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e3856e5c-aa11-4b1f-868b-2535d0fbb4e7 | self-supervised-image-prior-learning-with-gmm | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Liu_Self-Supervised_Image_Prior_Learning_With_GMM_From_a_Single_Noisy_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Liu_Self-Supervised_Image_Prior_Learning_With_GMM_From_a_Single_Noisy_ICCV_2021_paper.pdf | Self-Supervised Image Prior Learning With GMM From a Single Noisy Image | The lack of clean images undermines the practicability of supervised image prior learning methods, of which the training schemes require a large number of clean images. To free image prior learning from the image collection burden, a novel Self-Supervised learning method for Gaussian Mixture Model (SS-GMM) is proposed in this paper. It can simultaneously achieve the noise level estimation and the image prior learning directly from only a single noisy image. This work is derived from our study on eigenvalues of the GMM's covariance matrix. Through statistical experiments and theoretical analysis, we conclude that (1) covariance eigenvalues for clean images hold the sparsity; and that (2) those for noisy images contain sufficient information for noise estimation. The first conclusion inspires us to impose a sparsity constraint on covariance eigenvalues during the learning process to suppress the influence of noise. The second conclusion leads to a self-contained noise estimation module of high accuracy in our proposed method. This module serves to estimate the noise level and automatically determine the specific level of the sparsity constraint. Our final derived method requires only minor modifications to the standard expectation-maximization algorithm. This makes it easy to implement. Very interestingly, the GMM learned via our proposed self-supervised learning method can even achieve better image denoising performance than its supervised counterpart, i.e., the EPLL. Also, it is on par with the state-of-the-art self-supervised deep learning method, i.e., the Self2Self. Code is available at https://github.com/HUST-Tan/SS-GMM. | ['Shan Tan', 'Jiangbo Lu', 'Xuan Liu', 'Haosen Liu'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['noise-estimation'] | ['medical'] | [ 1.78934753e-01 -1.12917364e-01 1.34007186e-01 -2.47022435e-01
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44b2baaf-13cb-40f8-b0da-eb50587f0e64 | interpretability-and-causal-discovery-of-the | 2212.10718 | null | https://arxiv.org/abs/2212.10718v1 | https://arxiv.org/pdf/2212.10718v1.pdf | Interpretability and causal discovery of the machine learning models to predict the production of CBM wells after hydraulic fracturing | Machine learning approaches are widely studied in the production prediction of CBM wells after hydraulic fracturing, but merely used in practice due to the low generalization ability and the lack of interpretability. A novel methodology is proposed in this article to discover the latent causality from observed data, which is aimed at finding an indirect way to interpret the machine learning results. Based on the theory of causal discovery, a causal graph is derived with explicit input, output, treatment and confounding variables. Then, SHAP is employed to analyze the influence of the factors on the production capability, which indirectly interprets the machine learning models. The proposed method can capture the underlying nonlinear relationship between the factors and the output, which remedies the limitation of the traditional machine learning routines based on the correlation analysis of factors. The experiment on the data of CBM shows that the detected relationship between the production and the geological/engineering factors by the presented method, is coincident with the actual physical mechanism. Meanwhile, compared with traditional methods, the interpretable machine learning models have better performance in forecasting production capability, averaging 20% improvement in accuracy. | ['Zhaozhong Yang', 'Xiaogang Li', 'Liangjie Gou', 'Guoquan Wen', 'Chao Min'] | 2022-12-21 | null | null | null | null | ['causal-discovery', 'interpretable-machine-learning'] | ['knowledge-base', 'methodology'] | [ 1.55693501e-01 2.03056723e-01 -3.54876876e-01 -2.80320887e-02
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12298351-ea54-4c37-a8e9-82d04bc93b06 | zju-reler-submission-for-epic-kitchen-1 | 2307.02508 | null | https://arxiv.org/abs/2307.02508v2 | https://arxiv.org/pdf/2307.02508v2.pdf | ZJU ReLER Submission for EPIC-KITCHEN Challenge 2023: TREK-150 Single Object Tracking | The Associating Objects with Transformers (AOT) framework has exhibited exceptional performance in a wide range of complex scenarios for video object tracking and segmentation. In this study, we convert the bounding boxes to masks in reference frames with the help of the Segment Anything Model (SAM) and Alpha-Refine, and then propagate the masks to the current frame, transforming the task from Video Object Tracking (VOT) to video object segmentation (VOS). Furthermore, we introduce MSDeAOT, a variant of the AOT series that incorporates transformers at multiple feature scales. MSDeAOT efficiently propagates object masks from previous frames to the current frame using two feature scales of 16 and 8. As a testament to the effectiveness of our design, we achieved the 1st place in the EPIC-KITCHENS TREK-150 Object Tracking Challenge. | ['Yueting Zhuang', 'Yi Yang', 'Zongxin Yang', 'Jiahao Li', 'Yuanyou Xu'] | 2023-07-05 | null | null | null | null | ['object-tracking', 'video-object-tracking', 'video-object-segmentation', 'video-semantic-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 3.20198059e-01 -3.97283584e-01 -1.31468982e-01 -1.30053759e-01
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98101bb7-53c4-4bd5-9ec0-07984a873ab0 | fpaenet-pneumonia-detection-network-based-on | 2011.08706 | null | http://arxiv.org/abs/2011.08706v1 | http://arxiv.org/pdf/2011.08706v1.pdf | FPAENet: Pneumonia Detection Network Based on Feature Pyramid Attention Enhancement | Automatic pneumonia Detection based on deep learning has increasing clinical
value. Although the existing Feature Pyramid Network (FPN) and its variants
have already achieved some great successes, their detection accuracies for
pneumonia lesions in medical images are still unsatisfactory. In this paper, we
propose a pneumonia detection network based on feature pyramid attention
enhancement, which integrates attended high-level semantic features with
low-level information. We add another information extracting path equipped with
feature enhancement modules, which are conducted with an attention mechanism.
Experimental results show that our proposed method can achieve much better
performances, as a higher value of 4.02% and 3.19%, than the baselines in
detecting pneumonia lesions. | [] | 2020-11-16 | null | null | null | null | ['pneumonia-detection'] | ['medical'] | [ 1.43651426e-01 -4.42387998e-01 3.16016600e-02 -1.99290603e-01
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7488423d-b36b-4901-b533-0742cc442324 | oair-object-aware-image-retargeting-using-pso | 2209.04804 | null | https://arxiv.org/abs/2209.04804v1 | https://arxiv.org/pdf/2209.04804v1.pdf | OAIR: Object-Aware Image Retargeting Using PSO and Aesthetic Quality Assessment | Image retargeting aims at altering an image size while preserving important content and minimizing noticeable distortions. However, previous image retargeting methods create outputs that suffer from artifacts and distortions. Besides, most previous works attempt to retarget the background and foreground of the input image simultaneously. Simultaneous resizing of the foreground and background causes changes in the aspect ratios of the objects. The change in the aspect ratio is specifically not desirable for human objects. We propose a retargeting method that overcomes these problems. The proposed approach consists of the following steps. Firstly, an inpainting method uses the input image and the binary mask of foreground objects to produce a background image without any foreground objects. Secondly, the seam carving method resizes the background image to the target size. Then, a super-resolution method increases the input image quality, and we then extract the foreground objects. Finally, the retargeted background and the extracted super-resolued objects are fed into a particle swarm optimization algorithm (PSO). The PSO algorithm uses aesthetic quality assessment as its objective function to identify the best location and size for the objects to be placed in the background. We used image quality assessment and aesthetic quality assessment measures to show our superior results compared to popular image retargeting techniques. | ['Shadrokh Samavi', 'Shahram Shirani', 'Nader Karimi', 'Mohammad Hossein Givkashi', 'Mohammad Reza Naderi'] | 2022-09-11 | null | null | null | null | ['image-retargeting'] | ['computer-vision'] | [ 7.10438490e-01 -4.03157435e-02 1.64494455e-01 1.78819716e-01
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ad270add-4c5d-41ab-acf0-43a75261d78d | event-based-camera-pose-tracking-using-a | 1510.01972 | null | http://arxiv.org/abs/1510.01972v1 | http://arxiv.org/pdf/1510.01972v1.pdf | Event-based Camera Pose Tracking using a Generative Event Model | Event-based vision sensors mimic the operation of biological retina and they
represent a major paradigm shift from traditional cameras. Instead of providing
frames of intensity measurements synchronously, at artificially chosen rates,
event-based cameras provide information on brightness changes asynchronously,
when they occur. Such non-redundant pieces of information are called "events".
These sensors overcome some of the limitations of traditional cameras (response
time, bandwidth and dynamic range) but require new methods to deal with the
data they output. We tackle the problem of event-based camera localization in a
known environment, without additional sensing, using a probabilistic generative
event model in a Bayesian filtering framework. Our main contribution is the
design of the likelihood function used in the filter to process the observed
events. Based on the physical characteristics of the sensor and on empirical
evidence of the Gaussian-like distribution of spiked events with respect to the
brightness change, we propose to use the contrast residual as a measure of how
well the estimated pose of the event-based camera and the environment explain
the observed events. The filter allows for localization in the general case of
six degrees-of-freedom motions. | ['Guillermo Gallego', 'Davide Scaramuzza', 'Christian Forster', 'Elias Mueggler'] | 2015-10-07 | null | null | null | null | ['camera-localization', 'event-based-vision'] | ['computer-vision', 'computer-vision'] | [ 5.17982602e-01 -2.17605621e-01 5.27250230e-01 -2.71475285e-01
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273e41da-15e9-4902-91ff-76c508b7dc7d | specific-differential-entropy-rate-estimation | 1606.02615 | null | http://arxiv.org/abs/1606.02615v1 | http://arxiv.org/pdf/1606.02615v1.pdf | Specific Differential Entropy Rate Estimation for Continuous-Valued Time Series | We introduce a method for quantifying the inherent unpredictability of a
continuous-valued time series via an extension of the differential Shannon
entropy rate. Our extension, the specific entropy rate, quantifies the amount
of predictive uncertainty associated with a specific state, rather than
averaged over all states. We relate the specific entropy rate to popular
`complexity' measures such as Approximate and Sample Entropies. We provide a
data-driven approach for estimating the specific entropy rate of an observed
time series. Finally, we consider three case studies of estimating specific
entropy rate from synthetic and physiological data relevant to the analysis of
heart rate variability. | ['David Darmon'] | 2016-06-08 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 3.79933476e-01 2.08534360e-01 -1.11512631e-01 -5.04725993e-01
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d34ba617-cfec-4f52-966e-99516487726c | self-supervision-and-spatial-sequential | 2110.10734 | null | https://arxiv.org/abs/2110.10734v1 | https://arxiv.org/pdf/2110.10734v1.pdf | Self-Supervision and Spatial-Sequential Attention Based Loss for Multi-Person Pose Estimation | Bottom-up based multi-person pose estimation approaches use heatmaps with auxiliary predictions to estimate joint positions and belonging at one time. Recently, various combinations between auxiliary predictions and heatmaps have been proposed for higher performance, these predictions are supervised by the corresponding L2 loss function directly. However, the lack of more explicit supervision results in low features utilization and contradictions between predictions in one model. To solve these problems, this paper proposes (i) a new loss organization method which uses self-supervised heatmaps to reduce prediction contradictions and spatial-sequential attention to enhance networks' features extraction; (ii) a new combination of predictions composed by heatmaps, Part Affinity Fields (PAFs) and our block-inside offsets to fix pixel-level joints positions and further demonstrates the effectiveness of proposed loss function. Experiments are conducted on the MS COCO keypoint dataset and adopting OpenPose as the baseline model. Our method outperforms the baseline overall. On the COCO verification dataset, the mAP of OpenPose trained with our proposals outperforms the OpenPose baseline by over 5.5%. | ['Takeshi Ikenaga', 'Songlin Du', 'Dingli Luo', 'Haiyang Liu'] | 2021-10-20 | null | null | null | null | ['multi-person-pose-estimation'] | ['computer-vision'] | [-6.17479272e-02 5.58166504e-01 -1.93714112e-01 -5.99237919e-01
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f64e6644-4dd3-4b7f-9727-6bdce530c464 | transitional-adaptation-of-pretrained-models | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Yu_Transitional_Adaptation_of_Pretrained_Models_for_Visual_Storytelling_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Yu_Transitional_Adaptation_of_Pretrained_Models_for_Visual_Storytelling_CVPR_2021_paper.pdf | Transitional Adaptation of Pretrained Models for Visual Storytelling | Previous models for vision-to-language generation tasks usually pretrain a visual encoder and a language generator in the respective domains and jointly finetune them with the target task. However, this direct transfer practice may suffer from the discord between visual specificity and language fluency since they are often separately trained from large corpora of visual and text data with no common ground. In this work, we claim that a transitional adaptation task is required between pretraining and finetuning to harmonize the visual encoder and the language model for challenging downstream target tasks like visual storytelling. We propose a novel approach named Transitional Adaptation of Pretrained Model (TAPM) that adapts the multi-modal modules to each other with a simpler alignment task between visual inputs only with no need for text labels. Through extensive experiments, we show that the adaptation step significantly improves the performance of multiple language models for sequential video and image captioning tasks. We achieve new state-of-the-art performance on both language metrics and human evaluation in the multi-sentence description task of LSMDC 2019 and the image storytelling task of VIST. Our experiments reveal that this improvement in caption quality does not depend on the specific choice of language models. | ['Gunhee Kim', 'Jongseok Kim', 'Heeseung Yun', 'Jiwan Chung', 'Youngjae Yu'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['visual-storytelling'] | ['natural-language-processing'] | [ 4.30006385e-01 2.64416367e-01 -6.05357736e-02 -3.66828054e-01
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5b5ef402-cbc5-44ca-b969-2d85e59a2029 | interactive-visual-hull-refinement-for | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Zuo_Interactive_Visual_Hull_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Zuo_Interactive_Visual_Hull_ICCV_2015_paper.pdf | Interactive Visual Hull Refinement for Specular and Transparent Object Surface Reconstruction | In this paper we present a method of using standard multi-view images for 3D surface reconstruction of non-Lambertian objects. We extend the original visual hull concept to incorporate 3D cues presented by internal occluding contours, i.e., occluding contours that are inside the object's silhouettes. We discovered that these internal contours, which are results of convex parts on an object's surface, can lead to a tighter fit than the original visual hull. We formulated a new visual hull refinement scheme - Locally Convex Carving that can completely reconstruct concavity caused by two or more intersecting convex surfaces. In addition we develop a novel approach for contour tracking given labeled contours in sparse key frames. It is designed specifically for highly specular or transparent objects, for which assumptions made in traditional contour detection/tracking methods, such as highest gradient and stationary texture edges, are no longer valid. It is formulated as an energy minimization function where several novel terms are developed to increase robustness. Based on the two core algorithms, we have developed an interactive system for 3D modeling. We have validated our system, both quantitatively and qualitatively, with four datasets of different object materials. Results show that we are able to generate visually pleasing models for very challenging cases. | ['Ruigang Yang', 'Xinxin Zuo', 'Sen Wang', 'Chao Du', 'Jiangbin Zheng'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['transparent-objects', 'contour-detection'] | ['computer-vision', 'computer-vision'] | [ 2.14870855e-01 1.86649725e-01 4.44304854e-01 -1.25086591e-01
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ec598ff5-69ad-448c-bb4c-cf8878c12338 | conservative-safety-critics-for-exploration-1 | 2010.14497 | null | https://arxiv.org/abs/2010.14497v2 | https://arxiv.org/pdf/2010.14497v2.pdf | Conservative Safety Critics for Exploration | Safe exploration presents a major challenge in reinforcement learning (RL): when active data collection requires deploying partially trained policies, we must ensure that these policies avoid catastrophically unsafe regions, while still enabling trial and error learning. In this paper, we target the problem of safe exploration in RL by learning a conservative safety estimate of environment states through a critic, and provably upper bound the likelihood of catastrophic failures at every training iteration. We theoretically characterize the tradeoff between safety and policy improvement, show that the safety constraints are likely to be satisfied with high probability during training, derive provable convergence guarantees for our approach, which is no worse asymptotically than standard RL, and demonstrate the efficacy of the proposed approach on a suite of challenging navigation, manipulation, and locomotion tasks. Empirically, we show that the proposed approach can achieve competitive task performance while incurring significantly lower catastrophic failure rates during training than prior methods. Videos are at this url https://sites.google.com/view/conservative-safety-critics/home | ['Animesh Garg', 'Florian Shkurti', 'Sergey Levine', 'Nicholas Rhinehart', 'Aviral Kumar', 'Homanga Bharadhwaj'] | 2020-10-27 | conservative-safety-critics-for-exploration | https://openreview.net/forum?id=iaO86DUuKi | https://openreview.net/pdf?id=iaO86DUuKi | iclr-2021-1 | ['safe-exploration'] | ['robots'] | [ 3.91625836e-02 4.49319303e-01 -4.20849353e-01 3.19859125e-02
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554c64cd-c67e-4773-b9ec-88c098e047bb | multi-task-balanced-and-recalibrated-network | 2109.02418 | null | https://arxiv.org/abs/2109.02418v3 | https://arxiv.org/pdf/2109.02418v3.pdf | Multitask Balanced and Recalibrated Network for Medical Code Prediction | Human coders assign standardized medical codes to clinical documents generated during patients' hospitalization, which is error-prone and labor-intensive. Automated medical coding approaches have been developed using machine learning methods such as deep neural networks. Nevertheless, automated medical coding is still challenging because of the imbalanced class problem, complex code association, and noise in lengthy documents. To solve these issues, we propose a novel neural network called Multitask Balanced and Recalibrated Neural Network. Significantly, the multitask learning scheme shares the relationship knowledge between different code branches to capture the code association. A recalibrated aggregation module is developed by cascading convolutional blocks to extract high-level semantic features that mitigate the impact of noise in documents. Also, the cascaded structure of the recalibrated module can benefit the learning from lengthy notes. To solve the class imbalanced problem, we deploy the focal loss to redistribute the attention of low and high-frequency medical codes. Experimental results show that our proposed model outperforms competitive baselines on a real-world clinical dataset MIMIC-III. | ['Pekka Marttinen', 'Erik Cambria', 'Shaoxiong Ji', 'Wei Sun'] | 2021-09-06 | null | null | null | null | ['medical-code-prediction'] | ['medical'] | [ 2.46354178e-01 -8.02672878e-02 -1.36687815e-01 -5.20578682e-01
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554080a5-affc-4b56-b5c1-08a17619d9e7 | mumu-cooperative-multitask-learning-based | null | null | https://www.researchgate.net/publication/358345510_MuMu_Cooperative_Multitask_Learning-based_Guided_Multimodal_Fusion | https://www.researchgate.net/publication/358345510_MuMu_Cooperative_Multitask_Learning-based_Guided_Multimodal_Fusion | MuMu: Cooperative Multitask Learning-based Guided Multimodal Fusion | Multimodal sensors (visual, non-visual, and wearable) can provide complementary information to develop robust perception systems for recognizing activities accurately. However, it is challenging to extract robust multimodal representations due to the heterogeneous characteristics of data from multimodal sensors and disparate human activities, especially in the presence of noisy and misaligned sensor data. In this work, we propose a cooperative multitask learning-based guided multimodal fusion approach, MuMu, to extract robust multimodal representations for human activity recognition (HAR). MuMu employs an auxiliary task learning approach to extract features specific to each set of activities with shared characteristics (activity-group). MuMu then utilizes activity-group-specific features to direct our proposed Guided Multimodal Fusion Approach (GM-Fusion) for extracting complementary multimodal representations, designed as the target task. We evaluated MuMu by comparing its performance to state-of-the-art multimodal HAR approaches on three activity datasets. Our extensive experimental results suggest that MuMu outperforms all the evaluated approaches across all three datasets. Additionally, the ablation study suggests that MuMu significantly outperforms the baseline models (p<0.05), which do not use our guided multimodal fusion. Finally, the robust performance of MuMu on noisy and misaligned sensor data posits that our approach is suitable for HAR in real-world settings. | ['Tariq Iqbal', 'Md Mofijul Islam'] | 2022-02-22 | null | null | null | aaai-2022-2 | ['multimodal-activity-recognition'] | ['computer-vision'] | [ 5.04462302e-01 -4.46187884e-01 -1.06017210e-01 -8.08488280e-02
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df9590ee-112d-4ec5-ac26-97b4a2aedaac | state-of-the-art-vietnamese-word-segmentation | 1906.07662 | null | https://arxiv.org/abs/1906.07662v1 | https://arxiv.org/pdf/1906.07662v1.pdf | State-of-the-Art Vietnamese Word Segmentation | Word segmentation is the first step of any tasks in Vietnamese language processing. This paper reviews stateof-the-art approaches and systems for word segmentation in Vietnamese. To have an overview of all stages from building corpora to developing toolkits, we discuss building the corpus stage, approaches applied to solve the word segmentation and existing toolkits to segment words in Vietnamese sentences. In addition, this study shows clearly the motivations on building corpus and implementing machine learning techniques to improve the accuracy for Vietnamese word segmentation. According to our observation, this study also reports a few of achivements and limitations in existing Vietnamese word segmentation systems. | ['Rachsuda Jiamthapthaksin', 'Song Nguyen Duc Cong', 'Quoc Hung Ngo'] | 2019-06-18 | null | null | null | null | ['vietnamese-word-segmentation'] | ['natural-language-processing'] | [-2.34797657e-01 1.58080637e-01 -3.95173281e-01 -5.06910861e-01
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f96c6754-b1e4-4731-b6c0-59038b956fe9 | sta-vpr-spatio-temporal-alignment-for-visual | 2103.1358 | null | https://arxiv.org/abs/2103.13580v2 | https://arxiv.org/pdf/2103.13580v2.pdf | STA-VPR: Spatio-temporal Alignment for Visual Place Recognition | Recently, the methods based on Convolutional Neural Networks (CNNs) have gained popularity in the field of visual place recognition (VPR). In particular, the features from the middle layers of CNNs are more robust to drastic appearance changes than handcrafted features and high-layer features. Unfortunately, the holistic mid-layer features lack robustness to large viewpoint changes. Here we split the holistic mid-layer features into local features, and propose an adaptive dynamic time warping (DTW) algorithm to align local features from the spatial domain while measuring the distance between two images. This realizes viewpoint-invariant and condition-invariant place recognition. Meanwhile, a local matching DTW (LM-DTW) algorithm is applied to perform image sequence matching based on temporal alignment, which achieves further improvements and ensures linear time complexity. We perform extensive experiments on five representative VPR datasets. The results show that the proposed method significantly improves the CNN-based methods. Moreover, our method outperforms several state-of-the-art methods while maintaining good run-time performance. This work provides a novel way to boost the performance of CNN methods without any re-training for VPR. The code is available at https://github.com/Lu-Feng/STA-VPR. | ['Dezhen Song', 'Xiang-Dong Zhou', 'Baifan Chen', 'Feng Lu'] | 2021-03-25 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [-6.13028966e-02 -9.33478475e-01 -3.30966294e-01 -3.21608633e-01
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9c81a341-07c4-4edf-bcc4-50653479416b | argus-context-based-detection-of-stealthy-iot | 2302.07589 | null | https://arxiv.org/abs/2302.07589v2 | https://arxiv.org/pdf/2302.07589v2.pdf | ARGUS: Context-Based Detection of Stealthy IoT Infiltration Attacks | IoT application domains, device diversity and connectivity are rapidly growing. IoT devices control various functions in smart homes and buildings, smart cities, and smart factories, making these devices an attractive target for attackers. On the other hand, the large variability of different application scenarios and inherent heterogeneity of devices make it very challenging to reliably detect abnormal IoT device behaviors and distinguish these from benign behaviors. Existing approaches for detecting attacks are mostly limited to attacks directly compromising individual IoT devices, or, require predefined detection policies. They cannot detect attacks that utilize the control plane of the IoT system to trigger actions in an unintended/malicious context, e.g., opening a smart lock while the smart home residents are absent. In this paper, we tackle this problem and propose ARGUS, the first self-learning intrusion detection system for detecting contextual attacks on IoT environments, in which the attacker maliciously invokes IoT device actions to reach its goals. ARGUS monitors the contextual setting based on the state and actions of IoT devices in the environment. An unsupervised Deep Neural Network (DNN) is used for modeling the typical contextual device behavior and detecting actions taking place in abnormal contextual settings. This unsupervised approach ensures that ARGUS is not restricted to detecting previously known attacks but is also able to detect new attacks. We evaluated ARGUS on heterogeneous real-world smart-home settings and achieve at least an F1-Score of 99.64% for each setup, with a false positive rate (FPR) of at most 0.03%. | ['Ahmad-Reza Sadeghi', 'Hossein Fereidooni', 'Markus Miettinen', 'Reham Mohamed', 'Marco Chilese', 'Phillip Rieger'] | 2023-02-15 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [ 2.98530042e-01 -2.61427581e-01 -1.83231965e-01 8.94238353e-02
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f2e2f2a5-6d00-4dfd-8e3e-c21d41169936 | visual-speech-enhancement | 1711.08789 | null | http://arxiv.org/abs/1711.08789v3 | http://arxiv.org/pdf/1711.08789v3.pdf | Visual Speech Enhancement | When video is shot in noisy environment, the voice of a speaker seen in the
video can be enhanced using the visible mouth movements, reducing background
noise. While most existing methods use audio-only inputs, improved performance
is obtained with our visual speech enhancement, based on an audio-visual neural
network. We include in the training data videos to which we added the voice of
the target speaker as background noise. Since the audio input is not sufficient
to separate the voice of a speaker from his own voice, the trained model better
exploits the visual input and generalizes well to different noise types. The
proposed model outperforms prior audio visual methods on two public lipreading
datasets. It is also the first to be demonstrated on a dataset not designed for
lipreading, such as the weekly addresses of Barack Obama. | ['Shmuel Peleg', 'Asaph Shamir', 'Aviv Gabbay'] | 2017-11-23 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 2.58146673e-01 8.20429400e-02 -3.53334755e-01 2.45250762e-02
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cc5799f3-de3c-4013-bf03-c1e43694206f | revisiting-table-detection-datasets-for | 2305.04833 | null | https://arxiv.org/abs/2305.04833v1 | https://arxiv.org/pdf/2305.04833v1.pdf | Revisiting Table Detection Datasets for Visually Rich Documents | Table Detection has become a fundamental task for visually rich document understanding with the surging number of electronic documents. There have been some open datasets widely used in many studies. However, popular available datasets have some inherent limitations, including the noisy and inconsistent samples, and the limit number of training samples, and the limit number of data-sources. These limitations make these datasets unreliable to evaluate the model performance and cannot reflect the actual capacity of models. Therefore, in this paper, we revisit some open datasets with high quality of annotations, identify and clean the noise, and align the annotation definitions of these datasets to merge a larger dataset, termed with Open-Tables. Moreover, to enrich the data sources, we propose a new dataset, termed with ICT-TD, using the PDF files of Information and communication technologies (ICT) commodities which is a different domain containing unique samples that hardly appear in open datasets. To ensure the label quality of the dataset, we annotated the dataset manually following the guidance of a domain expert. The proposed dataset has a larger intra-variance and smaller inter-variance, making it more challenging and can be a sample of actual cases in the business context. We built strong baselines using various state-of-the-art object detection models and also built the baselines in the cross-domain setting. Our experimental results show that the domain difference among existing open datasets are small, even they have different data-sources. Our proposed Open-tables and ICT-TD are more suitable for the cross domain setting, and can provide more reliable evaluation for model because of their high quality and consistent annotations. | ['Ala Abu Alkheir', 'Burak Kantarci', 'Murat Simsek', 'Bin Xiao'] | 2023-05-04 | null | null | null | null | ['table-detection'] | ['miscellaneous'] | [-1.42950684e-01 -2.14648530e-01 -3.54331344e-01 -1.29257396e-01
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a134834c-09e8-4abb-9bf3-f6e071bc037d | conditional-online-learning-for-keyword | 2305.13332 | null | https://arxiv.org/abs/2305.13332v1 | https://arxiv.org/pdf/2305.13332v1.pdf | Conditional Online Learning for Keyword Spotting | Modern approaches for keyword spotting rely on training deep neural networks on large static datasets with i.i.d. distributions. However, the resulting models tend to underperform when presented with changing data regimes in real-life applications. This work investigates a simple but effective online continual learning method that updates a keyword spotter on-device via SGD as new data becomes available. Contrary to previous research, this work focuses on learning the same KWS task, which covers most commercial applications. During experiments with dynamic audio streams in different scenarios, that method improves the performance of a pre-trained small-footprint model by 34%. Moreover, experiments demonstrate that, compared to a naive online learning implementation, conditional model updates based on its performance in a small hold-out set drawn from the training distribution mitigate catastrophic forgetting. | ['Bruno Iwami', 'Michel Meneses'] | 2023-05-19 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 3.40568461e-02 -1.74541980e-01 -3.40375543e-01 2.48624682e-02
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d812de91-ec09-4ff3-a404-6b11ba493be2 | noise-pollution-in-hospital-readmission | 2005.01259 | null | https://arxiv.org/abs/2005.01259v2 | https://arxiv.org/pdf/2005.01259v2.pdf | Noise Pollution in Hospital Readmission Prediction: Long Document Classification with Reinforcement Learning | This paper presents a reinforcement learning approach to extract noise in long clinical documents for the task of readmission prediction after kidney transplant. We face the challenges of developing robust models on a small dataset where each document may consist of over 10K tokens with full of noise including tabular text and task-irrelevant sentences. We first experiment four types of encoders to empirically decide the best document representation, and then apply reinforcement learning to remove noisy text from the long documents, which models the noise extraction process as a sequential decision problem. Our results show that the old bag-of-words encoder outperforms deep learning-based encoders on this task, and reinforcement learning is able to improve upon baseline while pruning out 25% text segments. Our analysis depicts that reinforcement learning is able to identify both typical noisy tokens and task-specific noisy text. | ['Rachel E. Patzer', 'Julien Hogan', 'Liyan Xu', 'Jinho D. Choi'] | 2020-05-04 | noise-pollution-in-hospital-readmission-1 | https://aclanthology.org/2020.bionlp-1.10 | https://aclanthology.org/2020.bionlp-1.10.pdf | ws-2020-7 | ['readmission-prediction'] | ['medical'] | [ 1.86590642e-01 2.85221934e-01 -1.23764731e-01 -4.17178601e-01
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39cf39be-6375-469b-bc6b-3c55a6ee9e27 | denoising-of-mr-images-with-rician-noise | null | null | https://www.sciencedirect.com/science/article/abs/pii/S0730725X18306751 | https://drive.google.com/file/d/1XRzZNc4tqJnaK0sYzLHdEiNJ7B8ZokHh/view?usp=sharing | Denoising of MR images with Rician noise using a wider neural network and noise range division | Magnetic resonance (MR) images denoising is important in medical image analysis. Denoising methods based on deep learning have shown great promise and outperform all of the other conventional methods. However, deep- learning methods are limited by the number of training samples. In this article, using a small sample size, we applied a wider denoising neural network to MR images with Rician noise and trained several denoising models. The first model is specific to a certain noise, while the other applies to a wide range of noise levels. We con- sidered the noise range as one interval, two sub-intervals, three sub-intervals, or even more sub-intervals to train the corresponding models. Experimental results demonstrate that for MR images, the proposed deep-learning models are efficient in terms of peak-signal-to-noise ratio, structure-similarity-index metrics and normalized mutual information. In addition, for blind noise, the effect of the three sub-intervals is better than that of the other sub-intervals. | ['C', 'Wei Wanga', 'Minghe Maoa', 'Hao Lua', '⁎', 'Ning Caoa', 'B', 'Xuexiao Youa'] | 2019-06-17 | null | null | null | magnetic-resonance-imaging-2019-6 | ['denoising'] | ['computer-vision'] | [ 1.05293445e-01 -2.66558319e-01 1.27307668e-01 -3.13859195e-01
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fe12f35b-5155-4b5b-99e3-fb9e4bc1e681 | hub-at-semeval-2021-task-5-toxic-span | null | null | https://aclanthology.org/2021.semeval-1.122 | https://aclanthology.org/2021.semeval-1.122.pdf | hub at SemEval-2021 Task 5: Toxic Span Detection Based on Word-Level Classification | This article introduces the system description of the hub team, which explains the related work and experimental results of our team{'}s participation in SemEval 2021 Task 5: Toxic Spans Detection. The data for this shared task comes from some posts on the Internet. The task goal is to identify the toxic content contained in these text data. We need to find the span of the toxic text in the text data as accurately as possible. In the same post, the toxic text may be one paragraph or multiple paragraphs. Our team uses a classification scheme based on word-level to accomplish this task. The system we used to submit the results is ALBERT+BILSTM+CRF. The result evaluation index of the task submission is the F1 score, and the final score of the prediction result of the test set submitted by our team is 0.6640226029. | ['Xiaobing Zhou', 'Yang Bai', 'Bo Huang'] | 2021-08-01 | null | null | null | semeval-2021 | ['toxic-spans-detection'] | ['natural-language-processing'] | [-1.97367072e-01 -1.15503848e-01 -4.99254055e-02 -1.25489891e-01
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7ef4ae0b-f7fa-4fd4-b59e-5608f3d66edb | trufor-leveraging-all-round-clues-for | 2212.10957 | null | https://arxiv.org/abs/2212.10957v3 | https://arxiv.org/pdf/2212.10957v3.pdf | TruFor: Leveraging all-round clues for trustworthy image forgery detection and localization | In this paper we present TruFor, a forensic framework that can be applied to a large variety of image manipulation methods, from classic cheapfakes to more recent manipulations based on deep learning. We rely on the extraction of both high-level and low-level traces through a transformer-based fusion architecture that combines the RGB image and a learned noise-sensitive fingerprint. The latter learns to embed the artifacts related to the camera internal and external processing by training only on real data in a self-supervised manner. Forgeries are detected as deviations from the expected regular pattern that characterizes each pristine image. Looking for anomalies makes the approach able to robustly detect a variety of local manipulations, ensuring generalization. In addition to a pixel-level localization map and a whole-image integrity score, our approach outputs a reliability map that highlights areas where localization predictions may be error-prone. This is particularly important in forensic applications in order to reduce false alarms and allow for a large scale analysis. Extensive experiments on several datasets show that our method is able to reliably detect and localize both cheapfakes and deepfakes manipulations outperforming state-of-the-art works. Code is publicly available at https://grip-unina.github.io/TruFor/ | ['Luisa Verdoliva', 'Nicholas Dufour', 'Avneesh Sud', 'Davide Cozzolino', 'Fabrizio Guillaro'] | 2022-12-21 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Guillaro_TruFor_Leveraging_All-Round_Clues_for_Trustworthy_Image_Forgery_Detection_and_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Guillaro_TruFor_Leveraging_All-Round_Clues_for_Trustworthy_Image_Forgery_Detection_and_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-manipulation-detection', 'image-manipulation'] | ['computer-vision', 'computer-vision'] | [ 3.68072718e-01 -3.16734135e-01 2.31089607e-01 -1.97208777e-01
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c2118417-ec35-4ac3-a7d6-7608f4254e78 | retinex-image-enhancement-based-on-sequential | 2210.05436 | null | https://arxiv.org/abs/2210.05436v2 | https://arxiv.org/pdf/2210.05436v2.pdf | Retinex Image Enhancement Based on Sequential Decomposition With a Plug-and-Play Framework | The Retinex model is one of the most representative and effective methods for low-light image enhancement. However, the Retinex model does not explicitly tackle the noise problem, and shows unsatisfactory enhancing results. In recent years, due to the excellent performance, deep learning models have been widely used in low-light image enhancement. However, these methods have two limitations: i) The desirable performance can only be achieved by deep learning when a large number of labeled data are available. However, it is not easy to curate massive low/normal-light paired data; ii) Deep learning is notoriously a black-box model [1]. It is difficult to explain their inner-working mechanism and understand their behaviors. In this paper, using a sequential Retinex decomposition strategy, we design a plug-and-play framework based on the Retinex theory for simultaneously image enhancement and noise removal. Meanwhile, we develop a convolutional neural network-based (CNN-based) denoiser into our proposed plug-and-play framework to generate a reflectance component. The final enhanced image is produced by integrating the illumination and reflectance with gamma correction. The proposed plug-and-play framework can facilitate both post hoc and ad hoc interpretability. Extensive experiments on different datasets demonstrate that our framework outcompetes the state-of-the-art methods in both image enhancement and denoising. | ['Tieyong Zeng', 'Feng-Lei Fan', 'Ying Yang', 'Wenna Wu', 'Tingting Wu'] | 2022-10-11 | null | null | null | null | ['low-light-image-enhancement'] | ['computer-vision'] | [ 2.32480183e-01 -6.31545722e-01 2.62558639e-01 -2.26841837e-01
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581398dd-52ee-47f7-8d49-8bb5d74273ad | closure-assessing-systematic-generalization | 1912.05783 | null | https://arxiv.org/abs/1912.05783v2 | https://arxiv.org/pdf/1912.05783v2.pdf | CLOSURE: Assessing Systematic Generalization of CLEVR Models | The CLEVR dataset of natural-looking questions about 3D-rendered scenes has recently received much attention from the research community. A number of models have been proposed for this task, many of which achieved very high accuracies of around 97-99%. In this work, we study how systematic the generalization of such models is, that is to which extent they are capable of handling novel combinations of known linguistic constructs. To this end, we test models' understanding of referring expressions based on matching object properties (such as e.g. "another cube that is the same size as the brown cube") in novel contexts. Our experiments on the thereby constructed CLOSURE benchmark show that state-of-the-art models often do not exhibit systematicity after being trained on CLEVR. Surprisingly, we find that an explicitly compositional Neural Module Network model also generalizes badly on CLOSURE, even when it has access to the ground-truth programs at test time. We improve the NMN's systematic generalization by developing a novel Vector-NMN module architecture with vector-valued inputs and outputs. Lastly, we investigate how much few-shot transfer learning can help models that are pretrained on CLEVR to adapt to CLOSURE. Our few-shot learning experiments contrast the adaptation behavior of the models with intermediate discrete programs with that of the end-to-end continuous models. | ['Yoshua Bengio', "Timothy J. O'Donnell", 'Harm de Vries', 'Shikhar Murty', 'Philippe Beaudoin', 'Dzmitry Bahdanau', 'Aaron Courville'] | 2019-12-12 | null | null | null | null | ['systematic-generalization'] | ['reasoning'] | [ 7.63688385e-02 1.52195901e-01 -1.32648587e-01 -7.86467135e-01
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f1919a61-f5e5-40e8-9d74-1f6970d6db1d | heterogeneous-graph-learning-for-acoustic | 2303.02665 | null | https://arxiv.org/abs/2303.02665v2 | https://arxiv.org/pdf/2303.02665v2.pdf | Heterogeneous Graph Learning for Acoustic Event Classification | Heterogeneous graphs provide a compact, efficient, and scalable way to model data involving multiple disparate modalities. This makes modeling audiovisual data using heterogeneous graphs an attractive option. However, graph structure does not appear naturally in audiovisual data. Graphs for audiovisual data are constructed manually which is both difficult and sub-optimal. In this work, we address this problem by (i) proposing a parametric graph construction strategy for the intra-modal edges, and (ii) learning the crossmodal edges. To this end, we develop a new model, heterogeneous graph crossmodal network (HGCN) that learns the crossmodal edges. Our proposed model can adapt to various spatial and temporal scales owing to its parametric construction, while the learnable crossmodal edges effectively connect the relevant nodes across modalities. Experiments on a large benchmark dataset (AudioSet) show that our model is state-of-the-art (0.53 mean average precision), outperforming transformer-based models and other graph-based models. | ['Tanaya Guha', 'Krishna Somandepalli', 'Mona Ahmadian', 'Amir Shirian'] | 2023-03-05 | null | null | null | null | ['graph-construction'] | ['graphs'] | [-1.72180369e-01 1.19539060e-01 -3.67726117e-01 -2.88518798e-02
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8f33d013-ed7d-432b-804d-ac965d85c8cd | mixed-vine-copulas-as-joint-models-of-spike | null | null | http://papers.nips.cc/paper/6069-mixed-vine-copulas-as-joint-models-of-spike-counts-and-local-field-potentials | http://papers.nips.cc/paper/6069-mixed-vine-copulas-as-joint-models-of-spike-counts-and-local-field-potentials.pdf | Mixed vine copulas as joint models of spike counts and local field potentials | Concurrent measurements of neural activity at multiple scales, sometimes performed with multimodal techniques, become increasingly important for studying brain function. However, statistical methods for their concurrent analysis are currently lacking. Here we introduce such techniques in a framework based on vine copulas with mixed margins to construct multivariate stochastic models. These models can describe detailed mixed interactions between discrete variables such as neural spike counts, and continuous variables such as local field potentials. We propose efficient methods for likelihood calculation, inference, sampling and mutual information estimation within this framework. We test our methods on simulated data and demonstrate applicability on mixed data generated by a biologically realistic neural network. Our methods hold the promise to considerably improve statistical analysis of neural data recorded simultaneously at different scales. | ['Arno Onken', 'Stefano Panzeri'] | 2016-12-01 | null | null | null | neurips-2016-12 | ['mutual-information-estimation'] | ['methodology'] | [ 3.76247525e-01 -6.30222023e-01 5.65274537e-01 -3.53833675e-01
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6503bfc5-992e-48f0-8dd6-5b7c78918bf6 | gpr-net-multi-view-layout-estimation-via-a | 2210.11419 | null | https://arxiv.org/abs/2210.11419v2 | https://arxiv.org/pdf/2210.11419v2.pdf | GPR-Net: Multi-view Layout Estimation via a Geometry-aware Panorama Registration Network | Reconstructing 3D layouts from multiple $360^{\circ}$ panoramas has received increasing attention recently as estimating a complete layout of a large-scale and complex room from a single panorama is very difficult. The state-of-the-art method, called PSMNet, introduces the first learning-based framework that jointly estimates the room layout and registration given a pair of panoramas. However, PSMNet relies on an approximate (i.e., "noisy") registration as input. Obtaining this input requires a solution for wide baseline registration which is a challenging problem. In this work, we present a complete multi-view panoramic layout estimation framework that jointly learns panorama registration and layout estimation given a pair of panoramas without relying on a pose prior. The major improvement over PSMNet comes from a novel Geometry-aware Panorama Registration Network or GPR-Net that effectively tackles the wide baseline registration problem by exploiting the layout geometry and computing fine-grained correspondences on the layout boundaries, instead of the global pixel-space. Our architecture consists of two parts. First, given two panoramas, we adopt a vision transformer to learn a set of 1D horizon features sampled on the panorama. These 1D horizon features encode the depths of individual layout boundary samples and the correspondence and covisibility maps between layout boundaries. We then exploit a non-linear registration module to convert these 1D horizon features into a set of corresponding 2D boundary points on the layout. Finally, we estimate the final relative camera pose via RANSAC and obtain the complete layout simply by taking the union of registered layouts. Experimental results indicate that our method achieves state-of-the-art performance in both panorama registration and layout estimation on a large-scale indoor panorama dataset ZInD. | ['Hung-Kuo Chu', 'Peter Wonka', 'Chi-Han Peng', 'Jheng-Wei Su'] | 2022-10-20 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 4.41380084e-01 -2.65630126e-01 2.39636883e-01 -4.39797133e-01
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120974fb-dd53-4962-b655-aa6c068c6e5d | memory-like-adaptive-modeling-multi-agent | 2212.07646 | null | https://arxiv.org/abs/2212.07646v2 | https://arxiv.org/pdf/2212.07646v2.pdf | Adaptive Multi-Agent Continuous Learning System | We propose an adaptive multi-agent clustering recognition system that can be self-supervised driven, based on a temporal sequences continuous learning mechanism with adaptability. The system is designed to use some different functional agents to build up a connection structure to improve adaptability to cope with environmental diverse demands, by predicting the input of the agent to drive the agent to achieve the act of clustering recognition of sequences using the traditional algorithmic approach. Finally, the feasibility experiments of video behavior clustering demonstrate the feasibility of the system to cope with dynamic situations. Our work is placed here\footnote{https://github.com/qian-git/MAMMALS}. | ['Zhitang Song', 'Weibang Dai', 'Shunfen Li', 'Xiaogang Chen', 'Wen-Chi Yang', 'Longfei Liang', 'Aximu Yuemaier', 'Xingyu Qian'] | 2022-12-15 | null | null | null | null | ['temporal-sequences'] | ['reasoning'] | [-1.73560843e-01 -4.36293691e-01 -2.33798951e-01 -2.31379882e-01
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b1def502-6170-40eb-acb1-dda2bda88cb8 | mtstereo-2-0-improved-accuracy-of-stereo | 2006.15373 | null | https://arxiv.org/abs/2006.15373v1 | https://arxiv.org/pdf/2006.15373v1.pdf | MTStereo 2.0: improved accuracy of stereo depth estimation withMax-trees | Efficient yet accurate extraction of depth from stereo image pairs is required by systems with low power resources, such as robotics and embedded systems. State-of-the-art stereo matching methods based on convolutional neural networks require intensive computations on GPUs and are difficult to deploy on embedded systems. In this paper, we propose a stereo matching method, called MTStereo 2.0, for limited-resource systems that require efficient and accurate depth estimation. It is based on a Max-tree hierarchical representation of image pairs, which we use to identify matching regions along image scan-lines. The method includes a cost function that considers similarity of region contextual information based on the Max-trees and a disparity border preserving cost aggregation approach. MTStereo 2.0 improves on its predecessor MTStereo 1.0 as it a) deploys a more robust cost function, b) performs more thorough detection of incorrect matches, c) computes disparity maps with pixel-level rather than node-level precision. MTStereo provides accurate sparse and semi-dense depth estimation and does not require intensive GPU computations like methods based on CNNs. Thus it can run on embedded and robotics devices with low-power requirements. We tested the proposed approach on several benchmark data sets, namely KITTI 2015, Driving, FlyingThings3D, Middlebury 2014, Monkaa and the TrimBot2020 garden data sets, and achieved competitive accuracy and efficiency. The code is available at https://github.com/rbrandt1/MaxTreeS. | ['Nicolai Petkov', 'Rafael Brandt', 'Nicola Strisciuglio'] | 2020-06-27 | null | null | null | null | ['stereo-depth-estimation'] | ['computer-vision'] | [ 1.21862767e-02 -2.07267910e-01 4.53138947e-02 -4.32901621e-01
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969a60f8-9f41-4c11-9c8e-21d6021907b5 | cab-empathetic-dialogue-generation-with | 2302.01935 | null | https://arxiv.org/abs/2302.01935v2 | https://arxiv.org/pdf/2302.01935v2.pdf | CAB: Empathetic Dialogue Generation with Cognition, Affection and Behavior | Empathy is an important characteristic to be considered when building a more intelligent and humanized dialogue agent. However, existing methods did not fully comprehend empathy as a complex process involving three aspects: cognition, affection and behavior. In this paper, we propose CAB, a novel framework that takes a comprehensive perspective of cognition, affection and behavior to generate empathetic responses. For cognition, we build paths between critical keywords in the dialogue by leveraging external knowledge. This is because keywords in a dialogue are the core of sentences. Building the logic relationship between keywords, which is overlooked by the majority of existing works, can improve the understanding of keywords and contextual logic, thus enhance the cognitive ability. For affection, we capture the emotional dependencies with dual latent variables that contain both interlocutors' emotions. The reason is that considering both interlocutors' emotions simultaneously helps to learn the emotional dependencies. For behavior, we use appropriate dialogue acts to guide the dialogue generation to enhance the empathy expression. Extensive experiments demonstrate that our multi-perspective model outperforms the state-of-the-art models in both automatic and manual evaluation. | ['Zikun Wang', 'Xuejiao Zhang', 'Rui Zhou', 'Donghong Han', 'Pan Gao'] | 2023-02-03 | null | null | null | null | ['dialogue-generation', 'dialogue-generation'] | ['natural-language-processing', 'speech'] | [-3.63313138e-01 2.19827816e-01 -9.94093642e-02 -6.77883685e-01
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74d0de05-4650-4019-b594-80e83e615c99 | vulnerability-analysis-of-face-morphing | 2012.05344 | null | https://arxiv.org/abs/2012.05344v1 | https://arxiv.org/pdf/2012.05344v1.pdf | Vulnerability Analysis of Face Morphing Attacks from Landmarks and Generative Adversarial Networks | Morphing attacks is a threat to biometric systems where the biometric reference in an identity document can be altered. This form of attack presents an important issue in applications relying on identity documents such as border security or access control. Research in face morphing attack detection is developing rapidly, however very few datasets with several forms of attacks are publicly available. This paper bridges this gap by providing a new dataset with four different types of morphing attacks, based on OpenCV, FaceMorpher, WebMorph and a generative adversarial network (StyleGAN), generated with original face images from three public face datasets. We also conduct extensive experiments to assess the vulnerability of the state-of-the-art face recognition systems, notably FaceNet, VGG-Face, and ArcFace. The experiments demonstrate that VGG-Face, while being less accurate face recognition system compared to FaceNet, is also less vulnerable to morphing attacks. Also, we observed that na\"ive morphs generated with a StyleGAN do not pose a significant threat. | ['Sébastien Marcel', 'Laurent Colbois', 'Pavel Korshunov', 'Eklavya Sarkar'] | 2020-12-09 | null | null | null | null | ['image-morphing'] | ['computer-vision'] | [ 8.16900730e-02 9.17701870e-02 3.62612993e-01 -4.40283209e-01
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1b8342c0-26be-48ed-837e-fd4c5b949b6d | autonomous-apex-detection-and-micro | null | null | https://ijnaa.semnan.ac.ir/article_4707.html | https://ijnaa.semnan.ac.ir/article_4707.html | Autonomous Apex Detection and Micro-Expression Recognition using Proposed Diagonal Planes | Micro-expression as the main way of non-verbal communication occurs quickly and subtle in highrisk situations. Since it cannot be misleading, it discloses the real human aim. Nonetheless, feature extraction is an arduous task due to its two particular features. To resolve this problem in this paper, we propose Local Binary Pattern on Four Diagonal Planes. In fact, we utilize it after motion magnification for feature extraction in apex detection task. Also, we combine it and optical flow for micro-expression recognition. Simulation results show that automatic micro-expression apex frame detection and micro-expression recognition is promising using our method on CASME2 and CASME in comparison with other methods. | ['Seyed Omid Shahdi', 'Mahmood Mohassel Feghhi', 'Vida Esmaeili'] | 2020-04-01 | null | null | null | int-j-nonlinear-anal-appl-2020-4 | ['motion-magnification', 'micro-expression-recognition'] | ['computer-vision', 'computer-vision'] | [-4.47361954e-02 -3.31461221e-01 -3.52138817e-01 -8.88572037e-02
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c1c2e7ad-d8bc-49ad-9752-7a7fa9253ce3 | dece-decision-explorer-with-counterfactual | 2008.08353 | null | https://arxiv.org/abs/2008.08353v1 | https://arxiv.org/pdf/2008.08353v1.pdf | DECE: Decision Explorer with Counterfactual Explanations for Machine Learning Models | With machine learning models being increasingly applied to various decision-making scenarios, people have spent growing efforts to make machine learning models more transparent and explainable. Among various explanation techniques, counterfactual explanations have the advantages of being human-friendly and actionable -- a counterfactual explanation tells the user how to gain the desired prediction with minimal changes to the input. Besides, counterfactual explanations can also serve as efficient probes to the models' decisions. In this work, we exploit the potential of counterfactual explanations to understand and explore the behavior of machine learning models. We design DECE, an interactive visualization system that helps understand and explore a model's decisions on individual instances and data subsets, supporting users ranging from decision-subjects to model developers. DECE supports exploratory analysis of model decisions by combining the strengths of counterfactual explanations at instance- and subgroup-levels. We also introduce a set of interactions that enable users to customize the generation of counterfactual explanations to find more actionable ones that can suit their needs. Through three use cases and an expert interview, we demonstrate the effectiveness of DECE in supporting decision exploration tasks and instance explanations. | ['Huamin Qu', 'Yao Ming', 'Furui Cheng'] | 2020-08-19 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [-3.79637517e-02 7.79848218e-01 -5.81174552e-01 -7.89073110e-01
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803c5846-ffcf-44ff-a784-3afdef61b2d7 | real-time-variational-method-for-learning | 2305.11278 | null | https://arxiv.org/abs/2305.11278v1 | https://arxiv.org/pdf/2305.11278v1.pdf | Real-Time Variational Method for Learning Neural Trajectory and its Dynamics | Latent variable models have become instrumental in computational neuroscience for reasoning about neural computation. This has fostered the development of powerful offline algorithms for extracting latent neural trajectories from neural recordings. However, despite the potential of real time alternatives to give immediate feedback to experimentalists, and enhance experimental design, they have received markedly less attention. In this work, we introduce the exponential family variational Kalman filter (eVKF), an online recursive Bayesian method aimed at inferring latent trajectories while simultaneously learning the dynamical system generating them. eVKF works for arbitrary likelihoods and utilizes the constant base measure exponential family to model the latent state stochasticity. We derive a closed-form variational analogue to the predict step of the Kalman filter which leads to a provably tighter bound on the ELBO compared to another online variational method. We validate our method on synthetic and real-world data, and, notably, show that it achieves competitive performance | ['Il Memming Park', 'Yuan Zhao', 'Matthew Dowling'] | 2023-05-18 | null | null | null | null | ['experimental-design'] | ['methodology'] | [ 1.00444622e-01 5.91293164e-03 -6.11432865e-02 -1.52520791e-01
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ee559c70-a3a9-4049-a1a7-4b8c4ead980f | geracao-de-expressoes-de-referencia-usando | null | null | https://aclanthology.org/W13-4810 | https://aclanthology.org/W13-4810.pdf | Gera\cc\~ao de Express\~oes de Refer\^encia usando Rela\cc\~oes Espaciais (Referring Expression Generation Using Spatial Relations) [in Portuguese] | null | ["r{\\'e}", 'Diego dos Santos Silva', 'Iv Paraboni'] | 2013-01-01 | null | null | null | ws-2013-1 | ['referring-expression-generation'] | ['computer-vision'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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1f0e3734-d5ba-4fe2-99b8-ec1cb17f7446 | matrix-completion-with-sparse-noisy-rows | 2204.0153 | null | https://arxiv.org/abs/2204.01530v2 | https://arxiv.org/pdf/2204.01530v2.pdf | Matrix Completion with Sparse Noisy Rows | Exact matrix completion and low rank matrix estimation problems has been studied in different underlying conditions. In this work we study exact low-rank completion under non-degenerate noise model. Non-degenerate random noise model has been previously studied by many researchers under given condition that the noise is sparse and existing in some of the columns. In this paper, we assume that each row can receive random noise instead of columns and propose an interactive algorithm that is robust to this noise. We show that we use a parametrization technique to give a condition when the underlying matrix could be recoverable and suggest an algorithm which recovers the underlying matrix. | ['Jafar Jafarov'] | 2022-04-01 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 5.04468560e-01 9.13723782e-02 1.69947445e-01 6.56313449e-02
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34a019a2-de0b-4fb2-865c-692daba40103 | type-i-tobit-bayesian-additive-regression | 2211.07506 | null | https://arxiv.org/abs/2211.07506v2 | https://arxiv.org/pdf/2211.07506v2.pdf | Type I Tobit Bayesian Additive Regression Trees for Censored Outcome Regression | This paper introduces Type I Tobit Bayesian Additive Regression Trees (TOBART-1). Simulation results and applications to real data sets demonstrate that TOBART-1 produces more accurate predictions than competing methods. TOBART-1 provides accurate posterior intervals for the conditional expectation and other quantities of interest. A Dirichlet Process mixture of normal distributions can flexibly model the error term for improved uncertainty quantification. | ["Eoghan O'Neill"] | 2022-11-14 | null | null | null | null | ['type'] | ['speech'] | [ 2.24223044e-02 -4.16975990e-02 -3.09234262e-01 -9.84690607e-01
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abfbd0bc-eb83-469c-8c30-0923126751a0 | looking-for-the-devil-in-the-details-learning | 1903.0615 | null | https://arxiv.org/abs/1903.06150v2 | https://arxiv.org/pdf/1903.06150v2.pdf | Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-grained Image Recognition | Learning subtle yet discriminative features (e.g., beak and eyes for a bird) plays a significant role in fine-grained image recognition. Existing attention-based approaches localize and amplify significant parts to learn fine-grained details, which often suffer from a limited number of parts and heavy computational cost. In this paper, we propose to learn such fine-grained features from hundreds of part proposals by Trilinear Attention Sampling Network (TASN) in an efficient teacher-student manner. Specifically, TASN consists of 1) a trilinear attention module, which generates attention maps by modeling the inter-channel relationships, 2) an attention-based sampler which highlights attended parts with high resolution, and 3) a feature distiller, which distills part features into a global one by weight sharing and feature preserving strategies. Extensive experiments verify that TASN yields the best performance under the same settings with the most competitive approaches, in iNaturalist-2017, CUB-Bird, and Stanford-Cars datasets. | ['Zheng-Jun Zha', 'Jiebo Luo', 'Jianlong Fu', 'Heliang Zheng'] | 2019-03-14 | looking-for-the-devil-in-the-details-learning-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Zheng_Looking_for_the_Devil_in_the_Details_Learning_Trilinear_Attention_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Zheng_Looking_for_the_Devil_in_the_Details_Learning_Trilinear_Attention_CVPR_2019_paper.pdf | cvpr-2019-6 | ['fine-grained-image-recognition'] | ['computer-vision'] | [-1.18092276e-01 -2.68529296e-01 -1.55409932e-01 -4.10695553e-01
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cb0b5fa6-06e2-4d50-96ec-3f7a4f9709a6 | sml-a-new-semantic-embedding-alignment | 2103.09635 | null | https://arxiv.org/abs/2103.09635v3 | https://arxiv.org/pdf/2103.09635v3.pdf | SILT: Efficient transformer training for inter-lingual inference | The ability of transformers to perform precision tasks such as question answering, Natural Language Inference (NLI) or summarising, have enabled them to be ranked as one of the best paradigm to address Natural Language Processing (NLP) tasks. NLI is one of the best scenarios to test these architectures, due to the knowledge required to understand complex sentences and established relationships between a hypothesis and a premise. Nevertheless, these models suffer from incapacity to generalise to other domains or difficulties to face multilingual and interlingual scenarios. The leading pathway in the literature to address these issues involve designing and training extremely large architectures, which leads to unpredictable behaviours and to establish barriers which impede broad access and fine tuning. In this paper, we propose a new architecture called Siamese Inter-Lingual Transformer (SILT), to efficiently align multilingual embeddings for Natural Language Inference, allowing for unmatched language pairs to be processed. SILT leverages siamese pre-trained multi-lingual transformers with frozen weights where the two input sentences attend each other to later be combined through a matrix alignment method. The experimental results carried out in this paper evidence that SILT allows to reduce drastically the number of trainable parameters while allowing for inter-lingual NLI and achieving state-of-the-art performance on common benchmarks. We make our code and dataset available at https://github.com/jahuerta92/siamese-inter-lingual-transformer. | ['David Camacho', 'Alejandro Martín', 'Javier Huertas-Tato'] | 2021-03-17 | null | null | null | null | ['cross-lingual-natural-language-inference'] | ['natural-language-processing'] | [ 7.85454959e-02 8.53241161e-02 2.47204360e-02 -3.73755634e-01
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78f2d790-fa90-4daa-91c7-a1b6e8bb7f02 | sumhis-extractive-summarization-exploiting | null | null | https://openreview.net/forum?id=ZaM7EsLb5X | https://openreview.net/pdf?id=ZaM7EsLb5X | SumHiS: Extractive Summarization Exploiting Hidden Sctructure | Extractive summarization is a task of highlighting the most important parts of the text. We introduce a new approach to extractive summarization task using hidden clustering structure of the text. Experimental results on CNN/DailyMail demonstrate that our approach generates more accurate summaries than both extractive and abstractive methods, achieving state-of-the-art results in terms of ROUGE-2 metric exceeding the previous approaches by 10\%. Additionally, we show that hidden structure of the text could be interpreted as aspects. | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['extractive-summarization'] | ['natural-language-processing'] | [ 2.67682135e-01 7.23915696e-01 -1.19319558e-01 -9.21986848e-02
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9b200548-5ba8-48b1-b8ba-587f0d0aa4a8 | mimic-iii-a-freely-accessible-critical-care | null | null | https://www.nature.com/articles/sdata201635 | https://www.nature.com/articles/sdata201635.pdf | MIMIC-III, a freely accessible critical care database | MIMIC-III (‘Medical Information Mart for Intensive Care’) is a large, single-center database comprising information relating to patients admitted to critical care units at a large tertiary care hospital. Data includes vital signs, medications, laboratory measurements, observations and notes charted by care providers, fluid balance, procedure codes, diagnostic codes, imaging reports, hospital length of stay, survival data, and more. The database supports applications including academic and industrial research, quality improvement initiatives, and higher education coursework.
In recent years there has been a concerted move towards the adoption of digital health record systems in hospitals. In the US, for example, the number of non-federal acute care hospitals with basic digital systems increased from 9.4 to 75.5% over the 7-year period between 2008 and 2014 (ref. 1).
Despite this advance, interoperability of digital systems remains an open issue, leading to challenges in data integration. As a result, the potential that hospital data offers in terms of understanding and improving care is yet to be fully realized. In parallel, the scientific research community is increasingly coming under criticism for the lack of reproducibility of studies2.
Here we report the release of the MIMIC-III database, an update to the widely-used MIMIC-II database (Data Citation 1). MIMIC-III integrates de-identified, comprehensive clinical data of patients admitted to the Beth Israel Deaconess Medical Center in Boston, Massachusetts, and makes it widely accessible to researchers internationally under a data use agreement (Fig. 1). The open nature of the data allows clinical studies to be reproduced and improved in ways that would not otherwise be possible. | ['Roger G. Mark', 'Leo Anthony Celi', 'Peter Szolovits', 'Benjamin Moody', 'Mohammad Ghassemi', 'Mengling Feng', 'Li-wei H. Lehman', 'Lu Shen', 'Tom J. Pollard', 'Alistair E.W. Johnson'] | 2016-05-24 | null | null | null | nature-2016-5 | ['data-integration', 'blood-pressure-estimation', 'multi-label-classification-of-biomedical', 'medical-code-prediction', 'length-of-stay-prediction', 'multi-label-text-classification', 'multi-label-text-classification'] | ['knowledge-base', 'medical', 'medical', 'medical', 'medical', 'methodology', 'natural-language-processing'] | [-2.32092943e-02 -1.37576789e-01 -3.42169493e-01 -1.08117834e-01
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ca53e290-f126-4b59-a182-780b6e052caf | contrastive-representation-learning-for-gaze | 2210.13404 | null | https://arxiv.org/abs/2210.13404v1 | https://arxiv.org/pdf/2210.13404v1.pdf | Contrastive Representation Learning for Gaze Estimation | Self-supervised learning (SSL) has become prevalent for learning representations in computer vision. Notably, SSL exploits contrastive learning to encourage visual representations to be invariant under various image transformations. The task of gaze estimation, on the other hand, demands not just invariance to various appearances but also equivariance to the geometric transformations. In this work, we propose a simple contrastive representation learning framework for gaze estimation, named Gaze Contrastive Learning (GazeCLR). GazeCLR exploits multi-view data to promote equivariance and relies on selected data augmentation techniques that do not alter gaze directions for invariance learning. Our experiments demonstrate the effectiveness of GazeCLR for several settings of the gaze estimation task. Particularly, our results show that GazeCLR improves the performance of cross-domain gaze estimation and yields as high as 17.2% relative improvement. Moreover, the GazeCLR framework is competitive with state-of-the-art representation learning methods for few-shot evaluation. The code and pre-trained models are available at https://github.com/jswati31/gazeclr. | ['Roberto Manduchi', 'Swati Jindal'] | 2022-10-24 | null | null | null | null | ['gaze-estimation'] | ['computer-vision'] | [ 1.75249875e-01 -1.29886851e-01 -4.62657809e-01 -4.59699154e-01
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c6b1c266-55aa-4087-a1dc-4a94315609db | rsgt-relational-structure-guided-temporal | null | null | https://aclanthology.org/2022.coling-1.174 | https://aclanthology.org/2022.coling-1.174.pdf | RSGT: Relational Structure Guided Temporal Relation Extraction | Temporal relation extraction aims to extract temporal relations between event pairs, which is crucial for natural language understanding. Few efforts have been devoted to capturing the global features. In this paper, we propose RSGT: Relational Structure Guided Temporal Relation Extraction to extract the relational structure features that can fit for both inter-sentence and intra-sentence relations. Specifically, we construct a syntactic-and-semantic-based graph to extract relational structures. Then we present a graph neural network based model to learn the representation of this graph. After that, an auxiliary temporal neighbor prediction task is used to fine-tune the encoder to get more comprehensive node representations. Finally, we apply a conflict detection and correction algorithm to adjust the wrongly predicted labels. Experiments on two well-known datasets, MATRES and TB-Dense, demonstrate the superiority of our method (2.3% F1 improvement on MATRES, 3.5% F1 improvement on TB-Dense). | ['Yong Dou', 'Xiaodong Wang', 'Hongkui Tu', 'Shenpo Dong', 'Jie zhou'] | null | null | null | null | coling-2022-10 | ['temporal-relation-extraction', 'temporal-relation-classification'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.72538802e-01 2.62540102e-01 -5.65600634e-01 -7.38715529e-01
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291e550d-03a2-45df-ab1b-a471a2617a0b | better-distractions-transformer-based | 2010.09598 | null | https://arxiv.org/abs/2010.09598v1 | https://arxiv.org/pdf/2010.09598v1.pdf | Better Distractions: Transformer-based Distractor Generation and Multiple Choice Question Filtering | For the field of education, being able to generate semantically correct and educationally relevant multiple choice questions (MCQs) could have a large impact. While question generation itself is an active research topic, generating distractors (the incorrect multiple choice options) receives much less attention. A missed opportunity, since there is still a lot of room for improvement in this area. In this work, we train a GPT-2 language model to generate three distractors for a given question and text context, using the RACE dataset. Next, we train a BERT language model to answer MCQs, and use this model as a filter, to select only questions that can be answered and therefore presumably make sense. To evaluate our work, we start by using text generation metrics, which show that our model outperforms earlier work on distractor generation (DG) and achieves state-of-the-art performance. Also, by calculating the question answering ability, we show that larger base models lead to better performance. Moreover, we conducted a human evaluation study, which confirmed the quality of the generated questions, but showed no statistically significant effect of the QA filter. | ['Tessa Verhoef', 'Suzan Verberne', 'Jeroen Offerijns'] | 2020-10-19 | null | null | null | null | ['distractor-generation'] | ['natural-language-processing'] | [ 7.61085972e-02 5.22900403e-01 2.96618015e-01 -1.60531700e-01
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0d66a95d-65b1-4794-b218-4bd070251cac | sips-unsupervised-succinct-interest-points | 1805.01358 | null | https://arxiv.org/abs/1805.01358v2 | https://arxiv.org/pdf/1805.01358v2.pdf | SIPs: Succinct Interest Points from Unsupervised Inlierness Probability Learning | A wide range of computer vision algorithms rely on identifying sparse interest points in images and establishing correspondences between them. However, only a subset of the initially identified interest points results in true correspondences (inliers). In this paper, we seek a detector that finds the minimum number of points that are likely to result in an application-dependent "sufficient" number of inliers k. To quantify this goal, we introduce the "k-succinctness" metric. Extracting a minimum number of interest points is attractive for many applications, because it can reduce computational load, memory, and data transmission. Alongside succinctness, we introduce an unsupervised training methodology for interest point detectors that is based on predicting the probability of a given pixel being an inlier. In comparison to previous learned detectors, our method requires the least amount of data pre-processing. Our detector and other state-of-the-art detectors are extensively evaluated with respect to succinctness on popular public datasets covering both indoor and outdoor scenes, and both wide and narrow baselines. In certain cases, our detector is able to obtain an equivalent amount of inliers with as little as 60% of the amount of points of other detectors. The code and trained networks are provided at https://github.com/uzh-rpg/sips2_open . | ['Davide Scaramuzza', 'Konstantinos G. Derpanis', 'Titus Cieslewski'] | 2018-05-03 | null | null | null | null | ['interest-point-detection'] | ['computer-vision'] | [-6.93139136e-02 -1.57508582e-01 -1.99737936e-01 -2.24793494e-01
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9f34944e-5212-42cc-9cc6-cac5906db8a0 | peace-cross-platform-hate-speech-detection-a | 2306.08804 | null | https://arxiv.org/abs/2306.08804v1 | https://arxiv.org/pdf/2306.08804v1.pdf | PEACE: Cross-Platform Hate Speech Detection- A Causality-guided Framework | Hate speech detection refers to the task of detecting hateful content that aims at denigrating an individual or a group based on their religion, gender, sexual orientation, or other characteristics. Due to the different policies of the platforms, different groups of people express hate in different ways. Furthermore, due to the lack of labeled data in some platforms it becomes challenging to build hate speech detection models. To this end, we revisit if we can learn a generalizable hate speech detection model for the cross platform setting, where we train the model on the data from one (source) platform and generalize the model across multiple (target) platforms. Existing generalization models rely on linguistic cues or auxiliary information, making them biased towards certain tags or certain kinds of words (e.g., abusive words) on the source platform and thus not applicable to the target platforms. Inspired by social and psychological theories, we endeavor to explore if there exist inherent causal cues that can be leveraged to learn generalizable representations for detecting hate speech across these distribution shifts. To this end, we propose a causality-guided framework, PEACE, that identifies and leverages two intrinsic causal cues omnipresent in hateful content: the overall sentiment and the aggression in the text. We conduct extensive experiments across multiple platforms (representing the distribution shift) showing if causal cues can help cross-platform generalization. | ['Huan Liu', 'Aman Chadha', 'Raha Moraffah', 'Tharindu Kumarage', 'Paras Sheth'] | 2023-06-15 | null | null | null | null | ['hate-speech-detection'] | ['natural-language-processing'] | [-2.17141896e-01 -1.47957548e-01 -4.62043077e-01 -1.70266837e-01
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6e36864b-a3db-4823-be74-a8672020ae72 | rethinking-so-3-equivariance-with-bilinear | 2303.11288 | null | https://arxiv.org/abs/2303.11288v1 | https://arxiv.org/pdf/2303.11288v1.pdf | Rethinking SO(3)-equivariance with Bilinear Tensor Networks | Many datasets in scientific and engineering applications are comprised of objects which have specific geometric structure. A common example is data which inhabits a representation of the group SO$(3)$ of 3D rotations: scalars, vectors, tensors, \textit{etc}. One way for a neural network to exploit prior knowledge of this structure is to enforce SO$(3)$-equivariance throughout its layers, and several such architectures have been proposed. While general methods for handling arbitrary SO$(3)$ representations exist, they computationally intensive and complicated to implement. We show that by judicious symmetry breaking, we can efficiently increase the expressiveness of a network operating only on vector and order-2 tensor representations of SO$(2)$. We demonstrate the method on an important problem from High Energy Physics known as \textit{b-tagging}, where particle jets originating from b-meson decays must be discriminated from an overwhelming QCD background. In this task, we find that augmenting a standard architecture with our method results in a \ensuremath{2.3\times} improvement in rejection score. | ['Ema Smith', 'Zhelun Li', 'Chase Shimmin'] | 2023-03-20 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [ 1.74042717e-01 -6.87752888e-02 7.53218085e-02 -7.54442155e-01
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ad5f6aaf-90cd-4b70-86e6-d5b3ee01ec26 | wavenet-based-low-rate-speech-coding | 1712.0112 | null | http://arxiv.org/abs/1712.01120v1 | http://arxiv.org/pdf/1712.01120v1.pdf | Wavenet based low rate speech coding | Traditional parametric coding of speech facilitates low rate but provides
poor reconstruction quality because of the inadequacy of the model used. We
describe how a WaveNet generative speech model can be used to generate high
quality speech from the bit stream of a standard parametric coder operating at
2.4 kb/s. We compare this parametric coder with a waveform coder based on the
same generative model and show that approximating the signal waveform incurs a
large rate penalty. Our experiments confirm the high performance of the WaveNet
based coder and show that the speech produced by the system is able to
additionally perform implicit bandwidth extension and does not significantly
impair recognition of the original speaker for the human listener, even when
that speaker has not been used during the training of the generative model. | ['Thomas C. Walters', 'W. Bastiaan Kleijn', 'Jan Skoglund', 'Alejandro Luebs', 'Quan Wang', 'Florian Stimberg', 'Felicia S. C. Lim'] | 2017-12-01 | null | null | null | null | ['bandwidth-extension', 'bandwidth-extension'] | ['audio', 'speech'] | [ 2.08029911e-01 2.53618121e-01 2.28916436e-01 -1.12540670e-01
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a8b85e25-d319-4859-b3bd-4100b308a385 | textual-echo-cancellation | 2008.06006 | null | https://arxiv.org/abs/2008.06006v4 | https://arxiv.org/pdf/2008.06006v4.pdf | Textual Echo Cancellation | In this paper, we propose Textual Echo Cancellation (TEC) - a framework for cancelling the text-to-speech (TTS) playback echo from overlapping speech recordings. Such a system can largely improve speech recognition performance and user experience for intelligent devices such as smart speakers, as the user can talk to the device while the device is still playing the TTS signal responding to the previous query. We implement this system by using a novel sequence-to-sequence model with multi-source attention that takes both the microphone mixture signal and source text of the TTS playback as inputs, and predicts the enhanced audio. Experiments show that the textual information of the TTS playback is critical to enhancement performance. Besides, the text sequence is much smaller in size compared with the raw acoustic signal of the TTS playback, and can be immediately transmitted to the device or ASR server even before the playback is synthesized. Therefore, our proposed approach effectively reduces Internet communication and latency compared with alternative approaches such as acoustic echo cancellation (AEC). | ['Ye Jia', 'Shaojin Ding', 'Ke Hu', 'Quan Wang'] | 2020-08-13 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 6.10677242e-01 -2.59178281e-01 3.15518171e-01 -1.28934816e-01
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2d0fead3-68f4-4352-9ca8-1adbcffe9578 | crts-a-type-system-for-representing-clinical | 1609.01592 | null | http://arxiv.org/abs/1609.01592v1 | http://arxiv.org/pdf/1609.01592v1.pdf | CRTS: A type system for representing clinical recommendations | Background: Clinical guidelines and recommendations are the driving wheels of
the evidence-based medicine (EBM) paradigm, but these are available primarily
as unstructured text and are generally highly heterogeneous in nature. This
significantly reduces the dissemination and automatic application of these
recommendations at the point of care. A comprehensive structured representation
of these recommendations is highly beneficial in this regard. Objective: The
objective of this paper to present Clinical Recommendation Type System (CRTS),
a common type system that can effectively represent a clinical recommendation
in a structured form. Methods: CRTS is built by analyzing 125 recommendations
and 195 research articles corresponding to 6 different diseases available from
UpToDate, a publicly available clinical knowledge system, and from the National
Guideline Clearinghouse, a public resource for evidence-based clinical practice
guidelines. Results: We show that CRTS not only covers the recommendations but
also is flexible to be extended to represent information from primary
literature. We also describe how our developed type system can be applied for
clinical decision support, medical knowledge summarization, and citation
retrieval. Conclusion: We showed that our proposed type system is precise and
comprehensive in representing a large sample of recommendations available for
various disorders. CRTS can now be used to build interoperable information
extraction systems that automatically extract clinical recommendations and
related data elements from clinical evidence resources, guidelines, systematic
reviews and primary publications.
Keywords: guidelines and recommendations, type system, clinical decision
support, evidence-based medicine, information storage and retrieval | ['Siddhartha R. Jonnalagadda', 'Ravi P Garg', 'Kalpana Raja'] | 2016-09-06 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [ 9.81338769e-02 2.73899317e-01 -9.91227746e-01 -2.38573011e-02
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71da0d83-e6b6-40d3-b8c0-eb9dc357d0a1 | causalapm-generalizable-literal | 2305.02865 | null | https://arxiv.org/abs/2305.02865v1 | https://arxiv.org/pdf/2305.02865v1.pdf | CausalAPM: Generalizable Literal Disentanglement for NLU Debiasing | Dataset bias, i.e., the over-reliance on dataset-specific literal heuristics, is getting increasing attention for its detrimental effect on the generalization ability of NLU models. Existing works focus on eliminating dataset bias by down-weighting problematic data in the training process, which induce the omission of valid feature information while mitigating bias. In this work, We analyze the causes of dataset bias from the perspective of causal inference and propose CausalAPM, a generalizable literal disentangling framework to ameliorate the bias problem from feature granularity. The proposed approach projects literal and semantic information into independent feature subspaces, and constrains the involvement of literal information in subsequent predictions. Extensive experiments on three NLP benchmarks (MNLI, FEVER, and QQP) demonstrate that our proposed framework significantly improves the OOD generalization performance while maintaining ID performance. | ['Xuanjing Huang', 'Qi Zhang', 'Junjie Shan', 'Shihan Dou', 'Songyang Gao'] | 2023-05-04 | null | null | null | null | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [ 0.28159782 0.20540154 -0.8782854 -0.6681401 -0.35455146 -0.51895094
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b48defff-34fc-4ea5-a0ec-f447e0a203bc | salsa-spatial-cue-augmented-log-spectrogram | 2110.00275 | null | https://arxiv.org/abs/2110.00275v3 | https://arxiv.org/pdf/2110.00275v3.pdf | SALSA: Spatial Cue-Augmented Log-Spectrogram Features for Polyphonic Sound Event Localization and Detection | Sound event localization and detection (SELD) consists of two subtasks, which are sound event detection and direction-of-arrival estimation. While sound event detection mainly relies on time-frequency patterns to distinguish different sound classes, direction-of-arrival estimation uses amplitude and/or phase differences between microphones to estimate source directions. As a result, it is often difficult to jointly optimize these two subtasks. We propose a novel feature called Spatial cue-Augmented Log-SpectrogrAm (SALSA) with exact time-frequency mapping between the signal power and the source directional cues, which is crucial for resolving overlapping sound sources. The SALSA feature consists of multichannel log-spectrograms stacked along with the normalized principal eigenvector of the spatial covariance matrix at each corresponding time-frequency bin. Depending on the microphone array format, the principal eigenvector can be normalized differently to extract amplitude and/or phase differences between the microphones. As a result, SALSA features are applicable for different microphone array formats such as first-order ambisonics (FOA) and multichannel microphone array (MIC). Experimental results on the TAU-NIGENS Spatial Sound Events 2021 dataset with directional interferences showed that SALSA features outperformed other state-of-the-art features. Specifically, the use of SALSA features in the FOA format increased the F1 score and localization recall by 6% each, compared to the multichannel log-mel spectrograms with intensity vectors. For the MIC format, using SALSA features increased F1 score and localization recall by 16% and 7%, respectively, compared to using multichannel log-mel spectrograms with generalized cross-correlation spectra. | ['Woon-Seng Gan', 'Douglas L. Jones', 'Ngoc Khanh Nguyen', 'Karn N. Watcharasupat', 'Thi Ngoc Tho Nguyen'] | 2021-10-01 | null | null | null | null | ['direction-of-arrival-estimation', 'sound-event-localization-and-detection'] | ['audio', 'audio'] | [ 1.47532851e-01 -8.54816437e-01 5.70904613e-01 3.55881490e-02
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2010658b-22f0-43d6-b514-b7c459a30153 | incorporating-transformer-designs-into | 2303.14324 | null | https://arxiv.org/abs/2303.14324v1 | https://arxiv.org/pdf/2303.14324v1.pdf | Incorporating Transformer Designs into Convolutions for Lightweight Image Super-Resolution | In recent years, the use of large convolutional kernels has become popular in designing convolutional neural networks due to their ability to capture long-range dependencies and provide large receptive fields. However, the increase in kernel size also leads to a quadratic growth in the number of parameters, resulting in heavy computation and memory requirements. To address this challenge, we propose a neighborhood attention (NA) module that upgrades the standard convolution with a self-attention mechanism. The NA module efficiently extracts long-range dependencies in a sliding window pattern, thereby achieving similar performance to large convolutional kernels but with fewer parameters. Building upon the NA module, we propose a lightweight single image super-resolution (SISR) network named TCSR. Additionally, we introduce an enhanced feed-forward network (EFFN) in TCSR to improve the SISR performance. EFFN employs a parameter-free spatial-shift operation for efficient feature aggregation. Our extensive experiments and ablation studies demonstrate that TCSR outperforms existing lightweight SISR methods and achieves state-of-the-art performance. Our codes are available at \url{https://github.com/Aitical/TCSR}. | ['Xianming Liu', 'Yuanchao Bai', 'Junjun Jiang', 'Gang Wu'] | 2023-03-25 | null | null | null | null | ['image-super-resolution'] | ['computer-vision'] | [ 3.47178094e-02 -3.00964504e-01 -4.67221178e-02 -5.05640864e-01
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0f522584-78b2-4ea5-9bb2-5389a7a1f0f9 | convolutional-neural-networks-for-no | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Kang_Convolutional_Neural_Networks_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Kang_Convolutional_Neural_Networks_2014_CVPR_paper.pdf | Convolutional Neural Networks for No-Reference Image Quality Assessment | In this work we describe a Convolutional Neural Network (CNN) to accurately predict image quality without a reference image. Taking image patches as input, the CNN works in the spatial domain without using hand-crafted features that are employed by most previous methods. The network consists of one convolutional layer with max and min pooling, two fully connected layers and an output node. Within the network structure, feature learning and regression are integrated into one optimization process, which leads to a more effective model for estimating image quality. This approach achieves state of the art performance on the LIVE dataset and shows excellent generalization ability in cross dataset experiments. Further experiments on images with local distortions demonstrate the local quality estimation ability of our CNN, which is rarely reported in previous literature. | ['David Doermann', 'Yi Li', 'Le Kang', 'Peng Ye'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [ 1.23146974e-01 -2.10113376e-01 -2.07875729e-01 -4.38500047e-01
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56a8aa58-6acd-47ad-b245-db284ba544f6 | modular-representation-underlies-systematic | 2004.14623 | null | https://arxiv.org/abs/2004.14623v4 | https://arxiv.org/pdf/2004.14623v4.pdf | Neural Natural Language Inference Models Partially Embed Theories of Lexical Entailment and Negation | We address whether neural models for Natural Language Inference (NLI) can learn the compositional interactions between lexical entailment and negation, using four methods: the behavioral evaluation methods of (1) challenge test sets and (2) systematic generalization tasks, and the structural evaluation methods of (3) probes and (4) interventions. To facilitate this holistic evaluation, we present Monotonicity NLI (MoNLI), a new naturalistic dataset focused on lexical entailment and negation. In our behavioral evaluations, we find that models trained on general-purpose NLI datasets fail systematically on MoNLI examples containing negation, but that MoNLI fine-tuning addresses this failure. In our structural evaluations, we look for evidence that our top-performing BERT-based model has learned to implement the monotonicity algorithm behind MoNLI. Probes yield evidence consistent with this conclusion, and our intervention experiments bolster this, showing that the causal dynamics of the model mirror the causal dynamics of this algorithm on subsets of MoNLI. This suggests that the BERT model at least partially embeds a theory of lexical entailment and negation at an algorithmic level. | ['Christopher Potts', 'Kyle Richardson', 'Atticus Geiger'] | 2020-04-30 | null | https://aclanthology.org/2020.blackboxnlp-1.16 | https://aclanthology.org/2020.blackboxnlp-1.16.pdf | emnlp-blackboxnlp-2020-11 | ['systematic-generalization'] | ['reasoning'] | [ 2.60880500e-01 3.94012600e-01 -5.01263916e-01 -5.22573948e-01
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c28ec404-34d9-47ad-8eeb-d5d8b89a339c | hyper-laplacian-regularized-concept | 2304.11435 | null | https://arxiv.org/abs/2304.11435v1 | https://arxiv.org/pdf/2304.11435v1.pdf | Hyper-Laplacian Regularized Concept Factorization in Low-rank Tensor Space for Multi-view Clustering | Tensor-oriented multi-view subspace clustering has achieved significant strides in assessing high-order correlations and improving clustering analysis of multi-view data. Nevertheless, most of existing investigations are typically hampered by the two flaws. First, self-representation based tensor subspace learning usually induces high time and space complexity, and is limited in perceiving nonlinear local structure in the embedding space. Second, the tensor singular value decomposition (t-SVD) model redistributes each singular value equally without considering the diverse importance among them. To well cope with the issues, we propose a hyper-Laplacian regularized concept factorization (HLRCF) in low-rank tensor space for multi-view clustering. Specifically, we adopt the concept factorization to explore the latent cluster-wise representation of each view. Further, the hypergraph Laplacian regularization endows the model with the capability of extracting the nonlinear local structures in the latent space. Considering that different tensor singular values associate structural information with unequal importance, we develop a self-weighted tensor Schatten p-norm to constrain the tensor comprised of all cluster-wise representations. Notably, the tensor with smaller size greatly decreases the time and space complexity in the low-rank optimization. Finally, experimental results on eight benchmark datasets exhibit that HLRCF outperforms other multi-view methods, showingcasing its superior performance. | ['Zhoumin Lu', 'Zhiling Cai', 'Lele Fu', 'Zixiao Yu'] | 2023-04-22 | null | null | null | null | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-3.79965633e-01 -6.61943972e-01 -2.03386873e-01 1.25909358e-01
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c6f9e525-7df1-400a-a26a-67147bfcbc5a | foundations-of-coupled-nonlinear | 1509.0888 | null | http://arxiv.org/abs/1509.08880v2 | http://arxiv.org/pdf/1509.08880v2.pdf | Foundations of Coupled Nonlinear Dimensionality Reduction | In this paper we introduce and analyze the learning scenario of \emph{coupled
nonlinear dimensionality reduction}, which combines two major steps of machine
learning pipeline: projection onto a manifold and subsequent supervised
learning. First, we present new generalization bounds for this scenario and,
second, we introduce an algorithm that follows from these bounds. The
generalization error bound is based on a careful analysis of the empirical
Rademacher complexity of the relevant hypothesis set. In particular, we show an
upper bound on the Rademacher complexity that is in $\widetilde
O(\sqrt{\Lambda_{(r)}/m})$, where $m$ is the sample size and $\Lambda_{(r)}$
the upper bound on the Ky-Fan $r$-norm of the associated kernel matrix. We give
both upper and lower bound guarantees in terms of that Ky-Fan $r$-norm, which
strongly justifies the definition of our hypothesis set. To the best of our
knowledge, these are the first learning guarantees for the problem of coupled
dimensionality reduction. Our analysis and learning guarantees further apply to
several special cases, such as that of using a fixed kernel with supervised
dimensionality reduction or that of unsupervised learning of a kernel for
dimensionality reduction followed by a supervised learning algorithm. Based on
theoretical analysis, we suggest a structural risk minimization algorithm
consisting of the coupled fitting of a low dimensional manifold and a
separation function on that manifold. | ['Mehryar Mohri', 'Dmitry Storcheus', 'Afshin Rostamizadeh'] | 2015-09-29 | null | null | null | null | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [ 1.42421752e-01 4.48508352e-01 1.76971465e-01 -1.50057256e-01
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ed52e195-d755-4a1f-aa25-16c6efd5aa3b | vggin-net-deep-transfer-network-for | null | null | https://ieeexplore.ieee.org/document/9744541 | https://drive.google.com/file/d/1catVKX8IgJX_aTLfgS72JUCmTsNBXE3m/view?usp=sharing | VGGIN-Net: Deep Transfer Network for Imbalanced Breast Cancer Dataset | In this paper, we have presented a novel deep neural network architecture involving transfer learning approach, formed
by freezing and concatenating all the layers till block4 pool layer of VGG16 pre-trained model (at the lower level) with the layers
of a randomly initialized naïve Inception block module (at the higher level). Further, we have added the batch normalization, flatten,
dropout and dense layers in the proposed architecture. Our transfer network, called VGGIN-Net, facilitates the transfer of domain
knowledge from the larger ImageNet object dataset to the smaller imbalanced breast cancer dataset. To improve the performance of
the proposed model, regularization was used in the form of dropout and data augmentation. A detailed block-wise fine tuning has
been conducted on the proposed deep transfer network for images of different magnification factors. The results of extensive
experiments indicate a significant improvement of classification performance after the application of fine-tuning. The proposed deep
learning architecture with transfer learning and fine-tuning yields the highest accuracies in comparison to other state-of-the-art
approaches for the classification of BreakHis breast cancer dataset. The articulated architecture is designed in a way that it can be
effectively transfer learned on other breast cancer datasets. | ['Seba Susan', 'Manisha Saini'] | 2022-03-29 | null | null | null | ieee-acm-transactions-on-computational-1 | ['breast-cancer-detection', 'breast-cancer-detection', 'breast-cancer-histology-image-classification'] | ['knowledge-base', 'medical', 'medical'] | [ 1.61907852e-01 4.56431419e-01 -4.54710759e-02 -6.57635927e-01
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c57f590e-eda3-4df9-8280-a0648d92dba1 | reasoning-chain-based-adversarial-attack-for | 2112.09658 | null | https://arxiv.org/abs/2112.09658v1 | https://arxiv.org/pdf/2112.09658v1.pdf | Reasoning Chain Based Adversarial Attack for Multi-hop Question Answering | Recent years have witnessed impressive advances in challenging multi-hop QA tasks. However, these QA models may fail when faced with some disturbance in the input text and their interpretability for conducting multi-hop reasoning remains uncertain. Previous adversarial attack works usually edit the whole question sentence, which has limited effect on testing the entity-based multi-hop inference ability. In this paper, we propose a multi-hop reasoning chain based adversarial attack method. We formulate the multi-hop reasoning chains starting from the query entity to the answer entity in the constructed graph, which allows us to align the question to each reasoning hop and thus attack any hop. We categorize the questions into different reasoning types and adversarially modify part of the question corresponding to the selected reasoning hop to generate the distracting sentence. We test our adversarial scheme on three QA models on HotpotQA dataset. The results demonstrate significant performance reduction on both answer and supporting facts prediction, verifying the effectiveness of our reasoning chain based attack method for multi-hop reasoning models and the vulnerability of them. Our adversarial re-training further improves the performance and robustness of these models. | ['Zhongyu Wei', 'Qin Chen', 'Siyuan Wang', 'Jiayu Ding'] | 2021-12-17 | null | null | null | null | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 1.12473853e-01 7.70504296e-01 2.05086455e-01 -3.34609866e-01
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e5a51767-490d-48c0-b238-42126ed57df5 | temporal-consistency-learning-of-inter-frames | 2211.01639 | null | https://arxiv.org/abs/2211.01639v1 | https://arxiv.org/pdf/2211.01639v1.pdf | Temporal Consistency Learning of inter-frames for Video Super-Resolution | Video super-resolution (VSR) is a task that aims to reconstruct high-resolution (HR) frames from the low-resolution (LR) reference frame and multiple neighboring frames. The vital operation is to utilize the relative misaligned frames for the current frame reconstruction and preserve the consistency of the results. Existing methods generally explore information propagation and frame alignment to improve the performance of VSR. However, few studies focus on the temporal consistency of inter-frames. In this paper, we propose a Temporal Consistency learning Network (TCNet) for VSR in an end-to-end manner, to enhance the consistency of the reconstructed videos. A spatio-temporal stability module is designed to learn the self-alignment from inter-frames. Especially, the correlative matching is employed to exploit the spatial dependency from each frame to maintain structural stability. Moreover, a self-attention mechanism is utilized to learn the temporal correspondence to implement an adaptive warping operation for temporal consistency among multi-frames. Besides, a hybrid recurrent architecture is designed to leverage short-term and long-term information. We further present a progressive fusion module to perform a multistage fusion of spatio-temporal features. And the final reconstructed frames are refined by these fused features. Objective and subjective results of various experiments demonstrate that TCNet has superior performance on different benchmark datasets, compared to several state-of-the-art methods. | ['Yao Zhao', 'Chunyu Lin', 'Chao Yao', 'Shuo Jin', 'Meiqin Liu'] | 2022-11-03 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [ 1.97402481e-02 -5.98908484e-01 -2.66765058e-01 -3.86092752e-01
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1b733a5d-7797-41f8-be5a-f4a08ddb36b9 | pose-aware-instance-segmentation-framework | 2002.02143 | null | https://arxiv.org/abs/2002.02143v1 | https://arxiv.org/pdf/2002.02143v1.pdf | Pose-Aware Instance Segmentation Framework from Cone Beam CT Images for Tooth Segmentation | Individual tooth segmentation from cone beam computed tomography (CBCT) images is an essential prerequisite for an anatomical understanding of orthodontic structures in several applications, such as tooth reformation planning and implant guide simulations. However, the presence of severe metal artifacts in CBCT images hinders the accurate segmentation of each individual tooth. In this study, we propose a neural network for pixel-wise labeling to exploit an instance segmentation framework that is robust to metal artifacts. Our method comprises of three steps: 1) image cropping and realignment by pose regressions, 2) metal-robust individual tooth detection, and 3) segmentation. We first extract the alignment information of the patient by pose regression neural networks to attain a volume-of-interest (VOI) region and realign the input image, which reduces the inter-overlapping area between tooth bounding boxes. Then, individual tooth regions are localized within a VOI realigned image using a convolutional detector. We improved the accuracy of the detector by employing non-maximum suppression and multiclass classification metrics in the region proposal network. Finally, we apply a convolutional neural network (CNN) to perform individual tooth segmentation by converting the pixel-wise labeling task to a distance regression task. Metal-intensive image augmentation is also employed for a robust segmentation of metal artifacts. The result shows that our proposed method outperforms other state-of-the-art methods, especially for teeth with metal artifacts. The primary significance of the proposed method is two-fold: 1) an introduction of pose-aware VOI realignment followed by a robust tooth detection and 2) a metal-robust CNN framework for accurate tooth segmentation. | ['Yeong-Gil Shin', 'Sanguk Park', 'Jingyu Lee', 'Jeongjin Lee', 'Minyoung Chung', 'Minkyung Lee', 'Jusang Lee', 'Jioh Hong'] | 2020-02-06 | null | null | null | null | ['image-cropping'] | ['computer-vision'] | [ 7.30852604e-01 6.67938769e-01 -1.34529859e-01 -4.68439639e-01
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aa6bd19d-87c4-4136-ace1-64edfdca59e8 | pad-program-aided-distillation-specializes | 2305.13888 | null | https://arxiv.org/abs/2305.13888v1 | https://arxiv.org/pdf/2305.13888v1.pdf | PaD: Program-aided Distillation Specializes Large Models in Reasoning | While Large Language Models (LLMs) excel in several natural language processing tasks, their size and inaccessibility present challenges for extensive practical application. Previous studies acquire specialized skills through distillation on LLMs, which result in trading generic abilities, called model specialization. As for reasoning ability, chain-of-thought was synthesized to subsequent distillation. However, due to hallucination, synthetic chain-of-thought from LLMs contains faulty reasoning. These incorrect reasoning steps damage the reasoning capability. To tackle above issues, we propose Program-aided Distillation (PaD), which distills LLMs to obtain specialized small models in reasoning tasks. In PaD, we strengthen specialized models with program-aided reasoning, and help them overcome faulty reasoning steps with automated error checking. Experimental results demonstrate that, on the GSM8K benchmark, a 0.06B model using PaD can not only outperform certain LLMs (e.g., LLaMA), but also achieves a 10% improvement over baselines with a significantly smaller scale of parameters and data. Data pruning analysis reveals that PaD possesses higher training efficiency. | ['BoWen Zhou', 'Xingwei Long', 'Kaiyan Zhang', 'Biqing Qi', 'Xuekai Zhu'] | 2023-05-23 | null | null | null | null | ['gsm8k'] | ['natural-language-processing'] | [ 8.47106799e-02 5.28758824e-01 -3.52437437e-01 -2.97183126e-01
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4ccf1fd5-a7f0-46d1-918b-3279454b3045 | multi-2oie-multilingual-open-information | 2009.08128 | null | https://arxiv.org/abs/2009.08128v2 | https://arxiv.org/pdf/2009.08128v2.pdf | Multi$^2$OIE: Multilingual Open Information Extraction Based on Multi-Head Attention with BERT | In this paper, we propose Multi$^2$OIE, which performs open information extraction (open IE) by combining BERT with multi-head attention. Our model is a sequence-labeling system with an efficient and effective argument extraction method. We use a query, key, and value setting inspired by the Multimodal Transformer to replace the previously used bidirectional long short-term memory architecture with multi-head attention. Multi$^2$OIE outperforms existing sequence-labeling systems with high computational efficiency on two benchmark evaluation datasets, Re-OIE2016 and CaRB. Additionally, we apply the proposed method to multilingual open IE using multilingual BERT. Experimental results on new benchmark datasets introduced for two languages (Spanish and Portuguese) demonstrate that our model outperforms other multilingual systems without training data for the target languages. | ['Yukyung Lee', 'Youngbin Ro', 'Pilsung Kang'] | 2020-09-17 | null | null | null | null | ['open-information-extraction'] | ['natural-language-processing'] | [ 1.35268671e-02 2.12883130e-01 -6.06281698e-01 2.77121142e-02
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f70fadc3-81af-4687-a371-5c09abf7bdd7 | logo-2k-a-large-scale-logo-dataset-for | 1911.07924 | null | https://arxiv.org/abs/1911.07924v1 | https://arxiv.org/pdf/1911.07924v1.pdf | Logo-2K+: A Large-Scale Logo Dataset for Scalable Logo Classification | Logo classification has gained increasing attention for its various applications, such as copyright infringement detection, product recommendation and contextual advertising. Compared with other types of object images, the real-world logo images have larger variety in logo appearance and more complexity in their background. Therefore, recognizing the logo from images is challenging. To support efforts towards scalable logo classification task, we have curated a dataset, Logo-2K+, a new large-scale publicly available real-world logo dataset with 2,341 categories and 167,140 images. Compared with existing popular logo datasets, such as FlickrLogos-32 and LOGO-Net, Logo-2K+ has more comprehensive coverage of logo categories and larger quantity of logo images. Moreover, we propose a Discriminative Region Navigation and Augmentation Network (DRNA-Net), which is capable of discovering more informative logo regions and augmenting these image regions for logo classification. DRNA-Net consists of four sub-networks: the navigator sub-network first selected informative logo-relevant regions guided by the teacher sub-network, which can evaluate its confidence belonging to the ground-truth logo class. The data augmentation sub-network then augments the selected regions via both region cropping and region dropping. Finally, the scrutinizer sub-network fuses features from augmented regions and the whole image for logo classification. Comprehensive experiments on Logo-2K+ and other three existing benchmark datasets demonstrate the effectiveness of proposed method. Logo-2K+ and the proposed strong baseline DRNA-Net are expected to further the development of scalable logo image recognition, and the Logo-2K+ dataset can be found at https://github.com/msn199959/Logo-2k-plus-Dataset. | ['Yuanjie Zheng', 'Weiqing Min', 'Sujuan Hou', 'Jing Wang', 'Shengnan Ma', 'Shuqiang Jiang', 'Haishuai Wang'] | 2019-11-11 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [ 3.00911367e-01 -2.79796600e-01 -8.69742393e-01 -2.74113268e-01
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8542c2c9-3823-4608-8696-d42d61611d41 | from-2d-images-to-3d-model-weakly-supervised | 2204.03842 | null | https://arxiv.org/abs/2204.03842v2 | https://arxiv.org/pdf/2204.03842v2.pdf | From 2D Images to 3D Model:Weakly Supervised Multi-View Face Reconstruction with Deep Fusion | We consider the problem of Multi-view 3D Face Reconstruction (MVR) with weakly supervised learning that leverages a limited number of 2D face images (e.g. 3) to generate a high-quality 3D face model with very light annotation. Despite their encouraging performance, present MVR methods simply concatenate multi-view image features and pay less attention to critical areas (e.g. eye, brow, nose, and mouth). To this end, we propose a novel model called Deep Fusion MVR (DF-MVR) and design a multi-view encoding to a single decoding framework with skip connections, able to extract, integrate, and compensate deep features with attention from multi-view images. In addition, we develop a multi-view face parse network to learn, identify, and emphasize the critical common face area. Finally, though our model is trained with a few 2D images, it can reconstruct an accurate 3D model even if one single 2D image is input. We conduct extensive experiments to evaluate various multi-view 3D face reconstruction methods. Experiments on Pixel-Face and Bosphorus datasets indicate the superiority of our model. Without 3D landmarks annotation, DF-MVR achieves 5.2% and 3.0% RMSE improvements over the existing best weakly supervised MVRs respectively on Pixel-Face and Bosphorus datasets; with 3D landmarks annotation, DF-MVR attains superior performance particularly on Pixel-Face dataset, leading to 13.4% RMSE improvement over the best weakly supervised MVR model. | ['Kaizhu Huang', 'Xi Yang', 'Yuyao Yan', 'Jianan Ye', 'Chaolong Yang', 'Weiguang Zhao'] | 2022-04-08 | null | null | null | null | ['3d-face-reconstruction', 'face-model', 'face-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 9.60031617e-03 2.32691139e-01 -1.29170224e-01 -5.50045788e-01
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f37d8e44-1ebe-4505-be71-99839f41f783 | introduction-to-medical-imaging-informatics | 2306.00421 | null | https://arxiv.org/abs/2306.00421v3 | https://arxiv.org/pdf/2306.00421v3.pdf | Introduction to Medical Imaging Informatics | Medical imaging informatics is a rapidly growing field that combines the principles of medical imaging and informatics to improve the acquisition, management, and interpretation of medical images. This chapter introduces the basic concepts of medical imaging informatics, including image processing, feature engineering, and machine learning. It also discusses the recent advancements in computer vision and deep learning technologies and how they are used to develop new quantitative image markers and prediction models for disease detection, diagnosis, and prognosis prediction. By covering the basic knowledge of medical imaging informatics, this chapter provides a foundation for understanding the role of informatics in medicine and its potential impact on patient care. | ['Sajedul Talukder', 'Md Jahangir Alam', 'Md. Mahim Anjum Haque', 'MD Abdullah Al Nasim', 'Riadul Islam', 'Ruksat Hossain', 'Md. Zihad Bin Jahangir'] | 2023-06-01 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [ 5.09056687e-01 -6.89144358e-02 -4.36827302e-01 -5.08290708e-01
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ebcb11d0-fa2b-4d51-a800-1046752f1013 | a-novel-model-based-heuristic-for-energy | 1712.03719 | null | http://arxiv.org/abs/1712.03719v2 | http://arxiv.org/pdf/1712.03719v2.pdf | A novel model-based heuristic for energy optimal motion planning for automated driving | Predictive motion planning is the key to achieve energy-efficient driving,
which is one of the main benefits of automated driving. Researchers have been
studying the planning of velocity trajectories, a simpler form of motion
planning, for over a decade now and many different methods are available.
Dynamic programming has shown to be the most common choice due to its numerical
background and ability to include nonlinear constraints and models. Although
planning of an optimal trajectory is done in a systematic way, dynamic
programming does not use any knowledge about the considered problem to guide
the exploration and therefore explores all possible trajectories.
A* is a search algorithm which enables using knowledge about the problem to
guide the exploration to the most promising solutions first. Knowledge has to
be represented in a form of a heuristic function, which gives an optimistic
estimate of cost for transitioning to the final state, which is not a
straightforward task. This paper presents a novel heuristics incorporating air
drag and auxiliary power as well as operational costs of the vehicle, besides
kinetic and potential energy and rolling resistance known in the literature.
Furthermore, optimal cruising velocity, which depends on vehicle aerodynamic
properties and auxiliary power, is derived. Results are compared for different
variants of heuristic functions and dynamic programming as well. | ['Zlatan Ajanovic', 'Michael Stolz', 'Martin Horn'] | 2017-12-11 | null | null | null | null | ['optimal-motion-planning'] | ['robots'] | [-1.34824291e-01 1.66472763e-01 -4.15356278e-01 -3.89952436e-02
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43010faa-f5b5-4e8f-acad-a3ba5d52c3e1 | realistic-bokeh-effect-rendering-on-mobile | 2211.06769 | null | https://arxiv.org/abs/2211.06769v1 | https://arxiv.org/pdf/2211.06769v1.pdf | Realistic Bokeh Effect Rendering on Mobile GPUs, Mobile AI & AIM 2022 challenge: Report | As mobile cameras with compact optics are unable to produce a strong bokeh effect, lots of interest is now devoted to deep learning-based solutions for this task. In this Mobile AI challenge, the target was to develop an efficient end-to-end AI-based bokeh effect rendering approach that can run on modern smartphone GPUs using TensorFlow Lite. The participants were provided with a large-scale EBB! bokeh dataset consisting of 5K shallow / wide depth-of-field image pairs captured using the Canon 7D DSLR camera. The runtime of the resulting models was evaluated on the Kirin 9000's Mali GPU that provides excellent acceleration results for the majority of common deep learning ops. A detailed description of all models developed in this challenge is provided in this paper. | ['Lei Lei', 'Xiaotao Wang', 'Yanan Li', 'Huixin Ma', 'Mingyang Qian', 'Munchurl Kim', 'Byeongjun Kwon', 'Hyebin Cho', 'Huaijin Chen', 'Lei Fei', 'Brian Lee', 'Guangjing Yan', 'Ziping Wang', 'Pan Mu', 'Wentao Tong', 'Haotian Qian', 'Minsu Kwon', 'Hongbin Wang', 'Zhe Ma', 'Gaocheng Yu', 'Feng Zhang', 'Jin Zhang', 'Radu Timofte', 'Andrey Ignatov'] | 2022-11-07 | null | null | null | null | ['bokeh-effect-rendering'] | ['computer-vision'] | [ 2.73172557e-02 -4.27223712e-01 4.13947046e-01 -3.86179388e-01
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4c6a154b-d96c-482b-870e-c6658d0cf9c9 | language-model-based-chinese-handwriting | null | null | https://aclanthology.org/2022.rocling-1.1 | https://aclanthology.org/2022.rocling-1.1.pdf | Language Model Based Chinese Handwriting Address Recognition | Chinese handwritten address recognition of consignment note is an important challenge of smart logistics automation. Chinese handwritten characters detection and recognition is the key technology for this application. Since the writing mode of handwritten characters is more complex and diverse than printed characters, it is easy misjudgment for recognition. Moreover, the address text occupies a small proportion in the image of the consignment note and arranged closely, which is easy to cause difficulties in detection. Therefore, how to detect the address text on the consignment note accurately is a focus of this paper. The consignment note address automatic detection and recognition system proposed in this paper detects and recognizes address characters, reduces the probability of misjudgment of Chinese handwriting recognition through language model, and improves the accuracy. | ['Yun-Wei Hung', 'Yung-Ping Tien', 'Chieh-Jen Wang'] | null | null | null | null | rocling-2022-11 | ['handwriting-recognition'] | ['computer-vision'] | [ 3.27809528e-02 -8.70279849e-01 -4.50698398e-02 5.47644794e-02
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8476d1f9-ffd7-4c63-b8fb-5a163d670e0a | human-face-recognition-from-part-of-a-facial | 2203.05601 | null | https://arxiv.org/abs/2203.05601v1 | https://arxiv.org/pdf/2203.05601v1.pdf | Human Face Recognition from Part of a Facial Image based on Image Stitching | Most of the current techniques for face recognition require the presence of a full face of the person to be recognized, and this situation is difficult to achieve in practice, the required person may appear with a part of his face, which requires prediction of the part that did not appear. Most of the current forecasting processes are done by what is known as image interpolation, which does not give reliable results, especially if the missing part is large. In this work, we adopted the process of stitching the face by completing the missing part with the flipping of the part shown in the picture, depending on the fact that the human face is characterized by symmetry in most cases. To create a complete model, two facial recognition methods were used to prove the efficiency of the algorithm. The selected face recognition algorithms that are applied here are Eigenfaces and geometrical methods. Image stitching is the process during which distinctive photographic images are combined to make a complete scene or a high-resolution image. Several images are integrated to form a wide-angle panoramic image. The quality of the image stitching is determined by calculating the similarity among the stitched image and original images and by the presence of the seam lines through the stitched images. The Eigenfaces approach utilizes PCA calculation to reduce the feature vector dimensions. It provides an effective approach for discovering the lower-dimensional space. In addition, to enable the proposed algorithm to recognize the face, it also ensures a fast and effective way of classifying faces. The phase of feature extraction is followed by the classifier phase. | ['Ahmed I. Taloba', 'Rasha M. Abd El-Aziz', 'Alanazi Rayan', 'Rami Ayedi', 'Osama R. Shahin'] | 2022-03-10 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [ 4.62735504e-01 -1.65218070e-01 1.96625084e-01 -2.69169152e-01
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e5748aa8-d477-425c-b0a6-61c0c576caab | shifting-attention-to-relevance-towards-the | 2307.01379 | null | https://arxiv.org/abs/2307.01379v1 | https://arxiv.org/pdf/2307.01379v1.pdf | Shifting Attention to Relevance: Towards the Uncertainty Estimation of Large Language Models | Although Large Language Models (LLMs) have shown great potential in Natural Language Generation, it is still challenging to characterize the uncertainty of model generations, i.e., when users could trust model outputs. Our research is derived from the heuristic facts that tokens are created unequally in reflecting the meaning of generations by auto-regressive LLMs, i.e., some tokens are more relevant (or representative) than others, yet all the tokens are equally valued when estimating uncertainty. It is because of the linguistic redundancy where mostly a few keywords are sufficient to convey the meaning of a long sentence. We name these inequalities as generative inequalities and investigate how they affect uncertainty estimation. Our results reveal that considerable tokens and sentences containing limited semantics are weighted equally or even heavily when estimating uncertainty. To tackle these biases posed by generative inequalities, we propose to jointly Shifting Attention to more Relevant (SAR) components from both the token level and the sentence level while estimating uncertainty. We conduct experiments over popular "off-the-shelf" LLMs (e.g., OPT, LLaMA) with model sizes up to 30B and powerful commercial LLMs (e.g., Davinci from OpenAI), across various free-form question-answering tasks. Experimental results and detailed demographic analysis indicate the superior performance of SAR. Code is available at https://github.com/jinhaoduan/shifting-attention-to-relevance. | ['Kaidi Xu', 'Bhavya Kailkhura', 'Renjing Xu', 'Alex Zavalny', 'Chenan Wang', 'Shiqi Wang', 'Hao Cheng', 'Jinhao Duan'] | 2023-07-03 | null | null | null | null | ['text-generation', 'question-answering'] | ['natural-language-processing', 'natural-language-processing'] | [-1.92127571e-01 4.06224638e-01 -3.45130831e-01 -6.20319784e-01
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bb29d5d8-591e-4a92-ae06-7b0076a4dee0 | seeing-is-not-always-believing-a-quantitative | 2304.13023 | null | https://arxiv.org/abs/2304.13023v2 | https://arxiv.org/pdf/2304.13023v2.pdf | Seeing is not always believing: Benchmarking Human and Model Perception of AI-Generated Images | Photos serve as a way for humans to record what they experience in their daily lives, and they are often regarded as trustworthy sources of information. However, there is a growing concern that the advancement of artificial intelligence (AI) technology may produce fake photos, which can create confusion and diminish trust in photographs. This study aims to comprehensively evaluate agents for distinguishing state-of-the-art AI-generated visual content. Our study benchmarks both human capability and cutting-edge fake image detection AI algorithms, using a newly collected large-scale fake image dataset Fake2M. In our human perception evaluation, titled HPBench, we discovered that humans struggle significantly to distinguish real photos from AI-generated ones, with a misclassification rate of 38.7%. Along with this, we conduct the model capability of AI-Generated images detection evaluation MPBench and the top-performing model from MPBench achieves a 13% failure rate under the same setting used in the human evaluation. We hope that our study can raise awareness of the potential risks of AI-generated images and facilitate further research to prevent the spread of false information. More information can refer to https://github.com/Inf-imagine/Sentry. | ['Wanli Ouyang', 'Chengyue Wu', 'Jingjing Qu', 'Xihui Liu', 'Lei Bai', 'Di Huang', 'Zeyu Lu'] | 2023-04-25 | null | null | null | null | ['fake-image-detection'] | ['computer-vision'] | [ 3.92554849e-02 3.40546757e-01 -1.04393288e-02 -7.46179819e-02
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6ce6028d-4d38-4283-94b7-2ddadb65019c | construe-a-software-solution-for-the | 2003.07596 | null | https://arxiv.org/abs/2003.07596v1 | https://arxiv.org/pdf/2003.07596v1.pdf | Construe: a software solution for the explanation-based interpretation of time series | This paper presents a software implementation of a general framework for time series interpretation based on abductive reasoning. The software provides a data model and a set of algorithms to make inference to the best explanation of a time series, resulting in a description in multiple abstraction levels of the processes underlying the time series. As a proof of concept, a comprehensive knowledge base for the electrocardiogram (ECG) domain is provided, so it can be used directly as a tool for ECG analysis. This tool has been successfully validated in several noteworthy problems, such as heartbeat classification or atrial fibrillation detection. | ['Paulo Felix', 'Tomas Teijeiro'] | 2020-03-17 | null | null | null | null | ['heartbeat-classification', 'atrial-fibrillation-detection'] | ['medical', 'medical'] | [ 9.52279568e-02 5.89652181e-01 -1.89245552e-01 -4.87526715e-01
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00efecff-91f2-46b3-ac6c-6a23a0597fcb | deep-graph-level-clustering-using-pseudo | 2302.02369 | null | https://arxiv.org/abs/2302.02369v1 | https://arxiv.org/pdf/2302.02369v1.pdf | Deep Graph-Level Clustering Using Pseudo-Label-Guided Mutual Information Maximization Network | In this work, we study the problem of partitioning a set of graphs into different groups such that the graphs in the same group are similar while the graphs in different groups are dissimilar. This problem was rarely studied previously, although there have been a lot of work on node clustering and graph classification. The problem is challenging because it is difficult to measure the similarity or distance between graphs. One feasible approach is using graph kernels to compute a similarity matrix for the graphs and then performing spectral clustering, but the effectiveness of existing graph kernels in measuring the similarity between graphs is very limited. To solve the problem, we propose a novel method called Deep Graph-Level Clustering (DGLC). DGLC utilizes a graph isomorphism network to learn graph-level representations by maximizing the mutual information between the representations of entire graphs and substructures, under the regularization of a clustering module that ensures discriminative representations via pseudo labels. DGLC achieves graph-level representation learning and graph-level clustering in an end-to-end manner. The experimental results on six benchmark datasets of graphs show that our DGLC has state-of-the-art performance in comparison to many baselines. | ['Jicong Fan', 'Wenzhong Guo', 'Yi Han', 'Jinyu Cai'] | 2023-02-05 | null | null | null | null | ['graph-classification'] | ['graphs'] | [-1.40072092e-01 7.37889856e-02 -1.57040909e-01 -2.65007526e-01
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3198ed7a-3133-4979-883f-09a79d11898d | knod-domain-knowledge-distilled-tree-decoder | 2302.01857 | null | https://arxiv.org/abs/2302.01857v3 | https://arxiv.org/pdf/2302.01857v3.pdf | KNOD: Domain Knowledge Distilled Tree Decoder for Automated Program Repair | Automated Program Repair (APR) improves software reliability by generating patches for a buggy program automatically. Recent APR techniques leverage deep learning (DL) to build models to learn to generate patches from existing patches and code corpora. While promising, DL-based APR techniques suffer from the abundant syntactically or semantically incorrect patches in the patch space. These patches often disobey the syntactic and semantic domain knowledge of source code and thus cannot be the correct patches to fix a bug. We propose a DL-based APR approach KNOD, which incorporates domain knowledge to guide patch generation in a direct and comprehensive way. KNOD has two major novelties, including (1) a novel three-stage tree decoder, which directly generates Abstract Syntax Trees of patched code according to the inherent tree structure, and (2) a novel domain-rule distillation, which leverages syntactic and semantic rules and teacher-student distributions to explicitly inject the domain knowledge into the decoding procedure during both the training and inference phases. We evaluate KNOD on three widely-used benchmarks. KNOD fixes 72 bugs on the Defects4J v1.2, 25 bugs on the QuixBugs, and 50 bugs on the additional Defects4J v2.0 benchmarks, outperforming all existing APR tools. | ['Xiangyu Zhang', 'Dan Goldwasser', 'Lin Tan', 'Yiling Lou', 'Thibaud Lutellier', 'Nan Jiang'] | 2023-02-03 | null | null | null | null | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [-3.46993655e-02 3.09469044e-01 -5.81007659e-01 -2.10462272e-01
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b8fab4f2-b697-4e1c-93be-b65da433e86a | streamyolo-real-time-object-detection-for | 2207.10433 | null | https://arxiv.org/abs/2207.10433v1 | https://arxiv.org/pdf/2207.10433v1.pdf | StreamYOLO: Real-time Object Detection for Streaming Perception | The perceptive models of autonomous driving require fast inference within a low latency for safety. While existing works ignore the inevitable environmental changes after processing, streaming perception jointly evaluates the latency and accuracy into a single metric for video online perception, guiding the previous works to search trade-offs between accuracy and speed. In this paper, we explore the performance of real time models on this metric and endow the models with the capacity of predicting the future, significantly improving the results for streaming perception. Specifically, we build a simple framework with two effective modules. One is a Dual Flow Perception module (DFP). It consists of dynamic flow and static flow in parallel to capture moving tendency and basic detection feature, respectively. Trend Aware Loss (TAL) is the other module which adaptively generates loss weight for each object with its moving speed. Realistically, we consider multiple velocities driving scene and further propose Velocity-awared streaming AP (VsAP) to jointly evaluate the accuracy. In this realistic setting, we design a efficient mix-velocity training strategy to guide detector perceive any velocities. Our simple method achieves the state-of-the-art performance on Argoverse-HD dataset and improves the sAP and VsAP by 4.7% and 8.2% respectively compared to the strong baseline, validating its effectiveness. | ['Jian Sun', 'Xiaoping Li', 'Zeming Li', 'Songtao Liu', 'Jinrong Yang'] | 2022-07-21 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [-1.78600419e-02 -2.74268717e-01 -1.25803158e-01 -4.37392384e-01
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3c735cd2-5d02-4245-a576-ef8b146834cd | iterative-scale-up-expansioniou-and-deep | 2306.13074 | null | https://arxiv.org/abs/2306.13074v1 | https://arxiv.org/pdf/2306.13074v1.pdf | Iterative Scale-Up ExpansionIoU and Deep Features Association for Multi-Object Tracking in Sports | Multi-object tracking algorithms have made significant advancements due to the recent developments in object detection. However, most existing methods primarily focus on tracking pedestrians or vehicles, which exhibit relatively simple and regular motion patterns. Consequently, there is a scarcity of algorithms that address the tracking of targets with irregular or non-linear motion, such as multi-athlete tracking. Furthermore, popular tracking algorithms often rely on the Kalman filter for object motion modeling, which fails to track objects when their motion contradicts the linear motion assumption of the Kalman filter. Due to this reason, we proposed a novel online and robust multi-object tracking approach, named Iterative Scale-Up ExpansionIoU and Deep Features for multi-object tracking. Unlike conventional methods, we abandon the use of the Kalman filter and propose utilizing the iterative scale-up expansion IoU. This approach achieves superior tracking performance without requiring additional training data or adopting a more robust detector, all while maintaining a lower computational cost compared to other appearance-based methods. Our proposed method demonstrates remarkable effectiveness in tracking irregular motion objects, achieving a score of 75.3% in HOTA. It outperforms all state-of-the-art online tracking algorithms on the SportsMOT dataset, covering various kinds of sport scenarios. | ['Chung-I Huang', 'Jenq-Neng Hwang', 'Cheng-Yen Yang', 'Hsiang-Wei Huang'] | 2023-06-22 | null | null | null | null | ['object-tracking', 'multi-object-tracking'] | ['computer-vision', 'computer-vision'] | [-3.28925490e-01 -9.13742125e-01 -2.38559112e-01 2.50698060e-01
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e1aa2bef-4801-4d53-bb60-a8490578afc3 | sentence-compression-via-dc-programming | 1902.07248 | null | http://arxiv.org/abs/1902.07248v1 | http://arxiv.org/pdf/1902.07248v1.pdf | Sentence Compression via DC Programming Approach | Sentence compression is an important problem in natural language processing.
In this paper, we firstly establish a new sentence compression model based on
the probability model and the parse tree model. Our sentence compression model
is equivalent to an integer linear program (ILP) which can both guarantee the
syntax correctness of the compression and save the main meaning. We propose
using a DC (Difference of convex) programming approach (DCA) for finding local
optimal solution of our model. Combing DCA with a parallel-branch-and-bound
framework, we can find global optimal solution. Numerical results demonstrate
the good quality of our sentence compression model and the excellent
performance of our proposed solution algorithm. | ['Xi-Wei Hu', 'Yi-Shuai Niu', 'Faouzi Mohamed Benammour', 'Yu You', 'Hu Zhang'] | 2019-02-13 | null | null | null | null | ['sentence-compression'] | ['natural-language-processing'] | [ 5.13760924e-01 2.42413267e-01 -2.95406997e-01 -4.85650867e-01
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79772f71-6fce-46ef-b1d9-5890a72dd979 | graph-less-collaborative-filtering | 2303.08537 | null | https://arxiv.org/abs/2303.08537v3 | https://arxiv.org/pdf/2303.08537v3.pdf | Graph-less Collaborative Filtering | Graph neural networks (GNNs) have shown the power in representation learning over graph-structured user-item interaction data for collaborative filtering (CF) task. However, with their inherently recursive message propagation among neighboring nodes, existing GNN-based CF models may generate indistinguishable and inaccurate user (item) representations due to the over-smoothing and noise effect with low-pass Laplacian smoothing operators. In addition, the recursive information propagation with the stacked aggregators in the entire graph structures may result in poor scalability in practical applications. Motivated by these limitations, we propose a simple and effective collaborative filtering model (SimRec) that marries the power of knowledge distillation and contrastive learning. In SimRec, adaptive transferring knowledge is enabled between the teacher GNN model and a lightweight student network, to not only preserve the global collaborative signals, but also address the over-smoothing issue with representation recalibration. Empirical results on public datasets show that SimRec archives better efficiency while maintaining superior recommendation performance compared with various strong baselines. Our implementations are publicly available at: https://github.com/HKUDS/SimRec. | ['Yong Xu', 'Jiao Shi', 'Chao Huang', 'Lianghao Xia'] | 2023-03-15 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [ 5.09830341e-02 6.92790374e-02 -5.68219908e-02 -2.15056092e-01
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44567f82-9f45-4ec7-ac60-dc4e16871d4b | aggregating-multiple-types-of-complex-data-in | 1805.05617 | null | http://arxiv.org/abs/1805.05617v1 | http://arxiv.org/pdf/1805.05617v1.pdf | Aggregating multiple types of complex data in stock market prediction: A model-independent framework | The increasing richness in volume, and especially types of data in the
financial domain provides unprecedented opportunities to understand the stock
market more comprehensively and makes the price prediction more accurate than
before. However, they also bring challenges to classic statistic approaches
since those models might be constrained to a certain type of data. Aiming at
aggregating differently sourced information and offering type-free capability
to existing models, a framework for predicting stock market of scenarios with
mixed data, including scalar data, compositional data (pie-like) and functional
data (curve-like), is established. The presented framework is
model-independent, as it serves like an interface to multiple types of data and
can be combined with various prediction models. And it is proved to be
effective through numerical simulations. Regarding to price prediction, we
incorporate the trading volume (scalar data), intraday return series
(functional data), and investors' emotions from social media (compositional
data) through the framework to competently forecast whether the market goes up
or down at opening in the next day. The strong explanatory power of the
framework is further demonstrated. Specifically, it is found that the intraday
returns impact the following opening prices differently between bearish market
and bullish market. And it is not at the beginning of the bearish market but
the subsequent period in which the investors' "fear" comes to be indicative.
The framework would help extend existing prediction models easily to scenarios
with multiple types of data and shed light on a more systemic understanding of
the stock market. | [] | 2018-05-15 | null | null | null | null | ['stock-market-prediction'] | ['time-series'] | [-7.76928544e-01 -2.23706633e-01 -5.06929338e-01 -2.09077656e-01
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19e05d8e-befd-4bd2-9c03-4aa4dfb3731e | few-shot-geometry-aware-keypoint-localization | 2303.17216 | null | https://arxiv.org/abs/2303.17216v1 | https://arxiv.org/pdf/2303.17216v1.pdf | Few-shot Geometry-Aware Keypoint Localization | Supervised keypoint localization methods rely on large manually labeled image datasets, where objects can deform, articulate, or occlude. However, creating such large keypoint labels is time-consuming and costly, and is often error-prone due to inconsistent labeling. Thus, we desire an approach that can learn keypoint localization with fewer yet consistently annotated images. To this end, we present a novel formulation that learns to localize semantically consistent keypoint definitions, even for occluded regions, for varying object categories. We use a few user-labeled 2D images as input examples, which are extended via self-supervision using a larger unlabeled dataset. Unlike unsupervised methods, the few-shot images act as semantic shape constraints for object localization. Furthermore, we introduce 3D geometry-aware constraints to uplift keypoints, achieving more accurate 2D localization. Our general-purpose formulation paves the way for semantically conditioned generative modeling and attains competitive or state-of-the-art accuracy on several datasets, including human faces, eyes, animals, cars, and never-before-seen mouth interior (teeth) localization tasks, not attempted by the previous few-shot methods. Project page: https://xingzhehe.github.io/FewShot3DKP/}{https://xingzhehe.github.io/FewShot3DKP/ | ['Pablo Garrido', 'Helge Rhodin', 'David Ferman', 'Gaurav Bharaj', 'Xingzhe He'] | 2023-03-30 | null | http://openaccess.thecvf.com//content/CVPR2023/html/He_Few-Shot_Geometry-Aware_Keypoint_Localization_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/He_Few-Shot_Geometry-Aware_Keypoint_Localization_CVPR_2023_paper.pdf | cvpr-2023-1 | ['object-localization'] | ['computer-vision'] | [-2.80012190e-01 -6.32905364e-02 -4.61812913e-01 -4.78909105e-01
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aaaccae4-9f79-4308-8889-55b651ce6946 | thompson-sampling-under-bernoulli-rewards | 2307.00863 | null | https://arxiv.org/abs/2307.00863v1 | https://arxiv.org/pdf/2307.00863v1.pdf | Thompson Sampling under Bernoulli Rewards with Local Differential Privacy | This paper investigates the problem of regret minimization for multi-armed bandit (MAB) problems with local differential privacy (LDP) guarantee. Given a fixed privacy budget $\epsilon$, we consider three privatizing mechanisms under Bernoulli scenario: linear, quadratic and exponential mechanisms. Under each mechanism, we derive stochastic regret bound for Thompson Sampling algorithm. Finally, we simulate to illustrate the convergence of different mechanisms under different privacy budgets. | ['Ming Li', 'Tianchi Zhao', 'Bo Jiang'] | 2023-07-03 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [-2.34241694e-01 4.89292443e-02 -6.39139473e-01 -7.20250547e-01
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b064e6be-d581-4ea1-8649-abad5c180db4 | few-shot-multi-domain-knowledge-rearming-for | 2306.07685 | null | https://arxiv.org/abs/2306.07685v2 | https://arxiv.org/pdf/2306.07685v2.pdf | Few-shot Multi-domain Knowledge Rearming for Context-aware Defence against Advanced Persistent Threats | Advanced persistent threats (APTs) have novel features such as multi-stage penetration, highly-tailored intention, and evasive tactics. APTs defense requires fusing multi-dimensional Cyber threat intelligence data to identify attack intentions and conducts efficient knowledge discovery strategies by data-driven machine learning to recognize entity relationships. However, data-driven machine learning lacks generalization ability on fresh or unknown samples, reducing the accuracy and practicality of the defense model. Besides, the private deployment of these APT defense models on heterogeneous environments and various network devices requires significant investment in context awareness (such as known attack entities, continuous network states, and current security strategies). In this paper, we propose a few-shot multi-domain knowledge rearming (FMKR) scheme for context-aware defense against APTs. By completing multiple small tasks that are generated from different network domains with meta-learning, the FMKR firstly trains a model with good discrimination and generalization ability for fresh and unknown APT attacks. In each FMKR task, both threat intelligence and local entities are fused into the support/query sets in meta-learning to identify possible attack stages. Secondly, to rearm current security strategies, an finetuning-based deployment mechanism is proposed to transfer learned knowledge into the student model, while minimizing the defense cost. Compared to multiple model replacement strategies, the FMKR provides a faster response to attack behaviors while consuming less scheduling cost. Based on the feedback from multiple real users of the Industrial Internet of Things (IIoT) over 2 months, we demonstrate that the proposed scheme can improve the defense satisfaction rate. | ['Yuchen Liu', 'Wenqi Wei', 'YuanYuan Zhao', 'Gaolei Li'] | 2023-06-13 | null | null | null | null | ['meta-learning'] | ['methodology'] | [ 1.34853600e-02 -3.42593610e-01 -6.72979414e-01 -1.64840728e-01
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90ea26ab-f076-4fb4-b123-6cd140b513d9 | stereonet-guided-hierarchical-refinement-for | 1807.08865 | null | http://arxiv.org/abs/1807.08865v1 | http://arxiv.org/pdf/1807.08865v1.pdf | StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Prediction | This paper presents StereoNet, the first end-to-end deep architecture for
real-time stereo matching that runs at 60 fps on an NVidia Titan X, producing
high-quality, edge-preserved, quantization-free disparity maps. A key insight
of this paper is that the network achieves a sub-pixel matching precision than
is a magnitude higher than those of traditional stereo matching approaches.
This allows us to achieve real-time performance by using a very low resolution
cost volume that encodes all the information needed to achieve high disparity
precision. Spatial precision is achieved by employing a learned edge-aware
upsampling function. Our model uses a Siamese network to extract features from
the left and right image. A first estimate of the disparity is computed in a
very low resolution cost volume, then hierarchically the model re-introduces
high-frequency details through a learned upsampling function that uses compact
pixel-to-pixel refinement networks. Leveraging color input as a guide, this
function is capable of producing high-quality edge-aware output. We achieve
compelling results on multiple benchmarks, showing how the proposed method
offers extreme flexibility at an acceptable computational budget. | ['Julien Valentin', 'Adarsh Kowdle', 'Christoph Rhemann', 'Sean Fanello', 'Sameh Khamis', 'Shahram Izadi'] | 2018-07-24 | stereonet-guided-hierarchical-refinement-for-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Sameh_Khamis_StereoNet_Guided_Hierarchical_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Sameh_Khamis_StereoNet_Guided_Hierarchical_ECCV_2018_paper.pdf | eccv-2018-9 | ['stereo-depth-estimation'] | ['computer-vision'] | [ 2.64315337e-01 -1.73506036e-01 2.77346633e-02 -4.35482532e-01
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7dd67799-bf3f-4530-90bf-a12e1a68c359 | monocular-3d-human-pose-estimation-by-1 | 1904.01324 | null | https://arxiv.org/abs/1904.01324v2 | https://arxiv.org/pdf/1904.01324v2.pdf | Monocular 3D Human Pose Estimation by Generation and Ordinal Ranking | Monocular 3D human-pose estimation from static images is a challenging problem, due to the curse of dimensionality and the ill-posed nature of lifting 2D-to-3D. In this paper, we propose a Deep Conditional Variational Autoencoder based model that synthesizes diverse anatomically plausible 3D-pose samples conditioned on the estimated 2D-pose. We show that CVAE-based 3D-pose sample set is consistent with the 2D-pose and helps tackling the inherent ambiguity in 2D-to-3D lifting. We propose two strategies for obtaining the final 3D pose- (a) depth-ordering/ordinal relations to score and weight-average the candidate 3D-poses, referred to as OrdinalScore, and (b) with supervision from an Oracle. We report close to state of-the-art results on two benchmark datasets using OrdinalScore, and state-of-the-art results using the Oracle. We also show that our pipeline yields competitive results without paired image-to-3D annotations. The training and evaluation code is available at https://github.com/ssfootball04/generative_pose. | ['Prashast Bindal', 'Arjun Jain', 'Saurabh Sharma', 'Abhishek Sharma', 'Pavan Teja Varigonda'] | 2019-04-02 | monocular-3d-human-pose-estimation-by-2 | http://openaccess.thecvf.com/content_ICCV_2019/html/Sharma_Monocular_3D_Human_Pose_Estimation_by_Generation_and_Ordinal_Ranking_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Sharma_Monocular_3D_Human_Pose_Estimation_by_Generation_and_Ordinal_Ranking_ICCV_2019_paper.pdf | iccv-2019-10 | ['multi-hypotheses-3d-human-pose-estimation', 'monocular-3d-human-pose-estimation', 'image-to-3d'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-2.86122747e-02 1.68911442e-01 -2.34074414e-01 -3.26784998e-01
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be3d9186-74c4-485b-9a1e-79ae1e6cb467 | softpoolnet-shape-descriptor-for-point-cloud | 2008.07358 | null | https://arxiv.org/abs/2008.07358v1 | https://arxiv.org/pdf/2008.07358v1.pdf | SoftPoolNet: Shape Descriptor for Point Cloud Completion and Classification | Point clouds are often the default choice for many applications as they exhibit more flexibility and efficiency than volumetric data. Nevertheless, their unorganized nature -- points are stored in an unordered way -- makes them less suited to be processed by deep learning pipelines. In this paper, we propose a method for 3D object completion and classification based on point clouds. We introduce a new way of organizing the extracted features based on their activations, which we name soft pooling. For the decoder stage, we propose regional convolutions, a novel operator aimed at maximizing the global activation entropy. Furthermore, inspired by the local refining procedure in Point Completion Network (PCN), we also propose a patch-deforming operation to simulate deconvolutional operations for point clouds. This paper proves that our regional activation can be incorporated in many point cloud architectures like AtlasNet and PCN, leading to better performance for geometric completion. We evaluate our approach on different 3D tasks such as object completion and classification, achieving state-of-the-art accuracy. | ['Federico Tombari', 'David Joseph Tan', 'Yida Wang', 'Nassir Navab'] | 2020-08-17 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/5457_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480069.pdf | eccv-2020-8 | ['point-cloud-completion'] | ['computer-vision'] | [-1.14483394e-01 1.45019263e-01 3.18708122e-01 -6.77942216e-01
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cdf81121-e0cc-46e2-a429-5055769aa3ea | associating-frailty-and-dynamic-dysregulation | 2303.13591 | null | https://arxiv.org/abs/2303.13591v1 | https://arxiv.org/pdf/2303.13591v1.pdf | Associating Frailty and Dynamic Dysregulation between Motor and Cardiac Autonomic Systems | Frailty is a geriatric syndrome associated with the lack of physiological reserve and consequent adverse outcomes (therapy complications and death) in older adults. Recent research has shown associations between heart rate (HR) dynamics (HR changes during physical activity) with frailty. The goal of the present study was to determine the effect of frailty on the interconnection between motor and cardiac systems during a localized upper-extremity function (UEF) test. Fifty-six older adults aged 65 or older were recruited and performed the UEF task of rapid elbow flexion for 20-seconds with the right arm. Frailty was assessed using the Fried phenotype. Wearable gyroscopes and electrocardiography were used to measure motor function and HR dynamics. Using convergent cross-mapping (CCM) the interconnection between motor (angular displacement) and cardiac (HR) performance was assessed. A significantly weaker interconnection was observed among pre-frail and frail participants compared to non-frail individuals (p<0.01, effect size=0.81$\pm$0.08). Using logistic models pre-frailty and frailty were identified with sensitivity and specificity of 82% to 89%, using motor, HR dynamics, and interconnection parameters. Findings suggested a strong association between cardiac-motor interconnection and frailty. Adding CCM parameters in a multimodal model may provide a promising measure of frailty. | ['Nima Toosizadeh', 'Kaveh Laksari', 'Patricio Arrué'] | 2023-03-23 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [-3.33474278e-01 -4.52486351e-02 -6.18686318e-01 1.17816404e-01
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18ddbcf5-1eaf-4f4d-b393-91af8c8b5f20 | f-pabee-flexible-patience-based-early-exiting | 2305.11916 | null | https://arxiv.org/abs/2305.11916v1 | https://arxiv.org/pdf/2305.11916v1.pdf | F-PABEE: Flexible-patience-based Early Exiting for Single-label and Multi-label text Classification Tasks | Computational complexity and overthinking problems have become the bottlenecks for pre-training language models (PLMs) with millions or even trillions of parameters. A Flexible-Patience-Based Early Exiting method (F-PABEE) has been proposed to alleviate the problems mentioned above for single-label classification (SLC) and multi-label classification (MLC) tasks. F-PABEE makes predictions at the classifier and will exit early if predicted distributions of cross-layer are consecutively similar. It is more flexible than the previous state-of-the-art (SOTA) early exiting method PABEE because it can simultaneously adjust the similarity score thresholds and the patience parameters. Extensive experiments show that: (1) F-PABEE makes a better speedup-accuracy balance than existing early exiting strategies on both SLC and MLC tasks. (2) F-PABEE achieves faster inference and better performances on different PLMs such as BERT and ALBERT. (3) F-PABEE-JSKD performs best for F-PABEE with different similarity measures. | ['Congrui Yin', 'Jiasheng Gao', 'Wei Zhu', 'Xiangxiang Gao'] | 2023-05-21 | null | null | null | null | ['multi-label-text-classification', 'multi-label-text-classification'] | ['methodology', 'natural-language-processing'] | [-2.51049876e-01 -1.36784911e-01 -5.03339887e-01 -6.13193750e-01
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db6a8f08-9499-44bc-820c-08faedd12f85 | adaptivepose-a-powerful-single-stage-network | 2210.04014 | null | https://arxiv.org/abs/2210.04014v1 | https://arxiv.org/pdf/2210.04014v1.pdf | AdaptivePose++: A Powerful Single-Stage Network for Multi-Person Pose Regression | Multi-person pose estimation generally follows top-down and bottom-up paradigms. Both of them use an extra stage ($\boldsymbol{e.g.,}$ human detection in top-down paradigm or grouping process in bottom-up paradigm) to build the relationship between the human instance and corresponding keypoints, thus leading to the high computation cost and redundant two-stage pipeline. To address the above issue, we propose to represent the human parts as adaptive points and introduce a fine-grained body representation method. The novel body representation is able to sufficiently encode the diverse pose information and effectively model the relationship between the human instance and corresponding keypoints in a single-forward pass. With the proposed body representation, we further deliver a compact single-stage multi-person pose regression network, termed as AdaptivePose. During inference, our proposed network only needs a single-step decode operation to form the multi-person pose without complex post-processes and refinements. We employ AdaptivePose for both 2D/3D multi-person pose estimation tasks to verify the effectiveness of AdaptivePose. Without any bells and whistles, we achieve the most competitive performance on MS COCO and CrowdPose in terms of accuracy and speed. Furthermore, the outstanding performance on MuCo-3DHP and MuPoTS-3D further demonstrates the effectiveness and generalizability on 3D scenes. Code is available at https://github.com/buptxyb666/AdaptivePose. | ['Jian Zhao', 'Shuicheng Yan', 'Mei Song', 'Lei Jin', 'Kai Su', 'Dongdong Yu', 'Xiaojuan Wang', 'Yabo Xiao'] | 2022-10-08 | null | null | null | null | ['3d-multi-person-pose-estimation', 'multi-person-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-2.14700088e-01 -2.20564201e-01 2.81053185e-01 -3.58270735e-01
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7b47c5a0-9151-452c-8c26-a6b8a48639b5 | r5-rule-discovery-with-reinforced-and-1 | 2205.06454 | null | https://arxiv.org/abs/2205.06454v1 | https://arxiv.org/pdf/2205.06454v1.pdf | R5: Rule Discovery with Reinforced and Recurrent Relational Reasoning | Systematicity, i.e., the ability to recombine known parts and rules to form new sequences while reasoning over relational data, is critical to machine intelligence. A model with strong systematicity is able to train on small-scale tasks and generalize to large-scale tasks. In this paper, we propose R5, a relational reasoning framework based on reinforcement learning that reasons over relational graph data and explicitly mines underlying compositional logical rules from observations. R5 has strong systematicity and being robust to noisy data. It consists of a policy value network equipped with Monte Carlo Tree Search to perform recurrent relational prediction and a backtrack rewriting mechanism for rule mining. By alternately applying the two components, R5 progressively learns a set of explicit rules from data and performs explainable and generalizable relation prediction. We conduct extensive evaluations on multiple datasets. Experimental results show that R5 outperforms various embedding-based and rule induction baselines on relation prediction tasks while achieving a high recall rate in discovering ground truth rules. | ['Di Niu', 'Shangling Jui', 'Keith G. Mills', 'Bang Liu', 'Shengyao Lu'] | 2022-05-13 | r5-rule-discovery-with-reinforced-and | https://openreview.net/forum?id=2eXhNpHeW6E | https://openreview.net/pdf?id=2eXhNpHeW6E | iclr-2022-4 | ['relational-reasoning'] | ['natural-language-processing'] | [ 4.47823942e-01 1.00482476e+00 -8.27009320e-01 -5.61155736e-01
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2e330c63-7779-4d51-abda-b8a99c8d1d76 | learning-for-online-mixed-integer-model | 2303.12152 | null | https://arxiv.org/abs/2303.12152v2 | https://arxiv.org/pdf/2303.12152v2.pdf | Learning for Online Mixed-Integer Model Predictive Control with Parametric Optimality Certificates | We propose a supervised learning framework for computing solutions of multi-parametric Mixed Integer Linear Programs (MILPs) that arise in Model Predictive Control. Our approach also quantifies sub-optimality for the computed solutions. Inspired by Branch-and-Bound techniques, the key idea is to train a Neural Network/Random Forest, which for a given parameter, predicts a strategy consisting of (1) a set of Linear Programs (LPs) such that their feasible sets form a partition of the feasible set of the MILP and (2) a candidate integer solution. For control computation and sub-optimality quantification, we solve a set of LPs online in parallel. We demonstrate our approach for a motion planning example and compare against various commercial and open-source mixed-integer programming solvers. | ['Francesco Borrelli', 'Luigi Glielmo', 'Siddharth H. Nair', 'Luigi Russo'] | 2023-03-21 | null | null | null | null | ['motion-planning'] | ['robots'] | [ 2.62070835e-01 4.21422303e-01 -1.08886349e+00 -3.66168432e-02
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f09a2aeb-78bf-41a4-8309-7c5ff64194ec | eider-evidence-enhanced-document-level-1 | null | null | https://openreview.net/forum?id=Z9arKXstUo5 | https://openreview.net/pdf?id=Z9arKXstUo5 | EIDER: Evidence-enhanced Document-level Relation Extraction | Document-level relation extraction (DocRE) aims at extracting the semantic relations among entity pairs in a document. In DocRE, a subset of the sentences in a document, called the evidence sentences, might be sufficient for predicting the relation between a specific entity pair. To make better use of the evidence sentences, in this paper, we propose a three-stage evidence-enhanced DocRE framework called Eider consisting of joint relation and evidence extraction, evidence-centered relation extraction (RE), and fusion of extraction results. We first jointly train an RE model with a simple and memory-efficient evidence extraction model. Then, we construct pseudo documents based on the extracted evidence sentences and run the RE model again. Finally, we fuse the extraction results of the first two stages using a blending layer and make a final prediction. Extensive experiments show that our proposed framework achieves state-of-the-art performance on the DocRED dataset, outperforming the second-best method by 1.37/1.26 Ign F1/F1. In particular, Eider-RoBERTa$_\text{large}$ significantly improves the performance on entity pairs requiring co-reference and multi-hop reasoning by 1.98/2.08 F1, respectively, which cover around 75\% of the cross-sentence samples. | ['Anonymous'] | 2021-07-17 | null | https://openreview.net/forum?id=_5lTEMDR2e1 | https://openreview.net/pdf?id=_5lTEMDR2e1 | acl-arr-november-2021-11 | ['document-level-relation-extraction'] | ['natural-language-processing'] | [-3.42896767e-02 3.70317757e-01 -3.88784558e-01 -2.98720837e-01
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79b39fb1-2e54-4f45-8acd-3e3a30920c9a | leveraging-bev-representation-for-360-degree | 2305.13814 | null | https://arxiv.org/abs/2305.13814v1 | https://arxiv.org/pdf/2305.13814v1.pdf | Leveraging BEV Representation for 360-degree Visual Place Recognition | This paper investigates the advantages of using Bird's Eye View (BEV) representation in 360-degree visual place recognition (VPR). We propose a novel network architecture that utilizes the BEV representation in feature extraction, feature aggregation, and vision-LiDAR fusion, which bridges visual cues and spatial awareness. Our method extracts image features using standard convolutional networks and combines the features according to pre-defined 3D grid spatial points. To alleviate the mechanical and time misalignments between cameras, we further introduce deformable attention to learn the compensation. Upon the BEV feature representation, we then employ the polar transform and the Discrete Fourier transform for aggregation, which is shown to be rotation-invariant. In addition, the image and point cloud cues can be easily stated in the same coordinates, which benefits sensor fusion for place recognition. The proposed BEV-based method is evaluated in ablation and comparative studies on two datasets, including on-the-road and off-the-road scenarios. The experimental results verify the hypothesis that BEV can benefit VPR by its superior performance compared to baseline methods. To the best of our knowledge, this is the first trial of employing BEV representation in this task. | ['Yue Wang', 'Rong Xiong', 'Xiaqing Ding', 'Sha Lu', 'Yanmei Jiao', 'Xuecheng Xu'] | 2023-05-23 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [-2.29154881e-02 -3.55068922e-01 -1.60843775e-01 -4.76410747e-01
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c03f84a3-325c-47f1-b6cb-294444a2e966 | fast-bayesian-inference-with-batch-bayesian | 2206.04734 | null | https://arxiv.org/abs/2206.04734v4 | https://arxiv.org/pdf/2206.04734v4.pdf | Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination | Calculation of Bayesian posteriors and model evidences typically requires numerical integration. Bayesian quadrature (BQ), a surrogate-model-based approach to numerical integration, is capable of superb sample efficiency, but its lack of parallelisation has hindered its practical applications. In this work, we propose a parallelised (batch) BQ method, employing techniques from kernel quadrature, that possesses an empirically exponential convergence rate. Additionally, just as with Nested Sampling, our method permits simultaneous inference of both posteriors and model evidence. Samples from our BQ surrogate model are re-selected to give a sparse set of samples, via a kernel recombination algorithm, requiring negligible additional time to increase the batch size. Empirically, we find that our approach significantly outperforms the sampling efficiency of both state-of-the-art BQ techniques and Nested Sampling in various real-world datasets, including lithium-ion battery analytics. | ['Michael A. Osborne', 'Harald Oberhauser', 'Martin Jørgensen', 'Satoshi Hayakawa', 'Masaki Adachi'] | 2022-06-09 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [-1.04568817e-01 -1.62196532e-01 -2.29953721e-01 -2.88881093e-01
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438f9179-b739-4a15-86e5-3cf101d6ba50 | kernel-dependence-regularizers-and-gaussian | 1911.04322 | null | https://arxiv.org/abs/1911.04322v1 | https://arxiv.org/pdf/1911.04322v1.pdf | Kernel Dependence Regularizers and Gaussian Processes with Applications to Algorithmic Fairness | Current adoption of machine learning in industrial, societal and economical activities has raised concerns about the fairness, equity and ethics of automated decisions. Predictive models are often developed using biased datasets and thus retain or even exacerbate biases in their decisions and recommendations. Removing the sensitive covariates, such as gender or race, is insufficient to remedy this issue since the biases may be retained due to other related covariates. We present a regularization approach to this problem that trades off predictive accuracy of the learned models (with respect to biased labels) for the fairness in terms of statistical parity, i.e. independence of the decisions from the sensitive covariates. In particular, we consider a general framework of regularized empirical risk minimization over reproducing kernel Hilbert spaces and impose an additional regularizer of dependence between predictors and sensitive covariates using kernel-based measures of dependence, namely the Hilbert-Schmidt Independence Criterion (HSIC) and its normalized version. This approach leads to a closed-form solution in the case of squared loss, i.e. ridge regression. Moreover, we show that the dependence regularizer has an interpretation as modifying the corresponding Gaussian process (GP) prior. As a consequence, a GP model with a prior that encourages fairness to sensitive variables can be derived, allowing principled hyperparameter selection and studying of the relative relevance of covariates under fairness constraints. Experimental results in synthetic examples and in real problems of income and crime prediction illustrate the potential of the approach to improve fairness of automated decisions. | ['Gustau Camps-Valls', 'Adrian Perez-Suay', 'Dino Sejdinovic', 'Zhu Li'] | 2019-11-11 | null | null | null | null | ['crime-prediction'] | ['miscellaneous'] | [ 2.91243225e-01 4.24139112e-01 -4.38953280e-01 -7.75081098e-01
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e6476e81-a132-4db0-bd55-7284b1048c52 | differentiable-data-augmentation-with-kornia | 2011.09832 | null | https://arxiv.org/abs/2011.09832v1 | https://arxiv.org/pdf/2011.09832v1.pdf | Differentiable Data Augmentation with Kornia | In this paper we present a review of the Kornia differentiable data augmentation (DDA) module for both for spatial (2D) and volumetric (3D) tensors. This module leverages differentiable computer vision solutions from Kornia, with an aim of integrating data augmentation (DA) pipelines and strategies to existing PyTorch components (e.g. autograd for differentiability, optim for optimization). In addition, we provide a benchmark comparing different DA frameworks and a short review for a number of approaches that make use of Kornia DDA. | ['Anguelos Nicolaou', 'Francesc Moreno', 'Dmytro Mishkin', 'Edgar Riba', 'Jian Shi'] | 2020-11-19 | null | null | null | null | ['image-smoothing'] | ['computer-vision'] | [-3.76663923e-01 9.14489105e-02 7.77546838e-02 -1.38939813e-01
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43026d08-6fe9-4d1a-a51b-6780b3fac0c9 | learning-to-reconstruct-missing-data-from | 2205.13479 | null | https://arxiv.org/abs/2205.13479v2 | https://arxiv.org/pdf/2205.13479v2.pdf | Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations | Modeling multivariate time series as temporal signals over a (possibly dynamic) graph is an effective representational framework that allows for developing models for time series analysis. In fact, discrete sequences of graphs can be processed by autoregressive graph neural networks to recursively learn representations at each discrete point in time and space. Spatiotemporal graphs are often highly sparse, with time series characterized by multiple, concurrent, and long sequences of missing data, e.g., due to the unreliable underlying sensor network. In this context, autoregressive models can be brittle and exhibit unstable learning dynamics. The objective of this paper is, then, to tackle the problem of learning effective models to reconstruct, i.e., impute, missing data points by conditioning the reconstruction only on the available observations. In particular, we propose a novel class of attention-based architectures that, given a set of highly sparse discrete observations, learn a representation for points in time and space by exploiting a spatiotemporal propagation architecture aligned with the imputation task. Representations are trained end-to-end to reconstruct observations w.r.t. the corresponding sensor and its neighboring nodes. Compared to the state of the art, our model handles sparse data without propagating prediction errors or requiring a bidirectional model to encode forward and backward time dependencies. Empirical results on representative benchmarks show the effectiveness of the proposed method. | ['Cesare Alippi', 'Andrea Cini', 'Ivan Marisca'] | 2022-05-26 | null | null | null | null | ['multivariate-time-series-imputation', 'traffic-data-imputation'] | ['time-series', 'time-series'] | [ 3.56752604e-01 2.70198971e-01 -1.36650234e-01 -2.06369609e-01
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9380eac1-600d-4894-acc4-b5c40b7c5a57 | task-embedded-control-networks-for-few-shot | 1810.03237 | null | http://arxiv.org/abs/1810.03237v1 | http://arxiv.org/pdf/1810.03237v1.pdf | Task-Embedded Control Networks for Few-Shot Imitation Learning | Much like humans, robots should have the ability to leverage knowledge from
previously learned tasks in order to learn new tasks quickly in new and
unfamiliar environments. Despite this, most robot learning approaches have
focused on learning a single task, from scratch, with a limited notion of
generalisation, and no way of leveraging the knowledge to learn other tasks
more efficiently. One possible solution is meta-learning, but many of the
related approaches are limited in their ability to scale to a large number of
tasks and to learn further tasks without forgetting previously learned ones.
With this in mind, we introduce Task-Embedded Control Networks, which employ
ideas from metric learning in order to create a task embedding that can be used
by a robot to learn new tasks from one or more demonstrations. In the area of
visually-guided manipulation, we present simulation results in which we surpass
the performance of a state-of-the-art method when using only visual information
from each demonstration. Additionally, we demonstrate that our approach can
also be used in conjunction with domain randomisation to train our few-shot
learning ability in simulation and then deploy in the real world without any
additional training. Once deployed, the robot can learn new tasks from a single
real-world demonstration. | ['Andrew J. Davison', 'Stephen James', 'Michael Bloesch'] | 2018-10-08 | null | null | null | null | ['few-shot-imitation-learning'] | ['methodology'] | [ 3.55674446e-01 2.84688413e-01 2.64545023e-01 -1.67440370e-01
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671ae52b-ce54-4a9a-841a-6515e0033453 | arabic-dialect-identification-an-arabic-bert | null | null | https://aclanthology.org/2020.wanlp-1.28 | https://aclanthology.org/2020.wanlp-1.28.pdf | Arabic dialect identification: An Arabic-BERT model with data augmentation and ensembling strategy | This paper presents the ArabicProcessors team’s deep learning system designed for the NADI 2020 Subtask 1 (country-level dialect identification) and Subtask 2 (province-level dialect identification). We used Arabic-Bert in combination with data augmentation and ensembling methods. Unlabeled data provided by task organizers (10 Million tweets) was split into multiple subparts, to which we applied semi-supervised learning method, and finally ran a specific ensembling process on the resulting models. This system ranked 3rd in Subtask 1 with 23.26% F1-score and 2nd in Subtask 2 with 5.75% F1-score. | ['Imade Benelallam', 'Kamel Gaanoun'] | null | null | null | null | coling-wanlp-2020-12 | ['dialect-identification'] | ['natural-language-processing'] | [-4.06983018e-01 2.13024870e-01 6.29590228e-02 -6.72840416e-01
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6.55832440e-02 1.60143077e-01 6.83236569e-02 -3.19222122e-01
-1.07114863e+00 3.91924642e-02 8.06403100e-01 -1.50240615e-01
1.42464012e-01 -5.37992060e-01 -9.65919137e-01 -5.02844334e-01
4.64233160e-02 3.14717084e-01 6.09022617e-01 8.44992399e-01
-8.64103287e-02 -1.12665528e-02 9.87362623e-01 -8.10366869e-01
-4.32725012e-01 -1.76355684e+00 -4.94831264e-01 1.30044684e-01
4.18489546e-01 -1.96343765e-01 -5.27579561e-02 -6.99515804e-04] | [10.16657829284668, 10.783455848693848] |
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