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3e323792-b289-4c3b-98c2-26b62b18d6d5 | pu-gan-a-point-cloud-upsampling-adversarial | 1907.10844 | null | https://arxiv.org/abs/1907.10844v1 | https://arxiv.org/pdf/1907.10844v1.pdf | PU-GAN: a Point Cloud Upsampling Adversarial Network | Point clouds acquired from range scans are often sparse, noisy, and non-uniform. This paper presents a new point cloud upsampling network called PU-GAN, which is formulated based on a generative adversarial network (GAN), to learn a rich variety of point distributions from the latent space and upsample points over patches on object surfaces. To realize a working GAN network, we construct an up-down-up expansion unit in the generator for upsampling point features with error feedback and self-correction, and formulate a self-attention unit to enhance the feature integration. Further, we design a compound loss with adversarial, uniform and reconstruction terms, to encourage the discriminator to learn more latent patterns and enhance the output point distribution uniformity. Qualitative and quantitative evaluations demonstrate the quality of our results over the state-of-the-arts in terms of distribution uniformity, proximity-to-surface, and 3D reconstruction quality. | ['Pheng-Ann Heng', 'Daniel Cohen-Or', 'Chi-Wing Fu', 'Xianzhi Li', 'Ruihui Li'] | 2019-07-25 | pu-gan-a-point-cloud-upsampling-adversarial-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Li_PU-GAN_A_Point_Cloud_Upsampling_Adversarial_Network_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Li_PU-GAN_A_Point_Cloud_Upsampling_Adversarial_Network_ICCV_2019_paper.pdf | iccv-2019-10 | ['point-cloud-super-resolution'] | ['computer-vision'] | [ 1.82355136e-01 2.25304991e-01 8.25240836e-02 -4.36762750e-01
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c4eafb0f-576c-4eeb-9ef8-f8091de0b828 | unsupervised-video-anomaly-detection-with | 2307.01533 | null | https://arxiv.org/abs/2307.01533v1 | https://arxiv.org/pdf/2307.01533v1.pdf | Unsupervised Video Anomaly Detection with Diffusion Models Conditioned on Compact Motion Representations | This paper aims to address the unsupervised video anomaly detection (VAD) problem, which involves classifying each frame in a video as normal or abnormal, without any access to labels. To accomplish this, the proposed method employs conditional diffusion models, where the input data is the spatiotemporal features extracted from a pre-trained network, and the condition is the features extracted from compact motion representations that summarize a given video segment in terms of its motion and appearance. Our method utilizes a data-driven threshold and considers a high reconstruction error as an indicator of anomalous events. This study is the first to utilize compact motion representations for VAD and the experiments conducted on two large-scale VAD benchmarks demonstrate that they supply relevant information to the diffusion model, and consequently improve VAD performances w.r.t the prior art. Importantly, our method exhibits better generalization performance across different datasets, notably outperforming both the state-of-the-art and baseline methods. The code of our method is available at https://github.com/AnilOsmanTur/conditioned_video_anomaly_diffusion | ['Elisa Ricci', 'Cigdem Beyan', "Nicola Dall'Asen", 'Anil Osman Tur'] | 2023-07-04 | null | null | null | null | ['video-anomaly-detection', 'anomaly-detection'] | ['computer-vision', 'methodology'] | [ 1.36161774e-01 -3.06359619e-01 -3.99536699e-01 -1.59935132e-01
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5ad34c60-5094-46a9-bc14-e88e0296608b | camel-curvature-augmented-manifold-embedding | 2303.02561 | null | https://arxiv.org/abs/2303.02561v1 | https://arxiv.org/pdf/2303.02561v1.pdf | CAMEL: Curvature-Augmented Manifold Embedding and Learning | A novel method, named Curvature-Augmented Manifold Embedding and Learning (CAMEL), is proposed for high dimensional data classification, dimension reduction, and visualization. CAMEL utilizes a topology metric defined on the Riemannian manifold, and a unique Riemannian metric for both distance and curvature to enhance its expressibility. The method also employs a smooth partition of unity operator on the Riemannian manifold to convert localized orthogonal projection to global embedding, which captures both the overall topological structure and local similarity simultaneously. The local orthogonal vectors provide a physical interpretation of the significant characteristics of clusters. Therefore, CAMEL not only provides a low-dimensional embedding but also interprets the physics behind this embedding. CAMEL has been evaluated on various benchmark datasets and has shown to outperform state-of-the-art methods, especially for high-dimensional datasets. The method's distinct benefits are its high expressibility, interpretability, and scalability. The paper provides a detailed discussion on Riemannian distance and curvature metrics, physical interpretability, hyperparameter effect, manifold stability, and computational efficiency for a holistic understanding of CAMEL. Finally, the paper presents the limitations and future work of CAMEL along with key conclusions. | ['Yongming Liu', 'Nan Xu'] | 2023-03-05 | null | null | null | null | ['unity'] | ['computer-vision'] | [-7.21470892e-01 -1.01483107e-01 8.76840763e-03 -2.62802273e-01
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16aadca9-dbbc-47fa-9082-10d4b4118892 | kasam-spline-additive-models-for-function | 2205.06376 | null | https://arxiv.org/abs/2205.06376v1 | https://arxiv.org/pdf/2205.06376v1.pdf | KASAM: Spline Additive Models for Function Approximation | Neural networks have been criticised for their inability to perform continual learning due to catastrophic forgetting and rapid unlearning of a past concept when a new concept is introduced. Catastrophic forgetting can be alleviated by specifically designed models and training techniques. This paper outlines a novel Spline Additive Model (SAM). SAM exhibits intrinsic memory retention with sufficient expressive power for many practical tasks, but is not a universal function approximator. SAM is extended with the Kolmogorov-Arnold representation theorem to a novel universal function approximator, called the Kolmogorov-Arnold Spline Additive Model - KASAM. The memory retention, expressive power and limitations of SAM and KASAM are illustrated analytically and empirically. SAM exhibited robust but imperfect memory retention, with small regions of overlapping interference in sequential learning tasks. KASAM exhibited greater susceptibility to catastrophic forgetting. KASAM in combination with pseudo-rehearsal training techniques exhibited superior performance in regression tasks and memory retention. | ['Anna Bosman', 'Pieter Janse van Rensburg', 'Heinrich van Deventer'] | 2022-05-12 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 3.86621319e-02 -1.39152437e-01 6.88331127e-02 -1.00336313e-01
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3e30488b-e5cd-4505-ad1f-f5b4716b0111 | patch-based-deep-autoencoder-for-point-cloud | 2110.09109 | null | https://arxiv.org/abs/2110.09109v1 | https://arxiv.org/pdf/2110.09109v1.pdf | Patch-Based Deep Autoencoder for Point Cloud Geometry Compression | The ever-increasing 3D application makes the point cloud compression unprecedentedly important and needed. In this paper, we propose a patch-based compression process using deep learning, focusing on the lossy point cloud geometry compression. Unlike existing point cloud compression networks, which apply feature extraction and reconstruction on the entire point cloud, we divide the point cloud into patches and compress each patch independently. In the decoding process, we finally assemble the decompressed patches into a complete point cloud. In addition, we train our network by a patch-to-patch criterion, i.e., use the local reconstruction loss for optimization, to approximate the global reconstruction optimality. Our method outperforms the state-of-the-art in terms of rate-distortion performance, especially at low bitrates. Moreover, the compression process we proposed can guarantee to generate the same number of points as the input. The network model of this method can be easily applied to other point cloud reconstruction problems, such as upsampling. | ['Pan Gao', 'Kang You'] | 2021-10-18 | null | null | null | null | ['point-cloud-reconstruction'] | ['computer-vision'] | [ 1.41308188e-01 -7.04346672e-02 -2.19735764e-02 -1.52009815e-01
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815edc1f-d99e-4ee1-945e-22ba2f4105c1 | inductive-biased-estimation-learning | 2110.01571 | null | https://arxiv.org/abs/2110.01571v4 | https://arxiv.org/pdf/2110.01571v4.pdf | Causal Representation Learning for Context-Aware Face Transfer | Human face synthesis involves transferring knowledge about the identity and identity-dependent face shape (IDFS) of a human face to target face images where the context (e.g., facial expressions, head poses, and other background factors) may change dramatically. Human faces are non-rigid, so facial expression leads to deformation of face shape, and head pose also affects the face observed in 2D images. A key challenge in face transfer is to match the face with unobserved new contexts, adapting the face appearance to different poses and expressions accordingly. In this work, we find a way to provide prior knowledge for generative models to reason about the appropriate appearance of a human face in response to various expressions and poses. We propose a novel context-aware face transfer method, called CarTrans, that incorporates causal effects of contextual factors into face representation, and thus is able to be aware of the uncertainty of new contexts. We estimate the effect of facial expression and head pose in terms of counterfactual inference by designing a controlled intervention trial, thus avoiding the requirement of a large number of observations to cover the pose-expression space well. Moreover, we propose a kernel regression-based encoder that eliminates the identity specificity of target faces when encoding contextual information from target images. The resulting method shows impressive performance, allowing fine-grained control over face shape and appearance under various contextual conditions. | ['Ran He', 'Chaoyou Fu', 'Huaibo Huang', 'Gege Gao'] | 2021-10-04 | null | null | null | null | ['face-transfer', 'counterfactual-inference'] | ['computer-vision', 'miscellaneous'] | [ 4.71223652e-01 3.70401859e-01 -6.62460774e-02 -7.64264047e-01
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47b3b3b3-9200-4aca-b475-6cdabd992fac | neural-network-based-sleep-phases | 2105.11452 | null | https://arxiv.org/abs/2105.11452v1 | https://arxiv.org/pdf/2105.11452v1.pdf | Neural Network Based Sleep Phases Classification for Resource Constraint Environments | Sleep is restoration process of the body. The efficiency of this restoration process is directly correlated to the amount of time spent at each sleep phase. Hence, automatic tracking of sleep via wearable devices has attracted both the researchers and industry. Current state-of-the-art sleep tracking solutions are memory and processing greedy and they require cloud or mobile phone connectivity. We propose a memory efficient sleep tracking architecture which can work in the embedded environment without needing any cloud or mobile phone connection. In this study, a novel architecture is proposed that consists of a feature extraction and Artificial Neural Networks based stacking classifier. Besides, we discussed how to tackle with sequential nature of the sleep staging for the memory constraint environments through the proposed framework. To verify the system, a dataset is collected from 24 different subjects for 31 nights with a wrist worn device having 3-axis accelerometer (ACC) and photoplethysmogram (PPG) sensors. Over the collected dataset, the proposed classification architecture achieves 20\% and 14\% better F1 scores than its competitors. Apart from the superior performance, proposed architecture is a promising solution for resource constraint embedded systems by allocating only 4.2 kilobytes of memory (RAM). | ['Alisher Kholmatov', 'Murat Aslan', 'Berkay Köprü'] | 2021-05-25 | null | null | null | null | ['sleep-staging'] | ['medical'] | [ 2.10186556e-01 -2.07213357e-01 -2.43803576e-01 -2.95521796e-01
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7d9fed27-1e1b-4007-affa-087cc6263750 | pacer-a-fully-push-forward-based | 2306.06637 | null | https://arxiv.org/abs/2306.06637v1 | https://arxiv.org/pdf/2306.06637v1.pdf | PACER: A Fully Push-forward-based Distributional Reinforcement Learning Algorithm | In this paper, we propose the first fully push-forward-based Distributional Reinforcement Learning algorithm, called Push-forward-based Actor-Critic EncourageR (PACER). Specifically, PACER establishes a stochastic utility value policy gradient theorem and simultaneously leverages the push-forward operator in the construction of both the actor and the critic. Moreover, based on maximum mean discrepancies (MMD), a novel sample-based encourager is designed to incentivize exploration. Experimental evaluations on various continuous control benchmarks demonstrate the superiority of our algorithm over the state-of-the-art. | ['Bin Dai', 'Hui Qian', 'Lingwei Peng', 'Yichao Fu', 'Chao Zhang', 'Wensong Bai'] | 2023-06-11 | null | null | null | null | ['distributional-reinforcement-learning', 'continuous-control'] | ['methodology', 'playing-games'] | [-3.58175248e-01 2.44064182e-01 -6.32144809e-01 -1.74395010e-01
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e2c6c19b-0319-4e85-a8fe-84d40dc2b157 | a-parameterised-quantum-circuit-approach-to | 2102.06697 | null | https://arxiv.org/abs/2102.06697v2 | https://arxiv.org/pdf/2102.06697v2.pdf | Matching Point Sets with Quantum Circuit Learning | In this work, we propose a parameterised quantum circuit learning approach to point set matching problem. In contrast to previous annealing-based methods, we propose a quantum circuit-based framework whose parameters are optimised via descending the gradients w.r.t a kernel-based loss function. We formulate the shape matching problem into a distribution learning task; that is, to learn the distribution of the optimal transformation parameters. We show that this framework is able to find multiple optimal solutions for symmetric shapes and is more accurate, scalable and robust than the previous annealing-based method. Code, data and pre-trained weights are available at the project page: \href{https://hansen7.github.io/qKC}{https://hansen7.github.io/qKC} | ['Hanchen Wang', 'Mohammadreza Noormandipour'] | 2021-02-12 | null | null | null | null | ['set-matching'] | ['computer-vision'] | [ 1.47000611e-01 -7.40852728e-02 -1.78171560e-01 -2.53437400e-01
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a5d32989-575b-4f30-a2db-348e93465b49 | skip-prediction-using-boosting-trees-based-on | 1903.11833 | null | http://arxiv.org/abs/1903.11833v1 | http://arxiv.org/pdf/1903.11833v1.pdf | Skip prediction using boosting trees based on acoustic features of tracks in sessions | The Spotify Sequential Skip Prediction Challenge focuses on predicting if a
track in a session will be skipped by the user or not. In this paper, we
describe our approach to this problem and the final system that was submitted
to the challenge by our team from the Music Technology Group (MTG) under the
name "aferraro". This system consists in combining the predictions of multiple
boosting trees models trained with features extracted from the sessions and the
tracks. The proposed approach achieves good overall performance (MAA of 0.554),
with our model ranked 14th out of more than 600 submissions in the final
leaderboard. | ['Dmitry Bogdanov', 'Andrés Ferraro', 'Xavier Serra'] | 2019-03-28 | null | null | null | null | ['sequential-skip-prediction'] | ['time-series'] | [-6.05148030e-03 -2.05035761e-01 -4.46360677e-01 -3.91746342e-01
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6771f9a6-0594-44ec-bfee-4f69d7653c0f | table-structure-recognition-based-on-cell | null | null | https://aclanthology.org/R19-1001 | https://aclanthology.org/R19-1001.pdf | Table Structure Recognition Based on Cell Relationship, a Bottom-Up Approach | In this paper, we present a relationship extraction based methodology for table structure recognition in PDF documents. The proposed deep learning-based method takes a bottom-up approach to table recognition in PDF documents. We outline the shortcomings of conventional approaches based on heuristics and machine learning-based top-down approaches. In this work, we explain how the task of table structure recognition can be modeled as a cell relationship extraction task and the importance of the bottom-up approach in recognizing the table cells. We use Multilayer Feedforward Neural Network for table structure recognition and compare the results of three feature sets. To gauge the performance of the proposed method, we prepared a training dataset using 250 tables in PDF documents, carefully selecting the table structures that are most commonly found in the documents. Our model achieves an overall accuracy of 97.95{\%} and an F1-Score of 92.62{\%} on the test dataset. | ['Viveka Vyeth', 'Muzaffar Bashir Shah', 'Shabir Ahmad Bhat', 'Darshan Adiga'] | 2019-09-01 | null | null | null | ranlp-2019-9 | ['table-recognition'] | ['computer-vision'] | [ 5.12149483e-02 1.32085666e-01 -3.11401248e-01 -5.04754364e-01
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60720feb-9091-4025-85bc-6cebad0cc859 | aced-accelerated-computational | 2011.04426 | null | https://arxiv.org/abs/2011.04426v4 | https://arxiv.org/pdf/2011.04426v4.pdf | AutoMat: Accelerated Computational Electrochemical systems Discovery | Large-scale electrification is vital to addressing the climate crisis, but several scientific and technological challenges remain to fully electrify both the chemical industry and transportation. In both of these areas, new electrochemical materials will be critical, but their development currently relies heavily on human-time-intensive experimental trial and error and computationally expensive first-principles, meso-scale and continuum simulations. We present an automated workflow, AutoMat, that accelerates these computational steps by introducing both automated input generation and management of simulations across scales from first principles to continuum device modeling. Furthermore, we show how to seamlessly integrate multi-fidelity predictions such as machine learning surrogates or automated robotic experiments "in-the-loop". The automated framework is implemented with design space search techniques to dramatically accelerate the overall materials discovery pipeline by implicitly learning design features that optimize device performance across several metrics. We discuss the benefits of AutoMat using examples in electrocatalysis and energy storage and highlight lessons learned. | ['Christopher Rackauckas', 'Bharath Ramsundar', 'Hongyi Lin', 'Adarsh Dave', 'David Farina', 'Jiankun Pu', 'Shang Zhu', 'Valentin Sulzer', 'Vinay I. Hegde', 'Eric Muckley', 'Rachel Kurchin', 'Emil Annevelink', 'Venkatasubramanian Viswanathan', 'Viral Shah', 'Bryce Meredig', 'James Saal', 'Alan Edelman', 'Matthew Johnson', 'Dhairya Gandhi', 'Lance Kavalsky'] | 2020-11-03 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [ 2.66749382e-01 -3.25925887e-01 8.69147480e-02 8.55769739e-02
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60ee7843-62ff-454b-95c7-f81e8727d255 | semi-supervised-relational-contrastive | 2304.05047 | null | https://arxiv.org/abs/2304.05047v2 | https://arxiv.org/pdf/2304.05047v2.pdf | Semi-Supervised Relational Contrastive Learning | Disease diagnosis from medical images via supervised learning is usually dependent on tedious, error-prone, and costly image labeling by medical experts. Alternatively, semi-supervised learning and self-supervised learning offer effectiveness through the acquisition of valuable insights from readily available unlabeled images. We present Semi-Supervised Relational Contrastive Learning (SRCL), a novel semi-supervised learning model that leverages self-supervised contrastive loss and sample relation consistency for the more meaningful and effective exploitation of unlabeled data. Our experimentation with the SRCL model explores both pre-train/fine-tune and joint learning of the pretext (contrastive learning) and downstream (diagnostic classification) tasks. We validate against the ISIC 2018 Challenge benchmark skin lesion classification dataset and demonstrate the effectiveness of our semi-supervised method on varying amounts of labeled data. | ['Abdullah-Al-Zubaer Imran', 'Demetri Terzopoulos', 'Adam Wang', 'Attiano Purpura-Pontoniere'] | 2023-04-11 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 8.25325549e-01 6.74173295e-01 -7.67870247e-01 -6.47461057e-01
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f178c512-212c-49c8-acda-4d24b28b2872 | on-the-performance-of-time-pooling-strategies | null | null | https://aclanthology.org/2020.lrec-1.438 | https://aclanthology.org/2020.lrec-1.438.pdf | On The Performance of Time-Pooling Strategies for End-to-End Spoken Language Identification | Automatic speech processing applications often have to deal with the problem of aggregating local descriptors (i.e., representations of input speech data corresponding to specific portions across the time dimension) and turning them into a single fixed-dimension representation, known as global descriptor, on top of which downstream classification tasks can be performed. In this paper, we provide an empirical assessment of different time pooling strategies when used with state-of-the-art representation learning models. In particular, insights are provided as to when it is suitable to use simple statistics of local descriptors or when more sophisticated approaches are needed. Here, language identification is used as a case study and a database containing ten oriental languages under varying test conditions (short-duration test recordings, confusing languages, unseen languages) is used. Experiments are performed with classifiers trained on top of global descriptors to provide insights on open-set evaluation performance and show that appropriate selection of such pooling strategies yield embeddings able to outperform well-known benchmark systems as well as previously results based on attention only. | ['Tiago Falk', 'Md Jahangir Alam', 'Joao Monteiro'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['spoken-language-identification'] | ['speech'] | [ 2.83984691e-01 -1.98312968e-01 -1.99278854e-02 -3.93782854e-01
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33e94240-5a50-45f1-a5f0-eaeb5bec16fe | floweval-a-consensus-based-dialogue | 2202.06633 | null | https://arxiv.org/abs/2202.06633v2 | https://arxiv.org/pdf/2202.06633v2.pdf | FlowEval: A Consensus-Based Dialogue Evaluation Framework Using Segment Act Flows | Despite recent progress in open-domain dialogue evaluation, how to develop automatic metrics remains an open problem. We explore the potential of dialogue evaluation featuring dialog act information, which was hardly explicitly modeled in previous methods. However, defined at the utterance level in general, dialog act is of coarse granularity, as an utterance can contain multiple segments possessing different functions. Hence, we propose segment act, an extension of dialog act from utterance level to segment level, and crowdsource a large-scale dataset for it. To utilize segment act flows, sequences of segment acts, for evaluation, we develop the first consensus-based dialogue evaluation framework, FlowEval. This framework provides a reference-free approach for dialog evaluation by finding pseudo-references. Extensive experiments against strong baselines on three benchmark datasets demonstrate the effectiveness and other desirable characteristics of our FlowEval, pointing out a potential path for better dialogue evaluation. | ['LiWei Wang', 'Michael R. Lyu', 'Dong Yu', 'Yangfeng Ji', 'Wanyu Du', 'Yanyang Li', 'Jianqiao Zhao'] | 2022-02-14 | null | null | null | null | ['dialogue-evaluation'] | ['natural-language-processing'] | [ 9.02015716e-02 4.51969028e-01 -9.55247581e-02 -7.31953323e-01
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c9932343-3033-49fd-a510-d288c4ce7a1b | joint-self-supervised-image-volume | 2212.01893 | null | https://arxiv.org/abs/2212.01893v1 | https://arxiv.org/pdf/2212.01893v1.pdf | Joint Self-Supervised Image-Volume Representation Learning with Intra-Inter Contrastive Clustering | Collecting large-scale medical datasets with fully annotated samples for training of deep networks is prohibitively expensive, especially for 3D volume data. Recent breakthroughs in self-supervised learning (SSL) offer the ability to overcome the lack of labeled training samples by learning feature representations from unlabeled data. However, most current SSL techniques in the medical field have been designed for either 2D images or 3D volumes. In practice, this restricts the capability to fully leverage unlabeled data from numerous sources, which may include both 2D and 3D data. Additionally, the use of these pre-trained networks is constrained to downstream tasks with compatible data dimensions. In this paper, we propose a novel framework for unsupervised joint learning on 2D and 3D data modalities. Given a set of 2D images or 2D slices extracted from 3D volumes, we construct an SSL task based on a 2D contrastive clustering problem for distinct classes. The 3D volumes are exploited by computing vectored embedding at each slice and then assembling a holistic feature through deformable self-attention mechanisms in Transformer, allowing incorporating long-range dependencies between slices inside 3D volumes. These holistic features are further utilized to define a novel 3D clustering agreement-based SSL task and masking embedding prediction inspired by pre-trained language models. Experiments on downstream tasks, such as 3D brain segmentation, lung nodule detection, 3D heart structures segmentation, and abnormal chest X-ray detection, demonstrate the effectiveness of our joint 2D and 3D SSL approach. We improve plain 2D Deep-ClusterV2 and SwAV by a significant margin and also surpass various modern 2D and 3D SSL approaches. | ['Daniel Sonntag', 'Pengtao Xie', 'Shadi Albarqouni', 'Paul Swoboda', 'Nhat Ho', 'Binh T. Nguyen', 'Tri Cao', 'Mai T. N. Truong', 'Hoang Nguyen', 'Duy M. H. Nguyen'] | 2022-12-04 | null | null | null | null | ['lung-nodule-detection', 'brain-segmentation'] | ['medical', 'medical'] | [ 1.60443291e-01 4.47123021e-01 -4.28029984e-01 -6.40530825e-01
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1e9039c5-3684-4cbf-a363-637202504b8b | disenhcn-disentangled-hypergraph | 2208.06794 | null | https://arxiv.org/abs/2208.06794v1 | https://arxiv.org/pdf/2208.06794v1.pdf | DisenHCN: Disentangled Hypergraph Convolutional Networks for Spatiotemporal Activity Prediction | Spatiotemporal activity prediction, aiming to predict user activities at a specific location and time, is crucial for applications like urban planning and mobile advertising. Existing solutions based on tensor decomposition or graph embedding suffer from the following two major limitations: 1) ignoring the fine-grained similarities of user preferences; 2) user's modeling is entangled. In this work, we propose a hypergraph neural network model called DisenHCN to bridge the above gaps. In particular, we first unify the fine-grained user similarity and the complex matching between user preferences and spatiotemporal activity into a heterogeneous hypergraph. We then disentangle the user representations into different aspects (location-aware, time-aware, and activity-aware) and aggregate corresponding aspect's features on the constructed hypergraph, capturing high-order relations from different aspects and disentangles the impact of each aspect for final prediction. Extensive experiments show that our DisenHCN outperforms the state-of-the-art methods by 14.23% to 18.10% on four real-world datasets. Further studies also convincingly verify the rationality of each component in our DisenHCN. | ['Yong Li', 'Depeng Jin', 'Tong Li', 'Quanming Yao', 'Chen Gao', 'Yinfeng Li'] | 2022-08-14 | null | null | null | null | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [-1.63082987e-01 -1.58627033e-01 -7.65814841e-01 -2.24941790e-01
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eaa4fe45-732f-49f0-8b24-73da0cd31f1a | exploring-non-contrastive-representation-1 | 2111.11821 | null | https://arxiv.org/abs/2111.11821v2 | https://arxiv.org/pdf/2111.11821v2.pdf | Learning Representation for Clustering via Prototype Scattering and Positive Sampling | Existing deep clustering methods rely on either contrastive or non-contrastive representation learning for downstream clustering task. Contrastive-based methods thanks to negative pairs learn uniform representations for clustering, in which negative pairs, however, may inevitably lead to the class collision issue and consequently compromise the clustering performance. Non-contrastive-based methods, on the other hand, avoid class collision issue, but the resulting non-uniform representations may cause the collapse of clustering. To enjoy the strengths of both worlds, this paper presents a novel end-to-end deep clustering method with prototype scattering and positive sampling, termed ProPos. Specifically, we first maximize the distance between prototypical representations, named prototype scattering loss, which improves the uniformity of representations. Second, we align one augmented view of instance with the sampled neighbors of another view -- assumed to be truly positive pair in the embedding space -- to improve the within-cluster compactness, termed positive sampling alignment. The strengths of ProPos are avoidable class collision issue, uniform representations, well-separated clusters, and within-cluster compactness. By optimizing ProPos in an end-to-end expectation-maximization framework, extensive experimental results demonstrate that ProPos achieves competing performance on moderate-scale clustering benchmark datasets and establishes new state-of-the-art performance on large-scale datasets. Source code is available at \url{https://github.com/Hzzone/ProPos}. | ['Hongming Shan', 'Junping Zhang', 'Jie Chen', 'Zhizhong Huang'] | 2021-11-23 | exploring-non-contrastive-representation | https://openreview.net/forum?id=JZrETJlgyq | https://openreview.net/pdf?id=JZrETJlgyq | null | ['image-clustering'] | ['computer-vision'] | [-2.88620323e-01 1.49307288e-02 2.07110733e-01 -3.70163769e-01
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4d86d041-91fd-484a-b740-ed61f84ad822 | a-survey-on-graph-neural-networks-for-time | 2307.03759 | null | https://arxiv.org/abs/2307.03759v1 | https://arxiv.org/pdf/2307.03759v1.pdf | A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection | Time series are the primary data type used to record dynamic system measurements and generated in great volume by both physical sensors and online processes (virtual sensors). Time series analytics is therefore crucial to unlocking the wealth of information implicit in available data. With the recent advancements in graph neural networks (GNNs), there has been a surge in GNN-based approaches for time series analysis. Approaches can explicitly model inter-temporal and inter-variable relationships, which traditional and other deep neural network-based methods struggle to do. In this survey, we provide a comprehensive review of graph neural networks for time series analysis (GNN4TS), encompassing four fundamental dimensions: Forecasting, classification, anomaly detection, and imputation. Our aim is to guide designers and practitioners to understand, build applications, and advance research of GNN4TS. At first, we provide a comprehensive task-oriented taxonomy of GNN4TS. Then, we present and discuss representative research works and, finally, discuss mainstream applications of GNN4TS. A comprehensive discussion of potential future research directions completes the survey. This survey, for the first time, brings together a vast array of knowledge on GNN-based time series research, highlighting both the foundations, practical applications, and opportunities of graph neural networks for time series analysis. | ['Shirui Pan', 'Irwin King', 'Geoffrey I. Webb', 'Cesare Alippi', 'Daniele Zambon', 'Qingsong Wen', 'Huan Yee Koh', 'Ming Jin'] | 2023-07-07 | null | null | null | null | ['imputation', 'anomaly-detection', 'imputation', 'time-series-forecasting', 'imputation', 'time-series'] | ['computer-vision', 'methodology', 'miscellaneous', 'time-series', 'time-series', 'time-series'] | [ 7.90243503e-03 -4.33725387e-01 -1.53868169e-01 2.27039605e-02
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53b32c84-093c-4a6f-9e3b-f7ba701e8f30 | nonlinear-constructive-observer-design-for | 2303.05900 | null | https://arxiv.org/abs/2303.05900v1 | https://arxiv.org/pdf/2303.05900v1.pdf | Nonlinear constructive observer design for direct homography estimation | Feature-based homography estimation approaches rely on extensive image processing for feature extraction and matching, and do not adequately account for the information provided by the image. Therefore, developing efficient direct techniques to extract the homography from images is essential. This paper presents a novel nonlinear direct homography observer that exploits the Lie group structure of $\mathbf{SL}(3)$ and its action on the space of image maps. Theoretical analysis demonstrates local asymptotic convergence of the observer. The observer design is also extended for partial measurements of velocity under the assumption that the unknown component is constant or slowly time-varying. Finally, simulation results demonstrate the performance of the proposed solutions on real images. | ['Tarek Hamel', 'Robert Mahony', 'Pieter van Goor', 'Tarek Bouazza'] | 2023-03-10 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [-1.25267589e-02 -7.79214140e-04 1.96572137e-03 -2.31718067e-02
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047a8571-7298-4c16-b00d-abe5d13dae0c | non-adversarial-training-of-neural-sdes-with | 2305.16274 | null | https://arxiv.org/abs/2305.16274v1 | https://arxiv.org/pdf/2305.16274v1.pdf | Non-adversarial training of Neural SDEs with signature kernel scores | Neural SDEs are continuous-time generative models for sequential data. State-of-the-art performance for irregular time series generation has been previously obtained by training these models adversarially as GANs. However, as typical for GAN architectures, training is notoriously unstable, often suffers from mode collapse, and requires specialised techniques such as weight clipping and gradient penalty to mitigate these issues. In this paper, we introduce a novel class of scoring rules on pathspace based on signature kernels and use them as objective for training Neural SDEs non-adversarially. By showing strict properness of such kernel scores and consistency of the corresponding estimators, we provide existence and uniqueness guarantees for the minimiser. With this formulation, evaluating the generator-discriminator pair amounts to solving a system of linear path-dependent PDEs which allows for memory-efficient adjoint-based backpropagation. Moreover, because the proposed kernel scores are well-defined for paths with values in infinite dimensional spaces of functions, our framework can be easily extended to generate spatiotemporal data. Our procedure permits conditioning on a rich variety of market conditions and significantly outperforms alternative ways of training Neural SDEs on a variety of tasks including the simulation of rough volatility models, the conditional probabilistic forecasts of real-world forex pairs where the conditioning variable is an observed past trajectory, and the mesh-free generation of limit order book dynamics. | ['Cristopher Salvi', 'Maud Lemercier', 'Blanka Horvath', 'Zacharia Issa'] | 2023-05-25 | null | null | null | null | ['irregular-time-series'] | ['time-series'] | [ 1.71281502e-01 1.38000652e-01 3.12684178e-01 1.31791145e-01
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21bf810f-e019-4320-8532-1ca99ef73239 | scannet-richly-annotated-3d-reconstructions | 1702.04405 | null | http://arxiv.org/abs/1702.04405v2 | http://arxiv.org/pdf/1702.04405v2.pdf | ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes | A key requirement for leveraging supervised deep learning methods is the
availability of large, labeled datasets. Unfortunately, in the context of RGB-D
scene understanding, very little data is available -- current datasets cover a
small range of scene views and have limited semantic annotations. To address
this issue, we introduce ScanNet, an RGB-D video dataset containing 2.5M views
in 1513 scenes annotated with 3D camera poses, surface reconstructions, and
semantic segmentations. To collect this data, we designed an easy-to-use and
scalable RGB-D capture system that includes automated surface reconstruction
and crowdsourced semantic annotation. We show that using this data helps
achieve state-of-the-art performance on several 3D scene understanding tasks,
including 3D object classification, semantic voxel labeling, and CAD model
retrieval. The dataset is freely available at http://www.scan-net.org. | ['Matthias Nießner', 'Thomas Funkhouser', 'Manolis Savva', 'Maciej Halber', 'Angel X. Chang', 'Angela Dai'] | 2017-02-14 | scannet-richly-annotated-3d-reconstructions-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Dai_ScanNet_Richly-Annotated_3D_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Dai_ScanNet_Richly-Annotated_3D_CVPR_2017_paper.pdf | cvpr-2017-7 | ['3d-object-classification'] | ['computer-vision'] | [ 1.76991150e-01 1.68667361e-01 -2.33517066e-01 -8.86166930e-01
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8ad6ba6d-d6c8-406e-9b01-d3356c36ab01 | a-dual-attention-hierarchical-recurrent | 1810.09154 | null | https://arxiv.org/abs/1810.09154v3 | https://arxiv.org/pdf/1810.09154v3.pdf | A Dual-Attention Hierarchical Recurrent Neural Network for Dialogue Act Classification | Recognising dialogue acts (DA) is important for many natural language processing tasks such as dialogue generation and intention recognition. In this paper, we propose a dual-attention hierarchical recurrent neural network for DA classification. Our model is partially inspired by the observation that conversational utterances are normally associated with both a DA and a topic, where the former captures the social act and the latter describes the subject matter. However, such a dependency between DAs and topics has not been utilised by most existing systems for DA classification. With a novel dual task-specific attention mechanism, our model is able, for utterances, to capture information about both DAs and topics, as well as information about the interactions between them. Experimental results show that by modelling topic as an auxiliary task, our model can significantly improve DA classification, yielding better or comparable performance to the state-of-the-art method on three public datasets. | ['Guanyi Chen', 'Matthew Collinson', 'Ruizhe Li', 'Xiao Li', 'Chenghua Lin'] | 2018-10-22 | a-dual-attention-hierarchical-recurrent-1 | https://aclanthology.org/K19-1036 | https://aclanthology.org/K19-1036.pdf | conll-2019-11 | ['dialogue-act-classification'] | ['natural-language-processing'] | [ 1.57732859e-01 5.25186002e-01 1.53908413e-02 -4.31448907e-01
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a1888134-2907-42a3-9367-0caf9bd0f4aa | safe-critical-modular-deep-reinforcement | 2109.02791 | null | https://arxiv.org/abs/2109.02791v7 | https://arxiv.org/pdf/2109.02791v7.pdf | Safety-Critical Learning of Robot Control with Temporal Logic Specifications | Reinforcement learning (RL) is a promising approach. However, success is limited to real-world applications, because ensuring safe exploration and facilitating adequate exploitation is a challenge for controlling robotic systems with unknown models and measurement uncertainties. The learning problem becomes even more difficult for complex tasks over continuous state-action. In this paper, we propose a learning-based robotic control framework consisting of several aspects: (1) we leverage Linear Temporal Logic (LTL) to express complex tasks over infinite horizons that are translated to a novel automaton structure; (2) we detail an innovative reward scheme for LTL satisfaction with a probabilistic guarantee. Then, by applying a reward shaping technique, we develop a modular policy-gradient architecture exploiting the benefits of the automaton structure to decompose overall tasks and enhance the performance of learned controllers; (3) by incorporating Gaussian Processes (GPs) to estimate the uncertain dynamic systems, we synthesize a model-based safe exploration during the learning process using Exponential Control Barrier Functions (ECBFs) that generalize systems with high-order relative degrees; (4) to further improve the efficiency of exploration, we utilize the properties of LTL automata and ECBFs to propose a safe guiding process. Finally, we demonstrate the effectiveness of the framework via several robotic environments. We show an ECBF-based modular deep RL algorithm that achieves near-perfect success rates and safety guarding with high probability confidence during training. | ['Cristian-Ioan Vasile', 'Mingyu Cai'] | 2021-09-07 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 2.44236123e-02 4.96824324e-01 -4.05526191e-01 2.33426958e-01
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5a9331d9-8313-45b2-ad02-103c5cd74c74 | fast-and-incremental-loop-closure-detection-1 | 2010.11703 | null | https://arxiv.org/abs/2010.11703v2 | https://arxiv.org/pdf/2010.11703v2.pdf | Fast and Incremental Loop Closure Detection with Deep Features and Proximity Graphs | In recent years, the robotics community has extensively examined methods concerning the place recognition task within the scope of simultaneous localization and mapping applications.This article proposes an appearance-based loop closure detection pipeline named ``FILD++" (Fast and Incremental Loop closure Detection).First, the system is fed by consecutive images and, via passing them twice through a single convolutional neural network, global and local deep features are extracted.Subsequently, a hierarchical navigable small-world graph incrementally constructs a visual database representing the robot's traversed path based on the computed global features.Finally, a query image, grabbed each time step, is set to retrieve similar locations on the traversed route.An image-to-image pairing follows, which exploits local features to evaluate the spatial information. Thus, in the proposed article, we propose a single network for global and local feature extraction in contrast to our previous work (FILD), while an exhaustive search for the verification process is adopted over the generated deep local features avoiding the utilization of hash codes. Exhaustive experiments on eleven publicly available datasets exhibit the system's high performance (achieving the highest recall score on eight of them) and low execution times (22.05 ms on average in New College, which is the largest one containing 52480 images) compared to other state-of-the-art approaches. | ['Antonios Gasteratos', 'Konstantinos A. Tsintotas', 'Dong Wei', 'Haogang Zhu', 'Shan An'] | 2020-09-29 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [-1.93117969e-02 -8.87002144e-03 -7.90420100e-02 -1.57698423e-01
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56366352-095e-4b59-94bf-419af92cb38b | a-real-time-hand-gesture-recognition-and | 1704.07296 | null | http://arxiv.org/abs/1704.07296v1 | http://arxiv.org/pdf/1704.07296v1.pdf | A Real-time Hand Gesture Recognition and Human-Computer Interaction System | In this project, we design a real-time human-computer interaction system
based on hand gesture. The whole system consists of three components: hand
detection, gesture recognition and human-computer interaction (HCI) based on
recognition; and realizes the robust control of mouse and keyboard events with
a higher accuracy of gesture recognition. Specifically, we use the
convolutional neural network (CNN) to recognize gestures and makes it
attainable to identify relatively complex gestures using only one cheap
monocular camera. We introduce the Kalman filter to estimate the hand position
based on which the mouse cursor control is realized in a stable and smooth way.
During the HCI stage, we develop a simple strategy to avoid the false
recognition caused by noises - mostly transient, false gestures, and thus to
improve the reliability of interaction. The developed system is highly
extendable and can be used in human-robotic or other human-machine interaction
scenarios with more complex command formats rather than just mouse and keyboard
events. | ['Pei Xu'] | 2017-04-24 | null | null | null | null | ['hand-detection'] | ['computer-vision'] | [ 1.28565222e-01 -6.55502021e-01 4.49087247e-02 -5.27184978e-02
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561b834d-8feb-4753-8ade-a6e44e94d626 | valuing-player-actions-in-counter-strike | 2011.01324 | null | https://arxiv.org/abs/2011.01324v2 | https://arxiv.org/pdf/2011.01324v2.pdf | Valuing Player Actions in Counter-Strike: Global Offensive | Esports, despite its expanding interest, lacks fundamental sports analytics resources such as accessible data or proven and reproducible analytical frameworks. Even Counter-Strike: Global Offensive (CSGO), the second most popular esport, suffers from these problems. Thus, quantitative evaluation of CSGO players, a task important to teams, media, bettors and fans, is difficult. To address this, we introduce (1) a data model for CSGO with an open-source implementation; (2) a graph distance measure for defining distances in CSGO; and (3) a context-aware framework to value players' actions based on changes in their team's chances of winning. Using over 70 million in-game CSGO events, we demonstrate our framework's consistency and independence compared to existing valuation frameworks. We also provide use cases demonstrating high-impact play identification and uncertainty estimation. | ['Claudio Silva', 'Harish Doraiswamy', 'Peter Xenopoulos'] | 2020-11-02 | null | null | null | null | ['sports-analytics'] | ['computer-vision'] | [-4.71623242e-01 -1.08449511e-01 -6.12200856e-01 -7.33915344e-02
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701051f4-1084-497c-9e20-797c7d378093 | speech-bandwidth-extension-with-wavenet | 1907.04927 | null | https://arxiv.org/abs/1907.04927v1 | https://arxiv.org/pdf/1907.04927v1.pdf | Speech bandwidth extension with WaveNet | Large-scale mobile communication systems tend to contain legacy transmission channels with narrowband bottlenecks, resulting in characteristic "telephone-quality" audio. While higher quality codecs exist, due to the scale and heterogeneity of the networks, transmitting higher sample rate audio with modern high-quality audio codecs can be difficult in practice. This paper proposes an approach where a communication node can instead extend the bandwidth of a band-limited incoming speech signal that may have been passed through a low-rate codec. To this end, we propose a WaveNet-based model conditioned on a log-mel spectrogram representation of a bandwidth-constrained speech audio signal of 8 kHz and audio with artifacts from GSM full-rate (FR) compression to reconstruct the higher-resolution signal. In our experimental MUSHRA evaluation, we show that a model trained to upsample to 24kHz speech signals from audio passed through the 8kHz GSM-FR codec is able to reconstruct audio only slightly lower in quality to that of the Adaptive Multi-Rate Wideband audio codec (AMR-WB) codec at 16kHz, and closes around half the gap in perceptual quality between the original encoded signal and the original speech sampled at 24kHz. We further show that when the same model is passed 8kHz audio that has not been compressed, is able to again reconstruct audio of slightly better quality than 16kHz AMR-WB, in the same MUSHRA evaluation. | ['Thomas C. Walters', 'Yannis Assael', 'Brendan Shillingford', 'Archit Gupta'] | 2019-07-05 | null | null | null | null | ['bandwidth-extension', 'bandwidth-extension'] | ['audio', 'speech'] | [ 3.47493887e-01 2.88585901e-01 9.63552892e-02 1.49745420e-01
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41f84b1a-1068-4e3a-a497-a47628e82507 | exoskeleton-for-the-mind-exploring-strategies | 2304.08759 | null | https://arxiv.org/abs/2304.08759v1 | https://arxiv.org/pdf/2304.08759v1.pdf | Exoskeleton for the Mind: Exploring Strategies Against Misinformation with a Metacognitive Agent | Misinformation is a global problem in modern social media platforms with few solutions known to be effective. Social media platforms have offered tools to raise awareness of information, but these are closed systems that have not been empirically evaluated. Others have developed novel tools and strategies, but most have been studied out of context using static stimuli, researcher prompts, or low fidelity prototypes. We offer a new anti-misinformation agent grounded in theories of metacognition that was evaluated within Twitter. We report on a pilot study (n=17) and multi-part experimental study (n=57, n=49) where participants experienced three versions of the agent, each deploying a different strategy. We found that no single strategy was superior over the control. We also confirmed the necessity of transparency and clarity about the agent's underlying logic, as well as concerns about repeated exposure to misinformation and lack of user engagement. | ['Yuto Sawa', 'Jacqueline Urakami', 'Hiroki Oura', 'Katie Seaborn', 'Takane Ueno', 'Yeongdae Kim'] | 2023-04-18 | null | null | null | null | ['misinformation'] | ['miscellaneous'] | [-2.24280745e-01 5.87840319e-01 5.76008819e-02 -7.63399377e-02
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2a242e01-ffa9-4d74-9763-42b2061236b6 | recognizing-complex-gestures-on-minimalistic | 2303.10336 | null | https://arxiv.org/abs/2303.10336v1 | https://arxiv.org/pdf/2303.10336v1.pdf | Recognizing Complex Gestures on Minimalistic Knitted Sensors: Toward Real-World Interactive Systems | Developments in touch-sensitive textiles have enabled many novel interactive techniques and applications. Our digitally-knitted capacitive active sensors can be manufactured at scale with little human intervention. Their sensitive areas are created from a single conductive yarn, and they require only few connections to external hardware. This technique increases their robustness and usability, while shifting the complexity of enabling interactivity from the hardware to computational models. This work advances the capabilities of such sensors by creating the foundation for an interactive gesture recognition system. It uses a novel sensor design, and a neural network-based recognition model to classify 12 relatively complex, single touch point gesture classes with 89.8% accuracy, unfolding many possibilities for future applications. We also demonstrate the system's applicability and robustness to real-world conditions through its performance while being worn and the impact of washing and drying on the sensor's resistance. | ['Ali Shokoufandeh', 'Genevieve Dion', 'Lev Saunders', 'Richard Valett', 'Denisa Qori McDonald'] | 2023-03-18 | null | null | null | null | ['gesture-recognition'] | ['computer-vision'] | [ 8.83150280e-01 -1.75289482e-01 -1.11051239e-01 -1.70571759e-01
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5739d953-2b2b-4ab2-a6ff-2398e08140f0 | surveillance-face-anti-spoofing | 2301.00975 | null | https://arxiv.org/abs/2301.00975v1 | https://arxiv.org/pdf/2301.00975v1.pdf | Surveillance Face Anti-spoofing | Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. However, recent research generally focuses on short-distance applications (i.e., phone unlocking) while lacking consideration of long-distance scenes (i.e., surveillance security checks). In order to promote relevant research and fill this gap in the community, we collect a large-scale Surveillance High-Fidelity Mask (SuHiFiMask) dataset captured under 40 surveillance scenes, which has 101 subjects from different age groups with 232 3D attacks (high-fidelity masks), 200 2D attacks (posters, portraits, and screens), and 2 adversarial attacks. In this scene, low image resolution and noise interference are new challenges faced in surveillance FAS. Together with the SuHiFiMask dataset, we propose a Contrastive Quality-Invariance Learning (CQIL) network to alleviate the performance degradation caused by image quality from three aspects: (1) An Image Quality Variable module (IQV) is introduced to recover image information associated with discrimination by combining the super-resolution network. (2) Using generated sample pairs to simulate quality variance distributions to help contrastive learning strategies obtain robust feature representation under quality variation. (3) A Separate Quality Network (SQN) is designed to learn discriminative features independent of image quality. Finally, a large number of experiments verify the quality of the SuHiFiMask dataset and the superiority of the proposed CQIL. | ['Zhen Lei', 'Stan Z. Li', 'Xu Zhang', 'Chenxu Zhao', 'Sergio Escalera', 'Jun Wan', 'Ajian Liu', 'Hao Fang'] | 2023-01-03 | null | null | null | null | ['face-anti-spoofing'] | ['computer-vision'] | [ 7.63885558e-01 -5.68917394e-01 -1.88446388e-01 -3.02834153e-01
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69c11eb8-d030-4ada-a33c-0d18aa4a0116 | eye-of-the-beholder-improved-relation | 2106.05387 | null | https://arxiv.org/abs/2106.05387v2 | https://arxiv.org/pdf/2106.05387v2.pdf | Eye of the Beholder: Improved Relation Generalization for Text-based Reinforcement Learning Agents | Text-based games (TBGs) have become a popular proving ground for the demonstration of learning-based agents that make decisions in quasi real-world settings. The crux of the problem for a reinforcement learning agent in such TBGs is identifying the objects in the world, and those objects' relations with that world. While the recent use of text-based resources for increasing an agent's knowledge and improving its generalization have shown promise, we posit in this paper that there is much yet to be learned from visual representations of these same worlds. Specifically, we propose to retrieve images that represent specific instances of text observations from the world and train our agents on such images. This improves the agent's overall understanding of the game 'scene' and objects' relationships to the world around them, and the variety of visual representations on offer allow the agent to generate a better generalization of a relationship. We show that incorporating such images improves the performance of agents in various TBG settings. | ['Kartik Talamadupula', 'Subhajit Chaudhury', 'Keerthiram Murugesan'] | 2021-06-09 | null | null | null | null | ['text-based-games'] | ['playing-games'] | [ 3.47389765e-02 1.09537579e-01 1.64215378e-02 -1.26454130e-01
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e379c14e-2944-4976-b302-075060cc486c | revisiting-pretraining-with-adapters | null | null | https://aclanthology.org/2021.repl4nlp-1.11 | https://aclanthology.org/2021.repl4nlp-1.11.pdf | Revisiting Pretraining with Adapters | Pretrained language models have served as the backbone for many state-of-the-art NLP results. These models are large and expensive to train. Recent work suggests that continued pretraining on task-specific data is worth the effort as pretraining leads to improved performance on downstream tasks. We explore alternatives to full-scale task-specific pretraining of language models through the use of adapter modules, a parameter-efficient approach to transfer learning. We find that adapter-based pretraining is able to achieve comparable results to task-specific pretraining while using a fraction of the overall trainable parameters. We further explore direct use of adapters without pretraining and find that the direct fine-tuning performs mostly on par with pretrained adapter models, contradicting previously proposed benefits of continual pretraining in full pretraining fine-tuning strategies. Lastly, we perform an ablation study on task-adaptive pretraining to investigate how different hyperparameter settings can change the effectiveness of the pretraining. | ['Jonathan Hilgart', 'Nathan Susanj', 'Alex Shum', 'Seungwon Kim'] | null | null | null | null | acl-repl4nlp-2021-8 | ['continual-pretraining'] | ['methodology'] | [ 3.18582624e-01 2.30108872e-01 -3.66687626e-01 -5.63175976e-01
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84272800-49cc-4d54-a2a7-9dd0879a8a05 | intercode-standardizing-and-benchmarking | 2306.14898 | null | https://arxiv.org/abs/2306.14898v2 | https://arxiv.org/pdf/2306.14898v2.pdf | InterCode: Standardizing and Benchmarking Interactive Coding with Execution Feedback | Humans write code in a fundamentally interactive manner and rely on constant execution feedback to correct errors, resolve ambiguities, and decompose tasks. While LLMs have recently exhibited promising coding capabilities, current coding benchmarks mostly consider a static instruction-to-code sequence transduction process, which has the potential for error propagation and a disconnect between the generated code and its final execution environment. To address this gap, we introduce InterCode, a lightweight, flexible, and easy-to-use framework of interactive coding as a standard reinforcement learning (RL) environment, with code as actions and execution feedback as observations. Our framework is language and platform agnostic, uses self-contained Docker environments to provide safe and reproducible execution, and is compatible out-of-the-box with traditional seq2seq coding methods, while enabling the development of new methods for interactive code generation. We use InterCode to create two interactive code environments with Bash and SQL as action spaces, leveraging data from the static Spider and NL2Bash datasets. We demonstrate InterCode's viability as a testbed by evaluating multiple state-of-the-art LLMs configured with different prompting strategies such as ReAct and Plan & Solve. Our results showcase the benefits of interactive code generation and demonstrate that InterCode can serve as a challenging benchmark for advancing code understanding and generation capabilities. InterCode is designed to be easily extensible and can even be used to incorporate new tasks such as Capture the Flag, a popular coding puzzle that is inherently multi-step and involves multiple programming languages. Project site with code and data: https://intercode-benchmark.github.io | ['Shunyu Yao', 'Karthik Narasimhan', 'Akshara Prabhakar', 'John Yang'] | 2023-06-26 | null | null | null | null | ['code-generation', 'benchmarking', 'benchmarking'] | ['computer-code', 'miscellaneous', 'robots'] | [-1.47866160e-01 1.00001171e-01 -1.51094258e-01 -3.43674630e-01
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0c515d7d-1d1f-4bc4-801e-4a7b3e6ac97c | make-the-most-out-of-your-net-alternating | 2303.15792 | null | https://arxiv.org/abs/2303.15792v1 | https://arxiv.org/pdf/2303.15792v1.pdf | Make the Most Out of Your Net: Alternating Between Canonical and Hard Datasets for Improved Image Demosaicing | Image demosaicing is an important step in the image processing pipeline for digital cameras, and it is one of the many tasks within the field of image restoration. A well-known characteristic of natural images is that most patches are smooth, while high-content patches like textures or repetitive patterns are much rarer, which results in a long-tailed distribution. This distribution can create an inductive bias when training machine learning algorithms for image restoration tasks and for image demosaicing in particular. There have been many different approaches to address this challenge, such as utilizing specific losses or designing special network architectures. What makes our work is unique in that it tackles the problem from a training protocol perspective. Our proposed training regime consists of two key steps. The first step is a data-mining stage where sub-categories are created and then refined through an elimination process to only retain the most helpful sub-categories. The second step is a cyclic training process where the neural network is trained on both the mined sub-categories and the original dataset. We have conducted various experiments to demonstrate the effectiveness of our training method for the image demosaicing task. Our results show that this method outperforms standard training across a range of architecture sizes and types, including CNNs and Transformers. Moreover, we are able to achieve state-of-the-art results with a significantly smaller neural network, compared to previous state-of-the-art methods. | ['Tomer Peleg', 'Raz Z. Nossek', 'Yuval Becker'] | 2023-03-28 | null | null | null | null | ['demosaicking'] | ['computer-vision'] | [ 6.15855455e-01 -1.12121426e-01 -3.20748352e-02 -3.52381855e-01
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4d41bffd-564f-4ab4-8085-dafd0ac28fd9 | tab-iais-flexible-table-recognition-and | 2105.11879 | null | https://arxiv.org/abs/2105.11879v2 | https://arxiv.org/pdf/2105.11879v2.pdf | Flexible Table Recognition and Semantic Interpretation System | Table extraction is an important but still unsolved problem. In this paper, we introduce a flexible and modular table extraction system. We develop two rule-based algorithms that perform the complete table recognition process, including table detection and segmentation, and support the most frequent table formats. Moreover, to incorporate the extraction of semantic information, we develop a graph-based table interpretation method. We conduct extensive experiments on the challenging table recognition benchmarks ICDAR 2013 and ICDAR 2019, achieving results competitive with state-of-the-art approaches. Our complete information extraction system exhibited a high F1 score of 0.7380. To support future research on information extraction from documents, we make the resources (ground-truth annotations, evaluation scripts, algorithm parameters) from our table interpretation experiment publicly available. | ['Joachim köhler', 'Sven Behnke', 'Alexander M. Esser', 'Marcin Namysl'] | 2021-05-25 | null | null | null | null | ['table-recognition', 'table-detection', 'table-extraction'] | ['computer-vision', 'miscellaneous', 'miscellaneous'] | [ 3.72844398e-01 1.67942002e-01 -4.16062176e-01 -3.65928411e-01
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6bbd2737-cf25-46b6-8f9d-eeb76418cb23 | m2trec-metadata-aware-multi-task-transformer | 2209.11824 | null | https://arxiv.org/abs/2209.11824v1 | https://arxiv.org/pdf/2209.11824v1.pdf | M2TRec: Metadata-aware Multi-task Transformer for Large-scale and Cold-start free Session-based Recommendations | Session-based recommender systems (SBRSs) have shown superior performance over conventional methods. However, they show limited scalability on large-scale industrial datasets since most models learn one embedding per item. This leads to a large memory requirement (of storing one vector per item) and poor performance on sparse sessions with cold-start or unpopular items. Using one public and one large industrial dataset, we experimentally show that state-of-the-art SBRSs have low performance on sparse sessions with sparse items. We propose M2TRec, a Metadata-aware Multi-task Transformer model for session-based recommendations. Our proposed method learns a transformation function from item metadata to embeddings, and is thus, item-ID free (i.e., does not need to learn one embedding per item). It integrates item metadata to learn shared representations of diverse item attributes. During inference, new or unpopular items will be assigned identical representations for the attributes they share with items previously observed during training, and thus will have similar representations with those items, enabling recommendations of even cold-start and sparse items. Additionally, M2TRec is trained in a multi-task setting to predict the next item in the session along with its primary category and subcategories. Our multi-task strategy makes the model converge faster and significantly improves the overall performance. Experimental results show significant performance gains using our proposed approach on sparse items on the two datasets. | ['Xiquan Cui', 'Srijan Kumar', 'Amir Afsharinejad', 'Sejoon Oh', 'Walid Shalaby'] | 2022-09-23 | null | null | null | null | ['session-based-recommendations'] | ['miscellaneous'] | [-1.13303676e-01 -4.98285443e-01 -6.27150893e-01 -4.09806490e-01
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d8bb9be3-788e-4d0c-8443-741629997949 | multivariate-time-series-classification-a | 2307.02253 | null | https://arxiv.org/abs/2307.02253v1 | https://arxiv.org/pdf/2307.02253v1.pdf | Multivariate Time Series Classification: A Deep Learning Approach | This paper investigates different methods and various neural network architectures applicable in the time series classification domain. The data is obtained from a fleet of gas sensors that measure and track quantities such as oxygen and sound. With the help of this data, we can detect events such as occupancy in a specific environment. At first, we analyze the time series data to understand the effect of different parameters, such as the sequence length, when training our models. These models employ Fully Convolutional Networks (FCN) and Long Short-Term Memory (LSTM) for supervised learning and Recurrent Autoencoders for semisupervised learning. Throughout this study, we spot the differences between these methods based on metrics such as precision and recall identifying which technique best suits this problem. | ['Aida Farahani', 'Julien Vitay', 'Mohamed Abouelnaga'] | 2023-07-05 | null | null | null | null | ['classification-1', 'time-series-classification'] | ['methodology', 'time-series'] | [ 4.63033617e-02 -5.57781696e-01 -5.79187907e-02 -3.47126275e-01
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4c48826d-6582-40b6-b9e6-79a6dc0bd4a9 | hrpose-real-time-high-resolution-6d-pose | 2204.09429 | null | https://arxiv.org/abs/2204.09429v1 | https://arxiv.org/pdf/2204.09429v1.pdf | HRPose: Real-Time High-Resolution 6D Pose Estimation Network Using Knowledge Distillation | Real-time 6D object pose estimation is essential for many real-world applications, such as robotic grasping and augmented reality. To achieve an accurate object pose estimation from RGB images in real-time, we propose an effective and lightweight model, namely High-Resolution 6D Pose Estimation Network (HRPose). We adopt the efficient and small HRNetV2-W18 as a feature extractor to reduce computational burdens while generating accurate 6D poses. With only 33\% of the model size and lower computational costs, our HRPose achieves comparable performance compared with state-of-the-art models. Moreover, by transferring knowledge from a large model to our proposed HRPose through output and feature-similarity distillations, the performance of our HRPose is improved in effectiveness and efficiency. Numerical experiments on the widely-used benchmark LINEMOD demonstrate the superiority of our proposed HRPose against state-of-the-art methods. | ['Shibei Xue', 'Zihao Sheng', 'Qi Guan'] | 2022-04-20 | null | null | null | null | ['6d-pose-estimation-1', '6d-pose-estimation', 'robotic-grasping'] | ['computer-vision', 'computer-vision', 'robots'] | [-1.38477772e-01 -3.55373472e-02 -7.89154917e-02 -2.62040466e-01
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646bb901-daa0-48a7-9bd9-d9d3f72c6bc3 | learning-from-web-data-the-benefit-of | 1812.09232 | null | http://arxiv.org/abs/1812.09232v1 | http://arxiv.org/pdf/1812.09232v1.pdf | Learning from Web Data: the Benefit of Unsupervised Object Localization | Annotating a large number of training images is very time-consuming. In this
background, this paper focuses on learning from easy-to-acquire web data and
utilizes the learned model for fine-grained image classification in labeled
datasets. Currently, the performance gain from training with web data is
incremental, like a common saying "better than nothing, but not by much".
Conventionally, the community looks to correcting the noisy web labels to
select informative samples. In this work, we first systematically study the
built-in gap between the web and standard datasets, i.e. different data
distributions between the two kinds of data. Then, in addition to using web
labels, we present an unsupervised object localization method, which provides
critical insights into the object density and scale in web images.
Specifically, we design two constraints on web data to substantially reduce the
difference of data distributions for the web and standard datasets. First, we
present a method to control the scale, localization and number of objects in
the detected region. Second, we propose to select the regions containing
objects that are consistent with the web tag. Based on the two constraints, we
are able to process web images to reduce the gap, and the processed web data is
used to better assist the standard dataset to train CNNs. Experiments on
several fine-grained image classification datasets confirm that our method
performs favorably against the state-of-the-art methods. | ['Jufeng Yang', 'Yu-Kun Lai', 'Xiaoxiao Sun', 'Liang Zheng'] | 2018-12-21 | null | null | null | null | ['unsupervised-object-localization'] | ['computer-vision'] | [ 1.31082565e-01 -1.56501845e-01 -1.74492493e-01 -6.15736783e-01
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9d4d7ac3-5389-42c9-a00d-bf7d847f91c3 | soft-prompt-tuning-to-predict-lung-cancer | 2303.15846 | null | https://arxiv.org/abs/2303.15846v1 | https://arxiv.org/pdf/2303.15846v1.pdf | Soft-prompt tuning to predict lung cancer using primary care free-text Dutch medical notes | We investigate different natural language processing (NLP) approaches based on contextualised word representations for the problem of early prediction of lung cancer using free-text patient medical notes of Dutch primary care physicians. Because lung cancer has a low prevalence in primary care, we also address the problem of classification under highly imbalanced classes. Specifically, we use large Transformer-based pretrained language models (PLMs) and investigate: 1) how \textit{soft prompt-tuning} -- an NLP technique used to adapt PLMs using small amounts of training data -- compares to standard model fine-tuning; 2) whether simpler static word embedding models (WEMs) can be more robust compared to PLMs in highly imbalanced settings; and 3) how models fare when trained on notes from a small number of patients. We find that 1) soft-prompt tuning is an efficient alternative to standard model fine-tuning; 2) PLMs show better discrimination but worse calibration compared to simpler static word embedding models as the classification problem becomes more imbalanced; and 3) results when training models on small number of patients are mixed and show no clear differences between PLMs and WEMs. All our code is available open source in \url{https://bitbucket.org/aumc-kik/prompt_tuning_cancer_prediction/}. | ['Iacer Calixto', 'Ameen Abu-Hanna', 'Iacopo Vagliano', 'Auke Elfrink'] | 2023-03-28 | null | null | null | null | ['contextualised-word-representations'] | ['natural-language-processing'] | [ 1.55063644e-01 3.95106733e-01 -6.11229539e-01 -2.10886598e-01
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7b7ee84c-380d-4349-b664-9cbca4b221bc | deep-multi-modal-networks-for-book-genre | 2011.07658 | null | https://arxiv.org/abs/2011.07658v1 | https://arxiv.org/pdf/2011.07658v1.pdf | Deep multi-modal networks for book genre classification based on its cover | Book covers are usually the very first impression to its readers and they often convey important information about the content of the book. Book genre classification based on its cover would be utterly beneficial to many modern retrieval systems, considering that the complete digitization of books is an extremely expensive task. At the same time, it is also an extremely challenging task due to the following reasons: First, there exists a wide variety of book genres, many of which are not concretely defined. Second, book covers, as graphic designs, vary in many different ways such as colors, styles, textual information, etc, even for books of the same genre. Third, book cover designs may vary due to many external factors such as country, culture, target reader populations, etc. With the growing competitiveness in the book industry, the book cover designers and typographers push the cover designs to its limit in the hope of attracting sales. The cover-based book classification systems become a particularly exciting research topic in recent years. In this paper, we propose a multi-modal deep learning framework to solve this problem. The contribution of this paper is four-fold. First, our method adds an extra modality by extracting texts automatically from the book covers. Second, image-based and text-based, state-of-the-art models are evaluated thoroughly for the task of book cover classification. Third, we develop an efficient and salable multi-modal framework based on the images and texts shown on the covers only. Fourth, a thorough analysis of the experimental results is given and future works to improve the performance is suggested. The results show that the multi-modal framework significantly outperforms the current state-of-the-art image-based models. However, more efforts and resources are needed for this classification task in order to reach a satisfactory level. | ['Lukun Zheng', 'Chandra Kundu'] | 2020-11-15 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [-8.63243043e-02 -6.25444353e-01 -3.50209832e-01 -2.05303803e-01
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139ff613-8b65-4918-acd7-8da8c6326d8d | online-real-time-recurrent-learning-using | 2302.05326 | null | https://arxiv.org/abs/2302.05326v2 | https://arxiv.org/pdf/2302.05326v2.pdf | Scalable Real-Time Recurrent Learning Using Sparse Connections and Selective Learning | State construction from sensory observations is an important component of a reinforcement learning agent. One solution for state construction is to use recurrent neural networks. Back-propagation through time (BPTT), and real-time recurrent learning (RTRL) are two popular gradient-based methods for recurrent learning. BPTT requires the complete sequence of observations before computing gradients and is unsuitable for online real-time updates. RTRL can do online updates but scales poorly to large networks. In this paper, we propose two constraints that make RTRL scalable. We show that by either decomposing the network into independent modules, or learning the network incrementally, we can make RTRL scale linearly with the number of parameters. Unlike prior scalable gradient estimation algorithms, such as UORO and Truncated-BPTT, our algorithms do not add noise or bias to the gradient estimate. Instead, they trade-off the functional capacity of the network to achieve scalable learning. We demonstrate the effectiveness of our approach over Truncated-BPTT on a benchmark inspired by animal learning and by doing policy evaluation for pre-trained Rainbow-DQN agents in the Arcade Learning Environment (ALE). | ['Martha White', 'Rich Sutton', 'Haseeb Shah', 'Khurram Javed'] | 2023-01-20 | null | null | null | null | ['atari-games'] | ['playing-games'] | [-2.15356484e-01 -9.79335159e-02 -4.73074734e-01 -1.26858652e-01
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aad136da-d94d-43e4-82f1-de653a5cbf18 | an-adaptive-and-scalable-ann-based-model | 2203.10515 | null | https://arxiv.org/abs/2203.10515v1 | https://arxiv.org/pdf/2203.10515v1.pdf | An Adaptive and Scalable ANN-based Model-Order-Reduction Method for Large-Scale TO Designs | Topology Optimization (TO) provides a systematic approach for obtaining structure design with optimum performance of interest. However, the process requires numerical evaluation of objective function and constraints at each iteration, which is computational expensive especially for large-scale design. Deep learning-based models have been developed to accelerate the process either by acting as surrogate models replacing the simulation process, or completely replacing the optimization process. However, most of them require a large set of labelled training data, which are generated mostly through simulations. The data generation time scales rapidly with the design domain size, decreasing the efficiency of the method itself. Another major issue is the weak generalizability of most deep learning models. Most models are trained to work with the design problem similar to that used for data generation and require retraining if the design problem changes. In this work a scalable deep learning-based model-order-reduction method is proposed to accelerate large-scale TO process, by utilizing MapNet, a neural network which maps the field of interest from coarse-scale to fine-scale. The proposed method allows for each simulation of the TO process to be performed at a coarser mesh, thereby greatly reducing the total computational time. Moreover, by using domain fragmentation, the transferability of the MapNet is largely improved. Specifically, it has been demonstrated that the MapNet trained using data from one cantilever beam design with a specific loading condition can be directly applied to other structure design problems with different domain shapes, sizes, boundary and loading conditions. | ['Wenjing Ye', 'Dan Xu', 'Chao Qian', 'Ren Kai Tan'] | 2022-03-20 | null | null | null | null | ['cantilever-beam'] | ['miscellaneous'] | [-1.16339602e-01 -2.66289823e-02 5.60438819e-02 -4.39626537e-02
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0e96b7a2-a2b0-4f25-8c2c-930b36d01e4f | superline3d-self-supervised-line-segmentation | 2208.01925 | null | https://arxiv.org/abs/2208.01925v1 | https://arxiv.org/pdf/2208.01925v1.pdf | SuperLine3D: Self-supervised Line Segmentation and Description for LiDAR Point Cloud | Poles and building edges are frequently observable objects on urban roads, conveying reliable hints for various computer vision tasks. To repetitively extract them as features and perform association between discrete LiDAR frames for registration, we propose the first learning-based feature segmentation and description model for 3D lines in LiDAR point cloud. To train our model without the time consuming and tedious data labeling process, we first generate synthetic primitives for the basic appearance of target lines, and build an iterative line auto-labeling process to gradually refine line labels on real LiDAR scans. Our segmentation model can extract lines under arbitrary scale perturbations, and we use shared EdgeConv encoder layers to train the two segmentation and descriptor heads jointly. Base on the model, we can build a highly-available global registration module for point cloud registration, in conditions without initial transformation hints. Experiments have demonstrated that our line-based registration method is highly competitive to state-of-the-art point-based approaches. Our code is available at https://github.com/zxrzju/SuperLine3D.git. | ['Yong liu', 'Mingyang Li', 'Teng Ma', 'Jun Chen', 'Tianxin Huang', 'Sheng Yang', 'Xiangrui Zhao'] | 2022-08-03 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [-9.46160108e-02 1.54686440e-02 -2.45161355e-01 -6.48048103e-01
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482fb6a9-eefe-4d97-bbb8-7e8820c94f9b | backdoor-vulnerabilities-in-normally-trained | 2211.15929 | null | https://arxiv.org/abs/2211.15929v1 | https://arxiv.org/pdf/2211.15929v1.pdf | Backdoor Vulnerabilities in Normally Trained Deep Learning Models | We conduct a systematic study of backdoor vulnerabilities in normally trained Deep Learning models. They are as dangerous as backdoors injected by data poisoning because both can be equally exploited. We leverage 20 different types of injected backdoor attacks in the literature as the guidance and study their correspondences in normally trained models, which we call natural backdoor vulnerabilities. We find that natural backdoors are widely existing, with most injected backdoor attacks having natural correspondences. We categorize these natural backdoors and propose a general detection framework. It finds 315 natural backdoors in the 56 normally trained models downloaded from the Internet, covering all the different categories, while existing scanners designed for injected backdoors can at most detect 65 backdoors. We also study the root causes and defense of natural backdoors. | ['Xiangyu Zhang', 'Yunshu Mao', 'Zhuo Zhang', 'Guangyu Shen', 'Yingqi Liu', 'Shengwei An', 'Shiqing Ma', 'Siyuan Cheng', 'Zhenting Wang', 'Guanhong Tao'] | 2022-11-29 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [-2.47403100e-01 -1.12669908e-01 -7.07218289e-01 1.37393653e-01
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abd2f5b1-dfa6-4ae4-8697-5720f3f7591d | set-transformer-beamsnet-for-auv-velocity | 2212.11671 | null | https://arxiv.org/abs/2212.11671v1 | https://arxiv.org/pdf/2212.11671v1.pdf | Set-Transformer BeamsNet for AUV Velocity Forecasting in Complete DVL Outage Scenarios | Autonomous underwater vehicles (AUVs) are regularly used for deep ocean applications. Commonly, the autonomous navigation task is carried out by a fusion between two sensors: the inertial navigation system and the Doppler velocity log (DVL). The DVL operates by transmitting four acoustic beams to the sea floor, and once reflected back, the AUV velocity vector can be estimated. However, in real-life scenarios, such as an uneven seabed, sea creatures blocking the DVL's view and, roll/pitch maneuvers, the acoustic beams' reflection is resulting in a scenario known as DVL outage. Consequently, a velocity update is not available to bind the inertial solution drift. To cope with such situations, in this paper, we leverage our BeamsNet framework and propose a Set-Transformer-based BeamsNet (ST-BeamsNet) that utilizes inertial data readings and previous DVL velocity measurements to regress the current AUV velocity in case of a complete DVL outage. The proposed approach was evaluated using data from experiments held in the Mediterranean Sea with the Snapir AUV and was compared to a moving average (MA) estimator. Our ST-BeamsNet estimated the AUV velocity vector with an 8.547% speed error, which is 26% better than the MA approach. | ['Itzik Klein', 'Zeev Yampolsky', 'Nadav Cohen'] | 2022-12-22 | null | null | null | null | ['blocking'] | ['natural-language-processing'] | [-3.94643731e-02 1.49872497e-01 4.97521192e-01 -2.13689227e-02
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e4c514fa-2c95-4e52-9eb5-c7bd8c6499a3 | qi-tts-questioning-intonation-control-for | 2303.07682 | null | https://arxiv.org/abs/2303.07682v1 | https://arxiv.org/pdf/2303.07682v1.pdf | QI-TTS: Questioning Intonation Control for Emotional Speech Synthesis | Recent expressive text to speech (TTS) models focus on synthesizing emotional speech, but some fine-grained styles such as intonation are neglected. In this paper, we propose QI-TTS which aims to better transfer and control intonation to further deliver the speaker's questioning intention while transferring emotion from reference speech. We propose a multi-style extractor to extract style embedding from two different levels. While the sentence level represents emotion, the final syllable level represents intonation. For fine-grained intonation control, we use relative attributes to represent intonation intensity at the syllable level.Experiments have validated the effectiveness of QI-TTS for improving intonation expressiveness in emotional speech synthesis. | ['Jing Xiao', 'Ning Cheng', 'Jianzong Wang', 'xulong Zhang', 'Haobin Tang'] | 2023-03-14 | null | null | null | null | ['emotional-speech-synthesis', 'speech-synthesis'] | ['speech', 'speech'] | [ 1.08611137e-01 5.85480966e-03 -1.89036131e-01 -5.16731441e-01
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f732c75e-e4c8-4372-9f5b-729b3aa13899 | paco-provocation-involving-action-culture-and | 2303.12808 | null | https://arxiv.org/abs/2303.12808v1 | https://arxiv.org/pdf/2303.12808v1.pdf | PACO: Provocation Involving Action, Culture, and Oppression | In India, people identify with a particular group based on certain attributes such as religion. The same religious groups are often provoked against each other. Previous studies show the role of provocation in increasing tensions between India's two prominent religious groups: Hindus and Muslims. With the advent of the Internet, such provocation also surfaced on social media platforms such as WhatsApp. By leveraging an existing dataset of Indian WhatsApp posts, we identified three categories of provoking sentences against Indian Muslims. Further, we labeled 7,000 sentences for three provocation categories and called this dataset PACO. We leveraged PACO to train a model that can identify provoking sentences from a WhatsApp post. Our best model is fine-tuned RoBERTa and achieved a 0.851 average AUC score over five-fold cross-validation. Automatically identifying provoking sentences could stop provoking text from reaching out to the masses, and can prevent possible discrimination or violence against the target religious group. Further, we studied the provocative speech through a pragmatic lens, by identifying the dialog acts and impoliteness super-strategies used against the religious group. | ['Munindar P. Singh', 'Ganning Xu', 'Vaibhav Garg'] | 2023-03-19 | null | null | null | null | ['culture'] | ['speech'] | [-7.67440945e-02 5.60764909e-01 -3.78200948e-01 -3.76907736e-01
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f5d9a122-2f69-4fe4-a44b-438886b2beb1 | a-complex-network-approach-to-time-series | 2108.06920 | null | https://arxiv.org/abs/2108.06920v1 | https://arxiv.org/pdf/2108.06920v1.pdf | A complex network approach to time series analysis with application in diagnosis of neuromuscular disorders | Electromyography (EMG) refers to a biomedical signal indicating neuromuscular activity and muscle morphology. Experts accurately diagnose neuromuscular disorders using this time series. Modern data analysis techniques have recently led to introducing novel approaches for mapping time series data to graphs and complex networks with applications in diverse fields, including medicine. The resulting networks develop a completely different visual acuity that can be used to complement physician findings of time series. This can lead to a more enriched analysis, reduced error, more accurate diagnosis of the disease, and increased accuracy and speed of the treatment process. The mapping process may cause the loss of essential data from the time series and not retain all the time series features. As a result, achieving an approach that can provide a good representation of the time series while maintaining essential features is crucial. This paper proposes a new approach to network development named GraphTS to overcome the limited accuracy of existing methods through EMG time series using the visibility graph method. For this purpose, EMG signals are pre-processed and mapped to a complex network by a standard visibility graph algorithm. The resulting networks can differentiate between healthy and patient samples. In the next step, the properties of the developed networks are given in the form of a feature matrix as input to classifiers after extracting optimal features. Performance evaluation of the proposed approach with deep neural network shows 99.30% accuracy for training data and 99.18% for test data. Therefore, in addition to enriched network representation and covering the features of time series for healthy, myopathy, and neuropathy EMG, the proposed technique improves accuracy, precision, recall, and F-score. | ['Behnaz Ansari', 'Nasser Ghadiri', 'Samaneh Samiei'] | 2021-08-16 | null | null | null | null | ['electromyography-emg'] | ['medical'] | [ 3.71668577e-01 -6.68274760e-02 -1.08596392e-01 2.45057717e-02
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9e4cd924-22ac-4c62-a0be-26cd77e77b18 | plug-and-play-controller-for-story-completion | null | null | https://aclanthology.org/2022.in2writing-1.6 | https://aclanthology.org/2022.in2writing-1.6.pdf | Plug-and-Play Controller for Story Completion: A Pilot Study toward Emotion-aware Story Writing Assistance | Emotions are essential for storytelling and narrative generation, and as such, the relationship between stories and emotions has been extensively studied. The authors of this paper, including a professional novelist, have examined the use of natural language processing to address the problems of novelists from the perspective of practical creative writing. In particular, the story completion task, which requires understanding the existing unfinished context, was studied from the perspective of creative support for human writers, to generate appropriate content to complete the unfinished parts. It was found that unsupervised pre-trained large neural models of the sequence-to-sequence type are useful for this task. Furthermore, based on the plug-and-play module for controllable text generation using GPT-2, an additional module was implemented to consider emotions. Although this is a preliminary study, and the results leave room for improvement before incorporating the model into a practical system, this effort is an important step in complementing the emotional trajectory of the story. | ['Tatsuya Harada', 'Ryohei Shimizu', 'Hiroaki Yamane', 'Yusuke Mori'] | null | null | null | null | in2writing-acl-2022-5 | ['story-completion'] | ['natural-language-processing'] | [ 3.82076830e-01 5.38309932e-01 2.03140110e-01 -3.58270377e-01
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5188d410-758d-44c3-ab0d-775d878ee496 | the-stoic2021-covid-19-ai-challenge-applying | 2306.10484 | null | https://arxiv.org/abs/2306.10484v2 | https://arxiv.org/pdf/2306.10484v2.pdf | The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private data | Challenges drive the state-of-the-art of automated medical image analysis. The quantity of public training data that they provide can limit the performance of their solutions. Public access to the training methodology for these solutions remains absent. This study implements the Type Three (T3) challenge format, which allows for training solutions on private data and guarantees reusable training methodologies. With T3, challenge organizers train a codebase provided by the participants on sequestered training data. T3 was implemented in the STOIC2021 challenge, with the goal of predicting from a computed tomography (CT) scan whether subjects had a severe COVID-19 infection, defined as intubation or death within one month. STOIC2021 consisted of a Qualification phase, where participants developed challenge solutions using 2000 publicly available CT scans, and a Final phase, where participants submitted their training methodologies with which solutions were trained on CT scans of 9724 subjects. The organizers successfully trained six of the eight Final phase submissions. The submitted codebases for training and running inference were released publicly. The winning solution obtained an area under the receiver operating characteristic curve for discerning between severe and non-severe COVID-19 of 0.815. The Final phase solutions of all finalists improved upon their Qualification phase solutions.HSUXJM-TNZF9CHSUXJM-TNZF9C | ['Marie-Pierre Revel Dubois', 'Bram van Ginneken', 'Nikos Paragios', 'Razvan Ionasec', 'Christoph Russ', 'Alexander Lemm', 'James A. Meakin', 'Marco Aiello', 'Pasquale Borrelli', 'Mario Verdicchio', 'Reda Bouadjenek', 'Imran Razzak', 'Ngoc Dung Huynh', 'Panagiotis Gonidakis', 'Nikos Deligiannis', 'Hichem Sahli', 'Jef Vandemeulebroucke', 'Tanmoy Mukherjee', 'Abel Diaz Berenguer', 'Matias Nicolas Bossa', 'Ine Dirks', 'Miriam Elia', 'Fabio Hellmann', 'Niklas Schroter', 'Silvan Mertes', 'Dominik Muller', 'Mayug Maniparambil', 'Derik Shi', 'Qi Wang', 'Cong Chen', 'Guang Li', 'Simon Jegou', 'Rainer Lienhart', 'Katja Ludwig', 'Robin Schon', 'Daniel Kienzle', 'Julian Lorenz', 'Luuk H. Boulogne'] | 2023-06-18 | null | null | null | null | ['computed-tomography-ct'] | ['methodology'] | [-3.28413993e-02 8.63363296e-02 -8.05726871e-02 -4.49210346e-01
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e204a7aa-b7fc-4bc5-9c7a-ba8908d36f63 | centermask-real-time-anchor-free-instance-3 | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Lee_CenterMask_Real-Time_Anchor-Free_Instance_Segmentation_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Lee_CenterMask_Real-Time_Anchor-Free_Instance_Segmentation_CVPR_2020_paper.pdf | CenterMask: Real-Time Anchor-Free Instance Segmentation | We propose a simple yet efficient anchor-free instance segmentation, called CenterMask, that adds a novel spatial attention-guided mask (SAG-Mask) branch to anchor-free one stage object detector (FCOS) in the same vein with Mask R-CNN. Plugged into the FCOS object detector, the SAG-Mask branch predicts a segmentation mask on each box with the spatial attention map that helps to focus on informative pixels and suppress noise. We also present an improved backbone networks, VoVNetV2, with two effective strategies: (1) residual connection for alleviating the optimization problem of larger VoVNet [??] and (2) effective Squeeze-Excitation (eSE) dealing with the channel information loss problem of original SE. With SAG-Mask and VoVNetV2, we deign CenterMask and CenterMask-Lite that are targeted to large and small models, respectively. Using the same ResNet-101-FPN backbone, CenterMask achieves 38.3%, surpassing all previous state-of-the-art methods while at a much faster speed. CenterMask-Lite also outperforms the state-of-the-art by large margins at over 35fps on Titan Xp. We hope that CenterMask and VoVNetV2 can serve as a solid baseline of real-time instance segmentation and backbone network for various vision tasks, respectively. The Code is available at https://github.com/youngwanLEE/CenterMask.
| [' Jongyoul Park', 'Youngwan Lee'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['real-time-instance-segmentation'] | ['computer-vision'] | [ 1.94223776e-01 5.42567015e-01 -2.32856035e-01 -2.46353000e-01
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bf8ed96b-def1-47b1-a6b8-7c7bbd0d0990 | boltzmann-machine-learning-and-regularization | 1909.05006 | null | https://arxiv.org/abs/1909.05006v4 | https://arxiv.org/pdf/1909.05006v4.pdf | Boltzmann machine learning and regularization methods for inferring evolutionary fields and couplings from a multiple sequence alignment | The inverse Potts problem to infer a Boltzmann distribution for homologous protein sequences from their single-site and pairwise amino acid frequencies recently attracts a great deal of attention in the studies of protein structure and evolution. We study regularization and learning methods and how to tune regularization parameters to correctly infer interactions in Boltzmann machine learning. Using $L_2$ regularization for fields, group $L_1$ for couplings is shown to be very effective for sparse couplings in comparison with $L_2$ and $L_1$. Two regularization parameters are tuned to yield equal values for both the sample and ensemble averages of evolutionary energy. Both averages smoothly change and converge, but their learning profiles are very different between learning methods. The Adam method is modified to make stepsize proportional to the gradient for sparse couplings and to use a soft-thresholding function for group $L_1$. It is shown by first inferring interactions from protein sequences and then from Monte Carlo samples that the fields and couplings can be well recovered, but that recovering the pairwise correlations in the resolution of a total energy is harder for the natural proteins than for the protein-like sequences. Selective temperature for folding/structural constrains in protein evolution is also estimated. | ['Sanzo Miyazawa'] | 2019-09-10 | null | null | null | null | ['multiple-sequence-alignment'] | ['medical'] | [ 3.42910945e-01 3.28757688e-02 4.39739525e-02 -6.79521382e-01
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3d3a78c7-c0b6-4d74-adc2-9bd12d401d38 | ideal-improved-dense-local-contrastive | 2210.15075 | null | https://arxiv.org/abs/2210.15075v2 | https://arxiv.org/pdf/2210.15075v2.pdf | IDEAL: Improved DEnse locAL Contrastive Learning for Semi-Supervised Medical Image Segmentation | Due to the scarcity of labeled data, Contrastive Self-Supervised Learning (SSL) frameworks have lately shown great potential in several medical image analysis tasks. However, the existing contrastive mechanisms are sub-optimal for dense pixel-level segmentation tasks due to their inability to mine local features. To this end, we extend the concept of metric learning to the segmentation task, using a dense (dis)similarity learning for pre-training a deep encoder network, and employing a semi-supervised paradigm to fine-tune for the downstream task. Specifically, we propose a simple convolutional projection head for obtaining dense pixel-level features, and a new contrastive loss to utilize these dense projections thereby improving the local representations. A bidirectional consistency regularization mechanism involving two-stream model training is devised for the downstream task. Upon comparison, our IDEAL method outperforms the SoTA methods by fair margins on cardiac MRI segmentation. Code available: https://github.com/hritam-98/IDEAL-ICASSP23 | ['Rammohan Mallipeddi', 'Sayan Nag', 'Rohit Kundu', 'Soumitri Chattopadhyay', 'Hritam Basak'] | 2022-10-26 | null | null | null | null | ['semi-supervised-medical-image-segmentation'] | ['computer-vision'] | [ 3.28451872e-01 3.16619515e-01 -2.33743683e-01 -7.91274607e-01
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1832ebf3-fb0b-4a1b-a260-e0dfaf115b6b | residual-lstm-design-of-a-deep-recurrent | 1701.03360 | null | http://arxiv.org/abs/1701.03360v3 | http://arxiv.org/pdf/1701.03360v3.pdf | Residual LSTM: Design of a Deep Recurrent Architecture for Distant Speech Recognition | In this paper, a novel architecture for a deep recurrent neural network,
residual LSTM is introduced. A plain LSTM has an internal memory cell that can
learn long term dependencies of sequential data. It also provides a temporal
shortcut path to avoid vanishing or exploding gradients in the temporal domain.
The residual LSTM provides an additional spatial shortcut path from lower
layers for efficient training of deep networks with multiple LSTM layers.
Compared with the previous work, highway LSTM, residual LSTM separates a
spatial shortcut path with temporal one by using output layers, which can help
to avoid a conflict between spatial and temporal-domain gradient flows.
Furthermore, residual LSTM reuses the output projection matrix and the output
gate of LSTM to control the spatial information flow instead of additional gate
networks, which effectively reduces more than 10% of network parameters. An
experiment for distant speech recognition on the AMI SDM corpus shows that
10-layer plain and highway LSTM networks presented 13.7% and 6.2% increase in
WER over 3-layer aselines, respectively. On the contrary, 10-layer residual
LSTM networks provided the lowest WER 41.0%, which corresponds to 3.3% and 2.8%
WER reduction over plain and highway LSTM networks, respectively. | ['Mostafa El-Khamy', 'Jungwon Lee', 'Jaeyoung Kim'] | 2017-01-10 | null | null | null | null | ['distant-speech-recognition'] | ['speech'] | [-1.40652815e-02 4.96739417e-01 7.63212666e-02 -2.42280319e-01
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d4fe6532-12f9-4eca-8bc0-f9e41fdb3d12 | english-malay-cross-lingual-embedding | null | null | https://aclanthology.org/2022.acl-srw.16 | https://aclanthology.org/2022.acl-srw.16.pdf | English-Malay Cross-Lingual Embedding Alignment using Bilingual Lexicon Augmentation | As high-quality Malay language resources are still a scarcity, cross lingual word embeddings make it possible for richer English resources to be leveraged for downstream Malay text classification tasks. This paper focuses on creating an English-Malay cross-lingual word embeddings using embedding alignment by exploiting existing language resources. We augmented the training bilingual lexicons using machine translation with the goal to improve the alignment precision of our cross-lingual word embeddings. We investigated the quality of the current state-of-the-art English-Malay bilingual lexicon and worked on improving its quality using Google Translate. We also examined the effect of Malay word coverage on the quality of cross-lingual word embeddings. Experimental results with a precision up till 28.17% show that the alignment precision of the cross-lingual word embeddings would inevitably degrade after 1-NN but a better seed lexicon and cleaner nearest neighbours can reduce the number of word pairs required to achieve satisfactory performance. As the English and Malay monolingual embeddings are pre-trained on informal language corpora, our proposed English-Malay embeddings alignment approach is also able to map non-standard Malay translations in the English nearest neighbours. | ['Jasy Suet Yan Liew', 'Ying Hao Lim'] | null | null | null | null | acl-2022-5 | ['cross-lingual-word-embeddings'] | ['natural-language-processing'] | [-2.49435261e-01 7.38724768e-02 -2.74520487e-01 -3.65607083e-01
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3a112783-d94e-4dd4-8807-1751adfdf0fe | cnn-based-rgb-d-salient-object-detection | 1909.09309 | null | https://arxiv.org/abs/1909.09309v1 | https://arxiv.org/pdf/1909.09309v1.pdf | CNN-based RGB-D Salient Object Detection: Learn, Select and Fuse | The goal of this work is to present a systematic solution for RGB-D salient object detection, which addresses the following three aspects with a unified framework: modal-specific representation learning, complementary cue selection and cross-modal complement fusion. To learn discriminative modal-specific features, we propose a hierarchical cross-modal distillation scheme, in which the well-learned source modality provides supervisory signals to facilitate the learning process for the new modality. To better extract the complementary cues, we formulate a residual function to incorporate complements from the paired modality adaptively. Furthermore, a top-down fusion structure is constructed for sufficient cross-modal interactions and cross-level transmissions. The experimental results demonstrate the effectiveness of the proposed cross-modal distillation scheme in zero-shot saliency detection and pre-training on a new modality, as well as the advantages in selecting and fusing cross-modal/cross-level complements. | ['Youfu Li', 'Hao Chen'] | 2019-09-20 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 4.45262879e-01 -1.19487613e-01 -3.55132192e-01 -2.06904218e-01
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516de9a5-9e34-4288-a84a-1331205cbeb5 | mocanet-motion-retargeting-in-the-wild-via | 2112.10082 | null | https://arxiv.org/abs/2112.10082v2 | https://arxiv.org/pdf/2112.10082v2.pdf | MoCaNet: Motion Retargeting in-the-wild via Canonicalization Networks | We present a novel framework that brings the 3D motion retargeting task from controlled environments to in-the-wild scenarios. In particular, our method is capable of retargeting body motion from a character in a 2D monocular video to a 3D character without using any motion capture system or 3D reconstruction procedure. It is designed to leverage massive online videos for unsupervised training, needless of 3D annotations or motion-body pairing information. The proposed method is built upon two novel canonicalization operations, structure canonicalization and view canonicalization. Trained with the canonicalization operations and the derived regularizations, our method learns to factorize a skeleton sequence into three independent semantic subspaces, i.e., motion, structure, and view angle. The disentangled representation enables motion retargeting from 2D to 3D with high precision. Our method achieves superior performance on motion transfer benchmarks with large body variations and challenging actions. Notably, the canonicalized skeleton sequence could serve as a disentangled and interpretable representation of human motion that benefits action analysis and motion retrieval. | ['Chen Change Loy', 'Yizhou Wang', 'Wayne Wu', 'Ziang Di', 'Zhuoqian Yang', 'Wentao Zhu'] | 2021-12-19 | null | null | null | null | ['action-analysis', 'motion-retargeting'] | ['computer-vision', 'computer-vision'] | [ 1.35597825e-01 -1.24833196e-01 -5.37175119e-01 -1.21431937e-02
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3924e078-abeb-411e-903f-beea299d2553 | foreground-aware-semantic-representations-for | 2006.00809 | null | https://arxiv.org/abs/2006.00809v1 | https://arxiv.org/pdf/2006.00809v1.pdf | Foreground-aware Semantic Representations for Image Harmonization | Image harmonization is an important step in photo editing to achieve visual consistency in composite images by adjusting the appearances of foreground to make it compatible with background. Previous approaches to harmonize composites are based on training of encoder-decoder networks from scratch, which makes it challenging for a neural network to learn a high-level representation of objects. We propose a novel architecture to utilize the space of high-level features learned by a pre-trained classification network. We create our models as a combination of existing encoder-decoder architectures and a pre-trained foreground-aware deep high-resolution network. We extensively evaluate the proposed method on existing image harmonization benchmark and set up a new state-of-the-art in terms of MSE and PSNR metrics. The code and trained models are available at \url{https://github.com/saic-vul/image_harmonization}. | ['Polina Popenova', 'Konstantin Sofiiuk', 'Anton Konushin'] | 2020-06-01 | null | null | null | null | ['image-harmonization'] | ['computer-vision'] | [ 6.02788448e-01 -1.36315733e-01 3.77333164e-02 -3.20164412e-01
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764a66d1-9a70-42dc-9766-43e1d965bb20 | simple-primitives-with-feasibility-and | 2211.02895 | null | https://arxiv.org/abs/2211.02895v1 | https://arxiv.org/pdf/2211.02895v1.pdf | Simple Primitives with Feasibility- and Contextuality-Dependence for Open-World Compositional Zero-shot Learning | The task of Compositional Zero-Shot Learning (CZSL) is to recognize images of novel state-object compositions that are absent during the training stage. Previous methods of learning compositional embedding have shown effectiveness in closed-world CZSL. However, in Open-World CZSL (OW-CZSL), their performance tends to degrade significantly due to the large cardinality of possible compositions. Some recent works separately predict simple primitives (i.e., states and objects) to reduce cardinality. However, they consider simple primitives as independent probability distributions, ignoring the heavy dependence between states, objects, and compositions. In this paper, we model the dependence of compositions via feasibility and contextuality. Feasibility-dependence refers to the unequal feasibility relations between simple primitives, e.g., \textit{hairy} is more feasible with \textit{cat} than with \textit{building} in the real world. Contextuality-dependence represents the contextual variance in images, e.g., \textit{cat} shows diverse appearances under the state of \textit{dry} and \textit{wet}. We design Semantic Attention (SA) and generative Knowledge Disentanglement (KD) to learn the dependence of feasibility and contextuality, respectively. SA captures semantics in compositions to alleviate impossible predictions, driven by the visual similarity between simple primitives. KD disentangles images into unbiased feature representations, easing contextual bias in predictions. Moreover, we complement the current compositional probability model with feasibility and contextuality in a compatible format. Finally, we conduct comprehensive experiments to analyze and validate the superior or competitive performance of our model, Semantic Attention and knowledge Disentanglement guided Simple Primitives (SAD-SP), on three widely-used benchmark OW-CZSL datasets. | ['Yi Yang', 'XiaoJun Wu', 'Wei Fang', 'Xiaojun Chang', 'Lina Yao', 'Yun Li', 'Zhe Liu'] | 2022-11-05 | null | null | null | null | ['compositional-zero-shot-learning'] | ['computer-vision'] | [ 4.49340641e-01 2.43239645e-02 -2.53535390e-01 -1.78333044e-01
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9202c343-f565-46d0-a400-107c529d9c59 | enhancing-mr-image-segmentation-with | 2108.03429 | null | https://arxiv.org/abs/2108.03429v3 | https://arxiv.org/pdf/2108.03429v3.pdf | Enhancing MR Image Segmentation with Realistic Adversarial Data Augmentation | The success of neural networks on medical image segmentation tasks typically relies on large labeled datasets for model training. However, acquiring and manually labeling a large medical image set is resource-intensive, expensive, and sometimes impractical due to data sharing and privacy issues. To address this challenge, we propose AdvChain, a generic adversarial data augmentation framework, aiming at improving both the diversity and effectiveness of training data for medical image segmentation tasks. AdvChain augments data with dynamic data augmentation, generating randomly chained photo-metric and geometric transformations to resemble realistic yet challenging imaging variations to expand training data. By jointly optimizing the data augmentation model and a segmentation network during training, challenging examples are generated to enhance network generalizability for the downstream task. The proposed adversarial data augmentation does not rely on generative networks and can be used as a plug-in module in general segmentation networks. It is computationally efficient and applicable for both low-shot supervised and semi-supervised learning. We analyze and evaluate the method on two MR image segmentation tasks: cardiac segmentation and prostate segmentation with limited labeled data. Results show that the proposed approach can alleviate the need for labeled data while improving model generalization ability, indicating its practical value in medical imaging applications. | ['Huaqi Qiu', 'Shuo Wang', 'Zeju Li', 'Daniel Rueckert', 'Wenjia Bai', 'Giacomo Tarroni', 'Liang Chen', 'Cheng Ouyang', 'Chen Qin', 'Chen Chen'] | 2021-08-07 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 8.90488446e-01 6.43057346e-01 -1.46673098e-01 -7.07933962e-01
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ef9b19bc-2582-4dd2-880a-1491bf3f0899 | neural-bee-colony-optimization-a-case-study | 2306.00720 | null | https://arxiv.org/abs/2306.00720v1 | https://arxiv.org/pdf/2306.00720v1.pdf | Neural Bee Colony Optimization: A Case Study in Public Transit Network Design | In this work we explore the combination of metaheuristics and learned neural network solvers for combinatorial optimization. We do this in the context of the transit network design problem, a uniquely challenging combinatorial optimization problem with real-world importance. We train a neural network policy to perform single-shot planning of individual transit routes, and then incorporate it as one of several sub-heuristics in a modified Bee Colony Optimization (BCO) metaheuristic algorithm. Our experimental results demonstrate that this hybrid algorithm outperforms the learned policy alone by up to 20% and the original BCO algorithm by up to 53% on realistic problem instances. We perform a set of ablations to study the impact of each component of the modified algorithm. | ['Gregory Dudek', 'Andrew Holliday'] | 2023-05-18 | null | null | null | null | ['combinatorial-optimization'] | ['methodology'] | [ 4.77528006e-01 2.20795423e-01 -4.51825529e-01 2.47899681e-01
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d774e223-2115-48db-884e-971c47853c34 | comparison-of-possibilistic-fuzzy-local | 1904.01014 | null | http://arxiv.org/abs/1904.01014v1 | http://arxiv.org/pdf/1904.01014v1.pdf | Comparison of Possibilistic Fuzzy Local Information C-Means and Possibilistic K-Nearest Neighbors for Synthetic Aperture Sonar Image Segmentation | Synthetic aperture sonar (SAS) imagery can generate high resolution images of
the seafloor. Thus, segmentation algorithms can be used to partition the images
into different seafloor environments. In this paper, we compare two
possibilistic segmentation approaches. Possibilistic approaches allow for the
ability to detect novel or outlier environments as well as well known classes.
The Possibilistic Fuzzy Local Information C-Means (PFLICM) algorithm has been
previously applied to segment SAS imagery. Additionally, the Possibilistic
K-Nearest Neighbors (PKNN) algorithm has been used in other domains such as
landmine detection and hyperspectral imagery. In this paper, we compare the
segmentation performance of a semi-supervised approach using PFLICM and a
supervised method using Possibilistic K-NN. We include final segmentation
results on multiple SAS images and a quantitative assessment of each algorithm. | ['Matthew Cook', 'James Keller', 'Joshua Peeples', 'Alina Zare', 'Daniel Suen'] | 2019-04-01 | null | null | null | null | ['landmine'] | ['computer-vision'] | [ 4.23075795e-01 -1.06557623e-01 3.46805066e-01 -3.69519889e-01
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1a45f9e1-c30e-42ad-b613-fc02676c9929 | joint-adaptive-sparsity-and-low-rankness-on | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Wen_Joint_Adaptive_Sparsity_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Wen_Joint_Adaptive_Sparsity_ICCV_2017_paper.pdf | Joint Adaptive Sparsity and Low-Rankness on the Fly: An Online Tensor Reconstruction Scheme for Video Denoising | Recent works on adaptive sparse and low-rank signal modeling have demonstrated their usefulness, especially in image/video processing applications. While a patch-based sparse model imposes local structure, low-rankness of the grouped patches exploits non-local correlation. Applying either approach alone usually limits performance in various low-level vision tasks. In this work, we propose a novel video denoising method, based on an online tensor reconstruction scheme with a joint adaptive sparse and low-rank model, dubbed SALT. An efficient and unsupervised online unitary sparsifying transform learning method is introduced to impose adaptive sparsity on the fly. We develop an efficient 3D spatio-temporal data reconstruction framework based on the proposed online learning method, which exhibits low latency and can potentially handle streaming videos. To the best of our knowledge, this is the first work that combines adaptive sparsity and low-rankness for video denoising, and the first work of solving the proposed problem in an online fashion. We demonstrate video denoising results over commonly used videos from public datasets. Numerical experiments show that the proposed video denoising method outperforms competing methods.
| ['Bihan Wen', 'Yoram Bresler', 'Luke Pfister', 'Yanjun Li'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['video-denoising'] | ['computer-vision'] | [ 3.44825327e-01 -6.64231420e-01 4.34463248e-02 -1.70469239e-01
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8f52f992-4e11-4880-9c2f-3a49ac1c4127 | block-simultaneous-direction-method-of | 1708.09066 | null | http://arxiv.org/abs/1708.09066v1 | http://arxiv.org/pdf/1708.09066v1.pdf | Block-Simultaneous Direction Method of Multipliers: A proximal primal-dual splitting algorithm for nonconvex problems with multiple constraints | We introduce a generalization of the linearized Alternating Direction Method
of Multipliers to optimize a real-valued function $f$ of multiple arguments
with potentially multiple constraints $g_\circ$ on each of them. The function
$f$ may be nonconvex as long as it is convex in every argument, while the
constraints $g_\circ$ need to be convex but not smooth. If $f$ is smooth, the
proposed Block-Simultaneous Direction Method of Multipliers (bSDMM) can be
interpreted as a proximal analog to inexact coordinate descent methods under
constraints. Unlike alternative approaches for joint solvers of
multiple-constraint problems, we do not require linear operators $L$ of a
constraint function $g(L\ \cdot)$ to be invertible or linked between each
other. bSDMM is well-suited for a range of optimization problems, in particular
for data analysis, where $f$ is the likelihood function of a model and $L$
could be a transformation matrix describing e.g. finite differences or basis
transforms. We apply bSDMM to the Non-negative Matrix Factorization task of a
hyperspectral unmixing problem and demonstrate convergence and effectiveness of
multiple constraints on both matrix factors. The algorithms are implemented in
python and released as an open-source package. | ['Fred Moolekamp', 'Peter Melchior'] | 2017-08-30 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 1.77271992e-01 -7.85200670e-02 -9.10126269e-02 -1.54171541e-01
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8acccc69-bd5b-4dc6-8893-5b40bd82b872 | differentiable-agent-based-epidemiology | 2207.09714 | null | https://arxiv.org/abs/2207.09714v2 | https://arxiv.org/pdf/2207.09714v2.pdf | Differentiable Agent-based Epidemiology | Mechanistic simulators are an indispensable tool for epidemiology to explore the behavior of complex, dynamic infections under varying conditions and navigate uncertain environments. Agent-based models (ABMs) are an increasingly popular simulation paradigm that can represent the heterogeneity of contact interactions with granular detail and agency of individual behavior. However, conventional ABM frameworks are not differentiable and present challenges in scalability; due to which it is non-trivial to connect them to auxiliary data sources. In this paper, we introduce GradABM: a scalable, differentiable design for agent-based modeling that is amenable to gradient-based learning with automatic differentiation. GradABM can quickly simulate million-size populations in few seconds on commodity hardware, integrate with deep neural networks and ingest heterogeneous data sources. This provides an array of practical benefits for calibration, forecasting, and evaluating policy interventions. We demonstrate the efficacy of GradABM via extensive experiments with real COVID-19 and influenza datasets. | ['Balaji Krishnamurthy', 'Arnau Quera-Bofarull', 'Ramesh Raskar', 'B. Aditya Prakash', 'Jayakumar Subramanian', 'Alexander Rodríguez', 'Ayush Chopra'] | 2022-07-20 | null | null | null | null | ['epidemiology'] | ['medical'] | [-3.61966729e-01 -5.29870868e-01 -1.72450289e-01 9.71520618e-02
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eddfaa14-695a-4009-ab23-820dd14947df | contextual-squeeze-and-excitation-for | 2206.09843 | null | https://arxiv.org/abs/2206.09843v3 | https://arxiv.org/pdf/2206.09843v3.pdf | Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification | Recent years have seen a growth in user-centric applications that require effective knowledge transfer across tasks in the low-data regime. An example is personalization, where a pretrained system is adapted by learning on small amounts of labeled data belonging to a specific user. This setting requires high accuracy under low computational complexity, therefore the Pareto frontier of accuracy vs. adaptation cost plays a crucial role. In this paper we push this Pareto frontier in the few-shot image classification setting with a key contribution: a new adaptive block called Contextual Squeeze-and-Excitation (CaSE) that adjusts a pretrained neural network on a new task to significantly improve performance with a single forward pass of the user data (context). We use meta-trained CaSE blocks to conditionally adapt the body of a network and a fine-tuning routine to adapt a linear head, defining a method called UpperCaSE. UpperCaSE achieves a new state-of-the-art accuracy relative to meta-learners on the 26 datasets of VTAB+MD and on a challenging real-world personalization benchmark (ORBIT), narrowing the gap with leading fine-tuning methods with the benefit of orders of magnitude lower adaptation cost. | ['Richard E. Turner', 'Sebastian Nowozin', 'Katja Hofmann', 'Aliaksandra Shysheya', 'John Bronskill', 'Massimiliano Patacchiola'] | 2022-06-20 | null | null | null | null | ['few-shot-image-classification'] | ['computer-vision'] | [ 4.94932562e-01 3.56560163e-02 -2.31414735e-01 -5.69049776e-01
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24bb5656-5f6e-4143-b729-23ee4d9844d0 | vani-very-lightweight-accent-controllable-tts | 2303.07578 | null | https://arxiv.org/abs/2303.07578v1 | https://arxiv.org/pdf/2303.07578v1.pdf | VANI: Very-lightweight Accent-controllable TTS for Native and Non-native speakers with Identity Preservation | We introduce VANI, a very lightweight multi-lingual accent controllable speech synthesis system. Our model builds upon disentanglement strategies proposed in RADMMM and supports explicit control of accent, language, speaker and fine-grained $F_0$ and energy features for speech synthesis. We utilize the Indic languages dataset, released for LIMMITS 2023 as part of ICASSP Signal Processing Grand Challenge, to synthesize speech in 3 different languages. Our model supports transferring the language of a speaker while retaining their voice and the native accent of the target language. We utilize the large-parameter RADMMM model for Track $1$ and lightweight VANI model for Track $2$ and $3$ of the competition. | ['Bryan Catanzaro', 'Boris Ginsburg', 'João Felipe Santos', 'Kevin J. Shih', 'Rafael Valle', 'Subhankar Ghosh', 'Akshit Arora', 'Rohan Badlani'] | 2023-03-14 | null | null | null | null | ['speech-synthesis'] | ['speech'] | [-2.70349890e-01 5.83723187e-02 -5.11835575e-01 -4.25078899e-01
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b871a9a0-89f7-4129-8c75-ca9fe198837b | a-biomedical-knowledge-graph-for-biomarker | 2302.04737 | null | https://arxiv.org/abs/2302.04737v2 | https://arxiv.org/pdf/2302.04737v2.pdf | A Biomedical Knowledge Graph for Biomarker Discovery in Cancer | Structured and unstructured data and facts about drugs, genes, protein, viruses, and their mechanism are spread across a huge number of scientific articles. These articles are a large-scale knowledge source and can have a huge impact on disseminating knowledge about the mechanisms of certain biological processes. A domain-specific knowledge graph~(KG) is an explicit conceptualization of a specific subject-matter domain represented w.r.t semantically interrelated entities and relations. A KG can be constructed by integrating such facts and data and be used for data integration, exploration, and federated queries. However, exploration and querying large-scale KGs is tedious for certain groups of users due to a lack of knowledge about underlying data assets or semantic technologies. Such a KG will not only allow deducing new knowledge and question answering(QA) but also allows domain experts to explore. Since cross-disciplinary explanations are important for accurate diagnosis, it is important to query the KG to provide interactive explanations about learned biomarkers. Inspired by these, we construct a domain-specific KG, particularly for cancer-specific biomarker discovery. The KG is constructed by integrating cancer-related knowledge and facts from multiple sources. First, we construct a domain-specific ontology, which we call OncoNet Ontology (ONO). The ONO ontology is developed to enable semantic reasoning for verification of the predictions for relations between diseases and genes. The KG is then developed and enriched by harmonizing the ONO, additional metadata schemas, ontologies, controlled vocabularies, and additional concepts from external sources using a BERT-based information extraction method. BioBERT and SciBERT are finetuned with the selected articles crawled from PubMed. We listed down some queries and some examples of QA and deducing knowledge based on the KG. | ['Stefan Decker', 'Lina Molinas Comet', 'Dietrich Rebholz-Schuhmann', 'Oya Beyan', 'Md. Rezaul Karim'] | 2023-02-09 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [-3.48031104e-01 5.16292512e-01 -6.72865093e-01 -2.61719555e-01
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0e755ec6-95df-4995-ba79-27564f9d62b6 | end-to-end-deep-learning-based-adaptation-1 | 2306.02450 | null | https://arxiv.org/abs/2306.02450v1 | https://arxiv.org/pdf/2306.02450v1.pdf | End-To-End Deep Learning-based Adaptation Control for Linear Acoustic Echo Cancellation | The attenuation of acoustic loudspeaker echoes remains to be one of the open challenges to achieve pleasant full-duplex hands free speech communication. In many modern signal enhancement interfaces, this problem is addressed by a linear acoustic echo canceler which subtracts a loudspeaker echo estimate from the recorded microphone signal. To obtain precise echo estimates, the parameters of the echo canceler, i.e., the filter coefficients, need to be estimated quickly and precisely from the observed loudspeaker and microphone signals. For this a sophisticated adaptation control is required to deal with high-power double-talk and rapidly track time-varying acoustic environments which are often faced with portable devices. In this paper, we address this problem by end-to-end deep learning. In particular, we suggest to infer the step-size for a least mean squares frequency-domain adaptive filter update by a Deep Neural Network (DNN). Two different step-size inference approaches are investigated. On the one hand broadband approaches, which use a single DNN to jointly infer step-sizes for all frequency bands, and on the other hand narrowband methods, which exploit individual DNNs per frequency band. The discussion of benefits and disadvantages of both approaches leads to a novel hybrid approach which shows improved echo cancellation while requiring only small DNN architectures. Furthermore, we investigate the effect of different loss functions, signal feature vectors, and DNN output layer architectures on the echo cancellation performance from which we obtain valuable insights into the general design and functionality of DNN-based adaptation control algorithms. | ['Walter Kellermann', 'Andreas Brendel', 'Thomas Haubner'] | 2023-06-04 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 2.72979975e-01 -1.05873868e-01 4.93464351e-01 -1.58198297e-01
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1c247c0d-21f3-4698-8ed8-91672fa6de20 | adversarial-training-for-multi-context-joint | 1808.06876 | null | http://arxiv.org/abs/1808.06876v3 | http://arxiv.org/pdf/1808.06876v3.pdf | Adversarial training for multi-context joint entity and relation extraction | Adversarial training (AT) is a regularization method that can be used to
improve the robustness of neural network methods by adding small perturbations
in the training data. We show how to use AT for the tasks of entity recognition
and relation extraction. In particular, we demonstrate that applying AT to a
general purpose baseline model for jointly extracting entities and relations,
allows improving the state-of-the-art effectiveness on several datasets in
different contexts (i.e., news, biomedical, and real estate data) and for
different languages (English and Dutch). | ['Giannis Bekoulis', 'Johannes Deleu', 'Chris Develder', 'Thomas Demeester'] | 2018-08-21 | adversarial-training-for-multi-context-joint-1 | https://aclanthology.org/D18-1307 | https://aclanthology.org/D18-1307.pdf | emnlp-2018-10 | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [ 1.77818224e-01 6.22959554e-01 -2.76024789e-01 -3.55684131e-01
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d9f96017-6fe3-4202-a9ea-62c53c69e83e | wadenet-wavelet-decomposition-based-cnn-for | 2011.05594 | null | https://arxiv.org/abs/2011.05594v1 | https://arxiv.org/pdf/2011.05594v1.pdf | WaDeNet: Wavelet Decomposition based CNN for Speech Processing | Existing speech processing systems consist of different modules, individually optimized for a specific task such as acoustic modelling or feature extraction. In addition to not assuring optimality of the system, the disjoint nature of current speech processing systems make them unsuitable for ubiquitous health applications. We propose WaDeNet, an end-to-end model for mobile speech processing. In order to incorporate spectral features, WaDeNet embeds wavelet decomposition of the speech signal within the architecture. This allows WaDeNet to learn from spectral features in an end-to-end manner, thus alleviating the need for feature extraction and successive modules that are currently present in speech processing systems. WaDeNet outperforms the current state of the art in datasets that involve speech for mobile health applications such as non-invasive emotion recognition. WaDeNet achieves an average increase in accuracy of 6.36% when compared to the existing state of the art models. Additionally, WaDeNet is considerably lighter than a simple CNNs with a similar architecture. | ['Abhijith Ragav', 'Prithvi Suresh'] | 2020-11-11 | null | null | null | null | ['acoustic-modelling'] | ['speech'] | [ 2.52900839e-01 2.89484084e-01 2.34302774e-01 -4.33472484e-01
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3888debe-cf74-4e49-9d8f-b9df1571a2a1 | document-level-relation-extraction-via-pair | null | null | https://aclanthology.org/2022.coling-1.213 | https://aclanthology.org/2022.coling-1.213.pdf | Document-Level Relation Extraction via Pair-Aware and Entity-Enhanced Representation Learning | Document-level relation extraction aims to recognize relations among multiple entity pairs from a whole piece of article. Recent methods achieve considerable performance but still suffer from two challenges: a) the relational entity pairs are sparse, b) the representation of entity pairs is insufficient. In this paper, we propose Pair-Aware and Entity-Enhanced(PAEE) model to solve the aforementioned two challenges. For the first challenge, we design a Pair-Aware Representation module to predict potential relational entity pairs, which constrains the relation extraction to the predicted entity pairs subset rather than all pairs; For the second, we introduce a Entity-Enhanced Representation module to assemble directional entity pairs and obtain a holistic understanding of the entire document. Experimental results show that our approach can obtain state-of-the-art performance on four benchmark datasets DocRED, DWIE, CDR and GDA. | ['Zuyu Zhao', 'Weijian Sun', 'Kang Liu', 'Jun Zhao', 'Yubo Chen', 'Hang Yang', 'Xiusheng Huang'] | null | null | null | null | coling-2022-10 | ['document-level-relation-extraction'] | ['natural-language-processing'] | [-4.80051078e-02 3.31830055e-01 -5.88713467e-01 -2.31607080e-01
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bfaafa55-4048-453b-b9ff-1a69eda99353 | topic-driven-adaptive-network-for-cross | 2111.14094 | null | https://arxiv.org/abs/2111.14094v2 | https://arxiv.org/pdf/2111.14094v2.pdf | Topic Driven Adaptive Network for Cross-Domain Sentiment Classification | Cross-domain sentiment classification has been a hot spot these years, which aims to learn a reliable classifier using labeled data from a source domain and evaluate it on a target domain. In this vein, most approaches utilized domain adaptation that maps data from different domains into a common feature space. To further improve the model performance, several methods targeted to mine domain-specific information were proposed. However, most of them only utilized a limited part of domain-specific information. In this study, we first develop a method of extracting domain-specific words based on the topic information derived from topic models. Then, we propose a Topic Driven Adaptive Network (TDAN) for cross-domain sentiment classification. The network consists of two sub-networks: a semantics attention network and a domain-specific word attention network, the structures of which are based on transformers. These sub-networks take different forms of input and their outputs are fused as the feature vector. Experiments validate the effectiveness of our TDAN on sentiment classification across domains. Case studies also indicate that topic models have the potential to add value to cross-domain sentiment classification by discovering interpretable and low-dimensional subspaces. | ['Yanghui Rao', 'Fu Lee Wang', 'Qingyuan Wu', 'Yiqiao Qiu', 'Yicheng Zhu'] | 2021-11-28 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [ 5.80176413e-02 -3.55243832e-02 -3.20557982e-01 -8.84039044e-01
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201cd48d-82f3-4d46-981c-b7cf9f234368 | faceinpainter-high-fidelity-face-adaptation | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Li_FaceInpainter_High_Fidelity_Face_Adaptation_to_Heterogeneous_Domains_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Li_FaceInpainter_High_Fidelity_Face_Adaptation_to_Heterogeneous_Domains_CVPR_2021_paper.pdf | FaceInpainter: High Fidelity Face Adaptation to Heterogeneous Domains | In this work, we propose a novel two-stage framework named FaceInpainter to implement controllable Identity-Guided Face Inpainting (IGFI) under heterogeneous domains. Concretely, by explicitly disentangling foreground and background of the target face, the first stage focuses on adaptive face fitting to the fixed background via a Styled Face Inpainting Network (SFI-Net), with 3D priors and texture code of the target, as well as identity factor of the source face. It is challenging to deal with the inconsistency between the new identity of the source and the original background of the target, concerning the face shape and appearance on the fused boundary. The second stage consists of a Joint Refinement Network (JR-Net) to refine the swapped face. It leverages AdaIN considering identity and multi-scale texture codes, for feature transformation of the decoded face from SFI-Net with facial occlusions. We adopt the contextual loss to implicitly preserve the attributes, encouraging face deformation and fewer texture distortions. Experimental results demonstrate that our approach handles high-quality identity adaptation to heterogeneous domains, exhibiting the competitive performance compared with state-of-the-art methods concerning both attribute and identity fidelity. | ['Ran He', 'Xingguang Song', 'Jie Cao', 'Zhaoyang Li', 'Jia Li'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['facial-inpainting'] | ['computer-vision'] | [ 5.09085119e-01 4.07916218e-01 1.67822331e-01 -3.40173006e-01
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e48c892e-feb5-4960-84b5-8585afb08cca | post-processing-networks-method-for | 2207.12185 | null | https://arxiv.org/abs/2207.12185v1 | https://arxiv.org/pdf/2207.12185v1.pdf | Post-processing Networks: Method for Optimizing Pipeline Task-oriented Dialogue Systems using Reinforcement Learning | Many studies have proposed methods for optimizing the dialogue performance of an entire pipeline task-oriented dialogue system by jointly training modules in the system using reinforcement learning. However, these methods are limited in that they can only be applied to modules implemented using trainable neural-based methods. To solve this problem, we propose a method for optimizing a pipeline system composed of modules implemented with arbitrary methods for dialogue performance. With our method, neural-based components called post-processing networks (PPNs) are installed inside such a system to post-process the output of each module. All PPNs are updated to improve the overall dialogue performance of the system by using reinforcement learning, not necessitating each module to be differentiable. Through dialogue simulation and human evaluation on the MultiWOZ dataset, we show that our method can improve the dialogue performance of pipeline systems consisting of various modules. | ['Ryuichiro Higashinaka', 'Atsumoto Ohashi'] | 2022-07-25 | null | https://aclanthology.org/2022.sigdial-1.1 | https://aclanthology.org/2022.sigdial-1.1.pdf | sigdial-acl-2022-9 | ['task-oriented-dialogue-systems'] | ['natural-language-processing'] | [-2.69825369e-01 8.45138490e-01 3.34139347e-01 -5.65604508e-01
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f39332ec-f169-40cb-9b75-ea911c6aec96 | self-supervised-learning-of-object-parts-for | 2204.13101 | null | https://arxiv.org/abs/2204.13101v2 | https://arxiv.org/pdf/2204.13101v2.pdf | Self-Supervised Learning of Object Parts for Semantic Segmentation | Progress in self-supervised learning has brought strong general image representation learning methods. Yet so far, it has mostly focused on image-level learning. In turn, tasks such as unsupervised image segmentation have not benefited from this trend as they require spatially-diverse representations. However, learning dense representations is challenging, as in the unsupervised context it is not clear how to guide the model to learn representations that correspond to various potential object categories. In this paper, we argue that self-supervised learning of object parts is a solution to this issue. Object parts are generalizable: they are a priori independent of an object definition, but can be grouped to form objects a posteriori. To this end, we leverage the recently proposed Vision Transformer's capability of attending to objects and combine it with a spatially dense clustering task for fine-tuning the spatial tokens. Our method surpasses the state-of-the-art on three semantic segmentation benchmarks by 17%-3%, showing that our representations are versatile under various object definitions. Finally, we extend this to fully unsupervised segmentation - which refrains completely from using label information even at test-time - and demonstrate that a simple method for automatically merging discovered object parts based on community detection yields substantial gains. | ['Yuki M. Asano', 'Adrian Ziegler'] | 2022-04-27 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Ziegler_Self-Supervised_Learning_of_Object_Parts_for_Semantic_Segmentation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Ziegler_Self-Supervised_Learning_of_Object_Parts_for_Semantic_Segmentation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['unsupervised-semantic-segmentation'] | ['computer-vision'] | [ 5.31783819e-01 2.28447035e-01 -1.01371676e-01 -3.90344173e-01
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cab329a7-5b70-4763-882a-9b919fcbd229 | learning-by-applying-a-general-framework-for | 2302.05717 | null | https://arxiv.org/abs/2302.05717v1 | https://arxiv.org/pdf/2302.05717v1.pdf | Learning by Applying: A General Framework for Mathematical Reasoning via Enhancing Explicit Knowledge Learning | Mathematical reasoning is one of the crucial abilities of general artificial intelligence, which requires machines to master mathematical logic and knowledge from solving problems. However, existing approaches are not transparent (thus not interpretable) in terms of what knowledge has been learned and applied in the reasoning process. In this paper, we propose a general Learning by Applying (LeAp) framework to enhance existing models (backbones) in a principled way by explicit knowledge learning. In LeAp, we perform knowledge learning in a novel problem-knowledge-expression paradigm, with a Knowledge Encoder to acquire knowledge from problem data and a Knowledge Decoder to apply knowledge for expression reasoning. The learned mathematical knowledge, including word-word relations and word-operator relations, forms an explicit knowledge graph, which bridges the knowledge "learning" and "applying" organically. Moreover, for problem solving, we design a semantics-enhanced module and a reasoning-enhanced module that apply knowledge to improve the problem comprehension and symbol reasoning abilities of any backbone, respectively. We theoretically prove the superiority of LeAp's autonomous learning mechanism. Experiments on three real-world datasets show that LeAp improves all backbones' performances, learns accurate knowledge, and achieves a more interpretable reasoning process. | ['Qi Liu', 'ChengXiang Zhai', 'Zhenya Huang', 'Jiayu Liu'] | 2023-02-11 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [ 1.96242914e-01 3.92178357e-01 -2.82554120e-01 -4.42389905e-01
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0abe2055-a4af-438e-8721-6638d225e09d | hirl-a-general-framework-for-hierarchical | 2205.13159 | null | https://arxiv.org/abs/2205.13159v1 | https://arxiv.org/pdf/2205.13159v1.pdf | HIRL: A General Framework for Hierarchical Image Representation Learning | Learning self-supervised image representations has been broadly studied to boost various visual understanding tasks. Existing methods typically learn a single level of image semantics like pairwise semantic similarity or image clustering patterns. However, these methods can hardly capture multiple levels of semantic information that naturally exists in an image dataset, e.g., the semantic hierarchy of "Persian cat to cat to mammal" encoded in an image database for species. It is thus unknown whether an arbitrary image self-supervised learning (SSL) approach can benefit from learning such hierarchical semantics. To answer this question, we propose a general framework for Hierarchical Image Representation Learning (HIRL). This framework aims to learn multiple semantic representations for each image, and these representations are structured to encode image semantics from fine-grained to coarse-grained. Based on a probabilistic factorization, HIRL learns the most fine-grained semantics by an off-the-shelf image SSL approach and learns multiple coarse-grained semantics by a novel semantic path discrimination scheme. We adopt six representative image SSL methods as baselines and study how they perform under HIRL. By rigorous fair comparison, performance gain is observed on all the six methods for diverse downstream tasks, which, for the first time, verifies the general effectiveness of learning hierarchical image semantics. All source code and model weights are available at https://github.com/hirl-team/HIRL | ['Bingbing Ni', 'Yi Xu', 'Jian Tang', 'Zhenbang Sun', 'Jiawen Li', 'Xuanyu Zhu', 'Yuanfan Guo', 'Minghao Xu'] | 2022-05-26 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [ 4.85355914e-01 2.33392920e-02 -6.34346008e-01 -7.81422913e-01
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8eefc1c1-a413-4e36-b795-eb49622e9ed0 | probabilistic-monocular-3d-human-pose | 2107.13788 | null | https://arxiv.org/abs/2107.13788v2 | https://arxiv.org/pdf/2107.13788v2.pdf | Probabilistic Monocular 3D Human Pose Estimation with Normalizing Flows | 3D human pose estimation from monocular images is a highly ill-posed problem due to depth ambiguities and occlusions. Nonetheless, most existing works ignore these ambiguities and only estimate a single solution. In contrast, we generate a diverse set of hypotheses that represents the full posterior distribution of feasible 3D poses. To this end, we propose a normalizing flow based method that exploits the deterministic 3D-to-2D mapping to solve the ambiguous inverse 2D-to-3D problem. Additionally, uncertain detections and occlusions are effectively modeled by incorporating uncertainty information of the 2D detector as condition. Further keys to success are a learned 3D pose prior and a generalization of the best-of-M loss. We evaluate our approach on the two benchmark datasets Human3.6M and MPI-INF-3DHP, outperforming all comparable methods in most metrics. The implementation is available on GitHub. | ['Bastian Wandt', 'Bodo Rosenhahn', 'Marco Rudolph', 'Tom Wehrbein'] | 2021-07-29 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Wehrbein_Probabilistic_Monocular_3D_Human_Pose_Estimation_With_Normalizing_Flows_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Wehrbein_Probabilistic_Monocular_3D_Human_Pose_Estimation_With_Normalizing_Flows_ICCV_2021_paper.pdf | iccv-2021-1 | ['multi-hypotheses-3d-human-pose-estimation', 'monocular-3d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-7.66106993e-02 1.72981784e-01 -5.12187853e-02 -3.74706239e-01
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1f8e52e8-77fa-4418-b0f6-de15a6048e2b | speaker-separation-using-speaker-inventories | 2010.10556 | null | https://arxiv.org/abs/2010.10556v1 | https://arxiv.org/pdf/2010.10556v1.pdf | Speaker Separation Using Speaker Inventories and Estimated Speech | We propose speaker separation using speaker inventories and estimated speech (SSUSIES), a framework leveraging speaker profiles and estimated speech for speaker separation. SSUSIES contains two methods, speaker separation using speaker inventories (SSUSI) and speaker separation using estimated speech (SSUES). SSUSI performs speaker separation with the help of speaker inventory. By combining the advantages of permutation invariant training (PIT) and speech extraction, SSUSI significantly outperforms conventional approaches. SSUES is a widely applicable technique that can substantially improve speaker separation performance using the output of first-pass separation. We evaluate the models on both speaker separation and speech recognition metrics. | ['Yifan Gong', 'Jinyu Li', 'DeLiang Wang', 'Zhuo Chen', 'Peidong Wang'] | 2020-10-20 | null | null | null | null | ['speech-extraction', 'speaker-separation'] | ['speech', 'speech'] | [ 3.32087547e-01 -1.51095435e-01 -1.97103590e-01 -5.21160543e-01
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224dedb8-017f-4056-9a38-32e6cd14146f | instance-similarity-deep-hashing-for-multi | 1803.02987 | null | https://arxiv.org/abs/1803.02987v3 | https://arxiv.org/pdf/1803.02987v3.pdf | Improved Deep Hashing with Soft Pairwise Similarity for Multi-label Image Retrieval | Hash coding has been widely used in the approximate nearest neighbor search for large-scale image retrieval. Recently, many deep hashing methods have been proposed and shown largely improved performance over traditional feature-learning-based methods. Most of these methods examine the pairwise similarity on the semantic-level labels, where the pairwise similarity is generally defined in a hard-assignment way. That is, the pairwise similarity is '1' if they share no less than one class label and '0' if they do not share any. However, such similarity definition cannot reflect the similarity ranking for pairwise images that hold multiple labels. In this paper, a new deep hashing method is proposed for multi-label image retrieval by re-defining the pairwise similarity into an instance similarity, where the instance similarity is quantified into a percentage based on the normalized semantic labels. Based on the instance similarity, a weighted cross-entropy loss and a minimum mean square error loss are tailored for loss-function construction, and are efficiently used for simultaneous feature learning and hash coding. Experiments on three popular datasets demonstrate that, the proposed method outperforms the competing methods and achieves the state-of-the-art performance in multi-label image retrieval. | ['Song Wang', 'Yuewei Lin', 'Qin Zou', 'Long Chen', 'Zheng Zhang'] | 2018-03-08 | null | null | null | null | ['multi-label-image-retrieval'] | ['computer-vision'] | [ 5.59188938e-03 -4.68996674e-01 -4.26440299e-01 -6.84381902e-01
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0d503f9f-e3ef-4a76-a829-c02dde92049b | mention-annotations-alone-enable-efficient | 2210.07602 | null | https://arxiv.org/abs/2210.07602v2 | https://arxiv.org/pdf/2210.07602v2.pdf | Mention Annotations Alone Enable Efficient Domain Adaptation for Coreference Resolution | Although recent neural models for coreference resolution have led to substantial improvements on benchmark datasets, transferring these models to new target domains containing out-of-vocabulary spans and requiring differing annotation schemes remains challenging. Typical approaches involve continued training on annotated target-domain data, but obtaining annotations is costly and time-consuming. We show that annotating mentions alone is nearly twice as fast as annotating full coreference chains. Accordingly, we propose a method for efficiently adapting coreference models, which includes a high-precision mention detection objective and requires annotating only mentions in the target domain. Extensive evaluation across three English coreference datasets: CoNLL-2012 (news/conversation), i2b2/VA (medical notes), and previously unstudied child welfare notes, reveals that our approach facilitates annotation-efficient transfer and results in a 7-14% improvement in average F1 without increasing annotator time. | ['Emma Strubell', 'Anjalie Field', 'Nupoor Gandhi'] | 2022-10-14 | null | null | null | null | ['coreference-resolution'] | ['natural-language-processing'] | [ 4.07254040e-01 7.30438828e-01 -5.20217180e-01 -5.18965304e-01
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683841d8-8b0d-48d9-9b42-81f58170fbab | abcd-a-graph-framework-to-convert-complex | 2106.12027 | null | https://arxiv.org/abs/2106.12027v1 | https://arxiv.org/pdf/2106.12027v1.pdf | ABCD: A Graph Framework to Convert Complex Sentences to a Covering Set of Simple Sentences | Atomic clauses are fundamental text units for understanding complex sentences. Identifying the atomic sentences within complex sentences is important for applications such as summarization, argument mining, discourse analysis, discourse parsing, and question answering. Previous work mainly relies on rule-based methods dependent on parsing. We propose a new task to decompose each complex sentence into simple sentences derived from the tensed clauses in the source, and a novel problem formulation as a graph edit task. Our neural model learns to Accept, Break, Copy or Drop elements of a graph that combines word adjacency and grammatical dependencies. The full processing pipeline includes modules for graph construction, graph editing, and sentence generation from the output graph. We introduce DeSSE, a new dataset designed to train and evaluate complex sentence decomposition, and MinWiki, a subset of MinWikiSplit. ABCD achieves comparable performance as two parsing baselines on MinWiki. On DeSSE, which has a more even balance of complex sentence types, our model achieves higher accuracy on the number of atomic sentences than an encoder-decoder baseline. Results include a detailed error analysis. | ['Rebecca J. Passonneau', 'Huang', 'Ting-Hao', 'Yanjun Gao'] | 2021-06-22 | null | https://aclanthology.org/2021.acl-long.303 | https://aclanthology.org/2021.acl-long.303.pdf | acl-2021-5 | ['discourse-parsing'] | ['natural-language-processing'] | [ 5.75567007e-01 9.68551636e-01 1.58575550e-02 -6.08923733e-01
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df78a80c-6c4f-40a5-a06c-7eabc0030f1b | pointwise-shape-adaptive-dct-for-high-quality | null | null | https://ieeexplore.ieee.org/document/7071698?arnumber=7071698 | https://www.cs.tut.fi/~foi/papers/Eusipco2006-SA-DCT_Deblocking.pdf | Pointwise shape-adaptive DCT for high-quality deblocking of compressed color images | We present an high-quality image deblocking algorithm based on the shape-adaptive DCT (SA-DCT). The SA-DCT is a low-complexity transform which can be computed on a support of arbitrary shape. This transform has been adopted by the MPEG-4 standard and it is found implemented in modern video hardware. The use of this shape-adaptive transform for denoising and deblurring has been recently proposed, showing a remarkable performance. In this paper we discuss and analyze the use of this approach for the deblocking of block-DCT compressed images. Particular emphasis is given to the deblocking of highly-compressed color images. Extensive simulation experiments attest the advanced performance of the proposed filtering method. The visual quality of the estimates is high, with sharp detail preservation, clean edges. Blocking and ringing artifacts are suppressed while salient image features are preserved. The SA-DCT filtering used for the chrominance channels allows to faithfully reconstruct the missing structural information of the chrominances, thus correcting color-bleeding artifacts. | ['Karen Egiazarian', 'Vladimir Katkovnik', 'Alessandro Foi'] | 2006-09-04 | null | null | null | null | ['image-deblocking'] | ['computer-vision'] | [ 5.29289842e-01 -5.43934524e-01 2.73391247e-01 1.86193697e-02
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b7825748-26d9-4bbb-ac40-69cc9043843c | language-guided-networks-for-cross-modal | 2006.10457 | null | https://arxiv.org/abs/2006.10457v2 | https://arxiv.org/pdf/2006.10457v2.pdf | Language Guided Networks for Cross-modal Moment Retrieval | We address the challenging task of cross-modal moment retrieval, which aims to localize a temporal segment from an untrimmed video described by a natural language query. It poses great challenges over the proper semantic alignment between vision and linguistic domains. Existing methods independently extract the features of videos and sentences and purely utilize the sentence embedding in the multi-modal fusion stage, which do not make full use of the potential of language. In this paper, we present Language Guided Networks (LGN), a new framework that leverages the sentence embedding to guide the whole process of moment retrieval. In the first feature extraction stage, we propose to jointly learn visual and language features to capture the powerful visual information which can cover the complex semantics in the sentence query. Specifically, the early modulation unit is designed to modulate the visual feature extractor's feature maps by a linguistic embedding. Then we adopt a multi-modal fusion module in the second fusion stage. Finally, to get a precise localizer, the sentence information is utilized to guide the process of predicting temporal positions. Specifically, the late guidance module is developed to linearly transform the output of localization networks via the channel attention mechanism. The experimental results on two popular datasets demonstrate the superior performance of our proposed method on moment retrieval (improving by 5.8\% in terms of Rank1@IoU0.5 on Charades-STA and 5.2\% on TACoS). The source code for the complete system will be publicly available. | ['Kun Liu', 'Huadong Ma', 'Chuang Gan'] | 2020-06-18 | null | null | null | null | ['moment-retrieval'] | ['computer-vision'] | [-3.76052074e-02 -5.16653895e-01 -3.27049375e-01 -2.95144469e-01
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7389cc4b-31fb-4460-a354-3a19d5d1b462 | dynamic-range-independent-image-quality | null | null | https://resources.mpi-inf.mpg.de/hdr/vis_metric/ | https://resources.mpi-inf.mpg.de/hdr/vis_metric/aydin_sg08.pdf | Dynamic Range Independent Image Quality Assessment | The diversity of display technologies and introduction of high dynamic range imagery introduces the necessity of comparing images of radically different dynamic ranges. Current quality assessment metrics are not suitable for this task, as they assume that both reference and test images have the same dynamic range. Image fidelity measures employed by a majority of current metrics, based on the difference of pixel intensity or contrast values between test and reference images, result in meaningless predictions if this assumption does not hold. We present a novel image quality metric capable of operating on an image pair where both images have arbitrary dynamic ranges. Our metric utilizes a model of the human visual system, and its central idea is a new definition of visible distortion based on the detection and classification of visible changes in the image structure. Our metric is carefully calibrated and its performance is validated through perceptual experiments. We demonstrate possible applications of our metric to the evaluation of direct and inverse tone mapping operators as well as the analysis of the image appearance on displays with various characteristics. | ['Hans-Peter Seidel', 'Karol Myszkowski', 'Rafal Mantiuk', 'Tunç O. Aydın'] | 2008-08-01 | null | null | null | siggraph-2008-8 | ['image-quality-assessment', 'tone-mapping', 'inverse-tone-mapping'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 8.37009966e-01 -4.33590561e-01 3.57832938e-01 -5.15140295e-01
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8d0501b0-0454-411e-bece-b28ffcac95d4 | spatiotemporal-attention-based-semantic | 2305.12796 | null | https://arxiv.org/abs/2305.12796v1 | https://arxiv.org/pdf/2305.12796v1.pdf | Spatiotemporal Attention-based Semantic Compression for Real-time Video Recognition | This paper studies the computational offloading of video action recognition in edge computing. To achieve effective semantic information extraction and compression, following semantic communication we propose a novel spatiotemporal attention-based autoencoder (STAE) architecture, including a frame attention module and a spatial attention module, to evaluate the importance of frames and pixels in each frame. Additionally, we use entropy encoding to remove statistical redundancy in the compressed data to further reduce communication overhead. At the receiver, we develop a lightweight decoder that leverages a 3D-2D CNN combined architecture to reconstruct missing information by simultaneously learning temporal and spatial information from the received data to improve accuracy. To fasten convergence, we use a step-by-step approach to train the resulting STAE-based vision transformer (ViT_STAE) models. Experimental results show that ViT_STAE can compress the video dataset HMDB51 by 104x with only 5% accuracy loss, outperforming the state-of-the-art baseline DeepISC. The proposed ViT_STAE achieves faster inference and higher accuracy than the DeepISC-based ViT model under time-varying wireless channel, which highlights the effectiveness of STAE in guaranteeing higher accuracy under time constraints. | ['Qi Zhang', 'Alexandros Iosifidis', 'Mehdi Bennis', 'Nan Li'] | 2023-05-22 | null | null | null | null | ['video-recognition', 'action-recognition-in-videos', 'action-recognition', 'edge-computing'] | ['computer-vision', 'computer-vision', 'computer-vision', 'time-series'] | [ 2.75663584e-01 -8.01603571e-02 -1.37851223e-01 -2.16994494e-01
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effc9667-9a93-450e-ba09-5398a9fe3c0d | measuring-feature-diversity-in-native | null | null | https://aclanthology.org/W15-0606 | https://aclanthology.org/W15-0606.pdf | Measuring Feature Diversity in Native Language Identification | null | ['Aoife Cahill', 'Shervin Malmasi'] | 2015-06-01 | null | null | null | ws-2015-6 | ['native-language-identification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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b7df65b3-7981-4a63-a19b-fc366ab326ea | towards-supervised-extractive-text | 1911.06121 | null | https://arxiv.org/abs/1911.06121v1 | https://arxiv.org/pdf/1911.06121v1.pdf | Towards Supervised Extractive Text Summarization via RNN-based Sequence Classification | This article briefly explains our submitted approach to the DocEng'19 competition on extractive summarization. We implemented a recurrent neural network based model that learns to classify whether an article's sentence belongs to the corresponding extractive summary or not. We bypass the lack of large annotated news corpora for extractive summarization by generating extractive summaries from abstractive ones, which are available from the CNN corpus. | ['Max Lübbering', 'Eduardo Brito', 'David Biesner', 'Christian Bauckhage', 'Lars Patrick Hillebrand'] | 2019-11-13 | null | null | null | null | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 4.85546440e-01 8.17867279e-01 -5.09173155e-01 -3.08888823e-01
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42905805-5608-4e32-96fc-5f7c568b7a46 | locate-end-to-end-localization-of-actions-in | 2203.10719 | null | https://arxiv.org/abs/2203.10719v1 | https://arxiv.org/pdf/2203.10719v1.pdf | LocATe: End-to-end Localization of Actions in 3D with Transformers | Understanding a person's behavior from their 3D motion is a fundamental problem in computer vision with many applications. An important component of this problem is 3D Temporal Action Localization (3D-TAL), which involves recognizing what actions a person is performing, and when. State-of-the-art 3D-TAL methods employ a two-stage approach in which the action span detection task and the action recognition task are implemented as a cascade. This approach, however, limits the possibility of error-correction. In contrast, we propose LocATe, an end-to-end approach that jointly localizes and recognizes actions in a 3D sequence. Further, unlike existing autoregressive models that focus on modeling the local context in a sequence, LocATe's transformer model is capable of capturing long-term correlations between actions in a sequence. Unlike transformer-based object-detection and classification models which consider image or patch features as input, the input in 3D-TAL is a long sequence of highly correlated frames. To handle the high-dimensional input, we implement an effective input representation, and overcome the diffuse attention across long time horizons by introducing sparse attention in the model. LocATe outperforms previous approaches on the existing PKU-MMD 3D-TAL benchmark (mAP=93.2%). Finally, we argue that benchmark datasets are most useful where there is clear room for performance improvement. To that end, we introduce a new, challenging, and more realistic benchmark dataset, BABEL-TAL-20 (BT20), where the performance of state-of-the-art methods is significantly worse. The dataset and code for the method will be available for research purposes. | ['Arjun Chandrasekaran', 'Michael J. Black', 'Bolei Zhou', 'Jiankai Sun'] | 2022-03-21 | null | null | null | null | ['action-localization'] | ['computer-vision'] | [ 1.40822157e-01 -5.39431453e-01 -1.55284300e-01 -2.75775999e-01
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22c86d4d-ef58-4694-a1c2-9890b2f22797 | sentiment-classification-using-document | null | null | https://aclanthology.org/P19-2057 | https://aclanthology.org/P19-2057.pdf | Sentiment Classification Using Document Embeddings Trained with Cosine Similarity | In document-level sentiment classification, each document must be mapped to a fixed length vector. Document embedding models map each document to a dense, low-dimensional vector in continuous vector space. This paper proposes training document embeddings using cosine similarity instead of dot product. Experiments on the IMDB dataset show that accuracy is improved when using cosine similarity compared to using dot product, while using feature combination with Naive Bayes weighted bag of n-grams achieves a new state of the art accuracy of 97.42{\%}. Code to reproduce all experiments is available at https://github.com/tanthongtan/dv-cosine | ['Tan Thongtan', 'Tanasanee Phienthrakul'] | 2019-07-01 | null | null | null | acl-2019-7 | ['document-embedding'] | ['methodology'] | [-4.16828722e-01 -3.95356536e-01 -5.66459477e-01 -5.95616043e-01
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046eec6a-7a60-4cfd-bc15-77e908dbc9be | psychology-guided-controllable-story-1 | 2210.07493 | null | https://arxiv.org/abs/2210.07493v1 | https://arxiv.org/pdf/2210.07493v1.pdf | Psychology-guided Controllable Story Generation | Controllable story generation is a challenging task in the field of NLP, which has attracted increasing research interest in recent years. However, most existing works generate a whole story conditioned on the appointed keywords or emotions, ignoring the psychological changes of the protagonist. Inspired by psychology theories, we introduce global psychological state chains, which include the needs and emotions of the protagonists, to help a story generation system create more controllable and well-planned stories. In this paper, we propose a Psychology-guIded Controllable Story Generation System (PICS) to generate stories that adhere to the given leading context and desired psychological state chains for the protagonist. Specifically, psychological state trackers are employed to memorize the protagonist's local psychological states to capture their inner temporal relationships. In addition, psychological state planners are adopted to gain the protagonist's global psychological states for story planning. Eventually, a psychology controller is designed to integrate the local and global psychological states into the story context representation for composing psychology-guided stories. Automatic and manual evaluations demonstrate that PICS outperforms baselines, and each part of PICS shows effectiveness for writing stories with more consistent psychological changes. | ['Wei Peng', 'Luxi Xing', 'Guanqun Bi', 'Yunpeng Li', 'Yue Hu', 'Yuqiang Xie'] | 2022-10-14 | psychology-guided-controllable-story | https://aclanthology.org/2022.coling-1.564 | https://aclanthology.org/2022.coling-1.564.pdf | coling-2022-10 | ['story-generation'] | ['natural-language-processing'] | [-1.29152257e-02 1.59740284e-01 -2.39663601e-01 -4.35204118e-01
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3c33aa59-e043-4fc0-9e64-a9deefe9e1ba | can-x2vec-save-lives-integrating-graph-and | 2001.01126 | null | https://arxiv.org/abs/2001.01126v1 | https://arxiv.org/pdf/2001.01126v1.pdf | Can x2vec Save Lives? Integrating Graph and Language Embeddings for Automatic Mental Health Classification | Graph and language embedding models are becoming commonplace in large scale analyses given their ability to represent complex sparse data densely in low-dimensional space. Integrating these models' complementary relational and communicative data may be especially helpful if predicting rare events or classifying members of hidden populations - tasks requiring huge and sparse datasets for generalizable analyses. For example, due to social stigma and comorbidities, mental health support groups often form in amorphous online groups. Predicting suicidality among individuals in these settings using standard network analyses is prohibitive due to resource limits (e.g., memory), and adding auxiliary data like text to such models exacerbates complexity- and sparsity-related issues. Here, I show how merging graph and language embedding models (metapath2vec and doc2vec) avoids these limits and extracts unsupervised clustering data without domain expertise or feature engineering. Graph and language distances to a suicide support group have little correlation (\r{ho} < 0.23), implying the two models are not embedding redundant information. When used separately to predict suicidality among individuals, graph and language data generate relatively accurate results (69% and 76%, respectively); however, when integrated, both data produce highly accurate predictions (90%, with 10% false-positives and 12% false-negatives). Visualizing graph embeddings annotated with predictions of potentially suicidal individuals shows the integrated model could classify such individuals even if they are positioned far from the support group. These results extend research on the importance of simultaneously analyzing behavior and language in massive networks and efforts to integrate embedding models for different kinds of data when predicting and classifying, particularly when they involve rare events. | ['Alexander Ruch'] | 2020-01-04 | null | null | null | null | ['activity-prediction', 'document-embedding', 'activity-prediction'] | ['computer-vision', 'methodology', 'time-series'] | [-4.51775156e-02 4.50627863e-01 -4.54583287e-01 -1.12204589e-01
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84889524-dd49-44f2-9bf1-9aa53f804647 | memory-enhanced-embedding-learning-for-cross | 2103.15686 | null | https://arxiv.org/abs/2103.15686v1 | https://arxiv.org/pdf/2103.15686v1.pdf | Memory Enhanced Embedding Learning for Cross-Modal Video-Text Retrieval | Cross-modal video-text retrieval, a challenging task in the field of vision and language, aims at retrieving corresponding instance giving sample from either modality. Existing approaches for this task all focus on how to design encoding model through a hard negative ranking loss, leaving two key problems unaddressed during this procedure. First, in the training stage, only a mini-batch of instance pairs is available in each iteration. Therefore, this kind of hard negatives is locally mined inside a mini-batch while ignoring the global negative samples among the dataset. Second, there are many text descriptions for one video and each text only describes certain local features of a video. Previous works for this task did not consider to fuse the multiply texts corresponding to a video during the training. In this paper, to solve the above two problems, we propose a novel memory enhanced embedding learning (MEEL) method for videotext retrieval. To be specific, we construct two kinds of memory banks respectively: cross-modal memory module and text center memory module. The cross-modal memory module is employed to record the instance embeddings of all the datasets for global negative mining. To avoid the fast evolving of the embedding in the memory bank during training, we utilize a momentum encoder to update the features by a moving-averaging strategy. The text center memory module is designed to record the center information of the multiple textual instances corresponding to a video, and aims at bridging these textual instances together. Extensive experimental results on two challenging benchmarks, i.e., MSR-VTT and VATEX, demonstrate the effectiveness of the proposed method. | ['Jiebo Luo', 'Hongtao Xie', 'Zheng-Jun Zha', 'Kecheng Zheng', 'Rui Zhao'] | 2021-03-29 | null | null | null | null | ['video-text-retrieval'] | ['computer-vision'] | [ 1.23643979e-01 -5.16128838e-01 -2.84108937e-01 -2.81402320e-01
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160887fe-cd60-4741-8b8f-539b5c1f763b | the-lmu-munich-system-for-the-wmt-2020 | 2010.13192 | null | https://arxiv.org/abs/2010.13192v1 | https://arxiv.org/pdf/2010.13192v1.pdf | The LMU Munich System for the WMT 2020 Unsupervised Machine Translation Shared Task | This paper describes the submission of LMU Munich to the WMT 2020 unsupervised shared task, in two language directions, German<->Upper Sorbian. Our core unsupervised neural machine translation (UNMT) system follows the strategy of Chronopoulou et al. (2020), using a monolingual pretrained language generation model (on German) and fine-tuning it on both German and Upper Sorbian, before initializing a UNMT model, which is trained with online backtranslation. Pseudo-parallel data obtained from an unsupervised statistical machine translation (USMT) system is used to fine-tune the UNMT model. We also apply BPE-Dropout to the low resource (Upper Sorbian) data to obtain a more robust system. We additionally experiment with residual adapters and find them useful in the Upper Sorbian->German direction. We explore sampling during backtranslation and curriculum learning to use SMT translations in a more principled way. Finally, we ensemble our best-performing systems and reach a BLEU score of 32.4 on German->Upper Sorbian and 35.2 on Upper Sorbian->German. | ['Alexander Fraser', 'Viktor Hangya', 'Dario Stojanovski', 'Alexandra Chronopoulou'] | 2020-10-25 | null | https://aclanthology.org/2020.wmt-1.128 | https://aclanthology.org/2020.wmt-1.128.pdf | wmt-emnlp-2020-11 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 4.25518274e-01 3.83106351e-01 -1.17545836e-01 -3.96193534e-01
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ca4e39c7-e7c1-4cf9-b29c-187735e38463 | catch-a-waveform-learning-to-generate-audio | 2106.06426 | null | https://arxiv.org/abs/2106.06426v2 | https://arxiv.org/pdf/2106.06426v2.pdf | Catch-A-Waveform: Learning to Generate Audio from a Single Short Example | Models for audio generation are typically trained on hours of recordings. Here, we illustrate that capturing the essence of an audio source is typically possible from as little as a few tens of seconds from a single training signal. Specifically, we present a GAN-based generative model that can be trained on one short audio signal from any domain (e.g. speech, music, etc.) and does not require pre-training or any other form of external supervision. Once trained, our model can generate random samples of arbitrary duration that maintain semantic similarity to the training waveform, yet exhibit new compositions of its audio primitives. This enables a long line of interesting applications, including generating new jazz improvisations or new a-cappella rap variants based on a single short example, producing coherent modifications to famous songs (e.g. adding a new verse to a Beatles song based solely on the original recording), filling-in of missing parts (inpainting), extending the bandwidth of a speech signal (super-resolution), and enhancing old recordings without access to any clean training example. We show that in all cases, no more than 20 seconds of training audio commonly suffice for our model to achieve state-of-the-art results. This is despite its complete lack of prior knowledge about the nature of audio signals in general. | ['Tomer Michaeli', 'Tamar Rott Shaham', 'Gal Greshler'] | 2021-06-11 | null | http://proceedings.neurips.cc/paper/2021/hash/af21d0c97db2e27e13572cbf59eb343d-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/af21d0c97db2e27e13572cbf59eb343d-Paper.pdf | neurips-2021-12 | ['audio-generation'] | ['audio'] | [ 6.55454099e-01 3.21356535e-01 3.52215379e-01 -1.82033852e-02
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44942d8c-1e92-49d8-a062-f69e1670d5dd | changepoint-detection-in-noisy-data-using-a | null | null | https://doi.org/10.3390/brainsci12050525 | https://www.mdpi.com/2076-3425/12/5/525/pdf?version=1650772641 | Changepoint Detection in Noisy Data Using a Novel Residuals Permutation-Based Method (RESPERM): Benchmarking and Application to Single Trial ERPs | An important problem in many fields dealing with noisy time series, such as psychophysiological single trial data during learning or monitoring treatment effects over time, is detecting a change in the model underlying a time series. Here, we present a new method for detecting a single changepoint in a linear time series regression model, termed residuals permutation-based method (RESPERM). The optimal changepoint in RESPERM maximizes Cohen’s effect size with the parameters estimated by the permutation of residuals in a linear model. RESPERM was compared with the SEGMENTED method, a well-established and recommended method for detecting changepoints, using extensive simulated data sets, varying the amount and distribution characteristics of noise and the location of the change point. In time series with medium to large amounts of noise, the variance of the detected changepoint was consistently smaller for RESPERM than SEGMENTED. Finally, both methods were applied to a sample dataset of single trial amplitudes of the N250 ERP component during face learning. In conclusion, RESPERM appears to be well suited for changepoint detection especially in noisy data, making it the method of choice in neuroscience, medicine and many other fields. | ['Grażyna Ślusarczyk', 'Anna Bereś', 'Jeremi Ochab', 'Piotr Fabian', 'Krzysztof Kotowski', 'Grzegorz Kończak', 'Katarzyna Stapor', 'Werner Sommer'] | 2022-04-21 | null | null | null | brain-sciences-2022-4 | ['time-series-regression'] | ['time-series'] | [ 4.53878224e-01 -4.19942856e-01 5.99468090e-02 -2.26735502e-01
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451b3629-62c5-4812-966e-f9c6f4748015 | visual-chain-of-thought-bridging-logical-gaps | 2305.02317 | null | https://arxiv.org/abs/2305.02317v1 | https://arxiv.org/pdf/2305.02317v1.pdf | Visual Chain of Thought: Bridging Logical Gaps with Multimodal Infillings | Recent advances in large language models elicit reasoning in a chain of thought that allows models to decompose problems in a human-like fashion. Though this paradigm improves multi-step reasoning ability in language models, it is limited by being unimodal and applied mainly to question-answering tasks. We claim that incorporating visual augmentation into reasoning is essential, especially for complex, imaginative tasks. Consequently, we introduce VCoT, a novel method that leverages chain of thought prompting with vision-language grounding to recursively bridge the logical gaps within sequential data. Our method uses visual guidance to generate synthetic multimodal infillings that add consistent and novel information to reduce the logical gaps for downstream tasks that can benefit from temporal reasoning, as well as provide interpretability into models' multi-step reasoning. We apply VCoT to the Visual Storytelling and WikiHow summarization datasets and demonstrate through human evaluation that VCoT offers novel and consistent synthetic data augmentation beating chain of thought baselines, which can be used to enhance downstream performance. | ['William Yang Wang', 'Diba Mirza', 'Chinmay Sonar', 'Michael Saxon', 'Yujie Lu', 'Alex Mei', 'Ryan He', 'Andy Ouyang', 'Vaishnavi Himakunthala', 'Daniel Rose'] | 2023-05-03 | null | null | null | null | ['visual-storytelling'] | ['natural-language-processing'] | [ 2.33782277e-01 7.46587813e-01 -3.93962227e-02 -1.28574520e-01
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3.82836312e-01 3.18382770e-01 7.51205564e-01 -8.41996253e-01
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4.19756770e-01 -1.26302436e-01 2.69731015e-01 1.19906211e+00
4.90782976e-01 9.25686359e-01 -3.88308048e-01 -7.66345501e-01
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4.38989729e-01 -2.54416019e-01 -9.42963138e-02 2.98245102e-01] | [10.872467994689941, 1.8985121250152588] |
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