suggestions still lack in stability. Suggestion Mining, a novel and challenging field of Natural Language Processing, is increasingly gaining attention, aiming to track user advice on online forums. In this paper, a carefully designed methodology to identify customer-to-company and customer-to-customer suggestions is presented. The methodology implements a rule-based classifier using heuristic, lexical and syntactic patterns. The approach ranked at 5th and 1st position, achieving an f1-score of 0.749 and 0.858 for SemEval-2019/Suggestion Mining sub-tasks A and B, respectively. In addition, we were able to improve performance results by combining the rule-based classifier with a recurrent convolutional neural network, that exhibits an f1-score of 0.79 for subtask A. | ['Georgios Siolas', 'ros', 'Rol Potamias', 'Alex Neofytou', 'os Alex'] | 2019-06-01 | null | null | null | semeval-2019-6 | ['suggestion-mining'] | ['natural-language-processing'] | [-4.89814952e-02 4.07040626e-01 -4.25996870e-01 -4.95767206e-01
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b8c70fe2-9350-4005-97cc-d5bde88a4d7c | on-manipulating-signals-of-user-item-graph-a | 2306.03624 | null | https://arxiv.org/abs/2306.03624v1 | https://arxiv.org/pdf/2306.03624v1.pdf | On Manipulating Signals of User-Item Graph: A Jacobi Polynomial-based Graph Collaborative Filtering | Collaborative filtering (CF) is an important research direction in recommender systems that aims to make recommendations given the information on user-item interactions. Graph CF has attracted more and more attention in recent years due to its effectiveness in leveraging high-order information in the user-item bipartite graph for better recommendations. Specifically, recent studies show the success of graph neural networks (GNN) for CF is attributed to its low-pass filtering effects. However, current researches lack a study of how different signal components contributes to recommendations, and how to design strategies to properly use them well. To this end, from the view of spectral transformation, we analyze the important factors that a graph filter should consider to achieve better performance. Based on the discoveries, we design JGCF, an efficient and effective method for CF based on Jacobi polynomial bases and frequency decomposition strategies. Extensive experiments on four widely used public datasets show the effectiveness and efficiency of the proposed methods, which brings at most 27.06% performance gain on Alibaba-iFashion. Besides, the experimental results also show that JGCF is better at handling sparse datasets, which shows potential in making recommendations for cold-start users. | ['Yan Zhang', 'Dongmei Zhang', 'Shi Han', 'Qiang Fu', 'Xiaojun Ma', 'Xu Chen', 'Lun Du', 'Jiayan Guo'] | 2023-06-06 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [-2.54503340e-01 -5.69013536e-01 -2.53541827e-01 -1.30014285e-01
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cbea1b4a-0201-415d-9294-d823b73bfd3c | augment-to-detect-anomalies-with-continuous | 2207.01112 | null | https://arxiv.org/abs/2207.01112v1 | https://arxiv.org/pdf/2207.01112v1.pdf | Augment to Detect Anomalies with Continuous Labelling | Anomaly detection is to recognize samples that differ in some respect from the training observations. These samples which do not conform to the distribution of normal data are called outliers or anomalies. In real-world anomaly detection problems, the outliers are absent, not well defined, or have a very limited number of instances. Recent state-of-the-art deep learning-based anomaly detection methods suffer from high computational cost, complexity, unstable training procedures, and non-trivial implementation, making them difficult to deploy in real-world applications. To combat this problem, we leverage a simple learning procedure that trains a lightweight convolutional neural network, reaching state-of-the-art performance in anomaly detection. In this paper, we propose to solve anomaly detection as a supervised regression problem. We label normal and anomalous data using two separable distributions of continuous values. To compensate for the unavailability of anomalous samples during training time, we utilize straightforward image augmentation techniques to create a distinct set of samples as anomalies. The distribution of the augmented set is similar but slightly deviated from the normal data, whereas real anomalies are expected to have an even further distribution. Therefore, training a regressor on these augmented samples will result in more separable distributions of labels for normal and real anomalous data points. Anomaly detection experiments on image and video datasets show the superiority of the proposed method over the state-of-the-art approaches. | ['Yalda Mohsenzadeh', 'Anthony Wong', 'Vahid Reza Khazaie'] | 2022-07-03 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 2.73087353e-01 -2.66012639e-01 1.10376455e-01 -4.69226122e-01
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5.75414360e-01 -2.69160289e-02 8.49398434e-01 -1.55214691e+00
1.06602997e-01 -4.45077628e-01 -1.07709193e+00 -5.84130764e-01
2.23377556e-01 -5.74574411e-01 1.64054826e-01 -1.00123274e+00
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1.31678963e+00 -4.88964200e-01 -2.06227526e-01 -3.56327266e-01
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9.65778053e-01 -1.51144695e+00 -6.13987207e-01 9.43877935e-01
6.01457596e-01 3.02556276e-01 2.45240271e-01 8.24719906e-01
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3.33705127e-01 -4.51916546e-01 -1.25243336e-01 -7.19391555e-02
7.25211918e-01 3.82316202e-01 1.22757055e-01 -6.12361610e-01
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1.12105119e+00 2.16368139e-01 -9.31630135e-02 5.09529293e-01
1.12346995e+00 -1.09938607e-01 -1.25472677e+00 -4.10506159e-01
3.19216579e-01 -8.38573873e-01 -3.11602235e-01 -3.16712201e-01
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1.31442487e+00 -2.46286497e-01 8.47491086e-01 4.71558213e-01
-1.23663493e-01 -1.08148384e+00 1.95670113e-01 5.73356867e-01
7.16133654e-01 -1.59259200e+00 -2.21896663e-01 -5.63043579e-02
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5.95424235e-01 1.19424211e-02 3.67900610e-01 -1.75592169e-01
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1.86029404e-01 -1.49891421e-01 1.52531844e-02 4.22636360e-01
1.59771234e-01 -4.44562197e-01 8.04531515e-01 1.10335851e+00
-4.17827338e-01 -7.31358767e-01 -6.39228582e-01 9.35844839e-01
-6.47940218e-01 1.36412084e-01 -1.60784930e-01 6.19130790e-01
9.25660506e-02 8.56365561e-01 6.32727563e-01 -2.80454338e-01
5.68788886e-01 2.98747092e-01 -1.65937692e-01 -4.69880253e-01
7.76403025e-03 -6.97343647e-02 -4.42418724e-01 -6.77768826e-01
5.95090576e-02 -6.22454822e-01 -1.54999125e+00 -1.74818292e-01
-2.11973339e-01 9.10003670e-04 4.17466402e-01 1.15191185e+00
3.08535159e-01 6.24917507e-01 8.56841922e-01 -6.32016540e-01
-6.66368306e-01 -8.31457019e-01 -7.64577746e-01 9.69799578e-01
9.00661647e-01 -6.15538478e-01 -6.77900910e-01 -1.86099842e-01] | [7.608783721923828, 2.3297250270843506] |
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