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eccfc125-42cc-4f71-9f18-c1b30b5eb58f | cross-network-social-user-embedding-with | 2209.01539 | null | https://arxiv.org/abs/2209.01539v1 | https://arxiv.org/pdf/2209.01539v1.pdf | Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees | Integrating multiple online social networks (OSNs) has important implications for many downstream social mining tasks, such as user preference modelling, recommendation, and link prediction. However, it is unfortunately accompanied by growing privacy concerns about leaking sensitive user information. How to fully utilize the data from different online social networks while preserving user privacy remains largely unsolved. To this end, we propose a Cross-network Social User Embedding framework, namely DP-CroSUE, to learn the comprehensive representations of users in a privacy-preserving way. We jointly consider information from partially aligned social networks with differential privacy guarantees. In particular, for each heterogeneous social network, we first introduce a hybrid differential privacy notion to capture the variation of privacy expectations for heterogeneous data types. Next, to find user linkages across social networks, we make unsupervised user embedding-based alignment in which the user embeddings are achieved by the heterogeneous network embedding technology. To further enhance user embeddings, a novel cross-network GCN embedding model is designed to transfer knowledge across networks through those aligned users. Extensive experiments on three real-world datasets demonstrate that our approach makes a significant improvement on user interest prediction tasks as well as defending user attribute inference attacks from embedding. | ['Philip S. Yu', 'Xu Bai', 'Jia Wu', 'Chaochao Chen', 'Zhiwei Liu', 'Lingjuan Lyu', 'Hao Peng', 'Lei Jiang', 'Jiaqian Ren'] | 2022-09-04 | null | null | null | null | ['network-embedding'] | ['methodology'] | [ 1.34420972e-02 2.27263033e-01 -4.55736667e-01 -4.63679940e-01
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151e4842-1dc2-474d-a6ff-a9699182d327 | dual-cross-attention-learning-for-fine | 2205.02151 | null | https://arxiv.org/abs/2205.02151v1 | https://arxiv.org/pdf/2205.02151v1.pdf | Dual Cross-Attention Learning for Fine-Grained Visual Categorization and Object Re-Identification | Recently, self-attention mechanisms have shown impressive performance in various NLP and CV tasks, which can help capture sequential characteristics and derive global information. In this work, we explore how to extend self-attention modules to better learn subtle feature embeddings for recognizing fine-grained objects, e.g., different bird species or person identities. To this end, we propose a dual cross-attention learning (DCAL) algorithm to coordinate with self-attention learning. First, we propose global-local cross-attention (GLCA) to enhance the interactions between global images and local high-response regions, which can help reinforce the spatial-wise discriminative clues for recognition. Second, we propose pair-wise cross-attention (PWCA) to establish the interactions between image pairs. PWCA can regularize the attention learning of an image by treating another image as distractor and will be removed during inference. We observe that DCAL can reduce misleading attentions and diffuse the attention response to discover more complementary parts for recognition. We conduct extensive evaluations on fine-grained visual categorization and object re-identification. Experiments demonstrate that DCAL performs on par with state-of-the-art methods and consistently improves multiple self-attention baselines, e.g., surpassing DeiT-Tiny and ViT-Base by 2.8% and 2.4% mAP on MSMT17, respectively. | ['Yi Shan', 'Lu Tian', 'Ji Liu', 'Dong Li', 'Wenjing Ke', 'Haowei Zhu'] | 2022-05-04 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhu_Dual_Cross-Attention_Learning_for_Fine-Grained_Visual_Categorization_and_Object_Re-Identification_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhu_Dual_Cross-Attention_Learning_for_Fine-Grained_Visual_Categorization_and_Object_Re-Identification_CVPR_2022_paper.pdf | cvpr-2022-1 | ['fine-grained-image-classification', 'fine-grained-visual-categorization'] | ['computer-vision', 'computer-vision'] | [ 4.70073670e-02 -4.32117075e-01 -1.22370133e-02 -5.27651310e-01
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54834823-28bd-43e6-aa7f-6f409b02f4b0 | evaluating-the-impact-of-bitcoin-on | 2205.00335 | null | https://arxiv.org/abs/2205.00335v1 | https://arxiv.org/pdf/2205.00335v1.pdf | Evaluating the Impact of Bitcoin on International Asset Allocation using Mean-Variance, Conditional Value-at-Risk (CVaR), and Markov Regime Switching Approaches | This paper aims to analyze the effect of Bitcoin on portfolio optimization using mean-variance, conditional value-at-risk (CVaR), and Markov regime switching approaches. I assessed each approach and developed the next based on the prior approach's weaknesses until I ended with a high level of confidence in the final approach. Though the results of mean-variance and CVaR frameworks indicate that Bitcoin improves the diversification of a well-diversified international portfolio, they assume that assets' returns are developed linearly and normally distributed. However, the Bitcoin return does not have both of these characteristics. Due to this, I developed a Markov regime switching approach to analyze the effect of Bitcoin on an international portfolio performance. The results show that there are two regimes based on the assets' returns: 1- bear state, where returns have low means and high volatility, 2- bull state, where returns have high means and low volatility. | ['Mohammadreza Mahmoudi'] | 2022-04-30 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-7.78281033e-01 -9.03284326e-02 -5.20115674e-01 1.04856610e-01
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46c1888d-85f4-42a2-97c3-ba11c025cbd8 | transfer-learning-based-multi-objective | 2109.15136 | null | https://arxiv.org/abs/2109.15136v2 | https://arxiv.org/pdf/2109.15136v2.pdf | Transfer Learning Based Multi-Objective Genetic Algorithm for Dynamic Community Detection | Dynamic community detection is the hotspot and basic problem of complex network and artificial intelligence research in recent years. It is necessary to maximize the accuracy of clustering as the network structure changes, but also to minimize the two consecutive clustering differences between the two results. There is a trade-off relationship between these two objectives. In this paper, we propose a Feature Transfer Based Multi-Objective Optimization Genetic Algorithm (TMOGA) based on transfer learning and traditional multi-objective evolutionary algorithm framework. The main idea is to extract stable features from past community structures, retain valuable feature information, and integrate this feature information into current optimization processes to improve the evolutionary algorithms. Additionally, a new theoretical framework is proposed in this paper to analyze community detection problem based on information theory. Then, we exploit this framework to prove the rationality of TMOGA. Finally, the experimental results show that our algorithm can achieve better clustering effects compared with the state-of-the-art dynamic network community detection algorithms in diverse test problems. | ['Gaoshan Deng', 'Siyu Gao', 'Gil Alterovitz', 'Wenhua Zeng', 'Fan Lin', 'Jungang Zou'] | 2021-09-30 | null | null | null | null | ['dynamic-community-detection'] | ['graphs'] | [ 9.83107602e-04 -7.10840523e-01 4.67053987e-02 7.25212321e-02
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8a82b7d4-f13b-424f-a81c-4b961b56296a | toward-training-at-imagenet-scale-with | 2201.12328 | null | https://arxiv.org/abs/2201.12328v2 | https://arxiv.org/pdf/2201.12328v2.pdf | Toward Training at ImageNet Scale with Differential Privacy | Differential privacy (DP) is the de facto standard for training machine learning (ML) models, including neural networks, while ensuring the privacy of individual examples in the training set. Despite a rich literature on how to train ML models with differential privacy, it remains extremely challenging to train real-life, large neural networks with both reasonable accuracy and privacy. We set out to investigate how to do this, using ImageNet image classification as a poster example of an ML task that is very challenging to resolve accurately with DP right now. This paper shares initial lessons from our effort, in the hope that it will inspire and inform other researchers to explore DP training at scale. We show approaches that help make DP training faster, as well as model types and settings of the training process that tend to work better in the DP setting. Combined, the methods we discuss let us train a Resnet-18 with DP to $47.9\%$ accuracy and privacy parameters $\epsilon = 10, \delta = 10^{-6}$. This is a significant improvement over "naive" DP training of ImageNet models, but a far cry from the $75\%$ accuracy that can be obtained by the same network without privacy. The model we use was pretrained on the Places365 data set as a starting point. We share our code at https://github.com/google-research/dp-imagenet, calling for others to build upon this new baseline to further improve DP at scale. | ['Abhradeep Thakurta', 'Andreas Terzis', 'Roxana Geambasu', 'Shuang Song', 'Steve Chien', 'Alexey Kurakin'] | 2022-01-28 | null | null | null | null | ['image-classification-with-dp'] | ['computer-vision'] | [ 9.39956978e-02 2.27750435e-01 2.43033632e-04 -6.86730266e-01
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f061c634-ad22-4af4-821b-a4c3b4f911ed | compressing-facial-makeup-transfer-networks | 2009.07604 | null | https://arxiv.org/abs/2009.07604v1 | https://arxiv.org/pdf/2009.07604v1.pdf | Compressing Facial Makeup Transfer Networks by Collaborative Distillation and Kernel Decomposition | Although the facial makeup transfer network has achieved high-quality performance in generating perceptually pleasing makeup images, its capability is still restricted by the massive computation and storage of the network architecture. We address this issue by compressing facial makeup transfer networks with collaborative distillation and kernel decomposition. The main idea of collaborative distillation is underpinned by a finding that the encoder-decoder pairs construct an exclusive collaborative relationship, which is regarded as a new kind of knowledge for low-level vision tasks. For kernel decomposition, we apply the depth-wise separation of convolutional kernels to build a light-weighted Convolutional Neural Network (CNN) from the original network. Extensive experiments show the effectiveness of the compression method when applied to the state-of-the-art facial makeup transfer network -- BeautyGAN. | ['Lu Yu', 'Xinyi Hu', 'Zi Hui', 'Haoji Hu', 'Bianjiang Yang'] | 2020-09-16 | null | null | null | null | ['facial-makeup-transfer'] | ['computer-vision'] | [ 2.56758481e-01 3.10080230e-01 -3.47036242e-01 -4.75228906e-01
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745e6315-c0df-4ab4-8a24-c640f461183c | do-deep-neural-networks-capture | 2302.07866 | null | https://arxiv.org/abs/2302.07866v1 | https://arxiv.org/pdf/2302.07866v1.pdf | Do Deep Neural Networks Capture Compositionality in Arithmetic Reasoning? | Compositionality is a pivotal property of symbolic reasoning. However, how well recent neural models capture compositionality remains underexplored in the symbolic reasoning tasks. This study empirically addresses this question by systematically examining recently published pre-trained seq2seq models with a carefully controlled dataset of multi-hop arithmetic symbolic reasoning. We introduce a skill tree on compositionality in arithmetic symbolic reasoning that defines the hierarchical levels of complexity along with three compositionality dimensions: systematicity, productivity, and substitutivity. Our experiments revealed that among the three types of composition, the models struggled most with systematicity, performing poorly even with relatively simple compositions. That difficulty was not resolved even after training the models with intermediate reasoning steps. | ['Kentaro Inui', 'Keisuke Sakaguchi', 'Masashi Yoshikawa', 'Ana Brassard', 'Tatsuki Kuribayashi', 'Yoichi Aoki', 'Keito Kudo'] | 2023-02-15 | null | null | null | null | ['arithmetic-reasoning'] | ['reasoning'] | [ 4.54284996e-01 2.23917633e-01 -2.29856014e-01 -1.40029043e-01
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b1451f60-7c92-4588-b17b-5e3f6d13f3ab | deeplogo-hitting-logo-recognition-with-the | 1510.02131 | null | http://arxiv.org/abs/1510.02131v1 | http://arxiv.org/pdf/1510.02131v1.pdf | DeepLogo: Hitting Logo Recognition with the Deep Neural Network Hammer | Recently, there has been a flurry of industrial activity around logo
recognition, such as Ditto's service for marketers to track their brands in
user-generated images, and LogoGrab's mobile app platform for logo recognition.
However, relatively little academic or open-source logo recognition progress
has been made in the last four years. Meanwhile, deep convolutional neural
networks (DCNNs) have revolutionized a broad range of object recognition
applications. In this work, we apply DCNNs to logo recognition. We propose
several DCNN architectures, with which we surpass published state-of-art
accuracy on a popular logo recognition dataset. | ['Forrest N. Iandola', 'Kurt Keutzer', 'Anting Shen', 'Peter Gao'] | 2015-10-07 | null | null | null | null | ['logo-recognition'] | ['computer-vision'] | [ 9.41755157e-03 -7.23411918e-01 -7.98286796e-01 -3.28718424e-01
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a28a3bd3-f91a-4750-b9dd-e7b957f63bc2 | blind-image-quality-assessment-for-mri-with-a | 2107.06888 | null | https://arxiv.org/abs/2107.06888v1 | https://arxiv.org/pdf/2107.06888v1.pdf | Blind Image Quality Assessment for MRI with A Deep Three-dimensional content-adaptive Hyper-Network | Image Quality Assessment (IQA) is of great value in the workflow of Magnetic Resonance Imaging (MRI)-based analysis. Blind IQA (BIQA) methods are especially required since high-quality reference MRI images are usually not available. Recently, many efforts have been devoted to developing deep learning-based BIQA approaches. However, the performance of these methods is limited due to the utilization of simple content-non-adaptive network parameters and the waste of the important 3D spatial information of the medical images. To address these issues, we design a 3D content-adaptive hyper-network for MRI BIQA. The overall 3D configuration enables the exploration of comprehensive 3D spatial information from MRI images, while the developed content-adaptive hyper-network contributes to the self-adaptive capacity of network parameters and thus, facilitates better BIQA performance. The effectiveness of the proposed method is extensively evaluated on the open dataset, MRIQC. Promising performance is achieved compared with the corresponding baseline and 4 state-of-the-art BIQA methods. We make our code available at \url{https://git.openi.org.cn/SIAT_Wangshanshan/HyS-Net}. | ['Shanshan Wang', 'Hairong Zheng', 'Cheng Li', 'Yu Gong', 'Chuyu Rong', 'Haoran Li', 'Kehan Qi'] | 2021-07-13 | null | null | null | null | ['blind-image-quality-assessment'] | ['computer-vision'] | [-3.90565321e-02 -2.23657757e-01 2.89333779e-02 -3.00261647e-01
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eaab419d-411d-4c05-b910-7022879c8015 | generative-low-bitwidth-data-free | 2003.03603 | null | https://arxiv.org/abs/2003.03603v3 | https://arxiv.org/pdf/2003.03603v3.pdf | Generative Low-bitwidth Data Free Quantization | Neural network quantization is an effective way to compress deep models and improve their execution latency and energy efficiency, so that they can be deployed on mobile or embedded devices. Existing quantization methods require original data for calibration or fine-tuning to get better performance. However, in many real-world scenarios, the data may not be available due to confidential or private issues, thereby making existing quantization methods not applicable. Moreover, due to the absence of original data, the recently developed generative adversarial networks (GANs) cannot be applied to generate data. Although the full-precision model may contain rich data information, such information alone is hard to exploit for recovering the original data or generating new meaningful data. In this paper, we investigate a simple-yet-effective method called Generative Low-bitwidth Data Free Quantization (GDFQ) to remove the data dependence burden. Specifically, we propose a knowledge matching generator to produce meaningful fake data by exploiting classification boundary knowledge and distribution information in the pre-trained model. With the help of generated data, we can quantize a model by learning knowledge from the pre-trained model. Extensive experiments on three data sets demonstrate the effectiveness of our method. More critically, our method achieves much higher accuracy on 4-bit quantization than the existing data free quantization method. Code is available at https://github.com/xushoukai/GDFQ. | ['Mingkui Tan', 'JieZhang Cao', 'Chuangrun Liang', 'Jing Liu', 'Bohan Zhuang', 'Shoukai Xu', 'Haokun Li'] | 2020-03-07 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1469_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123570001.pdf | eccv-2020-8 | ['data-free-quantization', 'data-free-quantization'] | ['computer-vision', 'methodology'] | [ 3.34013999e-01 6.74563646e-02 -6.28275752e-01 -2.75593579e-01
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b1b18fc0-5167-42d9-af00-28ce914c3b5e | ameli-enhancing-multimodal-entity-linking | 2305.14725 | null | https://arxiv.org/abs/2305.14725v1 | https://arxiv.org/pdf/2305.14725v1.pdf | AMELI: Enhancing Multimodal Entity Linking with Fine-Grained Attributes | We propose attribute-aware multimodal entity linking, where the input is a mention described with a text and image, and the goal is to predict the corresponding target entity from a multimodal knowledge base (KB) where each entity is also described with a text description, a visual image and a set of attributes and values. To support this research, we construct AMELI, a large-scale dataset consisting of 18,472 reviews and 35,598 products. To establish baseline performance on AMELI, we experiment with the current state-of-the-art multimodal entity linking approaches and our enhanced attribute-aware model and demonstrate the importance of incorporating the attribute information into the entity linking process. To be best of our knowledge, we are the first to build benchmark dataset and solutions for the attribute-aware multimodal entity linking task. Datasets and codes will be made publicly available. | ['Lifu Huang', 'Licheng Yu', 'Zhiyang Xu', 'Minqian Liu', 'Sijia Wang', 'Qifan Wang', 'Yu Chen', 'Barry Menglong Yao'] | 2023-05-24 | null | null | null | null | ['entity-linking'] | ['natural-language-processing'] | [ 1.02494724e-01 3.77471000e-01 -4.84449893e-01 -7.10924506e-01
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3fec2afa-35d3-4664-8c8d-1a7e1e5a4ac6 | learning-to-make-generalizable-and-diverse | 1910.09688 | null | https://arxiv.org/abs/1910.09688v1 | https://arxiv.org/pdf/1910.09688v1.pdf | Learning to Make Generalizable and Diverse Predictions for Retrosynthesis | We propose a new model for making generalizable and diverse retrosynthetic reaction predictions. Given a target compound, the task is to predict the likely chemical reactants to produce the target. This generative task can be framed as a sequence-to-sequence problem by using the SMILES representations of the molecules. Building on top of the popular Transformer architecture, we propose two novel pre-training methods that construct relevant auxiliary tasks (plausible reactions) for our problem. Furthermore, we incorporate a discrete latent variable model into the architecture to encourage the model to produce a diverse set of alternative predictions. On the 50k subset of reaction examples from the United States patent literature (USPTO-50k) benchmark dataset, our model greatly improves performance over the baseline, while also generating predictions that are more diverse. | ['Benson Chen', 'Regina Barzilay', 'Tommi S. Jaakkola', 'Tianxiao Shen'] | 2019-10-21 | null | https://openreview.net/forum?id=BygfrANKvB | https://openreview.net/pdf?id=BygfrANKvB | null | ['retrosynthesis'] | ['medical'] | [ 8.72396469e-01 4.16981548e-01 -5.04399359e-01 -4.10956711e-01
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6b497118-ec6e-439b-bb5f-55ccff310012 | short-text-clustering-via-convolutional | null | null | https://aclanthology.org/W15-1509 | https://aclanthology.org/W15-1509.pdf | Short Text Clustering via Convolutional Neural Networks | null | ['Hong-Wei Hao', 'Peng Wang', 'Bo Xu', 'Jun Zhao', 'Jiaming Xu', 'Guanhua Tian', 'Fangyuan Wang'] | 2015-06-01 | null | null | null | ws-2015-6 | ['text-clustering', 'short-text-clustering'] | ['natural-language-processing', 'natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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f91892fd-42bc-4d62-8464-d1b65e6e09d2 | toward-fault-detection-in-industrial-welding | 2106.10160 | null | https://arxiv.org/abs/2106.10160v1 | https://arxiv.org/pdf/2106.10160v1.pdf | Toward Fault Detection in Industrial Welding Processes with Deep Learning and Data Augmentation | With the rise of deep learning models in the field of computer vision, new possibilities for their application in industrial processes proves to return great benefits. Nevertheless, the actual fit of machine learning for highly standardised industrial processes is still under debate. This paper addresses the challenges on the industrial realization of the AI tools, considering the use case of Laser Beam Welding quality control as an example. We use object detection algorithms from the TensorFlow object detection API and adapt them to our use case using transfer learning. The baseline models we develop are used as benchmarks and evaluated and compared to models that undergo dataset scaling and hyperparameter tuning. We find that moderate scaling of the dataset via image augmentation leads to improvements in intersection over union (IoU) and recall, whereas high levels of augmentation and scaling may lead to deterioration of results. Finally, we put our results into perspective of the underlying use case and evaluate their fit. | ['Prof. Dr. Kristof Van Laerhoven', 'Markus Schmitz', 'Georgij Safronov', 'Dr. Florian Schlather', 'Jibinraj Antony'] | 2021-06-18 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 3.18068236e-01 2.67475873e-01 1.19983718e-01 -2.17104301e-01
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5addd9a6-1f77-4060-95a9-3ea053b0d978 | point2ssm-learning-morphological-variations | 2305.14486 | null | https://arxiv.org/abs/2305.14486v1 | https://arxiv.org/pdf/2305.14486v1.pdf | Point2SSM: Learning Morphological Variations of Anatomies from Point Cloud | We introduce Point2SSM, a novel unsupervised learning approach that can accurately construct correspondence-based statistical shape models (SSMs) of anatomy directly from point clouds. SSMs are crucial in clinical research for analyzing the population-level morphological variation in bones and organs. However, traditional methods for creating SSMs have limitations that hinder their widespread adoption, such as the need for noise-free surface meshes or binary volumes, reliance on assumptions or predefined templates, and simultaneous optimization of the entire cohort leading to lengthy inference times given new data. Point2SSM overcomes these barriers by providing a data-driven solution that infers SSMs directly from raw point clouds, reducing inference burdens and increasing applicability as point clouds are more easily acquired. Deep learning on 3D point clouds has seen recent success in unsupervised representation learning, point-to-point matching, and shape correspondence; however, their application to constructing SSMs of anatomies is largely unexplored. In this work, we benchmark state-of-the-art point cloud deep networks on the task of SSM and demonstrate that they are not robust to the challenges of anatomical SSM, such as noisy, sparse, or incomplete input and significantly limited training data. Point2SSM addresses these challenges via an attention-based module that provides correspondence mappings from learned point features. We demonstrate that the proposed method significantly outperforms existing networks in terms of both accurate surface sampling and correspondence, better capturing population-level statistics. | ['Shireen Elhabian', 'Jadie Adams'] | 2023-05-23 | null | null | null | null | ['anatomy'] | ['miscellaneous'] | [ 1.49367422e-01 2.14576185e-01 -2.78763503e-01 -4.32345331e-01
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9e55313b-f2e2-4381-a3c7-84ca9f460bb0 | learning-task-specific-strategies-for | 2304.12507 | null | https://arxiv.org/abs/2304.12507v1 | https://arxiv.org/pdf/2304.12507v1.pdf | Learning Task-Specific Strategies for Accelerated MRI | Compressed sensing magnetic resonance imaging (CS-MRI) seeks to recover visual information from subsampled measurements for diagnostic tasks. Traditional CS-MRI methods often separately address measurement subsampling, image reconstruction, and task prediction, resulting in suboptimal end-to-end performance. In this work, we propose TACKLE as a unified framework for designing CS-MRI systems tailored to specific tasks. Leveraging recent co-design techniques, TACKLE jointly optimizes subsampling, reconstruction, and prediction strategies to enhance the performance on the downstream task. Our results on multiple public MRI datasets show that the proposed framework achieves improved performance on various tasks over traditional CS-MRI methods. We also evaluate the generalization ability of TACKLE by experimentally collecting a new dataset using different acquisition setups from the training data. Without additional fine-tuning, TACKLE functions robustly and leads to both numerical and visual improvements. | ['Katherine L. Bouman', 'Adrian V. Dalca', 'Andre van der Kouwe', 'Robert Frost', 'Yu Sun', 'Tianwei Yin', 'Zihui Wu'] | 2023-04-25 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 8.38327706e-01 4.68258299e-02 8.68185759e-02 -4.37640011e-01
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d28a8b0e-8327-47c5-98c5-da13e763cf4b | dfanet-deep-feature-aggregation-for-real-time | 1904.02216 | null | http://arxiv.org/abs/1904.02216v1 | http://arxiv.org/pdf/1904.02216v1.pdf | DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation | This paper introduces an extremely efficient CNN architecture named DFANet
for semantic segmentation under resource constraints. Our proposed network
starts from a single lightweight backbone and aggregates discriminative
features through sub-network and sub-stage cascade respectively. Based on the
multi-scale feature propagation, DFANet substantially reduces the number of
parameters, but still obtains sufficient receptive field and enhances the model
learning ability, which strikes a balance between the speed and segmentation
performance. Experiments on Cityscapes and CamVid datasets demonstrate the
superior performance of DFANet with 8$\times$ less FLOPs and 2$\times$ faster
than the existing state-of-the-art real-time semantic segmentation methods
while providing comparable accuracy. Specifically, it achieves 70.3\% Mean IOU
on the Cityscapes test dataset with only 1.7 GFLOPs and a speed of 160 FPS on
one NVIDIA Titan X card, and 71.3\% Mean IOU with 3.4 GFLOPs while inferring on
a higher resolution image. | ['Pengfei Xiong', 'Hanchao Li', 'Jian Sun', 'Haoqiang Fan'] | 2019-04-03 | dfanet-deep-feature-aggregation-for-real-time-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Li_DFANet_Deep_Feature_Aggregation_for_Real-Time_Semantic_Segmentation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Li_DFANet_Deep_Feature_Aggregation_for_Real-Time_Semantic_Segmentation_CVPR_2019_paper.pdf | cvpr-2019-6 | ['smac-1'] | ['playing-games'] | [-1.90587476e-01 -2.58826226e-01 -8.94429609e-02 -5.33264399e-01
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6910a5f9-de2d-4d4c-82cd-d907d568cf18 | self-supervised-language-learning-from-raw | 2210.15759 | null | https://arxiv.org/abs/2210.15759v1 | https://arxiv.org/pdf/2210.15759v1.pdf | Self-supervised language learning from raw audio: Lessons from the Zero Resource Speech Challenge | Recent progress in self-supervised or unsupervised machine learning has opened the possibility of building a full speech processing system from raw audio without using any textual representations or expert labels such as phonemes, dictionaries or parse trees. The contribution of the Zero Resource Speech Challenge series since 2015 has been to break down this long-term objective into four well-defined tasks -- Acoustic Unit Discovery, Spoken Term Discovery, Discrete Resynthesis, and Spoken Language Modeling -- and introduce associated metrics and benchmarks enabling model comparison and cumulative progress. We present an overview of the six editions of this challenge series since 2015, discuss the lessons learned, and outline the areas which need more work or give puzzling results. | ['Emmanuel Dupoux', 'Nicolas Hamilakis', 'Ewan Dunbar'] | 2022-10-27 | null | null | null | null | ['acoustic-unit-discovery'] | ['speech'] | [ 4.37377125e-01 3.04805368e-01 -2.54801124e-01 -5.77820599e-01
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b55f313a-5540-4cb7-9efc-51f57e6fb780 | fml-based-dynamic-assessment-agent-for-human | 1707.04828 | null | http://arxiv.org/abs/1707.04828v1 | http://arxiv.org/pdf/1707.04828v1.pdf | FML-based Dynamic Assessment Agent for Human-Machine Cooperative System on Game of Go | In this paper, we demonstrate the application of Fuzzy Markup Language (FML)
to construct an FML-based Dynamic Assessment Agent (FDAA), and we present an
FML-based Human-Machine Cooperative System (FHMCS) for the game of Go. The
proposed FDAA comprises an intelligent decision-making and learning mechanism,
an intelligent game bot, a proximal development agent, and an intelligent
agent. The intelligent game bot is based on the open-source code of Facebook
Darkforest, and it features a representational state transfer application
programming interface mechanism. The proximal development agent contains a
dynamic assessment mechanism, a GoSocket mechanism, and an FML engine with a
fuzzy knowledge base and rule base. The intelligent agent contains a GoSocket
engine and a summarization agent that is based on the estimated win rate,
real-time simulation number, and matching degree of predicted moves.
Additionally, the FML for player performance evaluation and linguistic
descriptions for game results commentary are presented. We experimentally
verify and validate the performance of the FDAA and variants of the FHMCS by
testing five games in 2016 and 60 games of Google Master Go, a new version of
the AlphaGo program, in January 2017. The experimental results demonstrate that
the proposed FDAA can work effectively for Go applications. | ['Chia-Hsiu Kao', 'Ping-Chiang Chou', 'Chun-Hsun Chou', 'Su-Wei Lin', 'Pi-Hsia Hung', 'Sheng-Chi Yang', 'Mei-Hui Wang', 'Chang-Shing Lee', 'Nan Shuo', 'Naoyuki Kubota'] | 2017-07-16 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [-5.18797278e-01 2.70732909e-01 2.18917038e-02 6.06349949e-03
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32816177-a5dc-4735-a063-80ccb6e03160 | neural-360-circ-structured-light-with-learned | 2306.13361 | null | https://arxiv.org/abs/2306.13361v2 | https://arxiv.org/pdf/2306.13361v2.pdf | Neural 360$^\circ$ Structured Light with Learned Metasurfaces | Structured light has proven instrumental in 3D imaging, LiDAR, and holographic light projection. Metasurfaces, comprised of sub-wavelength-sized nanostructures, facilitate 180$^\circ$ field-of-view (FoV) structured light, circumventing the restricted FoV inherent in traditional optics like diffractive optical elements. However, extant metasurface-facilitated structured light exhibits sub-optimal performance in downstream tasks, due to heuristic pattern designs such as periodic dots that do not consider the objectives of the end application. In this paper, we present neural 360$^\circ$ structured light, driven by learned metasurfaces. We propose a differentiable framework, that encompasses a computationally-efficient 180$^\circ$ wave propagation model and a task-specific reconstructor, and exploits both transmission and reflection channels of the metasurface. Leveraging a first-order optimizer within our differentiable framework, we optimize the metasurface design, thereby realizing neural 360$^\circ$ structured light. We have utilized neural 360$^\circ$ structured light for holographic light projection and 3D imaging. Specifically, we demonstrate the first 360$^\circ$ light projection of complex patterns, enabled by our propagation model that can be computationally evaluated 50,000$\times$ faster than the Rayleigh-Sommerfeld propagation. For 3D imaging, we improve depth-estimation accuracy by 5.09$\times$ in RMSE compared to the heuristically-designed structured light. Neural 360$^\circ$ structured light promises robust 360$^\circ$ imaging and display for robotics, extended-reality systems, and human-computer interactions. | ['Junsuk Rho', 'Seung-Hwan Baek', 'Yujin Jeon', 'Jooyeong Yun', 'Gyeongtae Kim', 'Eunsue Choi'] | 2023-06-23 | null | null | null | null | ['depth-estimation'] | ['computer-vision'] | [ 6.04720056e-01 2.94353604e-01 6.72006488e-01 -8.80023688e-02
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9f401606-289e-402f-b41e-0d956163bc4a | compositional-sentence-representation-from | 1605.00482 | null | http://arxiv.org/abs/1605.00482v3 | http://arxiv.org/pdf/1605.00482v3.pdf | Compositional Sentence Representation from Character within Large Context Text | This paper describes a Hierarchical Composition Recurrent Network (HCRN)
consisting of a 3-level hierarchy of compositional models: character, word and
sentence. This model is designed to overcome two problems of representing a
sentence on the basis of a constituent word sequence. The first is a
data-sparsity problem in word embedding, and the other is a no usage of
inter-sentence dependency. In the HCRN, word representations are built from
characters, thus resolving the data-sparsity problem, and inter-sentence
dependency is embedded into sentence representation at the level of sentence
composition. We adopt a hierarchy-wise learning scheme in order to alleviate
the optimization difficulties of learning deep hierarchical recurrent network
in end-to-end fashion. The HCRN was quantitatively and qualitatively evaluated
on a dialogue act classification task. Especially, sentence representations
with an inter-sentence dependency are able to capture both implicit and
explicit semantics of sentence, significantly improving performance. In the
end, the HCRN achieved state-of-the-art performance with a test error rate of
22.7% for dialogue act classification on the SWBD-DAMSL database. | ['Soo-Young Lee', 'Geonmin Kim', 'Jisu Choi', 'Hwaran Lee'] | 2016-05-02 | null | null | null | null | ['dialogue-act-classification'] | ['natural-language-processing'] | [ 3.37289840e-01 4.42541033e-01 3.90362623e-03 -3.88293356e-01
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198ad8cb-4621-4027-aae2-b73689c45c28 | self-supervised-spatiotemporal-learning-via | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Xu_Self-Supervised_Spatiotemporal_Learning_via_Video_Clip_Order_Prediction_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Xu_Self-Supervised_Spatiotemporal_Learning_via_Video_Clip_Order_Prediction_CVPR_2019_paper.pdf | Self-Supervised Spatiotemporal Learning via Video Clip Order Prediction | We propose a self-supervised spatiotemporal learning technique which leverages the chronological order of videos. Our method can learn the spatiotemporal representation of the video by predicting the order of shuffled clips from the video. The category of the video is not required, which gives our technique the potential to take advantage of infinite unannotated videos. There exist related works which use frames, while compared to frames, clips are more consistent with the video dynamics. Clips can help to reduce the uncertainty of orders and are more appropriate to learn a video representation. The 3D convolutional neural networks are utilized to extract features for clips, and these features are processed to predict the actual order. The learned representations are evaluated via nearest neighbor retrieval experiments. We also use the learned networks as the pre-trained models and finetune them on the action recognition task. Three types of 3D convolutional neural networks are tested in experiments, and we gain large improvements compared to existing self-supervised methods.
| [' Yueting Zhuang', ' Di Xie', ' Jian Shao', ' Zhou Zhao', ' Jun Xiao', 'Dejing Xu'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['self-supervised-action-recognition'] | ['computer-vision'] | [-8.22589844e-02 -4.30449426e-01 -8.74797165e-01 -5.97018838e-01
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ede6e405-a392-4e40-a5b9-ea0561a9f130 | musiclm-generating-music-from-text | 2301.11325 | null | https://arxiv.org/abs/2301.11325v1 | https://arxiv.org/pdf/2301.11325v1.pdf | MusicLM: Generating Music From Text | We introduce MusicLM, a model generating high-fidelity music from text descriptions such as "a calming violin melody backed by a distorted guitar riff". MusicLM casts the process of conditional music generation as a hierarchical sequence-to-sequence modeling task, and it generates music at 24 kHz that remains consistent over several minutes. Our experiments show that MusicLM outperforms previous systems both in audio quality and adherence to the text description. Moreover, we demonstrate that MusicLM can be conditioned on both text and a melody in that it can transform whistled and hummed melodies according to the style described in a text caption. To support future research, we publicly release MusicCaps, a dataset composed of 5.5k music-text pairs, with rich text descriptions provided by human experts. | ['Christian Frank', 'Neil Zeghidour', 'Matt Sharifi', 'Marco Tagliasacchi', 'Adam Roberts', 'Aren Jansen', 'Qingqing Huang', 'Antoine Caillon', 'Mauro Verzetti', 'Jesse Engel', 'Zalán Borsos', 'Timo I. Denk', 'Andrea Agostinelli'] | 2023-01-26 | null | null | null | null | ['text-to-music-generation', 'music-generation', 'music-generation', 'text-to-music-generation'] | ['audio', 'audio', 'music', 'music'] | [ 3.06228310e-01 -1.37700766e-01 -2.31995489e-02 -2.32221484e-01
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631a262b-2ba4-4a9c-8f10-ae28cdd4d550 | aligning-artificial-neural-networks-to-the | null | null | https://openreview.net/forum?id=BJeY6sR9KX | https://openreview.net/pdf?id=BJeY6sR9KX | Aligning Artificial Neural Networks to the Brain yields Shallow Recurrent Architectures | Deep artificial neural networks with spatially repeated processing (a.k.a., deep convolutional ANNs) have been established as the best class of candidate models of visual processing in the primate ventral visual processing stream. Over the past five years, these ANNs have evolved from a simple feedforward eight-layer architecture in AlexNet to extremely deep and branching NASNet architectures, demonstrating increasingly better object categorization performance. Here we ask, as ANNs have continued to evolve in performance, are they also strong candidate models for the brain? To answer this question, we developed Brain-Score, a composite of neural and behavioral benchmarks for determining how brain-like a model is, together with an online platform where models can receive a Brain-Score and compare against other models.
Despite high scores, typical deep models from the machine learning community are often hard to map onto the brain's anatomy due to their vast number of layers and missing biologically-important connections, such as recurrence. To further map onto anatomy and validate our approach, we built CORnet-S: an ANN guided by Brain-Score with the anatomical constraints of compactness and recurrence. Although a shallow model with four anatomically mapped areas and recurrent connectivity, CORnet-S is a top model on Brain-Score and outperforms similarly compact models on ImageNet. Analyzing CORnet-S circuitry variants revealed recurrence as the main predictive factor of both Brain-Score and ImageNet top-1 performance.
| ['Daniel L. K. Yamins', 'Jonathan Prescott-Roy', 'Rishi Rajalingham', 'Najib J. Majaj', 'Jonas Kubilius', 'Daniel Bear', 'Pouya Bashivan', 'Kailyn Schmidt', 'Ha Hong', 'Elias B. Issa', 'Aran Nayebi', 'Martin Schrimpf', 'Kohitij Kar', 'James J. DiCarlo'] | 2019-05-01 | null | null | null | iclr-2019-5 | ['object-categorization'] | ['computer-vision'] | [-3.22296917e-02 1.64932191e-01 1.00949734e-01 -2.23154858e-01
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52a0d563-3d85-4bd7-b0d9-7213a71d266b | spaceyolo-a-human-inspired-model-for-real | 2302.00824 | null | https://arxiv.org/abs/2302.00824v1 | https://arxiv.org/pdf/2302.00824v1.pdf | SpaceYOLO: A Human-Inspired Model for Real-time, On-board Spacecraft Feature Detection | The rapid proliferation of non-cooperative spacecraft and space debris in orbit has precipitated a surging demand for on-orbit servicing and space debris removal at a scale that only autonomous missions can address, but the prerequisite autonomous navigation and flightpath planning to safely capture an unknown, non-cooperative, tumbling space object is an open problem. This requires algorithms for real-time, automated spacecraft feature recognition to pinpoint the locations of collision hazards (e.g. solar panels or antennas) and safe docking features (e.g. satellite bodies or thrusters) so safe, effective flightpaths can be planned. Prior work in this area reveals the performance of computer vision models are highly dependent on the training dataset and its coverage of scenarios visually similar to the real scenarios that occur in deployment. Hence, the algorithm may have degraded performance under certain lighting conditions even when the rendezvous maneuver conditions of the chaser to the target spacecraft are the same. This work delves into how humans perform these tasks through a survey of how aerospace engineering students experienced with spacecraft shapes and components recognize features of the three spacecraft: Landsat, Envisat, Anik, and the orbiter Mir. The survey reveals that the most common patterns in the human detection process were to consider the shape and texture of the features: antennas, solar panels, thrusters, and satellite bodies. This work introduces a novel algorithm SpaceYOLO, which fuses a state-of-the-art object detector YOLOv5 with a separate neural network based on these human-inspired decision processes exploiting shape and texture. Performance in autonomous spacecraft detection of SpaceYOLO is compared to ordinary YOLOv5 in hardware-in-the-loop experiments under different lighting and chaser maneuver conditions at the ORION Laboratory at Florida Tech. | ['Madhur Tiwari', 'Markus Wilde', 'Ryan T. White', 'Trupti Mahendrakar'] | 2023-02-02 | null | null | null | null | ['human-detection'] | ['computer-vision'] | [-2.13532805e-01 -3.40384841e-01 2.33900726e-01 1.54574811e-01
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b6b01322-833f-4ba2-aab1-0d5ecdf41975 | end-to-end-learning-for-early-classification | 1901.10681 | null | https://arxiv.org/abs/1901.10681v2 | https://arxiv.org/pdf/1901.10681v2.pdf | End-to-End Learned Early Classification of Time Series for In-Season Crop Type Mapping | Remote sensing satellites capture the cyclic dynamics of our Planet in regular time intervals recorded in satellite time series data. End-to-end trained deep learning models use this time series data to make predictions at a large scale, for instance, to produce up-to-date crop cover maps. Most time series classification approaches focus on the accuracy of predictions. However, the earliness of the prediction is also of great importance since coming to an early decision can make a crucial difference in time-sensitive applications. In this work, we present an End-to-End Learned Early Classification of Time Series (ELECTS) model that estimates a classification score and a probability of whether sufficient data has been observed to come to an early and still accurate decision. ELECTS is modular: any deep time series classification model can adopt the ELECTS conceptual idea by adding a second prediction head that outputs a probability of stopping the classification. The ELECTS loss function then optimizes the overall model on a balanced objective of earliness and accuracy. Our experiments on four crop classification datasets from Europe and Africa show that ELECTS allows reaching state-of-the-art accuracy while reducing the quantity of data massively to be downloaded, stored, and processed. The source code is available at https://github.com/marccoru/elects. | ['Devis Tuia', 'Sébastien Lefèvre', 'Rémi Emonet', 'Nicolas Courty', 'Marc Rußwurm', 'Romain Tavenard'] | 2019-01-30 | null | null | null | null | ['crop-classification'] | ['miscellaneous'] | [ 1.94594972e-02 -2.37992451e-01 -3.02055955e-01 -6.17662311e-01
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bb694010-4610-4b59-85d0-b336ca4e295d | vtp-volumetric-transformer-for-multi-view | 2205.12602 | null | https://arxiv.org/abs/2205.12602v1 | https://arxiv.org/pdf/2205.12602v1.pdf | VTP: Volumetric Transformer for Multi-view Multi-person 3D Pose Estimation | This paper presents Volumetric Transformer Pose estimator (VTP), the first 3D volumetric transformer framework for multi-view multi-person 3D human pose estimation. VTP aggregates features from 2D keypoints in all camera views and directly learns the spatial relationships in the 3D voxel space in an end-to-end fashion. The aggregated 3D features are passed through 3D convolutions before being flattened into sequential embeddings and fed into a transformer. A residual structure is designed to further improve the performance. In addition, the sparse Sinkhorn attention is empowered to reduce the memory cost, which is a major bottleneck for volumetric representations, while also achieving excellent performance. The output of the transformer is again concatenated with 3D convolutional features by a residual design. The proposed VTP framework integrates the high performance of the transformer with volumetric representations, which can be used as a good alternative to the convolutional backbones. Experiments on the Shelf, Campus and CMU Panoptic benchmarks show promising results in terms of both Mean Per Joint Position Error (MPJPE) and Percentage of Correctly estimated Parts (PCP). Our code will be available. | ['Gangyong Jia', 'Ouhan Huang', 'Renshu Gu', 'Yuxing Chen'] | 2022-05-25 | null | null | null | null | ['3d-pose-estimation', '3d-multi-person-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-4.11871672e-01 1.22150660e-01 -1.90056507e-02 -3.93280029e-01
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158382b3-a0f9-4d3a-a7c4-baca9991d249 | tampered-vae-for-improved-satellite-image | 2203.16149 | null | https://arxiv.org/abs/2203.16149v1 | https://arxiv.org/pdf/2203.16149v1.pdf | Tampered VAE for Improved Satellite Image Time Series Classification | The unprecedented availability of spatial and temporal high-resolution satellite image time series (SITS) for crop type mapping is believed to necessitate deep learning architectures to accommodate challenges arising from both dimensions. Recent state-of-the-art deep learning models have shown promising results by stacking spatial and temporal encoders. However, we present a Pyramid Time-Series Transformer (PTST) that operates solely on the temporal dimension, i.e., neglecting the spatial dimension, can produce superior results with a drastic reduction in GPU memory consumption and easy extensibility. Furthermore, we augment it to perform semi-supervised learning by proposing a classification-friendly VAE framework that introduces clustering mechanisms into latent space and can promote linear separability therein. Consequently, a few principal axes of the latent space can explain the majority of variance in raw data. Meanwhile, the VAE framework with proposed tweaks can maintain competitive classification performance as its purely discriminative counterpart when only $40\%$ of labelled data is used. We hope the proposed framework can serve as a baseline for crop classification with SITS for its modularity and simplicity. | ['Peter Nicholl', 'Yaxin Bi', 'Xin Cai'] | 2022-03-30 | null | null | null | null | ['crop-classification'] | ['miscellaneous'] | [ 1.25162020e-01 -4.31255251e-01 -3.59098405e-01 -3.53393972e-01
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61d70982-4a04-4021-b481-7da8c56c4c65 | neural-network-inspired-analog-to-digital | 1911.12815 | null | https://arxiv.org/abs/1911.12815v1 | https://arxiv.org/pdf/1911.12815v1.pdf | Neural Network-Inspired Analog-to-Digital Conversion to Achieve Super-Resolution with Low-Precision RRAM Devices | Recent works propose neural network- (NN-) inspired analog-to-digital converters (NNADCs) and demonstrate their great potentials in many emerging applications. These NNADCs often rely on resistive random-access memory (RRAM) devices to realize the NN operations and require high-precision RRAM cells (6~12-bit) to achieve a moderate quantization resolution (4~8-bit). Such optimistic assumption of RRAM resolution, however, is not supported by fabrication data of RRAM arrays in large-scale production process. In this paper, we propose an NN-inspired super-resolution ADC based on low-precision RRAM devices by taking the advantage of a co-design methodology that combines a pipelined hardware architecture with a custom NN training framework. Results obtained from SPICE simulations demonstrate that our method leads to robust design of a 14-bit super-resolution ADC using 3-bit RRAM devices with improved power and speed performance and competitive figure-of-merits (FoMs). In addition to the linear uniform quantization, the proposed ADC can also support configurable high-resolution nonlinear quantization with high conversion speed and low conversion energy, enabling future intelligent analog-to-information interfaces for near-sensor analytics and processing. | ['Ayan Chakrabarti', 'Xuan Zhang', 'Weidong Cao', 'Liu Ke'] | 2019-11-28 | null | null | null | null | ['robust-design'] | ['miscellaneous'] | [ 8.54829013e-01 -3.99145305e-01 -2.48063877e-01 -3.81148279e-01
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a813438c-d957-4bc4-9b1f-a1deeafbc3b1 | an-efficient-edge-detection-approach-to | null | null | https://ieeexplore.ieee.org/document/8667063/authors#authors | https://ieeexplore.ieee.org/document/8667063/authors#authors | An Efficient Edge Detection Approach to Provide Better Edge Connectivity for Image Analysis | An edge detection is important for its reliability and security which delivers a better understanding of object recognition in the applications of computer vision, such as pedestrian detection, face detection, and video surveillance. This paper introduced two fundamental limitations encountered in edge detection: edge connectivity and edge thickness, those have been used by various developments in the state-of-the-art. An optimal selection of the threshold for effectual edge detection has constantly been a key challenge in computer vision. Therefore, a robust edge detection algorithm using multiple threshold approaches (B-Edge) is proposed to cover both the limitations. The majorly used canny edge operator focuses on two thresholds selections and still witnesses a few gaps for optimal results. To handle the loopholes of the canny edge operator, our method selects the simulated triple thresholds that target to the prime issues of the edge detection: image contrast, effective edge pixels selection, errors handling, and similarity to the ground truth. The qualitative and quantitative experimental evaluations demonstrate that our edge detection method outperforms competing algorithms for mentioned issues. The proposed approach endeavors an improvement for both grayscale and colored images. | ['Mamta Mittal; Amit Verma; Iqbaldeep Kaur; Bhavneet Kaur; Meenakshi Sharma; Lalit Mohan Goyal; Sudipta Roy; TAI-HOON KIM'] | 2019-03-13 | null | null | null | ieee-2019-3 | ['face-detection', 'edge-detection'] | ['computer-vision', 'computer-vision'] | [ 1.84426069e-01 -3.27079415e-01 1.44054875e-01 -8.23762417e-02
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a6987ed9-5b98-4395-8bda-0edb644dafb3 | active-anomaly-detection-via-ensembles | 1809.06477 | null | http://arxiv.org/abs/1809.06477v1 | http://arxiv.org/pdf/1809.06477v1.pdf | Active Anomaly Detection via Ensembles | In critical applications of anomaly detection including computer security and
fraud prevention, the anomaly detector must be configurable by the analyst to
minimize the effort on false positives. One important way to configure the
anomaly detector is by providing true labels for a few instances. We study the
problem of label-efficient active learning to automatically tune anomaly
detection ensembles and make four main contributions. First, we present an
important insight into how anomaly detector ensembles are naturally suited for
active learning. This insight allows us to relate the greedy querying strategy
to uncertainty sampling, with implications for label-efficiency. Second, we
present a novel formalism called compact description to describe the discovered
anomalies and show that it can also be employed to improve the diversity of the
instances presented to the analyst without loss in the anomaly discovery rate.
Third, we present a novel data drift detection algorithm that not only detects
the drift robustly, but also allows us to take corrective actions to adapt the
detector in a principled manner. Fourth, we present extensive experiments to
evaluate our insights and algorithms in both batch and streaming settings. Our
results show that in addition to discovering significantly more anomalies than
state-of-the-art unsupervised baselines, our active learning algorithms under
the streaming-data setup are competitive with the batch setup. | ['Md. Rakibul Islam', 'Nitthilan Kannappan Jayakodi', 'Shubhomoy Das', 'Janardhan Rao Doppa'] | 2018-09-17 | null | null | null | null | ['computer-security'] | ['miscellaneous'] | [ 2.87800997e-01 3.66185270e-02 -1.90832198e-01 -6.06752217e-01
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3d546b72-b910-43c8-8b51-87b0ff5cd5de | polyglot-ner-massive-multilingual-named | 1410.3791 | null | http://arxiv.org/abs/1410.3791v1 | http://arxiv.org/pdf/1410.3791v1.pdf | POLYGLOT-NER: Massive Multilingual Named Entity Recognition | The increasing diversity of languages used on the web introduces a new level
of complexity to Information Retrieval (IR) systems. We can no longer assume
that textual content is written in one language or even the same language
family. In this paper, we demonstrate how to build massive multilingual
annotators with minimal human expertise and intervention. We describe a system
that builds Named Entity Recognition (NER) annotators for 40 major languages
using Wikipedia and Freebase. Our approach does not require NER human annotated
datasets or language specific resources like treebanks, parallel corpora, and
orthographic rules. The novelty of approach lies therein - using only language
agnostic techniques, while achieving competitive performance.
Our method learns distributed word representations (word embeddings) which
encode semantic and syntactic features of words in each language. Then, we
automatically generate datasets from Wikipedia link structure and Freebase
attributes. Finally, we apply two preprocessing stages (oversampling and exact
surface form matching) which do not require any linguistic expertise.
Our evaluation is two fold: First, we demonstrate the system performance on
human annotated datasets. Second, for languages where no gold-standard
benchmarks are available, we propose a new method, distant evaluation, based on
statistical machine translation. | ['Steven Skiena', 'Rami Al-Rfou', 'Bryan Perozzi', 'Vivek Kulkarni'] | 2014-10-14 | null | null | null | null | ['multilingual-named-entity-recognition'] | ['natural-language-processing'] | [-3.01705569e-01 7.94104561e-02 -3.59717548e-01 -2.54366457e-01
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3029c7ce-effb-4812-94ce-6ad5ddf39129 | tackling-ambiguity-with-images-improved | 2212.10140 | null | https://arxiv.org/abs/2212.10140v2 | https://arxiv.org/pdf/2212.10140v2.pdf | Tackling Ambiguity with Images: Improved Multimodal Machine Translation and Contrastive Evaluation | One of the major challenges of machine translation (MT) is ambiguity, which can in some cases be resolved by accompanying context such as images. However, recent work in multimodal MT (MMT) has shown that obtaining improvements from images is challenging, limited not only by the difficulty of building effective cross-modal representations, but also by the lack of specific evaluation and training data. We present a new MMT approach based on a strong text-only MT model, which uses neural adapters, a novel guided self-attention mechanism and which is jointly trained on both visually-conditioned masking and MMT. We also introduce CoMMuTE, a Contrastive Multilingual Multimodal Translation Evaluation set of ambiguous sentences and their possible translations, accompanied by disambiguating images corresponding to each translation. Our approach obtains competitive results compared to strong text-only models on standard English-to-French, English-to-German and English-to-Czech benchmarks and outperforms baselines and state-of-the-art MMT systems by a large margin on our contrastive test set. Our code and CoMMuTE are freely available. | ['Rachel Bawden', 'Benoît Sagot', 'Ivan Laptev', 'Cordelia Schmid', 'Matthieu Futeral'] | 2022-12-20 | null | null | null | null | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 5.39801598e-01 -7.12600276e-02 -5.28833382e-02 -2.40358844e-01
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0843198c-204b-4702-9276-4c4ff15b9ec4 | adversarial-examples-in-remote-sensing | 1805.10997 | null | http://arxiv.org/abs/1805.10997v1 | http://arxiv.org/pdf/1805.10997v1.pdf | Adversarial Examples in Remote Sensing | This paper considers attacks against machine learning algorithms used in
remote sensing applications, a domain that presents a suite of challenges that
are not fully addressed by current research focused on natural image data such
as ImageNet. In particular, we present a new study of adversarial examples in
the context of satellite image classification problems. Using a recently
curated data set and associated classifier, we provide a preliminary analysis
of adversarial examples in settings where the targeted classifier is permitted
multiple observations of the same location over time. While our experiments to
date are purely digital, our problem setup explicitly incorporates a number of
practical considerations that a real-world attacker would need to take into
account when mounting a physical attack. We hope this work provides a useful
starting point for future studies of potential vulnerabilities in this setting. | ['I-Jeng Wang', 'Neil Fendley', 'Christopher Ratto', 'Wojciech Czaja', 'Michael Pekala'] | 2018-05-28 | null | null | null | null | ['satellite-image-classification'] | ['computer-vision'] | [ 8.74072373e-01 7.50555396e-02 1.63895488e-01 -2.63269901e-01
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41218b9a-d322-4cee-933e-b2b5b9890efc | leveraging-key-information-modeling-to | 2210.04473 | null | https://arxiv.org/abs/2210.04473v1 | https://arxiv.org/pdf/2210.04473v1.pdf | Leveraging Key Information Modeling to Improve Less-Data Constrained News Headline Generation via Duality Fine-Tuning | Recent language generative models are mostly trained on large-scale datasets, while in some real scenarios, the training datasets are often expensive to obtain and would be small-scale. In this paper we investigate the challenging task of less-data constrained generation, especially when the generated news headlines are short yet expected by readers to keep readable and informative simultaneously. We highlight the key information modeling task and propose a novel duality fine-tuning method by formally defining the probabilistic duality constraints between key information prediction and headline generation tasks. The proposed method can capture more information from limited data, build connections between separate tasks, and is suitable for less-data constrained generation tasks. Furthermore, the method can leverage various pre-trained generative regimes, e.g., autoregressive and encoder-decoder models. We conduct extensive experiments to demonstrate that our method is effective and efficient to achieve improved performance in terms of language modeling metric and informativeness correctness metric on two public datasets. | ['Bo Ren', 'Shanshan Feng', 'Di Yin', 'Lingfeng Qiao', 'Zhuoxuan Jiang'] | 2022-10-10 | null | null | null | null | ['headline-generation'] | ['natural-language-processing'] | [ 1.77773163e-01 3.57222348e-01 -4.71236140e-01 -4.28759784e-01
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0af1e54f-ae76-462d-8334-462c21928e24 | controlled-natural-languages-and-default | 1905.04422 | null | https://arxiv.org/abs/1905.04422v1 | https://arxiv.org/pdf/1905.04422v1.pdf | Controlled Natural Languages and Default Reasoning | Controlled natural languages (CNLs) are effective languages for knowledge representation and reasoning. They are designed based on certain natural languages with restricted lexicon and grammar. CNLs are unambiguous and simple as opposed to their base languages. They preserve the expressiveness and coherence of natural languages. In this report, we focus on a class of CNLs, called machine-oriented CNLs, which have well-defined semantics that can be deterministically translated into formal languages, such as Prolog, to do logical reasoning. Over the past 20 years, a number of machine-oriented CNLs emerged and have been used in many application domains for problem solving and question answering. However, few of them support non-monotonic inference. In our work, we propose non-monotonic extensions of CNL to support defeasible reasoning. In the first part of this report, we survey CNLs and compare three influential systems: Attempto Controlled English (ACE), Processable English (PENG), and Computer-processable English (CPL). We compare their language design, semantic interpretations, and reasoning services. In the second part of this report, we first identify typical non-monotonicity in natural languages, such as defaults, exceptions and conversational implicatures. Then, we propose their representation in CNL and the corresponding formalizations in a form of defeasible reasoning known as Logic Programming with Defaults and Argumentation Theory (LPDA). | ['Tiantian Gao'] | 2019-05-11 | null | null | null | null | ['implicatures'] | ['natural-language-processing'] | [-1.25544325e-01 9.80326056e-01 -8.04233015e-01 -5.90541601e-01
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c90700b0-86df-4b06-babe-e5d5e7b2aba6 | seefar-vehicle-speed-estimation-and-flow | null | null | https://link.springer.com/chapter/10.1007/978-3-031-06433-3_24 | https://www.researchgate.net/publication/360607227_SeeFar_Vehicle_Speed_Estimation_and_Flow_Analysis_from_a_Moving_UAV | SeeFar: Vehicle Speed Estimation and Flow Analysis from a Moving UAV | Visual perception from drones has been largely investigated for Intelligent Traffic Monitoring System (ITMS) recently. In this paper, we introduce SeeFar to achieve vehicle speed estimation and traffic flow analysis based on YOLOv5 and DeepSORT from a moving drone. SeeFar differs from previous works in three key ways: the speed estimation and flow analysis components are integrated into a unified framework; our method of predicting car speed has the least constraints while maintaining a high accuracy; our flow analysor is direction-aware and outlier-aware. Specifically, we design the speed estimator only using the camera imaging geometry, where the transformation between world space and image space is completed by the variable Ground Sampling Distance. Besides, previous papers do not evaluate their speed estimators at scale due to the difficulty of obtaining the ground truth, we therefore propose a simple yet efficient approach to estimate the true speeds of vehicles via the prior size of the road signs. We evaluate SeeFar on our ten videos that contain 929 vehicle samples. Experiments on these sequences demonstrate the effectiveness of SeeFar by achieving 98.0% accuracy in speed estimation and 99.1% accuracy in traffic volume prediction, respectively. The code is available at https://github.com/forever208/SeeFar | ['Rita Cucchiara', 'Simone Calderara', 'Yao Lu', 'Xiaoliang Ma', 'Mang Ning'] | 2022-05-15 | null | null | null | iciap-2022-5 | ['vehicle-speed-estimation'] | ['computer-vision'] | [-5.33293068e-01 -5.46870351e-01 -2.29017407e-01 -3.18613440e-01
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24dea732-282f-4cfe-ae34-9c8be3ce6267 | the-adaptive-t-lasso-its-robustness-and | 2304.09310 | null | https://arxiv.org/abs/2304.09310v1 | https://arxiv.org/pdf/2304.09310v1.pdf | The Adaptive $τ$-Lasso: Its Robustness and Oracle Properties | This paper introduces a new regularized version of the robust $\tau$-regression estimator for analyzing high-dimensional data sets subject to gross contamination in the response variables and covariates. We call the resulting estimator adaptive $\tau$-Lasso that is robust to outliers and high-leverage points and simultaneously employs adaptive $\ell_1$-norm penalty term to reduce the bias associated with large true regression coefficients. More specifically, this adaptive $\ell_1$-norm penalty term assigns a weight to each regression coefficient. For a fixed number of predictors $p$, we show that the adaptive $\tau$-Lasso has the oracle property with respect to variable-selection consistency and asymptotic normality for the regression vector corresponding to the true support, assuming knowledge of the true regression vector support. We then characterize its robustness via the finite-sample breakdown point and the influence function. We carry-out extensive simulations to compare the performance of the adaptive $\tau$-Lasso estimator with that of other competing regularized estimators in terms of prediction and variable selection accuracy in the presence of contamination within the response vector/regression matrix and additive heavy-tailed noise. We observe from our simulations that the class of $\tau$-Lasso estimators exhibits robustness and reliable performance in both contaminated and uncontaminated data settings, achieving the best or close-to-best for many scenarios, except for oracle estimators. However, it is worth noting that no particular estimator uniformly dominates others. We also validate our findings on robustness properties through simulation experiments. | ['Visa Koivunen', 'Emadaldin Mozafari-Majd'] | 2023-04-18 | null | null | null | null | ['variable-selection'] | ['methodology'] | [ 1.07701700e-02 -1.50247529e-01 -5.18581450e-01 -5.09423614e-01
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07dcfd7f-6287-4f21-8f9d-2037b7f687a2 | somesci-a-5-star-open-data-gold-standard | 2108.09070 | null | https://arxiv.org/abs/2108.09070v1 | https://arxiv.org/pdf/2108.09070v1.pdf | SoMeSci- A 5 Star Open Data Gold Standard Knowledge Graph of Software Mentions in Scientific Articles | Knowledge about software used in scientific investigations is important for several reasons, for instance, to enable an understanding of provenance and methods involved in data handling. However, software is usually not formally cited, but rather mentioned informally within the scholarly description of the investigation, raising the need for automatic information extraction and disambiguation. Given the lack of reliable ground truth data, we present SoMeSci (Software Mentions in Science) a gold standard knowledge graph of software mentions in scientific articles. It contains high quality annotations (IRR: $\kappa{=}.82$) of 3756 software mentions in 1367 PubMed Central articles. Besides the plain mention of the software, we also provide relation labels for additional information, such as the version, the developer, a URL or citations. Moreover, we distinguish between different types, such as application, plugin or programming environment, as well as different types of mentions, such as usage or creation. To the best of our knowledge, SoMeSci is the most comprehensive corpus about software mentions in scientific articles, providing training samples for Named Entity Recognition, Relation Extraction, Entity Disambiguation, and Entity Linking. Finally, we sketch potential use cases and provide baseline results. | ['Frank Krüger', 'Stefan Dietze', 'Felix Bensmann', 'David Schindler'] | 2021-08-20 | null | null | null | null | ['entity-disambiguation'] | ['natural-language-processing'] | [-2.61403695e-02 2.84562588e-01 -5.01587689e-01 1.08788386e-02
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652b20a7-8e53-48dc-9cc5-e8abc3301508 | overcoming-limitations-of-mixture-density-1 | 1906.03631 | null | https://arxiv.org/abs/1906.03631v2 | https://arxiv.org/pdf/1906.03631v2.pdf | Overcoming Limitations of Mixture Density Networks: A Sampling and Fitting Framework for Multimodal Future Prediction | Future prediction is a fundamental principle of intelligence that helps plan actions and avoid possible dangers. As the future is uncertain to a large extent, modeling the uncertainty and multimodality of the future states is of great relevance. Existing approaches are rather limited in this regard and mostly yield a single hypothesis of the future or, at the best, strongly constrained mixture components that suffer from instabilities in training and mode collapse. In this work, we present an approach that involves the prediction of several samples of the future with a winner-takes-all loss and iterative grouping of samples to multiple modes. Moreover, we discuss how to evaluate predicted multimodal distributions, including the common real scenario, where only a single sample from the ground-truth distribution is available for evaluation. We show on synthetic and real data that the proposed approach triggers good estimates of multimodal distributions and avoids mode collapse. Source code is available at $\href{https://github.com/lmb-freiburg/Multimodal-Future-Prediction}{\text{this https URL.}}$ | ['Özgün Cicek', 'Osama Makansi', 'Thomas Brox', 'Eddy Ilg'] | 2019-06-09 | overcoming-limitations-of-mixture-density | http://openaccess.thecvf.com/content_CVPR_2019/html/Makansi_Overcoming_Limitations_of_Mixture_Density_Networks_A_Sampling_and_Fitting_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Makansi_Overcoming_Limitations_of_Mixture_Density_Networks_A_Sampling_and_Fitting_CVPR_2019_paper.pdf | cvpr-2019-6 | ['probabilistic-deep-learning'] | ['computer-vision'] | [-4.73229364e-02 2.75073498e-01 -1.00471154e-01 -5.39974749e-01
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f0dfd728-a4e6-47aa-a5dd-8002ac5b4c4b | motion-artifact-reduction-in | 2109.02755 | null | https://arxiv.org/abs/2109.02755v1 | https://arxiv.org/pdf/2109.02755v1.pdf | Motion Artifact Reduction In Photoplethysmography For Reliable Signal Selection | Photoplethysmography (PPG) is a non-invasive and economical technique to extract vital signs of the human body. Although it has been widely used in consumer and research grade wrist devices to track a user's physiology, the PPG signal is very sensitive to motion which can corrupt the signal's quality. Existing Motion Artifact (MA) reduction techniques have been developed and evaluated using either synthetic noisy signals or signals collected during high-intensity activities - both of which are difficult to generalize for real-life scenarios. Therefore, it is valuable to collect realistic PPG signals while performing Activities of Daily Living (ADL) to develop practical signal denoising and analysis methods. In this work, we propose an automatic pseudo clean PPG generation process for reliable PPG signal selection. For each noisy PPG segment, the corresponding pseudo clean PPG reduces the MAs and contains rich temporal details depicting cardiac features. Our experimental results show that 71% of the pseudo clean PPG collected from ADL can be considered as high quality segment where the derived MAE of heart rate and respiration rate are 1.46 BPM and 3.93 BrPM, respectively. Therefore, our proposed method can determine the reliability of the raw noisy PPG by considering quality of the corresponding pseudo clean PPG signal. | ['Fengqing Zhu', 'George R. Wodicka', 'Craig J. Goergen', 'Stephan W. Wegerich', 'Mackenzie Tweardy', 'Runyu Mao'] | 2021-09-06 | null | null | null | null | ['photoplethysmography-ppg'] | ['medical'] | [ 3.39033037e-01 -4.10447270e-01 2.21960500e-01 3.60492244e-02
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ca1a8749-b41b-469d-9e47-aacf55fef7fa | altiro3d-scene-representation-from-single | 2304.11161 | null | https://arxiv.org/abs/2304.11161v1 | https://arxiv.org/pdf/2304.11161v1.pdf | altiro3D: Scene representation from single image and novel view synthesis | We introduce altiro3D, a free extended library developed to represent reality starting from a given original RGB image or flat video. It allows to generate a light-field (or Native) image or video and get a realistic 3D experience. To synthesize N-number of virtual images and add them sequentially into a Quilt collage, we apply MiDaS models for the monocular depth estimation, simple OpenCV and Telea inpainting techniques to map all pixels, and implement a 'Fast' algorithm to handle 3D projection camera and scene transformations along N-viewpoints. We use the degree of depth to move proportionally the pixels, assuming the original image to be at the center of all the viewpoints. altiro3D can also be used with DIBR algorithm to compute intermediate snapshots from a equivalent 'Real (slower)' camera with N-geometric viewpoints, which requires to calibrate a priori several intrinsic and extrinsic camera parameters. We adopt a pixel- and device-based Lookup Table to optimize computing time. The multiple viewpoints and video generated from a single image or frame can be displayed in a free-view LCD display. | ['L. Tenze', 'E. Canessa'] | 2023-04-02 | null | null | null | null | ['monocular-depth-estimation', 'novel-view-synthesis'] | ['computer-vision', 'computer-vision'] | [ 4.46047038e-01 -1.19621344e-01 4.11589116e-01 -3.69509101e-01
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6264efd7-f146-4c62-8f1d-7de52a9dd9e8 | density-ratio-estimation-and-neyman-pearson | 2302.10655 | null | https://arxiv.org/abs/2302.10655v1 | https://arxiv.org/pdf/2302.10655v1.pdf | Density Ratio Estimation and Neyman Pearson Classification with Missing Data | Density Ratio Estimation (DRE) is an important machine learning technique with many downstream applications. We consider the challenge of DRE with missing not at random (MNAR) data. In this setting, we show that using standard DRE methods leads to biased results while our proposal (M-KLIEP), an adaptation of the popular DRE procedure KLIEP, restores consistency. Moreover, we provide finite sample estimation error bounds for M-KLIEP, which demonstrate minimax optimality with respect to both sample size and worst-case missingness. We then adapt an important downstream application of DRE, Neyman-Pearson (NP) classification, to this MNAR setting. Our procedure both controls Type I error and achieves high power, with high probability. Finally, we demonstrate promising empirical performance both synthetic data and real-world data with simulated missingness. | ['Henry W J Reeve', 'Song Liu', 'Josh Givens'] | 2023-02-21 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [ 7.18302205e-02 -2.23225709e-02 -7.03283370e-01 -3.37531567e-01
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62323590-b0cf-46c2-b8b2-01bddeca29d4 | tafsir-dataset-a-novel-multi-task-benchmark | null | null | https://aclanthology.org/2022.coling-1.330 | https://aclanthology.org/2022.coling-1.330.pdf | Tafsir Dataset: A Novel Multi-Task Benchmark for Named Entity Recognition and Topic Modeling in Classical Arabic Literature | Various historical languages, which used to be lingua franca of science and arts, deserve the attention of current NLP research. In this work, we take the first data-driven steps towards this research line for Classical Arabic (CA) by addressing named entity recognition (NER) and topic modeling (TM) on the example of CA literature. We manually annotate the encyclopedic work of Tafsir Al-Tabari with span-based NEs, sentence-based topics, and span-based subtopics, thus creating the Tafsir Dataset with over 51,000 sentences, the first large-scale multi-task benchmark for CA. Next, we analyze our newly generated dataset, which we make open-source available, with current language models (lightweight BiLSTM, transformer-based MaChAmP) along a novel script compression method, thereby achieving state-of-the-art performance for our target task CA-NER. We also show that CA-TM from the perspective of historical topic models, which are central to Arabic studies, is very challenging. With this interdisciplinary work, we lay the foundations for future research on automatic analysis of CA literature. | ['Gemma Roig', 'Alexander Mehler', 'Ömer Özsoy', 'Carl Kruse', 'Misbahur Rehman', 'Rob van der Goot', 'Sajawel Ahmed'] | null | null | null | null | coling-2022-10 | ['topic-models'] | ['natural-language-processing'] | [-1.86193064e-01 -2.19920456e-01 -1.38316318e-01 -5.19807786e-02
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67cf1c10-50f5-47dd-a203-16923de9b111 | audiogen-textually-guided-audio-generation | 2209.15352 | null | https://arxiv.org/abs/2209.15352v2 | https://arxiv.org/pdf/2209.15352v2.pdf | AudioGen: Textually Guided Audio Generation | We tackle the problem of generating audio samples conditioned on descriptive text captions. In this work, we propose AaudioGen, an auto-regressive generative model that generates audio samples conditioned on text inputs. AudioGen operates on a learnt discrete audio representation. The task of text-to-audio generation poses multiple challenges. Due to the way audio travels through a medium, differentiating ``objects'' can be a difficult task (e.g., separating multiple people simultaneously speaking). This is further complicated by real-world recording conditions (e.g., background noise, reverberation, etc.). Scarce text annotations impose another constraint, limiting the ability to scale models. Finally, modeling high-fidelity audio requires encoding audio at high sampling rate, leading to extremely long sequences. To alleviate the aforementioned challenges we propose an augmentation technique that mixes different audio samples, driving the model to internally learn to separate multiple sources. We curated 10 datasets containing different types of audio and text annotations to handle the scarcity of text-audio data points. For faster inference, we explore the use of multi-stream modeling, allowing the use of shorter sequences while maintaining a similar bitrate and perceptual quality. We apply classifier-free guidance to improve adherence to text. Comparing to the evaluated baselines, AudioGen outperforms over both objective and subjective metrics. Finally, we explore the ability of the proposed method to generate audio continuation conditionally and unconditionally. Samples: https://felixkreuk.github.io/audiogen | ['Yossi Adi', 'Yaniv Taigman', 'Devi Parikh', 'Jade Copet', 'Alexandre Défossez', 'Uriel Singer', 'Adam Polyak', 'Gabriel Synnaeve', 'Felix Kreuk'] | 2022-09-30 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 3.98449361e-01 -1.39565483e-01 1.94672585e-01 -1.95712388e-01
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4afa69ca-ddfe-4ae7-bfbb-65e5266c55bd | on-the-expressivity-of-persistent-homology-in | 2302.09826 | null | https://arxiv.org/abs/2302.09826v2 | https://arxiv.org/pdf/2302.09826v2.pdf | On the Expressivity of Persistent Homology in Graph Learning | Persistent homology, a technique from computational topology, has recently shown strong empirical performance in the context of graph classification. Being able to capture long range graph properties via higher-order topological features, such as cycles of arbitrary length, in combination with multi-scale topological descriptors, has improved predictive performance for data sets with prominent topological structures, such as molecules. At the same time, the theoretical properties of persistent homology have not been formally assessed in this context. This paper intends to bridge the gap between computational topology and graph machine learning by providing a brief introduction to persistent homology in the context of graphs, as well as a theoretical discussion and empirical analysis of its expressivity for graph learning tasks. | ['Bastian Rieck'] | 2023-02-20 | null | null | null | null | ['graph-classification'] | ['graphs'] | [ 1.93987504e-01 3.80673319e-01 -4.40230668e-01 3.38153988e-02
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95f57baa-0d97-4ece-ae4a-97aada370003 | temporal-perceiving-video-language-pre | 2301.07463 | null | https://arxiv.org/abs/2301.07463v1 | https://arxiv.org/pdf/2301.07463v1.pdf | Temporal Perceiving Video-Language Pre-training | Video-Language Pre-training models have recently significantly improved various multi-modal downstream tasks. Previous dominant works mainly adopt contrastive learning to achieve global feature alignment across modalities. However, the local associations between videos and texts are not modeled, restricting the pre-training models' generality, especially for tasks requiring the temporal video boundary for certain query texts. This work introduces a novel text-video localization pre-text task to enable fine-grained temporal and semantic alignment such that the trained model can accurately perceive temporal boundaries in videos given the text description. Specifically, text-video localization consists of moment retrieval, which predicts start and end boundaries in videos given the text description, and text localization which matches the subset of texts with the video features. To produce temporal boundaries, frame features in several videos are manually merged into a long video sequence that interacts with a text sequence. With the localization task, our method connects the fine-grained frame representations with the word representations and implicitly distinguishes representations of different instances in the single modality. Notably, comprehensive experimental results show that our method significantly improves the state-of-the-art performance on various benchmarks, covering text-to-video retrieval, video question answering, video captioning, temporal action localization and temporal moment retrieval. The code will be released soon. | ['Yi Yang', 'Jiashi Feng', 'Linchao Zhu', 'Jingjia Huang', 'Heng Wang', 'Xiaojie Jin', 'Fan Ma'] | 2023-01-18 | null | null | null | null | ['moment-retrieval', 'video-question-answering', 'video-retrieval', 'action-localization'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 2.93716580e-01 -5.60904562e-01 -7.29501605e-01 -3.40554625e-01
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4ca6e97c-f91c-4361-b4f8-c055cc8f19f4 | web-scale-academic-name-disambiguation-the | 2302.11848 | null | https://arxiv.org/abs/2302.11848v2 | https://arxiv.org/pdf/2302.11848v2.pdf | Web-Scale Academic Name Disambiguation: the WhoIsWho Benchmark, Leaderboard, and Toolkit | Name disambiguation -- a fundamental problem in online academic systems -- is now facing greater challenges with the increasing growth of research papers. For example, on AMiner, an online academic search platform, about 10% of names own more than 100 authors. Such real-world challenging cases have not been effectively addressed by existing researches due to the small-scale or low-quality datasets that they have used. The development of effective algorithms is further hampered by a variety of tasks and evaluation protocols designed on top of diverse datasets. To this end, we present WhoIsWho owning, a large-scale benchmark with over 1,000,000 papers built using an interactive annotation process, a regular leaderboard with comprehensive tasks, and an easy-to-use toolkit encapsulating the entire pipeline as well as the most powerful features and baseline models for tackling the tasks. Our developed strong baseline has already been deployed online in the AMiner system to enable daily arXiv paper assignments. The public leaderboard is available at http://whoiswho.biendata.xyz/. The toolkit is at https://github.com/THUDM/WhoIsWho. The online demo of daily arXiv paper assignments is at https://na-demo.aminer.cn/arxivpaper. | ['Jie Tang', 'Yuxiao Dong', 'Xiaoyan Li', 'Yuqing Cheng', 'Tianyi Han', 'Fanjin Zhang', 'Jing Zhang', 'Bo Chen'] | 2023-02-23 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [-7.50662029e-01 -2.82638878e-01 -2.81044155e-01 -1.48157224e-01
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f54e797b-fd07-4853-81eb-355676fba6ed | multi-granularity-interaction-simulation-for | 2303.13399 | null | https://arxiv.org/abs/2303.13399v1 | https://arxiv.org/pdf/2303.13399v1.pdf | Multi-granularity Interaction Simulation for Unsupervised Interactive Segmentation | Interactive segmentation enables users to segment as needed by providing cues of objects, which introduces human-computer interaction for many fields, such as image editing and medical image analysis. Typically, massive and expansive pixel-level annotations are spent to train deep models by object-oriented interactions with manually labeled object masks. In this work, we reveal that informative interactions can be made by simulation with semantic-consistent yet diverse region exploration in an unsupervised paradigm. Concretely, we introduce a Multi-granularity Interaction Simulation (MIS) approach to open up a promising direction for unsupervised interactive segmentation. Drawing on the high-quality dense features produced by recent self-supervised models, we propose to gradually merge patches or regions with similar features to form more extensive regions and thus, every merged region serves as a semantic-meaningful multi-granularity proposal. By randomly sampling these proposals and simulating possible interactions based on them, we provide meaningful interaction at multiple granularities to teach the model to understand interactions. Our MIS significantly outperforms non-deep learning unsupervised methods and is even comparable with some previous deep-supervised methods without any annotation. | ['Jie Chen', 'Chang Liu', 'Li Yuan', 'Xiangyang Ji', 'Peng Jin', 'Zesen Cheng', 'Zhennan Wang', 'Yian Zhao', 'Kehan Li'] | 2023-03-23 | null | null | null | null | ['interactive-segmentation'] | ['computer-vision'] | [ 3.64267379e-01 7.34306097e-01 -2.24261537e-01 -5.22455096e-01
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73f32574-af36-4a29-afce-37416e381cf3 | unsupervised-shot-boundary-detection-for | 2110.09067 | null | https://arxiv.org/abs/2110.09067v1 | https://arxiv.org/pdf/2110.09067v1.pdf | Unsupervised Shot Boundary Detection for Temporal Segmentation of Long Capsule Endoscopy Videos | Physicians use Capsule Endoscopy (CE) as a non-invasive and non-surgical procedure to examine the entire gastrointestinal (GI) tract for diseases and abnormalities. A single CE examination could last between 8 to 11 hours generating up to 80,000 frames which is compiled as a video. Physicians have to review and analyze the entire video to identify abnormalities or diseases before making diagnosis. This review task can be very tedious, time consuming and prone to error. While only as little as a single frame may capture useful content that is relevant to the physicians' final diagnosis, frames covering the small bowel region alone could be as much as 50,000. To minimize physicians' review time and effort, this paper proposes a novel unsupervised and computationally efficient temporal segmentation method to automatically partition long CE videos into a homogeneous and identifiable video segments. However, the search for temporal boundaries in a long video using high dimensional frame-feature matrix is computationally prohibitive and impracticable for real clinical application. Therefore, leveraging both spatial and temporal information in the video, we first extracted high level frame features using a pretrained CNN model and then projected the high-dimensional frame-feature matrix to lower 1-dimensional embedding. Using this 1-dimensional sequence embedding, we applied the Pruned Exact Linear Time (PELT) algorithm to searched for temporal boundaries that indicates the transition points from normal to abnormal frames and vice-versa. We experimented with multiple real patients' CE videos and our model achieved an AUC of 66\% on multiple test videos against expert provided labels. | ['Donald Brown', 'Sana Syed', 'Michael Porter', 'Andrew Copland', 'James Jablonski', 'Philip Fernandes', 'Sodiq Adewole'] | 2021-10-18 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 1.88400835e-01 -2.82764919e-02 -7.09782541e-02 8.01787674e-02
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eacf90eb-a781-46a3-8ec5-b93e6a8fd7a1 | learning-temporal-consistency-for-low-light | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Zhang_Learning_Temporal_Consistency_for_Low_Light_Video_Enhancement_From_Single_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Zhang_Learning_Temporal_Consistency_for_Low_Light_Video_Enhancement_From_Single_CVPR_2021_paper.pdf | Learning Temporal Consistency for Low Light Video Enhancement From Single Images | Single image low light enhancement is an important task and it has many practical applications. Most existing methods adopt a single image approach. Although their performance is satisfying on a static single image, we found, however, they suffer serious temporal instability when handling low light videos. We notice the problem is because existing data-driven methods are trained from single image pairs where no temporal information is available. Unfortunately, training from real temporally consistent data is also problematic because it is impossible to collect pixel-wisely paired low and normal light videos under controlled environments in large scale and diversities with noise of identical statistics. In this paper, we propose a novel method to enforce the temporal stability in low light video enhancement with only static images. The key idea is to learn and infer motion field (optical flow) from a single image and synthesize short range video sequences. Our strategy is general and can extend to large scale datasets directly. Based on this idea, we propose our method which can infer motion prior for single image low light video enhancement and enforce temporal consistency. Rigorous experiments and user study demonstrate the state-of-the-art performance of our proposed method. Our code and model will be publicly available at https://github.com/zkawfanx/StableLLVE. | ['Ying Fu', 'ShaoDi You', 'Yu Li', 'Fan Zhang'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['video-enhancement'] | ['computer-vision'] | [ 3.33348960e-01 -7.91543245e-01 -8.04343969e-02 -2.77621269e-01
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13416882-b858-4dbd-aca3-c54e0ad58c77 | karaoker-alignment-free-singing-voice | 2204.04127 | null | https://arxiv.org/abs/2204.04127v2 | https://arxiv.org/pdf/2204.04127v2.pdf | Karaoker: Alignment-free singing voice synthesis with speech training data | Existing singing voice synthesis models (SVS) are usually trained on singing data and depend on either error-prone time-alignment and duration features or explicit music score information. In this paper, we propose Karaoker, a multispeaker Tacotron-based model conditioned on voice characteristic features that is trained exclusively on spoken data without requiring time-alignments. Karaoker synthesizes singing voice and transfers style following a multi-dimensional template extracted from a source waveform of an unseen singer/speaker. The model is jointly conditioned with a single deep convolutional encoder on continuous data including pitch, intensity, harmonicity, formants, cepstral peak prominence and octaves. We extend the text-to-speech training objective with feature reconstruction, classification and speaker identification tasks that guide the model to an accurate result. In addition to multitasking, we also employ a Wasserstein GAN training scheme as well as new losses on the acoustic model's output to further refine the quality of the model. | ['Aimilios Chalamandaris', 'Pirros Tsiakoulis', 'Gunu Jho', 'June Sig Sung', 'Konstantinos Markopoulos', 'Georgios Vamvoukakis', 'Nikolaos Ellinas', 'Panos Kakoulidis'] | 2022-04-08 | null | null | null | null | ['speaker-identification', 'singing-voice-synthesis'] | ['speech', 'speech'] | [ 1.95100769e-01 -7.51683936e-02 3.35137039e-01 -3.71919870e-01
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e88bc2e6-9675-4957-b03e-9eb7b4cba894 | deepfakebench-a-comprehensive-benchmark-of | 2307.01426 | null | https://arxiv.org/abs/2307.01426v1 | https://arxiv.org/pdf/2307.01426v1.pdf | DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection | A critical yet frequently overlooked challenge in the field of deepfake detection is the lack of a standardized, unified, comprehensive benchmark. This issue leads to unfair performance comparisons and potentially misleading results. Specifically, there is a lack of uniformity in data processing pipelines, resulting in inconsistent data inputs for detection models. Additionally, there are noticeable differences in experimental settings, and evaluation strategies and metrics lack standardization. To fill this gap, we present the first comprehensive benchmark for deepfake detection, called DeepfakeBench, which offers three key contributions: 1) a unified data management system to ensure consistent input across all detectors, 2) an integrated framework for state-of-the-art methods implementation, and 3) standardized evaluation metrics and protocols to promote transparency and reproducibility. Featuring an extensible, modular-based codebase, DeepfakeBench contains 15 state-of-the-art detection methods, 9 deepfake datasets, a series of deepfake detection evaluation protocols and analysis tools, as well as comprehensive evaluations. Moreover, we provide new insights based on extensive analysis of these evaluations from various perspectives (e.g., data augmentations, backbones). We hope that our efforts could facilitate future research and foster innovation in this increasingly critical domain. All codes, evaluations, and analyses of our benchmark are publicly available at https://github.com/SCLBD/DeepfakeBench. | ['Baoyuan Wu', 'Siwei Lyu', 'Xinhang Yuan', 'Yong Zhang', 'Zhiyuan Yan'] | 2023-07-04 | null | null | null | null | ['deepfake-detection', 'face-swapping', 'management'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [-2.83750176e-01 -6.14364922e-01 -2.18755335e-01 -2.65242755e-01
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1a00b5d1-800a-4ebd-a9d3-be11903fec6f | fine-grained-coordinated-cross-lingual-text | null | null | https://aclanthology.org/D18-1271 | https://aclanthology.org/D18-1271.pdf | Fine-grained Coordinated Cross-lingual Text Stream Alignment for Endless Language Knowledge Acquisition | This paper proposes to study fine-grained coordinated cross-lingual text stream alignment through a novel information network decipherment paradigm. We use Burst Information Networks as media to represent text streams and present a simple yet effective network decipherment algorithm with diverse clues to decipher the networks for accurate text stream alignment. Experiments on Chinese-English news streams show our approach not only outperforms previous approaches on bilingual lexicon extraction from coordinated text streams but also can harvest high-quality alignments from large amounts of streaming data for endless language knowledge mining, which makes it promising to be a new paradigm for automatic language knowledge acquisition. | ['Qing Dou', 'Heng Ji', 'Furu Wei', 'Tao Ge', 'Ming Zhou', 'Lei Cui', 'Baobao Chang', 'Zhifang Sui'] | 2018-10-01 | null | null | null | emnlp-2018-10 | ['decipherment'] | ['natural-language-processing'] | [ 9.06683058e-02 -2.62491405e-01 -7.49712884e-01 -2.09561631e-01
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14c15a9a-1366-4fbe-bb99-5d35c11e23ff | svma-a-gan-based-model-for-monocular-3d-human | 2106.05616 | null | https://arxiv.org/abs/2106.05616v4 | https://arxiv.org/pdf/2106.05616v4.pdf | SVMAC: Unsupervised 3D Human Pose Estimation from a Single Image with Single-view-multi-angle Consistency | Recovering 3D human pose from 2D joints is still a challenging problem, especially without any 3D annotation, video information, or multi-view information. In this paper, we present an unsupervised GAN-based model consisting of multiple weight-sharing generators to estimate a 3D human pose from a single image without 3D annotations. In our model, we introduce single-view-multi-angle consistency (SVMAC) to significantly improve the estimation performance. With 2D joint locations as input, our model estimates a 3D pose and a camera simultaneously. During training, the estimated 3D pose is rotated by random angles and the estimated camera projects the rotated 3D poses back to 2D. The 2D reprojections will be fed into weight-sharing generators to estimate the corresponding 3D poses and cameras, which are then mixed to impose SVMAC constraints to self-supervise the training process. The experimental results show that our method outperforms the state-of-the-art unsupervised methods on Human 3.6M and MPI-INF-3DHP. Moreover, qualitative results on MPII and LSP show that our method can generalize well to unknown data. | ['Yongqi Sun', 'Cheng Sun', 'Jiahui Zhu', 'Yicheng Deng'] | 2021-06-10 | null | null | null | null | ['monocular-3d-human-pose-estimation', 'unsupervised-3d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-1.66930869e-01 2.54384845e-01 -6.28801882e-02 -3.32665235e-01
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9b330df0-0046-4bda-b0b8-aa56c9054d49 | lung-infection-quantification-of-covid-19-in | 2003.04655 | null | https://arxiv.org/abs/2003.04655v3 | https://arxiv.org/pdf/2003.04655v3.pdf | Lung Infection Quantification of COVID-19 in CT Images with Deep Learning | CT imaging is crucial for diagnosis, assessment and staging COVID-19 infection. Follow-up scans every 3-5 days are often recommended for disease progression. It has been reported that bilateral and peripheral ground glass opacification (GGO) with or without consolidation are predominant CT findings in COVID-19 patients. However, due to lack of computerized quantification tools, only qualitative impression and rough description of infected areas are currently used in radiological reports. In this paper, a deep learning (DL)-based segmentation system is developed to automatically quantify infection regions of interest (ROIs) and their volumetric ratios w.r.t. the lung. The performance of the system was evaluated by comparing the automatically segmented infection regions with the manually-delineated ones on 300 chest CT scans of 300 COVID-19 patients. For fast manual delineation of training samples and possible manual intervention of automatic results, a human-in-the-loop (HITL) strategy has been adopted to assist radiologists for infection region segmentation, which dramatically reduced the total segmentation time to 4 minutes after 3 iterations of model updating. The average Dice simiarility coefficient showed 91.6% agreement between automatic and manual infaction segmentations, and the mean estimation error of percentage of infection (POI) was 0.3% for the whole lung. Finally, possible applications, including but not limited to analysis of follow-up CT scans and infection distributions in the lobes and segments correlated with clinical findings, were discussed. | ['Yuxin Shi', 'Yaozong Gao', 'Nannan Shi', 'Miaofei Han', 'Dinggang Shen', 'Weiya Shi', 'Jun Wang', 'Fei Shan', 'Zhong Xue'] | 2020-03-10 | null | null | null | null | ['covid-19-image-segmentation'] | ['computer-vision'] | [-4.72334959e-02 -3.85808110e-01 -8.56628194e-02 8.70669540e-03
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b9207e72-0391-45f8-a93b-174c67751949 | gmf-general-multimodal-fusion-framework-for | 2211.00207 | null | https://arxiv.org/abs/2211.00207v1 | https://arxiv.org/pdf/2211.00207v1.pdf | GMF: General Multimodal Fusion Framework for Correspondence Outlier Rejection | Rejecting correspondence outliers enables to boost the correspondence quality, which is a critical step in achieving high point cloud registration accuracy. The current state-of-the-art correspondence outlier rejection methods only utilize the structure features of the correspondences. However, texture information is critical to reject the correspondence outliers in our human vision system. In this paper, we propose General Multimodal Fusion (GMF) to learn to reject the correspondence outliers by leveraging both the structure and texture information. Specifically, two cross-attention-based fusion layers are proposed to fuse the texture information from paired images and structure information from point correspondences. Moreover, we propose a convolutional position encoding layer to enhance the difference between Tokens and enable the encoding feature pay attention to neighbor information. Our position encoding layer will make the cross-attention operation integrate both local and global information. Experiments on multiple datasets(3DMatch, 3DLoMatch, KITTI) and recent state-of-the-art models (3DRegNet, DGR, PointDSC) prove that our GMF achieves wide generalization ability and consistently improves the point cloud registration accuracy. Furthermore, several ablation studies demonstrate the robustness of the proposed GMF on different loss functions, lighting conditions and noises.The code is available at https://github.com/XiaoshuiHuang/GMF. | ['Xiaowei Zhao', 'Yuming Fang', 'Yifan Zuo', 'Wentao Qu', 'Xiaoshui Huang'] | 2022-11-01 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [-3.38146716e-01 -3.87477070e-01 4.01895717e-02 -4.54147458e-01
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24eb379b-980c-4734-95a5-7e850c3b371c | local-differential-privacy-for-sequential | 2301.00561 | null | https://arxiv.org/abs/2301.00561v1 | https://arxiv.org/pdf/2301.00561v1.pdf | Local Differential Privacy for Sequential Decision Making in a Changing Environment | We study the problem of preserving privacy while still providing high utility in sequential decision making scenarios in a changing environment. We consider abruptly changing environment: the environment remains constant during periods and it changes at unknown time instants. To formulate this problem, we propose a variant of multi-armed bandits called non-stationary stochastic corrupt bandits. We construct an algorithm called SW-KLUCB-CF and prove an upper bound on its utility using the performance measure of regret. The proven regret upper bound for SW-KLUCB-CF is near-optimal in the number of time steps and matches the best known bound for analogous problems in terms of the number of time steps and the number of changes. Moreover, we present a provably optimal mechanism which can guarantee the desired level of local differential privacy while providing high utility. | ['Pratik Gajane'] | 2023-01-02 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 3.76090467e-01 7.28960931e-02 -4.74557817e-01 -3.13450843e-01
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2233c909-7fb1-43fb-bace-8060ffcbe032 | computer-vision-toolkit-for-non-invasive | 2005.06037 | null | https://arxiv.org/abs/2005.06037v1 | https://arxiv.org/pdf/2005.06037v1.pdf | Computer Vision Toolkit for Non-invasive Monitoring of Factory Floor Artifacts | Digitization has led to smart, connected technologies be an integral part of businesses, governments and communities. For manufacturing digitization, there has been active research and development with a focus on Cloud Manufacturing (CM) and the Industrial Internet of Things (IIoT). This work presents a computer vision toolkit (CV Toolkit) for non-invasive digitization of the factory floor in line with Industry 4.0 requirements for factory data collection. Currently, technical challenges persist towards digitization of legacy systems due to the limitation for changes in their design and sensors. This novel toolkit is developed to facilitate easy integration of legacy production machinery and factory floor artifacts with the digital and smart manufacturing environment with no requirement of any physical changes in the machines. The system developed is modular, and allows real-time monitoring of production machinery. Modularity aspect allows the incorporation of new software applications in the current framework of CV Toolkit. To allow connectivity of this toolkit with manufacturing floors in a simple, deployable and cost-effective manner, the toolkit is integrated with a known manufacturing data standard, MTConnect, to "translate" the digital inputs into data streams that can be read by commercial status tracking and reporting software solutions. The proposed toolkit is demonstrated using a mock-panel environment developed in house at the University of Cincinnati to highlight its usability. | ['David A. Wickelhaus', 'Sam Anand', 'Aditya M. Deshpande', 'Anil Kumar Telikicherla', 'Vinay Jakkali', 'Manish Kumar'] | 2020-05-12 | null | null | null | null | ['manufacturing-quality-control', 'curved-text-detection', 'contour-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.14680275e-01 -1.22078560e-01 4.15761828e-01 -5.32804467e-02
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2a8e87eb-b52a-4234-a93e-3de3d2c45fa9 | energy-loss-prediction-in-iot-energy-services | 2305.10238 | null | https://arxiv.org/abs/2305.10238v1 | https://arxiv.org/pdf/2305.10238v1.pdf | Energy Loss Prediction in IoT Energy Services | We propose a novel Energy Loss Prediction(ELP) framework that estimates the energy loss in sharing crowdsourced energy services. Crowdsourcing wireless energy services is a novel and convenient solution to enable the ubiquitous charging of nearby IoT devices. Therefore, capturing the wireless energy sharing loss is essential for the successful deployment of efficient energy service composition techniques. We propose Easeformer, a novel attention-based algorithm to predict the battery levels of IoT devices in a crowdsourced energy sharing environment. The predicted battery levels are used to estimate the energy loss. A set of experiments were conducted to demonstrate the feasibility and effectiveness of the proposed framework. We conducted extensive experiments on real wireless energy datasets to demonstrate that our framework significantly outperforms existing methods. | ['Athman Bouguettaya', 'Abdallah Lakhdari', 'Amani Abusafia', 'Pengwei Yang'] | 2023-05-16 | null | null | null | null | ['service-composition'] | ['miscellaneous'] | [-4.01017547e-01 -2.80626953e-01 -3.77405703e-01 -4.22009856e-01
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fa0174b2-5893-4763-9424-148a962c7ce9 | slice-connection-clustering-algorithm-for | 2203.03830 | null | https://arxiv.org/abs/2203.03830v1 | https://arxiv.org/pdf/2203.03830v1.pdf | Slice-Connection Clustering Algorithm for Tree Roots Recognition in Noisy 3D GPR Data | 3D mapping of tree roots is a popular ground-penetrating radar (GPR) application. In real field tests, the recognition of tree roots suffers due to noisey reflection patterns from subsurface targets that are not of interest, such as rocks, cavities, soil unevenness, etc. A Slice-Connection Clustering Algorithm (SCC) is applied to separate the regions of interest from each other in a reconstructed 3D image. The proposed method can successfully recognize the radar signatures of the roots and distinguish roots from other objects. Meanwhile, most noise radar features are ignored through our method. The final 3D mapping of the radargram obtained by the method can be used to estimate the location and extension trend of the tree roots. The effectiveness of the proposed system is tested on real GPR data. | ['Abdulkadir C. Yucel', 'Mohamed Lokman Mohd Yusof', 'Lai Fern Ow', 'Yee Hui Lee', 'Wenhao Luo'] | 2022-03-08 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 3.86798263e-01 -2.01465130e-01 4.70774472e-01 -1.56973064e-01
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f00ce65e-f5c5-47cb-9908-c0b316ff4b8e | modeling-video-as-stochastic-processes-for | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Modeling_Video_As_Stochastic_Processes_for_Fine-Grained_Video_Representation_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_Modeling_Video_As_Stochastic_Processes_for_Fine-Grained_Video_Representation_Learning_CVPR_2023_paper.pdf | Modeling Video As Stochastic Processes for Fine-Grained Video Representation Learning | A meaningful video is semantically coherent and changes smoothly. However, most existing fine-grained video representation learning methods learn frame-wise features by aligning frames across videos or exploring relevance between multiple views, neglecting the inherent dynamic process of each video. In this paper, we propose to learn video representations by modeling Video as Stochastic Processes (VSP) via a novel process-based contrastive learning framework, which aims to discriminate between video processes and simultaneously capture the temporal dynamics in the processes. Specifically, we enforce the embeddings of the frame sequence of interest to approximate a goal-oriented stochastic process, i.e., Brownian bridge, in the latent space via a process-based contrastive loss. To construct the Brownian bridge, we adapt specialized sampling strategies under different annotations for both self-supervised and weakly-supervised learning. Experimental results on four datasets show that VSP stands as a state-of-the-art method for various video understanding tasks, including phase progression, phase classification and frame retrieval. Code is available at 'https://github.com/hengRUC/VSP'. | ['Bing Su', 'Qi Zheng', 'Daqing Liu', 'Heng Zhang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['video-understanding'] | ['computer-vision'] | [ 8.84885713e-02 -3.04030150e-01 -3.99988621e-01 -2.84648210e-01
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98bdfbfc-b506-4223-9049-01d983ed255b | meranet-facial-micro-expression-recognition | 2012.04581 | null | https://arxiv.org/abs/2012.04581v2 | https://arxiv.org/pdf/2012.04581v2.pdf | MERANet: Facial Micro-Expression Recognition using 3D Residual Attention Network | Micro-expression has emerged as a promising modality in affective computing due to its high objectivity in emotion detection. Despite the higher recognition accuracy provided by the deep learning models, there are still significant scope for improvements in micro-expression recognition techniques. The presence of micro-expressions in small-local regions of the face, as well as the limited size of available databases, continue to limit the accuracy in recognizing micro-expressions. In this work, we propose a facial micro-expression recognition model using 3D residual attention network named MERANet to tackle such challenges. The proposed model takes advantage of spatial-temporal attention and channel attention together, to learn deeper fine-grained subtle features for classification of emotions. Further, the proposed model encompasses both spatial and temporal information simultaneously using the 3D kernels and residual connections. Moreover, the channel features and spatio-temporal features are re-calibrated using the channel and spatio-temporal attentions, respectively in each residual module. Our attention mechanism enables the model to learn to focus on different facial areas of interest. The experiments are conducted on benchmark facial micro-expression datasets. A superior performance is observed as compared to the state-of-the-art for facial micro-expression recognition on benchmark data. | ['Shiv Ram Dubey', 'Snehasis Mukherjee', 'Sai Prasanna Teja Reddy', 'Viswanatha Reddy Gajjala'] | 2020-12-07 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [-4.30212431e-02 -3.27599347e-01 1.07557066e-01 -5.71609914e-01
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57510f70-c682-4cf2-a1dc-3b1bd09716ee | neuroclip-neuromorphic-data-understanding-by | 2306.12073 | null | https://arxiv.org/abs/2306.12073v1 | https://arxiv.org/pdf/2306.12073v1.pdf | NeuroCLIP: Neuromorphic Data Understanding by CLIP and SNN | Recently, the neuromorphic vision sensor has received more and more interest. However, the neuromorphic data consists of asynchronous event spikes, which is not natural and difficult to construct a benchmark, thus limiting the neuromorphic data understanding for "unseen" objects by deep learning. Zero-shot and few-shot learning via Contrastive Vision-Language Pre-training (CLIP) have shown inspirational performance in 2D frame image recognition. To handle "unseen" recognition for the neuromorphic data, in this paper, we propose NeuroCLIP, which transfers the CLIP's 2D pre-trained knowledge to event spikes. To improve the few-shot performance, we also provide an inter-timestep adapter based on a spiking neural network. Our code is open-sourced at https://github.com/yfguo91/NeuroCLIP.git. | ['Yuanpei Chen', 'Yufei Guo'] | 2023-06-21 | null | null | null | null | ['few-shot-learning'] | ['methodology'] | [ 1.88568458e-01 -4.13033068e-01 4.41329747e-01 -4.17390674e-01
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f62c29d4-06be-4651-9392-1cdcab10a098 | mgpt-few-shot-learners-go-multilingual | 2204.07580 | null | https://arxiv.org/abs/2204.07580v1 | https://arxiv.org/pdf/2204.07580v1.pdf | mGPT: Few-Shot Learners Go Multilingual | Recent studies report that autoregressive language models can successfully solve many NLP tasks via zero- and few-shot learning paradigms, which opens up new possibilities for using the pre-trained language models. This paper introduces two autoregressive GPT-like models with 1.3 billion and 13 billion parameters trained on 60 languages from 25 language families using Wikipedia and Colossal Clean Crawled Corpus. We reproduce the GPT-3 architecture using GPT-2 sources and the sparse attention mechanism; Deepspeed and Megatron frameworks allow us to parallelize the training and inference steps effectively. The resulting models show performance on par with the recently released XGLM models by Facebook, covering more languages and enhancing NLP possibilities for low resource languages of CIS countries and Russian small nations. We detail the motivation for the choices of the architecture design, thoroughly describe the data preparation pipeline, and train five small versions of the model to choose the most optimal multilingual tokenization strategy. We measure the model perplexity in all covered languages and evaluate it on the wide spectre of multilingual tasks, including classification, generative, sequence labeling and knowledge probing. The models were evaluated with the zero-shot and few-shot methods. Furthermore, we compared the classification tasks with the state-of-the-art multilingual model XGLM. source code and the mGPT XL model are publicly released. | ['Tatiana Shavrina', 'Anastasia Kozlova', 'Vladislav Mikhailov', 'Maria Tikhonova', 'Alena Fenogenova', 'Oleh Shliazhko'] | 2022-04-15 | null | null | null | null | ['cross-lingual-natural-language-inference', 'few-shot-ner'] | ['natural-language-processing', 'natural-language-processing'] | [-4.57495511e-01 3.30574125e-01 -5.03251016e-01 -2.66785175e-01
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9563f369-b7d7-4948-8471-201578d63cf2 | bayesian-numerical-integration-with-neural | 2305.13248 | null | https://arxiv.org/abs/2305.13248v1 | https://arxiv.org/pdf/2305.13248v1.pdf | Bayesian Numerical Integration with Neural Networks | Bayesian probabilistic numerical methods for numerical integration offer significant advantages over their non-Bayesian counterparts: they can encode prior information about the integrand, and can quantify uncertainty over estimates of an integral. However, the most popular algorithm in this class, Bayesian quadrature, is based on Gaussian process models and is therefore associated with a high computational cost. To improve scalability, we propose an alternative approach based on Bayesian neural networks which we call Bayesian Stein networks. The key ingredients are a neural network architecture based on Stein operators, and an approximation of the Bayesian posterior based on the Laplace approximation. We show that this leads to orders of magnitude speed-ups on the popular Genz functions benchmark, and on challenging problems arising in the Bayesian analysis of dynamical systems, and the prediction of energy production for a large-scale wind farm. | ['François-Xavier Briol', 'Philipp Hennig', 'Michael Tiemann', 'Katharina Ott'] | 2023-05-22 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [-3.97635251e-01 -2.44405925e-01 1.50456101e-01 1.91071201e-02
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dc6bfd7f-8878-46b5-bf6c-161f3ba7b12c | efficient-large-scale-audio-tagging-via | 2211.04772 | null | https://arxiv.org/abs/2211.04772v3 | https://arxiv.org/pdf/2211.04772v3.pdf | Efficient Large-scale Audio Tagging via Transformer-to-CNN Knowledge Distillation | Audio Spectrogram Transformer models rule the field of Audio Tagging, outrunning previously dominating Convolutional Neural Networks (CNNs). Their superiority is based on the ability to scale up and exploit large-scale datasets such as AudioSet. However, Transformers are demanding in terms of model size and computational requirements compared to CNNs. We propose a training procedure for efficient CNNs based on offline Knowledge Distillation (KD) from high-performing yet complex transformers. The proposed training schema and the efficient CNN design based on MobileNetV3 results in models outperforming previous solutions in terms of parameter and computational efficiency and prediction performance. We provide models of different complexity levels, scaling from low-complexity models up to a new state-of-the-art performance of .483 mAP on AudioSet. Source Code available at: https://github.com/fschmid56/EfficientAT | ['Gerhard Widmer', 'Khaled Koutini', 'Florian Schmid'] | 2022-11-09 | null | null | null | null | ['audio-tagging'] | ['audio'] | [-9.37730446e-02 3.06292810e-02 3.90320388e-03 -2.78085560e-01
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2cc25fd5-ecf8-4421-8a91-15ebc11507ce | saliencycut-augmenting-plausible-anomalies | 2306.08366 | null | https://arxiv.org/abs/2306.08366v1 | https://arxiv.org/pdf/2306.08366v1.pdf | SaliencyCut: Augmenting Plausible Anomalies for Open-set Fine-Grained Anomaly Detection | Open-set fine-grained anomaly detection is a challenging task that requires learning discriminative fine-grained features to detect anomalies that were even unseen during training. As a cheap yet effective approach, data augmentation has been widely used to create pseudo anomalies for better training of such models. Recent wisdom of augmentation methods focuses on generating random pseudo instances that may lead to a mixture of augmented instances with seen anomalies, or out of the typical range of anomalies. To address this issue, we propose a novel saliency-guided data augmentation method, SaliencyCut, to produce pseudo but more common anomalies which tend to stay in the plausible range of anomalies. Furthermore, we deploy a two-head learning strategy consisting of normal and anomaly learning heads, to learn the anomaly score of each sample. Theoretical analyses show that this mechanism offers a more tractable and tighter lower bound of the data log-likelihood. We then design a novel patch-wise residual module in the anomaly learning head to extract and assess the fine-grained anomaly features from each sample, facilitating the learning of discriminative representations of anomaly instances. Extensive experiments conducted on six real-world anomaly detection datasets demonstrate the superiority of our method to the baseline and other state-of-the-art methods under various settings. | ['Kaizhu Huang', 'Chao Huang', 'Qiu-Feng Wang', 'Xi Yang', 'Yijie Hu', 'Jianan Ye'] | 2023-06-14 | null | null | null | null | ['anomaly-detection'] | ['methodology'] | [ 4.14003521e-01 6.98365793e-02 1.11622401e-02 -5.98767817e-01
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99e95721-7f91-4e1d-8e87-4b79f8c05272 | rethinking-the-two-stage-framework-for | 2112.05375 | null | https://arxiv.org/abs/2112.05375v1 | https://arxiv.org/pdf/2112.05375v1.pdf | Rethinking the Two-Stage Framework for Grounded Situation Recognition | Grounded Situation Recognition (GSR), i.e., recognizing the salient activity (or verb) category in an image (e.g., buying) and detecting all corresponding semantic roles (e.g., agent and goods), is an essential step towards "human-like" event understanding. Since each verb is associated with a specific set of semantic roles, all existing GSR methods resort to a two-stage framework: predicting the verb in the first stage and detecting the semantic roles in the second stage. However, there are obvious drawbacks in both stages: 1) The widely-used cross-entropy (XE) loss for object recognition is insufficient in verb classification due to the large intra-class variation and high inter-class similarity among daily activities. 2) All semantic roles are detected in an autoregressive manner, which fails to model the complex semantic relations between different roles. To this end, we propose a novel SituFormer for GSR which consists of a Coarse-to-Fine Verb Model (CFVM) and a Transformer-based Noun Model (TNM). CFVM is a two-step verb prediction model: a coarse-grained model trained with XE loss first proposes a set of verb candidates, and then a fine-grained model trained with triplet loss re-ranks these candidates with enhanced verb features (not only separable but also discriminative). TNM is a transformer-based semantic role detection model, which detects all roles parallelly. Owing to the global relation modeling ability and flexibility of the transformer decoder, TNM can fully explore the statistical dependency of the roles. Extensive validations on the challenging SWiG benchmark show that SituFormer achieves a new state-of-the-art performance with significant gains under various metrics. Code is available at https://github.com/kellyiss/SituFormer. | ['Tat-Seng Chua', 'Xiaoyu Yue', 'Wei Ji', 'Long Chen', 'Meng Wei'] | 2021-12-10 | null | null | null | null | ['grounded-situation-recognition', 'situation-recognition'] | ['computer-vision', 'computer-vision'] | [ 6.03590190e-01 -4.38855179e-02 -3.37422609e-01 -4.98913914e-01
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681755f4-e541-4579-9110-1f892aa6585f | mgtab-a-multi-relational-graph-based-twitter | 2301.01123 | null | https://arxiv.org/abs/2301.01123v2 | https://arxiv.org/pdf/2301.01123v2.pdf | MGTAB: A Multi-Relational Graph-Based Twitter Account Detection Benchmark | The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB. | ['Bin Yan', 'Linyuan Wang', 'Baojie Song', 'Jie Yang', 'Shuai Yang', 'Jian Chen', 'Kai Qiao', 'Shuhao Shi'] | 2023-01-03 | null | null | null | null | ['twitter-bot-detection', 'stance-detection'] | ['miscellaneous', 'natural-language-processing'] | [-2.71460712e-01 -2.15009786e-02 -9.19604897e-01 -1.16370723e-01
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d517b126-a21f-4f2f-a04e-9424cbd9d9bf | native-tongues-lost-and-found-resources-and | null | null | https://aclanthology.info/papers/C12-1158/c12-1158 | https://www.aclweb.org/anthology/C12-1158v2 | Native Tongues, Lost and Found: Resources and Empirical Evaluations in Native Language Identification | null | ['Martin Chodorow', 'Joel Tetreault', 'Daniel Blanchard', 'Aoife Cahill'] | 2012-12-01 | native-tongues-lost-and-found-resources-and-1 | https://aclanthology.org/C12-1158 | https://aclanthology.org/C12-1158.pdf | coling-2012-12 | ['native-language-identification'] | ['natural-language-processing'] | [-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01
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5242d735-cedb-4991-956d-e868cfe2a8ea | spatial-latent-representations-in-generative | 2303.14552 | null | https://arxiv.org/abs/2303.14552v1 | https://arxiv.org/pdf/2303.14552v1.pdf | Spatial Latent Representations in Generative Adversarial Networks for Image Generation | In the majority of GAN architectures, the latent space is defined as a set of vectors of given dimensionality. Such representations are not easily interpretable and do not capture spatial information of image content directly. In this work, we define a family of spatial latent spaces for StyleGAN2, capable of capturing more details and representing images that are out-of-sample in terms of the number and arrangement of object parts, such as an image of multiple faces or a face with more than two eyes. We propose a method for encoding images into our spaces, together with an attribute model capable of performing attribute editing in these spaces. We show that our spaces are effective for image manipulation and encode semantic information well. Our approach can be used on pre-trained generator models, and attribute edition can be done using pre-generated direction vectors making the barrier to entry for experimentation and use extremely low. We propose a regularization method for optimizing latent representations, which equalizes distributions of parts of latent spaces, making representations much closer to generated ones. We use it for encoding images into spatial spaces to obtain significant improvement in quality while keeping semantics and ability to use our attribute model for edition purposes. In total, using our methods gives encoding quality boost even as high as 30% in terms of LPIPS score comparing to standard methods, while keeping semantics. Additionally, we propose a StyleGAN2 training procedure on our spatial latent spaces, together with a custom spatial latent representation distribution to make spatially closer elements in the representation more dependent on each other than farther elements. Such approach improves the FID score by 29% on SpaceNet, and is able to generate consistent images of arbitrary sizes on spatially homogeneous datasets, like satellite imagery. | ['Maciej Sypetkowski'] | 2023-03-25 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [ 4.53040183e-01 4.03243840e-01 8.99922177e-02 -3.71656984e-01
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e656c687-4ca6-4dea-a2c3-3b707f0c1019 | lq-optimal-control-for-power-tracking | 2211.07690 | null | https://arxiv.org/abs/2211.07690v1 | https://arxiv.org/pdf/2211.07690v1.pdf | LQ Optimal Control for Power Tracking Operation of Wind Turbines | In this paper, an approach for active power control of individual wind turbines is presented. State-of-the-art controllers typically employ separate control loops for torque and pitch control. In contrast, we use a multivariable control approach. In detail, active power control is achieved by using reference trajectories for generator speed, generator torque, and pitch angle such that a desired power demand is met if weather conditions allow. Then, a linear quadratic (LQ) optimal controller is used for reference tracking. In an OpenFAST simulation environment, the controller is compared to a state-of-the-art approach. The simulations show a similar active power tracking performance, while the LQ optimal controller results in lower mechanical wear. Moreover, the presented approach exhibits good reference tracking and by improving the reference trajectory generation further performance increases can be expected. | ['Christian A. Hans', 'Jörg Raisch', 'Arnold Sterle', 'Aaron Grapentin'] | 2022-11-14 | null | null | null | null | ['pitch-control'] | ['audio'] | [-1.51736245e-01 6.72512054e-01 -2.45826051e-01 5.82051873e-01
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b8b600c8-3394-42c0-a3f0-e0d78f422e34 | skin-feature-point-tracking-using-deep | 2112.14159 | null | https://arxiv.org/abs/2112.14159v2 | https://arxiv.org/pdf/2112.14159v2.pdf | Skin feature point tracking using deep feature encodings | Facial feature tracking is a key component of imaging ballistocardiography (BCG) where accurate quantification of the displacement of facial keypoints is needed for good heart rate estimation. Skin feature tracking enables video-based quantification of motor degradation in Parkinson's disease. Traditional computer vision algorithms include Scale Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), and Lucas-Kanade method (LK). These have long represented the state-of-the-art in efficiency and accuracy but fail when common deformations, like affine local transformations or illumination changes, are present. Over the past five years, deep convolutional neural networks have outperformed traditional methods for most computer vision tasks. We propose a pipeline for feature tracking, that applies a convolutional stacked autoencoder to identify the most similar crop in an image to a reference crop containing the feature of interest. The autoencoder learns to represent image crops into deep feature encodings specific to the object category it is trained on. We train the autoencoder on facial images and validate its ability to track skin features in general using manually labeled face and hand videos. The tracking errors of distinctive skin features (moles) are so small that we cannot exclude that they stem from the manual labelling based on a $\chi^2$-test. With a mean error of 0.6-4.2 pixels, our method outperformed the other methods in all but one scenario. More importantly, our method was the only one to not diverge. We conclude that our method creates better feature descriptors for feature tracking, feature matching, and image registration than the traditional algorithms. | ['Torbjörn E. M. Nordling', 'Jose Ramon Chang'] | 2021-12-28 | null | null | null | null | ['heart-rate-estimation'] | ['medical'] | [ 1.74473614e-01 -2.44029284e-01 1.66572690e-01 -3.26762348e-01
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e5cac4ed-1fd9-4337-9bb6-2488f2d46179 | end-to-end-semi-supervised-learning-for-video | 2203.04251 | null | https://arxiv.org/abs/2203.04251v3 | https://arxiv.org/pdf/2203.04251v3.pdf | End-to-End Semi-Supervised Learning for Video Action Detection | In this work, we focus on semi-supervised learning for video action detection which utilizes both labeled as well as unlabeled data. We propose a simple end-to-end consistency based approach which effectively utilizes the unlabeled data. Video action detection requires both, action class prediction as well as a spatio-temporal localization of actions. Therefore, we investigate two types of constraints, classification consistency, and spatio-temporal consistency. The presence of predominant background and static regions in a video makes it challenging to utilize spatio-temporal consistency for action detection. To address this, we propose two novel regularization constraints for spatio-temporal consistency; 1) temporal coherency, and 2) gradient smoothness. Both these aspects exploit the temporal continuity of action in videos and are found to be effective for utilizing unlabeled videos for action detection. We demonstrate the effectiveness of the proposed approach on two different action detection benchmark datasets, UCF101-24 and JHMDB-21. In addition, we also show the effectiveness of the proposed approach for video object segmentation on the Youtube-VOS which demonstrates its generalization capability The proposed approach achieves competitive performance by using merely 20% of annotations on UCF101-24 when compared with recent fully supervised methods. On UCF101-24, it improves the score by +8.9% and +11% at 0.5 f-mAP and v-mAP respectively, compared to supervised approach. | ['Yogesh Singh Rawat', 'Akash Kumar'] | 2022-03-08 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Kumar_End-to-End_Semi-Supervised_Learning_for_Video_Action_Detection_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Kumar_End-to-End_Semi-Supervised_Learning_for_Video_Action_Detection_CVPR_2022_paper.pdf | cvpr-2022-1 | ['image-classification-shift-consistency'] | ['computer-vision'] | [ 2.69884080e-01 -2.01653644e-01 -5.33163011e-01 -1.79409996e-01
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b9550d68-21f6-4287-80f4-679e875528a5 | esad-end-to-end-deep-semi-supervised-anomaly | 2012.04905 | null | https://arxiv.org/abs/2012.04905v3 | https://arxiv.org/pdf/2012.04905v3.pdf | ESAD: End-to-end Deep Semi-supervised Anomaly Detection | This paper explores semi-supervised anomaly detection, a more practical setting for anomaly detection where a small additional set of labeled samples are provided. We propose a new KL-divergence based objective function for semi-supervised anomaly detection, and show that two factors: the mutual information between the data and latent representations, and the entropy of latent representations, constitute an integral objective function for anomaly detection. To resolve the contradiction in simultaneously optimizing the two factors, we propose a novel encoder-decoder-encoder structure, with the first encoder focusing on optimizing the mutual information and the second encoder focusing on optimizing the entropy. The two encoders are enforced to share similar encoding with a consistent constraint on their latent representations. Extensive experiments have revealed that the proposed method significantly outperforms several state-of-the-arts on multiple benchmark datasets, including medical diagnosis and several classic anomaly detection benchmarks. | ['Peisen Zhao', 'Qi Tian', 'Yan-Feng Wang', 'Ya zhang', 'Fei Ye', 'Chaoqin Huang'] | 2020-12-09 | null | null | null | null | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [ 2.11569846e-01 4.33082700e-01 -1.92013010e-01 -6.08288646e-01
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7c8dd6d2-2d6e-4d1c-9301-45a66dced59e | deep-structured-feature-networks-for-table | 2102.10287 | null | https://arxiv.org/abs/2102.10287v2 | https://arxiv.org/pdf/2102.10287v2.pdf | Deep Structured Feature Networks for Table Detection and Tabular Data Extraction from Scanned Financial Document Images | Automatic table detection in PDF documents has achieved a great success but tabular data extraction are still challenging due to the integrity and noise issues in detected table areas. The accurate data extraction is extremely crucial in finance area. Inspired by this, the aim of this research is proposing an automated table detection and tabular data extraction from financial PDF documents. We proposed a method that consists of three main processes, which are detecting table areas with a Faster R-CNN (Region-based Convolutional Neural Network) model with Feature Pyramid Network (FPN) on each page image, extracting contents and structures by a compounded layout segmentation technique based on optical character recognition (OCR) and formulating regular expression rules for table header separation. The tabular data extraction feature is embedded with rule-based filtering and restructuring functions that are highly scalable. We annotate a new Financial Documents dataset with table regions for the experiment. The excellent table detection performance of the detection model is obtained from our customized dataset. The main contributions of this paper are proposing the Financial Documents dataset with table-area annotations, the superior detection model and the rule-based layout segmentation technique for the tabular data extraction from PDF files. | ['Josiah Poon', 'Wanying Zhou', 'Yiwen Gong', 'Mengting Wu', 'Siwen Luo'] | 2021-02-20 | null | null | null | null | ['table-detection'] | ['miscellaneous'] | [ 1.24692947e-01 -3.15467864e-02 1.22771725e-01 -3.06825101e-01
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f68da2de-ddf0-4366-966d-a3589ecbd4c2 | incorporating-physical-constraints-in-a-deep | 1912.12976 | null | https://arxiv.org/abs/1912.12976v4 | https://arxiv.org/pdf/1912.12976v4.pdf | Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems | Data-based discovery of effective, coarse-grained (CG) models of high-dimensional dynamical systems presents a unique challenge in computational physics and particularly in the context of multiscale problems. The present paper offers a data-based, probablistic perspective that enables the quantification of predictive uncertainties. One of the outstanding problems has been the introduction of physical constraints in the probabilistic machine learning objectives. The primary utility of such constraints stems from the undisputed physical laws such as conservation of mass, energy etc. that they represent. Furthermore and apart from leading to physically realistic predictions, they can significantly reduce the requisite amount of training data which for high-dimensional, multiscale systems are expensive to obtain (Small Data regime). We formulate the coarse-graining process by employing a probabilistic state-space model and account for the aforementioned equality constraints as virtual observables in the associated densities. We demonstrate how probabilistic inference tools can be employed to identify the coarse-grained variables in combination with deep neural nets and their evolution model without ever needing to define a fine-to-coarse (restriction) projection and without needing time-derivatives of state variables. Furthermore, it is capable of reconstructing the evolution of the full, fine-scale system and therefore the observables of interest need not be selected a priori. We demonstrate the efficacy of the proposed framework by applying it to systems of interacting particles and an image-series of a nonlinear pendulum. | ['Phaedon-Stelios Koutsourelakis', 'Sebastian Kaltenbach'] | 2019-12-30 | null | null | null | null | ['small-data'] | ['computer-vision'] | [ 1.02547137e-02 -1.02078043e-01 2.96528757e-01 1.34228636e-02
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5fcec48b-a1b8-49af-8836-3d376329d914 | learning-graph-edit-distance-by-graph-neural | 2008.07641 | null | https://arxiv.org/abs/2008.07641v1 | https://arxiv.org/pdf/2008.07641v1.pdf | Learning Graph Edit Distance by Graph Neural Networks | The emergence of geometric deep learning as a novel framework to deal with graph-based representations has faded away traditional approaches in favor of completely new methodologies. In this paper, we propose a new framework able to combine the advances on deep metric learning with traditional approximations of the graph edit distance. Hence, we propose an efficient graph distance based on the novel field of geometric deep learning. Our method employs a message passing neural network to capture the graph structure, and thus, leveraging this information for its use on a distance computation. The performance of the proposed graph distance is validated on two different scenarios. On the one hand, in a graph retrieval of handwritten words~\ie~keyword spotting, showing its superior performance when compared with (approximate) graph edit distance benchmarks. On the other hand, demonstrating competitive results for graph similarity learning when compared with the current state-of-the-art on a recent benchmark dataset. | ['Alicia Fornés', 'Josep Lladós', 'Andreas Fischer', 'Pau Riba'] | 2020-08-17 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [ 8.99538845e-02 8.40604827e-02 2.07684234e-01 -3.70469362e-01
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64cb8a83-affb-4f8f-b13b-9790c9c99f70 | gradient-sparsification-for-efficient | 2304.04164 | null | https://arxiv.org/abs/2304.04164v1 | https://arxiv.org/pdf/2304.04164v1.pdf | Gradient Sparsification for Efficient Wireless Federated Learning with Differential Privacy | Federated learning (FL) enables distributed clients to collaboratively train a machine learning model without sharing raw data with each other. However, it suffers the leakage of private information from uploading models. In addition, as the model size grows, the training latency increases due to limited transmission bandwidth and the model performance degrades while using differential privacy (DP) protection. In this paper, we propose a gradient sparsification empowered FL framework over wireless channels, in order to improve training efficiency without sacrificing convergence performance. Specifically, we first design a random sparsification algorithm to retain a fraction of the gradient elements in each client's local training, thereby mitigating the performance degradation induced by DP and and reducing the number of transmission parameters over wireless channels. Then, we analyze the convergence bound of the proposed algorithm, by modeling a non-convex FL problem. Next, we formulate a time-sequential stochastic optimization problem for minimizing the developed convergence bound, under the constraints of transmit power, the average transmitting delay, as well as the client's DP requirement. Utilizing the Lyapunov drift-plus-penalty framework, we develop an analytical solution to the optimization problem. Extensive experiments have been implemented on three real life datasets to demonstrate the effectiveness of our proposed algorithm. We show that our proposed algorithms can fully exploit the interworking between communication and computation to outperform the baselines, i.e., random scheduling, round robin and delay-minimization algorithms. | ['Hongbo Zhu', 'Wen Chen', 'Haitao Zhao', 'Ming Ding', 'Chuan Ma', 'Jun Li', 'Kang Wei'] | 2023-04-09 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [ 4.07254100e-02 8.30213726e-02 -3.57258737e-01 -3.05602819e-01
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f8c0802e-0a08-47c0-a4a8-13d34ee14f8d | quantile-based-fuzzy-c-means-clustering-of | 2109.11027 | null | https://arxiv.org/abs/2109.11027v1 | https://arxiv.org/pdf/2109.11027v1.pdf | Quantile-based fuzzy C-means clustering of multivariate time series: Robust techniques | Three robust methods for clustering multivariate time series from the point of view of generating processes are proposed. The procedures are robust versions of a fuzzy C-means model based on: (i) estimates of the quantile cross-spectral density and (ii) the classical principal component analysis. Robustness to the presence of outliers is achieved by using the so-called metric, noise and trimmed approaches. The metric approach incorporates in the objective function a distance measure aimed at neutralizing the effect of the outliers, the noise approach builds an artificial cluster expected to contain the outlying series and the trimmed approach eliminates the most atypical series in the dataset. All the proposed techniques inherit the nice properties of the quantile cross-spectral density, as being able to uncover general types of dependence. Results from a broad simulation study including multivariate linear, nonlinear and GARCH processes indicate that the algorithms are substantially effective in coping with the presence of outlying series (i.e., series exhibiting a dependence structure different from that of the majority), clearly poutperforming alternative procedures. The usefulness of the suggested methods is highlighted by means of two specific applications regarding financial and environmental series. | ['Borja Lafuente-Rego', 'José Antonio Vilar', "Pierpaolo D'Urso", 'Ángel López-Oriona'] | 2021-09-22 | null | null | null | null | ['clustering-multivariate-time-series'] | ['time-series'] | [-2.80328244e-02 -1.52271152e-01 3.86376351e-01 -1.07109793e-01
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a9b6038f-afdd-443d-800d-78884ff00dba | bpnet-bezier-primitive-segmentation-on-3d | 2307.04013 | null | https://arxiv.org/abs/2307.04013v1 | https://arxiv.org/pdf/2307.04013v1.pdf | BPNet: Bézier Primitive Segmentation on 3D Point Clouds | This paper proposes BPNet, a novel end-to-end deep learning framework to learn B\'ezier primitive segmentation on 3D point clouds. The existing works treat different primitive types separately, thus limiting them to finite shape categories. To address this issue, we seek a generalized primitive segmentation on point clouds. Taking inspiration from B\'ezier decomposition on NURBS models, we transfer it to guide point cloud segmentation casting off primitive types. A joint optimization framework is proposed to learn B\'ezier primitive segmentation and geometric fitting simultaneously on a cascaded architecture. Specifically, we introduce a soft voting regularizer to improve primitive segmentation and propose an auto-weight embedding module to cluster point features, making the network more robust and generic. We also introduce a reconstruction module where we successfully process multiple CAD models with different primitives simultaneously. We conducted extensive experiments on the synthetic ABC dataset and real-scan datasets to validate and compare our approach with different baseline methods. Experiments show superior performance over previous work in terms of segmentation, with a substantially faster inference speed. | ['Pierre Alliez', 'Xiao Xiao', 'Qian Li', 'Cheng Wen', 'Rao Fu'] | 2023-07-08 | null | null | null | null | ['point-cloud-segmentation'] | ['computer-vision'] | [ 1.38637554e-02 -7.37058371e-02 2.73179915e-02 -6.23996377e-01
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2738460d-4894-40eb-b9c2-91222e28b2be | interleaver-design-for-deep-neural-networks | 1711.06935 | null | http://arxiv.org/abs/1711.06935v3 | http://arxiv.org/pdf/1711.06935v3.pdf | Interleaver Design for Deep Neural Networks | We propose a class of interleavers for a novel deep neural network (DNN)
architecture that uses algorithmically pre-determined, structured sparsity to
significantly lower memory and computational requirements, and speed up
training. The interleavers guarantee clash-free memory accesses to eliminate
idle operational cycles, optimize spread and dispersion to improve network
performance, and are designed to ease the complexity of memory address
computations in hardware. We present a design algorithm with mathematical
proofs for these properties. We also explore interleaver variations and analyze
the behavior of neural networks as a function of interleaver metrics. | ['Keith M. Chugg', 'Sourya Dey', 'Peter A. Beerel'] | 2017-11-18 | null | null | null | null | ['mathematical-proofs'] | ['miscellaneous'] | [ 2.30487406e-01 -2.47245401e-01 -3.46792012e-01 -3.76311481e-01
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ec237948-2080-44a1-a5c3-da22fb7fba51 | improving-data-augmentation-in-low-resource | null | null | https://openreview.net/forum?id=LINkFIdHsuZ | https://openreview.net/pdf?id=LINkFIdHsuZ | Improving Data Augmentation in Low-resource Question Answering with Active Learning in Multiple Stages | Neural approaches have become very popular in the domain of Question Answering, however they require a large amount of annotated data. Furthermore, they often yield very good performance but only in the domain they were trained on. In this work we propose a novel approach that combines data augmentation via question-answer generation and active learning to improve performance in low resource settings, where the target domain is vastly different from the source domain. Furthermore, we investigate data augmentation via generation for question answering in three different low-resource settings relevant in practice and how this can be improved: 1) No labels for the target domain, 2) static, labelled data for the target domain and 3) an Active Learning approach with labels for the target domain provided by an expert. In all settings we assume sufficient amount of labelled data from the source domain is available. We perform extensive experiments in each of the above conditions. Our findings show that our novel approach, which combines data augmentation with active learning, boosts performances in the low-resource, domain-specific setting, allowing for low-labelling-effort question answering systems in new, specialized domains. They further demonstrate how to best utilize data augmentation to boost performance in these settings. | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['question-answer-generation'] | ['natural-language-processing'] | [ 5.19410491e-01 4.88007814e-01 -1.88044593e-01 -4.76901352e-01
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08149138-05bd-457f-8a87-e4fad2adc42e | always-on-674uw-4gop-s-error-resilient-binary | 2007.08952 | null | https://arxiv.org/abs/2007.08952v1 | https://arxiv.org/pdf/2007.08952v1.pdf | Always-On 674uW @ 4GOP/s Error Resilient Binary Neural Networks with Aggressive SRAM Voltage Scaling on a 22nm IoT End-Node | Binary Neural Networks (BNNs) have been shown to be robust to random bit-level noise, making aggressive voltage scaling attractive as a power-saving technique for both logic and SRAMs. In this work, we introduce the first fully programmable IoT end-node system-on-chip (SoC) capable of executing software-defined, hardware-accelerated BNNs at ultra-low voltage. Our SoC exploits a hybrid memory scheme where error-vulnerable SRAMs are complemented by reliable standard-cell memories to safely store critical data under aggressive voltage scaling. On a prototype in 22nm FDX technology, we demonstrate that both the logic and SRAM voltage can be dropped to 0.5Vwithout any accuracy penalty on a BNN trained for the CIFAR-10 dataset, improving energy efficiency by 2.2X w.r.t. nominal conditions. Furthermore, we show that the supply voltage can be dropped to 0.42V (50% of nominal) while keeping more than99% of the nominal accuracy (with a bit error rate ~1/1000). In this operating point, our prototype performs 4Gop/s (15.4Inference/s on the CIFAR-10 dataset) by computing up to 13binary ops per pJ, achieving 22.8 Inference/s/mW while keeping within a peak power envelope of 674uW - low enough to enable always-on operation in ultra-low power smart cameras, long-lifetime environmental sensors, and insect-sized pico-drones. | ['Pasquale Davide Schiavone', 'Alfio Di Mauro', 'Luca Benini', 'Francesco Conti', 'Davide Rossi'] | 2020-07-17 | null | null | null | null | ['pico'] | ['natural-language-processing'] | [ 3.94231915e-01 -8.81072059e-02 -5.94890356e-01 -2.97107309e-01
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ecf389b7-a86c-4821-925a-a37ea86d0127 | muslcat-multi-scale-multi-level-convolutional | 2104.02309 | null | https://arxiv.org/abs/2104.02309v1 | https://arxiv.org/pdf/2104.02309v1.pdf | MuSLCAT: Multi-Scale Multi-Level Convolutional Attention Transformer for Discriminative Music Modeling on Raw Waveforms | In this work, we aim to improve the expressive capacity of waveform-based discriminative music networks by modeling both sequential (temporal) and hierarchical information in an efficient end-to-end architecture. We present MuSLCAT, or Multi-scale and Multi-level Convolutional Attention Transformer, a novel architecture for learning robust representations of complex music tags directly from raw waveform recordings. We also introduce a lightweight variant of MuSLCAT called MuSLCAN, short for Multi-scale and Multi-level Convolutional Attention Network. Both MuSLCAT and MuSLCAN model features from multiple scales and levels by integrating a frontend-backend architecture. The frontend targets different frequency ranges while modeling long-range dependencies and multi-level interactions by using two convolutional attention networks with attention-augmented convolution (AAC) blocks. The backend dynamically recalibrates multi-scale and level features extracted from the frontend by incorporating self-attention. The difference between MuSLCAT and MuSLCAN is their backend components. MuSLCAT's backend is a modified version of BERT. While MuSLCAN's is a simple AAC block. We validate the proposed MuSLCAT and MuSLCAN architectures by comparing them to state-of-the-art networks on four benchmark datasets for music tagging and genre recognition. Our experiments show that MuSLCAT and MuSLCAN consistently yield competitive results when compared to state-of-the-art waveform-based models yet require considerably fewer parameters. | ['David Guy Brizan', 'Shyam Sudhakaran', 'Kai Middlebrook'] | 2021-04-06 | null | null | null | null | ['music-modeling'] | ['music'] | [ 3.42158452e-02 -5.08142769e-01 -1.19505348e-02 -1.19549222e-01
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dd578d62-725f-454e-a2a3-6fb185ebb0d1 | knowledge-base-inference-for-regular | 2005.00480 | null | https://arxiv.org/abs/2005.00480v2 | https://arxiv.org/pdf/2005.00480v2.pdf | Regex Queries over Incomplete Knowledge Bases | We propose the novel task of answering regular expression queries (containing disjunction ($\vee$) and Kleene plus ($+$) operators) over incomplete KBs. The answer set of these queries potentially has a large number of entities, hence previous works for single-hop queries in KBC that model a query as a point in high-dimensional space are not as effective. In response, we develop RotatE-Box -- a novel combination of RotatE and box embeddings. It can model more relational inference patterns compared to existing embedding based models. Furthermore, we define baseline approaches for embedding based KBC models to handle regex operators. We demonstrate performance of RotatE-Box on two new regex-query datasets introduced in this paper, including one where the queries are harvested based on actual user query logs. We find that our final RotatE-Box model significantly outperforms models based on just RotatE and just box embeddings. | ['Parth Shah', 'Mausam', 'Vaibhav Adlakha', 'Srikanta Bedathur'] | 2020-05-01 | null | https://openreview.net/forum?id=4YQVfA5vEJS | https://openreview.net/pdf?id=4YQVfA5vEJS | akbc-2021-10 | ['knowledge-base-completion', 'knowledge-base-completion'] | ['graphs', 'knowledge-base'] | [-5.27069390e-01 3.86325121e-01 -6.99278295e-01 -4.51300889e-01
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a487b9fb-884a-43ec-85bc-f1fbb04a618a | teaching-clip-to-count-to-ten | 2302.12066 | null | https://arxiv.org/abs/2302.12066v1 | https://arxiv.org/pdf/2302.12066v1.pdf | Teaching CLIP to Count to Ten | Large vision-language models (VLMs), such as CLIP, learn rich joint image-text representations, facilitating advances in numerous downstream tasks, including zero-shot classification and text-to-image generation. Nevertheless, existing VLMs exhibit a prominent well-documented limitation - they fail to encapsulate compositional concepts such as counting. We introduce a simple yet effective method to improve the quantitative understanding of VLMs, while maintaining their overall performance on common benchmarks. Specifically, we propose a new counting-contrastive loss used to finetune a pre-trained VLM in tandem with its original objective. Our counting loss is deployed over automatically-created counterfactual examples, each consisting of an image and a caption containing an incorrect object count. For example, an image depicting three dogs is paired with the caption "Six dogs playing in the yard". Our loss encourages discrimination between the correct caption and its counterfactual variant which serves as a hard negative example. To the best of our knowledge, this work is the first to extend CLIP's capabilities to object counting. Furthermore, we introduce "CountBench" - a new image-text counting benchmark for evaluating a model's understanding of object counting. We demonstrate a significant improvement over state-of-the-art baseline models on this task. Finally, we leverage our count-aware CLIP model for image retrieval and text-conditioned image generation, demonstrating that our model can produce specific counts of objects more reliably than existing ones. | ['Tali Dekel', 'Michal Irani', 'Inbar Mosseri', 'Shiran Zada', 'Omer Tov', 'Ariel Ephrat', 'Roni Paiss'] | 2023-02-23 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [ 6.70313895e-01 -2.44364351e-01 -6.05845936e-02 -3.47295642e-01
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99dd2fe7-d7a4-4342-a936-b96ce0135b44 | evi-multilingual-spoken-dialogue-tasks-and | null | null | https://openreview.net/forum?id=p5jgs957DXh | https://openreview.net/pdf?id=p5jgs957DXh | EVI: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification | Knowledge-based authentication is crucial for task-oriented spoken dialogue systems that offer personalised and privacy-focused services. Such systems should be able to enrol (E), verify (V), and identify (I) new and recurring users based on their personal information, e.g. postcode, name, and date-of-birth. In this work, we formalise the three authentication tasks and their evaluation protocols, and we present EVI, a challenging spoken multilingual dataset with 5,506 dialogues in English, Polish, and French. Our proposed models set the first competitive benchmarks, explore the challenges of multilingual natural language processing of spoken dialogue, and set directions for future research. | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['spoken-dialogue-systems'] | ['speech'] | [-3.53950858e-01 1.88537166e-01 -1.77258160e-02 -5.67982793e-01
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c5bd5884-da79-42d0-af0b-72a483cd431f | part-of-speech-tagging-of-odia-language-using | 2207.03256 | null | https://arxiv.org/abs/2207.03256v1 | https://arxiv.org/pdf/2207.03256v1.pdf | Part-of-Speech Tagging of Odia Language Using statistical and Deep Learning-Based Approaches | Automatic Part-of-speech (POS) tagging is a preprocessing step of many natural language processing (NLP) tasks such as name entity recognition (NER), speech processing, information extraction, word sense disambiguation, and machine translation. It has already gained a promising result in English and European languages, but in Indian languages, particularly in Odia language, it is not yet well explored because of the lack of supporting tools, resources, and morphological richness of language. Unfortunately, we were unable to locate an open source POS tagger for Odia, and only a handful of attempts have been made to develop POS taggers for Odia language. The main contribution of this research work is to present a conditional random field (CRF) and deep learning-based approaches (CNN and Bidirectional Long Short-Term Memory) to develop Odia part-of-speech tagger. We used a publicly accessible corpus and the dataset is annotated with the Bureau of Indian Standards (BIS) tagset. However, most of the languages around the globe have used the dataset annotated with Universal Dependencies (UD) tagset. Hence, to maintain uniformity Odia dataset should use the same tagset. So we have constructed a simple mapping from BIS tagset to UD tagset. We experimented with various feature set inputs to the CRF model, observed the impact of constructed feature set. The deep learning-based model includes Bi-LSTM network, CNN network, CRF layer, character sequence information, and pre-trained word vector. Character sequence information was extracted by using convolutional neural network (CNN) and Bi-LSTM network. Six different combinations of neural sequence labelling models are implemented, and their performance measures are investigated. It has been observed that Bi-LSTM model with character sequence feature and pre-trained word vector achieved a significant state-of-the-art result. | ['Pankaj K Sa', 'Tapas Kumar Mishra', 'Tusarkanta Dalai'] | 2022-07-07 | null | null | null | null | ['word-sense-disambiguation'] | ['natural-language-processing'] | [-1.43256597e-02 2.80100405e-02 -2.04838768e-01 -3.36434513e-01
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d606bc79-aee2-4bc6-9de2-376b44f477d6 | decision-transformer-under-random-frame | 2303.03391 | null | https://arxiv.org/abs/2303.03391v1 | https://arxiv.org/pdf/2303.03391v1.pdf | Decision Transformer under Random Frame Dropping | Controlling agents remotely with deep reinforcement learning~(DRL) in the real world is yet to come. One crucial stepping stone is to devise RL algorithms that are robust in the face of dropped information from corrupted communication or malfunctioning sensors. Typical RL methods usually require considerable online interaction data that are costly and unsafe to collect in the real world. Furthermore, when applying to the frame dropping scenarios, they perform unsatisfactorily even with moderate drop rates. To address these issues, we propose Decision Transformer under Random Frame Dropping~(DeFog), an offline RL algorithm that enables agents to act robustly in frame dropping scenarios without online interaction. DeFog first randomly masks out data in the offline datasets and explicitly adds the time span of frame dropping as inputs. After that, a finetuning stage on the same offline dataset with a higher mask rate would further boost the performance. Empirical results show that DeFog outperforms strong baselines under severe frame drop rates like 90\%, while maintaining similar returns under non-frame-dropping conditions in the regular MuJoCo control benchmarks and the Atari environments. Our approach offers a robust and deployable solution for controlling agents in real-world environments with limited or unreliable data. | ['Huazhe Xu', 'Yang Gao', 'Ray Chen Zheng', 'Kaizhe Hu'] | 2023-03-03 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [ 7.12755369e-03 -3.07820350e-01 -1.57759711e-01 -3.33863527e-01
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e107a780-dd34-488d-ae72-16f1c2d7e687 | topic-relevant-response-generation-using | null | null | https://aclanthology.org/2020.coling-main.359 | https://aclanthology.org/2020.coling-main.359.pdf | Topic-relevant Response Generation using Optimal Transport for an Open-domain Dialog System | Conventional neural generative models tend to generate safe and generic responses which have little connection with previous utterances semantically and would disengage users in a dialog system. To generate relevant responses, we propose a method that employs two types of constraints - topical constraint and semantic constraint. Under the hypothesis that a response and its context have higher relevance when they share the same topics, the topical constraint encourages the topics of a response to match its context by conditioning response decoding on topic words{'} embeddings. The semantic constraint, which encourages a response to be semantically related to its context by regularizing the decoding objective function with semantic distance, is proposed. Optimal transport is applied to compute a weighted semantic distance between the representation of a response and the context. Generated responses are evaluated by automatic metrics, as well as human judgment, showing that the proposed method can generate more topic-relevant and content-rich responses than conventional models. | ['Tatsuya Kawahara', 'Tianyu Zhao', 'Shuying Zhang'] | 2020-12-01 | null | null | null | coling-2020-8 | ['open-domain-dialog'] | ['natural-language-processing'] | [ 3.47273022e-01 3.89728367e-01 4.08415198e-02 -8.56206656e-01
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8cd2b73c-5dbf-43da-b71b-639b95b65274 | slam-simultaneous-localisation-and-mapping-at | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Salas-Moreno_SLAM_Simultaneous_Localisation_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Salas-Moreno_SLAM_Simultaneous_Localisation_2013_CVPR_paper.pdf | SLAM++: Simultaneous Localisation and Mapping at the Level of Objects | We present the major advantages of a new 'object oriented' 3D SLAM paradigm, which takes full advantage in the loop of prior knowledge that many scenes consist of repeated, domain-specific objects and structures. As a hand-held depth camera browses a cluttered scene, realtime 3D object recognition and tracking provides 6DoF camera-object constraints which feed into an explicit graph of objects, continually refined by efficient pose-graph optimisation. This offers the descriptive and predictive power of SLAM systems which perform dense surface reconstruction, but with a huge representation compression. The object graph enables predictions for accurate ICP-based camera to model tracking at each live frame, and efficient active search for new objects in currently undescribed image regions. We demonstrate real-time incremental SLAM in large, cluttered environments, including loop closure, relocalisation and the detection of moved objects, and of course the generation of an object level scene description with the potential to enable interaction. | ['Paul H. J. Kelly', 'Andrew J. Davison', 'Renato F. Salas-Moreno', 'Hauke Strasdat', 'Richard A. Newcombe'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['3d-object-recognition'] | ['computer-vision'] | [ 5.26830375e-01 9.70061403e-03 2.27191061e-01 -3.53441983e-01
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a85a8a61-0b5d-487e-9962-1fa8a90ac353 | multi-temporal-lip-audio-memory-for-visual | 2305.04542 | null | https://arxiv.org/abs/2305.04542v1 | https://arxiv.org/pdf/2305.04542v1.pdf | Multi-Temporal Lip-Audio Memory for Visual Speech Recognition | Visual Speech Recognition (VSR) is a task to predict a sentence or word from lip movements. Some works have been recently presented which use audio signals to supplement visual information. However, existing methods utilize only limited information such as phoneme-level features and soft labels of Automatic Speech Recognition (ASR) networks. In this paper, we present a Multi-Temporal Lip-Audio Memory (MTLAM) that makes the best use of audio signals to complement insufficient information of lip movements. The proposed method is mainly composed of two parts: 1) MTLAM saves multi-temporal audio features produced from short- and long-term audio signals, and the MTLAM memorizes a visual-to-audio mapping to load stored multi-temporal audio features from visual features at the inference phase. 2) We design an audio temporal model to produce multi-temporal audio features capturing the context of neighboring words. In addition, to construct effective visual-to-audio mapping, the audio temporal models can generate audio features time-aligned with visual features. Through extensive experiments, we validate the effectiveness of the MTLAM achieving state-of-the-art performances on two public VSR datasets. | ['Yong Man Ro', 'Minsu Kim', 'Jeong Hun Yeo'] | 2023-05-08 | null | null | null | null | ['visual-speech-recognition'] | ['speech'] | [ 1.72058985e-01 -4.51509029e-01 -3.81037623e-01 -3.38127524e-01
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3f766e40-57cd-4775-adaa-f8fc2c24c5b6 | transrev-modeling-reviews-as-translations | 1801.10095 | null | http://arxiv.org/abs/1801.10095v2 | http://arxiv.org/pdf/1801.10095v2.pdf | TransRev: Modeling Reviews as Translations from Users to Items | The text of a review expresses the sentiment a customer has towards a
particular product. This is exploited in sentiment analysis where machine
learning models are used to predict the review score from the text of the
review. Furthermore, the products costumers have purchased in the past are
indicative of the products they will purchase in the future. This is what
recommender systems exploit by learning models from purchase information to
predict the items a customer might be interested in. We propose TransRev, an
approach to the product recommendation problem that integrates ideas from
recommender systems, sentiment analysis, and multi-relational learning into a
joint learning objective. TransRev learns vector representations for users,
items, and reviews. The embedding of a review is learned such that (a) it
performs well as input feature of a regression model for sentiment prediction;
and (b) it always translates the reviewer embedding to the embedding of the
reviewed items. This allows TransRev to approximate a review embedding at test
time as the difference of the embedding of each item and the user embedding.
The approximated review embedding is then used with the regression model to
predict the review score for each item. TransRev outperforms state of the art
recommender systems on a large number of benchmark data sets. Moreover, it is
able to retrieve, for each user and item, the review text from the training set
whose embedding is most similar to the approximated review embedding. | ['Daniel Onoro-Rubio', 'Alberto Garcia-Duran', 'Hui Li', 'Roberto Gonzalez', 'Mathias Niepert'] | 2018-01-30 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [-3.79813284e-01 5.66169210e-02 -7.31617153e-01 -5.62269866e-01
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29ec6df4-d15e-44d6-b078-ac7aa39d950d | deep-dependency-networks-for-multi-label | 2302.00633 | null | https://arxiv.org/abs/2302.00633v2 | https://arxiv.org/pdf/2302.00633v2.pdf | Deep Dependency Networks for Multi-Label Classification | We propose a simple approach which combines the strengths of probabilistic graphical models and deep learning architectures for solving the multi-label classification task, focusing specifically on image and video data. First, we show that the performance of previous approaches that combine Markov Random Fields with neural networks can be modestly improved by leveraging more powerful methods such as iterative join graph propagation, integer linear programming, and $\ell_1$ regularization-based structure learning. Then we propose a new modeling framework called deep dependency networks, which augments a dependency network, a model that is easy to train and learns more accurate dependencies but is limited to Gibbs sampling for inference, to the output layer of a neural network. We show that despite its simplicity, jointly learning this new architecture yields significant improvements in performance over the baseline neural network. In particular, our experimental evaluation on three video activity classification datasets: Charades, Textually Annotated Cooking Scenes (TACoS), and Wetlab, and three multi-label image classification datasets: MS-COCO, PASCAL VOC, and NUS-WIDE show that deep dependency networks are almost always superior to pure neural architectures that do not use dependency networks. | ['Vibhav Gogate', 'Yu Xiang', 'Shivvrat Arya'] | 2023-02-01 | null | null | null | null | ['action-classification', 'multi-label-image-classification'] | ['computer-vision', 'computer-vision'] | [ 2.23610118e-01 9.13066044e-02 -5.17386615e-01 -7.00801909e-01
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4395ef7c-a880-4667-b953-a5bcbf4e9bbc | few-shot-table-to-text-generation-with-prompt | 2302.04415 | null | https://arxiv.org/abs/2302.04415v2 | https://arxiv.org/pdf/2302.04415v2.pdf | Few-Shot Table-to-Text Generation with Prompt Planning and Knowledge Memorization | Pre-trained language models (PLM) have achieved remarkable advancement in table-to-text generation tasks. However, the lack of labeled domain-specific knowledge and the topology gap between tabular data and text make it difficult for PLMs to yield faithful text. Low-resource generation likewise faces unique challenges in this domain. Inspired by how humans descript tabular data with prior knowledge, we suggest a new framework: PromptMize, which targets table-to-text generation under few-shot settings. The design of our framework consists of two aspects: a prompt planner and a knowledge adapter. The prompt planner aims to generate a prompt signal that provides instance guidance for PLMs to bridge the topology gap between tabular data and text. Moreover, the knowledge adapter memorizes domain-specific knowledge from the unlabelled corpus to supply essential information during generation. Extensive experiments and analyses are investigated on three open domain few-shot NLG datasets: human, song, and book. Compared with previous state-of-the-art approaches, our model achieves remarkable performance in generating quality as judged by human and automatic evaluations. | ['Xinbing Wang', 'Guanjie Zheng', 'Zhouhan Lin', 'Ziwei He', 'Jianping Zhou', 'Jiexing Qi', 'Minyxuan Yan', 'Zhixin Guo'] | 2023-02-09 | null | null | null | null | ['memorization', 'table-to-text-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.66042757e-01 6.09442472e-01 -2.86670119e-01 -9.23624858e-02
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45c4f59c-fd08-4cf8-9afe-17f33416a043 | evolvegcn-evolving-graph-convolutional | 1902.10191 | null | https://arxiv.org/abs/1902.10191v3 | https://arxiv.org/pdf/1902.10191v3.pdf | EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs | Graph representation learning resurges as a trending research subject owing to the widespread use of deep learning for Euclidean data, which inspire various creative designs of neural networks in the non-Euclidean domain, particularly graphs. With the success of these graph neural networks (GNN) in the static setting, we approach further practical scenarios where the graph dynamically evolves. Existing approaches typically resort to node embeddings and use a recurrent neural network (RNN, broadly speaking) to regulate the embeddings and learn the temporal dynamics. These methods require the knowledge of a node in the full time span (including both training and testing) and are less applicable to the frequent change of the node set. In some extreme scenarios, the node sets at different time steps may completely differ. To resolve this challenge, we propose EvolveGCN, which adapts the graph convolutional network (GCN) model along the temporal dimension without resorting to node embeddings. The proposed approach captures the dynamism of the graph sequence through using an RNN to evolve the GCN parameters. Two architectures are considered for the parameter evolution. We evaluate the proposed approach on tasks including link prediction, edge classification, and node classification. The experimental results indicate a generally higher performance of EvolveGCN compared with related approaches. The code is available at \url{https://github.com/IBM/EvolveGCN}. | ['Charles E. Leiserson', 'Tao B. Schardl', 'Jie Chen', 'Giacomo Domeniconi', 'Toyotaro Suzumura', 'Tim Kaler', 'Tengfei Ma', 'Hiroki Kanezashi', 'Aldo Pareja'] | 2019-02-26 | null | null | null | null | ['dynamic-link-prediction'] | ['graphs'] | [-1.75970554e-01 -1.65741891e-02 -2.49264352e-02 -3.85840982e-02
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aeae39f9-b5a7-4d40-8d6e-a0c49a1bd163 | road-segmentation-using-cnn-with-gru | 1804.05164 | null | http://arxiv.org/abs/1804.05164v1 | http://arxiv.org/pdf/1804.05164v1.pdf | Road Segmentation Using CNN with GRU | This paper presents an accurate and fast algorithm for road segmentation
using convolutional neural network (CNN) and gated recurrent units (GRU). For
autonomous vehicles, road segmentation is a fundamental task that can provide
the drivable area for path planning. The existing deep neural network based
segmentation algorithms usually take a very deep encoder-decoder structure to
fuse pixels, which requires heavy computations, large memory and long
processing time. Hereby, a CNN-GRU network model is proposed and trained to
perform road segmentation using data captured by the front camera of a vehicle.
GRU network obtains a long spatial sequence with lower computational
complexity, comparing to traditional encoder-decoder architecture. The proposed
road detector is evaluated on the KITTI road benchmark and achieves high
accuracy for road segmentation at real-time processing speed. | ['Yecheng Lyu', 'Xinming Huang'] | 2018-04-14 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [ 3.65283281e-01 1.54573232e-01 -3.80847633e-01 -5.15786827e-01
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3a4d63a9-5289-498e-b1b3-12a54e1c0391 | shi-he-jian-dong-ren-shi-yong-zhi-yu-yin | null | null | https://aclanthology.org/2019.ijclclp-2.3 | https://aclanthology.org/2019.ijclclp-2.3.pdf | 適合漸凍人使用之語音轉換系統初步研究 (Deep Neural-Network Bandwidth Extension and Denoising Voice Conversion System for ALS Patients) | null | ['Daniel Hládek', 'Matúš Pleva', 'Guang-Feng Deng', 'Yuan-Fu Liao', 'Bai-Hong Huang'] | null | null | null | null | ijclclp-2019-12 | ['bandwidth-extension', 'bandwidth-extension'] | ['audio', 'speech'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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