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d6ec3abc-3e06-4ca5-aab3-3d1bd5577b43 | lssvm-abc-algorithm-for-stock-price | 1402.6366 | null | http://arxiv.org/abs/1402.6366v1 | http://arxiv.org/pdf/1402.6366v1.pdf | LSSVM-ABC Algorithm for Stock Price prediction | In this paper, Artificial Bee Colony (ABC) algorithm which inspired from the
behavior of honey bees swarm is presented. ABC is a stochastic population-based
evolutionary algorithm for problem solving. ABC algorithm, which is considered
one of the most recently swarm intelligent techniques, is proposed to optimize
least square support vector machine (LSSVM) to predict the daily stock prices.
The proposed model is based on the study of stocks historical data, technical
indicators and optimizing LSSVM with ABC algorithm. ABC selects best free
parameters combination for LSSVM to avoid over-fitting and local minima
problems and improve prediction accuracy. LSSVM optimized by Particle swarm
optimization (PSO) algorithm, LSSVM, and ANN techniques are used for comparison
with proposed model. Proposed model tested with twenty datasets representing
different sectors in S&P 500 stock market. Results presented in this paper show
that the proposed model has fast convergence speed, and it also achieves better
accuracy than compared techniques in most cases. | ['Mustafa Abdul Salam', 'Omar S. Soliman', 'Osman Hegazy'] | 2014-02-25 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-3.99415076e-01 -8.36972356e-01 -6.39013201e-02 1.92932814e-01
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472eb1e2-4360-4c5b-9606-138cd983e5a6 | scg-net-self-constructing-graph-neural | 2009.01599 | null | https://arxiv.org/abs/2009.01599v2 | https://arxiv.org/pdf/2009.01599v2.pdf | SCG-Net: Self-Constructing Graph Neural Networks for Semantic Segmentation | Capturing global contextual representations by exploiting long-range pixel-pixel dependencies has shown to improve semantic segmentation performance. However, how to do this efficiently is an open question as current approaches of utilising attention schemes or very deep models to increase the models field of view, result in complex models with large memory consumption. Inspired by recent work on graph neural networks, we propose the Self-Constructing Graph (SCG) module that learns a long-range dependency graph directly from the image and uses it to propagate contextual information efficiently to improve semantic segmentation. The module is optimised via a novel adaptive diagonal enhancement method and a variational lower bound that consists of a customized graph reconstruction term and a Kullback-Leibler divergence regularization term. When incorporated into a neural network (SCG-Net), semantic segmentation is performed in an end-to-end manner and competitive performance (mean F1-scores of 92.0% and 89.8% respectively) on the publicly available ISPRS Potsdam and Vaihingen datasets is achieved, with much fewer parameters, and at a lower computational cost compared to related pure convolutional neural network (CNN) based models. | ['Arnt-Børre Salberg', 'Qinghui Liu', 'Michael Kampffmeyer', 'Robert Jenssen'] | 2020-09-03 | null | null | null | null | ['graph-reconstruction'] | ['graphs'] | [ 5.49756110e-01 5.02647936e-01 5.68919331e-02 -4.33706284e-01
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7fd5c728-5fd0-45c2-9ddc-610aea96f78e | a-nonparametric-stochastic-set-model | 2302.04354 | null | https://arxiv.org/abs/2302.04354v1 | https://arxiv.org/pdf/2302.04354v1.pdf | A Nonparametric Stochastic Set Model: Identification, Optimization, and Prediction | The identification of choice models is crucial for understanding consumer behavior and informing marketing or operational strategies, policy design, and product development. The identification of parametric choice-based demand models is typically straightforward. However, nonparametric models, which are highly effective and flexible in explaining customer choice, may encounter the challenge of the dimensionality curse, hindering their identification. A prominent example of a nonparametric model is the ranking-based model, which mirrors the random utility maximization (RUM) class and is known to be nonidentifiable from the collection of choice probabilities alone. Our objective in this paper is to develop a new class of nonparametric models that is not subject to the problem of nonidentifiability. Our model assumes bounded rationality of consumers, which results in symmetric demand cannibalization and intriguingly enables full identification. Additionally, our choice model demonstrates competitive prediction accuracy compared to the state-of-the-art benchmarks in a real-world case study, despite incorporating the assumption of bounded rationality which could, in theory, limit the representation power of our model. In addition, we tackle the important problem of finding the optimal assortment under the proposed choice model. We demonstrate the NP-hardness of this problem and provide a fully polynomial-time approximation scheme through dynamic programming. Additionally, we propose an efficient estimation framework using a combination of column generation and expectation-maximization algorithms, which proves to be more tractable than the estimation algorithm of the aforementioned ranking-based model. | ['Dmitry Mitrofanov', 'Yi-Chun Chen'] | 2023-02-08 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [ 2.34578639e-01 5.28443158e-02 -6.36586547e-01 -2.68733382e-01
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0ccdd5ad-a6da-474e-b680-a28acb60b1a8 | cltc-a-chinese-english-cross-lingual-topic | null | null | https://aclanthology.org/L12-1197 | https://aclanthology.org/L12-1197.pdf | CLTC: A Chinese-English Cross-lingual Topic Corpus | Cross-lingual topic detection within text is a feasible solution to resolving the language barrier in accessing the information. This paper presents a Chinese-English cross-lingual topic corpus (CLTC), in which 90,000 Chinese articles and 90,000 English articles are organized within 150 topics. Compared with TDT corpora, CLTC has three advantages. First, CLTC is bigger in size. This makes it possible to evaluate the large-scale cross-lingual text clustering methods. Second, articles are evenly distributed within the topics. Thus it can be used to produce test datasets for different purposes. Third, CLTC can be used as a cross-lingual comparable corpus to develop methods for cross-lingual information access. A preliminary evaluation with CLTC corpus indicates that the corpus is effective in evaluating cross-lingual topic detection methods. | ['Yunqing Xia', 'Xia Yang', 'Peng Jin', 'Guoyu Tang'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['text-clustering'] | ['natural-language-processing'] | [-7.20423043e-01 -1.83256298e-01 -6.24843836e-01 -6.50090203e-02
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9ce730cf-6c31-4992-bca0-44b922093fb4 | deeprhythm-exposing-deepfakes-with | 2006.07634 | null | https://arxiv.org/abs/2006.07634v2 | https://arxiv.org/pdf/2006.07634v2.pdf | DeepRhythm: Exposing DeepFakes with Attentional Visual Heartbeat Rhythms | As the GAN-based face image and video generation techniques, widely known as DeepFakes, have become more and more matured and realistic, there comes a pressing and urgent demand for effective DeepFakes detectors. Motivated by the fact that remote visual photoplethysmography (PPG) is made possible by monitoring the minuscule periodic changes of skin color due to blood pumping through the face, we conjecture that normal heartbeat rhythms found in the real face videos will be disrupted or even entirely broken in a DeepFake video, making it a potentially powerful indicator for DeepFake detection. In this work, we propose DeepRhythm, a DeepFake detection technique that exposes DeepFakes by monitoring the heartbeat rhythms. DeepRhythm utilizes dual-spatial-temporal attention to adapt to dynamically changing face and fake types. Extensive experiments on FaceForensics++ and DFDC-preview datasets have confirmed our conjecture and demonstrated not only the effectiveness, but also the generalization capability of \emph{DeepRhythm} over different datasets by various DeepFakes generation techniques and multifarious challenging degradations. | ['Xiaofei Xie', 'Felix Juefei-Xu', 'Lei Ma', 'Jianjun Zhao', 'Yang Liu', 'Qing Guo', 'Wei Feng', 'Hua Qi'] | 2020-06-13 | null | null | null | null | ['photoplethysmography-ppg'] | ['medical'] | [-1.25530958e-01 3.71968672e-02 2.18299970e-01 -1.82144195e-01
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84df9e3e-034c-40f7-be0c-9a9dcd5453f3 | multimodal-adaptive-fusion-of-face-and-gait | 2303.13814 | null | https://arxiv.org/abs/2303.13814v1 | https://arxiv.org/pdf/2303.13814v1.pdf | Multimodal Adaptive Fusion of Face and Gait Features using Keyless attention based Deep Neural Networks for Human Identification | Biometrics plays a significant role in vision-based surveillance applications. Soft biometrics such as gait is widely used with face in surveillance tasks like person recognition and re-identification. Nevertheless, in practical scenarios, classical fusion techniques respond poorly to changes in individual users and in the external environment. To this end, we propose a novel adaptive multi-biometric fusion strategy for the dynamic incorporation of gait and face biometric cues by leveraging keyless attention deep neural networks. Various external factors such as viewpoint and distance to the camera, are investigated in this study. Extensive experiments have shown superior performanceof the proposed model compared with the state-of-the-art model. | ['Alexandre Bernardino', 'Athira Nambiar', 'Thejaswin S', 'Ashwin Prakash'] | 2023-03-24 | null | null | null | null | ['person-recognition'] | ['computer-vision'] | [ 2.99969222e-02 -8.26249421e-01 7.02900738e-02 -2.88501769e-01
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576cc0b3-b76b-488f-ad63-08f4613eee35 | small-data-reduced-order-modeling-of-chaotic | 2305.08036 | null | https://arxiv.org/abs/2305.08036v1 | https://arxiv.org/pdf/2305.08036v1.pdf | Small-data Reduced Order Modeling of Chaotic Dynamics through SyCo-AE: Synthetically Constrained Autoencoders | Data-driven reduced order modeling of chaotic dynamics can result in systems that either dissipate or diverge catastrophically. Leveraging non-linear dimensionality reduction of autoencoders and the freedom of non-linear operator inference with neural-networks, we aim to solve this problem by imposing a synthetic constraint in the reduced order space. The synthetic constraint allows our reduced order model both the freedom to remain fully non-linear and highly unstable while preventing divergence. We illustrate the methodology with the classical 40-variable Lorenz '96 equations, showing that our methodology is capable of producing medium-to-long range forecasts with lower error using less data. | ['Renato Zanetti', 'Andrey A. Popov'] | 2023-05-14 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [-2.91635394e-01 1.00068703e-01 8.10183883e-01 1.34888262e-01
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fb5f7bda-cdd6-494d-927b-bc4448e647f6 | caricatureshop-personalized-and | 1807.09064 | null | http://arxiv.org/abs/1807.09064v1 | http://arxiv.org/pdf/1807.09064v1.pdf | CaricatureShop: Personalized and Photorealistic Caricature Sketching | In this paper, we propose the first sketching system for interactively
personalized and photorealistic face caricaturing. Input an image of a human
face, the users can create caricature photos by manipulating its facial feature
curves. Our system firstly performs exaggeration on the recovered 3D face model
according to the edited sketches, which is conducted by assigning the laplacian
of each vertex a scaling factor. To construct the mapping between 2D sketches
and a vertex-wise scaling field, a novel deep learning architecture is
developed. With the obtained 3D caricature model, two images are generated, one
obtained by applying 2D warping guided by the underlying 3D mesh deformation
and the other obtained by re-rendering the deformed 3D textured model. These
two images are then seamlessly integrated to produce our final output. Due to
the severely stretching of meshes, the rendered texture is of blurry
appearances. A deep learning approach is exploited to infer the missing details
for enhancing these blurry regions. Moreover, a relighting operation is
invented to further improve the photorealism of the result. Both quantitative
and qualitative experiment results validated the efficiency of our sketching
system and the superiority of our proposed techniques against existing methods. | ['Shuguang Cui', 'Kangcheng Hou', 'Yuda Qiu', 'Kun Zhou', 'Yizhou Yu', 'Xiaoguang Han', 'Dong Du'] | 2018-07-24 | null | null | null | null | ['caricature'] | ['computer-vision'] | [ 3.48747462e-01 2.49339864e-01 3.56972963e-01 -2.90345281e-01
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e33bf211-3a20-4a6f-80c2-57f1823dab70 | prin-sprin-on-extracting-point-wise-rotation | 2102.12093 | null | https://arxiv.org/abs/2102.12093v2 | https://arxiv.org/pdf/2102.12093v2.pdf | PRIN/SPRIN: On Extracting Point-wise Rotation Invariant Features | Point cloud analysis without pose priors is very challenging in real applications, as the orientations of point clouds are often unknown. In this paper, we propose a brand new point-set learning framework PRIN, namely, Point-wise Rotation Invariant Network, focusing on rotation invariant feature extraction in point clouds analysis. We construct spherical signals by Density Aware Adaptive Sampling to deal with distorted point distributions in spherical space. Spherical Voxel Convolution and Point Re-sampling are proposed to extract rotation invariant features for each point. In addition, we extend PRIN to a sparse version called SPRIN, which directly operates on sparse point clouds. Both PRIN and SPRIN can be applied to tasks ranging from object classification, part segmentation, to 3D feature matching and label alignment. Results show that, on the dataset with randomly rotated point clouds, SPRIN demonstrates better performance than state-of-the-art methods without any data augmentation. We also provide thorough theoretical proof and analysis for point-wise rotation invariance achieved by our methods. Our code is available on https://github.com/qq456cvb/SPRIN. | ['Cewu Lu', 'Weiming Wang', 'Lizhuang Ma', 'Yu-Wing Tai', 'Qi Liu', 'Ruoxi Shi', 'Yujing Lou', 'Yang You'] | 2021-02-24 | null | null | null | null | ['3d-feature-matching'] | ['computer-vision'] | [ 3.03721465e-02 -1.66746587e-01 -1.09346613e-01 -4.11573112e-01
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612b4530-4e1c-4341-889c-0f66f9da16cf | learning-controllable-3d-diffusion-models | 2304.06700 | null | https://arxiv.org/abs/2304.06700v1 | https://arxiv.org/pdf/2304.06700v1.pdf | Learning Controllable 3D Diffusion Models from Single-view Images | Diffusion models have recently become the de-facto approach for generative modeling in the 2D domain. However, extending diffusion models to 3D is challenging due to the difficulties in acquiring 3D ground truth data for training. On the other hand, 3D GANs that integrate implicit 3D representations into GANs have shown remarkable 3D-aware generation when trained only on single-view image datasets. However, 3D GANs do not provide straightforward ways to precisely control image synthesis. To address these challenges, We present Control3Diff, a 3D diffusion model that combines the strengths of diffusion models and 3D GANs for versatile, controllable 3D-aware image synthesis for single-view datasets. Control3Diff explicitly models the underlying latent distribution (optionally conditioned on external inputs), thus enabling direct control during the diffusion process. Moreover, our approach is general and applicable to any type of controlling input, allowing us to train it with the same diffusion objective without any auxiliary supervision. We validate the efficacy of Control3Diff on standard image generation benchmarks, including FFHQ, AFHQ, and ShapeNet, using various conditioning inputs such as images, sketches, and text prompts. Please see the project website (\url{https://jiataogu.me/control3diff}) for video comparisons. | ['Josh Susskind', 'Lingjie Liu', 'Baoquan Chen', 'Shuangfei Zhai', 'Qingzhe Gao', 'Jiatao Gu'] | 2023-04-13 | null | null | null | null | ['3d-aware-image-synthesis'] | ['computer-vision'] | [ 8.59982669e-02 2.52945006e-01 -3.03111374e-01 -7.96722546e-02
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ebfa4c80-0e9b-4f18-bc38-50e175593d6b | localization-of-just-noticeable-difference | 2306.07678 | null | https://arxiv.org/abs/2306.07678v1 | https://arxiv.org/pdf/2306.07678v1.pdf | Localization of Just Noticeable Difference for Image Compression | The just noticeable difference (JND) is the minimal difference between stimuli that can be detected by a person. The picture-wise just noticeable difference (PJND) for a given reference image and a compression algorithm represents the minimal level of compression that causes noticeable differences in the reconstruction. These differences can only be observed in some specific regions within the image, dubbed as JND-critical regions. Identifying these regions can improve the development of image compression algorithms. Due to the fact that visual perception varies among individuals, determining the PJND values and JND-critical regions for a target population of consumers requires subjective assessment experiments involving a sufficiently large number of observers. In this paper, we propose a novel framework for conducting such experiments using crowdsourcing. By applying this framework, we created a novel PJND dataset, KonJND++, consisting of 300 source images, compressed versions thereof under JPEG or BPG compression, and an average of 43 ratings of PJND and 129 self-reported locations of JND-critical regions for each source image. Our experiments demonstrate the effectiveness and reliability of our proposed framework, which is easy to be adapted for collecting a large-scale dataset. The source code and dataset are available at https://github.com/angchen-dev/LocJND. | ['Dietmar Saupe', 'Oliver Wiedemann', 'Hanhe Lin', 'Guangan Chen'] | 2023-06-13 | null | null | null | null | ['image-compression'] | ['computer-vision'] | [ 2.08628491e-01 -4.52035397e-01 -5.71665578e-02 -3.08912575e-01
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5a769a26-3efa-440e-9aff-cfb15b40420e | modelling-general-properties-of-nouns-by | 2012.07580 | null | https://arxiv.org/abs/2012.07580v2 | https://arxiv.org/pdf/2012.07580v2.pdf | Modelling General Properties of Nouns by Selectively Averaging Contextualised Embeddings | While the success of pre-trained language models has largely eliminated the need for high-quality static word vectors in many NLP applications, such vectors continue to play an important role in tasks where words need to be modelled in the absence of linguistic context. In this paper, we explore how the contextualised embeddings predicted by BERT can be used to produce high-quality word vectors for such domains, in particular related to knowledge base completion, where our focus is on capturing the semantic properties of nouns. We find that a simple strategy of averaging the contextualised embeddings of masked word mentions leads to vectors that outperform the static word vectors learned by BERT, as well as those from standard word embedding models, in property induction tasks. We notice in particular that masking target words is critical to achieve this strong performance, as the resulting vectors focus less on idiosyncratic properties and more on general semantic properties. Inspired by this view, we propose a filtering strategy which is aimed at removing the most idiosyncratic mention vectors, allowing us to obtain further performance gains in property induction. | ['Steven Schockaert', 'Qing Gu', 'Luis Espinosa-Anke', 'Jose Camacho Collados', 'Zied Bouraoui', 'Na Li'] | 2020-12-04 | null | null | null | null | ['knowledge-base-completion', 'knowledge-base-completion'] | ['graphs', 'knowledge-base'] | [ 1.45679370e-01 2.38235548e-01 -3.25841874e-01 -3.25768590e-01
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a4633a1d-20d3-4dfc-97ad-fe566fcd4ab7 | language-model-classifier-aligns-better-with | 2211.07047 | null | https://arxiv.org/abs/2211.07047v2 | https://arxiv.org/pdf/2211.07047v2.pdf | Language Model Classifier Aligns Better with Physician Word Sensitivity than XGBoost on Readmission Prediction | Traditional evaluation metrics for classification in natural language processing such as accuracy and area under the curve fail to differentiate between models with different predictive behaviors despite their similar performance metrics. We introduce sensitivity score, a metric that scrutinizes models' behaviors at the vocabulary level to provide insights into disparities in their decision-making logic. We assess the sensitivity score on a set of representative words in the test set using two classifiers trained for hospital readmission classification with similar performance statistics. Our experiments compare the decision-making logic of clinicians and classifiers based on rank correlations of sensitivity scores. The results indicate that the language model's sensitivity score aligns better with the professionals than the xgboost classifier on tf-idf embeddings, which suggests that xgboost uses some spurious features. Overall, this metric offers a novel perspective on assessing models' robustness by quantifying their discrepancy with professional opinions. Our code is available on GitHub (https://github.com/nyuolab/Model_Sensitivity). | ['David Kurland', 'Eric K. Oermann', 'Kyunghyun Cho', 'Hannah Weiss', 'Alexander T. M. Cheung', 'Xujin C. Liu', 'Lavender Y. Jiang', 'Ming Cao', 'Grace Yang'] | 2022-11-13 | null | null | null | null | ['readmission-prediction'] | ['medical'] | [-1.99346259e-01 2.16438636e-01 -8.21082175e-01 -6.57579482e-01
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8d9d2c4d-85a5-40a9-b663-f14bfe9aed28 | pyramid-transformer-for-traffic-sign | 2207.06067 | null | https://arxiv.org/abs/2207.06067v2 | https://arxiv.org/pdf/2207.06067v2.pdf | Pyramid Transformer for Traffic Sign Detection | Traffic sign detection is a vital task in the visual system of self-driving cars and the automated driving system. Recently, novel Transformer-based models have achieved encouraging results for various computer vision tasks. We still observed that vanilla ViT could not yield satisfactory results in traffic sign detection because the overall size of the datasets is very small and the class distribution of traffic signs is extremely unbalanced. To overcome this problem, a novel Pyramid Transformer with locality mechanisms is proposed in this paper. Specifically, Pyramid Transformer has several spatial pyramid reduction layers to shrink and embed the input image into tokens with rich multi-scale context by using atrous convolutions. Moreover, it inherits an intrinsic scale invariance inductive bias and is able to learn local feature representation for objects at various scales, thereby enhancing the network robustness against the size discrepancy of traffic signs. The experiments are conducted on the German Traffic Sign Detection Benchmark (GTSDB). The results demonstrate the superiority of the proposed model in the traffic sign detection tasks. More specifically, Pyramid Transformer achieves 77.8% mAP on GTSDB when applied to the Cascade RCNN as the backbone, which surpasses most well-known and widely-used state-of-the-art models. | ['Shahriar B. Shokouhi', 'Amin Boudesh', 'Omid Nejati Manzari'] | 2022-07-13 | null | null | null | null | ['traffic-sign-detection'] | ['computer-vision'] | [ 3.25526558e-02 -3.98564160e-01 -2.29299501e-01 -3.20646077e-01
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74083b15-d392-48a3-b69d-ba38ba770064 | r2-ad2-detecting-anomalies-by-analysing-the | 2206.10259 | null | https://arxiv.org/abs/2206.10259v1 | https://arxiv.org/pdf/2206.10259v1.pdf | R2-AD2: Detecting Anomalies by Analysing the Raw Gradient | Neural networks follow a gradient-based learning scheme, adapting their mapping parameters by back-propagating the output loss. Samples unlike the ones seen during training cause a different gradient distribution. Based on this intuition, we design a novel semi-supervised anomaly detection method called R2-AD2. By analysing the temporal distribution of the gradient over multiple training steps, we reliably detect point anomalies in strict semi-supervised settings. Instead of domain dependent features, we input the raw gradient caused by the sample under test to an end-to-end recurrent neural network architecture. R2-AD2 works in a purely data-driven way, thus is readily applicable in a variety of important use cases of anomaly detection. | ['Konstantin Böttinger', 'Carla Sagebiel', 'Ana Răduţoiu', 'Philip Sperl', 'Jan-Philipp Schulze'] | 2022-06-21 | null | null | null | null | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [ 9.51693654e-02 1.29799485e-01 2.10861400e-01 -6.06550455e-01
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7efa4f98-1b67-4172-87cc-92b4ff42674a | aspect-and-opinion-term-extraction-for-aspect | 1909.11879 | null | https://arxiv.org/abs/1909.11879v5 | https://arxiv.org/pdf/1909.11879v5.pdf | Aspect and Opinion Term Extraction for Hotel Reviews using Transfer Learning and Auxiliary Labels | Aspect and opinion term extraction is a critical step in Aspect-Based Sentiment Analysis (ABSA). Our study focuses on evaluating transfer learning using pre-trained BERT (Devlin et al., 2018) to classify tokens from hotel reviews in bahasa Indonesia. The primary challenge is the language informality of the review texts. By utilizing transfer learning from a multilingual model, we achieved up to 2% difference on token level F1-score compared to the state-of-the-art Bi-LSTM model with fewer training epochs (3 vs. 200 epochs). The fine-tuned model clearly outperforms the Bi-LSTM model on the entity level. Furthermore, we propose a method to include CRF with auxiliary labels as an output layer for the BERT-based models. The CRF addition further improves the F1-score for both token and entity level. | ['Yosef Ardhito Winatmoko', 'Arie Pratama Sutiono', 'Ali Akbar Septiandri'] | 2019-09-26 | null | null | null | null | ['extract-aspect'] | ['natural-language-processing'] | [-1.93839639e-01 2.92677522e-01 -1.53838336e-01 -5.82032800e-01
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b2383688-cc45-4efe-a45c-66822d139318 | document-embedding-enhanced-event-detection | null | null | https://aclanthology.org/P18-2066 | https://aclanthology.org/P18-2066.pdf | Document Embedding Enhanced Event Detection with Hierarchical and Supervised Attention | Document-level information is very important for event detection even at sentence level. In this paper, we propose a novel Document Embedding Enhanced Bi-RNN model, called DEEB-RNN, to detect events in sentences. This model first learns event detection oriented embeddings of documents through a hierarchical and supervised attention based RNN, which pays word-level attention to event triggers and sentence-level attention to those sentences containing events. It then uses the learned document embedding to enhance another bidirectional RNN model to identify event triggers and their types in sentences. Through experiments on the ACE-2005 dataset, we demonstrate the effectiveness and merits of the proposed DEEB-RNN model via comparison with state-of-the-art methods. | ['Xue-Qi Cheng', 'Yue Zhao', 'Xiaolong Jin', 'Yuanzhuo Wang'] | 2018-07-01 | null | null | null | acl-2018-7 | ['document-embedding'] | ['methodology'] | [ 2.54567340e-02 1.13986740e-02 -2.01948047e-01 -5.03284276e-01
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4cc3b92c-bc1e-4451-a3e1-16df5d341869 | well-defined-morphology-is-sentence-level | null | null | https://aclanthology.org/2021.mrl-1.23 | https://aclanthology.org/2021.mrl-1.23.pdf | Well-Defined Morphology is Sentence-Level Morphology | Morphological tasks have gained decent popularity within the NLP community in the recent years, with large multi-lingual datasets providing morphological analysis of words, either in or out of context. However, the lack of a clear linguistic definition for words destines the annotative work to be incomplete and mired in inconsistencies, especially cross-linguistically. In this work we expand morphological inflection of words to inflection of sentences to provide true universality disconnected from orthographic traditions of white-space usage. To allow annotation for sentence-inflection we define a morphological annotation scheme by a fixed set of inflectional features. We present a small cross-linguistic dataset including semi-manually generated simple sentences in 4 typologically diverse languages annotated according to our suggested scheme, and show that the task of reinflection gets substantially more difficult but that the change of scope from words to well-defined sentences allows interface with contextualized language models. | ['Reut Tsarfaty', 'Omer Goldman'] | null | null | null | null | emnlp-mrl-2021-11 | ['morphological-inflection'] | ['natural-language-processing'] | [ 1.38599887e-01 -1.35468051e-01 4.60142823e-04 -5.43800473e-01
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8171251b-a7a6-472b-bb93-833477c6b8ee | provable-inductive-matrix-completion | 1306.0626 | null | http://arxiv.org/abs/1306.0626v1 | http://arxiv.org/pdf/1306.0626v1.pdf | Provable Inductive Matrix Completion | Consider a movie recommendation system where apart from the ratings
information, side information such as user's age or movie's genre is also
available. Unlike standard matrix completion, in this setting one should be
able to predict inductively on new users/movies. In this paper, we study the
problem of inductive matrix completion in the exact recovery setting. That is,
we assume that the ratings matrix is generated by applying feature vectors to a
low-rank matrix and the goal is to recover back the underlying matrix.
Furthermore, we generalize the problem to that of low-rank matrix estimation
using rank-1 measurements. We study this generic problem and provide conditions
that the set of measurements should satisfy so that the alternating
minimization method (which otherwise is a non-convex method with no convergence
guarantees) is able to recover back the {\em exact} underlying low-rank matrix.
In addition to inductive matrix completion, we show that two other low-rank
estimation problems can be studied in our framework: a) general low-rank matrix
sensing using rank-1 measurements, and b) multi-label regression with missing
labels. For both the problems, we provide novel and interesting bounds on the
number of measurements required by alternating minimization to provably
converges to the {\em exact} low-rank matrix. In particular, our analysis for
the general low rank matrix sensing problem significantly improves the required
storage and computational cost than that required by the RIP-based matrix
sensing methods \cite{RechtFP2007}. Finally, we provide empirical validation of
our approach and demonstrate that alternating minimization is able to recover
the true matrix for the above mentioned problems using a small number of
measurements. | ['Inderjit S. Dhillon', 'Prateek Jain'] | 2013-06-04 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [ 6.38840199e-01 1.79716766e-01 -3.03877052e-03 -2.64775246e-01
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6db07e1f-398b-4af7-9c14-6ccef57bcd1e | reinforced-structured-state-evolution-for | 2204.09280 | null | https://arxiv.org/abs/2204.09280v2 | https://arxiv.org/pdf/2204.09280v2.pdf | Reinforced Structured State-Evolution for Vision-Language Navigation | Vision-and-language Navigation (VLN) task requires an embodied agent to navigate to a remote location following a natural language instruction. Previous methods usually adopt a sequence model (e.g., Transformer and LSTM) as the navigator. In such a paradigm, the sequence model predicts action at each step through a maintained navigation state, which is generally represented as a one-dimensional vector. However, the crucial navigation clues (i.e., object-level environment layout) for embodied navigation task is discarded since the maintained vector is essentially unstructured. In this paper, we propose a novel Structured state-Evolution (SEvol) model to effectively maintain the environment layout clues for VLN. Specifically, we utilise the graph-based feature to represent the navigation state instead of the vector-based state. Accordingly, we devise a Reinforced Layout clues Miner (RLM) to mine and detect the most crucial layout graph for long-term navigation via a customised reinforcement learning strategy. Moreover, the Structured Evolving Module (SEM) is proposed to maintain the structured graph-based state during navigation, where the state is gradually evolved to learn the object-level spatial-temporal relationship. The experiments on the R2R and R4R datasets show that the proposed SEvol model improves VLN models' performance by large margins, e.g., +3% absolute SPL accuracy for NvEM and +8% for EnvDrop on the R2R test set. | ['Si Liu', 'Qiong Zhang', 'Erli Meng', 'Chen Gao', 'Jinyu Chen'] | 2022-04-20 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Chen_Reinforced_Structured_State-Evolution_for_Vision-Language_Navigation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Chen_Reinforced_Structured_State-Evolution_for_Vision-Language_Navigation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['vision-language-navigation'] | ['computer-vision'] | [ 2.05427572e-01 -2.29538098e-01 1.63292706e-01 -1.47903323e-01
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77c2a711-568d-47d9-a113-4e8f96261dad | rethinking-rotated-object-detection-with | 2101.11952 | null | https://arxiv.org/abs/2101.11952v4 | https://arxiv.org/pdf/2101.11952v4.pdf | Rethinking Rotated Object Detection with Gaussian Wasserstein Distance Loss | Boundary discontinuity and its inconsistency to the final detection metric have been the bottleneck for rotating detection regression loss design. In this paper, we propose a novel regression loss based on Gaussian Wasserstein distance as a fundamental approach to solve the problem. Specifically, the rotated bounding box is converted to a 2-D Gaussian distribution, which enables to approximate the indifferentiable rotational IoU induced loss by the Gaussian Wasserstein distance (GWD) which can be learned efficiently by gradient back-propagation. GWD can still be informative for learning even there is no overlapping between two rotating bounding boxes which is often the case for small object detection. Thanks to its three unique properties, GWD can also elegantly solve the boundary discontinuity and square-like problem regardless how the bounding box is defined. Experiments on five datasets using different detectors show the effectiveness of our approach. Codes are available at https://github.com/yangxue0827/RotationDetection and https://github.com/open-mmlab/mmrotate. | ['Qi Tian', 'Xiaopeng Zhang', 'Wentao Wang', 'Qi Ming', 'Junchi Yan', 'Xue Yang'] | 2021-01-28 | null | null | null | null | ['object-detection-in-aerial-images', 'small-object-detection'] | ['computer-vision', 'computer-vision'] | [-5.21445274e-01 -6.75709322e-02 -1.37010723e-01 -1.75065011e-01
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a38c71b6-5e10-42c9-bd85-38117b860066 | look-before-you-match-instance-understanding | 2212.06826 | null | https://arxiv.org/abs/2212.06826v1 | https://arxiv.org/pdf/2212.06826v1.pdf | Look Before You Match: Instance Understanding Matters in Video Object Segmentation | Exploring dense matching between the current frame and past frames for long-range context modeling, memory-based methods have demonstrated impressive results in video object segmentation (VOS) recently. Nevertheless, due to the lack of instance understanding ability, the above approaches are oftentimes brittle to large appearance variations or viewpoint changes resulted from the movement of objects and cameras. In this paper, we argue that instance understanding matters in VOS, and integrating it with memory-based matching can enjoy the synergy, which is intuitively sensible from the definition of VOS task, \ie, identifying and segmenting object instances within the video. Towards this goal, we present a two-branch network for VOS, where the query-based instance segmentation (IS) branch delves into the instance details of the current frame and the VOS branch performs spatial-temporal matching with the memory bank. We employ the well-learned object queries from IS branch to inject instance-specific information into the query key, with which the instance-augmented matching is further performed. In addition, we introduce a multi-path fusion block to effectively combine the memory readout with multi-scale features from the instance segmentation decoder, which incorporates high-resolution instance-aware features to produce final segmentation results. Our method achieves state-of-the-art performance on DAVIS 2016/2017 val (92.6% and 87.1%), DAVIS 2017 test-dev (82.8%), and YouTube-VOS 2018/2019 val (86.3% and 86.3%), outperforming alternative methods by clear margins. | ['Yu-Gang Jiang', 'Lu Yuan', 'Yujia Xie', 'Yucheng Zhao', 'Xiyang Dai', 'Chuanxin Tang', 'Chong Luo', 'Zuxuan Wu', 'Dongdong Chen', 'Junke Wang'] | 2022-12-13 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_Look_Before_You_Match_Instance_Understanding_Matters_in_Video_Object_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_Look_Before_You_Match_Instance_Understanding_Matters_in_Video_Object_CVPR_2023_paper.pdf | cvpr-2023-1 | ['semi-supervised-video-object-segmentation', 'video-object-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.88530856e-01 -3.06675911e-01 -2.79055625e-01 -4.02006984e-01
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cf993682-3927-48ae-86ad-b40899e0f9a0 | audiovisual-highlight-detection-in-videos | 2102.05811 | null | https://arxiv.org/abs/2102.05811v1 | https://arxiv.org/pdf/2102.05811v1.pdf | Audiovisual Highlight Detection in Videos | In this paper, we test the hypothesis that interesting events in unstructured videos are inherently audiovisual. We combine deep image representations for object recognition and scene understanding with representations from an audiovisual affect recognition model. To this set, we include content agnostic audio-visual synchrony representations and mel-frequency cepstral coefficients to capture other intrinsic properties of audio. These features are used in a modular supervised model. We present results from two experiments: efficacy study of single features on the task, and an ablation study where we leave one feature out at a time. For the video summarization task, our results indicate that the visual features carry most information, and including audiovisual features improves over visual-only information. To better study the task of highlight detection, we run a pilot experiment with highlights annotations for a small subset of video clips and fine-tune our best model on it. Results indicate that we can transfer knowledge from the video summarization task to a model trained specifically for the task of highlight detection. | ['Shiva Sundaram', 'Aparna Khare', 'Alexandra Fenster', 'Karel Mundnich'] | 2021-02-11 | null | null | null | null | ['highlight-detection'] | ['computer-vision'] | [ 4.78555769e-01 8.19460675e-02 1.03807241e-01 -2.41101116e-01
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8c8277f1-5e62-4ddc-98a9-62c6f6aaec22 | indoor-lighting-estimation-using-an-event | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Chen_Indoor_Lighting_Estimation_Using_an_Event_Camera_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Chen_Indoor_Lighting_Estimation_Using_an_Event_Camera_CVPR_2021_paper.pdf | Indoor Lighting Estimation Using an Event Camera | Image-based methods for indoor lighting estimation suffer from the problem of intensity-distance ambiguity. This paper introduces a novel setup to help alleviate the ambiguity based on the event camera. We further demonstrate that estimating the distance of a light source becomes a well-posed problem under this setup, based on which an optimization-based method and a learning-based method are proposed. Our experimental results validate that our approaches not only achieve superior performance for indoor lighting estimation (especially for the close light) but also significantly alleviate the intensity-distance ambiguity. | ['Gang Pan', 'Huajin Tang', 'Peisong Niu', 'Qian Zheng', 'Zehao Chen'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['lighting-estimation'] | ['computer-vision'] | [ 1.99583277e-01 -7.18182325e-01 4.93086249e-01 -6.47516191e-01
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5256cafd-f44f-4190-a20a-cb8f94b576f1 | few-shot-fine-grained-entity-typing-with | 2206.13746 | null | https://arxiv.org/abs/2206.13746v1 | https://arxiv.org/pdf/2206.13746v1.pdf | Few-Shot Fine-Grained Entity Typing with Automatic Label Interpretation and Instance Generation | We study the problem of few-shot Fine-grained Entity Typing (FET), where only a few annotated entity mentions with contexts are given for each entity type. Recently, prompt-based tuning has demonstrated superior performance to standard fine-tuning in few-shot scenarios by formulating the entity type classification task as a ''fill-in-the-blank'' problem. This allows effective utilization of the strong language modeling capability of Pre-trained Language Models (PLMs). Despite the success of current prompt-based tuning approaches, two major challenges remain: (1) the verbalizer in prompts is either manually designed or constructed from external knowledge bases, without considering the target corpus and label hierarchy information, and (2) current approaches mainly utilize the representation power of PLMs, but have not explored their generation power acquired through extensive general-domain pre-training. In this work, we propose a novel framework for few-shot FET consisting of two modules: (1) an entity type label interpretation module automatically learns to relate type labels to the vocabulary by jointly leveraging few-shot instances and the label hierarchy, and (2) a type-based contextualized instance generator produces new instances based on given instances to enlarge the training set for better generalization. On three benchmark datasets, our model outperforms existing methods by significant margins. Code can be found at https://github.com/teapot123/Fine-Grained-Entity-Typing. | ['Jiawei Han', 'Yu Meng', 'Jiaxin Huang'] | 2022-06-28 | null | null | null | null | ['entity-typing'] | ['natural-language-processing'] | [ 1.72201991e-01 3.35735023e-01 -6.20280683e-01 -6.45313025e-01
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44369397-8f8c-4428-ba27-425151cd4443 | identity-aware-graph-neural-networks | 2101.10320 | null | https://arxiv.org/abs/2101.10320v2 | https://arxiv.org/pdf/2101.10320v2.pdf | Identity-aware Graph Neural Networks | Message passing Graph Neural Networks (GNNs) provide a powerful modeling framework for relational data. However, the expressive power of existing GNNs is upper-bounded by the 1-Weisfeiler-Lehman (1-WL) graph isomorphism test, which means GNNs that are not able to predict node clustering coefficients and shortest path distances, and cannot differentiate between different d-regular graphs. Here we develop a class of message passing GNNs, named Identity-aware Graph Neural Networks (ID-GNNs), with greater expressive power than the 1-WL test. ID-GNN offers a minimal but powerful solution to limitations of existing GNNs. ID-GNN extends existing GNN architectures by inductively considering nodes' identities during message passing. To embed a given node, ID-GNN first extracts the ego network centered at the node, then conducts rounds of heterogeneous message passing, where different sets of parameters are applied to the center node than to other surrounding nodes in the ego network. We further propose a simplified but faster version of ID-GNN that injects node identity information as augmented node features. Altogether, both versions of ID-GNN represent general extensions of message passing GNNs, where experiments show that transforming existing GNNs to ID-GNNs yields on average 40% accuracy improvement on challenging node, edge, and graph property prediction tasks; 3% accuracy improvement on node and graph classification benchmarks; and 15% ROC AUC improvement on real-world link prediction tasks. Additionally, ID-GNNs demonstrate improved or comparable performance over other task-specific graph networks. | ['Jure Leskovec', 'Rex Ying', 'Jonathan Gomes-Selman', 'Jiaxuan You'] | 2021-01-25 | null | null | null | null | ['graph-property-prediction'] | ['graphs'] | [ 8.46740752e-02 6.88515067e-01 -6.07535839e-01 -2.29848742e-01
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3311c29f-9190-40c7-80fa-7db9efbc028d | visual-analogies-between-atari-games-for | 1807.11074 | null | http://arxiv.org/abs/1807.11074v1 | http://arxiv.org/pdf/1807.11074v1.pdf | Visual Analogies between Atari Games for Studying Transfer Learning in RL | In this work, we ask the following question: Can visual analogies, learned in
an unsupervised way, be used in order to transfer knowledge between pairs of
games and even play one game using an agent trained for another game? We
attempt to answer this research question by creating visual analogies between a
pair of games: a source game and a target game. For example, given a video
frame in the target game, we map it to an analogous state in the source game
and then attempt to play using a trained policy learned for the source game. We
demonstrate convincing visual mapping between four pairs of games (eight
mappings), which are used to evaluate three transfer learning approaches. | ['Doron Sobol', 'Yaniv Taigman', 'Lior Wolf'] | 2018-07-29 | null | null | null | null | ['visual-analogies'] | ['computer-vision'] | [ 1.93960786e-01 3.30882192e-01 1.07864223e-01 8.46649706e-03
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608d2725-c600-44d3-a3a4-d8c3eaf63d46 | knn-learning-techniques-for-proportional | 2109.08917 | null | https://arxiv.org/abs/2109.08917v1 | https://arxiv.org/pdf/2109.08917v1.pdf | KNN Learning Techniques for Proportional Myocontrol in Prosthetics | This work has been conducted in the context of pattern-recognition-based control for electromyographic prostheses. It presents a k-nearest neighbour (kNN) classification technique for gesture recognition, extended by a proportionality scheme. The methods proposed are practically implemented and validated. Datasets are captured by means of a state-of-the-art 8-channel electromyography (EMG) armband positioned on the forearm. Based on this data, the influence of kNN's parameters is analyzed in pilot experiments. Moreover, the effect of proportionality scaling and rest thresholding schemes is investigated. A randomized, double-blind user study is conducted to compare the implemented method with the state-of-research algorithm Ridge Regression with Random Fourier Features (RR-RFF) for different levels of gesture exertion. The results from these experiments show a statistically significant improvement in favour of the kNN-based algorithm. | ['Davide Brunelli', 'Markus Nowak', 'Tim Sziburis'] | 2021-09-18 | null | null | null | null | ['electromyography-emg'] | ['medical'] | [ 5.47622263e-01 -2.45009080e-01 -5.01448214e-01 -3.29517499e-02
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00336ca0-99f0-4333-a7aa-f9fa27b58187 | scope-resolution-of-predicted-negation-cues-a | 2109.07264 | null | https://arxiv.org/abs/2109.07264v1 | https://arxiv.org/pdf/2109.07264v1.pdf | Scope resolution of predicted negation cues: A two-step neural network-based approach | Neural network-based methods are the state of the art in negation scope resolution. However, they often use the unrealistic assumption that cue information is completely accurate. Even if this assumption holds, there remains a dependency on engineered features from state-of-the-art machine learning methods. The current study adopted a two-step negation resolving apporach to assess whether a Bidirectional Long Short-Term Memory-based method can be used for cue detection as well, and how inaccurate cue predictions would affect the scope resolution performance. Results suggest that this method is not suitable for negation detection. Scope resolution performance is most robust against inaccurate information for models with a recurrent layer only, compared to extensions with a Conditional Random Fields layer or a post-processing algorithm. We advocate for more research into the application of deep learning on negation detection and the effect of imperfect information on scope resolution. | ['Daan de Jong'] | 2021-09-15 | null | null | null | null | ['negation-detection', 'negation-scope-resolution'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.04008651e-01 7.72499442e-02 -3.34761649e-01 -4.80306864e-01
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7ea1d070-9d2a-4ae1-a7b0-a8de5b07dd18 | joint-representation-learning-of-cross | 1811.10776 | null | http://arxiv.org/abs/1811.10776v1 | http://arxiv.org/pdf/1811.10776v1.pdf | Joint Representation Learning of Cross-lingual Words and Entities via Attentive Distant Supervision | Joint representation learning of words and entities benefits many NLP tasks,
but has not been well explored in cross-lingual settings. In this paper, we
propose a novel method for joint representation learning of cross-lingual words
and entities. It captures mutually complementary knowledge, and enables
cross-lingual inferences among knowledge bases and texts. Our method does not
require parallel corpora, and automatically generates comparable data via
distant supervision using multi-lingual knowledge bases. We utilize two types
of regularizers to align cross-lingual words and entities, and design knowledge
attention and cross-lingual attention to further reduce noises. We conducted a
series of experiments on three tasks: word translation, entity relatedness, and
cross-lingual entity linking. The results, both qualitatively and
quantitatively, demonstrate the significance of our method. | ['Tiansi Dong', 'Yixin Cao', 'Chengjiang Li', 'Zhiyuan Liu', 'Xu Chen', 'Lei Hou', 'Juanzi Li'] | 2018-11-27 | joint-representation-learning-of-cross-1 | https://aclanthology.org/D18-1021 | https://aclanthology.org/D18-1021.pdf | emnlp-2018-10 | ['cross-lingual-entity-linking'] | ['natural-language-processing'] | [-3.56881171e-01 1.71673708e-02 -6.87118232e-01 -4.11581933e-01
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5d1cb4e3-5ff3-450b-b7ce-262a8bcc8dd5 | far-gan-for-one-shot-face-reenactment | 2005.06402 | null | https://arxiv.org/abs/2005.06402v1 | https://arxiv.org/pdf/2005.06402v1.pdf | FaR-GAN for One-Shot Face Reenactment | Animating a static face image with target facial expressions and movements is important in the area of image editing and movie production. This face reenactment process is challenging due to the complex geometry and movement of human faces. Previous work usually requires a large set of images from the same person to model the appearance. In this paper, we present a one-shot face reenactment model, FaR-GAN, that takes only one face image of any given source identity and a target expression as input, and then produces a face image of the same source identity but with the target expression. The proposed method makes no assumptions about the source identity, facial expression, head pose, or even image background. We evaluate our method on the VoxCeleb1 dataset and show that our method is able to generate a higher quality face image than the compared methods. | ['Sriram Baireddy', 'Edward J. Delp', 'Amy R. Reibman', 'Hanxiang Hao'] | 2020-05-13 | null | null | null | null | ['face-reenactment'] | ['computer-vision'] | [ 4.07059640e-01 2.68880934e-01 3.83535355e-01 -5.81929743e-01
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fefd24c7-9a36-4ab6-a3f3-a78fbf34b5ea | generative-cooperative-learning-for | 2203.03962 | null | https://arxiv.org/abs/2203.03962v1 | https://arxiv.org/pdf/2203.03962v1.pdf | Generative Cooperative Learning for Unsupervised Video Anomaly Detection | Video anomaly detection is well investigated in weakly-supervised and one-class classification (OCC) settings. However, unsupervised video anomaly detection methods are quite sparse, likely because anomalies are less frequent in occurrence and usually not well-defined, which when coupled with the absence of ground truth supervision, could adversely affect the performance of the learning algorithms. This problem is challenging yet rewarding as it can completely eradicate the costs of obtaining laborious annotations and enable such systems to be deployed without human intervention. To this end, we propose a novel unsupervised Generative Cooperative Learning (GCL) approach for video anomaly detection that exploits the low frequency of anomalies towards building a cross-supervision between a generator and a discriminator. In essence, both networks get trained in a cooperative fashion, thereby allowing unsupervised learning. We conduct extensive experiments on two large-scale video anomaly detection datasets, UCF crime, and ShanghaiTech. Consistent improvement over the existing state-of-the-art unsupervised and OCC methods corroborate the effectiveness of our approach. | ['Seung-Ik Lee', 'Fisher Yu', 'Mattia Segu', 'Muhammad Haris Khan', 'Arif Mahmood', 'Muhammad Zaigham Zaheer'] | 2022-03-08 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zaheer_Generative_Cooperative_Learning_for_Unsupervised_Video_Anomaly_Detection_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zaheer_Generative_Cooperative_Learning_for_Unsupervised_Video_Anomaly_Detection_CVPR_2022_paper.pdf | cvpr-2022-1 | ['one-class-classification'] | ['miscellaneous'] | [ 2.82551527e-01 -9.86994058e-02 -1.70386434e-01 -2.69471258e-01
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0ffc8170-7bf8-4249-bd54-678f08baf3e6 | semi-supervised-anomaly-detection-based-on | 2208.08265 | null | https://arxiv.org/abs/2208.08265v1 | https://arxiv.org/pdf/2208.08265v1.pdf | Semi-Supervised Anomaly Detection Based on Quadratic Multiform Separation | In this paper we propose a novel method for semi-supervised anomaly detection (SSAD). Our classifier is named QMS22 as its inception was dated 2022 upon the framework of quadratic multiform separation (QMS), a recently introduced classification model. QMS22 tackles SSAD by solving a multi-class classification problem involving both the training set and the test set of the original problem. The classification problem intentionally includes classes with overlapping samples. One of the classes contains mixture of normal samples and outliers, and all other classes contain only normal samples. An outlier score is then calculated for every sample in the test set using the outcome of the classification problem. We also include performance evaluation of QMS22 against top performing classifiers using ninety-five benchmark imbalanced datasets from the KEEL repository. These classifiers are BRM (Bagging-Random Miner), OCKRA (One-Class K-means with Randomly-projected features Algorithm), ISOF (Isolation Forest), and ocSVM (One-Class Support Vector Machine). It is shown by using the area under the curve of the receiver operating characteristic curve as the performance measure, QMS22 significantly outperforms ISOF and ocSVM. Moreover, the Wilcoxon signed-rank tests reveal that there is no statistically significant difference when testing QMS22 against BRM nor QMS22 against OCKRA. | ['Kuang-Hsiao-Yin Kongguoluo', 'Chih-Chung Chang', 'Ko-Hui Michael Fan'] | 2022-08-17 | null | null | null | null | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [ 1.39707729e-01 -1.31378546e-01 8.09579119e-02 -2.92814553e-01
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becc263a-0826-4cf1-9077-006840109980 | personalized-and-occupational-aware-age | 1711.09368 | null | http://arxiv.org/abs/1711.09368v2 | http://arxiv.org/pdf/1711.09368v2.pdf | Personalized and Occupational-aware Age Progression by Generative Adversarial Networks | Face age progression, which aims to predict the future looks, is important
for various applications and has been received considerable attentions.
Existing methods and datasets are limited in exploring the effects of
occupations which may influence the personal appearances. In this paper, we
firstly introduce an occupational face aging dataset for studying the
influences of occupations on the appearances. It includes five occupations,
which enables the development of new algorithms for age progression and
facilitate future researches. Second, we propose a new occupational-aware
adversarial face aging network, which learns human aging process under
different occupations. Two factors are taken into consideration in our aging
process: personality-preserving and visually plausible texture change for
different occupations. We propose personalized network with personalized loss
in deep autoencoder network for keeping personalized facial characteristics,
and occupational-aware adversarial network with occupational-aware adversarial
loss for obtaining more realistic texture changes. Experimental results well
demonstrate the advantages of the proposed method by comparing with other
state-of-the-arts age progression methods. | ['Shuicheng Yan', 'Jian Yin', 'Yan Pan', 'Siyu Zhou', 'Jiashi Feng', 'Weiqiang Zhao', 'Hanjiang Lai'] | 2017-11-26 | null | null | null | null | ['human-aging'] | ['miscellaneous'] | [ 2.99520697e-02 -1.92361779e-03 1.12927355e-01 -5.77943921e-01
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dca4a6ca-4f2b-469e-8d05-42f0d6ccbfc3 | aqe-argument-quadruplet-extraction-via-a-quad | 2305.19902 | null | https://arxiv.org/abs/2305.19902v1 | https://arxiv.org/pdf/2305.19902v1.pdf | AQE: Argument Quadruplet Extraction via a Quad-Tagging Augmented Generative Approach | Argument mining involves multiple sub-tasks that automatically identify argumentative elements, such as claim detection, evidence extraction, stance classification, etc. However, each subtask alone is insufficient for a thorough understanding of the argumentative structure and reasoning process. To learn a complete view of an argument essay and capture the interdependence among argumentative components, we need to know what opinions people hold (i.e., claims), why those opinions are valid (i.e., supporting evidence), which source the evidence comes from (i.e., evidence type), and how those claims react to the debating topic (i.e., stance). In this work, we for the first time propose a challenging argument quadruplet extraction task (AQE), which can provide an all-in-one extraction of four argumentative components, i.e., claims, evidence, evidence types, and stances. To support this task, we construct a large-scale and challenging dataset. However, there is no existing method that can solve the argument quadruplet extraction. To fill this gap, we propose a novel quad-tagging augmented generative approach, which leverages a quadruplet tagging module to augment the training of the generative framework. The experimental results on our dataset demonstrate the empirical superiority of our proposed approach over several strong baselines. | ['Lidong Bing', 'Xin Li', 'Stanley Kok', 'Wenxuan Zhang', 'Liying Cheng', 'Jia Guo'] | 2023-05-31 | null | null | null | null | ['argument-mining'] | ['natural-language-processing'] | [ 9.38275456e-02 3.87926847e-01 -7.13949025e-01 -2.11599693e-01
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748154c2-a9c7-42a2-a165-a1dda0b7fc2a | deep3dpose-realtime-reconstruction-of | 2106.11536 | null | https://arxiv.org/abs/2106.11536v1 | https://arxiv.org/pdf/2106.11536v1.pdf | Deep3DPose: Realtime Reconstruction of Arbitrarily Posed Human Bodies from Single RGB Images | We introduce an approach that accurately reconstructs 3D human poses and detailed 3D full-body geometric models from single images in realtime. The key idea of our approach is a novel end-to-end multi-task deep learning framework that uses single images to predict five outputs simultaneously: foreground segmentation mask, 2D joints positions, semantic body partitions, 3D part orientations and uv coordinates (uv map). The multi-task network architecture not only generates more visual cues for reconstruction, but also makes each individual prediction more accurate. The CNN regressor is further combined with an optimization based algorithm for accurate kinematic pose reconstruction and full-body shape modeling. We show that the realtime reconstruction reaches accurate fitting that has not been seen before, especially for wild images. We demonstrate the results of our realtime 3D pose and human body reconstruction system on various challenging in-the-wild videos. We show the system advances the frontier of 3D human body and pose reconstruction from single images by quantitative evaluations and comparisons with state-of-the-art methods. | ['Jinxiang Chai', 'Xinguo Liu', 'Juntao Ye', 'Congyi Wang', 'Jianjie Zhang', 'Miaopeng Li', 'Liguo Jiang'] | 2021-06-22 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [-1.58524379e-01 2.60277599e-01 -5.19478098e-02 -3.15407932e-01
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cb83e887-1898-458e-8898-e4d11a687469 | japanese-beauty-marketing-on-social-media | null | null | https://aclanthology.org/2021.nlp4dh-1.15 | https://aclanthology.org/2021.nlp4dh-1.15.pdf | Japanese Beauty Marketing on Social Media: Critical Discourse Analysis Meets NLP | This project is a pilot study intending to combine traditional corpus linguistics, Natural Language Processing, critical discourse analysis, and digital humanities to gain an up-to-date understanding of how beauty is being marketed on social media, specifically Instagram, to followers. We use topic modeling combined with critical discourse analysis and NLP tools for insights into the “Japanese Beauty Myth” and show an overview of the dataset that we make publicly available. | ['Amy Gracy Metcalfe', 'Emily Öhman'] | null | null | null | null | nlp4dh-icon-2021-12 | ['marketing', 'date-understanding'] | ['miscellaneous', 'reasoning'] | [ 1.24705479e-01 3.55106562e-01 -3.61034125e-01 3.40194926e-02
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ebc96da1-f678-4780-a2a1-49a8d43e0cac | learning-a-model-of-facial-shape-and | null | null | http://flame.is.tue.mpg.de/ | https://ps.is.tuebingen.mpg.de/uploads_file/attachment/attachment/400/paper.pdf | Learning a model of facial shape and expression from 4D scans | he field of 3D face modeling has a large gap between high-end and low-end methods. At the high end, the best facial animation is indistinguishablefrom real humans, but this comes at the cost of extensive manual labor. At the low end, face capture from consumer depth sensors relies on 3D face models that are not expressive enough to capture the variability in natural facial shape and expression. We seek a middle ground by learning a facial model from thousands of accurately aligned 3D scans. Our FLAME model (Faces Learned with an Articulated Model and Expressions) is designed to work with existing graphics software and be easy to fit to data. FLAME uses a linear shape space trained from 3800 scans of human heads. FLAME combines this linear shape space with an articulated jaw, neck, and eyeballs, pose-dependent corrective blendshapes, and additional global expression blendshapes. The pose and expression dependent articulations are learned from 4D face sequences in the D3DFACS dataset along with additional 4D sequences. We accurately register a template mesh to the scan sequences and make the D3DFACS registrations available for research purposes. In total the model is trained from over 33, 000 scans. FLAME is low-dimensional but more expressive than the FaceWarehouse model and the Basel Face Model. We compare FLAME to these models by fitting them to static 3D scans and 4D sequences using the same optimization method. FLAME is significantly more accurate and is available for research purposes. | ['Timo Bolkart', 'Michael J. Black', 'Tianye Li', 'Javier Romero', 'Hao Li'] | 2017-11-01 | null | null | null | siggraph-asia-2017-11 | ['3d-face-animation', '3d-face-modeling'] | ['computer-vision', 'computer-vision'] | [-2.87299782e-01 3.27088296e-01 -9.32796970e-02 -7.95446634e-01
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fa43cd5d-6914-4144-924f-0f4447b708bc | balance-bayesian-linear-attribution-for-root | 2301.13572 | null | https://arxiv.org/abs/2301.13572v1 | https://arxiv.org/pdf/2301.13572v1.pdf | BALANCE: Bayesian Linear Attribution for Root Cause Localization | Root Cause Analysis (RCA) plays an indispensable role in distributed data system maintenance and operations, as it bridges the gap between fault detection and system recovery. Existing works mainly study multidimensional localization or graph-based root cause localization. This paper opens up the possibilities of exploiting the recently developed framework of explainable AI (XAI) for the purpose of RCA. In particular, we propose BALANCE (BAyesian Linear AttributioN for root CausE localization), which formulates the problem of RCA through the lens of attribution in XAI and seeks to explain the anomalies in the target KPIs by the behavior of the candidate root causes. BALANCE consists of three innovative components. First, we propose a Bayesian multicollinear feature selection (BMFS) model to predict the target KPIs given the candidate root causes in a forward manner while promoting sparsity and concurrently paying attention to the correlation between the candidate root causes. Second, we introduce attribution analysis to compute the attribution score for each candidate in a backward manner. Third, we merge the estimated root causes related to each KPI if there are multiple KPIs. We extensively evaluate the proposed BALANCE method on one synthesis dataset as well as three real-world RCA tasks, that is, bad SQL localization, container fault localization, and fault type diagnosis for Exathlon. Results show that BALANCE outperforms the state-of-the-art (SOTA) methods in terms of accuracy with the least amount of running time, and achieves at least $6\%$ notably higher accuracy than SOTA methods for real tasks. BALANCE has been deployed to production to tackle real-world RCA problems, and the online results further advocate its usage for real-time diagnosis in distributed data systems. | ['Wenhui Shi', 'Jianchao Wang', 'Silin Hu', 'Tingkai Zhang', 'Shaokang Ren', 'Jianguo Li', 'Zhichao Lei', 'Hang Yu', 'Chaoyu Chen'] | 2023-01-31 | null | null | null | null | ['fault-localization', 'fault-detection'] | ['computer-code', 'miscellaneous'] | [-1.67224184e-01 -3.05475265e-01 -1.56226099e-01 3.26003581e-02
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014edc11-e075-4cd8-8e6e-68ade24159e2 | deep-learning-for-blocking-in-entity-matching | null | null | https://dl.acm.org/doi/abs/10.14778/3476249.3476294 | https://dl.acm.org/doi/pdf/10.14778/3476249.3476294 | Deep learning for blocking in entity matching: a design space exploration | Entity matching (EM) finds data instances that refer to the same real-world entity. Most EM solutions perform blocking then matching. Many works have applied deep learning (DL) to matching, but far fewer works have applied DL to blocking. These blocking works are also limited in that they consider only a simple form of DL and some of them require labeled training data. In this paper, we develop the DeepBlocker framework that significantly advances the state of the art in applying DL to blocking for EM. We first define a large space of DL solutions for blocking, which contains solutions of varying complexity and subsumes most previous works. Next, we develop eight representative solutions in this space. These solutions do not require labeled training data and exploit recent advances in DL (e.g., sequence modeling, transformer, self supervision). We empirically determine which solutions perform best on what kind of datasets (structured, textual, or dirty). We show that the best solutions (among the above eight) outperform the best existing DL solution and the best existing non-DL solutions (including a state-of-the-art industrial non-DL solution), on dirty and textual data, and are comparable on structured data. Finally, we show that the combination of the best DL and non-DL solutions can perform even better, suggesting a new venue for research. | ['AnHai Doan', 'Glenn Fung', 'Derek Paulsen', 'Yash Govind', 'Mourad Ouzzani', 'Nan Tang', 'Han Li', 'Saravanan Thirumuruganathan'] | 2021-07-01 | null | null | null | proceedings-of-the-vldb-endowment-2021-7 | ['blocking'] | ['natural-language-processing'] | [-1.87763348e-02 -5.44450246e-02 -5.21486580e-01 -2.22146377e-01
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ad25ba7f-a624-4952-bfee-e20ed41f68ae | multi-label-prediction-via-sparse-infinite | null | null | http://papers.nips.cc/paper/3673-multi-label-prediction-via-sparse-infinite-cca | http://papers.nips.cc/paper/3673-multi-label-prediction-via-sparse-infinite-cca.pdf | Multi-Label Prediction via Sparse Infinite CCA | Canonical Correlation Analysis (CCA) is a useful technique for modeling dependencies between two (or more) sets of variables. Building upon the recently suggested probabilistic interpretation of CCA, we propose a nonparametric, fully Bayesian framework that can automatically select the number of correlation components, and effectively capture the sparsity underlying the projections. In addition, given (partially) labeled data, our algorithm can also be used as a (semi)supervised dimensionality reduction technique, and can be applied to learn useful predictive features in the context of learning a set of related tasks. Experimental results demonstrate the efficacy of the proposed approach for both CCA as a stand-alone problem, and when applied to multi-label prediction. | ['Hal Daume', 'Piyush Rai'] | 2009-12-01 | null | null | null | neurips-2009-12 | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [ 4.52521533e-01 -3.05280238e-01 -2.64517009e-01 -5.40731311e-01
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f001ec8f-b29b-4c1d-a10d-6b7a3b0cc33b | real-time-object-detection-system-with-yolo | 2208.00773 | null | https://arxiv.org/abs/2208.00773v1 | https://arxiv.org/pdf/2208.00773v1.pdf | Real Time Object Detection System with YOLO and CNN Models: A Review | The field of artificial intelligence is built on object detection techniques. YOU ONLY LOOK ONCE (YOLO) algorithm and it's more evolved versions are briefly described in this research survey. This survey is all about YOLO and convolution neural networks (CNN)in the direction of real time object detection.YOLO does generalized object representation more effectively without precision losses than other object detection models.CNN architecture models have the ability to eliminate highlights and identify objects in any given image. When implemented appropriately, CNN models can address issues like deformity diagnosis, creating educational or instructive application, etc. This article reached atnumber of observations and perspective findings through the analysis.Also it provides support for the focused visual information and feature extraction in the financial and other industries, highlights the method of target detection and feature selection, and briefly describe the development process of YOLO algorithm. | ['Ramachandra A. C.', 'Chandana R K', 'Viswanatha V'] | 2022-07-23 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [-2.13774011e-01 -1.45546511e-01 -1.06127128e-01 2.42569014e-01
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6fb07b15-5e37-499e-8998-ec8c6833f40f | benchmarking-of-image-registration-methods | null | null | https://ieeexplore.ieee.org/document/8451040 | https://www.researchgate.net/publication/325019076_Benchmarking_of_Image_Registration_Methods_for_Differently_Stained_Histological_Slides | Benchmarking of image registration methods for differently stained histological slides | Image registration is a common task for many biomedical analysis applications. The present work focuses on the benchmarking of registration methods on differently stained histological slides. This is a challenging task due to the differences in the appearance model, the repetitive texture of the details and the large image size, between other issues. Our benchmarking data is composed of 616 image pairs at two different scales — average image diagonal 2.4k and 5k pixels. We compare eleven fully automatic registration methods covering the widely used similarity measures. For each method, the best parameter configuration is found and subsequently applied to all the image pairs. The performance of the algorithms is evaluated from several perspectives — the registrations (in)accuracy on manually annotated landmarks, the method robustness and its computation time. | ['Jan Kybic', 'Arrate Muñoz-Barrutia', 'Jiří Borovec'] | 2018-10-11 | null | null | null | ieee-international-conference-on-image-1 | ['birl-cima'] | ['medical'] | [ 2.56302297e-01 -1.06391884e-01 1.68524027e-01 -1.83670014e-01
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f3c90cff-337f-48ed-b7c6-926c8dc80ac3 | deep-random-projector-accelerated-deep-image | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_Deep_Random_Projector_Accelerated_Deep_Image_Prior_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_Deep_Random_Projector_Accelerated_Deep_Image_Prior_CVPR_2023_paper.pdf | Deep Random Projector: Accelerated Deep Image Prior | Deep image prior (DIP) has shown great promise in tackling a variety of image restoration (IR) and general visual inverse problems, needing no training data. However, the resulting optimization process is often very slow, inevitably hindering DIP's practical usage for time-sensitive scenarios. In this paper, we focus on IR, and propose two crucial modifications to DIP that help achieve substantial speedup: 1) optimizing the DIP seed while freezing randomly-initialized network weights, and 2) reducing the network depth. In addition, we reintroduce explicit priors, such as sparse gradient prior---encoded by total-variation regularization, to preserve the DIP peak performance. We evaluate the proposed method on three IR tasks, including image denoising, image super-resolution, and image inpainting, against the original DIP and variants, as well as the competing metaDIP that uses meta-learning to learn good initializers with extra data. Our method is a clear winner in obtaining competitive restoration quality in a minimal amount of time. Our code is available at https://github.com/sun-umn/Deep-Random-Projector. | ['Ju Sun', 'Zhong Zhuang', 'Hengkang Wang', 'Taihui Li'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['image-super-resolution', 'image-inpainting'] | ['computer-vision', 'computer-vision'] | [ 4.33769166e-01 -1.32246241e-01 -4.74491203e-03 -1.81812257e-01
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7b3a4ab4-fe70-4b0d-8220-80d6dfb5755e | when-physics-meets-machine-learning-a-survey | 2203.16797 | null | https://arxiv.org/abs/2203.16797v1 | https://arxiv.org/pdf/2203.16797v1.pdf | When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learning | Physics-informed machine learning (PIML), referring to the combination of prior knowledge of physics, which is the high level abstraction of natural phenomenons and human behaviours in the long history, with data-driven machine learning models, has emerged as an effective way to mitigate the shortage of training data, to increase models' generalizability and to ensure the physical plausibility of results. In this paper, we survey an abundant number of recent works in PIML and summarize them from three aspects: (1) motivations of PIML, (2) physics knowledge in PIML, (3) methods of physics knowledge integration in PIML. We also discuss current challenges and corresponding research opportunities in PIML. | ['Yan Liu', 'Sam Griesemer', 'Defu Cao', 'Sungyong Seo', 'Chuizheng Meng'] | 2022-03-31 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [ 7.51344487e-02 1.20795749e-01 -5.74846387e-01 -1.80715084e-01
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9032c83a-6923-4be1-bd6a-7b7ba138f170 | a-survey-on-3d-aware-image-synthesis | 2210.14267 | null | https://arxiv.org/abs/2210.14267v2 | https://arxiv.org/pdf/2210.14267v2.pdf | A Survey on 3D-aware Image Synthesis | Recent years have seen remarkable progress in deep learning powered visual content creation. This includes 3D-aware generative image synthesis, which produces high-fidelity images in a 3D-consistent manner while simultaneously capturing compact surfaces of objects from pure image collections without the need for any 3D supervision, thus bridging the gap between 2D imagery and 3D reality. The 3D-aware generative models have shown that the introduction of 3D information can lead to more controllable image generation. The task of 3D-aware image synthesis has taken the field of computer vision by storm, with hundreds of papers accepted to top-tier journals and conferences in recent year (mainly the past two years), but there lacks a comprehensive survey of this remarkable and swift progress. Our survey aims to introduce new researchers to this topic, provide a useful reference for related works, and stimulate future research directions through our discussion section. Apart from the presented papers, we aim to constantly update the latest relevant papers along with corresponding implementations at https://weihaox.github.io/awesome-3D-aware-synthesis. | ['Jing-Hao Xue', 'Weihao Xia'] | 2022-10-25 | null | null | null | null | ['3d-aware-image-synthesis'] | ['computer-vision'] | [ 3.14082384e-01 1.91259176e-01 6.77655451e-03 -7.61580020e-02
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8f6f0353-f0c8-4da2-a8b5-14f98db9c856 | cost-effective-training-in-low-resource | null | null | https://openreview.net/forum?id=0n-fDxhaAHj | https://openreview.net/pdf?id=0n-fDxhaAHj | Cost-Effective Training in Low-Resource Neural Machine Translation | While Active Learning (AL) techniques are explored in Neural Machine Translation (NMT), only a few works focus on tackling low annotation budgets where a limited number of sentences can get translated. Such situations are especially challenging and can occur for endangered languages with few human annotators or having cost constraints to label large amounts of data. Although AL is shown to be helpful with large budgets, it is not enough to build high-quality translation systems in these low-resource conditions. In this work, we propose a cost-effective training procedure to increase the performance of NMT models utilizing a small number of annotated sentences and dictionary entries. Our method leverages monolingual data with self-supervised objectives and a small-scale, inexpensive dictionary for additional supervision to initialize the NMT model before applying AL. We show that improving the model using a combination of these knowledge sources is essential to exploit AL strategies and increase gains in low-resource conditions. We also present a novel AL strategy inspired by domain adaptation for NMT and show that it is effective for low budgets. We propose a new hybrid data-driven approach, which samples sentences that are diverse from the labelled data as well as similar to unlabelled data. Finally, we show that initializing the NMT model and further using our AL strategy can achieve gains of up to $13$ BLEU compared to conventional AL methods | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 4.07489210e-01 3.35830420e-01 -6.06079638e-01 -5.01661658e-01
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ff51f5d4-9da6-4b9b-81bd-5fb908836f9a | a-simple-and-strong-baseline-for-end-to-end | 2210.08355 | null | https://arxiv.org/abs/2210.08355v2 | https://arxiv.org/pdf/2210.08355v2.pdf | A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing | To promote and further develop RST-style discourse parsing models, we need a strong baseline that can be regarded as a reference for reporting reliable experimental results. This paper explores a strong baseline by integrating existing simple parsing strategies, top-down and bottom-up, with various transformer-based pre-trained language models. The experimental results obtained from two benchmark datasets demonstrate that the parsing performance strongly relies on the pretrained language models rather than the parsing strategies. In particular, the bottom-up parser achieves large performance gains compared to the current best parser when employing DeBERTa. We further reveal that language models with a span-masking scheme especially boost the parsing performance through our analysis within intra- and multi-sentential parsing, and nuclearity prediction. | ['Masaaki Nagata', 'Manabu Okumura', 'Hidetaka Kamigaito', 'Tsutomu Hirao', 'Naoki Kobayashi'] | 2022-10-15 | null | null | null | null | ['discourse-parsing'] | ['natural-language-processing'] | [ 1.67847499e-01 5.34291089e-01 -4.54123020e-01 -5.14707625e-01
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6cfef62e-2eb7-44b7-9d53-bb38c94053b2 | challenging-america-modeling-language-in | null | null | https://openreview.net/forum?id=sYkhFm2kkzj | https://openreview.net/pdf?id=sYkhFm2kkzj | Challenging America: Modeling language in longer time scales | The aim of the paper is to apply, for historical texts, the methodology used commonly to solve various NLP tasks defined for contemporary data, i.e. pre-train and fine-tune large Transformer models. This paper introduces an ML challenge, named Challenging America (ChallAm), based on OCR-ed excerpts from historical newspapers collected from the Chronicling America portal. ChallAm provides a dataset of clippings, labeled with metadata on their origin, and paired with their textual contents retrieved by an OCR tool. Three, publicly available, ML tasks are defined in the challenge: to determine the article date, to detect the location of the issue, and to deduce a word in a text gap (cloze test). Strong baselines are provided for all three ChallAm tasks. In particular, we pre-trained a RoBERTa model from scratch from the historical texts. We also discuss the issues of discrimination and hate-speech present in the historical American texts. | ['Anonymous'] | 2021-12-17 | null | null | null | acl-arr-december-2022-12 | ['cloze-test'] | ['natural-language-processing'] | [ 2.84375131e-01 4.31659818e-02 -2.58875966e-01 -1.61364600e-01
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7fe11364-af94-4904-8ba2-09530096715e | the-merlin-corpus-learner-language-and-the | null | null | https://aclanthology.org/L14-1488 | https://aclanthology.org/L14-1488.pdf | The MERLIN corpus: Learner language and the CEFR | The MERLIN corpus is a written learner corpus for Czech, German,and Italian that has been designed to illustrate the Common European Framework of Reference for Languages (CEFR) with authentic learner data. The corpus contains 2,290 learner texts produced in standardized language certifications covering CEFR levels A1-C1. The MERLIN annotation scheme includes a wide range of language characteristics that enable research into the empirical foundations of the CEFR scales and provide language teachers, test developers, and Second Language Acquisition researchers with concrete examples of learner performance and progress across multiple proficiency levels. For computational linguistics, it provide a range of authentic learner data for three target languages, supporting a broadening of the scope of research in areas such as automatic proficiency classification or native language identification. The annotated corpus and related information will be freely available as a corpus resource and through a freely accessible, didactically-oriented online platform. | ["Barbora {\\v{S}}tindlov{\\'a}", 'Karin Sch{\\"o}ne', 'Katrin Wisniewski', 'Jirka Hana', 'Detmar Meurers', 'Andrea Abel', 'Lionel Nicolas', 'Chiara Vettori', 'Adriane Boyd'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['native-language-identification'] | ['natural-language-processing'] | [-1.49804607e-01 9.23129469e-02 -6.82653666e-01 -1.20506369e-01
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d45902f9-780e-4c24-b0f8-9478e5065dbe | z-order-recurrent-neural-networks-for-video | null | null | https://ieeexplore.ieee.org/document/8784821 | https://ieeexplore.ieee.org/document/8784821 | Z-Order Recurrent Neural Networks for Video Prediction | We present a Z-Order RNN (Znet) for predicting future video frames given historical observations. There are two main contributions respectively in deterministic and stochastic modeling perspective. First, we propose a new RNN architecture for modeling the deterministic dynamics, which updates hidden states along a z-order curve to enhance the consistency of the features of mirrored layers. Second, we introduce an adversarial training approach to two-stream Znet for modeling the stochastic variations, which forces the Znet-Predictor to imitate the behavior of the Znet-Probe. This two-stream architecture enables the adversarial training to be conducted in the feature space instead of the image space. Our model achieves the state-of-the-art prediction accuracy on two video datasets. | ['Philip S Yu', 'Wang Jianmin', 'Mingsheng Long', 'Yunbo Wang', 'Jianjin Zhang'] | 2019-07-08 | null | null | null | ieee-international-conference-on-multimedia-1 | ['video-prediction'] | ['computer-vision'] | [ 7.25297555e-02 -9.84910131e-03 -5.63917458e-02 -3.37602794e-01
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18f42776-cbd6-4d47-8ae0-44a2e5a12683 | consistency-guided-prompt-learning-for-vision | 2306.01195 | null | https://arxiv.org/abs/2306.01195v1 | https://arxiv.org/pdf/2306.01195v1.pdf | Consistency-guided Prompt Learning for Vision-Language Models | We propose Consistency-guided Prompt learning (CoPrompt), a new fine-tuning method for vision-language models that addresses the challenge of improving the generalization capability of large foundation models while fine-tuning them on downstream tasks in a few-shot setting. The basic idea of CoPrompt is to enforce a consistency constraint in the prediction of the trainable and pre-trained models to prevent overfitting on the downstream task. Additionally, we introduce the following two components into our consistency constraint to further boost the performance: enforcing consistency on two perturbed inputs and combining two dominant paradigms of tuning, prompting and adapter. Enforcing consistency on perturbed input further regularizes the consistency constraint, effectively improving generalization, while tuning additional parameters with prompting and adapters improves the performance on downstream tasks. Extensive experiments show that CoPrompt outperforms existing methods on a range of evaluation suites, including base-to-novel generalization, domain generalization, and cross-dataset evaluation tasks. On the generalization task, CoPrompt improves the state-of-the-art by 2.09% on the zero-shot task and 1.93% on the harmonic mean over 11 recognition datasets. Detailed ablation studies show the effectiveness of each of the components in CoPrompt. | ['Ali Etemad', 'Shuvendu Roy'] | 2023-06-01 | null | null | null | null | ['domain-generalization'] | ['methodology'] | [ 4.50439677e-02 -2.75571257e-01 -3.68814051e-01 -6.01780593e-01
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68e3b93d-39e7-4da8-90cc-094ee58fab68 | plane-geometry-diagram-parsing | 2205.09363 | null | https://arxiv.org/abs/2205.09363v1 | https://arxiv.org/pdf/2205.09363v1.pdf | Plane Geometry Diagram Parsing | Geometry diagram parsing plays a key role in geometry problem solving, wherein the primitive extraction and relation parsing remain challenging due to the complex layout and between-primitive relationship. In this paper, we propose a powerful diagram parser based on deep learning and graph reasoning. Specifically, a modified instance segmentation method is proposed to extract geometric primitives, and the graph neural network (GNN) is leveraged to realize relation parsing and primitive classification incorporating geometric features and prior knowledge. All the modules are integrated into an end-to-end model called PGDPNet to perform all the sub-tasks simultaneously. In addition, we build a new large-scale geometry diagram dataset named PGDP5K with primitive level annotations. Experiments on PGDP5K and an existing dataset IMP-Geometry3K show that our model outperforms state-of-the-art methods in four sub-tasks remarkably. Our code, dataset and appendix material are available at https://github.com/mingliangzhang2018/PGDP. | ['Cheng-Lin Liu', 'Yi-Han Hao', 'Fei Yin', 'Ming-Liang Zhang'] | 2022-05-19 | null | null | null | null | ['scene-parsing', 'mathematical-question-answering'] | ['computer-vision', 'natural-language-processing'] | [-2.21297368e-01 3.08452100e-01 -2.60505900e-02 -4.11941081e-01
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b46d958e-29f5-4d82-a81c-059bc356d283 | quantifying-jump-height-using-markerless | 2302.10749 | null | https://arxiv.org/abs/2302.10749v2 | https://arxiv.org/pdf/2302.10749v2.pdf | Quantifying Jump Height Using Markerless Motion Capture with a Single Smartphone | Goal: The countermovement jump (CMJ) is commonly used to measure lower-body explosive power. This study evaluates how accurately markerless motion capture (MMC) with a single smartphone can measure bilateral and unilateral CMJ jump height. Methods: First, three repetitions each of bilateral and unilateral CMJ were performed by sixteen healthy adults (mean age: 30.87$\pm$7.24 years; mean BMI: 23.14$\pm$2.55 $kg/m^2$) on force plates and simultaneously captured using optical motion capture (OMC) and one smartphone camera. Next, MMC was performed on the smartphone videos using OpenPose. Then, we evaluated MMC in quantifying jump height using the force plate and OMC as ground truths. Results: MMC quantifies jump heights with ICC between 0.84 and 0.99 without manual segmentation and camera calibration. Conclusions: Our results suggest that using a single smartphone for markerless motion capture is promising. Index Terms - Countermovement jump, Markerless motion capture, Optical motion capture, Jump height. Impact Statement - Countermovement jump height can be accurately quantified using markerless motion capture with a single smartphone, with a simple setup that requires neither camera calibration nor manual segmentation. | ['Georgiana Ifrim', 'Brian Caulfield', 'Darragh Whelan', 'Hananeh Younesian', 'Timilehin B. Aderinola'] | 2023-02-21 | null | null | null | null | ['camera-calibration', 'markerless-motion-capture'] | ['computer-vision', 'computer-vision'] | [-1.91361103e-02 -2.67797291e-01 -6.83084607e-01 4.39476937e-01
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ca214ae5-fe7a-44b7-b1ae-5be576692333 | attentive-sequence-to-sequence-translation | 1808.08803 | null | http://arxiv.org/abs/1808.08803v1 | http://arxiv.org/pdf/1808.08803v1.pdf | Attentive Sequence to Sequence Translation for Localizing Clips of Interest by Natural Language Descriptions | We propose a novel attentive sequence to sequence translator (ASST) for clip
localization in videos by natural language descriptions. We make two
contributions. First, we propose a bi-directional Recurrent Neural Network
(RNN) with a finely calibrated vision-language attentive mechanism to
comprehensively understand the free-formed natural language descriptions. The
RNN parses natural language descriptions in two directions, and the attentive
model attends every meaningful word or phrase to each frame, thereby resulting
in a more detailed understanding of video content and description semantics.
Second, we design a hierarchical architecture for the network to jointly model
language descriptions and video content. Given a video-description pair, the
network generates a matrix representation, i.e., a sequence of vectors. Each
vector in the matrix represents a video frame conditioned by the description.
The 2D representation not only preserves the temporal dependencies of frames
but also provides an effective way to perform frame-level video-language
matching. The hierarchical architecture exploits video content with multiple
granularities, ranging from subtle details to global context. Integration of
the multiple granularities yields a robust representation for multi-level
video-language abstraction. We validate the effectiveness of our ASST on two
large-scale datasets. Our ASST outperforms the state-of-the-art by $4.28\%$ in
Rank$@1$ on the DiDeMo dataset. On the Charades-STA dataset, we significantly
improve the state-of-the-art by $13.41\%$ in Rank$@1,IoU=0.5$. | ['Linchao Zhu', 'Ke Ning', 'Yi Yang', 'Fei Wu', 'Di Xie', 'Ming Cai'] | 2018-08-27 | null | null | null | null | ['video-description'] | ['computer-vision'] | [ 3.96360047e-02 -4.26549107e-01 -3.67413789e-01 -3.86163205e-01
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8abd10f2-315b-48f0-a7ec-15584474bb66 | punctuation-restoration | 2202.09695 | null | https://arxiv.org/abs/2202.09695v2 | https://arxiv.org/pdf/2202.09695v2.pdf | SemEval 2022 Task 12: Symlink- Linking Mathematical Symbols to their Descriptions | Given the increasing number of livestreaming videos, automatic speech recognition and post-processing for livestreaming video transcripts are crucial for efficient data management as well as knowledge mining. A key step in this process is punctuation restoration which restores fundamental text structures such as phrase and sentence boundaries from the video transcripts. This work presents a new human-annotated corpus, called BehancePR, for punctuation restoration in livestreaming video transcripts. Our experiments on BehancePR demonstrate the challenges of punctuation restoration for this domain. Furthermore, we show that popular natural language processing toolkits are incapable of detecting sentence boundary on non-punctuated transcripts of livestreaming videos, calling for more research effort to develop robust models for this area. | ['Thien Huu Nguyen', 'Franck Dernoncourt', 'Amir Pouran Ben Veyseh', 'Viet Dac Lai'] | 2022-02-19 | null | null | null | null | ['punctuation-restoration'] | ['natural-language-processing'] | [ 5.94475269e-01 -1.26709357e-01 -1.51940599e-01 -3.62371951e-01
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bfb930a1-dbb4-46c6-bd84-b2a1db68eb78 | medical-vlbert-medical-visual-language-bert | 2108.05067 | null | https://arxiv.org/abs/2108.05067v2 | https://arxiv.org/pdf/2108.05067v2.pdf | Medical-VLBERT: Medical Visual Language BERT for COVID-19 CT Report Generation With Alternate Learning | Medical imaging technologies, including computed tomography (CT) or chest X-Ray (CXR), are largely employed to facilitate the diagnosis of the COVID-19. Since manual report writing is usually too time-consuming, a more intelligent auxiliary medical system that could generate medical reports automatically and immediately is urgently needed. In this article, we propose to use the medical visual language BERT (Medical-VLBERT) model to identify the abnormality on the COVID-19 scans and generate the medical report automatically based on the detected lesion regions. To produce more accurate medical reports and minimize the visual-and-linguistic differences, this model adopts an alternate learning strategy with two procedures that are knowledge pretraining and transferring. To be more precise, the knowledge pretraining procedure is to memorize the knowledge from medical texts, while the transferring procedure is to utilize the acquired knowledge for professional medical sentences generations through observations of medical images. In practice, for automatic medical report generation on the COVID-19 cases, we constructed a dataset of 368 medical findings in Chinese and 1104 chest CT scans from The First Affiliated Hospital of Jinan University, Guangzhou, China, and The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China. Besides, to alleviate the insufficiency of the COVID-19 training samples, our model was first trained on the large-scale Chinese CX-CHR dataset and then transferred to the COVID-19 CT dataset for further fine-tuning. The experimental results showed that Medical-VLBERT achieved state-of-the-art performances on terminology prediction and report generation with the Chinese COVID-19 CT dataset and the CX-CHR dataset. The Chinese COVID-19 CT dataset is available at https://covid19ct.github.io/. | ['Shuguang Cui', 'Shuixing Zhang', 'Zhen Li', 'Shaolin Li', 'Xiang Wan', 'Xiaodan Liang', 'Lu Zhang', 'Bin Zhang', 'Fuyu Wang', 'Yinghong Liao', 'Guangyi Liu'] | 2021-08-11 | null | null | null | null | ['medical-report-generation'] | ['medical'] | [ 2.67322570e-01 7.38302469e-02 -1.36759475e-01 -8.78352225e-02
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e05acde4-39d5-4d79-a8df-43cb01f988f4 | solving-rubiks-cube-with-a-robot-hand | 1910.07113 | null | https://arxiv.org/abs/1910.07113v1 | https://arxiv.org/pdf/1910.07113v1.pdf | Solving Rubik's Cube with a Robot Hand | We demonstrate that models trained only in simulation can be used to solve a manipulation problem of unprecedented complexity on a real robot. This is made possible by two key components: a novel algorithm, which we call automatic domain randomization (ADR) and a robot platform built for machine learning. ADR automatically generates a distribution over randomized environments of ever-increasing difficulty. Control policies and vision state estimators trained with ADR exhibit vastly improved sim2real transfer. For control policies, memory-augmented models trained on an ADR-generated distribution of environments show clear signs of emergent meta-learning at test time. The combination of ADR with our custom robot platform allows us to solve a Rubik's cube with a humanoid robot hand, which involves both control and state estimation problems. Videos summarizing our results are available: https://openai.com/blog/solving-rubiks-cube/ | ['Raphael Ribas', 'Matthias Plappert', 'Arthur Petron', 'Qiming Yuan', 'Maciek Chociej', 'Jerry Tworek', 'Glenn Powell', 'Mateusz Litwin', 'Lilian Weng', 'Alex Paino', 'OpenAI', 'Lei Zhang', 'Wojciech Zaremba', 'Peter Welinder', 'Nikolas Tezak', 'Marcin Andrychowicz', 'Jonas Schneider', 'Ilge Akkaya', 'Bob McGrew'] | 2019-10-16 | null | null | null | null | ['rubik-s-cube'] | ['graphs'] | [ 7.68872425e-02 1.97829887e-01 -2.17387988e-03 2.63498932e-01
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ab0a148d-ec3f-4e03-a31a-e14c305e1f81 | self-supervised-video-object-segmentation-by | 2104.07658 | null | https://arxiv.org/abs/2104.07658v2 | https://arxiv.org/pdf/2104.07658v2.pdf | Self-supervised Video Object Segmentation by Motion Grouping | Animals have evolved highly functional visual systems to understand motion, assisting perception even under complex environments. In this paper, we work towards developing a computer vision system able to segment objects by exploiting motion cues, i.e. motion segmentation. We make the following contributions: First, we introduce a simple variant of the Transformer to segment optical flow frames into primary objects and the background. Second, we train the architecture in a self-supervised manner, i.e. without using any manual annotations. Third, we analyze several critical components of our method and conduct thorough ablation studies to validate their necessity. Fourth, we evaluate the proposed architecture on public benchmarks (DAVIS2016, SegTrackv2, and FBMS59). Despite using only optical flow as input, our approach achieves superior or comparable results to previous state-of-the-art self-supervised methods, while being an order of magnitude faster. We additionally evaluate on a challenging camouflage dataset (MoCA), significantly outperforming the other self-supervised approaches, and comparing favourably to the top supervised approach, highlighting the importance of motion cues, and the potential bias towards visual appearance in existing video segmentation models. | ['Weidi Xie', 'Andrew Zisserman', 'Erika Lu', 'Hala Lamdouar', 'Charig Yang'] | 2021-04-15 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Yang_Self-Supervised_Video_Object_Segmentation_by_Motion_Grouping_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Yang_Self-Supervised_Video_Object_Segmentation_by_Motion_Grouping_ICCV_2021_paper.pdf | iccv-2021-1 | ['unsupervised-object-segmentation', 'motion-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.11214566e-01 -7.09577426e-02 -2.49118179e-01 -2.00589553e-01
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8e958a38-158e-4ecd-a326-c091350eb3d0 | the-severity-prediction-of-the-binary-and | 2203.04921 | null | https://arxiv.org/abs/2203.04921v1 | https://arxiv.org/pdf/2203.04921v1.pdf | The Severity Prediction of The Binary And Multi-Class Cardiovascular Disease -- A Machine Learning-Based Fusion Approach | In today's world, a massive amount of data is available in almost every sector. This data has become an asset as we can use this enormous amount of data to find information. Mainly health care industry contains many data consisting of patient and disease-related information. By using the machine learning technique, we can look for hidden data patterns to predict various diseases. Recently CVDs, or cardiovascular disease, have become a leading cause of death around the world. The number of death due to CVDs is frightening. That is why many researchers are trying their best to design a predictive model that can save many lives using the data mining model. In this research, some fusion models have been constructed to diagnose CVDs along with its severity. Machine learning(ML) algorithms like artificial neural network, SVM, logistic regression, decision tree, random forest, and AdaBoost have been applied to the heart disease dataset to predict disease. Randomoversampler was implemented because of the class imbalance in multiclass classification. To improve the performance of classification, a weighted score fusion approach was taken. At first, the models were trained. After training, two algorithms' decision was combined using a weighted sum rule. A total of three fusion models have been developed from the six ML algorithms. The results were promising in the performance parameter. The proposed approach has been experimented with different test training ratios for binary and multiclass classification problems, and for both of them, the fusion models performed well. The highest accuracy for multiclass classification was found as 75%, and it was 95% for binary. The code can be found in : https://github.com/hafsa-kibria/Weighted_score_fusion_model_heart_disease_prediction | ['Abdul Matin', 'Hafsa Binte Kibria'] | 2022-03-09 | null | null | null | null | ['severity-prediction'] | ['computer-vision'] | [-1.30609095e-01 -2.59674639e-01 -4.67926532e-01 -5.31465650e-01
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8e3274bc-7823-4294-812e-5d50804dc8b7 | real-time-monitoring-of-user-stress-heart | 2210.01791 | null | https://arxiv.org/abs/2210.01791v1 | https://arxiv.org/pdf/2210.01791v1.pdf | Real-Time Monitoring of User Stress, Heart Rate and Heart Rate Variability on Mobile Devices | Stress is considered to be the epidemic of the 21st-century. Yet, mobile apps cannot directly evaluate the impact of their content and services on user stress. We introduce the Beam AI SDK to address this issue. Using our SDK, apps can monitor user stress through the selfie camera in real-time. Our technology extracts the user's pulse wave by analyzing subtle color variations across the skin regions of the user's face. The user's pulse wave is then used to determine stress (according to the Baevsky Stress Index), heart rate, and heart rate variability. We evaluate our technology on the UBFC dataset, the MMSE-HR dataset, and Beam AI's internal data. Our technology achieves 99.2%, 97.8% and 98.5% accuracy for heart rate estimation on each benchmark respectively, a nearly twice lower error rate than competing methods. We further demonstrate an average Pearson correlation of 0.801 in determining stress and heart rate variability, thus producing commercially useful readings to derive content decisions in apps. Our SDK is available for use at www.beamhealth.ai. | ['Leonid Sigal', 'Peyman Bateni'] | 2022-10-04 | null | null | null | null | ['photoplethysmography-ppg-heart-rate', 'photoplethysmography-ppg', 'heart-rate-variability', 'heart-rate-estimation', 'photoplethysmography-ppg-beat-detection', 'mental-stress-detection'] | ['medical', 'medical', 'medical', 'medical', 'medical', 'robots'] | [ 4.66472693e-02 -1.11067012e-01 -3.97938579e-01 -3.88820022e-01
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2f870e62-6eca-4d2f-bce4-01404da581bc | 3d-talkemo-learning-to-synthesize-3d | 2104.12051 | null | https://arxiv.org/abs/2104.12051v1 | https://arxiv.org/pdf/2104.12051v1.pdf | 3D-TalkEmo: Learning to Synthesize 3D Emotional Talking Head | Impressive progress has been made in audio-driven 3D facial animation recently, but synthesizing 3D talking-head with rich emotion is still unsolved. This is due to the lack of 3D generative models and available 3D emotional dataset with synchronized audios. To address this, we introduce 3D-TalkEmo, a deep neural network that generates 3D talking head animation with various emotions. We also create a large 3D dataset with synchronized audios and videos, rich corpus, as well as various emotion states of different persons with the sophisticated 3D face reconstruction methods. In the emotion generation network, we propose a novel 3D face representation structure - geometry map by classical multi-dimensional scaling analysis. It maps the coordinates of vertices on a 3D face to a canonical image plane, while preserving the vertex-to-vertex geodesic distance metric in a least-square sense. This maintains the adjacency relationship of each vertex and holds the effective convolutional structure for the 3D facial surface. Taking a neutral 3D mesh and a speech signal as inputs, the 3D-TalkEmo is able to generate vivid facial animations. Moreover, it provides access to change the emotion state of the animated speaker. We present extensive quantitative and qualitative evaluation of our method, in addition to user studies, demonstrating the generated talking-heads of significantly higher quality compared to previous state-of-the-art methods. | ['Shihong Xia', 'Zhenfeng Fan', 'Qianyun Wang'] | 2021-04-25 | null | null | null | null | ['talking-head-generation', 'talking-face-generation', 'face-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-4.69170809e-01 3.18539321e-01 5.27277708e-01 -5.03252923e-01
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2b4679ad-9246-4f1a-ae63-f379aa7c267c | community-detection-in-multi-frequency-eeg | 2209.12779 | null | https://arxiv.org/abs/2209.12779v1 | https://arxiv.org/pdf/2209.12779v1.pdf | Community Detection in Multi-frequency EEG Networks | Objective: In recent years, the functional connectivity of the human brain has been studied with graph theoretical tools. One such approach is community detection which is fundamental for uncovering the localized networks. Existing methods focus on networks constructed from a single frequency band while ignoring multi-frequency nature of functional connectivity. Therefore, there is a need to study multi-frequency functional connectivity to be able to capture the full view of neuronal connectivity. Methods: In this paper, we use multilayer networks to model multi-frequency functional connectivity. In the proposed model, each layer corresponds to a different frequency band. We then extend the definition of modularity to multilayer networks to develop a new community detection algorithm. Results} The proposed approach is applied to electroencephalogram data collected during a study of error monitoring in the human brain. The differences between the community structures within and across different frequency bands for two response types, i.e. error and correct, are studied. Conclusion: The results indicate that following an error response, the brain organizes itself to form communities across frequencies, in particular between theta and gamma bands while a similar cross-frequency community formation is not observed for the correct response. Moreover, the community structures detected for the error response were more consistent across subjects compared to the community structures for correct response. Significance: The multi-frequency functional connectivity network models combined with multilayer community detection algorithms can reveal changes in cross-frequency functional connectivity network formation across different tasks and response types. | ['Selin Aviyente', 'Tamanna T. K. Munia', 'Meiby Ortiz-Bouza', 'Abdullah Karaaslanli'] | 2022-09-26 | null | null | null | null | ['community-detection'] | ['graphs'] | [ 1.58180043e-01 -1.34816706e-01 2.74476111e-01 4.53866869e-02
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42661d99-a10a-4e07-8e9c-60ff670081fc | efficient-decision-based-black-box-patch | 2303.11917 | null | https://arxiv.org/abs/2303.11917v1 | https://arxiv.org/pdf/2303.11917v1.pdf | Efficient Decision-based Black-box Patch Attacks on Video Recognition | Although Deep Neural Networks (DNNs) have demonstrated excellent performance, they are vulnerable to adversarial patches that introduce perceptible and localized perturbations to the input. Generating adversarial patches on images has received much attention, while adversarial patches on videos have not been well investigated. Further, decision-based attacks, where attackers only access the predicted hard labels by querying threat models, have not been well explored on video models either, even if they are practical in real-world video recognition scenes. The absence of such studies leads to a huge gap in the robustness assessment for video models. To bridge this gap, this work first explores decision-based patch attacks on video models. We analyze that the huge parameter space brought by videos and the minimal information returned by decision-based models both greatly increase the attack difficulty and query burden. To achieve a query-efficient attack, we propose a spatial-temporal differential evolution (STDE) framework. First, STDE introduces target videos as patch textures and only adds patches on keyframes that are adaptively selected by temporal difference. Second, STDE takes minimizing the patch area as the optimization objective and adopts spatialtemporal mutation and crossover to search for the global optimum without falling into the local optimum. Experiments show STDE has demonstrated state-of-the-art performance in terms of threat, efficiency and imperceptibility. Hence, STDE has the potential to be a powerful tool for evaluating the robustness of video recognition models. | ['Wenqiang Zhang', 'Yan Wang', 'Bo Li', 'Dingkang Yang', 'Jiafeng Wang', 'Tony Huang', 'Zhaoyu Chen', 'Kaixun Jiang'] | 2023-03-21 | null | null | null | null | ['video-recognition'] | ['computer-vision'] | [ 4.50020671e-01 -2.07401991e-01 -1.72801502e-03 1.78356156e-01
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033a87c7-e368-47e3-9cec-c66ad276daca | auxiliary-objectives-for-neural-error | 1707.05227 | null | http://arxiv.org/abs/1707.05227v1 | http://arxiv.org/pdf/1707.05227v1.pdf | Auxiliary Objectives for Neural Error Detection Models | We investigate the utility of different auxiliary objectives and training
strategies within a neural sequence labeling approach to error detection in
learner writing. Auxiliary costs provide the model with additional linguistic
information, allowing it to learn general-purpose compositional features that
can then be exploited for other objectives. Our experiments show that a joint
learning approach trained with parallel labels on in-domain data improves
performance over the previous best error detection system. While the resulting
model has the same number of parameters, the additional objectives allow it to
be optimised more efficiently and achieve better performance. | ['Marek Rei', 'Helen Yannakoudakis'] | 2017-07-17 | auxiliary-objectives-for-neural-error-1 | https://aclanthology.org/W17-5004 | https://aclanthology.org/W17-5004.pdf | ws-2017-9 | ['grammatical-error-detection'] | ['natural-language-processing'] | [ 4.72247303e-01 2.00139493e-01 -6.15298808e-01 -5.74696660e-01
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1223ac78-f18e-4a79-bebf-4e1dec0397c3 | a-theoretical-perspective-on-subnetwork | 2307.03803 | null | https://arxiv.org/abs/2307.03803v1 | https://arxiv.org/pdf/2307.03803v1.pdf | A Theoretical Perspective on Subnetwork Contributions to Adversarial Robustness | The robustness of deep neural networks (DNNs) against adversarial attacks has been studied extensively in hopes of both better understanding how deep learning models converge and in order to ensure the security of these models in safety-critical applications. Adversarial training is one approach to strengthening DNNs against adversarial attacks, and has been shown to offer a means for doing so at the cost of applying computationally expensive training methods to the entire model. To better understand these attacks and facilitate more efficient adversarial training, in this paper we develop a novel theoretical framework that investigates how the adversarial robustness of a subnetwork contributes to the robustness of the entire network. To do so we first introduce the concept of semirobustness, which is a measure of the adversarial robustness of a subnetwork. Building on this concept, we then provide a theoretical analysis to show that if a subnetwork is semirobust and there is a sufficient dependency between it and each subsequent layer in the network, then the remaining layers are also guaranteed to be robust. We validate these findings empirically across multiple DNN architectures, datasets, and adversarial attacks. Experiments show the ability of a robust subnetwork to promote full-network robustness, and investigate the layer-wise dependencies required for this full-network robustness to be achieved. | ['Salimeh Yasaei Sekeh', 'Theodore S. Nowak', 'Josh Andle', 'Jovon Craig'] | 2023-07-07 | null | null | null | null | ['adversarial-robustness'] | ['adversarial'] | [ 2.46178016e-01 2.96008706e-01 1.21213682e-01 -1.11347660e-01
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cbe317f1-0b30-456e-adc9-276028cc8619 | non-parametric-stochastic-policy-gradient | 2203.14905 | null | https://arxiv.org/abs/2203.14905v1 | https://arxiv.org/pdf/2203.14905v1.pdf | Non-Parametric Stochastic Policy Gradient with Strategic Retreat for Non-Stationary Environment | In modern robotics, effectively computing optimal control policies under dynamically varying environments poses substantial challenges to the off-the-shelf parametric policy gradient methods, such as the Deep Deterministic Policy Gradient (DDPG) and Twin Delayed Deep Deterministic policy gradient (TD3). In this paper, we propose a systematic methodology to dynamically learn a sequence of optimal control policies non-parametrically, while autonomously adapting with the constantly changing environment dynamics. Specifically, our non-parametric kernel-based methodology embeds a policy distribution as the features in a non-decreasing Euclidean space, therefore allowing its search space to be defined as a very high (possible infinite) dimensional RKHS (Reproducing Kernel Hilbert Space). Moreover, by leveraging the similarity metric computed in RKHS, we augmented our non-parametric learning with the technique of AdaptiveH- adaptively selecting a time-frame window of finishing the optimal part of whole action-sequence sampled on some preceding observed state. To validate our proposed approach, we conducted extensive experiments with multiple classic benchmarks and one simulated robotics benchmark equipped with dynamically changing environments. Overall, our methodology has outperformed the well-established DDPG and TD3 methodology by a sizeable margin in terms of learning performance. | ['Mingjie Lin', 'Apan Dastider'] | 2022-03-24 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [-1.14745840e-01 -1.39426664e-01 -1.48811519e-01 2.67831776e-02
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30285a8f-cbcf-4a2b-a721-c1727f49a0a2 | fast-adaptive-federated-bilevel-optimization | 2211.01122 | null | https://arxiv.org/abs/2211.01122v3 | https://arxiv.org/pdf/2211.01122v3.pdf | Fast Adaptive Federated Bilevel Optimization | Bilevel optimization is a popular hierarchical model in machine learning, and has been widely applied to many machine learning tasks such as meta learning, hyperparameter learning and policy optimization. Although many bilevel optimization algorithms recently have been developed, few adaptive algorithm focuses on the bilevel optimization under the distributed setting. It is well known that the adaptive gradient methods show superior performances on both distributed and non-distributed optimization. In the paper, thus, we propose a novel adaptive federated bilevel optimization algorithm (i.e.,AdaFBiO) to solve the distributed bilevel optimization problems, where the objective function of Upper-Level (UL) problem is possibly nonconvex, and that of Lower-Level (LL) problem is strongly convex. Specifically, our AdaFBiO algorithm builds on the momentum-based variance reduced technique and local-SGD to obtain the best known sample and communication complexities simultaneously. In particular, our AdaFBiO algorithm uses the unified adaptive matrices to flexibly incorporate various adaptive learning rates to update variables in both UL and LL problems. Moreover, we provide a convergence analysis framework for our AdaFBiO algorithm, and prove it needs the sample complexity of $\tilde{O}(\epsilon^{-3})$ with communication complexity of $\tilde{O}(\epsilon^{-2})$ to obtain an $\epsilon$-stationary point. Experimental results on federated hyper-representation learning and federated data hyper-cleaning tasks verify efficiency of our algorithm. | ['Feihu Huang'] | 2022-11-02 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-4.69772488e-01 -1.39365077e-01 -3.45276594e-01 -9.23493430e-02
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4fa445c6-9993-4526-85a4-b9bae66c1c50 | cyclegan-based-unpaired-speech | 2203.15652 | null | https://arxiv.org/abs/2203.15652v1 | https://arxiv.org/pdf/2203.15652v1.pdf | CycleGAN-Based Unpaired Speech Dereverberation | Typically, neural network-based speech dereverberation models are trained on paired data, composed of a dry utterance and its corresponding reverberant utterance. The main limitation of this approach is that such models can only be trained on large amounts of data and a variety of room impulse responses when the data is synthetically reverberated, since acquiring real paired data is costly. In this paper we propose a CycleGAN-based approach that enables dereverberation models to be trained on unpaired data. We quantify the impact of using unpaired data by comparing the proposed unpaired model to a paired model with the same architecture and trained on the paired version of the same dataset. We show that the performance of the unpaired model is comparable to the performance of the paired model on two different datasets, according to objective evaluation metrics. Furthermore, we run two subjective evaluations and show that both models achieve comparable subjective quality on the AMI dataset, which was not seen during training. | ['John R. Hershey', 'Scott Wisdom', 'Marco Tagliasacchi', 'Felix de Chaumont Quitry', 'Hakan Erdogan', 'Aleksandr Safin', 'Hannah Muckenhirn'] | 2022-03-29 | null | null | null | null | ['speech-dereverberation'] | ['speech'] | [ 3.05308551e-01 1.98372975e-01 6.40171885e-01 -2.26654366e-01
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f81f46f3-d14f-4050-99c6-aee4efda3392 | first-order-ambisonics-domain-spatial | 1910.04388 | null | https://arxiv.org/abs/1910.04388v1 | https://arxiv.org/pdf/1910.04388v1.pdf | First Order Ambisonics Domain Spatial Augmentation for DNN-based Direction of Arrival Estimation | In this paper, we propose a novel data augmentation method for training neural networks for Direction of Arrival (DOA) estimation. This method focuses on expanding the representation of the DOA subspace of a dataset. Given some input data, it applies a transformation to it in order to change its DOA information and simulate new potentially unseen one. Such transformation, in general, is a combination of a rotation and a reflection. It is possible to apply such transformation due to a well-known property of First Order Ambisonics (FOA). The same transformation is applied also to the labels, in order to maintain consistency between input data and target labels. Three methods with different level of generality are proposed for applying this augmentation principle. Experiments are conducted on two different DOA networks. Results of both experiments demonstrate the effectiveness of the novel augmentation strategy by improving the DOA error by around 40%. | ['Luca Mazzon', 'Masahiro Yasuda', 'Yuma Koizumi', 'Noboru Harada'] | 2019-10-10 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [ 2.39877507e-01 -1.87169343e-01 2.33391508e-01 -3.40154111e-01
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03d344d8-bfcd-43be-8eba-66faa4ec3508 | density-ratio-estimation-via-infinitesimal | 2111.11010 | null | https://arxiv.org/abs/2111.11010v2 | https://arxiv.org/pdf/2111.11010v2.pdf | Density Ratio Estimation via Infinitesimal Classification | Density ratio estimation (DRE) is a fundamental machine learning technique for comparing two probability distributions. However, existing methods struggle in high-dimensional settings, as it is difficult to accurately compare probability distributions based on finite samples. In this work we propose DRE-\infty, a divide-and-conquer approach to reduce DRE to a series of easier subproblems. Inspired by Monte Carlo methods, we smoothly interpolate between the two distributions via an infinite continuum of intermediate bridge distributions. We then estimate the instantaneous rate of change of the bridge distributions indexed by time (the "time score") -- a quantity defined analogously to data (Stein) scores -- with a novel time score matching objective. Crucially, the learned time scores can then be integrated to compute the desired density ratio. In addition, we show that traditional (Stein) scores can be used to obtain integration paths that connect regions of high density in both distributions, improving performance in practice. Empirically, we demonstrate that our approach performs well on downstream tasks such as mutual information estimation and energy-based modeling on complex, high-dimensional datasets. | ['Stefano Ermon', 'Yang song', 'Chenlin Meng', 'Kristy Choi'] | 2021-11-22 | null | null | null | null | ['density-ratio-estimation', 'mutual-information-estimation'] | ['methodology', 'methodology'] | [-4.03674729e-02 -2.60977477e-01 -2.54831493e-01 -2.32562527e-01
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93c754d0-7337-4071-8d45-8f466cc5342b | enhancing-knowledge-graph-embedding-models | 2303.00286 | null | https://arxiv.org/abs/2303.00286v2 | https://arxiv.org/pdf/2303.00286v2.pdf | Enhancing Knowledge Graph Embedding Models with Semantic-driven Loss Functions | Knowledge graph embedding models (KGEMs) are used for various tasks related to knowledge graphs (KGs), including link prediction. They are trained with loss functions that are computed considering a batch of scored triples and their corresponding labels. Traditional approaches consider the label of a triple to be either true or false. However, recent works suggest that all negative triples should not be valued equally. In line with this recent assumption, we posit that semantically valid negative triples might be high-quality negative triples. As such, loss functions should treat them differently from semantically invalid negative ones. To this aim, we propose semantic-driven versions for the three main loss functions for link prediction. In particular, we treat the scores of negative triples differently by injecting background knowledge about relation domains and ranges into the loss functions. In an extensive and controlled experimental setting, we show that the proposed loss functions systematically provide satisfying results on three public benchmark KGs underpinned with different schemas, which demonstrates both the generality and superiority of our proposed approach. In fact, the proposed loss functions do (1) lead to better MRR and Hits@$10$ values, (2) drive KGEMs towards better semantic awareness. This highlights that semantic information globally improves KGEMs, and thus should be incorporated into loss functions. Domains and ranges of relations being largely available in schema-defined KGs, this makes our approach both beneficial and widely usable in practice. | ['Davy Monticolo', 'Armelle Brun', 'Pierre Monnin', 'Nicolas Hubert'] | 2023-03-01 | null | null | null | null | ['knowledge-graph-embedding'] | ['graphs'] | [ 2.42576692e-02 5.50129235e-01 -6.75240397e-01 -4.69173163e-01
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606ba05e-59d3-4f45-a1d3-c577be80f88a | study-of-keyword-extraction-techniques-for | 2111.07068 | null | https://arxiv.org/abs/2111.07068v1 | https://arxiv.org/pdf/2111.07068v1.pdf | Study of keyword extraction techniques for Electric Double Layer Capacitor domain using text similarity indexes: An experimental analysis | Keywords perform a significant role in selecting various topic-related documents quite easily. Topics or keywords assigned by humans or experts provide accurate information. However, this practice is quite expensive in terms of resources and time management. Hence, it is more satisfying to utilize automated keyword extraction techniques. Nevertheless, before beginning the automated process, it is necessary to check and confirm how similar expert-provided and algorithm-generated keywords are. This paper presents an experimental analysis of similarity scores of keywords generated by different supervised and unsupervised automated keyword extraction algorithms with expert provided keywords from the Electric Double Layer Capacitor (EDLC) domain. The paper also analyses which texts provide better keywords like positive sentences or all sentences of the document. From the unsupervised algorithms, YAKE, TopicRank, MultipartiteRank, and KPMiner are employed for keyword extraction. From the supervised algorithms, KEA and WINGNUS are employed for keyword extraction. To assess the similarity of the extracted keywords with expert-provided keywords, Jaccard, Cosine, and Cosine with word vector similarity indexes are employed in this study. The experiment shows that the MultipartiteRank keyword extraction technique measured with cosine with word vector similarity index produces the best result with 92% similarity with expert provided keywords. This study can help the NLP researchers working with the EDLC domain or recommender systems to select more suitable keyword extraction and similarity index calculation techniques. | ['Rajan Jose', 'Kamal Z. Zamli', 'Talha Bin Sarwar', 'Junaida Sulaiman', 'M. Saef Ullah Miah'] | 2021-11-13 | null | null | null | null | ['keyword-extraction'] | ['natural-language-processing'] | [ 5.72787598e-02 -3.00331086e-01 -2.88673759e-01 -1.12132297e-03
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b7f9b7d8-bbc2-4451-bc80-37c9976fc579 | tablext-a-combined-neural-network-and | 2104.11287 | null | https://arxiv.org/abs/2104.11287v1 | https://arxiv.org/pdf/2104.11287v1.pdf | Tablext: A Combined Neural Network And Heuristic Based Table Extractor | A significant portion of the data available today is found within tables. Therefore, it is necessary to use automated table extraction to obtain thorough results when data-mining. Today's popular state-of-the-art methods for table extraction struggle to adequately extract tables with machine-readable text and structural data. To make matters worse, many tables do not have machine-readable data, such as tables saved as images, making most extraction methods completely ineffective. In order to address these issues, a novel, general format table extractor tool, Tablext, is proposed. This tool uses a combination of computer vision techniques and machine learning methods to efficiently and effectively identify and extract data from tables. Tablext begins by using a custom Convolutional Neural Network (CNN) to identify and separate all potential tables. The identification process is optimized by combining the custom CNN with the YOLO object detection network. Then, the high-level structure of each table is identified with computer vision methods. This high-level, structural meta-data is used by another CNN to identify exact cell locations. As a final step, Optical Characters Recognition (OCR) is performed on every individual cell to extract their content without needing machine-readable text. This multi-stage algorithm allows for the neural networks to focus on completing complex tasks, while letting image processing methods efficiently complete the simpler ones. This leads to the proposed approach to be general-purpose enough to handle a large batch of tables regardless of their internal encodings or their layout complexity. Additionally, it becomes accurate enough to outperform competing state-of-the-art table extractors on the ICDAR 2013 table dataset. | ['Ronald Dreslinski', 'Shuyan Yu', 'Serafina Kamp', 'Zineb Benameur-El', 'Morteza Fayazi', 'Zach Colter'] | 2021-04-22 | null | null | null | null | ['table-extraction'] | ['miscellaneous'] | [ 2.77890295e-01 -2.12319884e-02 -1.39677897e-01 -8.36776271e-02
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2edf6861-7050-40d3-9ba2-cfe3de5381fb | lifting-monocular-events-to-3d-human-poses | 2104.10609 | null | https://arxiv.org/abs/2104.10609v1 | https://arxiv.org/pdf/2104.10609v1.pdf | Lifting Monocular Events to 3D Human Poses | This paper presents a novel 3D human pose estimation approach using a single stream of asynchronous events as input. Most of the state-of-the-art approaches solve this task with RGB cameras, however struggling when subjects are moving fast. On the other hand, event-based 3D pose estimation benefits from the advantages of event-cameras, especially their efficiency and robustness to appearance changes. Yet, finding human poses in asynchronous events is in general more challenging than standard RGB pose estimation, since little or no events are triggered in static scenes. Here we propose the first learning-based method for 3D human pose from a single stream of events. Our method consists of two steps. First, we process the event-camera stream to predict three orthogonal heatmaps per joint; each heatmap is the projection of of the joint onto one orthogonal plane. Next, we fuse the sets of heatmaps to estimate 3D localisation of the body joints. As a further contribution, we make available a new, challenging dataset for event-based human pose estimation by simulating events from the RGB Human3.6m dataset. Experiments demonstrate that our method achieves solid accuracy, narrowing the performance gap between standard RGB and event-based vision. The code is freely available at https://iit-pavis.github.io/lifting_events_to_3d_hpe. | ['Alessio Del Bue', 'Pietro Morerio', 'Gianluca Scarpellini'] | 2021-04-21 | null | null | null | null | ['3d-pose-estimation', 'event-based-vision'] | ['computer-vision', 'computer-vision'] | [ 1.29294256e-02 -3.39565396e-01 2.35868901e-01 -2.20228150e-01
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f7bcb86e-07bd-4174-b1b9-c63734bf9fed | exploring-the-multi-modal-demand-dynamics | 2307.00877 | null | https://arxiv.org/abs/2307.00877v1 | https://arxiv.org/pdf/2307.00877v1.pdf | Exploring the Multi-modal Demand Dynamics During Transport System Disruptions | Various forms of disruption in transport systems perturb urban mobility in different ways. Passengers respond heterogeneously to such disruptive events based on numerous factors. This study takes a data-driven approach to explore multi-modal demand dynamics under disruptions. We first develop a methodology to automatically detect anomalous instances through historical hourly travel demand data. Then we apply clustering to these anomalous hours to distinguish various forms of multi-modal demand dynamics occurring during disruptions. Our study provides a straightforward tool for categorising various passenger responses to disruptive events in terms of mode choice and paves the way for predictive analyses on estimating the scope of modal shift under distinct disruption scenarios. | ['Nour-Eddin El Faouzi', 'Angelo Furno', 'Ali Shateri Benam'] | 2023-07-03 | null | null | null | null | ['clustering'] | ['methodology'] | [-3.74173641e-01 -3.81572068e-01 -1.70842811e-01 -2.73046821e-01
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b26382dd-3083-48a1-8dc6-d52dc1d37ac4 | tetrasphere-a-neural-descriptor-for-o-3 | 2211.14456 | null | https://arxiv.org/abs/2211.14456v2 | https://arxiv.org/pdf/2211.14456v2.pdf | TetraSphere: A Neural Descriptor for O(3)-Invariant Point Cloud Classification | Rotation invariance is an important requirement for the analysis of 3D point clouds. In this paper, we present a learnable descriptor for rotation- and reflection-invariant 3D point cloud classification based on recently introduced steerable 3D spherical neurons and vector neurons. Specifically, we show that the two approaches are compatible, and we show how to apply steerable neurons in an end-to-end method for the first time. In our approach, we perform TetraTransform -- which lifts the 3D input to an equivariant 4D representation, constructed by the steerable neurons -- and extract deeper rotation-equivariant features using vector neurons, subsequently computing pair-wise O(3)-invariant inner products of these features. This integration of the TetraTransform into the VN-DGCNN framework, termed TetraSphere, is used to classify synthetic and real-world data in arbitrary orientations. Taking only 3D coordinates as input, TetraSphere sets a new state-of-the-art classification performance on randomly rotated objects of the hardest subset of ScanObjectNN, even when trained on data without additional rotation augmentation. Our results reveal the practical value of spherical decision surfaces for learning in 3D Euclidean space. | ['Michael Felsberg', 'Mårten Wadenbäck', 'Andreas Robinson', 'Pavlo Melnyk'] | 2022-11-26 | null | null | null | null | ['3d-point-cloud-classification', 'point-cloud-classification'] | ['computer-vision', 'computer-vision'] | [-7.17382729e-02 -6.78942353e-02 2.05407649e-01 -4.29981261e-01
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a5816a96-86ac-4fb2-afb7-8e3817771621 | uncertainty-estimation-for-end-to-end-learned | 2002.03663 | null | https://arxiv.org/abs/2002.03663v1 | https://arxiv.org/pdf/2002.03663v1.pdf | Uncertainty Estimation for End-To-End Learned Dense Stereo Matching via Probabilistic Deep Learning | Motivated by the need to identify erroneous disparity assignments, various approaches for uncertainty and confidence estimation of dense stereo matching have been presented in recent years. As in many other fields, especially deep learning based methods have shown convincing results. However, most of these methods only model the uncertainty contained in the data, while ignoring the uncertainty of the employed dense stereo matching procedure. Additionally modelling the latter, however, is particularly beneficial if the domain of the training data varies from that of the data to be processed. For this purpose, in the present work the idea of probabilistic deep learning is applied to the task of dense stereo matching for the first time. Based on the well-known and commonly employed GC-Net architecture, a novel probabilistic neural network is presented, for the task of joint depth and uncertainty estimation from epipolar rectified stereo image pairs. Instead of learning the network parameters directly, the proposed probabilistic neural network learns a probability distribution from which parameters are sampled for every prediction. The variations between multiple such predictions on the same image pair allow to approximate the model uncertainty. The quality of the estimated depth and uncertainty information is assessed in an extensive evaluation on three different datasets. | ['Max Mehltretter'] | 2020-02-10 | null | null | null | null | ['probabilistic-deep-learning'] | ['computer-vision'] | [ 1.90326169e-01 3.37308288e-01 1.04395345e-01 -6.39712811e-01
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cea7bc79-8b46-4d32-b0de-72bc536a5759 | document-level-abstractive-summarization | 2212.03013 | null | https://arxiv.org/abs/2212.03013v1 | https://arxiv.org/pdf/2212.03013v1.pdf | Document-Level Abstractive Summarization | The task of automatic text summarization produces a concise and fluent text summary while preserving key information and overall meaning. Recent approaches to document-level summarization have seen significant improvements in recent years by using models based on the Transformer architecture. However, the quadratic memory and time complexities with respect to the sequence length make them very expensive to use, especially with long sequences, as required by document-level summarization. Our work addresses the problem of document-level summarization by studying how efficient Transformer techniques can be used to improve the automatic summarization of very long texts. In particular, we will use the arXiv dataset, consisting of several scientific papers and the corresponding abstracts, as baselines for this work. Then, we propose a novel retrieval-enhanced approach based on the architecture which reduces the cost of generating a summary of the entire document by processing smaller chunks. The results were below the baselines but suggest a more efficient memory a consumption and truthfulness. | ['Ana Sofia Carmo', 'Afonso Raposo', 'Gonçalo Raposo'] | 2022-12-06 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 4.86621231e-01 3.85232121e-01 -9.38968435e-02 -1.34095743e-01
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75f41d86-fca1-4ed8-912d-4fd29e65b398 | flsl-feature-level-self-supervised-learning | 2306.06203 | null | https://arxiv.org/abs/2306.06203v1 | https://arxiv.org/pdf/2306.06203v1.pdf | FLSL: Feature-level Self-supervised Learning | Current self-supervised learning (SSL) methods (e.g., SimCLR, DINO, VICReg, MOCOv3) target primarily on representations at instance level and do not generalize well to dense prediction tasks, such as object detection and segmentation. Towards aligning SSL with dense predictions, this paper demonstrates for the first time the underlying mean-shift clustering process of Vision Transformers (ViT), which aligns well with natural image semantics (e.g., a world of objects and stuffs). By employing transformer for joint embedding and clustering, we propose a two-level feature clustering SSL method, coined Feature-Level Self-supervised Learning (FLSL). We present the formal definition of the FLSL problem and construct the objectives from the mean-shift and k-means perspectives. We show that FLSL promotes remarkable semantic cluster representations and learns an embedding scheme amenable to intra-view and inter-view feature clustering. Experiments show that FLSL yields significant improvements in dense prediction tasks, achieving 44.9 (+2.8)% AP and 46.5% AP in object detection, as well as 40.8 (+2.3)% AP and 42.1% AP in instance segmentation on MS-COCO, using Mask R-CNN with ViT-S/16 and ViT-S/8 as backbone, respectively. FLSL consistently outperforms existing SSL methods across additional benchmarks, including UAV object detection on UAVDT, and video instance segmentation on DAVIS 2017. We conclude by presenting visualization and various ablation studies to better 20 understand the success of FLSL. | ['Shihao Ji', 'Hai Li', 'Anton Netchaev', 'Qing Su'] | 2023-06-09 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 3.06128949e-01 8.48704949e-02 -3.38941634e-01 -2.84614623e-01
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09f7b2e7-5da4-4b45-91d9-789855e3db6a | metric-learning-for-novelty-and-anomaly | 1808.05492 | null | http://arxiv.org/abs/1808.05492v1 | http://arxiv.org/pdf/1808.05492v1.pdf | Metric Learning for Novelty and Anomaly Detection | When neural networks process images which do not resemble the distribution
seen during training, so called out-of-distribution images, they often make
wrong predictions, and do so too confidently. The capability to detect
out-of-distribution images is therefore crucial for many real-world
applications. We divide out-of-distribution detection between novelty detection
---images of classes which are not in the training set but are related to
those---, and anomaly detection ---images with classes which are unrelated to
the training set. By related we mean they contain the same type of objects,
like digits in MNIST and SVHN. Most existing work has focused on anomaly
detection, and has addressed this problem considering networks trained with the
cross-entropy loss. Differently from them, we propose to use metric learning
which does not have the drawback of the softmax layer (inherent to
cross-entropy methods), which forces the network to divide its prediction power
over the learned classes. We perform extensive experiments and evaluate both
novelty and anomaly detection, even in a relevant application such as traffic
sign recognition, obtaining comparable or better results than previous works. | ['Joost Van de Weijer', 'Marc Masana', 'Joan Serrat', 'Antonio M. Lopez', 'Idoia Ruiz'] | 2018-08-16 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 1.97140768e-01 6.69167265e-02 8.38262066e-02 -6.16047621e-01
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7dc3e656-062a-4530-b74a-ef257d08834c | an-ordinal-latent-variable-model-of-conflict | 2210.03971 | null | https://arxiv.org/abs/2210.03971v2 | https://arxiv.org/pdf/2210.03971v2.pdf | An Ordinal Latent Variable Model of Conflict Intensity | Measuring the intensity of events is crucial for monitoring and tracking armed conflict. Advances in automated event extraction have yielded massive data sets of "who did what to whom" micro-records that enable data-driven approaches to monitoring conflict. The Goldstein scale is a widely-used expert-based measure that scores events on a conflictual-cooperative scale. It is based only on the action category ("what") and disregards the subject ("who") and object ("to whom") of an event, as well as contextual information, like associated casualty count, that should contribute to the perception of an event's "intensity". This paper takes a latent variable-based approach to measuring conflict intensity. We introduce a probabilistic generative model that assumes each observed event is associated with a latent intensity class. A novel aspect of this model is that it imposes an ordering on the classes, such that higher-valued classes denote higher levels of intensity. The ordinal nature of the latent variable is induced from naturally ordered aspects of the data (e.g., casualty counts) where higher values naturally indicate higher intensity. We evaluate the proposed model both intrinsically and extrinsically, showing that it obtains comparatively good held-out predictive performance. | ['Aaron Schein', 'Ryan Cotterell', 'Robert West', 'Josef Valvoda', 'Lucas Torroba Hennigen', 'Niklas Stoehr'] | 2022-10-08 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [ 0.2573375 0.02046539 -0.74104345 -0.71346223 -0.4067611 -0.58801997
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b483135a-cb57-4245-ab60-74b28cae0e99 | exploring-resolution-fields-for-scalable | 2306.08941 | null | https://arxiv.org/abs/2306.08941v1 | https://arxiv.org/pdf/2306.08941v1.pdf | Exploring Resolution Fields for Scalable Image Compression with Uncertainty Guidance | Recently, there are significant advancements in learning-based image compression methods surpassing traditional coding standards. Most of them prioritize achieving the best rate-distortion performance for a particular compression rate, which limits their flexibility and adaptability in various applications with complex and varying constraints. In this work, we explore the potential of resolution fields in scalable image compression and propose the reciprocal pyramid network (RPN) that fulfills the need for more adaptable and versatile compression. Specifically, RPN first builds a compression pyramid and generates the resolution fields at different levels in a top-down manner. The key design lies in the cross-resolution context mining module between adjacent levels, which performs feature enriching and distillation to mine meaningful contextualized information and remove unnecessary redundancy, producing informative resolution fields as residual priors. The scalability is achieved by progressive bitstream reusing and resolution field incorporation varying at different levels. Furthermore, between adjacent compression levels, we explicitly quantify the aleatoric uncertainty from the bottom decoded representations and develop an uncertainty-guided loss to update the upper-level compression parameters, forming a reverse pyramid process that enforces the network to focus on the textured pixels with high variance for more reliable and accurate reconstruction. Combining resolution field exploration and uncertainty guidance in a pyramid manner, RPN can effectively achieve spatial and quality scalable image compression. Experiments show the superiority of RPN against existing classical and deep learning-based scalable codecs. Code will be available at https://github.com/JGIroro/RPNSIC. | ['Yao Zhao', 'Meng Wang', 'Huihui Bai', 'Runmin Cong', 'Man Liu', 'Feng Li', 'Dongyi Zhang'] | 2023-06-15 | null | null | null | null | ['image-compression'] | ['computer-vision'] | [ 5.43337524e-01 -1.49230078e-01 -3.90957892e-01 -1.80656433e-01
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58163d65-a97d-4628-bfc2-573e32eb9c6c | improved-relation-networks-for-end-to-end | 2203.17218 | null | https://arxiv.org/abs/2203.17218v2 | https://arxiv.org/pdf/2203.17218v2.pdf | Improved Relation Networks for End-to-End Speaker Verification and Identification | Speaker identification systems in a real-world scenario are tasked to identify a speaker amongst a set of enrolled speakers given just a few samples for each enrolled speaker. This paper demonstrates the effectiveness of meta-learning and relation networks for this use case. We propose improved relation networks for speaker verification and few-shot (unseen) speaker identification. The use of relation networks facilitates joint training of the frontend speaker encoder and the backend model. Inspired by the use of prototypical networks in speaker verification and to increase the discriminability of the speaker embeddings, we train the model to classify samples in the current episode amongst all speakers present in the training set. Furthermore, we propose a new training regime for faster model convergence by extracting more information from a given meta-learning episode with negligible extra computation. We evaluate the proposed techniques on VoxCeleb, SITW and VCTK datasets on the tasks of speaker verification and unseen speaker identification. The proposed approach outperforms the existing approaches consistently on both tasks. | ['Susmita Ghose', 'Sparsh Sinha', 'Ashutosh Chaubey'] | 2022-03-31 | null | null | null | null | ['speaker-identification'] | ['speech'] | [ 2.96758115e-01 2.36576557e-01 -1.86650515e-01 -7.57882774e-01
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9929196e-1613-4ee6-ac3e-cba8aa7aad30 | higher-order-of-motion-magnification-for | 1806.04955 | null | http://arxiv.org/abs/1806.04955v1 | http://arxiv.org/pdf/1806.04955v1.pdf | Higher Order of Motion Magnification for Vessel Localisation in Surgical Video | Locating vessels during surgery is critical for avoiding inadvertent damage,
yet vasculature can be difficult to identify. Video motion magnification can
potentially highlight vessels by exaggerating subtle motion embedded within the
video to become perceivable to the surgeon. In this paper, we explore a
physiological model of artery distension to extend motion magnification to
incorporate higher orders of motion, leveraging the difference in acceleration
over time (jerk) in pulsatile motion to highlight the vascular pulse wave. Our
method is compared to first and second order motion based Eulerian video
magnification algorithms. Using data from a surgical video retrieved during a
robotic prostatectomy, we show that our method can accentuate
cardio-physiological features and produce a more succinct and clearer video for
motion magnification, with more similarities in areas without motion to the
source video at large magnifications. We validate the approach with a Structure
Similarity (SSIM) and Peak Signal to Noise Ratio (PSNR) assessment of three
videos at an increasing working distance, using three different levels of
optical magnification. Spatio-temporal cross sections are presented to show the
effectiveness of our proposal and video samples are provided to demonstrates
qualitatively our results. | ['Danail Stoyanov', 'Mirek Janatka', 'John Kelly', 'Ashwin Sridhar'] | 2018-06-13 | null | null | null | null | ['motion-magnification'] | ['computer-vision'] | [ 3.52458447e-01 3.41955394e-01 -9.19994414e-02 2.98616886e-01
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ac55d10e-dda2-47a8-beda-f7d6da2ee79d | mini-net-multiple-instance-ranking-network | 2007.09833 | null | https://arxiv.org/abs/2007.09833v2 | https://arxiv.org/pdf/2007.09833v2.pdf | MINI-Net: Multiple Instance Ranking Network for Video Highlight Detection | We address the weakly supervised video highlight detection problem for learning to detect segments that are more attractive in training videos given their video event label but without expensive supervision of manually annotating highlight segments. While manually averting localizing highlight segments, weakly supervised modeling is challenging, as a video in our daily life could contain highlight segments with multiple event types, e.g., skiing and surfing. In this work, we propose casting weakly supervised video highlight detection modeling for a given specific event as a multiple instance ranking network (MINI-Net) learning. We consider each video as a bag of segments, and therefore, the proposed MINI-Net learns to enforce a higher highlight score for a positive bag that contains highlight segments of a specific event than those for negative bags that are irrelevant. In particular, we form a max-max ranking loss to acquire a reliable relative comparison between the most likely positive segment instance and the hardest negative segment instance. With this max-max ranking loss, our MINI-Net effectively leverages all segment information to acquire a more distinct video feature representation for localizing the highlight segments of a specific event in a video. The extensive experimental results on three challenging public benchmarks clearly validate the efficacy of our multiple instance ranking approach for solving the problem. | ['Wei-Shi Zheng', 'Wei-Hong Li', 'Fa-Ting Hong', 'Xuanteng Huang'] | 2020-07-20 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1880_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123580341.pdf | eccv-2020-8 | ['highlight-detection'] | ['computer-vision'] | [ 4.10050273e-01 1.35084957e-01 -6.03383660e-01 -3.91325027e-01
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1dbaf480-038a-44ef-8858-d39fdbae967d | debiasing-skin-lesion-datasets-and-models-not | 2004.11457 | null | https://arxiv.org/abs/2004.11457v1 | https://arxiv.org/pdf/2004.11457v1.pdf | Debiasing Skin Lesion Datasets and Models? Not So Fast | Data-driven models are now deployed in a plethora of real-world applications - including automated diagnosis - but models learned from data risk learning biases from that same data. When models learn spurious correlations not found in real-world situations, their deployment for critical tasks, such as medical decisions, can be catastrophic. In this work we address this issue for skin-lesion classification models, with two objectives: finding out what are the spurious correlations exploited by biased networks, and debiasing the models by removing such spurious correlations from them. We perform a systematic integrated analysis of 7 visual artifacts (which are possible sources of biases exploitable by networks), employ a state-of-the-art technique to prevent the models from learning spurious correlations, and propose datasets to test models for the presence of bias. We find out that, despite interesting results that point to promising future research, current debiasing methods are not ready to solve the bias issue for skin-lesion models. | ['Alceu Bissoto', 'Sandra Avila', 'Eduardo Valle'] | 2020-04-23 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 5.38609147e-01 7.35475183e-01 -4.58976954e-01 -3.48057181e-01
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b7018870-4936-4919-a127-b425f20e7120 | abnormal-event-detection-on-bmtt-pets-2017 | null | null | http://openaccess.thecvf.com/content_cvpr_2017_workshops/w34/html/Vignesh_Abnormal_Event_Detection_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017_workshops/w34/papers/Vignesh_Abnormal_Event_Detection_CVPR_2017_paper.pdf | Abnormal event detection on BMTT-PETS 2017 surveillance challenge | In this paper, we have proposed a method to detect abnormal events for human group activities. Our main contribution is to develop a strategy that learns with very few videos by isolating the action and by using supervised learning. First, we subtract the background of each frame by modeling each pixel as a mixture of Gaussians (MoG) to concatenate the higher order learning only on the foreground. Next, features are extracted from each frame using a convolutional neural network (CNN) that is trained to classify between normal and abnormal frames. These feature vectors are fed into long short term memory (LSTM) network to learn the long-term dependencies between frames. The LSTM is also trained to classify abnormal frames, while extracting the temporal features of the frames. Finally, we classify the frames as abnormal or normal depending on the output of a linear SVM, whose input are the features computed by the LSTM | ['Kothapalli Vignesh', 'Gaurav Yadav', 'Amit Sethi'] | 2017-07-26 | null | null | null | ieee-conference-on-computer-vision-and-4 | ['anomaly-detection-in-surveillance-videos', 'abnormal-event-detection-in-video', 'anomaly-detection-in-surveillance-videos', 'abnormal-event-detection-in-video'] | ['computer-vision', 'computer-vision', 'methodology', 'methodology'] | [ 4.64091629e-01 2.12210953e-01 -1.19354017e-01 -3.67757499e-01
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85c705ee-28f0-45ac-8763-9c55e6aa3ada | attention-guided-quality-assessment-for | 2007.05593 | null | https://arxiv.org/abs/2007.05593v2 | https://arxiv.org/pdf/2007.05593v2.pdf | Attention-guided Quality Assessment for Automated Cryo-EM Grid Screening | Cryogenic electron microscopy (cryo-EM) has become an enabling technology in drug discovery and in understanding molecular bases of disease by producing near-atomic resolution (less than 0.4 nm) 3D reconstructions of biological macromolecules. The imaging process required for 3D reconstructions involves a highly iterative and empirical screening process, starting with the acquisition of low magnification images of the cryo-EM grids. These images are inspected for squares that are likely to contain useful molecular signals. Potentially useful squares within the grid are then imaged at progressively higher magnifications, with the goal of identifying sub-micron areas within circular holes (bounded by the squares) for imaging at high magnification. This arduous, multi-step data acquisition process represents a bottleneck for obtaining a high throughput data collection. Here, we focus on automating the early decision making for the microscope operator, scoring low magnification images of squares, and proposing the first deep learning framework, XCryoNet, for automated cryo-EM grid screening. XCryoNet is a semi-supervised, attention-guided deep learning approach that provides explainable scoring of automatically extracted square images using limited amounts of labeled data. Results show up to 8% and 37% improvements over a fully supervised and a no-attention solution, respectively, when labeled data is scarce. | ['Shireen Y. Elhabian', 'Hong Xu', 'David E. Timm'] | 2020-07-10 | null | null | null | null | ['cryogenic-electron-microscopy-cryo-em'] | ['computer-vision'] | [ 4.69830990e-01 8.40635821e-02 5.27870357e-01 -2.95326799e-01
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0b760669-1333-4198-899a-1122db0d725a | neural-network-with-optimal-neuron-activation | 2301.05567 | null | https://arxiv.org/abs/2301.05567v2 | https://arxiv.org/pdf/2301.05567v2.pdf | Neural network with optimal neuron activation functions based on additive Gaussian process regression | Feed-forward neural networks (NN) are a staple machine learning method widely used in many areas of science and technology. While even a single-hidden layer NN is a universal approximator, its expressive power is limited by the use of simple neuron activation functions (such as sigmoid functions) that are typically the same for all neurons. More flexible neuron activation functions would allow using fewer neurons and layers and thereby save computational cost and improve expressive power. We show that additive Gaussian process regression (GPR) can be used to construct optimal neuron activation functions that are individual to each neuron. An approach is also introduced that avoids non-linear fitting of neural network parameters. The resulting method combines the advantage of robustness of a linear regression with the higher expressive power of a NN. We demonstrate the approach by fitting the potential energy surfaces of the water molecule and formaldehyde. Without requiring any non-linear optimization, the additive GPR based approach outperforms a conventional NN in the high accuracy regime, where a conventional NN suffers more from overfitting. | ['Manabu Ihara', 'Sergei Manzhos'] | 2023-01-13 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 2.73574442e-01 2.65512645e-01 7.03289211e-02 -2.02823371e-01
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ac24b425-1295-40ed-b1e8-086e06ee8514 | playing-2048-with-reinforcement-learning | 2110.10374 | null | https://arxiv.org/abs/2110.10374v1 | https://arxiv.org/pdf/2110.10374v1.pdf | Playing 2048 With Reinforcement Learning | The game of 2048 is a highly addictive game. It is easy to learn the game, but hard to master as the created game revealed that only about 1% games out of hundreds million ever played have been won. In this paper, we would like to explore reinforcement learning techniques to win 2048. The approaches we have took include deep Q-learning and beam search, with beam search reaching 2048 28.5 of time. | ['Veronica Peng', 'Shilun Li'] | 2021-10-20 | null | null | null | null | ['2048'] | ['playing-games'] | [-6.18059874e-01 1.20058037e-01 -3.03276535e-02 2.95069516e-01
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b8f5fa61-b50b-4740-b876-9e427ab1fa75 | inria-at-semeval-2019-task-9-suggestion | null | null | https://aclanthology.org/S19-2211 | https://aclanthology.org/S19-2211.pdf | INRIA at SemEval-2019 Task 9: Suggestion Mining Using SVM with Handcrafted Features | We present the INRIA approach to the suggestion mining task at SemEval 2019. The task consists of two subtasks: suggestion mining under single-domain (Subtask A) and cross-domain (Subtask B) settings. We used the Support Vector Machines algorithm trained on handcrafted features, function words, sentiment features, digits, and verbs for Subtask A, and handcrafted features for Subtask B. Our best run archived a F1-score of 51.18{\%} on Subtask A, and ranked in the top ten of the submissions for Subtask B with 73.30{\%} F1-score. | ['Ilia Markov', 'Eric Villemonte de la Clergerie'] | 2019-06-01 | null | null | null | semeval-2019-6 | ['suggestion-mining'] | ['natural-language-processing'] | [ 1.70804411e-01 3.44598770e-01 -3.41415882e-01 -3.04031819e-01
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b445de57-0935-4971-94ff-acb04596dc0e | program-transfer-and-ontology-awareness-for | 2110.05743 | null | https://arxiv.org/abs/2110.05743v3 | https://arxiv.org/pdf/2110.05743v3.pdf | Program Transfer for Answering Complex Questions over Knowledge Bases | Program induction for answering complex questions over knowledge bases (KBs) aims to decompose a question into a multi-step program, whose execution against the KB produces the final answer. Learning to induce programs relies on a large number of parallel question-program pairs for the given KB. However, for most KBs, the gold program annotations are usually lacking, making learning difficult. In this paper, we propose the approach of program transfer, which aims to leverage the valuable program annotations on the rich-resourced KBs as external supervision signals to aid program induction for the low-resourced KBs that lack program annotations. For program transfer, we design a novel two-stage parsing framework with an efficient ontology-guided pruning strategy. First, a sketch parser translates the question into a high-level program sketch, which is the composition of functions. Second, given the question and sketch, an argument parser searches the detailed arguments from the KB for functions. During the searching, we incorporate the KB ontology to prune the search space. The experiments on ComplexWebQuestions and WebQuestionSP show that our method outperforms SOTA methods significantly, demonstrating the effectiveness of program transfer and our framework. Our codes and datasets can be obtained from https://github.com/THU-KEG/ProgramTransfer. | ['Jinghui Xiao', 'Zhiyuan Liu', 'Juanzi Li', 'Jifan Yu', 'Xin Lv', 'Lei Hou', 'Zijun Yao', 'Jiaxin Shi', 'Shulin Cao'] | 2021-10-12 | null | https://aclanthology.org/2022.acl-long.559 | https://aclanthology.org/2022.acl-long.559.pdf | acl-2022-5 | ['program-induction'] | ['computer-code'] | [ 4.82170135e-02 2.50923842e-01 -5.78660011e-01 -4.29030925e-01
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0fd1806e-b9bc-4a59-a448-65ae6fdc4725 | textocr-towards-large-scale-end-to-end | 2105.05486 | null | https://arxiv.org/abs/2105.05486v1 | https://arxiv.org/pdf/2105.05486v1.pdf | TextOCR: Towards large-scale end-to-end reasoning for arbitrary-shaped scene text | A crucial component for the scene text based reasoning required for TextVQA and TextCaps datasets involve detecting and recognizing text present in the images using an optical character recognition (OCR) system. The current systems are crippled by the unavailability of ground truth text annotations for these datasets as well as lack of scene text detection and recognition datasets on real images disallowing the progress in the field of OCR and evaluation of scene text based reasoning in isolation from OCR systems. In this work, we propose TextOCR, an arbitrary-shaped scene text detection and recognition with 900k annotated words collected on real images from TextVQA dataset. We show that current state-of-the-art text-recognition (OCR) models fail to perform well on TextOCR and that training on TextOCR helps achieve state-of-the-art performance on multiple other OCR datasets as well. We use a TextOCR trained OCR model to create PixelM4C model which can do scene text based reasoning on an image in an end-to-end fashion, allowing us to revisit several design choices to achieve new state-of-the-art performance on TextVQA dataset. | ['Tal Hassner', 'Wojciech Galuba', 'Jing Huang', 'Mandy Toh', 'Guan Pang', 'Amanpreet Singh'] | 2021-05-12 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Singh_TextOCR_Towards_Large-Scale_End-to-End_Reasoning_for_Arbitrary-Shaped_Scene_Text_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Singh_TextOCR_Towards_Large-Scale_End-to-End_Reasoning_for_Arbitrary-Shaped_Scene_Text_CVPR_2021_paper.pdf | cvpr-2021-1 | ['scene-text-detection'] | ['computer-vision'] | [ 5.31668842e-01 -4.13099766e-01 3.33295882e-01 -5.18275023e-01
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9e1c2bbf-209f-421f-a746-3ac15e7f5780 | occupation2vec-a-general-approach-to | 2111.02528 | null | https://arxiv.org/abs/2111.02528v2 | https://arxiv.org/pdf/2111.02528v2.pdf | occ2vec: A principal approach to representing occupations using natural language processing | We propose \textbf{occ2vec}, a principal approach to representing occupations, which can be used in matching, predictive and causal modeling, and other economic areas. In particular, we use it to score occupations on any definable characteristic of interest, say the degree of \textquote{greenness}. Using more than 17,000 occupation-specific text descriptors, we transform each occupation into a high-dimensional vector using natural language processing. Similar, we assign a vector to the target characteristic and estimate the occupational degree of this characteristic as the cosine similarity between the vectors. The main advantages of this approach are its universal applicability and verifiability contrary to existing ad-hoc approaches. We extensively validate our approach on several exercises and then use it to estimate the occupational degree of charisma and emotional intelligence (EQ). We find that occupations that score high on these tend to have higher educational requirements. Turning to wages, highly charismatic occupations are either found in the lower or upper tail in the wage distribution. This is not found for EQ, where higher levels of EQ are generally correlated with higher wages. | ['Nicolaj Søndergaard Mühlbach'] | 2021-11-03 | null | null | null | null | ['emotional-intelligence'] | ['natural-language-processing'] | [-7.90754706e-03 1.82975039e-01 -3.47500741e-01 -4.25503880e-01
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93459d3b-2cc8-4003-97d9-553130575704 | eeg-based-sleep-staging-with-hybrid-attention | 2305.09543 | null | https://arxiv.org/abs/2305.09543v1 | https://arxiv.org/pdf/2305.09543v1.pdf | EEG-based Sleep Staging with Hybrid Attention | Sleep staging is critical for assessing sleep quality and diagnosing sleep disorders. However, capturing both the spatial and temporal relationships within electroencephalogram (EEG) signals during different sleep stages remains challenging. In this paper, we propose a novel framework called the Hybrid Attention EEG Sleep Staging (HASS) Framework. Specifically, we propose a well-designed spatio-temporal attention mechanism to adaptively assign weights to inter-channels and intra-channel EEG segments based on the spatio-temporal relationship of the brain during different sleep stages. Experiment results on the MASS and ISRUC datasets demonstrate that HASS can significantly improve typical sleep staging networks. Our proposed framework alleviates the difficulties of capturing the spatial-temporal relationship of EEG signals during sleep staging and holds promise for improving the accuracy and reliability of sleep assessment in both clinical and research settings. | ['Yang Liu', 'Jiaping Xiao', 'Chenyu Liu', 'Xinliang Zhou'] | 2023-05-16 | null | null | null | null | ['sleep-quality-prediction', 'sleep-staging', 'eeg', 'eeg-based-sleep-staging', 'eeg'] | ['medical', 'medical', 'methodology', 'time-series', 'time-series'] | [ 7.54794031e-02 -6.12395465e-01 -1.05886705e-01 -5.46846628e-01
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258f3a3a-17af-4f07-9533-a677995624cd | a-generalist-agent | 2205.06175 | null | https://arxiv.org/abs/2205.06175v3 | https://arxiv.org/pdf/2205.06175v3.pdf | A Generalist Agent | Inspired by progress in large-scale language modeling, we apply a similar approach towards building a single generalist agent beyond the realm of text outputs. The agent, which we refer to as Gato, works as a multi-modal, multi-task, multi-embodiment generalist policy. The same network with the same weights can play Atari, caption images, chat, stack blocks with a real robot arm and much more, deciding based on its context whether to output text, joint torques, button presses, or other tokens. In this report we describe the model and the data, and document the current capabilities of Gato. | ['Nando de Freitas', 'Mahyar Bordbar', 'Oriol Vinyals', 'Raia Hadsell', 'Yutian Chen', 'Nicolas Heess', 'Ashley Edwards', 'Ali Razavi', 'Jake Bruce', 'Tom Eccles', 'Jost Tobias Springenberg', 'Jackie Kay', 'Yury Sulsky', 'Mai Gimenez', 'Gabriel Barth-Maron', 'Alexander Novikov', 'Sergio Gomez Colmenarejo', 'Emilio Parisotto', 'Konrad Zolna', 'Scott Reed'] | 2022-05-12 | a-generalist-agent-1 | https://storage.googleapis.com/deepmind-media/A%20Generalist%20Agent/Generalist%20Agent.pdf | https://storage.googleapis.com/deepmind-media/A%20Generalist%20Agent/Generalist%20Agent.pdf | deepmind-2022-5 | ['skill-generalization', 'skill-mastery'] | ['robots', 'robots'] | [-1.67149261e-01 5.41472673e-01 -2.18947753e-01 -2.08822072e-01
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3f61bedc-ba77-48b0-999e-74526671bd21 | childpredictor-a-child-face-prediction | 2204.09962 | null | https://arxiv.org/abs/2204.09962v1 | https://arxiv.org/pdf/2204.09962v1.pdf | ChildPredictor: A Child Face Prediction Framework with Disentangled Learning | The appearances of children are inherited from their parents, which makes it feasible to predict them. Predicting realistic children's faces may help settle many social problems, such as age-invariant face recognition, kinship verification, and missing child identification. It can be regarded as an image-to-image translation task. Existing approaches usually assume domain information in the image-to-image translation can be interpreted by "style", i.e., the separation of image content and style. However, such separation is improper for the child face prediction, because the facial contours between children and parents are not the same. To address this issue, we propose a new disentangled learning strategy for children's face prediction. We assume that children's faces are determined by genetic factors (compact family features, e.g., face contour), external factors (facial attributes irrelevant to prediction, such as moustaches and glasses), and variety factors (individual properties for each child). On this basis, we formulate predictions as a mapping from parents' genetic factors to children's genetic factors, and disentangle them from external and variety factors. In order to obtain accurate genetic factors and perform the mapping, we propose a ChildPredictor framework. It transfers human faces to genetic factors by encoders and back by generators. Then, it learns the relationship between the genetic factors of parents and children through a mapping function. To ensure the generated faces are realistic, we collect a large Family Face Database to train ChildPredictor and evaluate it on the FF-Database validation set. Experimental results demonstrate that ChildPredictor is superior to other well-known image-to-image translation methods in predicting realistic and diverse child faces. Implementation codes can be found at https://github.com/zhaoyuzhi/ChildPredictor. | ['Buhua Liu', 'Wing-Yin Yu', 'Weifeng Ou', 'Chiu-Sing Pang', 'Chun-Kit Wong', 'Wei Liu', 'Yujia Zhang', 'Wei Shen', 'Qiong Yan', 'Xuehui Wang', 'Lai-Man Po', 'Yuzhi Zhao'] | 2022-04-21 | null | null | null | null | ['age-invariant-face-recognition'] | ['computer-vision'] | [ 2.58728445e-01 2.70114154e-01 -3.20314229e-01 -9.09813046e-01
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fb78300a-9e64-4bb9-838c-71ae69c187e6 | arrhythmia-detection-using-mutual-information | 1502.01733 | null | http://arxiv.org/abs/1502.01733v1 | http://arxiv.org/pdf/1502.01733v1.pdf | Arrhythmia Detection using Mutual Information-Based Integration Method | The aim of this paper is to propose an application of mutual
information-based ensemble methods to the analysis and classification of heart
beats associated with different types of Arrhythmia. Models of multilayer
perceptrons, support vector machines, and radial basis function neural networks
were trained and tested using the MIT-BIH arrhythmia database. This research
brings a focus to an ensemble method that, to our knowledge, is a novel
application in the area of ECG Arrhythmia detection. The proposed classifier
ensemble method showed improved performance, relative to either majority voting
classifier integration or to individual classifier performance. The overall
ensemble accuracy was 98.25%. | ['Samer Arafat', 'Othman Soufan'] | 2015-02-05 | null | null | null | null | ['arrhythmia-detection'] | ['medical'] | [ 2.78233290e-01 -1.78309932e-01 3.02169353e-01 -4.99583989e-01
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