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183344d3-b337-4690-8874-a304c70bc25c | influence-of-asr-and-language-model-on | 2110.15704 | null | https://arxiv.org/abs/2110.15704v1 | https://arxiv.org/pdf/2110.15704v1.pdf | Influence of ASR and Language Model on Alzheimer's Disease Detection | Alzheimer's Disease is the most common form of dementia. Automatic detection from speech could help to identify symptoms at early stages, so that preventive actions can be carried out. This research is a contribution to the ADReSSo Challenge, we analyze the usage of a SotA ASR system to transcribe participant's spoken descriptions from a picture. We analyse the loss of performance regarding the use of human transcriptions (measured using transcriptions from the 2020 ADReSS Challenge). Furthermore, we study the influence of a language model -- which tends to correct non-standard sequences of words -- with the lack of language model to decode the hypothesis from the ASR. This aims at studying the language bias and get more meaningful transcriptions based only on the acoustic information from patients. The proposed system combines acoustic -- based on prosody and voice quality -- and lexical features based on the first occurrence of the most common words. The reported results show the effect of using automatic transcripts with or without language model. The best fully automatic system achieves up to 76.06 % of accuracy (without language model), significantly higher, 3 % above, than a system employing word transcriptions decoded using general purpose language models. | ['Mireia Farrús', 'Jordi Luque', 'Guillermo Cámbara', 'Joan Codina-Filbà'] | 2021-09-20 | null | null | null | null | ['alzheimer-s-disease-detection'] | ['medical'] | [ 5.08975238e-02 3.66438255e-02 3.77436548e-01 -4.17256117e-01
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f284db24-b42b-4677-b4f0-ba532d58ea49 | motion-matters-neural-motion-transfer-for | 2303.12059 | null | https://arxiv.org/abs/2303.12059v2 | https://arxiv.org/pdf/2303.12059v2.pdf | Motion Matters: Neural Motion Transfer for Better Camera Physiological Sensing | Machine learning models for camera-based physiological measurement can have weak generalization due to a lack of representative training data. Body motion is one of the most significant sources of noise when attempting to recover the subtle cardiac pulse from a video. We explore motion transfer as a form of data augmentation to introduce motion variation while preserving physiological changes. We adapt a neural video synthesis approach to augment videos for the task of remote photoplethysmography (PPG) and study the effects of motion augmentation with respect to 1) the magnitude and 2) the type of motion. After training on motion-augmented versions of publicly available datasets, the presented inter-dataset results on five benchmark datasets show improvements of up to 75% over existing state-of-the-art results. Our findings illustrate the utility of motion transfer as a data augmentation technique for improving the generalization of models for camera-based physiological sensing. We release our code and pre-trained models for using motion transfer as a data augmentation technique on our project page: https://motion-matters.github.io/ | ['Soumyadip Sengupta', 'Daniel McDuff', 'Shwetak Patel', 'Yulu Pan', 'Xin Liu', 'Akshay Paruchuri'] | 2023-03-21 | null | null | null | null | ['photoplethysmography-ppg'] | ['medical'] | [ 3.96767825e-01 -1.67511910e-01 -2.42469355e-01 -2.69954145e-01
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2a66f524-7dea-4a71-9935-78d524190e18 | optimizing-non-autoregressive-transformers | 2305.13667 | null | https://arxiv.org/abs/2305.13667v2 | https://arxiv.org/pdf/2305.13667v2.pdf | Optimizing Non-Autoregressive Transformers with Contrastive Learning | Non-autoregressive Transformers (NATs) reduce the inference latency of Autoregressive Transformers (ATs) by predicting words all at once rather than in sequential order. They have achieved remarkable progress in machine translation as well as many other applications. However, a long-standing challenge for NATs is the learning of multi-modality data distribution, which is the main cause of the performance gap between NATs and ATs. In this paper, we propose to ease the difficulty of modality learning via sampling from the model distribution instead of the data distribution. We derive contrastive constraints to stabilize the training process and integrate this resulting objective with the state-of-the-art NAT architecture DA-Transformer. Our model \method is examined on 3 different tasks, including machine translation, text summarization, and paraphrasing with 5 benchmarks. Results show that our approach outperforms previous non-autoregressive baselines by a significant margin and establishes new state-of-the-art results for non-autoregressive transformers on all the benchmarks. | ['Lingpeng Kong', 'Xipeng Qiu', 'Fei Huang', 'Jiangtao Feng', 'Chenxin An'] | 2023-05-23 | null | null | null | null | ['text-summarization'] | ['natural-language-processing'] | [ 5.70511460e-01 1.98638570e-02 -4.01652217e-01 -3.43932748e-01
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76d1c035-5d39-484b-bf3e-e1dcda1fd681 | towards-speech-enhancement-using-a | 2012.03594 | null | https://arxiv.org/abs/2012.03594v2 | https://arxiv.org/pdf/2012.03594v2.pdf | Towards speech enhancement using a variational U-Net architecture | We investigate the viability of a variational U-Net architecture for denoising of single-channel audio data. Deep network speech enhancement systems commonly aim to estimate filter masks, or opt to work on the waveform signal, potentially neglecting relationships across higher dimensional spectro-temporal features. We study the adoption of a probabilistic bottleneck into the classic U-Net architecture for direct spectral reconstruction. Evaluation of several ablation network variants is carried out using signal-to-distortion ratio and perceptual measures, on audio data that includes known and unknown noise types as well as reverberation. Our experiments show that the residual (skip) connections in the proposed system are a prerequisite for successful spectral reconstruction, i.e., without filter mask estimation. Results show, on average, an advantage of the proposed variational U-Net architecture over its classic, non-variational version in signal enhancement performance under reverberant conditions of 0.31 and 6.98 in PESQ and STOI scores, respectively. Anecdotal evidence points to improved suppression of impulsive noise sources with the variational U-Net compared to the recurrent mask estimation network baseline. | ['Jörn Anemüller', 'Eike J. Nustede'] | 2020-12-07 | null | null | null | null | ['spectral-reconstruction'] | ['computer-vision'] | [ 2.43050307e-01 -9.10381898e-02 4.44804788e-01 5.67895994e-02
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fea1bb56-7ade-45dd-ba36-0f0fe5225a5f | mapping-the-ictal-interictal-injury-continuum | 2211.05207 | null | https://arxiv.org/abs/2211.05207v4 | https://arxiv.org/pdf/2211.05207v4.pdf | Interpretable Machine Learning System to EEG Patterns on the Ictal-Interictal-Injury Continuum | In intensive care units (ICUs), critically ill patients are monitored with electroencephalograms (EEGs) to prevent serious brain injury. The number of patients who can be monitored is constrained by the availability of trained physicians to read EEGs, and EEG interpretation can be subjective and prone to inter-observer variability. Automated deep learning systems for EEG could reduce human bias and accelerate the diagnostic process. However, black box deep learning models are untrustworthy, difficult to troubleshoot, and lack accountability in real-world applications, leading to a lack of trust and adoption by clinicians. To address these challenges, we propose a novel interpretable deep learning model that not only predicts the presence of harmful brainwave patterns but also provides high-quality case-based explanations of its decisions. Our model performs better than the corresponding black box model, despite being constrained to be interpretable. The learned 2D embedded space provides the first global overview of the structure of ictal-interictal-injury continuum brainwave patterns. The ability to understand how our model arrived at its decisions will not only help clinicians to diagnose and treat harmful brain activities more accurately but also increase their trust and adoption of machine learning models in clinical practice; this could be an integral component of the ICU neurologists' standard workflow. | ['M. Brandon Westover', 'Cynthia Rudin', 'Wendong Ge', 'Jin Jing', 'Zhicheng Guo', 'Alina Jade Barnett'] | 2022-11-09 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [ 4.68635745e-03 1.64733708e-01 2.22423911e-01 -5.38747430e-01
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697c64a0-1fa0-4c0f-8d15-545ed6e3e33d | a-unified-bev-model-for-joint-learning-of-3d | 2302.14511 | null | https://arxiv.org/abs/2302.14511v2 | https://arxiv.org/pdf/2302.14511v2.pdf | A Unified BEV Model for Joint Learning of 3D Local Features and Overlap Estimation | Pairwise point cloud registration is a critical task for many applications, which heavily depends on finding correct correspondences from the two point clouds. However, the low overlap between input point clouds causes the registration to fail easily, leading to mistaken overlapping and mismatched correspondences, especially in scenes where non-overlapping regions contain similar structures. In this paper, we present a unified bird's-eye view (BEV) model for jointly learning of 3D local features and overlap estimation to fulfill pairwise registration and loop closure. Feature description is performed by a sparse UNet-like network based on BEV representation, and 3D keypoints are extracted by a detection head for 2D locations, and a regression head for heights. For overlap detection, a cross-attention module is applied for interacting contextual information of input point clouds, followed by a classification head to estimate the overlapping region. We evaluate our unified model extensively on the KITTI dataset and Apollo-SouthBay dataset. The experiments demonstrate that our method significantly outperforms existing methods on overlap estimation, especially in scenes with small overlaps. It also achieves top registration performance on both datasets in terms of translation and rotation errors. | ['Guowei Wan', 'Yong liu', 'Yufei Liang', 'Yongkun Wen', 'Wendong Ding', 'Lin Li'] | 2023-02-28 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [-1.74612805e-01 -2.95531660e-01 4.45758784e-03 -4.77093667e-01
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67db56d4-471a-4674-8ad1-f36f3a8a33f9 | 3d-a-nets-3d-deep-dense-descriptor-for | 1711.10108 | null | http://arxiv.org/abs/1711.10108v1 | http://arxiv.org/pdf/1711.10108v1.pdf | 3D-A-Nets: 3D Deep Dense Descriptor for Volumetric Shapes with Adversarial Networks | Recently researchers have been shifting their focus towards learned 3D shape
descriptors from hand-craft ones to better address challenging issues of the
deformation and structural variation inherently present in 3D objects. 3D
geometric data are often transformed to 3D Voxel grids with regular format in
order to be better fed to a deep neural net architecture. However, the
computational intractability of direct application of 3D convolutional nets to
3D volumetric data severely limits the efficiency (i.e. slow processing) and
effectiveness (i.e. unsatisfied accuracy) in processing 3D geometric data. In
this paper, powered with a novel design of adversarial networks (3D-A-Nets), we
have developed a novel 3D deep dense shape descriptor (3D-DDSD) to address the
challenging issues of efficient and effective 3D volumetric data processing. We
developed new definition of 2D multilayer dense representation (MDR) of 3D
volumetric data to extract concise but geometrically informative shape
description and a novel design of adversarial networks that jointly train a set
of convolution neural network (CNN), recurrent neural network (RNN) and an
adversarial discriminator. More specifically, the generator network produces 3D
shape features that encourages the clustering of samples from the same category
with correct class label, whereas the discriminator network discourages the
clustering by assigning them misleading adversarial class labels. By addressing
the challenges posed by the computational inefficiency of direct application of
CNN to 3D volumetric data, 3D-A-Nets can learn high-quality 3D-DSDD which
demonstrates superior performance on 3D shape classification and retrieval over
other state-of-the-art techniques by a great margin. | ['Mengwei Ren', 'Liang Niu', 'Yi Fang'] | 2017-11-28 | null | null | null | null | ['3d-shape-retrieval'] | ['computer-vision'] | [ 5.10879867e-02 1.01214923e-01 3.70246261e-01 -3.35991502e-01
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01681300-05e0-4b52-b140-cae7107971b9 | delta-descriptors-change-based-place | 2006.05700 | null | https://arxiv.org/abs/2006.05700v2 | https://arxiv.org/pdf/2006.05700v2.pdf | Delta Descriptors: Change-Based Place Representation for Robust Visual Localization | Visual place recognition is challenging because there are so many factors that can cause the appearance of a place to change, from day-night cycles to seasonal change to atmospheric conditions. In recent years a large range of approaches have been developed to address this challenge including deep-learnt image descriptors, domain translation, and sequential filtering, all with shortcomings including generality and velocity-sensitivity. In this paper we propose a novel descriptor derived from tracking changes in any learned global descriptor over time, dubbed Delta Descriptors. Delta Descriptors mitigate the offsets induced in the original descriptor matching space in an unsupervised manner by considering temporal differences across places observed along a route. Like all other approaches, Delta Descriptors have a shortcoming - volatility on a frame to frame basis - which can be overcome by combining them with sequential filtering methods. Using two benchmark datasets, we first demonstrate the high performance of Delta Descriptors in isolation, before showing new state-of-the-art performance when combined with sequence-based matching. We also present results demonstrating the approach working with four different underlying descriptor types, and two other beneficial properties of Delta Descriptors in comparison to existing techniques: their increased inherent robustness to variations in camera motion and a reduced rate of performance degradation as dimensional reduction is applied. Source code is made available at https://github.com/oravus/DeltaDescriptors. | ['Gaurangi Anand', 'Sourav Garg', 'Michael Milford', 'Ben Harwood'] | 2020-06-10 | null | null | null | null | ['sequential-place-recognition'] | ['robots'] | [ 1.09683454e-01 -7.73068607e-01 -1.03527352e-01 -4.09978151e-01
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c0870a45-b6e9-4557-b436-88e44d4b6a94 | hashtag-guided-low-resource-tweet | 2302.10143 | null | https://arxiv.org/abs/2302.10143v1 | https://arxiv.org/pdf/2302.10143v1.pdf | Hashtag-Guided Low-Resource Tweet Classification | Social media classification tasks (e.g., tweet sentiment analysis, tweet stance detection) are challenging because social media posts are typically short, informal, and ambiguous. Thus, training on tweets is challenging and demands large-scale human-annotated labels, which are time-consuming and costly to obtain. In this paper, we find that providing hashtags to social media tweets can help alleviate this issue because hashtags can enrich short and ambiguous tweets in terms of various information, such as topic, sentiment, and stance. This motivates us to propose a novel Hashtag-guided Tweet Classification model (HashTation), which automatically generates meaningful hashtags for the input tweet to provide useful auxiliary signals for tweet classification. To generate high-quality and insightful hashtags, our hashtag generation model retrieves and encodes the post-level and entity-level information across the whole corpus. Experiments show that HashTation achieves significant improvements on seven low-resource tweet classification tasks, in which only a limited amount of training data is provided, showing that automatically enriching tweets with model-generated hashtags could significantly reduce the demand for large-scale human-labeled data. Further analysis demonstrates that HashTation is able to generate high-quality hashtags that are consistent with the tweets and their labels. The code is available at https://github.com/shizhediao/HashTation. | ['Tong Zhang', 'Yan Song', 'Zhiliang Tian', 'Liangming Pan', 'Sedrick Scott Keh', 'Shizhe Diao'] | 2023-02-20 | null | null | null | null | ['stance-detection'] | ['natural-language-processing'] | [-3.51587385e-02 3.89956176e-01 -4.33137208e-01 -7.56026745e-01
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95b0da27-d807-4cf5-ad28-7c25eb45887f | r2-d2-a-modular-baseline-for-open-domain | 2109.03502 | null | https://arxiv.org/abs/2109.03502v1 | https://arxiv.org/pdf/2109.03502v1.pdf | R2-D2: A Modular Baseline for Open-Domain Question Answering | This work presents a novel four-stage open-domain QA pipeline R2-D2 (Rank twice, reaD twice). The pipeline is composed of a retriever, passage reranker, extractive reader, generative reader and a mechanism that aggregates the final prediction from all system's components. We demonstrate its strength across three open-domain QA datasets: NaturalQuestions, TriviaQA and EfficientQA, surpassing state-of-the-art on the first two. Our analysis demonstrates that: (i) combining extractive and generative reader yields absolute improvements up to 5 exact match and it is at least twice as effective as the posterior averaging ensemble of the same models with different parameters, (ii) the extractive reader with fewer parameters can match the performance of the generative reader on extractive QA datasets. | ['Pavel Smrz', 'Karel Ondrej', 'Martin Docekal', 'Martin Fajcik'] | 2021-09-08 | null | https://aclanthology.org/2021.findings-emnlp.73 | https://aclanthology.org/2021.findings-emnlp.73.pdf | findings-emnlp-2021-11 | ['triviaqa'] | ['miscellaneous'] | [-1.52928904e-01 3.53180975e-01 1.35475636e-01 -7.44274035e-02
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a21f328b-82ec-4163-a746-d9be2c1334a5 | doing-good-or-doing-right-exploring-the | 2107.01791 | null | https://arxiv.org/abs/2107.01791v1 | https://arxiv.org/pdf/2107.01791v1.pdf | Doing Good or Doing Right? Exploring the Weakness of Commonsense Causal Reasoning Models | Pretrained language models (PLM) achieve surprising performance on the Choice of Plausible Alternatives (COPA) task. However, whether PLMs have truly acquired the ability of causal reasoning remains a question. In this paper, we investigate the problem of semantic similarity bias and reveal the vulnerability of current COPA models by certain attacks. Previous solutions that tackle the superficial cues of unbalanced token distribution still encounter the same problem of semantic bias, even more seriously due to the utilization of more training data. We mitigate this problem by simply adding a regularization loss and experimental results show that this solution not only improves the model's generalization ability, but also assists the models to perform more robustly on a challenging dataset, BCOPA-CE, which has unbiased token distribution and is more difficult for models to distinguish cause and effect. | ['Yinglin Wang', 'Mingyue Han'] | 2021-07-05 | null | https://aclanthology.org/2021.acl-short.20 | https://aclanthology.org/2021.acl-short.20.pdf | acl-2021-5 | ['commonsense-causal-reasoning'] | ['natural-language-processing'] | [ 6.30140528e-02 2.68499047e-01 -3.29971671e-01 -3.23697120e-01
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dd1fc2e6-b71e-49b0-8aa1-4cfb41f1bc12 | ph-sft-shape-from-template-with-a-physics | 2203.11938 | null | https://arxiv.org/abs/2203.11938v1 | https://arxiv.org/pdf/2203.11938v1.pdf | φ-SfT: Shape-from-Template with a Physics-Based Deformation Model | Shape-from-Template (SfT) methods estimate 3D surface deformations from a single monocular RGB camera while assuming a 3D state known in advance (a template). This is an important yet challenging problem due to the under-constrained nature of the monocular setting. Existing SfT techniques predominantly use geometric and simplified deformation models, which often limits their reconstruction abilities. In contrast to previous works, this paper proposes a new SfT approach explaining 2D observations through physical simulations accounting for forces and material properties. Our differentiable physics simulator regularises the surface evolution and optimises the material elastic properties such as bending coefficients, stretching stiffness and density. We use a differentiable renderer to minimise the dense reprojection error between the estimated 3D states and the input images and recover the deformation parameters using an adaptive gradient-based optimisation. For the evaluation, we record with an RGB-D camera challenging real surfaces exposed to physical forces with various material properties and textures. Our approach significantly reduces the 3D reconstruction error compared to multiple competing methods. For the source code and data, see https://4dqv.mpi-inf.mpg.de/phi-SfT/. | ['Vladislav Golyanik', 'Christian Theobalt', 'Mohamed Elgharib', 'Edith Tretschk', 'Navami Kairanda'] | 2022-03-22 | null | null | null | null | ['physical-simulations'] | ['miscellaneous'] | [ 4.43171740e-01 4.48391177e-02 4.36886311e-01 -9.78609174e-02
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f2ed84b3-9cd2-4c93-bee7-d2f4b2b1aebf | meta-learning-adversarial-bandit-algorithms | 2307.02295 | null | https://arxiv.org/abs/2307.02295v1 | https://arxiv.org/pdf/2307.02295v1.pdf | Meta-Learning Adversarial Bandit Algorithms | We study online meta-learning with bandit feedback, with the goal of improving performance across multiple tasks if they are similar according to some natural similarity measure. As the first to target the adversarial online-within-online partial-information setting, we design meta-algorithms that combine outer learners to simultaneously tune the initialization and other hyperparameters of an inner learner for two important cases: multi-armed bandits (MAB) and bandit linear optimization (BLO). For MAB, the meta-learners initialize and set hyperparameters of the Tsallis-entropy generalization of Exp3, with the task-averaged regret improving if the entropy of the optima-in-hindsight is small. For BLO, we learn to initialize and tune online mirror descent (OMD) with self-concordant barrier regularizers, showing that task-averaged regret varies directly with an action space-dependent measure they induce. Our guarantees rely on proving that unregularized follow-the-leader combined with two levels of low-dimensional hyperparameter tuning is enough to learn a sequence of affine functions of non-Lipschitz and sometimes non-convex Bregman divergences bounding the regret of OMD. | ['Zhiwei Steven Wu', 'Ron Meir', 'Kfir Y. Levy', 'Maria-Florina Balcan', 'Keegan Harris', 'Ilya Osadchiy', 'Mikhail Khodak'] | 2023-07-05 | null | null | null | null | ['meta-learning', 'multi-armed-bandits'] | ['methodology', 'miscellaneous'] | [-2.32971087e-01 4.62712109e-01 -6.04377866e-01 -2.56187975e-01
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61e147b7-a442-4fbf-8143-fd5ba2f50e30 | how-adults-understand-what-young-children-say | 2206.07807 | null | https://arxiv.org/abs/2206.07807v3 | https://arxiv.org/pdf/2206.07807v3.pdf | How Adults Understand What Young Children Say | Children's early speech often bears little resemblance to that of adults, and yet parents and other caregivers are able to interpret that speech and react accordingly. Here we investigate how these adult inferences as listeners reflect sophisticated beliefs about what children are trying to communicate, as well as how children are likely to pronounce words. Using a Bayesian framework for modeling spoken word recognition, we find that computational models can replicate adult interpretations of children's speech only when they include strong, context-specific prior expectations about the messages that children will want to communicate. This points to a critical role of adult cognitive processes in supporting early communication and reveals how children can actively prompt adults to take actions on their behalf even when they have only a nascent understanding of the adult language. We discuss the wide-ranging implications of the powerful listening capabilities of adults for theories of first language acquisition. | ['Roger P. Levy', 'Elika Bergelson', 'Nicole H. Wong', 'Ruthe Foushee', 'Stephan C. Meylan'] | 2022-06-15 | null | null | null | null | ['language-acquisition'] | ['natural-language-processing'] | [ 4.01375830e-01 6.90240085e-01 -1.06597513e-01 -8.03182483e-01
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9095e77a-86ef-421b-a729-a4d1b970ea5b | mutual-information-guided-knowledge-transfer | 2206.12063 | null | https://arxiv.org/abs/2206.12063v2 | https://arxiv.org/pdf/2206.12063v2.pdf | Mutual Information-guided Knowledge Transfer for Novel Class Discovery | We tackle the novel class discovery problem, aiming to discover novel classes in unlabeled data based on labeled data from seen classes. The main challenge is to transfer knowledge contained in the seen classes to unseen ones. Previous methods mostly transfer knowledge through sharing representation space or joint label space. However, they tend to neglect the class relation between seen and unseen categories, and thus the learned representations are less effective for clustering unseen classes. In this paper, we propose a principle and general method to transfer semantic knowledge between seen and unseen classes. Our insight is to utilize mutual information to measure the relation between seen classes and unseen classes in a restricted label space and maximizing mutual information promotes transferring semantic knowledge. To validate the effectiveness and generalization of our method, we conduct extensive experiments both on novel class discovery and general novel class discovery settings. Our results show that the proposed method outperforms previous SOTA by a significant margin on several benchmarks. | ['Xuming He', 'Qian He', 'Zhitong Gao', 'Ruijie Xu', 'Chuanyang Hu', 'Chuyu Zhang'] | 2022-06-24 | null | null | null | null | ['novel-class-discovery', 'novel-class-discovery'] | ['computer-vision', 'methodology'] | [ 3.05916458e-01 1.18905693e-01 -3.86610508e-01 -6.91129267e-01
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3816f2ee-990f-45fe-8f1d-6496d49c9ee8 | a-diachronic-analysis-of-the-nlp-research | 2305.12920 | null | https://arxiv.org/abs/2305.12920v1 | https://arxiv.org/pdf/2305.12920v1.pdf | A Diachronic Analysis of the NLP Research Paradigm Shift: When, How, and Why? | Understanding the fundamental concepts and trends in a scientific field is crucial for keeping abreast of its ongoing development. In this study, we propose a systematic framework for analyzing the evolution of research topics in a scientific field using causal discovery and inference techniques. By conducting extensive experiments on the ACL Anthology corpus, we demonstrate that our framework effectively uncovers evolutionary trends and the underlying causes for a wide range of natural language processing (NLP) research topics. | ['Iryna Gurevych', 'Yufang Hou', 'Aniket Pramanick'] | 2023-05-22 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [-5.44811338e-02 -1.99698538e-01 -7.73034334e-01 -1.09073773e-01
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8f9c2a59-4599-4f5a-b2d7-d1ccd73d7149 | xvfi-extreme-video-frame-interpolation | 2103.16206 | null | https://arxiv.org/abs/2103.16206v2 | https://arxiv.org/pdf/2103.16206v2.pdf | XVFI: eXtreme Video Frame Interpolation | In this paper, we firstly present a dataset (X4K1000FPS) of 4K videos of 1000 fps with the extreme motion to the research community for video frame interpolation (VFI), and propose an extreme VFI network, called XVFI-Net, that first handles the VFI for 4K videos with large motion. The XVFI-Net is based on a recursive multi-scale shared structure that consists of two cascaded modules for bidirectional optical flow learning between two input frames (BiOF-I) and for bidirectional optical flow learning from target to input frames (BiOF-T). The optical flows are stably approximated by a complementary flow reversal (CFR) proposed in BiOF-T module. During inference, the BiOF-I module can start at any scale of input while the BiOF-T module only operates at the original input scale so that the inference can be accelerated while maintaining highly accurate VFI performance. Extensive experimental results show that our XVFI-Net can successfully capture the essential information of objects with extremely large motions and complex textures while the state-of-the-art methods exhibit poor performance. Furthermore, our XVFI-Net framework also performs comparably on the previous lower resolution benchmark dataset, which shows a robustness of our algorithm as well. All source codes, pre-trained models, and proposed X4K1000FPS datasets are publicly available at https://github.com/JihyongOh/XVFI. | ['Munchurl Kim', 'Jihyong Oh', 'Hyeonjun Sim'] | 2021-03-30 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Sim_XVFI_eXtreme_Video_Frame_Interpolation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Sim_XVFI_eXtreme_Video_Frame_Interpolation_ICCV_2021_paper.pdf | iccv-2021-1 | ['extreme-video-frame-interpolation'] | ['computer-vision'] | [-5.20634428e-02 -3.60848188e-01 -2.52667099e-01 3.30395587e-02
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84e9e5b4-676a-40e2-b486-a3a25792eba2 | generalizable-features-from-unsupervised | 1612.03809 | null | http://arxiv.org/abs/1612.03809v1 | http://arxiv.org/pdf/1612.03809v1.pdf | Generalizable Features From Unsupervised Learning | Humans learn a predictive model of the world and use this model to reason
about future events and the consequences of actions. In contrast to most
machine predictors, we exhibit an impressive ability to generalize to unseen
scenarios and reason intelligently in these settings. One important aspect of
this ability is physical intuition(Lake et al., 2016). In this work, we explore
the potential of unsupervised learning to find features that promote better
generalization to settings outside the supervised training distribution. Our
task is predicting the stability of towers of square blocks. We demonstrate
that an unsupervised model, trained to predict future frames of a video
sequence of stable and unstable block configurations, can yield features that
support extrapolating stability prediction to blocks configurations outside the
training set distribution | ['Mehdi Mirza', 'Yoshua Bengio', 'Aaron Courville'] | 2016-12-12 | null | null | null | null | ['physical-intuition'] | ['reasoning'] | [ 3.57590586e-01 1.65081948e-01 -4.52445835e-01 -5.31826377e-01
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4daaea5a-6907-4e21-a276-a5d354b54872 | 3d-face-modeling-from-diverse-raw-scan-data | 1902.04943 | null | https://arxiv.org/abs/1902.04943v3 | https://arxiv.org/pdf/1902.04943v3.pdf | 3D Face Modeling From Diverse Raw Scan Data | Traditional 3D face models learn a latent representation of faces using linear subspaces from limited scans of a single database. The main roadblock of building a large-scale face model from diverse 3D databases lies in the lack of dense correspondence among raw scans. To address these problems, this paper proposes an innovative framework to jointly learn a nonlinear face model from a diverse set of raw 3D scan databases and establish dense point-to-point correspondence among their scans. Specifically, by treating input scans as unorganized point clouds, we explore the use of PointNet architectures for converting point clouds to identity and expression feature representations, from which the decoder networks recover their 3D face shapes. Further, we propose a weakly supervised learning approach that does not require correspondence label for the scans. We demonstrate the superior dense correspondence and representation power of our proposed method, and its contribution to single-image 3D face reconstruction. | ['Luan Tran', 'Xiaoming Liu', 'Feng Liu'] | 2019-02-13 | 3d-face-modeling-from-diverse-raw-scan-data-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Liu_3D_Face_Modeling_From_Diverse_Raw_Scan_Data_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Liu_3D_Face_Modeling_From_Diverse_Raw_Scan_Data_ICCV_2019_paper.pdf | iccv-2019-10 | ['3d-face-modeling'] | ['computer-vision'] | [-7.70146474e-02 2.60589838e-01 -2.73295105e-01 -9.49223280e-01
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ba9a3526-204c-4c26-9cc1-2ff869071629 | improving-sp-stock-prediction-with-time | 2002.05784 | null | https://arxiv.org/abs/2002.05784v1 | https://arxiv.org/pdf/2002.05784v1.pdf | Improving S&P stock prediction with time series stock similarity | Stock market prediction with forecasting algorithms is a popular topic these days where most of the forecasting algorithms train only on data collected on a particular stock. In this paper, we enriched the stock data with related stocks just as a professional trader would have done to improve the stock prediction models. We tested five different similarities functions and found co-integration similarity to have the best improvement on the prediction model. We evaluate the models on seven S&P stocks from various industries over five years period. The prediction model we trained on similar stocks had significantly better results with 0.55 mean accuracy, and 19.782 profit compare to the state of the art model with an accuracy of 0.52 and profit of 6.6. | ['Lior Sidi'] | 2020-02-08 | null | null | null | null | ['stock-market-prediction', 'stock-prediction'] | ['time-series', 'time-series'] | [-9.53318834e-01 -3.03328365e-01 -4.19525981e-01 -2.05899194e-01
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6a01a16f-8852-422e-aace-cbdad26bb4e6 | small-footprint-keyword-spotting-with-graph | 1912.05124 | null | https://arxiv.org/abs/1912.05124v1 | https://arxiv.org/pdf/1912.05124v1.pdf | Small-footprint Keyword Spotting with Graph Convolutional Network | Despite the recent successes of deep neural networks, it remains challenging to achieve high precision keyword spotting task (KWS) on resource-constrained devices. In this study, we propose a novel context-aware and compact architecture for keyword spotting task. Based on residual connection and bottleneck structure, we design a compact and efficient network for KWS task. To leverage the long range dependencies and global context of the convolutional feature maps, the graph convolutional network is introduced to encode the non-local relations. By evaluated on the Google Speech Command Dataset, the proposed method achieves state-of-the-art performance and outperforms the prior works by a large margin with lower computational cost. | ['Leibo Liu', 'Dandan song', 'Shouyi Yin', 'Shaojun Wei', 'Peng Ouyang', 'Xi Chen'] | 2019-12-11 | null | null | null | null | ['small-footprint-keyword-spotting'] | ['speech'] | [ 1.09042376e-02 -1.30216151e-01 -6.14817381e-01 -4.31091696e-01
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ea60267d-dcd5-4efb-bb0e-41d31cb9d53d | naming-objects-for-vision-and-language | 2303.02871 | null | https://arxiv.org/abs/2303.02871v1 | https://arxiv.org/pdf/2303.02871v1.pdf | Naming Objects for Vision-and-Language Manipulation | Robot manipulation tasks by natural language instructions need common understanding of the target object between human and the robot. However, the instructions often have an interpretation ambiguity, because the instruction lacks important information, or does not express the target object correctly to complete the task. To solve this ambiguity problem, we hypothesize that "naming" the target objects in advance will reduce the ambiguity of natural language instructions. We propose a robot system and method that incorporates naming with appearance of the objects in advance, so that in the later manipulation task, instruction can be performed with its unique name to disambiguate the objects easily. To demonstrate the effectiveness of our approach, we build a system that can memorize the target objects, and show that naming the objects facilitates detection of the target objects and improves the success rate of manipulation instructions. With this method, the success rate of object manipulation task increases by 31% in ambiguous instructions. | ['Jerry Jun Yokono', 'Tamaki Kojima', 'Yu Ishihara', 'Jianing Wu', 'Takayoshi Takayanagi', 'Shunichi Sekiguchi', 'Kazumi Aoyama', 'Tokuhiro Nishikawa'] | 2023-03-06 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 5.82358725e-02 1.74106106e-01 3.68610732e-02 -3.81461412e-01
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947e6893-75c3-49fc-845d-ba8f0511502b | cnn-assisted-steganography-integrating | 2304.12503 | null | https://arxiv.org/abs/2304.12503v1 | https://arxiv.org/pdf/2304.12503v1.pdf | CNN-Assisted Steganography -- Integrating Machine Learning with Established Steganographic Techniques | We propose a method to improve steganography by increasing the resilience of stego-media to discovery through steganalysis. Our approach enhances a class of steganographic approaches through the inclusion of a steganographic assistant convolutional neural network (SA-CNN). Previous research showed success in discovering the presence of hidden information within stego-images using trained neural networks as steganalyzers that are applied to stego-images. Our results show that such steganalyzers are less effective when SA-CNN is employed during the generation of a stego-image. We also explore the advantages and disadvantages of representing all the possible outputs of our SA-CNN within a smaller, discrete space, rather than a continuous space. Our SA-CNN enables certain classes of parametric steganographic algorithms to be customized based on characteristics of the cover media in which information is to be embedded. Thus, SA-CNN is adaptive in the sense that it enables the core steganographic algorithm to be especially configured for each particular instance of cover media. Experimental results are provided that employ a recent steganographic technique, S-UNIWARD, both with and without the use of SA-CNN. We then apply both sets of stego-images, those produced with and without SA-CNN, to an exmaple steganalyzer, Yedroudj-Net, and we compare the results. We believe that this approach for the integration of neural networks with hand-crafted algorithms increases the reliability and adaptability of steganographic algorithms. | ['Mitchell A. Thornton', 'Eric C. Larson', 'Theodore Manikas', 'Andrew Havard'] | 2023-04-25 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 8.77783895e-01 4.04405683e-01 2.31897041e-01 1.18316799e-01
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2034efa4-669a-4bcf-b756-fc90041705d0 | comparison-of-time-frequency-representations | 1706.07156 | null | http://arxiv.org/abs/1706.07156v1 | http://arxiv.org/pdf/1706.07156v1.pdf | Comparison of Time-Frequency Representations for Environmental Sound Classification using Convolutional Neural Networks | Recent successful applications of convolutional neural networks (CNNs) to
audio classification and speech recognition have motivated the search for
better input representations for more efficient training. Visual displays of an
audio signal, through various time-frequency representations such as
spectrograms offer a rich representation of the temporal and spectral structure
of the original signal. In this letter, we compare various popular signal
processing methods to obtain this representation, such as short-time Fourier
transform (STFT) with linear and Mel scales, constant-Q transform (CQT) and
continuous Wavelet transform (CWT), and assess their impact on the
classification performance of two environmental sound datasets using CNNs. This
study supports the hypothesis that time-frequency representations are valuable
in learning useful features for sound classification. Moreover, the actual
transformation used is shown to impact the classification accuracy, with
Mel-scaled STFT outperforming the other discussed methods slightly and baseline
MFCC features to a large degree. Additionally, we observe that the optimal
window size during transformation is dependent on the characteristics of the
audio signal and architecturally, 2D convolution yielded better results in most
cases compared to 1D. | ['M. Huzaifah'] | 2017-06-22 | null | null | null | null | ['environmental-sound-classification', 'sound-classification'] | ['audio', 'audio'] | [ 2.85296053e-01 -3.88389111e-01 2.99418658e-01 -1.41585350e-01
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060b463f-28f5-4d10-a36f-1e901de6cd01 | cross-domain-joint-dictionary-learning-for | 2101.02362 | null | https://arxiv.org/abs/2101.02362v1 | https://arxiv.org/pdf/2101.02362v1.pdf | Cross-domain Joint Dictionary Learning for ECG Inference from PPG | The inverse problem of inferring electrocardiogram (ECG) from photoplethysmogram (PPG) is an emerging research direction that combines the easy measurability of PPG and the rich clinical knowledge of ECG for long-term continuous cardiac monitoring. The prior art for reconstruction using a universal basis has limited fidelity for uncommon ECG waveform shapes due to the lack of rich representative power. In this paper, we design two dictionary learning frameworks, the cross-domain joint dictionary learning (XDJDL) and the label-consistent XDJDL (LC-XDJDL), to further improve the ECG inference quality and enrich the PPG-based diagnosis knowledge. Building on the K-SVD technique, our proposed joint dictionary learning frameworks aim to maximize the expressive power by optimizing simultaneously a pair of signal dictionaries for PPG and ECG with the transforms to relate their sparse codes and disease information. The proposed models are evaluated with 34,000+ ECG/PPG cycle pairs containing a variety of ECG morphologies and cardiovascular diseases. We demonstrate both visually and quantitatively that our proposed frameworks can achieve better inference performance than previous methods, suggesting an encouraging potential for ECG screening using PPG based on the proactive learned PPG-ECG relationship. | ['Min Wu', 'Yuenan Li', 'Qiang Zhu', 'Xin Tian'] | 2021-01-07 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [ 2.44966015e-01 -1.81094810e-01 -9.79443081e-03 -3.13733310e-01
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6ca4c8fd-94ca-44bb-9bda-66682dd28b9d | sa2sl-from-aspect-based-sentiment-analysis-to | 2105.15079 | null | https://arxiv.org/abs/2105.15079v2 | https://arxiv.org/pdf/2105.15079v2.pdf | SA2SL: From Aspect-Based Sentiment Analysis to Social Listening System for Business Intelligence | In this paper, we present a process of building a social listening system based on aspect-based sentiment analysis in Vietnamese from creating a dataset to building a real application. Firstly, we create UIT-ViSFD, a Vietnamese Smartphone Feedback Dataset as a new benchmark corpus built based on a strict annotation schemes for evaluating aspect-based sentiment analysis, consisting of 11,122 human-annotated comments for mobile e-commerce, which is freely available for research purposes. We also present a proposed approach based on the Bi-LSTM architecture with the fastText word embeddings for the Vietnamese aspect based sentiment task. Our experiments show that our approach achieves the best performances with the F1-score of 84.48% for the aspect task and 63.06% for the sentiment task, which performs several conventional machine learning and deep learning systems. Last but not least, we build SA2SL, a social listening system based on the best performance model on our dataset, which will inspire more social listening systems in future. | ['Kiet Van Nguyen', 'Tin Van Huynh', 'Luan Thanh Nguyen', 'Sieu Khai Huynh', 'Tham Thi Nguyen', 'Kim Thi-Thanh Nguyen', 'Phuc Huynh Pham', 'Luong Luc Phan'] | 2021-05-31 | null | null | null | null | ['classification', 'vietnamese-aspect-based-sentiment-analysis', 'vietnamese-datasets', 'vietnamese-sentiment-analysis'] | ['methodology', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-9.82355624e-02 3.28666940e-02 -1.35746405e-01 -5.74708164e-01
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d214d9b4-861b-4b31-86d7-40365bb882a1 | realistic-conversational-question-answering | 2302.05137 | null | https://arxiv.org/abs/2302.05137v1 | https://arxiv.org/pdf/2302.05137v1.pdf | Realistic Conversational Question Answering with Answer Selection based on Calibrated Confidence and Uncertainty Measurement | Conversational Question Answering (ConvQA) models aim at answering a question with its relevant paragraph and previous question-answer pairs that occurred during conversation multiple times. To apply such models to a real-world scenario, some existing work uses predicted answers, instead of unavailable ground-truth answers, as the conversation history for inference. However, since these models usually predict wrong answers, using all the predictions without filtering significantly hampers the model performance. To address this problem, we propose to filter out inaccurate answers in the conversation history based on their estimated confidences and uncertainties from the ConvQA model, without making any architectural changes. Moreover, to make the confidence and uncertainty values more reliable, we propose to further calibrate them, thereby smoothing the model predictions. We validate our models, Answer Selection-based realistic Conversation Question Answering, on two standard ConvQA datasets, and the results show that our models significantly outperform relevant baselines. Code is available at: https://github.com/starsuzi/AS-ConvQA. | ['Jong C. Park', 'Sung Ju Hwang', 'Jinheon Baek', 'Soyeong Jeong'] | 2023-02-10 | null | null | null | null | ['answer-selection'] | ['natural-language-processing'] | [-1.11976445e-01 4.63260114e-01 3.81742746e-01 -9.65885341e-01
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4605ac99-e52c-4d37-a690-fd8982d3eb14 | a-diffusion-map-based-algorithm-for-gradient | 2108.06988 | null | https://arxiv.org/abs/2108.06988v5 | https://arxiv.org/pdf/2108.06988v5.pdf | A diffusion-map-based algorithm for gradient computation on manifolds and applications | We recover the Riemannian gradient of a given function defined on interior points of a Riemannian submanifold in the Euclidean space based on a sample of function evaluations at points in the submanifold. This approach is based on the estimates of the Laplace-Beltrami operator proposed in the diffusion-maps theory. The Riemannian gradient estimates do not involve differential terms. Analytical convergence results of the Riemannian gradient expansion are proved. We apply the Riemannian gradient estimate in a gradient-based algorithm providing a derivative-free optimization method. We test and validate several applications, including tomographic reconstruction from an unknown random angle distribution, and the sphere packing problem in dimensions 2 and 3. | ['Jorge P. Zubelli', 'Antônio J. Silva Neto', 'Alvaro Almeida Gomez'] | 2021-08-16 | null | null | null | null | ['cryogenic-electron-microscopy-cryo-em'] | ['computer-vision'] | [-2.21958339e-01 2.85821348e-01 4.95517820e-01 -3.30875546e-01
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b0382bc9-7f61-4191-a991-b819fd63ad09 | altclip-altering-the-language-encoder-in-clip | 2211.06679 | null | https://arxiv.org/abs/2211.06679v2 | https://arxiv.org/pdf/2211.06679v2.pdf | AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities | In this work, we present a conceptually simple and effective method to train a strong bilingual/multilingual multimodal representation model. Starting from the pre-trained multimodal representation model CLIP released by OpenAI, we altered its text encoder with a pre-trained multilingual text encoder XLM-R, and aligned both languages and image representations by a two-stage training schema consisting of teacher learning and contrastive learning. We validate our method through evaluations of a wide range of tasks. We set new state-of-the-art performances on a bunch of tasks including ImageNet-CN, Flicker30k-CN, COCO-CN and XTD. Further, we obtain very close performances with CLIP on almost all tasks, suggesting that one can simply alter the text encoder in CLIP for extended capabilities such as multilingual understanding. Our models and code are available at https://github.com/FlagAI-Open/FlagAI. | ['Ledell Wu', 'Qinghong Yang', 'Fulong Ye', 'Bo-Wen Zhang', 'Guang Liu', 'Zhongzhi Chen'] | 2022-11-12 | null | null | null | null | ['zero-shot-transfer-image-classification', 'zero-shot-cross-modal-retrieval', 'xlm-r'] | ['computer-vision', 'miscellaneous', 'natural-language-processing'] | [ 3.00677330e-03 2.61494536e-02 -3.40776265e-01 -4.85224903e-01
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db545a66-fbb1-420f-937b-c477c5bc5be9 | color-constancy-by-learning-to-predict | 1506.02167 | null | http://arxiv.org/abs/1506.02167v2 | http://arxiv.org/pdf/1506.02167v2.pdf | Color Constancy by Learning to Predict Chromaticity from Luminance | Color constancy is the recovery of true surface color from observed color,
and requires estimating the chromaticity of scene illumination to correct for
the bias it induces. In this paper, we show that the per-pixel color statistics
of natural scenes---without any spatial or semantic context---can by themselves
be a powerful cue for color constancy. Specifically, we describe an illuminant
estimation method that is built around a "classifier" for identifying the true
chromaticity of a pixel given its luminance (absolute brightness across color
channels). During inference, each pixel's observed color restricts its true
chromaticity to those values that can be explained by one of a candidate set of
illuminants, and applying the classifier over these values yields a
distribution over the corresponding illuminants. A global estimate for the
scene illuminant is computed through a simple aggregation of these
distributions across all pixels. We begin by simply defining the
luminance-to-chromaticity classifier by computing empirical histograms over
discretized chromaticity and luminance values from a training set of natural
images. These histograms reflect a preference for hues corresponding to smooth
reflectance functions, and for achromatic colors in brighter pixels. Despite
its simplicity, the resulting estimation algorithm outperforms current
state-of-the-art color constancy methods. Next, we propose a method to learn
the luminance-to-chromaticity classifier "end-to-end". Using stochastic
gradient descent, we set chromaticity-luminance likelihoods to minimize errors
in the final scene illuminant estimates on a training set. This leads to
further improvements in accuracy, most significantly in the tail of the error
distribution. | ['Ayan Chakrabarti'] | 2015-06-06 | color-constancy-by-learning-to-predict-1 | http://papers.nips.cc/paper/5864-color-constancy-by-learning-to-predict-chromaticity-from-luminance | http://papers.nips.cc/paper/5864-color-constancy-by-learning-to-predict-chromaticity-from-luminance.pdf | neurips-2015-12 | ['color-constancy'] | ['computer-vision'] | [ 5.47188878e-01 -4.93165553e-01 -7.62957633e-02 -5.69735825e-01
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1e5013b6-e988-493e-bae3-2909132a9315 | some-options-for-l1-subspace-signal | 1309.1194 | null | http://arxiv.org/abs/1309.1194v1 | http://arxiv.org/pdf/1309.1194v1.pdf | Some Options for L1-Subspace Signal Processing | We describe ways to define and calculate $L_1$-norm signal subspaces which
are less sensitive to outlying data than $L_2$-calculated subspaces. We focus
on the computation of the $L_1$ maximum-projection principal component of a
data matrix containing N signal samples of dimension D and conclude that the
general problem is formally NP-hard in asymptotically large N, D. We prove,
however, that the case of engineering interest of fixed dimension D and
asymptotically large sample support N is not and we present an optimal
algorithm of complexity $O(N^D)$. We generalize to multiple
$L_1$-max-projection components and present an explicit optimal $L_1$ subspace
calculation algorithm in the form of matrix nuclear-norm evaluations. We
conclude with illustrations of $L_1$-subspace signal processing in the fields
of data dimensionality reduction and direction-of-arrival estimation. | ['Panos P. Markopoulos', 'George N. Karystinos', 'Dimitris A. Pados'] | 2013-09-04 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [ 1.91990137e-01 -7.00613186e-02 1.57809719e-01 -3.16028297e-01
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2c801bb0-62f7-4d33-8862-34f7bd575903 | pre-trained-embeddings-for-entity-resolution | 2304.12329 | null | https://arxiv.org/abs/2304.12329v1 | https://arxiv.org/pdf/2304.12329v1.pdf | Pre-trained Embeddings for Entity Resolution: An Experimental Analysis [Experiment, Analysis & Benchmark] | Many recent works on Entity Resolution (ER) leverage Deep Learning techniques involving language models to improve effectiveness. This is applied to both main steps of ER, i.e., blocking and matching. Several pre-trained embeddings have been tested, with the most popular ones being fastText and variants of the BERT model. However, there is no detailed analysis of their pros and cons. To cover this gap, we perform a thorough experimental analysis of 12 popular language models over 17 established benchmark datasets. First, we assess their vectorization overhead for converting all input entities into dense embeddings vectors. Second, we investigate their blocking performance, performing a detailed scalability analysis, and comparing them with the state-of-the-art deep learning-based blocking method. Third, we conclude with their relative performance for both supervised and unsupervised matching. Our experimental results provide novel insights into the strengths and weaknesses of the main language models, facilitating researchers and practitioners to select the most suitable ones in practice. | ['Manolis Koubarakis', 'Dimitrios Skoutas', 'George Papadakis', 'Alexandros Zeakis'] | 2023-04-24 | null | null | null | null | ['blocking', 'entity-resolution'] | ['natural-language-processing', 'natural-language-processing'] | [-2.84455597e-01 -7.71262916e-03 -7.56417811e-01 -2.42736578e-01
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af46a9db-0109-4077-9b77-7996c312fd4c | low-precision-quantization-aware-training-in | 2305.19295 | null | https://arxiv.org/abs/2305.19295v1 | https://arxiv.org/pdf/2305.19295v1.pdf | Low Precision Quantization-aware Training in Spiking Neural Networks with Differentiable Quantization Function | Deep neural networks have been proven to be highly effective tools in various domains, yet their computational and memory costs restrict them from being widely deployed on portable devices. The recent rapid increase of edge computing devices has led to an active search for techniques to address the above-mentioned limitations of machine learning frameworks. The quantization of artificial neural networks (ANNs), which converts the full-precision synaptic weights into low-bit versions, emerged as one of the solutions. At the same time, spiking neural networks (SNNs) have become an attractive alternative to conventional ANNs due to their temporal information processing capability, energy efficiency, and high biological plausibility. Despite being driven by the same motivation, the simultaneous utilization of both concepts has yet to be thoroughly studied. Therefore, this work aims to bridge the gap between recent progress in quantized neural networks and SNNs. It presents an extensive study on the performance of the quantization function, represented as a linear combination of sigmoid functions, exploited in low-bit weight quantization in SNNs. The presented quantization function demonstrates the state-of-the-art performance on four popular benchmarks, CIFAR10-DVS, DVS128 Gesture, N-Caltech101, and N-MNIST, for binary networks (64.05\%, 95.45\%, 68.71\%, and 99.43\% respectively) with small accuracy drops and up to 31$\times$ memory savings, which outperforms existing methods. | ['Ahmed Eltawil', 'Mohammed E. Fouda', 'Ayan Shymyrbay'] | 2023-05-30 | null | null | null | null | ['edge-computing'] | ['time-series'] | [ 4.81218696e-01 -3.54087561e-01 -1.23079456e-01 -5.12596555e-02
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75ce0864-3e8b-4653-9fcc-62609f82aee8 | leveraging-non-dialogue-summaries-for-1 | 2210.09474 | null | https://arxiv.org/abs/2210.09474v1 | https://arxiv.org/pdf/2210.09474v1.pdf | Leveraging Non-dialogue Summaries for Dialogue Summarization | To mitigate the lack of diverse dialogue summarization datasets in academia, we present methods to utilize non-dialogue summarization data for enhancing dialogue summarization systems. We apply transformations to document summarization data pairs to create training data that better befit dialogue summarization. The suggested transformations also retain desirable properties of non-dialogue datasets, such as improved faithfulness to the source text. We conduct extensive experiments across both English and Korean to verify our approach. Although absolute gains in ROUGE naturally plateau as more dialogue summarization samples are introduced, utilizing non-dialogue data for training significantly improves summarization performance in zero- and few-shot settings and enhances faithfulness across all training regimes. | ['Jihwa Lee', 'Dongchan Shin', 'Seongmin Park'] | 2022-10-17 | leveraging-non-dialogue-summaries-for | https://aclanthology.org/2022.tu-1.1 | https://aclanthology.org/2022.tu-1.1.pdf | tu-coling-2022-10 | ['document-summarization'] | ['natural-language-processing'] | [ 2.92069614e-01 7.03145146e-01 -4.75192547e-01 -3.08423519e-01
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424f315e-1e84-4e6c-9cb6-ede11085af20 | segmentation-renormalized-deep-feature | 2102.06315 | null | https://arxiv.org/abs/2102.06315v2 | https://arxiv.org/pdf/2102.06315v2.pdf | Segmentation-Renormalized Deep Feature Modulation for Unpaired Image Harmonization | Deep networks are now ubiquitous in large-scale multi-center imaging studies. However, the direct aggregation of images across sites is contraindicated for downstream statistical and deep learning-based image analysis due to inconsistent contrast, resolution, and noise. To this end, in the absence of paired data, variations of Cycle-consistent Generative Adversarial Networks have been used to harmonize image sets between a source and target domain. Importantly, these methods are prone to instability, contrast inversion, intractable manipulation of pathology, and steganographic mappings which limit their reliable adoption in real-world medical imaging. In this work, based on an underlying assumption that morphological shape is consistent across imaging sites, we propose a segmentation-renormalized image translation framework to reduce inter-scanner heterogeneity while preserving anatomical layout. We replace the affine transformations used in the normalization layers within generative networks with trainable scale and shift parameters conditioned on jointly learned anatomical segmentation embeddings to modulate features at every level of translation. We evaluate our methodologies against recent baselines across several imaging modalities (T1w MRI, FLAIR MRI, and OCT) on datasets with and without lesions. Segmentation-renormalization for translation GANs yields superior image harmonization as quantified by Inception distances, demonstrates improved downstream utility via post-hoc segmentation accuracy, and improved robustness to translation perturbation and self-adversarial attacks. | ['Guido Gerig', 'James Fishbaugh', 'Neel Dey', 'Mengwei Ren'] | 2021-02-11 | null | null | null | null | ['image-harmonization'] | ['computer-vision'] | [ 7.85763562e-01 2.09412389e-02 -3.21705341e-02 -3.39063108e-01
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f4ca696f-6cdc-4c47-a4de-3066c7987b9c | inertia-guided-flow-completion-and-style | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhang_Inertia-Guided_Flow_Completion_and_Style_Fusion_for_Video_Inpainting_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhang_Inertia-Guided_Flow_Completion_and_Style_Fusion_for_Video_Inpainting_CVPR_2022_paper.pdf | Inertia-Guided Flow Completion and Style Fusion for Video Inpainting | Physical objects have inertia, which resists changes in the velocity and motion direction. Inspired by this, we introduce inertia prior that optical flow, which reflects object motion in a local temporal window, keeps unchanged in the adjacent preceding or subsequent frame. We propose a flow completion network to align and aggregate flow features from the consecutive flow sequences based on the inertia prior. The corrupted flows are completed under the supervision of customized losses on reconstruction, flow smoothness, and consistent ternary census transform. The completed flows with high fidelity give rise to significant improvement on the video inpainting quality. Nevertheless, the existing flow-guided cross-frame warping methods fail to consider the lightening and sharpness variation across video frames, which leads to spatial incoherence after warping from other frames. To alleviate such problem, we propose the Adaptive Style Fusion Network (ASFN), which utilizes the style information extracted from the valid regions to guide the gradient refinement in the warped regions. Moreover, we design a data simulation pipeline to reduce the training difficulty of ASFN. Extensive experiments show the superiority of our method against the state-of-the-art methods quantitatively and qualitatively. The project page is at https://github.com/hitachinsk/ISVI | ['Dong Liu', 'Jingjing Fu', 'Kaidong Zhang'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['video-inpainting'] | ['computer-vision'] | [ 8.01042840e-03 -5.01850426e-01 4.49298397e-02 -9.46746692e-02
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8e0bb328-b7c5-4127-a2e6-0af35d8f87fa | mlrip-pre-training-a-military-language | 2207.13929 | null | https://arxiv.org/abs/2207.13929v1 | https://arxiv.org/pdf/2207.13929v1.pdf | MLRIP: Pre-training a military language representation model with informative factual knowledge and professional knowledge base | Incorporating prior knowledge into pre-trained language models has proven to be effective for knowledge-driven NLP tasks, such as entity typing and relation extraction. Current pre-training procedures usually inject external knowledge into models by using knowledge masking, knowledge fusion and knowledge replacement. However, factual information contained in the input sentences have not been fully mined, and the external knowledge for injecting have not been strictly checked. As a result, the context information cannot be fully exploited and extra noise will be introduced or the amount of knowledge injected is limited. To address these issues, we propose MLRIP, which modifies the knowledge masking strategies proposed by ERNIE-Baidu, and introduce a two-stage entity replacement strategy. Extensive experiments with comprehensive analyses illustrate the superiority of MLRIP over BERT-based models in military knowledge-driven NLP tasks. | ['Wei Sun', 'Jiping Zheng', 'Lin Yu', 'Xin Zhao', 'Xuekang Yang', 'Hui Li'] | 2022-07-28 | null | null | null | null | ['entity-typing'] | ['natural-language-processing'] | [-4.24046703e-02 4.22197253e-01 -7.76967704e-01 -2.83766598e-01
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25f71788-23f1-46b0-b80b-52aa46a77cff | timesnet-temporal-2d-variation-modeling-for | 2210.02186 | null | https://arxiv.org/abs/2210.02186v3 | https://arxiv.org/pdf/2210.02186v3.pdf | TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis | Time series analysis is of immense importance in extensive applications, such as weather forecasting, anomaly detection, and action recognition. This paper focuses on temporal variation modeling, which is the common key problem of extensive analysis tasks. Previous methods attempt to accomplish this directly from the 1D time series, which is extremely challenging due to the intricate temporal patterns. Based on the observation of multi-periodicity in time series, we ravel out the complex temporal variations into the multiple intraperiod- and interperiod-variations. To tackle the limitations of 1D time series in representation capability, we extend the analysis of temporal variations into the 2D space by transforming the 1D time series into a set of 2D tensors based on multiple periods. This transformation can embed the intraperiod- and interperiod-variations into the columns and rows of the 2D tensors respectively, making the 2D-variations to be easily modeled by 2D kernels. Technically, we propose the TimesNet with TimesBlock as a task-general backbone for time series analysis. TimesBlock can discover the multi-periodicity adaptively and extract the complex temporal variations from transformed 2D tensors by a parameter-efficient inception block. Our proposed TimesNet achieves consistent state-of-the-art in five mainstream time series analysis tasks, including short- and long-term forecasting, imputation, classification, and anomaly detection. Code is available at this repository: https://github.com/thuml/TimesNet. | ['Mingsheng Long', 'Jianmin Wang', 'Hang Zhou', 'Yong liu', 'Tengge Hu', 'Haixu Wu'] | 2022-10-05 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [-2.83778995e-01 -1.00010359e+00 -1.88636091e-02 -8.96513462e-02
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93f95d87-4bda-4f82-87fb-5afbc6ef3b3c | tadse-template-aware-dialogue-sentence | 2305.14299 | null | https://arxiv.org/abs/2305.14299v1 | https://arxiv.org/pdf/2305.14299v1.pdf | TaDSE: Template-aware Dialogue Sentence Embeddings | Learning high quality sentence embeddings from dialogues has drawn increasing attentions as it is essential to solve a variety of dialogue-oriented tasks with low annotation cost. However, directly annotating and gathering utterance relationships in conversations are difficult, while token-level annotations, \eg, entities, slots and templates, are much easier to obtain. General sentence embedding methods are usually sentence-level self-supervised frameworks and cannot utilize token-level extra knowledge. In this paper, we introduce Template-aware Dialogue Sentence Embedding (TaDSE), a novel augmentation method that utilizes template information to effectively learn utterance representation via self-supervised contrastive learning framework. TaDSE augments each sentence with its corresponding template and then conducts pairwise contrastive learning over both sentence and template. We further enhance the effect with a synthetically augmented dataset that enhances utterance-template relation, in which entity detection (slot-filling) is a preliminary step. We evaluate TaDSE performance on five downstream benchmark datasets. The experiment results show that TaDSE achieves significant improvements over previous SOTA methods, along with a consistent Intent Classification task performance improvement margin. We further introduce a novel analytic instrument of Semantic Compression method, for which we discover a correlation with uniformity and alignment. Our code will be released soon. | ['Guoyin Wang', 'Jiwei Li', 'Minsik Oh'] | 2023-05-23 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings', 'intent-classification', 'slot-filling'] | ['methodology', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 4.16326731e-01 4.54470426e-01 -2.13134140e-01 -7.62071550e-01
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f136f748-076a-47be-87eb-bebc52ae91cc | grammatical-analysis-of-pretrained-sentence-1 | null | null | https://openreview.net/forum?id=Hkx5cU26kN | https://openreview.net/pdf?id=Hkx5cU26kN | Grammatical Analysis of Pretrained Sentence Encoders with Acceptability Judgments | Recent pretrained sentence encoders achieve state of the art results on language understanding tasks, but does this mean they have implicit knowledge of syntactic structures? We introduce a grammatically annotated development set for the Corpus of Linguistic Acceptability (CoLA; Warstadt et al., 2018), which we use to investigate the grammatical knowledge of three pretrained encoders, including the popular OpenAI Transformer (Radford et al., 2018) and BERT (Devlin et al., 2018). We fine-tune these encoders to do acceptability classification over CoLA and compare the models’ performance on the annotated analysis set. Some phenomena, e.g. modification by adjuncts, are easy to learn for all models, while others, e.g. long-distance movement, are learned effectively only by models with strong overall performance, and others still, e.g. morphological agreement, are hardly learned by any model. | ['Anonymous'] | 2018-12-11 | null | null | null | null | ['linguistic-acceptability'] | ['natural-language-processing'] | [-3.45224403e-02 8.20550501e-01 1.82056531e-01 -6.81531787e-01
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83c587ae-1f00-46c7-939c-b015448512d0 | image-segmentation-based-on-multiscale-fast | 1812.04816 | null | http://arxiv.org/abs/1812.04816v1 | http://arxiv.org/pdf/1812.04816v1.pdf | Image Segmentation Based on Multiscale Fast Spectral Clustering | In recent years, spectral clustering has become one of the most popular
clustering algorithms for image segmentation. However, it has restricted
applicability to large-scale images due to its high computational complexity.
In this paper, we first propose a novel algorithm called Fast Spectral
Clustering based on quad-tree decomposition. The algorithm focuses on the
spectral clustering at superpixel level and its computational complexity is
O(nlogn) + O(m) + O(m^(3/2)); its memory cost is O(m), where n and m are the
numbers of pixels and the superpixels of a image. Then we propose Multiscale
Fast Spectral Clustering by improving Fast Spectral Clustering, which is based
on the hierarchical structure of the quad-tree. The computational complexity of
Multiscale Fast Spectral Clustering is O(nlogn) and its memory cost is O(m).
Extensive experiments on real large-scale images demonstrate that Multiscale
Fast Spectral Clustering outperforms Normalized cut in terms of lower
computational complexity and memory cost, with comparable clustering accuracy. | ['Chenjian Wu', 'Hong Chen', 'Minxin Chen', 'Guofeng Zhu', 'Chongyang Zhang'] | 2018-12-12 | null | null | null | null | ['tree-decomposition'] | ['graphs'] | [ 2.35353217e-01 -5.01284182e-01 -9.16338414e-02 1.14978291e-01
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2cbf309e-1aa0-487c-bd92-956f17b48fef | knowing-how-knowing-that-a-new-task-for | 2306.04187 | null | https://arxiv.org/abs/2306.04187v1 | https://arxiv.org/pdf/2306.04187v1.pdf | Knowing-how & Knowing-that: A New Task for Machine Reading Comprehension of User Manuals | The machine reading comprehension (MRC) of user manuals has huge potential in customer service. However,current methods have trouble answering complex questions. Therefore, we introduce the Knowing-how & Knowing-that task that requires the model to answer factoid-style, procedure-style, and inconsistent questions about user manuals. We resolve this task by jointly representing the steps and facts in a graph (TARA), which supports a unified inference of various questions. Towards a systematical benchmarking study, we design a heuristic method to automatically parse user manuals into TARAs and build an annotated dataset to test the model's ability in answering real-world questions. Empirical results demonstrate that representing user manuals as TARAs is a desired solution for the MRC of user manuals. An in-depth investigation of TARA further sheds light on the issues and broader impacts of future representations of user manuals. We hope our work can move the MRC of user manuals to a more complex and realistic stage. | ['Jiancheng Lv', 'Zujie Wen', 'Wenqiang Lei', 'dingnan jin', 'Weihong Du', 'Jia Liu', 'Hongru Liang'] | 2023-06-07 | null | null | null | null | ['reading-comprehension', 'machine-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.44494522e-01 8.56336415e-01 1.16997510e-01 -7.61314034e-01
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56f2703b-57ea-4c82-85d3-c8011dd43276 | towards-antigenic-peptide-discovery-with | null | null | https://www.researchgate.net/publication/370528518_Towards_antigenic_peptide_discovery_with_better_MHC-I_binding_prediction_and_improved_benchmark_methodology | https://drive.google.com/file/d/1GH1t4pWf1cI9ivVrdOjS1o6tfk61DBCl/view | Towards antigenic peptide discovery with better MHC-I binding prediction and improved benchmark methodology | The Major Histocompatibility Complex (MHC) is a crucial component of the cellular immune system in vertebrates, responsible for, among others, presenting peptides derived from intracellular proteins. The MHC-I presentation is vital in the immune response and holds great promise in vaccine development and cancer immunotherapy. In this study, we analyze the limitations of existing methods and benchmarks for MHC-I presentation. We introduce a new benchmark to measure crucial generalization properties and models’ reliability on unseen MHC molecules and peptides. Finally, we present HLABERT, a pre-trained language model which significantly surpasses prior methods on our benchmark and also sets new state-of-the-art on the old benchmarks. | ['Anna Gambin', 'Piotr Grzegorczyk', 'Michał Rembalski', 'Michał Tyrolski', 'Piotr Kucharski', 'Grzegorz Preibisch', 'Stanisław Giziński'] | 2023-05-05 | null | null | null | machine-learning-for-drug-discovery-workshop | ['mhc-presentation-prediction'] | ['medical'] | [ 2.95069873e-01 -5.47206819e-01 -5.99381268e-01 -1.30804881e-01
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4e675ce2-443e-4929-9740-60e3de799ee7 | a-transformer-architecture-for-online-gesture | 2211.02643 | null | https://arxiv.org/abs/2211.02643v1 | https://arxiv.org/pdf/2211.02643v1.pdf | A Transformer Architecture for Online Gesture Recognition of Mathematical Expressions | The Transformer architecture is shown to provide a powerful framework as an end-to-end model for building expression trees from online handwritten gestures corresponding to glyph strokes. In particular, the attention mechanism was successfully used to encode, learn and enforce the underlying syntax of expressions creating latent representations that are correctly decoded to the exact mathematical expression tree, providing robustness to ablated inputs and unseen glyphs. For the first time, the encoder is fed with spatio-temporal data tokens potentially forming an infinitely large vocabulary, which finds applications beyond that of online gesture recognition. A new supervised dataset of online handwriting gestures is provided for training models on generic handwriting recognition tasks and a new metric is proposed for the evaluation of the syntactic correctness of the output expression trees. A small Transformer model suitable for edge inference was successfully trained to an average normalised Levenshtein accuracy of 94%, resulting in valid postfix RPN tree representation for 94% of predictions. | ['Guénolé C. M. Silvestre', 'Mirco Ramo'] | 2022-11-04 | null | null | null | null | ['gesture-recognition', 'handwriting-recognition'] | ['computer-vision', 'computer-vision'] | [ 7.48619080e-01 4.13799316e-01 -1.30508557e-01 -5.22624314e-01
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f88a15a1-e7c3-453a-9f29-5d1b7fed09cb | real-time-lip-sync-for-live-2d-animation | 1910.08685 | null | https://arxiv.org/abs/1910.08685v1 | https://arxiv.org/pdf/1910.08685v1.pdf | Real-Time Lip Sync for Live 2D Animation | The emergence of commercial tools for real-time performance-based 2D animation has enabled 2D characters to appear on live broadcasts and streaming platforms. A key requirement for live animation is fast and accurate lip sync that allows characters to respond naturally to other actors or the audience through the voice of a human performer. In this work, we present a deep learning based interactive system that automatically generates live lip sync for layered 2D characters using a Long Short Term Memory (LSTM) model. Our system takes streaming audio as input and produces viseme sequences with less than 200ms of latency (including processing time). Our contributions include specific design decisions for our feature definition and LSTM configuration that provide a small but useful amount of lookahead to produce accurate lip sync. We also describe a data augmentation procedure that allows us to achieve good results with a very small amount of hand-animated training data (13-20 minutes). Extensive human judgement experiments show that our results are preferred over several competing methods, including those that only support offline (non-live) processing. Video summary and supplementary results at GitHub link: https://github.com/deepalianeja/CharacterLipSync2D | ['Wilmot Li', 'Deepali Aneja'] | 2019-10-19 | null | null | null | null | ['lip-sync-1'] | ['computer-vision'] | [ 3.90478352e-04 4.25624922e-02 -1.65998966e-01 -2.08992615e-01
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2a1846e5-4f28-4cf0-98bb-4e2ae4a980da | efficient-joint-dimensional-search-with | 2208.05271 | null | https://arxiv.org/abs/2208.05271v1 | https://arxiv.org/pdf/2208.05271v1.pdf | Efficient Joint-Dimensional Search with Solution Space Regularization for Real-Time Semantic Segmentation | Semantic segmentation is a popular research topic in computer vision, and many efforts have been made on it with impressive results. In this paper, we intend to search an optimal network structure that can run in real-time for this problem. Towards this goal, we jointly search the depth, channel, dilation rate and feature spatial resolution, which results in a search space consisting of about 2.78*10^324 possible choices. To handle such a large search space, we leverage differential architecture search methods. However, the architecture parameters searched using existing differential methods need to be discretized, which causes the discretization gap between the architecture parameters found by the differential methods and their discretized version as the final solution for the architecture search. Hence, we relieve the problem of discretization gap from the innovative perspective of solution space regularization. Specifically, a novel Solution Space Regularization (SSR) loss is first proposed to effectively encourage the supernet to converge to its discrete one. Then, a new Hierarchical and Progressive Solution Space Shrinking method is presented to further achieve high efficiency of searching. In addition, we theoretically show that the optimization of SSR loss is equivalent to the L_0-norm regularization, which accounts for the improved search-evaluation gap. Comprehensive experiments show that the proposed search scheme can efficiently find an optimal network structure that yields an extremely fast speed (175 FPS) of segmentation with a small model size (1 M) while maintaining comparable accuracy. | ['Wanli Ouyan', 'Qinghua Chi', 'Chongyan Zuo', 'Chen Lin', 'Zhen Mei', 'Jiayuan Fan', 'Tao Chen', 'Baopu Li', 'Peng Ye'] | 2022-08-10 | null | null | null | null | ['real-time-semantic-segmentation'] | ['computer-vision'] | [ 1.52887851e-01 2.02319957e-02 -4.75416034e-02 -8.14923346e-02
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d129164b-7822-46e1-b054-376911d4b706 | ranking-aggregation-with-interactive-feedback | null | null | https://bmvc2022.mpi-inf.mpg.de/386/ | https://bmvc2022.mpi-inf.mpg.de/0386.pdf | Ranking Aggregation with Interactive Feedback for Collaborative Person Re-identification | Person re-identification (re-ID) aims to retrieve the same person from a group of networking cameras. Ranking aggregation (RA), a method to aggregates multiple ranking results, can further improve the retrieval accuracy in re-ID tasks. Existing RA work can be generally divided into unsupervised methods and fully-supervised methods. Unsupervised methods lack external supervision, can hardly achieve the optimal results. In contrast, fully-supervised methods need massive labeling data for training, which is prohibitively expensive in the practical application. This paper studies interactive RA (IRA) to address the above challenges in existing RA research. The core idea is to utilize a small amount of supervisory information, obtained from users' relevance feedback, to supervise RA method to produce better re-ranking results. Compared with unsupervised methods, IRA introduces supervisory information and thus has better aggregation accuracy. Compared with fully-supervision methods, the supervisory information of IRA is more readily available, and can be targeted to specific queries. Particularly, we propose two IRA implementations, based on ranking positions and scores respectively, to adapt to diverse application scenarios, where rankers only give rankings, or rankers give similarity scores. Experiments on three public re-ID datasets have shown that IRA significantly outperforms the-state-of-art unsupervised baselines, and achieves similar accuracy with less labeling cost than the fully-supervised RA method. | ['Chunjie Zhang', 'Zhongyuan Wang', 'Yue Zhang', 'Chao Liang', 'Ji Huang'] | 2022-11-21 | null | null | null | the-33rd-british-machine-vision-conference | ['person-re-identification'] | ['computer-vision'] | [-6.81765452e-02 -3.84763688e-01 -2.94025332e-01 -4.48811352e-01
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092568f5-f142-48da-9ec2-9db0e823b01e | non-monotonic-value-function-factorization | 2104.01939 | null | https://arxiv.org/abs/2104.01939v4 | https://arxiv.org/pdf/2104.01939v4.pdf | NQMIX: Non-monotonic Value Function Factorization for Deep Multi-Agent Reinforcement Learning | Multi-agent value-based approaches recently make great progress, especially value decomposition methods. However, there are still a lot of limitations in value function factorization. In VDN, the joint action-value function is the sum of per-agent action-value function while the joint action-value function of QMIX is the monotonic mixing of per-agent action-value function. To some extent, QTRAN reduces the limitation of joint action-value functions that can be represented, but it has unsatisfied performance in complex tasks. In this paper, in order to extend the class of joint value functions that can be represented, we propose a novel actor-critic method called NQMIX. NQMIX introduces an off-policy policy gradient on QMIX and modify its network architecture, which can remove the monotonicity constraint of QMIX and implement a non-monotonic value function factorization for the joint action-value function. In addition, NQMIX takes the state-value as the learning target, which overcomes the problem in QMIX that the learning target is overestimated. Furthermore, NQMIX can be extended to continuous action space settings by introducing deterministic policy gradient on itself. Finally, we evaluate our actor-critic methods on SMAC domain, and show that it has a stronger performance than COMA and QMIX on complex maps with heterogeneous agent types. In addition, our ablation results show that our modification of mixer is effective. | ['Quanlin Chen'] | 2021-04-05 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-4.05913860e-01 2.66282618e-01 -6.21858537e-01 2.52202693e-02
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953a6078-fe5f-4624-b9d6-32ebca9782d5 | geometry-aligned-variational-transformer-for | 2209.00852 | null | https://arxiv.org/abs/2209.00852v1 | https://arxiv.org/pdf/2209.00852v1.pdf | Geometry Aligned Variational Transformer for Image-conditioned Layout Generation | Layout generation is a novel task in computer vision, which combines the challenges in both object localization and aesthetic appraisal, widely used in advertisements, posters, and slides design. An accurate and pleasant layout should consider both the intra-domain relationship within layout elements and the inter-domain relationship between layout elements and the image. However, most previous methods simply focus on image-content-agnostic layout generation, without leveraging the complex visual information from the image. To this end, we explore a novel paradigm entitled image-conditioned layout generation, which aims to add text overlays to an image in a semantically coherent manner. Specifically, we propose an Image-Conditioned Variational Transformer (ICVT) that autoregressively generates various layouts in an image. First, self-attention mechanism is adopted to model the contextual relationship within layout elements, while cross-attention mechanism is used to fuse the visual information of conditional images. Subsequently, we take them as building blocks of conditional variational autoencoder (CVAE), which demonstrates appealing diversity. Second, in order to alleviate the gap between layout elements domain and visual domain, we design a Geometry Alignment module, in which the geometric information of the image is aligned with the layout representation. In addition, we construct a large-scale advertisement poster layout designing dataset with delicate layout and saliency map annotations. Experimental results show that our model can adaptively generate layouts in the non-intrusive area of the image, resulting in a harmonious layout design. | ['Yuning Jiang', 'Tiezheng Ge', 'Hongtao Xie', 'Chuanbin Liu', 'Min Zhou', 'Ye Ma', 'Yunning Cao'] | 2022-09-02 | null | null | null | null | ['layout-design'] | ['computer-vision'] | [ 4.17857580e-02 -2.25620344e-01 3.30720782e-01 -4.52909440e-01
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ff69fd26-7c98-4fd3-a8df-d3db3ee1726e | supervised-contrastive-learning-for-3 | 2210.16192 | null | https://arxiv.org/abs/2210.16192v2 | https://arxiv.org/pdf/2210.16192v2.pdf | Learning Audio Features with Metadata and Contrastive Learning | Methods based on supervised learning using annotations in an end-to-end fashion have been the state-of-the-art for classification problems. However, they may be limited in their generalization capability, especially in the low data regime. In this study, we address this issue using supervised contrastive learning combined with available metadata to solve multiple pretext tasks that learn a good representation of data. We apply our approach on ICBHI, a respiratory sound classification dataset suited for this setting. We show that learning representations using only metadata, without class labels, obtains similar performance as using cross entropy with those labels only. In addition, we obtain state-of-the-art score when combining class labels with metadata using multiple supervised contrastive learning. This work suggests the potential of using multiple metadata sources in supervised contrastive settings, in particular in settings with class imbalance and few data. Our code is released at https://github.com/ilyassmoummad/scl_icbhi2017 | ['Nicolas Farrugia', 'Ilyass Moummad'] | 2022-10-27 | null | null | null | null | ['sound-classification'] | ['audio'] | [ 2.59822041e-01 2.63838083e-01 -4.76591259e-01 -4.74529624e-01
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57b0ee8c-189b-4960-855d-5e0e30046c8b | low-light-image-enhancement-via-structure | 2305.05839 | null | https://arxiv.org/abs/2305.05839v1 | https://arxiv.org/pdf/2305.05839v1.pdf | Low-Light Image Enhancement via Structure Modeling and Guidance | This paper proposes a new framework for low-light image enhancement by simultaneously conducting the appearance as well as structure modeling. It employs the structural feature to guide the appearance enhancement, leading to sharp and realistic results. The structure modeling in our framework is implemented as the edge detection in low-light images. It is achieved with a modified generative model via designing a structure-aware feature extractor and generator. The detected edge maps can accurately emphasize the essential structural information, and the edge prediction is robust towards the noises in dark areas. Moreover, to improve the appearance modeling, which is implemented with a simple U-Net, a novel structure-guided enhancement module is proposed with structure-guided feature synthesis layers. The appearance modeling, edge detector, and enhancement module can be trained end-to-end. The experiments are conducted on representative datasets (sRGB and RAW domains), showing that our model consistently achieves SOTA performance on all datasets with the same architecture. | ['Jiangbo Lu', 'RuiXing Wang', 'Xiaogang Xu'] | 2023-05-10 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xu_Low-Light_Image_Enhancement_via_Structure_Modeling_and_Guidance_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xu_Low-Light_Image_Enhancement_via_Structure_Modeling_and_Guidance_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-enhancement', 'low-light-image-enhancement', 'edge-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.73459631e-01 -1.24915272e-01 2.45542452e-01 -2.90402770e-01
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8e308c2f-f936-4953-808a-fdf0c4d03c17 | deep-learning-automated-quantification-of | 2303.11130 | null | https://arxiv.org/abs/2303.11130v1 | https://arxiv.org/pdf/2303.11130v1.pdf | Deep learning automated quantification of lung disease in pulmonary hypertension on CT pulmonary angiography: A preliminary clinical study with external validation | Purpose: Lung disease assessment in precapillary pulmonary hypertension (PH) is essential for appropriate patient management. This study aims to develop an artificial intelligence (AI) deep learning model for lung texture classification in CT Pulmonary Angiography (CTPA), and evaluate its correlation with clinical assessment methods. Materials and Methods: In this retrospective study with external validation, 122 patients with pre-capillary PH were used to train (n=83), validate (n=17) and test (n=10 internal test, n=12 external test) a patch based DenseNet-121 classification model. "Normal", "Ground glass", "Ground glass with reticulation", "Honeycombing", and "Emphysema" were classified as per the Fleishner Society glossary of terms. Ground truth classes were segmented by two radiologists with patches extracted from the labelled regions. Proportion of lung volume for each texture was calculated by classifying patches throughout the entire lung volume to generate a coarse texture classification mapping throughout the lung parenchyma. AI output was assessed against diffusing capacity of carbon monoxide (DLCO) and specialist radiologist reported disease severity. Results: Micro-average AUCs for the validation, internal test, and external test were 0.92, 0.95, and 0.94, respectively. The model had consistent performance across parenchymal textures, demonstrated strong correlation with diffusing capacity of carbon monoxide (DLCO), and showed good correspondence with disease severity reported by specialist radiologists. Conclusion: The classification model demonstrates excellent performance on external validation. The clinical utility of its output has been demonstrated. This objective, repeatable measure of disease severity can aid in patient management in adjunct to radiological reporting. | ['Andrew J. Swift', 'Samer Alabed', 'Krit Dwivedi', 'Michael J. Sharkey'] | 2023-03-20 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 7.29203783e-03 1.09333105e-01 -2.79588968e-01 -4.54068966e-02
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7f1edf17-3634-4838-bb22-3a65e857abfb | unifier-a-unified-retriever-for-large-scale | 2205.11194 | null | https://arxiv.org/abs/2205.11194v2 | https://arxiv.org/pdf/2205.11194v2.pdf | UnifieR: A Unified Retriever for Large-Scale Retrieval | Large-scale retrieval is to recall relevant documents from a huge collection given a query. It relies on representation learning to embed documents and queries into a common semantic encoding space. According to the encoding space, recent retrieval methods based on pre-trained language models (PLM) can be coarsely categorized into either dense-vector or lexicon-based paradigms. These two paradigms unveil the PLMs' representation capability in different granularities, i.e., global sequence-level compression and local word-level contexts, respectively. Inspired by their complementary global-local contextualization and distinct representing views, we propose a new learning framework, UnifieR which unifies dense-vector and lexicon-based retrieval in one model with a dual-representing capability. Experiments on passage retrieval benchmarks verify its effectiveness in both paradigms. A uni-retrieval scheme is further presented with even better retrieval quality. We lastly evaluate the model on BEIR benchmark to verify its transferability. | ['Kai Zhang', 'Guodong Long', 'Daxin Jiang', 'Can Xu', 'Chongyang Tao', 'Xiubo Geng', 'Tao Shen'] | 2022-05-23 | null | null | null | null | ['passage-retrieval'] | ['natural-language-processing'] | [ 1.93602845e-01 -5.82563519e-01 -6.74365163e-01 -8.96402001e-02
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d52181e6-d25f-4943-a91b-50471539ec81 | making-invisible-visible-data-driven-seismic | 2106.11892 | null | https://arxiv.org/abs/2106.11892v3 | https://arxiv.org/pdf/2106.11892v3.pdf | Making Invisible Visible: Data-Driven Seismic Inversion with Spatio-temporally Constrained Data Augmentation | Deep learning and data-driven approaches have shown great potential in scientific domains. The promise of data-driven techniques relies on the availability of a large volume of high-quality training datasets. Due to the high cost of obtaining data through expensive physical experiments, instruments, and simulations, data augmentation techniques for scientific applications have emerged as a new direction for obtaining scientific data recently. However, existing data augmentation techniques originating from computer vision, yield physically unacceptable data samples that are not helpful for the domain problems that we are interested in. In this paper, we develop new data augmentation techniques based on convolutional neural networks. Specifically, our generative models leverage different physics knowledge (such as governing equations, observable perception, and physics phenomena) to improve the quality of the synthetic data. To validate the effectiveness of our data augmentation techniques, we apply them to solve a subsurface seismic full-waveform inversion using simulated CO$_2$ leakage data. Our interest is to invert for subsurface velocity models associated with very small CO$_2$ leakage. We validate the performance of our methods using comprehensive numerical tests. Via comparison and analysis, we show that data-driven seismic imaging can be significantly enhanced by using our data augmentation techniques. Particularly, the imaging quality has been improved by 15% in test scenarios of general-sized leakage and 17% in small-sized leakage when using an augmented training set obtained with our techniques. | ['Youzuo Lin', 'Qiang Guan', 'Xitong Zhang', 'Yuxin Yang'] | 2021-06-22 | null | null | null | null | ['seismic-imaging', 'seismic-inversion'] | ['miscellaneous', 'miscellaneous'] | [ 2.59985000e-01 -4.29769605e-02 4.25342679e-01 -1.55101970e-01
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a740601b-499c-414f-a9f1-a162e15cac11 | similarity-preserving-representation-learning | 1702.03584 | null | https://arxiv.org/abs/1702.03584v3 | https://arxiv.org/pdf/1702.03584v3.pdf | Similarity Preserving Representation Learning for Time Series Clustering | A considerable amount of clustering algorithms take instance-feature matrices as their inputs. As such, they cannot directly analyze time series data due to its temporal nature, usually unequal lengths, and complex properties. This is a great pity since many of these algorithms are effective, robust, efficient, and easy to use. In this paper, we bridge this gap by proposing an efficient representation learning framework that is able to convert a set of time series with various lengths to an instance-feature matrix. In particular, we guarantee that the pairwise similarities between time series are well preserved after the transformation, thus the learned feature representation is particularly suitable for the time series clustering task. Given a set of $n$ time series, we first construct an $n\times n$ partially-observed similarity matrix by randomly sampling $\mathcal{O}(n \log n)$ pairs of time series and computing their pairwise similarities. We then propose an efficient algorithm that solves a non-convex and NP-hard problem to learn new features based on the partially-observed similarity matrix. By conducting extensive empirical studies, we show that the proposed framework is more effective, efficient, and flexible, compared to other state-of-the-art time series clustering methods. | ['Jin-Feng Yi', 'Inderjit S. Dhillon', 'Lingfei Wu', 'Roman Vaculin', 'Qi Lei'] | 2017-02-12 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [ 1.15036629e-01 -7.10688591e-01 3.20183299e-02 -3.71118665e-01
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9e623db6-704d-4199-ba11-429707dcfff3 | a-constraint-programming-approach-for-mining | 1311.6907 | null | http://arxiv.org/abs/1311.6907v1 | http://arxiv.org/pdf/1311.6907v1.pdf | A Constraint Programming Approach for Mining Sequential Patterns in a Sequence Database | Constraint-based pattern discovery is at the core of numerous data mining
tasks. Patterns are extracted with respect to a given set of constraints
(frequency, closedness, size, etc). In the context of sequential pattern
mining, a large number of devoted techniques have been developed for solving
particular classes of constraints. The aim of this paper is to investigate the
use of Constraint Programming (CP) to model and mine sequential patterns in a
sequence database. Our CP approach offers a natural way to simultaneously
combine in a same framework a large set of constraints coming from various
origins. Experiments show the feasibility and the interest of our approach. | ['Jean-Philippe Métivier', 'Thierry Charnois', 'Samir Loudni'] | 2013-11-27 | null | null | null | null | ['sequential-pattern-mining'] | ['natural-language-processing'] | [ 4.95046437e-01 -9.68016386e-02 -3.39642704e-01 -3.61817360e-01
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2059c45e-33a8-4576-9547-5fc36f362571 | learning-to-measure-change-fully | 1810.09111 | null | http://arxiv.org/abs/1810.09111v3 | http://arxiv.org/pdf/1810.09111v3.pdf | Learning to Measure Change: Fully Convolutional Siamese Metric Networks for Scene Change Detection | A critical challenge problem of scene change detection is that noisy changes
generated by varying illumination, shadows and camera viewpoint make variances
of a scene difficult to define and measure since the noisy changes and semantic
ones are entangled. Following the intuitive idea of detecting changes by
directly comparing dissimilarities between a pair of features, we propose a
novel fully Convolutional siamese metric Network(CosimNet) to measure changes
by customizing implicit metrics. To learn more discriminative metrics, we
utilize contrastive loss to reduce the distance between the unchanged feature
pairs and to enlarge the distance between the changed feature pairs.
Specifically, to address the issue of large viewpoint differences, we propose
Thresholded Contrastive Loss (TCL) with a more tolerant strategy to punish
noisy changes. We demonstrate the effectiveness of the proposed approach with
experiments on three challenging datasets: CDnet, PCD2015, and VL-CMU-CD. Our
approach is robust to lots of challenging conditions, such as illumination
changes, large viewpoint difference caused by camera motion and zooming. In
addition, we incorporate the distance metric into the segmentation framework
and validate the effectiveness through visualization of change maps and feature
distribution. The source code is available at
https://github.com/gmayday1997/ChangeDet. | ['Min Deng', 'Yu Liu', 'Haifeng Li', 'Qing Zhu', 'Xinsha Fu', 'Jiawei Zhu', 'Enqiang Guo'] | 2018-10-22 | null | null | null | null | ['scene-change-detection'] | ['computer-vision'] | [ 3.92981768e-02 -8.40529919e-01 4.05111969e-01 -5.57735682e-01
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ee27e813-d324-442b-be77-954d197d709e | a-classification-scheme-for-local-energy | 2210.15344 | null | https://arxiv.org/abs/2210.15344v1 | https://arxiv.org/pdf/2210.15344v1.pdf | A Classification Scheme for Local Energy Trading | The current trend towards more renewable and sustainable energy generation leads to an increased interest in new energy management systems and the concept of a smart grid. One important aspect of this is local energy trading, which is an extension of existing electricity markets by including prosumers, who are consumers also producing electricity. Prosumers having a surplus of energy may directly trade this surplus with other prosumers, which are currently in demand. In this paper, we present an overview of the literature in the area of local energy trading. In order to provide structure to the broad range of publications, we identify key characteristics, define the various settings, and cluster the considered literature along these characteristics. We identify three main research lines, each with a distinct setting and research question. We analyze and compare the settings, the used techniques, and the results and findings within each cluster and derive connections between the clusters. In addition, we identify important aspects, which up to now have to a large extent been neglected in the considered literature and highlight interesting research directions, and open problems for future work. | ['Bert Zwart', 'Johann L. Hurink', 'Jens Hönen'] | 2022-10-27 | null | null | null | null | ['energy-management'] | ['time-series'] | [-2.72246718e-01 3.27273384e-02 -3.38159412e-01 -2.80543268e-02
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885379f1-f286-4830-8cc0-6a21676b1bd8 | near-optimal-multiple-testing-in-bayesian | 2211.02778 | null | https://arxiv.org/abs/2211.02778v2 | https://arxiv.org/pdf/2211.02778v2.pdf | Near-optimal multiple testing in Bayesian linear models with finite-sample FDR control | In high dimensional variable selection problems, statisticians often seek to design multiple testing procedures that control the False Discovery Rate (FDR), while concurrently identifying a greater number of relevant variables. Model-X methods, such as Knockoffs and conditional randomization tests, achieve the primary goal of finite-sample FDR control, assuming a known distribution of covariates. However, whether these methods can also achieve the secondary goal of maximizing discoveries remains uncertain. In fact, designing procedures to discover more relevant variables with finite-sample FDR control is a largely open question, even within the arguably simplest linear models. In this paper, we develop near-optimal multiple testing procedures for high dimensional Bayesian linear models with isotropic covariates. We introduce Model-X procedures that provably control the frequentist FDR from finite samples, even when the model is misspecified, and conjecturally achieve near-optimal power when the data follow the Bayesian linear model. Our proposed procedure, PoEdCe, incorporates three key ingredients: Posterior Expectation, distilled Conditional randomization test (dCRT), and the Benjamini-Hochberg procedure with e-values (eBH). The optimality conjecture of PoEdCe is based on a heuristic calculation of its asymptotic true positive proportion (TPP) and false discovery proportion (FDP), which is supported by methods from statistical physics as well as extensive numerical simulations. Our result establishes the Bayesian linear model as a benchmark for comparing the power of various multiple testing procedures. | ['Song Mei', 'Licong Lin', 'Taejoo Ahn'] | 2022-11-04 | null | null | null | null | ['variable-selection'] | ['methodology'] | [ 3.38887930e-01 -5.89257479e-02 -3.09868187e-01 -2.83188134e-01
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297d5ab3-7967-4276-8e1a-fe0250177f8e | attentionmask-attentive-efficient-object | 1811.08728 | null | http://arxiv.org/abs/1811.08728v1 | http://arxiv.org/pdf/1811.08728v1.pdf | AttentionMask: Attentive, Efficient Object Proposal Generation Focusing on Small Objects | We propose a novel approach for class-agnostic object proposal generation,
which is efficient and especially well-suited to detect small objects.
Efficiency is achieved by scale-specific objectness attention maps which focus
the processing on promising parts of the image and reduce the amount of sampled
windows strongly. This leads to a system, which is $33\%$ faster than the
state-of-the-art and clearly outperforming state-of-the-art in terms of average
recall. Secondly, we add a module for detecting small objects, which are often
missed by recent models. We show that this module improves the average recall
for small objects by about $53\%$. | ['Simone Frintrop', 'Christian Wilms'] | 2018-11-21 | null | null | null | null | ['object-proposal-generation'] | ['computer-vision'] | [ 0.11038441 0.17748722 -0.04186622 -0.3082973 -0.91136336 -0.13438025
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10c2f1e5-27a0-4ebb-8074-68b52dfd772c | end-to-end-learning-with-multiple-modalities | 2304.07151 | null | https://arxiv.org/abs/2304.07151v1 | https://arxiv.org/pdf/2304.07151v1.pdf | End-to-End Learning with Multiple Modalities for System-Optimised Renewables Nowcasting | With the increasing penetration of renewable power sources such as wind and solar, accurate short-term, nowcasting renewable power prediction is becoming increasingly important. This paper investigates the multi-modal (MM) learning and end-to-end (E2E) learning for nowcasting renewable power as an intermediate to energy management systems. MM combines features from all-sky imagery and meteorological sensor data as two modalities to predict renewable power generation that otherwise could not be combined effectively. The combined, predicted values are then input to a differentiable optimal power flow (OPF) formulation simulating the energy management. For the first time, MM is combined with E2E training of the model that minimises the expected total system cost. The case study tests the proposed methodology on the real sky and meteorological data from the Netherlands. In our study, the proposed MM-E2E model reduced system cost by 30% compared to uni-modal baselines. | ['Jochen L. Cremer', 'Ali Rajaei', 'Rushil Vohra'] | 2023-04-14 | null | null | null | null | ['energy-management'] | ['time-series'] | [-8.84600282e-02 -7.62662962e-02 -9.32913497e-02 8.78135711e-02
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4bc1ff57-59e5-4e8b-a10d-68739ce5195e | reference-aware-language-models | 1611.01628 | null | http://arxiv.org/abs/1611.01628v5 | http://arxiv.org/pdf/1611.01628v5.pdf | Reference-Aware Language Models | We propose a general class of language models that treat reference as an
explicit stochastic latent variable. This architecture allows models to create
mentions of entities and their attributes by accessing external databases
(required by, e.g., dialogue generation and recipe generation) and internal
state (required by, e.g. language models which are aware of coreference). This
facilitates the incorporation of information that can be accessed in
predictable locations in databases or discourse context, even when the targets
of the reference may be rare words. Experiments on three tasks shows our model
variants based on deterministic attention. | ['Wang Ling', 'Chris Dyer', 'Phil Blunsom', 'Zichao Yang'] | 2016-11-05 | reference-aware-language-models-1 | https://aclanthology.org/D17-1197 | https://aclanthology.org/D17-1197.pdf | emnlp-2017-9 | ['recipe-generation'] | ['miscellaneous'] | [-2.34108984e-01 9.66194212e-01 -3.72845203e-01 -3.25630605e-01
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772ded2a-f013-4966-a35e-d68f72030454 | robust-design-of-power-minimizing-symbol | 1805.02395 | null | http://arxiv.org/abs/1805.02395v2 | http://arxiv.org/pdf/1805.02395v2.pdf | Robust Design of Power Minimizing Symbol-Level Precoder under Channel Uncertainty | In this paper, we investigate the downlink transmission of a multiuser
multiple-input single-output (MISO) channel under a symbol-level precoding
(SLP) scheme, having imperfect channel knowledge at the transmitter. In
defining the SLP problem, a general category of constructive interference
regions (CIR) called distance preserving CIR (DPCIR) is adopted. In particular,
we are interested in the robust SLP design minimizing the total transmit power
while satisfying the users' quality-of-service (QoS) requirements. We consider
two common models for the channel uncertainty region, namely, norm-bounded
spherical and stochastic. For the spherical uncertainty model, a worst-case
robust precoder is proposed, while for the stochastic uncertainties, we define
a convex optimization problem with probabilistic constraints. We simulate the
performance of the proposed robust approaches, and compare them with the
existing methods. Through the simulation results, we also show that there is an
essential trade-off between the two robust approaches. | [] | 2018-08-12 | null | null | null | null | ['robust-design'] | ['miscellaneous'] | [ 1.30211905e-01 2.64561266e-01 6.29366711e-02 -3.55845168e-02
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f25665b3-ebdf-4208-a0bc-b222ca8ab2b7 | few-shot-learning-for-cross-target-stance | 2301.04535 | null | https://arxiv.org/abs/2301.04535v2 | https://arxiv.org/pdf/2301.04535v2.pdf | Few-shot Learning for Cross-Target Stance Detection by Aggregating Multimodal Embeddings | Despite the increasing popularity of the stance detection task, existing approaches are predominantly limited to using the textual content of social media posts for the classification, overlooking the social nature of the task. The stance detection task becomes particularly challenging in cross-target classification scenarios, where even in few-shot training settings the model needs to predict the stance towards new targets for which the model has only seen few relevant samples during training. To address the cross-target stance detection in social media by leveraging the social nature of the task, we introduce CT-TN, a novel model that aggregates multimodal embeddings derived from both textual and network features of the data. We conduct experiments in a few-shot cross-target scenario on six different combinations of source-destination target pairs. By comparing CT-TN with state-of-the-art cross-target stance detection models, we demonstrate the effectiveness of our model by achieving average performance improvements ranging from 11% to 21% across different baseline models. Experiments with different numbers of shots show that CT-TN can outperform other models after seeing 300 instances of the destination target. Further, ablation experiments demonstrate the positive contribution of each of the components of CT-TN towards the final performance. We further analyse the network interactions between social media users, which reveal the potential of using social features for cross-target stance detection. | ['Arkaitz Zubiaga', 'Parisa Jamadi Khiabani'] | 2023-01-11 | null | null | null | null | ['stance-detection'] | ['natural-language-processing'] | [ 1.96138710e-01 1.78117067e-01 -6.85452819e-01 -1.28071100e-01
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ce2f8d93-9157-4ba0-a6c6-ddc4b98ee77c | what-makes-for-effective-few-shot-point-cloud | 2304.00022 | null | https://arxiv.org/abs/2304.00022v1 | https://arxiv.org/pdf/2304.00022v1.pdf | What Makes for Effective Few-shot Point Cloud Classification? | Due to the emergence of powerful computing resources and large-scale annotated datasets, deep learning has seen wide applications in our daily life. However, most current methods require extensive data collection and retraining when dealing with novel classes never seen before. On the other hand, we humans can quickly recognize new classes by looking at a few samples, which motivates the recent popularity of few-shot learning (FSL) in machine learning communities. Most current FSL approaches work on 2D image domain, however, its implication in 3D perception is relatively under-explored. Not only needs to recognize the unseen examples as in 2D domain, 3D few-shot learning is more challenging with unordered structures, high intra-class variances, and subtle inter-class differences. Moreover, different architectures and learning algorithms make it difficult to study the effectiveness of existing 2D methods when migrating to the 3D domain. In this work, for the first time, we perform systematic and extensive studies of recent 2D FSL and 3D backbone networks for benchmarking few-shot point cloud classification, and we suggest a strong baseline and learning architectures for 3D FSL. Then, we propose a novel plug-and-play component called Cross-Instance Adaptation (CIA) module, to address the high intra-class variances and subtle inter-class differences issues, which can be easily inserted into current baselines with significant performance improvement. Extensive experiments on two newly introduced benchmark datasets, ModelNet40-FS and ShapeNet70-FS, demonstrate the superiority of our proposed network for 3D FSL. | ['Jiayuan Fan', 'Tao Chen', 'Yanggang Zhang', 'Yongbin Liao', 'Hongyuan Zhu', 'Chuangguan Ye'] | 2023-03-31 | null | null | null | null | ['few-shot-point-cloud-classification', 'point-cloud-classification'] | ['computer-vision', 'computer-vision'] | [ 9.23972726e-02 -2.71520615e-01 -1.76397324e-01 -3.48601907e-01
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49795470-ddcc-4604-9723-9771cbca4f3e | doctor-a-multi-disease-detection-continual | 2305.05738 | null | https://arxiv.org/abs/2305.05738v1 | https://arxiv.org/pdf/2305.05738v1.pdf | DOCTOR: A Multi-Disease Detection Continual Learning Framework Based on Wearable Medical Sensors | Modern advances in machine learning (ML) and wearable medical sensors (WMSs) in edge devices have enabled ML-driven disease detection for smart healthcare. Conventional ML-driven disease detection methods rely on customizing individual models for each disease and its corresponding WMS data. However, such methods lack adaptability to distribution shifts and new task classification classes. Also, they need to be rearchitected and retrained from scratch for each new disease. Moreover, installing multiple ML models in an edge device consumes excessive memory, drains the battery faster, and complicates the detection process. To address these challenges, we propose DOCTOR, a multi-disease detection continual learning (CL) framework based on WMSs. It employs a multi-headed deep neural network (DNN) and an exemplar-replay-style CL algorithm. The CL algorithm enables the framework to continually learn new missions where different data distributions, classification classes, and disease detection tasks are introduced sequentially. It counteracts catastrophic forgetting with a data preservation method and a synthetic data generation module. The data preservation method efficiently preserves the most informative subset of training data from previous missions based on the average training loss of each data instance. The synthetic data generation module models the probability distribution of the real training data and then generates as much synthetic data as needed for replays while maintaining data privacy. The multi-headed DNN enables DOCTOR to detect multiple diseases simultaneously based on user WMS data. We demonstrate DOCTOR's efficacy in maintaining high multi-disease classification accuracy with a single DNN model in various CL experiments. DOCTOR achieves very competitive performance across all CL scenarios relative to the ideal joint-training framework while maintaining a small model size. | ['Niraj K. Jha', 'Chia-Hao Li'] | 2023-05-09 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 1.30038217e-01 3.58972587e-02 -4.13192183e-01 -2.10080698e-01
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a660febf-e78a-46dd-aa75-bec1006dd175 | fairness-in-face-presentation-attack | 2209.09035 | null | https://arxiv.org/abs/2209.09035v1 | https://arxiv.org/pdf/2209.09035v1.pdf | Fairness in Face Presentation Attack Detection | Face presentation attack detection (PAD) is critical to secure face recognition (FR) applications from presentation attacks. FR performance has been shown to be unfair to certain demographic and non-demographic groups. However, the fairness of face PAD is an understudied issue, mainly due to the lack of appropriately annotated data. To address this issue, this work first presents a Combined Attribute Annotated PAD Dataset (CAAD-PAD) by combining several well-known PAD datasets where we provide seven human-annotated attribute labels. This work then comprehensively analyses the fairness of a set of face PADs and its relation to the nature of training data and the Operational Decision Threshold Assignment (ODTA) on different data groups by studying four face PAD approaches on our CAAD-PAD. To simultaneously represent both the PAD fairness and the absolute PAD performance, we introduce a novel metric, namely the Accuracy Balanced Fairness (ABF). Extensive experiments on CAAD-PAD show that the training data and ODTA induce unfairness on gender, occlusion, and other attribute groups. Based on these analyses, we propose a data augmentation method, FairSWAP, which aims to disrupt the identity/semantic information and guide models to mine attack cues rather than attribute-related information. Detailed experimental results demonstrate that FairSWAP generally enhances both the PAD performance and the fairness of face PAD. | ['Naser Damer', 'Vitomir Struc', 'Arjan Kuijper', 'Wufei Yang', 'Meiling Fang'] | 2022-09-19 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 5.11000678e-03 7.94267505e-02 -3.04329038e-01 -6.85249209e-01
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f5a0fe64-d610-4e13-b28c-41178f07cd31 | registration-free-face-ssd-single-shot | 1902.04042 | null | http://arxiv.org/abs/1902.04042v1 | http://arxiv.org/pdf/1902.04042v1.pdf | Registration-free Face-SSD: Single shot analysis of smiles, facial attributes, and affect in the wild | In this paper, we present a novel single shot face-related task analysis
method, called Face-SSD, for detecting faces and for performing various
face-related (classification/regression) tasks including smile recognition,
face attribute prediction and valence-arousal estimation in the wild. Face-SSD
uses a Fully Convolutional Neural Network (FCNN) to detect multiple faces of
different sizes and recognise/regress one or more face-related classes.
Face-SSD has two parallel branches that share the same low-level filters, one
branch dealing with face detection and the other one with face analysis tasks.
The outputs of both branches are spatially aligned heatmaps that are produced
in parallel - therefore Face-SSD does not require that face detection, facial
region extraction, size normalisation, and facial region processing are
performed in subsequent steps. Our contributions are threefold: 1) Face-SSD is
the first network to perform face analysis without relying on pre-processing
such as face detection and registration in advance - Face-SSD is a simple and a
single FCNN architecture simultaneously performing face detection and
face-related task analysis - those are conventionally treated as separate
consecutive tasks; 2) Face-SSD is a generalised architecture that is applicable
for various face analysis tasks without modifying the network structure - this
is in contrast to designing task-specific architectures; and 3) Face-SSD
achieves real-time performance (21 FPS) even when detecting multiple faces and
recognising multiple classes in a given image. Experimental results show that
Face-SSD achieves state-of-the-art performance in various face analysis tasks
by reaching a recognition accuracy of 95.76% for smile detection, 90.29% for
attribute prediction, and Root Mean Square (RMS) error of 0.44 and 0.39 for
valence and arousal estimation. | ['Hatice Gunes', 'Youngkyoon Jang', 'Ioannis Patras'] | 2019-02-11 | null | null | null | null | ['smile-recognition'] | ['computer-vision'] | [ 5.13627648e-01 2.02735662e-01 2.89552838e-01 -7.59238124e-01
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7bfc772b-a257-4cf0-8913-da7191405f61 | prior-aware-synthetic-data-to-the-rescue | 2208.13944 | null | https://arxiv.org/abs/2208.13944v1 | https://arxiv.org/pdf/2208.13944v1.pdf | Prior-Aware Synthetic Data to the Rescue: Animal Pose Estimation with Very Limited Real Data | Accurately annotated image datasets are essential components for studying animal behaviors from their poses. Compared to the number of species we know and may exist, the existing labeled pose datasets cover only a small portion of them, while building comprehensive large-scale datasets is prohibitively expensive. Here, we present a very data efficient strategy targeted for pose estimation in quadrupeds that requires only a small amount of real images from the target animal. It is confirmed that fine-tuning a backbone network with pretrained weights on generic image datasets such as ImageNet can mitigate the high demand for target animal pose data and shorten the training time by learning the the prior knowledge of object segmentation and keypoint estimation in advance. However, when faced with serious data scarcity (i.e., $<10^2$ real images), the model performance stays unsatisfactory, particularly for limbs with considerable flexibility and several comparable parts. We therefore introduce a prior-aware synthetic animal data generation pipeline called PASyn to augment the animal pose data essential for robust pose estimation. PASyn generates a probabilistically-valid synthetic pose dataset, SynAP, through training a variational generative model on several animated 3D animal models. In addition, a style transfer strategy is utilized to blend the synthetic animal image into the real backgrounds. We evaluate the improvement made by our approach with three popular backbone networks and test their pose estimation accuracy on publicly available animal pose images as well as collected from real animals in a zoo. | ['Sarah Ostadabbas', 'Xiangyu Bai', 'Shuangjun Liu', 'Le Jiang'] | 2022-08-30 | null | null | null | null | ['animal-pose-estimation'] | ['computer-vision'] | [ 1.43727392e-01 2.15157151e-01 1.65478766e-01 -4.51104581e-01
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de8b029f-9529-4cfc-abe6-c5825eb095bd | reduce-reuse-recycle-modular-multi-object | 2304.03696 | null | https://arxiv.org/abs/2304.03696v1 | https://arxiv.org/pdf/2304.03696v1.pdf | Reduce, Reuse, Recycle: Modular Multi-Object Navigation | Our work focuses on the Multi-Object Navigation (MultiON) task, where an agent needs to navigate to multiple objects in a given sequence. We systematically investigate the inherent modularity of this task by dividing our approach to contain four modules: (a) an object detection module trained to identify objects from RGB images, (b) a map building module to build a semantic map of the observed objects, (c) an exploration module enabling the agent to explore its surroundings, and finally (d) a navigation module to move to identified target objects. We focus on the navigation and the exploration modules in this work. We show that we can effectively leverage a PointGoal navigation model in the MultiON task instead of learning to navigate from scratch. Our experiments show that a PointGoal agent-based navigation module outperforms analytical path planning on the MultiON task. We also compare exploration strategies and surprisingly find that a random exploration strategy significantly outperforms more advanced exploration methods. We additionally create MultiON 2.0, a new large-scale dataset as a test-bed for our approach. | ['Angel X. Chang', 'Manolis Savva', 'Unnat Jain', 'Tommaso Campari', 'Sonia Raychaudhuri'] | 2023-04-07 | null | null | null | null | ['pointgoal-navigation'] | ['robots'] | [ 1.25818923e-01 8.53731558e-02 3.04613352e-01 -2.63335165e-02
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fcacf1e8-ea69-40db-a059-437586a55ce4 | adversarially-masking-synthetic-to-mimic-real | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_Adversarially_Masking_Synthetic_To_Mimic_Real_Adaptive_Noise_Injection_for_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_Adversarially_Masking_Synthetic_To_Mimic_Real_Adaptive_Noise_Injection_for_CVPR_2023_paper.pdf | Adversarially Masking Synthetic To Mimic Real: Adaptive Noise Injection for Point Cloud Segmentation Adaptation | This paper considers the synthetic-to-real adaptation of point cloud semantic segmentation, which aims to segment the real-world point clouds with only synthetic labels available. Contrary to synthetic data which is integral and clean, point clouds collected by real-world sensors typically contain unexpected and irregular noise because the sensors may be impacted by various environmental conditions. Consequently, the model trained on ideal synthetic data may fail to achieve satisfactory segmentation results on real data. Influenced by such noise, previous adversarial training methods, which are conventional for 2D adaptation tasks, become less effective. In this paper, we aim to mitigate the domain gap caused by target noise via learning to mask the source points during the adaptation procedure. To this end, we design a novel learnable masking module, which takes source features and 3D coordinates as inputs. We incorporate Gumbel-Softmax operation into the masking module so that it can generate binary masks and be trained end-to-end via gradient back-propagation. With the help of adversarial training, the masking module can learn to generate source masks to mimic the pattern of irregular target noise, thereby narrowing the domain gap. We name our method "Adversarial Masking" as adversarial training and learnable masking module depend on each other and cooperate with each other to mitigate the domain gap. Experiments on two synthetic-to-real adaptation benchmarks verify the effectiveness of the proposed method. | ['Yi Yang', 'Yunchao Wei', 'Xiaohan Wang', 'Guoliang Kang', 'Guangrui Li'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['point-cloud-segmentation'] | ['computer-vision'] | [ 5.14984012e-01 3.63472760e-01 1.13472052e-01 -4.37557369e-01
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68614c89-5d51-4e19-8389-6e52cf991675 | neural-part-priors-learning-to-optimize-part | 2203.09375 | null | https://arxiv.org/abs/2203.09375v2 | https://arxiv.org/pdf/2203.09375v2.pdf | Neural Part Priors: Learning to Optimize Part-Based Object Completion in RGB-D Scans | 3D object recognition has seen significant advances in recent years, showing impressive performance on real-world 3D scan benchmarks, but lacking in object part reasoning, which is fundamental to higher-level scene understanding such as inter-object similarities or object functionality. Thus, we propose to leverage large-scale synthetic datasets of 3D shapes annotated with part information to learn Neural Part Priors (NPPs), optimizable spaces characterizing geometric part priors. Crucially, we can optimize over the learned part priors in order to fit to real-world scanned 3D scenes at test time, enabling robust part decomposition of the real objects in these scenes that also estimates the complete geometry of the object while fitting accurately to the observed real geometry. Moreover, this enables global optimization over geometrically similar detected objects in a scene, which often share strong geometric commonalities, enabling scene-consistent part decompositions. Experiments on the ScanNet dataset demonstrate that NPPs significantly outperforms state of the art in part decomposition and object completion in real-world scenes. | ['Angela Dai', 'Alexey Bokhovkin'] | 2022-03-17 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Bokhovkin_Neural_Part_Priors_Learning_To_Optimize_Part-Based_Object_Completion_in_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Bokhovkin_Neural_Part_Priors_Learning_To_Optimize_Part-Based_Object_Completion_in_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-object-recognition'] | ['computer-vision'] | [ 2.71021575e-01 2.53445596e-01 1.46856353e-01 -5.08807182e-01
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39d274f0-d318-4700-9529-0cc9a191c39b | adaptive-radial-projection-on-fourier | null | null | https://ieeexplore.ieee.org/document/9897910 | https://www.researchgate.net/publication/364320913_ADAPTIVE_RADIAL_PROJECTION_ON_FOURIER_MAGNITUDE_SPECTRUM_FOR_DOCUMENT_IMAGE_SKEW_ESTIMATION | Adaptive Radial Projection on Fourier Magnitude Spectrum for Document Image Skew Estimation | Skew estimation is one of the vital tasks in document processing systems, especially for scanned document images, because its performance impacts subsequent steps directly. Over the years, an enormous number of researches focus on this challenging problem in the rise of digitization age. In this research, we first propose a novel skew estimation method that extracts the dominant skew angle of the given document image by applying an Adaptive Radial Projection on the 2D Discrete Fourier Magnitude spectrum. Second, we introduce a high quality skew estimation dataset DISE-2021 to assess the performance of different estimators. Finally, we provide comprehensive analyses that focus on multiple improvement aspects of Fourier-based methods. Our results show that the proposed method is robust, reliable, and outperforms all compared methods. Data and code are available at https://github.com/phamquiluan/jdeskew. | ['Luan Pham; Phu Hao Hoang; Xuan Toan Mai; Tuan Anh Tran'] | 2022-10-18 | null | null | null | ieee-international-conference-on-image-8 | ['document-image-skew-estimation'] | ['computer-vision'] | [ 1.96281657e-01 -5.96861064e-01 -1.13590635e-01 -3.08601052e-01
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f1e62a26-08c5-4db5-8581-4c51b177fbe6 | improved-modulation-spectrum-histogram | null | null | https://aclanthology.org/O13-1014 | https://aclanthology.org/O13-1014.pdf | 改良調變頻譜統計圖等化法於強健性語音辨識之研究 (Improved Modulation Spectrum Histogram Equalization for Robust Speech Recognition) [In Chinese] | null | ['Yu-Chen Kao', 'Berlin Chen'] | 2013-10-01 | improved-modulation-spectrum-histogram-1 | https://aclanthology.org/O13-1014 | https://aclanthology.org/O13-1014.pdf | roclingijclclp-2013-10 | ['robust-speech-recognition'] | ['speech'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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e176efeb-bcce-4456-b7e9-3c84ff7ea9b7 | an-efficient-anchor-free-universal-lesion | 2203.16074 | null | https://arxiv.org/abs/2203.16074v1 | https://arxiv.org/pdf/2203.16074v1.pdf | An Efficient Anchor-free Universal Lesion Detection in CT-scans | Existing universal lesion detection (ULD) methods utilize compute-intensive anchor-based architectures which rely on predefined anchor boxes, resulting in unsatisfactory detection performance, especially in small and mid-sized lesions. Further, these default fixed anchor-sizes and ratios do not generalize well to different datasets. Therefore, we propose a robust one-stage anchor-free lesion detection network that can perform well across varying lesions sizes by exploiting the fact that the box predictions can be sorted for relevance based on their center rather than their overlap with the object. Furthermore, we demonstrate that the ULD can be improved by explicitly providing it the domain-specific information in the form of multi-intensity images generated using multiple HU windows, followed by self-attention based feature-fusion and backbone initialization using weights learned via self-supervision over CT-scans. We obtain comparable results to the state-of-the-art methods, achieving an overall sensitivity of 86.05% on the DeepLesion dataset, which comprises of approximately 32K CT-scans with lesions annotated across various body organs. | ['Lovekesh Vig', 'Monika Sharma', 'Meghal Dani', 'Manu Sheoran'] | 2022-03-30 | null | null | null | null | ['medical-object-detection'] | ['computer-vision'] | [ 2.95068204e-01 7.98012987e-02 -5.84085524e-01 -2.62168467e-01
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0be20703-be64-4cb9-8e5e-2b6fbce65bbc | an-efficient-circuit-compilation-flow-for | null | null | https://ieeexplore.ieee.org/abstract/document/9218558 | https://ieeexplore.ieee.org/abstract/document/9218558/figures#figures | An Efficient Circuit Compilation Flow for Quantum Approximate Optimization Algorithm | Quantum approximate optimization algorithm (QAOA) is a promising quantum-classical hybrid algorithm to solve hard combinatorial optimization problems. The two-qubits gates used in quantum circuit for QAOA are commutative i.e., the order of gates can be altered without changing the logical output. This re-ordering leads to execution of more gates in parallel and a smaller number of additional gates to compile the QAOA circuit resulting in lower circuit depth and gate-count which is beneficial for circuit run-time and noise. A lower number of gates means a lower accumulation of gate errors, and a lower circuit depth means the quantum bits will have a lower time to decohere (lose state). However, finding the best re-ordered circuit is a difficult problem and does not scale well with circuit size. This paper presents a compilation flow with 3 approaches to find an optimal re-ordered circuit with reduced depth and gate count. Our approaches can reduce gate count up to 23.21% and circuit depth up to 53.65%. Our approaches are compiler agnostic, can be integrated with existing compilers, and scalable. | ['Swaroop Ghosh Authors Info & Claims', 'Abdullah Ash- Saki', 'Mahabubul Alam'] | 2020-10-09 | null | null | null | acm-ieee-design-automation-conference-dac | ['combinatorial-optimization'] | ['methodology'] | [ 6.30644783e-02 -6.50428981e-02 1.83246240e-01 -1.70012921e-01
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898737a7-49fc-425e-b6ee-9a79d6f08497 | deepsleep-fast-and-accurate-delineation-of | null | null | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3445559 | https://papers.ssrn.com/sol3/Delivery.cfm/TLDIGITALHEALTH-S-19-00424.pdf?abstractid=3445559&mirid=1 | Deepsleep: Fast and Accurate Delineation of Sleep Arousals at Millisecond Resolution by Deep Learning | Background: Sleep arousals are transient periods of wakefulness punctuated into sleep. Excessive sleep arousals are associated with many negative effects including daytime sleepiness and sleep disorders. High-quality annotation of polysomnographic recordings is crucial for the diagnosis of sleep arousal disorders. Currently, sleep arousals are mainly annotated by human experts through looking at millions of data points manually, which requires considerable time and effort.
Methods: We used the polysomnograms of 2,994 individuals from two independent datasets (i) PhysioNet Challenge dataset (n=994), and (ii) Sleep Heart Health Study dataset (n=2000) for model training (60%), validation (15%), and testing (25%). We developed a deep convolutional neural network approach, DeepSleep, to automatically segment sleep arousal events. Our method captured the long-range and short-range interactions among physiological signals at multiple time scales to empower the detection of sleep arousals. A novel augmentation strategy by randomly swapping similar physiological channels was further applied to improve the prediction accuracy.
Findings: Compared with other computational methods in sleep study, DeepSleep features accurate (area under receiver operating characteristic curve of 0.93 and area under the precision recall curve of 0.55), high-resolution (5-millisecond resolution), and fast (10 seconds per sleep record) delineation of sleep arousals. This method ranked first in segmenting non-apenic arousals when evaluated on a large held-out dataset (n=989) in the 2018 PhysioNet Challenge. We found that DeepSleep provided more detailed delineations than humans, especially at the low-confident boundary regions between arousal and non-arousal events. This indicates that in silico annotations is a complement to human annotations and potentially advances the current binary label system and scoring criteria for sleep arousals.
Interpretation: The proposed deep learning model achieved state-of-the-art performance in detection of sleep arousals. By introducing the probability of annotation confidence, this model would provide more accurate information for the diagnosis of sleep disorders and the evaluation of sleep quality. | ['Yuanfang Guan', 'Hongyang Li'] | 2019-09-07 | null | null | null | lancet-2019-9 | ['sleep-quality-prediction', 'sleep-micro-event-detection', 'sleep-arousal-detection'] | ['medical', 'medical', 'medical'] | [ 1.14789099e-01 -1.72857314e-01 2.59530041e-02 -6.85389638e-01
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d526a8da-9e07-4f03-975b-3c2ef7b2dab9 | deep-learning-for-logo-recognition | 1701.02620 | null | http://arxiv.org/abs/1701.02620v2 | http://arxiv.org/pdf/1701.02620v2.pdf | Deep Learning for Logo Recognition | In this paper we propose a method for logo recognition using deep learning.
Our recognition pipeline is composed of a logo region proposal followed by a
Convolutional Neural Network (CNN) specifically trained for logo
classification, even if they are not precisely localized. Experiments are
carried out on the FlickrLogos-32 database, and we evaluate the effect on
recognition performance of synthetic versus real data augmentation, and image
pre-processing. Moreover, we systematically investigate the benefits of
different training choices such as class-balancing, sample-weighting and
explicit modeling the background class (i.e. no-logo regions). Experimental
results confirm the feasibility of the proposed method, that outperforms the
methods in the state of the art. | ['Raimondo Schettini', 'Marco Buzzelli', 'Simone Bianco', 'Davide Mazzini'] | 2017-01-10 | null | null | null | null | ['logo-recognition'] | ['computer-vision'] | [ 3.72555941e-01 -1.72137111e-01 -3.54822487e-01 -2.01394558e-01
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aeb841b7-dfaa-4212-add6-cfca0a641d09 | variational-relational-point-completion | 2104.10154 | null | https://arxiv.org/abs/2104.10154v1 | https://arxiv.org/pdf/2104.10154v1.pdf | Variational Relational Point Completion Network | Real-scanned point clouds are often incomplete due to viewpoint, occlusion, and noise. Existing point cloud completion methods tend to generate global shape skeletons and hence lack fine local details. Furthermore, they mostly learn a deterministic partial-to-complete mapping, but overlook structural relations in man-made objects. To tackle these challenges, this paper proposes a variational framework, Variational Relational point Completion network (VRCNet) with two appealing properties: 1) Probabilistic Modeling. In particular, we propose a dual-path architecture to enable principled probabilistic modeling across partial and complete clouds. One path consumes complete point clouds for reconstruction by learning a point VAE. The other path generates complete shapes for partial point clouds, whose embedded distribution is guided by distribution obtained from the reconstruction path during training. 2) Relational Enhancement. Specifically, we carefully design point self-attention kernel and point selective kernel module to exploit relational point features, which refines local shape details conditioned on the coarse completion. In addition, we contribute a multi-view partial point cloud dataset (MVP dataset) containing over 100,000 high-quality scans, which renders partial 3D shapes from 26 uniformly distributed camera poses for each 3D CAD model. Extensive experiments demonstrate that VRCNet outperforms state-of-theart methods on all standard point cloud completion benchmarks. Notably, VRCNet shows great generalizability and robustness on real-world point cloud scans. | ['Ziwei Liu', 'Shuai Yi', 'Haiyu Zhao', 'Junzhe Zhang', 'Zhongang Cai', 'Xinyi Chen', 'Liang Pan'] | 2021-04-20 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Pan_Variational_Relational_Point_Completion_Network_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Pan_Variational_Relational_Point_Completion_Network_CVPR_2021_paper.pdf | cvpr-2021-1 | ['point-cloud-completion'] | ['computer-vision'] | [-1.77819118e-01 1.26554757e-01 -1.05032496e-01 -2.50893563e-01
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ce87fa30-38a4-4b20-b4eb-74edc93dfe26 | hatebr-large-expert-annotated-corpus-of | null | null | https://openreview.net/forum?id=Nd1L1GfqOBS | https://openreview.net/pdf?id=Nd1L1GfqOBS | HateBR: Large expert annotated corpus of Brazilian Instagram comments for abusive language detection | Due to the severity of the social media abusive comments in Brazil, and the lack of research in Portuguese, this paper provides the first large-scale annotated corpus of Brazilian Instagram comments for hate speech and offensive language detection on the web and social media. The HateBR corpus was collected from Brazilian Instagram comments of political personalities and manually annotated by specialists, being composed of 7,000 documents annotated according to three different layers: a binary classification (offensive versus non-offensive comments), offense-level classes (highly, moderately, and slightly offensive messages), as well as nine hate speech targets (xenophobia, racism, homophobia, sexism, religious intolerance, partyism, apology to the dictatorship, antisemitism, and fatphobia). Each comment was annotated by three different annotators and achieved high inter-annotator agreement. | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['abusive-language'] | ['natural-language-processing'] | [-1.36943713e-01 5.17857194e-01 -1.72669023e-01 6.40361197e-03
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eac2f4f8-53f0-41a5-b293-4da2ebbc6b12 | genetic-algorithm-based-proportional-integral | 2304.10137 | null | https://arxiv.org/abs/2304.10137v1 | https://arxiv.org/pdf/2304.10137v1.pdf | Genetic-Algorithm-Based Proportional Integral Controller (GAPI) for ROV Steering Control | This article presents the design and real-time implementation of an optimal controller for precise steering control of a remotely operated underwater vehicle (ROV). A PI controller is investigated to achieve the desired steering performance. The gain parameters of the controller are tuned using the genetic algorithm (GA). The experimental response corresponding to the step waveform for the GA is obtained. A root-locus-tuned PI controller alongside a simulated-annealing-based PI controller (SAPI) is used to benchmark the response characteristics such as overshoot, peak time, and settling time. The experimental findings indicate that GAPI provides considerably better performance than SAPI and the root-locus-tuned controller. | ['Sarvat Mushtaq Ahmad', 'Ahsan Tanveer'] | 2023-04-20 | null | null | null | null | ['steering-control'] | ['computer-vision'] | [ 6.72539249e-02 3.50318141e-02 1.69663742e-01 -1.04390956e-01
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00f81ec0-bd47-4e7d-8a72-5f61d2b12254 | a-three-player-gan-for-super-resolution-in | 2303.13900 | null | https://arxiv.org/abs/2303.13900v1 | https://arxiv.org/pdf/2303.13900v1.pdf | A Three-Player GAN for Super-Resolution in Magnetic Resonance Imaging | Learning based single image super resolution (SISR) task is well investigated in 2D images. However, SISR for 3D Magnetics Resonance Images (MRI) is more challenging compared to 2D, mainly due to the increased number of neural network parameters, the larger memory requirement and the limited amount of available training data. Current SISR methods for 3D volumetric images are based on Generative Adversarial Networks (GANs), especially Wasserstein GANs due to their training stability. Other common architectures in the 2D domain, e.g. transformer models, require large amounts of training data and are therefore not suitable for the limited 3D data. However, Wasserstein GANs can be problematic because they may not converge to a global optimum and thus produce blurry results. Here, we propose a new method for 3D SR based on the GAN framework. Specifically, we use instance noise to balance the GAN training. Furthermore, we use a relativistic GAN loss function and an updating feature extractor during the training process. We show that our method produces highly accurate results. We also show that we need very few training samples. In particular, we need less than 30 samples instead of thousands of training samples that are typically required in previous studies. Finally, we show improved out-of-sample results produced by our model. | ['Gabriele Lohmann', 'Klaus Scheffler', 'Florian Birk', 'Julius Steiglechner', 'Lucas Mahler', 'Qi Wang'] | 2023-03-24 | null | null | null | null | ['image-super-resolution'] | ['computer-vision'] | [ 3.78206700e-01 2.89339989e-01 2.09523812e-01 -1.76487893e-01
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9b86dc7e-669a-4116-8d66-d0012a40de9d | finding-coordinated-paths-for-multiple | 1402.3613 | null | http://arxiv.org/abs/1402.3613v1 | http://arxiv.org/pdf/1402.3613v1.pdf | Finding Coordinated Paths for Multiple Holonomic Agents in 2-d Polygonal Environment | Avoiding collisions is one of the vital tasks for systems of autonomous
mobile agents. We focus on the problem of finding continuous coordinated paths
for multiple mobile disc agents in a 2-d environment with polygonal obstacles.
The problem is PSPACE-hard, with the state space growing exponentially in the
number of agents. Therefore, the state of the art methods include mainly
reactive techniques and sampling-based iterative algorithms.
We compare the performance of a widely-used reactive method ORCA with three
variants of a popular planning algorithm RRT* applied to multi-agent path
planning and find that an algorithm combining reactive collision avoidance and
RRT* planning, which we call ORCA-RRT* can be used to solve instances that are
out of the reach of either of the techniques. We experimentally show that: 1)
the reactive part of the algorithm can efficiently solve many multi-agent path
finding problems involving large number of agents, for which RRT* algorithm is
often unable to find a solution in limited time and 2) the planning component
of the algorithm is able to solve many instances containing local minima, where
reactive techniques typically fail. | ['Jiří Vokřínek', 'Michal Čáp', 'Pavel Janovský'] | 2014-02-14 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 2.17491284e-01 5.08328974e-01 -2.71727555e-02 5.66939414e-01
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10693d7c-3bcd-4ea7-8ecd-ce6a186ff93d | csboundary-city-scale-road-boundary-detection | 2111.06020 | null | https://arxiv.org/abs/2111.06020v2 | https://arxiv.org/pdf/2111.06020v2.pdf | csBoundary: City-scale Road-boundary Detection in Aerial Images for High-definition Maps | High-Definition (HD) maps can provide precise geometric and semantic information of static traffic environments for autonomous driving. Road-boundary is one of the most important information contained in HD maps since it distinguishes between road areas and off-road areas, which can guide vehicles to drive within road areas. But it is labor-intensive to annotate road boundaries for HD maps at the city scale. To enable automatic HD map annotation, current work uses semantic segmentation or iterative graph growing for road-boundary detection. However, the former could not ensure topological correctness since it works at the pixel level, while the latter suffers from inefficiency and drifting issues. To provide a solution to the aforementioned problems, in this letter, we propose a novel system termed csBoundary to automatically detect road boundaries at the city scale for HD map annotation. Our network takes as input an aerial image patch, and directly infers the continuous road-boundary graph (i.e., vertices and edges) from this image. To generate the city-scale road-boundary graph, we stitch the obtained graphs from all the image patches. Our csBoundary is evaluated and compared on a public benchmark dataset. The results demonstrate our superiority. The accompanied demonstration video is available at our project page \url{https://sites.google.com/view/csboundary/}. | ['Ming Liu', 'Lujia Wang', 'Yuxiang Sun', 'Xiangcheng Hu', 'Lu Gan', 'Yuxuan Liu', 'Zhenhua Xu'] | 2021-11-11 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 3.58903348e-01 3.07237059e-01 -9.43588093e-02 -3.79107803e-01
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09a52d37-0546-49d8-baf5-3d4977368630 | higher-order-pooling-of-cnn-features-via | 1701.05432 | null | http://arxiv.org/abs/1701.05432v1 | http://arxiv.org/pdf/1701.05432v1.pdf | Higher-order Pooling of CNN Features via Kernel Linearization for Action Recognition | Most successful deep learning algorithms for action recognition extend models
designed for image-based tasks such as object recognition to video. Such
extensions are typically trained for actions on single video frames or very
short clips, and then their predictions from sliding-windows over the video
sequence are pooled for recognizing the action at the sequence level. Usually
this pooling step uses the first-order statistics of frame-level action
predictions. In this paper, we explore the advantages of using higher-order
correlations; specifically, we introduce Higher-order Kernel (HOK) descriptors
generated from the late fusion of CNN classifier scores from all the frames in
a sequence. To generate these descriptors, we use the idea of kernel
linearization. Specifically, a similarity kernel matrix, which captures the
temporal evolution of deep classifier scores, is first linearized into kernel
feature maps. The HOK descriptors are then generated from the higher-order
co-occurrences of these feature maps, and are then used as input to a
video-level classifier. We provide experiments on two fine-grained action
recognition datasets and show that our scheme leads to state-of-the-art
results. | ['Piotr Koniusz', 'Stephen Gould', 'Anoop Cherian'] | 2017-01-19 | null | null | null | null | ['fine-grained-action-recognition'] | ['computer-vision'] | [ 3.77795607e-01 -5.20092964e-01 -3.60936105e-01 -4.53925103e-01
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2445a531-9c15-424b-a7b5-1ee19a4e36df | text-to-motion-retrieval-towards-joint | 2305.15842 | null | https://arxiv.org/abs/2305.15842v1 | https://arxiv.org/pdf/2305.15842v1.pdf | Text-to-Motion Retrieval: Towards Joint Understanding of Human Motion Data and Natural Language | Due to recent advances in pose-estimation methods, human motion can be extracted from a common video in the form of 3D skeleton sequences. Despite wonderful application opportunities, effective and efficient content-based access to large volumes of such spatio-temporal skeleton data still remains a challenging problem. In this paper, we propose a novel content-based text-to-motion retrieval task, which aims at retrieving relevant motions based on a specified natural-language textual description. To define baselines for this uncharted task, we employ the BERT and CLIP language representations to encode the text modality and successful spatio-temporal models to encode the motion modality. We additionally introduce our transformer-based approach, called Motion Transformer (MoT), which employs divided space-time attention to effectively aggregate the different skeleton joints in space and time. Inspired by the recent progress in text-to-image/video matching, we experiment with two widely-adopted metric-learning loss functions. Finally, we set up a common evaluation protocol by defining qualitative metrics for assessing the quality of the retrieved motions, targeting the two recently-introduced KIT Motion-Language and HumanML3D datasets. The code for reproducing our results is available at https://github.com/mesnico/text-to-motion-retrieval. | ['Tomáš Rebok', 'Fabrizio Falchi', 'Jan Sedmidubsky', 'Nicola Messina'] | 2023-05-25 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [ 2.48303264e-01 -5.48377216e-01 -2.86727786e-01 -1.18995883e-01
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3e138e37-b081-416a-9a99-bb9e9abddf24 | disc-differential-spectral-clustering-of | 2211.05314 | null | https://arxiv.org/abs/2211.05314v1 | https://arxiv.org/pdf/2211.05314v1.pdf | DiSC: Differential Spectral Clustering of Features | Selecting subsets of features that differentiate between two conditions is a key task in a broad range of scientific domains. In many applications, the features of interest form clusters with similar effects on the data at hand. To recover such clusters we develop DiSC, a data-driven approach for detecting groups of features that differentiate between conditions. For each condition, we construct a graph whose nodes correspond to the features and whose weights are functions of the similarity between them for that condition. We then apply a spectral approach to compute subsets of nodes whose connectivity differs significantly between the condition-specific feature graphs. On the theoretical front, we analyze our approach with a toy example based on the stochastic block model. We evaluate DiSC on a variety of datasets, including MNIST, hyperspectral imaging, simulated scRNA-seq and task fMRI, and demonstrate that DiSC uncovers features that better differentiate between conditions compared to competing methods. | ['Ariel Jaffe', 'Gal Mishne', 'Ram Dyuthi Sristi'] | 2022-11-10 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 7.02074945e-01 -5.20549953e-01 -6.58721551e-02 -3.15756232e-01
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893b6483-0dc7-41a3-a7e6-159701221489 | sentence-embedding-leaks-more-information | 2305.03010 | null | https://arxiv.org/abs/2305.03010v1 | https://arxiv.org/pdf/2305.03010v1.pdf | Sentence Embedding Leaks More Information than You Expect: Generative Embedding Inversion Attack to Recover the Whole Sentence | Sentence-level representations are beneficial for various natural language processing tasks. It is commonly believed that vector representations can capture rich linguistic properties. Currently, large language models (LMs) achieve state-of-the-art performance on sentence embedding. However, some recent works suggest that vector representations from LMs can cause information leakage. In this work, we further investigate the information leakage issue and propose a generative embedding inversion attack (GEIA) that aims to reconstruct input sequences based only on their sentence embeddings. Given the black-box access to a language model, we treat sentence embeddings as initial tokens' representations and train or fine-tune a powerful decoder model to decode the whole sequences directly. We conduct extensive experiments to demonstrate that our generative inversion attack outperforms previous embedding inversion attacks in classification metrics and generates coherent and contextually similar sentences as the original inputs. | ['Yangqiu Song', 'Mingshi Xu', 'Haoran Li'] | 2023-05-04 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings'] | ['methodology', 'natural-language-processing'] | [ 4.76324141e-01 2.22157612e-01 -3.17194700e-01 -2.79931843e-01
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7f9f6e35-b47c-4415-bd62-0798e414f87e | adaptive-risk-tendency-nano-drone-navigation | 2203.14749 | null | https://arxiv.org/abs/2203.14749v2 | https://arxiv.org/pdf/2203.14749v2.pdf | Adaptive Risk-Tendency: Nano Drone Navigation in Cluttered Environments with Distributional Reinforcement Learning | Enabling the capability of assessing risk and making risk-aware decisions is essential to applying reinforcement learning to safety-critical robots like drones. In this paper, we investigate a specific case where a nano quadcopter robot learns to navigate an apriori-unknown cluttered environment under partial observability. We present a distributional reinforcement learning framework to generate adaptive risk-tendency policies. Specifically, we propose to use lower tail conditional variance of the learnt return distribution as intrinsic uncertainty estimation, and use exponentially weighted average forecasting (EWAF) to adapt the risk-tendency in accordance with the estimated uncertainty. In simulation and real-world empirical results, we show that (1) the most effective risk-tendency vary across states, (2) the agent with adaptive risk-tendency achieves superior performance compared to risk-neutral policy or risk-averse policy baselines. | ['Guido C. H. E. de Croon', 'Erik-Jan van Kampen', 'Cheng Liu'] | 2022-03-28 | null | null | null | null | ['drone-navigation', 'distributional-reinforcement-learning'] | ['computer-vision', 'methodology'] | [-3.27019215e-01 4.61704642e-01 -2.76352882e-01 -1.64569989e-01
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d9b2c823-6eeb-4af3-bd13-a9abb722428b | feature-combination-meets-attention-baidu | 2106.14447 | null | https://arxiv.org/abs/2106.14447v1 | https://arxiv.org/pdf/2106.14447v1.pdf | Feature Combination Meets Attention: Baidu Soccer Embeddings and Transformer based Temporal Detection | With rapidly evolving internet technologies and emerging tools, sports related videos generated online are increasing at an unprecedentedly fast pace. To automate sports video editing/highlight generation process, a key task is to precisely recognize and locate the events in the long untrimmed videos. In this tech report, we present a two-stage paradigm to detect what and when events happen in soccer broadcast videos. Specifically, we fine-tune multiple action recognition models on soccer data to extract high-level semantic features, and design a transformer based temporal detection module to locate the target events. This approach achieved the state-of-the-art performance in both two tasks, i.e., action spotting and replay grounding, in the SoccerNet-v2 Challenge, under CVPR 2021 ActivityNet workshop. Our soccer embedding features are released at https://github.com/baidu-research/vidpress-sports. By sharing these features with the broader community, we hope to accelerate the research into soccer video understanding. | ['Jingyu Xin', 'Bo He', 'Zhiyu Cheng', 'Le Kang', 'Xin Zhou'] | 2021-06-28 | null | null | null | null | ['action-spotting', 'replay-grounding'] | ['computer-vision', 'computer-vision'] | [ 2.49412596e-01 -6.26551449e-01 -6.23080492e-01 -8.79468396e-02
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56c790b6-3f58-4a18-ba1a-1073edda2b7c | graph-neural-network-surrogates-of-fair-graph | 2303.08157 | null | https://arxiv.org/abs/2303.08157v2 | https://arxiv.org/pdf/2303.08157v2.pdf | Graph Neural Network Surrogates of Fair Graph Filtering | Graph filters that transform prior node values to posterior scores via edge propagation often support graph mining tasks affecting humans, such as recommendation and ranking. Thus, it is important to make them fair in terms of satisfying statistical parity constraints between groups of nodes (e.g., distribute score mass between genders proportionally to their representation). To achieve this while minimally perturbing the original posteriors, we introduce a filter-aware universal approximation framework for posterior objectives. This defines appropriate graph neural networks trained at runtime to be similar to filters but also locally optimize a large class of objectives, including fairness-aware ones. Experiments on a collection of 8 filters and 5 graphs show that our approach performs equally well or better than alternatives in meeting parity constraints while preserving the AUC of score-based community member recommendation and creating minimal utility loss in prior diffusion. | ['Symeon Papadopoulos', 'Emmanouil Krasanakis'] | 2023-03-14 | null | null | null | null | ['graph-mining'] | ['graphs'] | [ 1.84489861e-01 6.27132952e-01 -4.63393986e-01 -5.42156518e-01
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4e3b964b-4506-4906-a231-2a1bf21fa092 | multi-level-multiple-instance-learning-with | 2306.05029 | null | https://arxiv.org/abs/2306.05029v1 | https://arxiv.org/pdf/2306.05029v1.pdf | Multi-level Multiple Instance Learning with Transformer for Whole Slide Image Classification | Whole slide image (WSI) refers to a type of high-resolution scanned tissue image, which is extensively employed in computer-assisted diagnosis (CAD). The extremely high resolution and limited availability of region-level annotations make it challenging to employ deep learning methods for WSI-based digital diagnosis. Multiple instance learning (MIL) is a powerful tool to address the weak annotation problem, while Transformer has shown great success in the field of visual tasks. The combination of both should provide new insights for deep learning based image diagnosis. However, due to the limitations of single-level MIL and the attention mechanism's constraints on sequence length, directly applying Transformer to WSI-based MIL tasks is not practical. To tackle this issue, we propose a Multi-level MIL with Transformer (MMIL-Transformer) approach. By introducing a hierarchical structure to MIL, this approach enables efficient handling of MIL tasks that involve a large number of instances. To validate its effectiveness, we conducted a set of experiments on WSIs classification task, where MMIL-Transformer demonstrate superior performance compared to existing state-of-the-art methods. Our proposed approach achieves test AUC 94.74% and test accuracy 93.41% on CAMELYON16 dataset, test AUC 99.04% and test accuracy 94.37% on TCGA-NSCLC dataset, respectively. All code and pre-trained models are available at: https://github.com/hustvl/MMIL-Transformer | ['Xinggang Wang', 'Yan Liu', 'Hao Xin', 'Yingzhuang Liu', 'Qiaozhe Zhang', 'Ruijie Zhang'] | 2023-06-08 | null | null | null | null | ['multiple-instance-learning'] | ['methodology'] | [ 4.37350363e-01 -1.00522421e-01 -5.38446128e-01 -9.95989665e-02
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1aea74df-b319-48d5-9293-ea725ffa91e0 | physnet-a-neural-network-for-predicting | 1902.08408 | null | http://arxiv.org/abs/1902.08408v2 | http://arxiv.org/pdf/1902.08408v2.pdf | PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments and Partial Charges | In recent years, machine learning (ML) methods have become increasingly
popular in computational chemistry. After being trained on appropriate ab
initio reference data, these methods allow to accurately predict the properties
of chemical systems, circumventing the need for explicitly solving the
electronic Schr\"odinger equation. Because of their computational efficiency
and scalability to large datasets, deep neural networks (DNNs) are a
particularly promising ML algorithm for chemical applications. This work
introduces PhysNet, a DNN architecture designed for predicting energies, forces
and dipole moments of chemical systems. PhysNet achieves state-of-the-art
performance on the QM9, MD17 and ISO17 benchmarks. Further, two new datasets
are generated in order to probe the performance of ML models for describing
chemical reactions, long-range interactions, and condensed phase systems. It is
shown that explicitly including electrostatics in energy predictions is crucial
for a qualitatively correct description of the asymptotic regions of a
potential energy surface (PES). PhysNet models trained on a systematically
constructed set of small peptide fragments (at most eight heavy atoms) are able
to generalize to considerably larger proteins like deca-alanine (Ala$_{10}$):
The optimized geometry of helical Ala$_{10}$ predicted by PhysNet is virtually
identical to ab initio results (RMSD = 0.21 \r{A}). By running unbiased
molecular dynamics (MD) simulations of Ala$_{10}$ on the PhysNet-PES in gas
phase, it is found that instead of a helical structure, Ala$_{10}$ folds into a
wreath-shaped configuration, which is more stable than the helical form by 0.46
kcal mol$^{-1}$ according to the reference ab initio calculations. | ['Oliver T. Unke', 'Markus Meuwly'] | 2019-02-22 | physnet-a-neural-network-for-predicting-1 | null | null | j-chem-theory-comput-2019-2 | ['formation-energy'] | ['miscellaneous'] | [ 4.44518365e-02 9.52456594e-02 -1.73607975e-01 -2.97091901e-01
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e3d79c6a-72cf-409d-ac7b-6634dad9c745 | a-deep-learning-approach-for-real-time-3d | 1907.03520 | null | https://arxiv.org/abs/1907.03520v2 | https://arxiv.org/pdf/1907.03520v2.pdf | A Deep Learning Approach for Real-Time 3D Human Action Recognition from Skeletal Data | We present a new deep learning approach for real-time 3D human action recognition from skeletal data and apply it to develop a vision-based intelligent surveillance system. Given a skeleton sequence, we propose to encode skeleton poses and their motions into a single RGB image. An Adaptive Histogram Equalization (AHE) algorithm is then applied on the color images to enhance their local patterns and generate more discriminative features. For learning and classification tasks, we design Deep Neural Networks based on the Densely Connected Convolutional Architecture (DenseNet) to extract features from enhanced-color images and classify them into classes. Experimental results on two challenging datasets show that the proposed method reaches state-of-the-art accuracy, whilst requiring low computational time for training and inference. This paper also introduces CEMEST, a new RGB-D dataset depicting passenger behaviors in public transport. It consists of 203 untrimmed real-world surveillance videos of realistic normal and anomalous events. We achieve promising results on real conditions of this dataset with the support of data augmentation and transfer learning techniques. This enables the construction of real-world applications based on deep learning for enhancing monitoring and security in public transport. | ['Sergio A. Velastin', 'Houssam Salmane', 'Alain Crouzil', 'Pablo Zegers', 'Louahdi Khoudour', 'Huy Hieu Pham'] | 2019-07-08 | null | null | null | null | ['3d-human-action-recognition'] | ['computer-vision'] | [ 5.79354703e-01 -8.18597302e-02 -1.48065895e-01 -5.08299053e-01
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45e71b26-b68f-49d9-a9f2-f80e2cc65b9f | global-counterfactual-explanations | 2204.06917 | null | https://arxiv.org/abs/2204.06917v1 | https://arxiv.org/pdf/2204.06917v1.pdf | Global Counterfactual Explanations: Investigations, Implementations and Improvements | Counterfactual explanations have been widely studied in explainability, with a range of application dependent methods emerging in fairness, recourse and model understanding. However, the major shortcoming associated with these methods is their inability to provide explanations beyond the local or instance-level. While some works touch upon the notion of a global explanation, typically suggesting to aggregate masses of local explanations in the hope of ascertaining global properties, few provide frameworks that are either reliable or computationally tractable. Meanwhile, practitioners are requesting more efficient and interactive explainability tools. We take this opportunity to investigate existing global methods, with a focus on implementing and improving Actionable Recourse Summaries (AReS), the only known global counterfactual explanation framework for recourse. | ['Daniele Magazzeni', 'Saumitra Mishra', 'Dan Ley'] | 2022-04-14 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 4.57031988e-02 6.47847831e-01 -1.04959512e+00 -4.11631197e-01
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73259a1e-82f1-4fef-9d08-eca335e6acc5 | knowledge-representation-learning-a | 1812.10901 | null | http://arxiv.org/abs/1812.10901v1 | http://arxiv.org/pdf/1812.10901v1.pdf | Knowledge Representation Learning: A Quantitative Review | Knowledge representation learning (KRL) aims to represent entities and
relations in knowledge graph in low-dimensional semantic space, which have been
widely used in massive knowledge-driven tasks. In this article, we introduce
the reader to the motivations for KRL, and overview existing approaches for
KRL. Afterwards, we extensively conduct and quantitative comparison and
analysis of several typical KRL methods on three evaluation tasks of knowledge
acquisition including knowledge graph completion, triple classification, and
relation extraction. We also review the real-world applications of KRL, such as
language modeling, question answering, information retrieval, and recommender
systems. Finally, we discuss the remaining challenges and outlook the future
directions for KRL. The codes and datasets used in the experiments can be found
in https://github.com/thunlp/OpenKE. | ['Maosong Sun', 'Ruobing Xie', 'Yankai Lin', 'Zhiyuan Liu', 'Xu Han'] | 2018-12-28 | null | null | null | null | ['triple-classification'] | ['graphs'] | [-3.23663414e-01 3.11552376e-01 -5.63651800e-01 -2.14569911e-01
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1c2bb955-5ef9-45dc-934f-11f8cea1d95c | disentangled-representation-learning-gan-for | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Tran_Disentangled_Representation_Learning_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Tran_Disentangled_Representation_Learning_CVPR_2017_paper.pdf | Disentangled Representation Learning GAN for Pose-Invariant Face Recognition | The large pose discrepancy between two face images is one of the key challenges in face recognition. Conventional approaches for pose-invariant face recognition either perform face frontalization on, or learn a pose-invariant representation from, a non-frontal face image. We argue that it is more desirable to perform both tasks jointly to allow them to leverage each other. To this end, this paper proposes Disentangled Representation learning-Generative Adversarial Network (DR-GAN) with three distinct novelties. First, the encoder-decoder structure of the generator allows DR-GAN to learn a generative and discriminative representation, in addition to image synthesis. Second, this representation is explicitly disentangled from other face variations such as pose, through the pose code provided to the decoder and pose estimation in the discriminator. Third, DR-GAN can take one or multiple images as the input, and generate one unified representation along with an arbitrary number of synthetic images. Quantitative and qualitative evaluation on both controlled and in-the-wild databases demonstrate the superiority of DR-GAN over the state of the art.
| ['Xiaoming Liu', 'Luan Tran', 'Xi Yin'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['robust-face-recognition'] | ['computer-vision'] | [ 4.41070288e-01 3.27807426e-01 -8.89127515e-03 -5.77658176e-01
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54f4d4b0-0160-462d-a27b-5f0943be1b2c | late-breaking-results-scalable-and-efficient | 2304.06728 | null | https://arxiv.org/abs/2304.06728v1 | https://arxiv.org/pdf/2304.06728v1.pdf | Late Breaking Results: Scalable and Efficient Hyperdimensional Computing for Network Intrusion Detection | Cybersecurity has emerged as a critical challenge for the industry. With the large complexity of the security landscape, sophisticated and costly deep learning models often fail to provide timely detection of cyber threats on edge devices. Brain-inspired hyperdimensional computing (HDC) has been introduced as a promising solution to address this issue. However, existing HDC approaches use static encoders and require very high dimensionality and hundreds of training iterations to achieve reasonable accuracy. This results in a serious loss of learning efficiency and causes huge latency for detecting attacks. In this paper, we propose CyberHD, an innovative HDC learning framework that identifies and regenerates insignificant dimensions to capture complicated patterns of cyber threats with remarkably lower dimensionality. Additionally, the holographic distribution of patterns in high dimensional space provides CyberHD with notably high robustness against hardware errors. | ['Mohsen Imani', 'Sitao Huang', 'Mariam Issa', 'Hanning Chen', 'Junyao Wang'] | 2023-04-11 | null | null | null | null | ['network-intrusion-detection'] | ['miscellaneous'] | [-3.88914347e-02 -4.45000529e-01 7.65149817e-02 1.78571478e-01
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5aef9a20-8974-4a78-b6a3-78e2952c9fa5 | mask-textspotter-v3-segmentation-proposal | 2007.09482 | null | https://arxiv.org/abs/2007.09482v1 | https://arxiv.org/pdf/2007.09482v1.pdf | Mask TextSpotter v3: Segmentation Proposal Network for Robust Scene Text Spotting | Recent end-to-end trainable methods for scene text spotting, integrating detection and recognition, showed much progress. However, most of the current arbitrary-shape scene text spotters use region proposal networks (RPN) to produce proposals. RPN relies heavily on manually designed anchors and its proposals are represented with axis-aligned rectangles. The former presents difficulties in handling text instances of extreme aspect ratios or irregular shapes, and the latter often includes multiple neighboring instances into a single proposal, in cases of densely oriented text. To tackle these problems, we propose Mask TextSpotter v3, an end-to-end trainable scene text spotter that adopts a Segmentation Proposal Network (SPN) instead of an RPN. Our SPN is anchor-free and gives accurate representations of arbitrary-shape proposals. It is therefore superior to RPN in detecting text instances of extreme aspect ratios or irregular shapes. Furthermore, the accurate proposals produced by SPN allow masked RoI features to be used for decoupling neighboring text instances. As a result, our Mask TextSpotter v3 can handle text instances of extreme aspect ratios or irregular shapes, and its recognition accuracy won't be affected by nearby text or background noise. Specifically, we outperform state-of-the-art methods by 21.9 percent on the Rotated ICDAR 2013 dataset (rotation robustness), 5.9 percent on the Total-Text dataset (shape robustness), and achieve state-of-the-art performance on the MSRA-TD500 dataset (aspect ratio robustness). Code is available at: https://github.com/MhLiao/MaskTextSpotterV3 | ['Xiang Bai', 'Tal Hassner', 'Minghui Liao', 'Jing Huang', 'Guan Pang'] | 2020-07-18 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1436_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123560681.pdf | eccv-2020-8 | ['text-spotting'] | ['computer-vision'] | [ 2.41005599e-01 -1.65346377e-02 -4.22377251e-02 -2.74101824e-01
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e0b3e8d5-e2f5-454e-a59a-2a24f831da95 | incremental-online-learning-algorithms | 2209.00591 | null | https://arxiv.org/abs/2209.00591v1 | https://arxiv.org/pdf/2209.00591v1.pdf | Incremental Online Learning Algorithms Comparison for Gesture and Visual Smart Sensors | Tiny machine learning (TinyML) in IoT systems exploits MCUs as edge devices for data processing. However, traditional TinyML methods can only perform inference, limited to static environments or classes. Real case scenarios usually work in dynamic environments, thus drifting the context where the original neural model is no more suitable. For this reason, pre-trained models reduce accuracy and reliability during their lifetime because the data recorded slowly becomes obsolete or new patterns appear. Continual learning strategies maintain the model up to date, with runtime fine-tuning of the parameters. This paper compares four state-of-the-art algorithms in two real applications: i) gesture recognition based on accelerometer data and ii) image classification. Our results confirm these systems' reliability and the feasibility of deploying them in tiny-memory MCUs, with a drop in the accuracy of a few percentage points with respect to the original models for unconstrained computing platforms. | ['Davide Brunelli', 'Andrea Albanese', 'Alessandro Avi'] | 2022-09-01 | null | null | null | null | ['gesture-recognition'] | ['computer-vision'] | [ 1.93796065e-02 1.12019435e-01 -4.37528074e-01 -3.86959225e-01
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2.10184783e-01 -4.38024014e-01 -8.53517186e-03 2.05252260e-01] | [8.038177490234375, 2.539698362350464] |
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