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4d80b483-9d32-4251-9ce6-cbf293006401 | a-question-answering-approach-to-emotion | 1708.05482 | null | http://arxiv.org/abs/1708.05482v2 | http://arxiv.org/pdf/1708.05482v2.pdf | A Question Answering Approach to Emotion Cause Extraction | Emotion cause extraction aims to identify the reasons behind a certain
emotion expressed in text. It is a much more difficult task compared to emotion
classification. Inspired by recent advances in using deep memory networks for
question answering (QA), we propose a new approach which considers emotion
cause identification as a reading comprehension task in QA. Inspired by
convolutional neural networks, we propose a new mechanism to store relevant
context in different memory slots to model context information. Our proposed
approach can extract both word level sequence features and lexical features.
Performance evaluation shows that our method achieves the state-of-the-art
performance on a recently released emotion cause dataset, outperforming a
number of competitive baselines by at least 3.01% in F-measure. | ['Lin Gui', 'Jiachen Du', 'Jiannan Hu', 'Ruifeng Xu', 'Yulan He', 'Qin Lu'] | 2017-08-18 | null | null | null | null | ['emotion-cause-extraction'] | ['natural-language-processing'] | [ 4.37361985e-01 9.72051546e-02 1.05143063e-01 -5.06662548e-01
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f7d19781-c6e6-4141-a0c0-be40f0bc9603 | quality-aware-pre-trained-models-for-blind | 2303.00521 | null | https://arxiv.org/abs/2303.00521v2 | https://arxiv.org/pdf/2303.00521v2.pdf | Quality-aware Pre-trained Models for Blind Image Quality Assessment | Blind image quality assessment (BIQA) aims to automatically evaluate the perceived quality of a single image, whose performance has been improved by deep learning-based methods in recent years. However, the paucity of labeled data somewhat restrains deep learning-based BIQA methods from unleashing their full potential. In this paper, we propose to solve the problem by a pretext task customized for BIQA in a self-supervised learning manner, which enables learning representations from orders of magnitude more data. To constrain the learning process, we propose a quality-aware contrastive loss based on a simple assumption: the quality of patches from a distorted image should be similar, but vary from patches from the same image with different degradations and patches from different images. Further, we improve the existing degradation process and form a degradation space with the size of roughly $2\times10^7$. After pre-trained on ImageNet using our method, models are more sensitive to image quality and perform significantly better on downstream BIQA tasks. Experimental results show that our method obtains remarkable improvements on popular BIQA datasets. | ['Xing Wen', 'Mading Li', 'Ming Sun', 'Kun Yuan', 'Kai Zhao'] | 2023-03-01 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhao_Quality-Aware_Pre-Trained_Models_for_Blind_Image_Quality_Assessment_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhao_Quality-Aware_Pre-Trained_Models_for_Blind_Image_Quality_Assessment_CVPR_2023_paper.pdf | cvpr-2023-1 | ['blind-image-quality-assessment', 'image-quality-assessment'] | ['computer-vision', 'computer-vision'] | [ 2.60940999e-01 -3.70800614e-01 1.56712934e-01 -5.18011928e-01
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7b2c8cf5-5497-4aa3-9ba9-7a7618734247 | pose-guided-knowledge-transfer-for-object | null | null | https://www.researchgate.net/publication/340684849_Pose-Guided_Knowledge_Transfer_for_Object_Part_Segmentation | http://vision.soic.indiana.edu/papers/poseguided2020cvprw.pdf | Pose-Guided Knowledge Transfer for Object Part Segmentation | Object part segmentation is an important problem for many applications, but generating the annotations to train a part segmentation model is typically quite labor-intensive. Recently, Fang et al. augmented object part segmentation datasets by using keypoint locations as weak supervision to transfer a source object instance’s part annotations to an unlabeled target object. We show that while their approach works well when the source and target objects have clearly visible keypoints, it often fails for severely articulated poses. Also, their model does not generalize well across multiple object classes, even if they are very similar. In this paper, we propose and evaluate a new model for transferring part segmentations using keypoints, even for complex object poses and across different object classes. | ['David J. Crandall', 'Md Alimoor Reza', 'Prianka Banik', 'Qingyang Xiao', 'Shujon Naha'] | 2020-04-01 | null | null | null | null | ['semantic-part-detection'] | ['computer-vision'] | [ 4.70016748e-01 2.39899710e-01 -3.77680987e-01 -4.89330351e-01
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ef380980-0ad6-4b78-a9a0-0cdcc37b4379 | transfer-learning-for-tensor-gaussian | 2211.09391 | null | https://arxiv.org/abs/2211.09391v1 | https://arxiv.org/pdf/2211.09391v1.pdf | Transfer learning for tensor Gaussian graphical models | Tensor Gaussian graphical models (GGMs), interpreting conditional independence structures within tensor data, have important applications in numerous areas. Yet, the available tensor data in one single study is often limited due to high acquisition costs. Although relevant studies can provide additional data, it remains an open question how to pool such heterogeneous data. In this paper, we propose a transfer learning framework for tensor GGMs, which takes full advantage of informative auxiliary domains even when non-informative auxiliary domains are present, benefiting from the carefully designed data-adaptive weights. Our theoretical analysis shows substantial improvement of estimation errors and variable selection consistency on the target domain under much relaxed conditions, by leveraging information from auxiliary domains. Extensive numerical experiments are conducted on both synthetic tensor graphs and a brain functional connectivity network data, which demonstrates the satisfactory performance of the proposed method. | ['Junhui Wang', 'Yaoming Zhen', 'Mingyang Ren'] | 2022-11-17 | null | null | null | null | ['variable-selection'] | ['methodology'] | [ 4.93786000e-02 -1.39971495e-01 -4.45241839e-01 -3.64654601e-01
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ce79c145-d6d8-4053-957f-6fbdb2485bc5 | non-invasive-experimental-identification-of-a | 2306.14195 | null | https://arxiv.org/abs/2306.14195v1 | https://arxiv.org/pdf/2306.14195v1.pdf | Non-Invasive Experimental Identification of a Single Particle Model for LiFePO4 Cells | The rapid spread of Lithium-ions batteries (LiBs) for electric vehicles calls for the development of accurate physical models for Battery Management Systems (BMSs). In this work, the electrochemical Single Particle Model (SPM) for a high-power LiFePO4 cell is experimentally identified through a set of non-invasive tests (based on voltage-current measurements only). The SPM is identified through a two-step procedure in which the equilibrium potentials and the kinetics parameters are characterized sequentially. The proposed identification procedure is specifically tuned for LiFePO4 chemistry, which is particularly challenging to model due to the non-linearity of its open circuit voltage (OCV) characteristic. The identified SPM is compared with a second-order Equivalent Circuit Model (ECM) with State of Charge dependency. Models performance is compared on dynamic current profiles. They exhibit similar performance when discharge currents peak up to 1C (RMSE between simulation and measures smaller than 20 mV) while, increasing the discharge peaks up to 3C, ECM's performance significantly deteriorates while SPM maintains acceptable RMSE (< 50 mV). | ['Sergio M. Savaresi', 'Stefano Radrizzani', 'Matteo Corno', 'Andrea Trivella'] | 2023-06-25 | null | null | null | null | ['management'] | ['miscellaneous'] | [-6.66809604e-02 -4.48621064e-01 -1.63564518e-01 1.04211360e-01
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f7dbfa07-8cac-4254-9c69-36c0ebc427ed | ucnn-exploiting-computational-reuse-in-deep | 1804.06508 | null | http://arxiv.org/abs/1804.06508v1 | http://arxiv.org/pdf/1804.06508v1.pdf | UCNN: Exploiting Computational Reuse in Deep Neural Networks via Weight Repetition | Convolutional Neural Networks (CNNs) have begun to permeate all corners of
electronic society (from voice recognition to scene generation) due to their
high accuracy and machine efficiency per operation. At their core, CNN
computations are made up of multi-dimensional dot products between weight and
input vectors. This paper studies how weight repetition ---when the same weight
occurs multiple times in or across weight vectors--- can be exploited to save
energy and improve performance during CNN inference. This generalizes a popular
line of work to improve efficiency from CNN weight sparsity, as reducing
computation due to repeated zero weights is a special case of reducing
computation due to repeated weights.
To exploit weight repetition, this paper proposes a new CNN accelerator
called the Unique Weight CNN Accelerator (UCNN). UCNN uses weight repetition to
reuse CNN sub-computations (e.g., dot products) and to reduce CNN model size
when stored in off-chip DRAM ---both of which save energy. UCNN further
improves performance by exploiting sparsity in weights. We evaluate UCNN with
an accelerator-level cycle and energy model and with an RTL implementation of
the UCNN processing element. On three contemporary CNNs, UCNN improves
throughput-normalized energy consumption by 1.2x - 4x, relative to a similarly
provisioned baseline accelerator that uses Eyeriss-style sparsity
optimizations. At the same time, the UCNN processing element adds only 17-24%
area overhead relative to the same baseline. | ['Christopher W. Fletcher', 'Jiyong Yu', 'Rohit Agrawal', 'Mengjia Yan', 'Michael Pellauer', 'Kartik Hegde'] | 2018-04-18 | null | null | null | null | ['scene-generation'] | ['computer-vision'] | [ 2.37553164e-01 -5.25553189e-02 -4.65235978e-01 -3.43276113e-01
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5d70cd95-da5d-43c7-8c20-9b772562dfea | fisher-matrix-based-fault-detection-for-pmus | 2208.04637 | null | https://arxiv.org/abs/2208.04637v1 | https://arxiv.org/pdf/2208.04637v1.pdf | Fisher Matrix Based Fault Detection for PMUs Data in Power Grids | Abnormal event detection is critical in the safe operation of power system. In this paper, using the data collected from phasor measurement units (PMUs), two methods based on Fisher random matrix are proposed to detect faults in power grids. Firstly, the fault detection matrix is constructed and the event detection problem is reformatted as a two-sample covariance matrices test problem. Secondly, the central limit theorem for the linear spectral statistic of the Fisher matrix is derived and a test statistic for testing faults is proposed. To save computing resources, the screening step of fault interval based on the test statistic is designed to check the existence of faults. Then two point-by-point methods are proposed to determine the time of the fault in the interval. One method detects faults by checking whether the largest sample eigenvalue falls outside the supporting set of limiting spectral distribution of the standard Fisher matrix, which can detect the faults with higher accuracy. The other method tests the faults based on the statistic proposed, which has a faster detection speed. Compared with existing works, the simulation results illustrate that two methods proposed in this paper cost less computational time and provide a higher degree of accuracy. | ['Hongxia Wang', 'Bo wang', 'Dandan Jiang', 'Ke Chen'] | 2022-08-09 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [-1.13969937e-01 -4.21794951e-01 2.80606329e-01 3.90788727e-02
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f2dd0ad6-0241-48ae-83d0-babafc058847 | universal-recurrent-neural-network-grammar | null | null | https://aclanthology.org/2021.rocling-1.1 | https://aclanthology.org/2021.rocling-1.1.pdf | Universal Recurrent Neural Network Grammar | Modern approaches to Constituency Parsing are mono-lingual supervised approaches which require large amount of labelled data to be trained on, thus limiting their utility to only a handful of high-resource languages. To address this issue of data-sparsity for low-resource languages we propose Universal Recurrent Neural Network Grammars (UniRNNG) which is a multi-lingual variant of the popular Recurrent Neural Network Grammars (RNNG) model for constituency parsing. UniRNNG involves Cross-lingual Transfer Learning for Constituency Parsing task. The architecture of UniRNNG is inspired by Principle and Parameter theory proposed by Noam Chomsky. UniRNNG utilises the linguistic typology knowledge available as feature-values within WALS database, to generalize over multiple languages. Once trained on sufficiently diverse polyglot corpus UniRNNG can be applied to any natural language thus making it Language-agnostic constituency parser. Experiments reveal that our proposed UniRNNG outperform state-of-the-art baseline approaches for most of the target languages, for which these are tested. | ['Colm O’riordan', 'Chinmay Choudhary'] | null | null | null | null | rocling-2021-10 | ['constituency-parsing'] | ['natural-language-processing'] | [ 1.87334701e-01 3.94179553e-01 -5.06288528e-01 -4.77622181e-01
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520d7a33-9db7-4ae0-b379-02eb633f4104 | dynamic-texture-recognition-via-nuclear | 2102.00841 | null | https://arxiv.org/abs/2102.00841v1 | https://arxiv.org/pdf/2102.00841v1.pdf | Dynamic Texture Recognition via Nuclear Distances on Kernelized Scattering Histogram Spaces | Distance-based dynamic texture recognition is an important research field in multimedia processing with applications ranging from retrieval to segmentation of video data. Based on the conjecture that the most distinctive characteristic of a dynamic texture is the appearance of its individual frames, this work proposes to describe dynamic textures as kernelized spaces of frame-wise feature vectors computed using the Scattering transform. By combining these spaces with a basis-invariant metric, we get a framework that produces competitive results for nearest neighbor classification and state-of-the-art results for nearest class center classification. | ['Hao Shen', 'Julian Wörmann', 'Alexander Sagel'] | 2021-02-01 | null | null | null | null | ['dynamic-texture-recognition'] | ['computer-vision'] | [ 2.29037598e-01 -7.76599288e-01 -1.97014362e-01 -6.29001558e-01
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884c99a1-d7a1-4f43-a54c-19c165ad1a1a | graphvf-controllable-protein-specific-3d | 2304.12825 | null | https://arxiv.org/abs/2304.12825v1 | https://arxiv.org/pdf/2304.12825v1.pdf | GraphVF: Controllable Protein-Specific 3D Molecule Generation with Variational Flow | Designing molecules that bind to specific target proteins is a fundamental task in drug discovery. Recent models leverage geometric constraints to generate ligand molecules that bind cohesively with specific protein pockets. However, these models cannot effectively generate 3D molecules with 2D skeletal curtailments and property constraints, which are pivotal to drug potency and development. To tackle this challenge, we propose GraphVF, a variational flow-based framework that combines 2D topology and 3D geometry, for controllable generation of binding 3D molecules. Empirically, our method achieves state-of-the-art binding affinity and realistic sub-structural layouts for protein-specific generation. In particular, GraphVF represents the first controllable geometry-aware, protein-specific molecule generation method, which can generate binding 3D molecules with tailored sub-structures and physio-chemical properties. Our code is available at https://github.com/Franco-Solis/GraphVF-code. | ['Jian Tang', 'Ming Zhang', 'Hongyu Guo', 'Zhihao Zhan', 'Fang Sun'] | 2023-02-23 | null | null | null | null | ['drug-discovery', '3d-molecule-generation'] | ['medical', 'medical'] | [ 1.12471983e-01 1.67339876e-01 -4.71056074e-01 -9.21481848e-02
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d7f11d1d-3bcc-4225-987d-efc6b904f483 | deforming-autoencoders-unsupervised | 1806.06503 | null | http://arxiv.org/abs/1806.06503v1 | http://arxiv.org/pdf/1806.06503v1.pdf | Deforming Autoencoders: Unsupervised Disentangling of Shape and Appearance | In this work we introduce Deforming Autoencoders, a generative model for
images that disentangles shape from appearance in an unsupervised manner. As in
the deformable template paradigm, shape is represented as a deformation between
a canonical coordinate system (`template') and an observed image, while
appearance is modeled in `canonical', template, coordinates, thus discarding
variability due to deformations. We introduce novel techniques that allow this
approach to be deployed in the setting of autoencoders and show that this
method can be used for unsupervised group-wise image alignment. We show
experiments with expression morphing in humans, hands, and digits, face
manipulation, such as shape and appearance interpolation, as well as
unsupervised landmark localization. A more powerful form of unsupervised
disentangling becomes possible in template coordinates, allowing us to
successfully decompose face images into shading and albedo, and further
manipulate face images. | ['Nikos Paragios', 'Zhixin Shu', 'Mihir Sahasrabudhe', 'Alp Guler', 'Iasonas Kokkinos', 'Dimitris Samaras'] | 2018-06-18 | deforming-autoencoders-unsupervised-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Zhixin_Shu_Deforming_Autoencoders_Unsupervised_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Zhixin_Shu_Deforming_Autoencoders_Unsupervised_ECCV_2018_paper.pdf | eccv-2018-9 | ['unsupervised-facial-landmark-detection'] | ['computer-vision'] | [ 2.63614029e-01 2.51354396e-01 5.19437909e-01 -5.91524243e-01
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fd0d3228-2447-4cba-b030-a1be7af14e6a | lesionpaste-one-shot-anomaly-detection-for | 2203.06354 | null | https://arxiv.org/abs/2203.06354v1 | https://arxiv.org/pdf/2203.06354v1.pdf | LesionPaste: One-Shot Anomaly Detection for Medical Images | Due to the high cost of manually annotating medical images, especially for large-scale datasets, anomaly detection has been explored through training models with only normal data. Lacking prior knowledge of true anomalies is the main reason for the limited application of previous anomaly detection methods, especially in the medical image analysis realm. In this work, we propose a one-shot anomaly detection framework, namely LesionPaste, that utilizes true anomalies from a single annotated sample and synthesizes artificial anomalous samples for anomaly detection. First, a lesion bank is constructed by applying augmentation to randomly selected lesion patches. Then, MixUp is adopted to paste patches from the lesion bank at random positions in normal images to synthesize anomalous samples for training. Finally, a classification network is trained using the synthetic abnormal samples and the true normal data. Extensive experiments are conducted on two publicly-available medical image datasets with different types of abnormalities. On both datasets, our proposed LesionPaste largely outperforms several state-of-the-art unsupervised and semi-supervised anomaly detection methods, and is on a par with the fully-supervised counterpart. To note, LesionPaste is even better than the fully-supervised method in detecting early-stage diabetic retinopathy. | ['Xiaoying Tang', 'Yijin Huang', 'Weikai Huang'] | 2022-03-12 | null | null | null | null | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [ 6.98200285e-01 2.47711703e-01 1.13810122e-01 -4.38115418e-01
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af3d3a28-b3c9-4c70-873d-4086e6fec012 | analysing-the-effect-of-clarifying-questions | 2008.03717 | null | https://arxiv.org/abs/2008.03717v2 | https://arxiv.org/pdf/2008.03717v2.pdf | Analysing the Effect of Clarifying Questions on Document Ranking in Conversational Search | Recent research on conversational search highlights the importance of mixed-initiative in conversations. To enable mixed-initiative, the system should be able to ask clarifying questions to the user. However, the ability of the underlying ranking models (which support conversational search) to account for these clarifying questions and answers has not been analysed when ranking documents, at large. To this end, we analyse the performance of a lexical ranking model on a conversational search dataset with clarifying questions. We investigate, both quantitatively and qualitatively, how different aspects of clarifying questions and user answers affect the quality of ranking. We argue that there needs to be some fine-grained treatment of the entire conversational round of clarification, based on the explicit feedback which is present in such mixed-initiative settings. Informed by our findings, we introduce a simple heuristic-based lexical baseline, that significantly outperforms the existing naive baselines. Our work aims to enhance our understanding of the challenges present in this particular task and inform the design of more appropriate conversational ranking models. | ['Nikos Voskarides', 'Mohammad Aliannejadi', 'Evangelos Kanoulas', 'Antonios Minas Krasakis'] | 2020-08-09 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 1.37561485e-01 6.02953613e-01 -1.15149550e-01 -3.35567385e-01
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1de85c6b-3d0f-4e59-89a2-a4535578a5e3 | supervised-anomaly-detection-method-combining | 2212.11507 | null | https://arxiv.org/abs/2212.11507v1 | https://arxiv.org/pdf/2212.11507v1.pdf | Supervised Anomaly Detection Method Combining Generative Adversarial Networks and Three-Dimensional Data in Vehicle Inspections | The external visual inspections of rolling stock's underfloor equipment are currently being performed via human visual inspection. In this study, we attempt to partly automate visual inspection by investigating anomaly inspection algorithms that use image processing technology. As the railroad maintenance studies tend to have little anomaly data, unsupervised learning methods are usually preferred for anomaly detection; however, training cost and accuracy is still a challenge. Additionally, a researcher created anomalous images from normal images by adding noise, etc., but the anomalous targeted in this study is the rotation of piping cocks that was difficult to create using noise. Therefore, in this study, we propose a new method that uses style conversion via generative adversarial networks on three-dimensional computer graphics and imitates anomaly images to apply anomaly detection based on supervised learning. The geometry-consistent style conversion model was used to convert the image, and because of this the color and texture of the image were successfully made to imitate the real image while maintaining the anomalous shape. Using the generated anomaly images as supervised data, the anomaly detection model can be easily trained without complex adjustments and successfully detects anomalies. | ['Gaurang Gavai', 'Ryosuke Mori', 'Takuro Hoshi', 'Yohei Baba'] | 2022-12-22 | null | null | null | null | ['supervised-anomaly-detection'] | ['computer-vision'] | [ 2.24806368e-01 5.39100403e-03 6.96852744e-01 -7.51937255e-02
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caf497f6-6455-4573-baf4-06bf4d5c876f | nerf-lidar-generating-realistic-lidar-point | 2304.14811 | null | https://arxiv.org/abs/2304.14811v1 | https://arxiv.org/pdf/2304.14811v1.pdf | NeRF-LiDAR: Generating Realistic LiDAR Point Clouds with Neural Radiance Fields | Labeling LiDAR point clouds for training autonomous driving is extremely expensive and difficult. LiDAR simulation aims at generating realistic LiDAR data with labels for training and verifying self-driving algorithms more efficiently. Recently, Neural Radiance Fields (NeRF) have been proposed for novel view synthesis using implicit reconstruction of 3D scenes. Inspired by this, we present NeRF-LIDAR, a novel LiDAR simulation method that leverages real-world information to generate realistic LIDAR point clouds. Different from existing LiDAR simulators, we use real images and point cloud data collected by self-driving cars to learn the 3D scene representation, point cloud generation and label rendering. We verify the effectiveness of our NeRF-LiDAR by training different 3D segmentation models on the generated LiDAR point clouds. It reveals that the trained models are able to achieve similar accuracy when compared with the same model trained on the real LiDAR data. Besides, the generated data is capable of boosting the accuracy through pre-training which helps reduce the requirements of the real labeled data. | ['Li Zhang', 'Shaochen Kuang', 'Feihu Zhang', 'Junge Zhang'] | 2023-04-28 | null | null | null | null | ['self-driving-cars', 'point-cloud-generation', 'novel-view-synthesis'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.09339602e-01 -2.62665981e-03 -4.67485376e-03 -7.38414049e-01
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aebcdf6c-5a20-4945-bb2c-7a909447cb81 | photi-lakeice-dataset | null | null | https://arxiv.org/abs/2002.07875v1 | https://arxiv.org/pdf/2002.07875.pdf | Photi-LakeIce Dataset | Lake ice is a strong climate indicator and has been recognised as part of the Essential Climate Variables (ECV) by the Global Climate Observing System (GCOS). The dynamics of freezing and thawing, and possible shifts of freezing patterns over time, can help in understanding the local and global climate systems. One way to acquire the spatio-temporal information about lake ice formation, independent of clouds, is to analyse webcam images. This paper intends to move towards a universal model for monitoring lake ice with freely available webcam data. We demonstrate good performance, including the ability to generalise across different winters and different lakes, with a state-of-the-art Convolutional Neural Network (CNN) model for semantic image segmentation, Deeplab v3+. Moreover, we design a variant of that model, termed Deep-U-Lab, which predicts sharper, more correct segmentation boundaries. We have tested the model's ability to generalise with data from multiple camera views and two different winters. On average, it achieves intersection-over-union (IoU) values of ~71% across different cameras and ~69% across different winters, greatly outperforming prior work. Going even further, we show that the model even achieves 60% IoU on arbitrary images scraped from photo-sharing web sites. As part of the work, we introduce a new benchmark dataset of webcam images, Photi-LakeIce, from multiple cameras and two different winters, along with pixel-wise ground truth annotations. | ['Laura Leal-Taixe', 'Manu Tom', 'Konrad Schindler', 'Emmanuel Baltsavias', 'Rajanie Prabha', 'Mathias Rothermel'] | 2020-02-18 | null | null | null | isprs-congress-2020-2 | ['webcam-rgb-image-classification', 'lake-ice-detection', 'lake-detection', 'change-detection-for-remote-sensing-images', 'lake-ice-detection', 'segmentation-of-remote-sensing-imagery', 'remote-sensing-image-classification', 'the-semantic-segmentation-of-remote-sensing'] | ['computer-code', 'computer-vision', 'computer-vision', 'miscellaneous', 'miscellaneous', 'miscellaneous', 'miscellaneous', 'miscellaneous'] | [ 8.71278625e-03 -3.46492529e-01 3.50989223e-01 -6.04254961e-01
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9d7b2b91-777a-44e4-aeff-db9b2494171c | probing-the-intra-component-correlations | 1604.04473 | null | http://arxiv.org/abs/1604.04473v1 | http://arxiv.org/pdf/1604.04473v1.pdf | Probing the Intra-Component Correlations within Fisher Vector for Material Classification | Fisher vector (FV) has become a popular image representation. One notable
underlying assumption of the FV framework is that local descriptors are well
decorrelated within each cluster so that the covariance matrix for each
Gaussian can be simplified to be diagonal. Though the FV usually relies on the
Principal Component Analysis (PCA) to decorrelate local features, the PCA is
applied to the entire training data and hence it only diagonalizes the
\textit{universal} covariance matrix, rather than those w.r.t. the local
components. As a result, the local decorrelation assumption is usually not
supported in practice.
To relax this assumption, this paper proposes a completed model of the Fisher
vector, which is termed as the Completed Fisher vector (CFV). The CFV is a more
general framework of the FV, since it encodes not only the variances but also
the correlations of the whitened local descriptors. The CFV thus leads to
improved discriminative power. We take the task of material categorization as
an example and experimentally show that: 1) the CFV outperforms the FV under
all parameter settings; 2) the CFV is robust to the changes in the number of
components in the mixture; 3) even with a relatively small visual vocabulary
the CFV still works well on two challenging datasets. | ['Matti Pietikäinen', 'Xiaopeng Hong', 'Xianbiao Qi', 'Guoying Zhao'] | 2016-04-15 | null | null | null | null | ['material-classification'] | ['computer-vision'] | [-1.01281397e-01 -5.06244659e-01 6.26885891e-03 -1.73467949e-01
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16955120-41dd-44d5-8790-f24a69f3534b | quantum-model-discovery | 2111.06376 | null | https://arxiv.org/abs/2111.06376v1 | https://arxiv.org/pdf/2111.06376v1.pdf | Quantum Model-Discovery | Quantum computing promises to speed up some of the most challenging problems in science and engineering. Quantum algorithms have been proposed showing theoretical advantages in applications ranging from chemistry to logistics optimization. Many problems appearing in science and engineering can be rewritten as a set of differential equations. Quantum algorithms for solving differential equations have shown a provable advantage in the fault-tolerant quantum computing regime, where deep and wide quantum circuits can be used to solve large linear systems like partial differential equations (PDEs) efficiently. Recently, variational approaches to solving non-linear PDEs also with near-term quantum devices were proposed. One of the most promising general approaches is based on recent developments in the field of scientific machine learning for solving PDEs. We extend the applicability of near-term quantum computers to more general scientific machine learning tasks, including the discovery of differential equations from a dataset of measurements. We use differentiable quantum circuits (DQCs) to solve equations parameterized by a library of operators, and perform regression on a combination of data and equations. Our results show a promising path to Quantum Model Discovery (QMoD), on the interface between classical and quantum machine learning approaches. We demonstrate successful parameter inference and equation discovery using QMoD on different systems including a second-order, ordinary differential equation and a non-linear, partial differential equation. | ['Vincent E. Elfving', 'Oleksandr Kyriienko', 'Atiyo Ghosh', 'Niklas Heim'] | 2021-11-11 | null | null | null | null | ['model-discovery'] | ['miscellaneous'] | [ 1.35446653e-01 -3.91961128e-01 1.21564776e-01 -2.73204297e-01
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0526c485-947f-4461-b6c6-e7c914622f55 | multi-class-versus-one-class-classifier-in | 2203.10837 | null | https://arxiv.org/abs/2203.10837v1 | https://arxiv.org/pdf/2203.10837v1.pdf | Multi-class versus One-class classifier in spontaneous speech analysis oriented to Alzheimer Disease diagnosis | Most of medical developments require the ability to identify samples that are anomalous with respect to a target group or control group, in the sense they could belong to a new, previously unseen class or are not class data. In this case when there are not enough data to train two-class One-class classification appear like an available solution. On the other hand non-linear approaches could give very useful information. The aim of our project is to contribute to earlier diagnosis of AD and better estimates of its severity by using automatic analysis performed through new biomarkers extracted from speech signal. The methods selected in this case are speech biomarkers oriented to Spontaneous Speech and Emotional Response Analysis. In this approach One-class classifiers and two-class classifiers are analyzed. The use of information about outlier and Fractal Dimension features improves the system performance. | ['Pilar Calvo', 'Fernando Zelarin', 'Jordi Solé-Casals', 'Marcos Faundez-Zanuy', 'K. López-de-Ipiña'] | 2022-03-21 | null | null | null | null | ['one-class-classifier', 'one-class-classification'] | ['methodology', 'miscellaneous'] | [ 2.47063100e-01 2.53587961e-01 1.60888180e-01 -5.50449789e-01
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44e22210-1880-4f01-a784-5724592786e8 | ensure-the-correctness-of-the-summary | null | null | https://aclanthology.org/C18-1121 | https://aclanthology.org/C18-1121.pdf | Ensure the Correctness of the Summary: Incorporate Entailment Knowledge into Abstractive Sentence Summarization | In this paper, we investigate the sentence summarization task that produces a summary from a source sentence. Neural sequence-to-sequence models have gained considerable success for this task, while most existing approaches only focus on improving the informativeness of the summary, which ignore the correctness, i.e., the summary should not contain unrelated information with respect to the source sentence. We argue that correctness is an essential requirement for summarization systems. Considering a correct summary is semantically entailed by the source sentence, we incorporate entailment knowledge into abstractive summarization models. We propose an entailment-aware encoder under multi-task framework (i.e., summarization generation and entailment recognition) and an entailment-aware decoder by entailment Reward Augmented Maximum Likelihood (RAML) training. Experiment results demonstrate that our models significantly outperform baselines from the aspects of informativeness and correctness. | ['Cheng-qing Zong', 'Junnan Zhu', 'Jiajun Zhang', 'Haoran Li'] | 2018-08-01 | ensure-the-correctness-of-the-summary-1 | https://aclanthology.org/C18-1121 | https://aclanthology.org/C18-1121.pdf | coling-2018-8 | ['summarization', 'abstractive-sentence-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.37023354e-01 6.61859453e-01 -2.68686205e-01 -6.57277524e-01
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e8148034-bbdb-40d0-a1d6-1e9e0e299b70 | audio-visual-character-profiles-for-detecting | 2203.11368 | null | https://arxiv.org/abs/2203.11368v1 | https://arxiv.org/pdf/2203.11368v1.pdf | Audio visual character profiles for detecting background characters in entertainment media | An essential goal of computational media intelligence is to support understanding how media stories -- be it news, commercial or entertainment media -- represent and reflect society and these portrayals are perceived. People are a central element of media stories. This paper focuses on understanding the representation and depiction of background characters in media depictions, primarily movies and TV shows. We define the background characters as those who do not participate vocally in any scene throughout the movie and address the problem of localizing background characters in videos. We use an active speaker localization system to extract high-confidence face-speech associations and generate audio-visual profiles for talking characters in a movie by automatically clustering them. Using a face verification system, we then prune all the face-tracks which match any of the generated character profiles and obtain the background character face-tracks. We curate a background character dataset which provides annotations for background character for a set of TV shows, and use it to evaluate the performance of the background character detection framework. | ['Shrikanth Narayanan', 'Rahul Sharma'] | 2022-03-21 | null | null | null | null | ['active-speaker-localization'] | ['audio'] | [ 4.97357816e-01 -1.29625350e-05 -1.54338881e-01 -4.58549410e-01
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fed79abc-4c28-4a63-9941-d36d97ce625a | improving-deep-learning-based-semi-supervised | 2102.08183 | null | https://arxiv.org/abs/2102.08183v2 | https://arxiv.org/pdf/2102.08183v2.pdf | Comparison of semi-supervised deep learning algorithms for audio classification | In this article, we adapted five recent SSL methods to the task of audio classification. The first two methods, namely Deep Co-Training (DCT) and Mean Teacher (MT), involve two collaborative neural networks. The three other algorithms, called MixMatch (MM), ReMixMatch (RMM), and FixMatch (FM), are single-model methods that rely primarily on data augmentation strategies. Using the Wide-ResNet-28-2 architecture in all our experiments, 10% of labeled data and the remaining 90% as unlabeled data for training, we first compare the error rates of the five methods on three standard benchmark audio datasets: Environmental Sound Classification (ESC-10), UrbanSound8K (UBS8K), and Google Speech Commands (GSC). In all but one cases, MM, RMM, and FM outperformed MT and DCT significantly, MM and RMM being the best methods in most experiments. On UBS8K and GSC, MM achieved 18.02% and 3.25% error rate (ER), respectively, outperforming models trained with 100% of the available labeled data, which reached 23.29% and 4.94%, respectively. RMM achieved the best results on ESC-10 (12.00% ER), followed by FM which reached 13.33%. Second, we explored adding the mixup augmentation, used in MM and RMM, to DCT, MT, and FM. In almost all cases, mixup brought consistent gains. For instance, on GSC, FM reached 4.44% and 3.31% ER without and with mixup. Our PyTorch code will be made available upon paper acceptance at https:// github. com/ Labbe ti/ SSLH. | ['Thomas Pellegrini', 'Etienne Labbé', 'Léo Cances'] | 2021-02-16 | null | null | null | null | ['audio-tagging', 'environmental-sound-classification', 'sound-classification'] | ['audio', 'audio', 'audio'] | [-1.35421485e-01 -2.27803111e-01 6.69662729e-02 -1.12793647e-01
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dcf89f57-e140-4e95-866e-648bd5eb3ff7 | deep-depth-estimation-from-thermal-image | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Shin_Deep_Depth_Estimation_From_Thermal_Image_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Shin_Deep_Depth_Estimation_From_Thermal_Image_CVPR_2023_paper.pdf | Deep Depth Estimation From Thermal Image | Robust and accurate geometric understanding against adverse weather conditions is one top prioritized conditions to achieve a high-level autonomy of self-driving cars. However, autonomous driving algorithms relying on the visible spectrum band are easily impacted by weather and lighting conditions. A long-wave infrared camera, also known as a thermal imaging camera, is a potential rescue to achieve high-level robustness. However, the missing necessities are the well-established large-scale dataset and public benchmark results. To this end, in this paper, we first built a large-scale Multi-Spectral Stereo (MS^2) dataset, including stereo RGB, stereo NIR, stereo thermal, and stereo LiDAR data along with GNSS/IMU information. The collected dataset provides about 195K synchronized data pairs taken from city, residential, road, campus, and suburban areas in the morning, daytime, and nighttime under clear-sky, cloudy, and rainy conditions. Secondly, we conduct an exhaustive validation process of monocular and stereo depth estimation algorithms designed on visible spectrum bands to benchmark their performance in the thermal image domain. Lastly, we propose a unified depth network that effectively bridges monocular depth and stereo depth tasks from a conditional random field approach perspective. Our dataset and source code are available at https://github.com/UkcheolShin/MS2-MultiSpectralStereoDataset. | ['In So Kweon', 'Jinsun Park', 'Ukcheol Shin'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['stereo-depth-estimation', 'self-driving-cars'] | ['computer-vision', 'computer-vision'] | [ 3.57476920e-02 -5.61345279e-01 -1.59091845e-01 -7.10638821e-01
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827c7f96-cdda-4f45-aaee-e51e9a757c86 | mining-themes-in-clinical-notes-to-identify | 2305.19373 | null | https://arxiv.org/abs/2305.19373v1 | https://arxiv.org/pdf/2305.19373v1.pdf | Mining Themes in Clinical Notes to Identify Phenotypes and to Predict Length of Stay in Patients admitted with Heart Failure | Heart failure is a syndrome which occurs when the heart is not able to pump blood and oxygen to support other organs in the body. Identifying the underlying themes in the diagnostic codes and procedure reports of patients admitted for heart failure could reveal the clinical phenotypes associated with heart failure and to group patients based on their similar characteristics which could also help in predicting patient outcomes like length of stay. These clinical phenotypes usually have a probabilistic latent structure and hence, as there has been no previous work on identifying phenotypes in clinical notes of heart failure patients using a probabilistic framework and to predict length of stay of these patients using data-driven artificial intelligence-based methods, we apply natural language processing technique, topic modeling, to identify the themes present in diagnostic codes and in procedure reports of 1,200 patients admitted for heart failure at the University of Illinois Hospital and Health Sciences System (UI Health). Topic modeling identified twelve themes each in diagnostic codes and procedure reports which revealed information about different phenotypes related to various perspectives about heart failure, to study patients' profiles and to discover new relationships among medical concepts. Each theme had a set of keywords and each clinical note was labeled with two themes - one corresponding to its diagnostic code and the other corresponding to its procedure reports along with their percentage contribution. We used these themes and their percentage contribution to predict length of stay. We found that the themes discovered in diagnostic codes and procedure reports using topic modeling together were able to predict length of stay of the patients with an accuracy of 61.1% and an Area under the Receiver Operating Characteristic Curve (ROC AUC) value of 0.828. | ['Mia Cajita', 'Lingwei Chen', 'Krishnaprasad Thirunarayan', 'William L. Romine', 'Tanvi Banerjee', 'Ankita Agarwal'] | 2023-05-30 | null | null | null | null | ['predicting-patient-outcomes'] | ['medical'] | [ 2.55844817e-02 1.87246680e-01 -1.97767600e-01 -2.76505560e-01
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7c22d574-b991-4afe-b498-e50105c59fce | 3dctn-3d-convolution-transformer-network-for | 2203.00828 | null | https://arxiv.org/abs/2203.00828v1 | https://arxiv.org/pdf/2203.00828v1.pdf | 3DCTN: 3D Convolution-Transformer Network for Point Cloud Classification | Although accurate and fast point cloud classification is a fundamental task in 3D applications, it is difficult to achieve this purpose due to the irregularity and disorder of point clouds that make it challenging to achieve effective and efficient global discriminative feature learning. Lately, 3D Transformers have been adopted to improve point cloud processing. Nevertheless, massive Transformer layers tend to incur huge computational and memory costs. This paper presents a novel hierarchical framework that incorporates convolution with Transformer for point cloud classification, named 3D Convolution-Transformer Network (3DCTN), to combine the strong and efficient local feature learning ability of convolution with the remarkable global context modeling capability of Transformer. Our method has two main modules operating on the downsampling point sets, and each module consists of a multi-scale local feature aggregating (LFA) block and a global feature learning (GFL) block, which are implemented by using Graph Convolution and Transformer respectively. We also conduct a detailed investigation on a series of Transformer variants to explore better performance for our network. Various experiments on ModelNet40 demonstrate that our method achieves state-of-the-art classification performance, in terms of both accuracy and efficiency. | ['Jonathan Li', 'Linlin Xu', 'Qian Xie', 'Dening Lu'] | 2022-03-02 | null | null | null | null | ['point-cloud-classification'] | ['computer-vision'] | [-2.32699677e-01 -8.65816176e-01 2.58186400e-01 -4.05011058e-01
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f48adae2-0807-440a-a2e6-fe4047b43390 | predicting-multiple-icd-10-codes-from | 2008.01515 | null | https://arxiv.org/abs/2008.01515v1 | https://arxiv.org/pdf/2008.01515v1.pdf | Predicting Multiple ICD-10 Codes from Brazilian-Portuguese Clinical Notes | ICD coding from electronic clinical records is a manual, time-consuming and expensive process. Code assignment is, however, an important task for billing purposes and database organization. While many works have studied the problem of automated ICD coding from free text using machine learning techniques, most use records in the English language, especially from the MIMIC-III public dataset. This work presents results for a dataset with Brazilian Portuguese clinical notes. We develop and optimize a Logistic Regression model, a Convolutional Neural Network (CNN), a Gated Recurrent Unit Neural Network and a CNN with Attention (CNN-Att) for prediction of diagnosis ICD codes. We also report our results for the MIMIC-III dataset, which outperform previous work among models of the same families, as well as the state of the art. Compared to MIMIC-III, the Brazilian Portuguese dataset contains far fewer words per document, when only discharge summaries are used. We experiment concatenating additional documents available in this dataset, achieving a great boost in performance. The CNN-Att model achieves the best results on both datasets, with micro-averaged F1 score of 0.537 on MIMIC-III and 0.485 on our dataset with additional documents. | ['Marcia M. de Souza e Sá', 'Saulo Pedro', 'Danilo Silva', 'Arthur D. Reys', 'Daniel Severo', 'Guilherme A. C. Salgado'] | 2020-07-29 | null | null | null | null | ['multi-label-classification-of-biomedical'] | ['medical'] | [-4.05126587e-02 1.43836588e-01 -2.45988458e-01 -4.98546988e-01
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be25d8f7-0bde-470a-a616-b5f8fdb6562f | alternating-gradient-descent-and-mixture-of | 2305.06324 | null | https://arxiv.org/abs/2305.06324v1 | https://arxiv.org/pdf/2305.06324v1.pdf | Alternating Gradient Descent and Mixture-of-Experts for Integrated Multimodal Perception | We present Integrated Multimodal Perception (IMP), a simple and scalable multimodal multi-task training and modeling approach. IMP integrates multimodal inputs including image, video, text, and audio into a single Transformer encoder with minimal modality-specific components. IMP makes use of a novel design that combines Alternating Gradient Descent (AGD) and Mixture-of-Experts (MoE) for efficient model \& task scaling. We conduct extensive empirical studies about IMP and reveal the following key insights: 1) performing gradient descent updates by alternating on diverse heterogeneous modalities, loss functions, and tasks, while also varying input resolutions, efficiently improves multimodal understanding. 2) model sparsification with MoE on a single modality-agnostic encoder substantially improves the performance, outperforming dense models that use modality-specific encoders or additional fusion layers and greatly mitigating the conflicts between modalities. IMP achieves competitive performance on a wide range of downstream tasks including image classification, video classification, image-text, and video-text retrieval. Most notably, we train a sparse IMP-MoE-L focusing on video tasks that achieves new state-of-the-art in zero-shot video classification. Our model achieves 77.0% on Kinetics-400, 76.8% on Kinetics-600, and 76.8% on Kinetics-700 zero-shot classification accuracy, improving the previous state-of-the-art by +5%, +6.7%, and +5.8%, respectively, while using only 15% of their total training computational cost. | ['Hartwig Adam', 'Huisheng Wang', 'Rachel Hornung', 'Yin Cui', 'Dan Kondratyuk', 'Hassan Akbari'] | 2023-05-10 | null | null | null | null | ['zero-shot-action-recognition', 'video-classification', 'video-text-retrieval'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.06773654e-01 -4.24322188e-01 -4.26424474e-01 -2.45387420e-01
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a158837a-1cf5-4326-897e-9b244d379946 | learning-situation-hyper-graphs-for-video | 2304.08682 | null | https://arxiv.org/abs/2304.08682v2 | https://arxiv.org/pdf/2304.08682v2.pdf | Learning Situation Hyper-Graphs for Video Question Answering | Answering questions about complex situations in videos requires not only capturing the presence of actors, objects, and their relations but also the evolution of these relationships over time. A situation hyper-graph is a representation that describes situations as scene sub-graphs for video frames and hyper-edges for connected sub-graphs and has been proposed to capture all such information in a compact structured form. In this work, we propose an architecture for Video Question Answering (VQA) that enables answering questions related to video content by predicting situation hyper-graphs, coined Situation Hyper-Graph based Video Question Answering (SHG-VQA). To this end, we train a situation hyper-graph decoder to implicitly identify graph representations with actions and object/human-object relationships from the input video clip. and to use cross-attention between the predicted situation hyper-graphs and the question embedding to predict the correct answer. The proposed method is trained in an end-to-end manner and optimized by a VQA loss with the cross-entropy function and a Hungarian matching loss for the situation graph prediction. The effectiveness of the proposed architecture is extensively evaluated on two challenging benchmarks: AGQA and STAR. Our results show that learning the underlying situation hyper-graphs helps the system to significantly improve its performance for novel challenges of video question-answering tasks. | ['Mubarak Shah', 'Niels Lobo', 'Chuang Gan', 'Walid Bousselham', 'Kim Chheu', 'Bo Wu', 'Hilde Kuehne', 'Aisha Urooj Khan'] | 2023-04-18 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Urooj_Learning_Situation_Hyper-Graphs_for_Video_Question_Answering_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Urooj_Learning_Situation_Hyper-Graphs_for_Video_Question_Answering_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-question-answering'] | ['computer-vision'] | [ 1.74913675e-01 2.71847516e-01 2.00132176e-01 -5.57831526e-01
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bfa131e2-99e0-4ca1-87a1-c3e6bf7b702a | multimodal-transformer-for-multimodal-machine | null | null | https://aclanthology.org/2020.acl-main.400 | https://aclanthology.org/2020.acl-main.400.pdf | Multimodal Transformer for Multimodal Machine Translation | Multimodal Machine Translation (MMT) aims to introduce information from other modality, generally static images, to improve the translation quality. Previous works propose various incorporation methods, but most of them do not consider the relative importance of multiple modalities. Equally treating all modalities may encode too much useless information from less important modalities. In this paper, we introduce the multimodal self-attention in Transformer to solve the issues above in MMT. The proposed method learns the representation of images based on the text, which avoids encoding irrelevant information in images. Experiments and visualization analysis demonstrate that our model benefits from visual information and substantially outperforms previous works and competitive baselines in terms of various metrics. | ['Xiaojun Wan', 'Shaowei Yao'] | 2020-07-01 | null | null | null | acl-2020-6 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 4.24381316e-01 -2.58091241e-02 -4.97318119e-01 -1.09500974e-01
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2daa39e5-1868-4a4f-924d-93055755b713 | probing-pretrained-models-of-source-code | 2202.08975 | null | https://arxiv.org/abs/2202.08975v3 | https://arxiv.org/pdf/2202.08975v3.pdf | Probing Pretrained Models of Source Code | Deep learning models are widely used for solving challenging code processing tasks, such as code generation or code summarization. Traditionally, a specific model architecture was carefully built to solve a particular code processing task. However, recently general pretrained models such as CodeBERT or CodeT5 have been shown to outperform task-specific models in many applications. While pretrained models are known to learn complex patterns from data, they may fail to understand some properties of source code. To test diverse aspects of code understanding, we introduce a set of diagnosting probing tasks. We show that pretrained models of code indeed contain information about code syntactic structure and correctness, the notions of identifiers, data flow and namespaces, and natural language naming. We also investigate how probing results are affected by using code-specific pretraining objectives, varying the model size, or finetuning. | ['Nadezhda Chirkova', 'Sergey Troshin'] | 2022-02-16 | null | null | null | null | ['variable-misuse'] | ['computer-code'] | [ 1.32056415e-01 2.89202891e-02 -5.82417428e-01 -5.76115191e-01
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21c784f5-13aa-4294-9a9b-4450a503b6f2 | rcaa-relational-context-aware-agents-for | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Xiaojun_Chang_RCAA_Relational_Context-Aware_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Xiaojun_Chang_RCAA_Relational_Context-Aware_ECCV_2018_paper.pdf | RCAA: Relational Context-Aware Agents for Person Search | We aim to search for a target person from a gallery of whole scene images for which the annotations of pedestrian bounding boxes are unavailable. Previous approaches to this problem have relied on a pedestrian proposal net, which may generate redundant proposals and increase the computational burden. In this paper, we address this problem by training relational context-aware agents which learn the actions to localize the target person from the gallery of whole scene images. We incorporate the relational spatial and temporal contexts into the framework. Specifically, we propose to use the target person as the query in the query-dependent relational network. The agent determines the best action to take at each time step by simultaneously considering the local visual information, the relational and temporal contexts, together with the target person. To validate the performance of our approach, we conduct extensive experiments on the large-scale Person Search benchmark dataset and achieve significant improvements over the compared approaches. It is also worth noting that the proposed model even performs better than traditional methods with perfect pedestrian detectors. | ['Yi-Dong Shen', 'Po-Yao Huang', 'Xiaojun Chang', 'Xiaodan Liang', 'Alexander G. Hauptmann', 'Yi Yang'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['person-search'] | ['computer-vision'] | [-2.90083438e-01 -2.56213337e-01 -8.39394182e-02 -4.29037631e-01
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25adeb47-fba1-451d-a096-67433da8fefa | college-student-retention-risk-analysis-from | 2109.05178 | null | https://arxiv.org/abs/2109.05178v1 | https://arxiv.org/pdf/2109.05178v1.pdf | College Student Retention Risk Analysis From Educational Database using Multi-Task Multi-Modal Neural Fusion | We develop a Multimodal Spatiotemporal Neural Fusion network for Multi-Task Learning (MSNF-MTCL) to predict 5 important students' retention risks: future dropout, next semester dropout, type of dropout, duration of dropout and cause of dropout. First, we develop a general purpose multi-modal neural fusion network model MSNF for learning students' academic information representation by fusing spatial and temporal unstructured advising notes with spatiotemporal structured data. MSNF combines a Bidirectional Encoder Representations from Transformers (BERT)-based document embedding framework to represent each advising note, Long-Short Term Memory (LSTM) network to model temporal advising note embeddings, LSTM network to model students' temporal performance variables and students' static demographics altogether. The final fused representation from MSNF has been utilized on a Multi-Task Cascade Learning (MTCL) model towards building MSNF-MTCL for predicting 5 student retention risks. We evaluate MSNFMTCL on a large educational database consists of 36,445 college students over 18 years period of time that provides promising performances comparing with the nearest state-of-art models. Additionally, we test the fairness of such model given the existence of biases. | ['Mohammad Arif Ul Alam'] | 2021-09-11 | null | null | null | null | ['document-embedding'] | ['methodology'] | [-2.02261820e-01 -3.93709660e-01 -4.49613959e-01 -5.48066080e-01
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0fee637f-237a-43e3-b06f-a609f665aa3a | co-occurrence-based-texture-synthesis | 2005.08186 | null | https://arxiv.org/abs/2005.08186v2 | https://arxiv.org/pdf/2005.08186v2.pdf | Co-occurrence Based Texture Synthesis | As image generation techniques mature, there is a growing interest in explainable representations that are easy to understand and intuitive to manipulate. In this work, we turn to co-occurrence statistics, which have long been used for texture analysis, to learn a controllable texture synthesis model. We propose a fully convolutional generative adversarial network, conditioned locally on co-occurrence statistics, to generate arbitrarily large images while having local, interpretable control over the texture appearance. To encourage fidelity to the input condition, we introduce a novel differentiable co-occurrence loss that is integrated seamlessly into our framework in an end-to-end fashion. We demonstrate that our solution offers a stable, intuitive and interpretable latent representation for texture synthesis, which can be used to generate a smooth texture morph between different textures. We further show an interactive texture tool that allows a user to adjust local characteristics of the synthesized texture image using the co-occurrence values directly. | ['Hadar Averbuch-Elor', 'Shai Avidan', 'Anna Darzi', 'Itai Lang', 'Ashutosh Taklikar'] | 2020-05-17 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 5.77205122e-01 3.92955571e-01 -2.12692246e-02 -4.15043771e-01
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6650b719-85c7-4213-970a-e8d14937a3e4 | fast-monocular-scene-reconstruction-with-1 | 2305.13220 | null | https://arxiv.org/abs/2305.13220v1 | https://arxiv.org/pdf/2305.13220v1.pdf | Fast Monocular Scene Reconstruction with Global-Sparse Local-Dense Grids | Indoor scene reconstruction from monocular images has long been sought after by augmented reality and robotics developers. Recent advances in neural field representations and monocular priors have led to remarkable results in scene-level surface reconstructions. The reliance on Multilayer Perceptrons (MLP), however, significantly limits speed in training and rendering. In this work, we propose to directly use signed distance function (SDF) in sparse voxel block grids for fast and accurate scene reconstruction without MLPs. Our globally sparse and locally dense data structure exploits surfaces' spatial sparsity, enables cache-friendly queries, and allows direct extensions to multi-modal data such as color and semantic labels. To apply this representation to monocular scene reconstruction, we develop a scale calibration algorithm for fast geometric initialization from monocular depth priors. We apply differentiable volume rendering from this initialization to refine details with fast convergence. We also introduce efficient high-dimensional Continuous Random Fields (CRFs) to further exploit the semantic-geometry consistency between scene objects. Experiments show that our approach is 10x faster in training and 100x faster in rendering while achieving comparable accuracy to state-of-the-art neural implicit methods. | ['Anima Anandkumar', 'Yuke Zhu', 'Or Litany', 'Charles Loop', 'Chris Choy', 'Wei Dong'] | 2023-05-22 | fast-monocular-scene-reconstruction-with | http://openaccess.thecvf.com//content/CVPR2023/html/Dong_Fast_Monocular_Scene_Reconstruction_With_Global-Sparse_Local-Dense_Grids_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Dong_Fast_Monocular_Scene_Reconstruction_With_Global-Sparse_Local-Dense_Grids_CVPR_2023_paper.pdf | cvpr-2023-1 | ['indoor-scene-reconstruction'] | ['computer-vision'] | [ 3.13435704e-01 -1.17820270e-01 1.12157263e-01 -7.63467312e-01
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d2d5b705-4b35-4911-b0bf-6fe346e09f62 | mot-masked-optimal-transport-for-partial | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Luo_MOT_Masked_Optimal_Transport_for_Partial_Domain_Adaptation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Luo_MOT_Masked_Optimal_Transport_for_Partial_Domain_Adaptation_CVPR_2023_paper.pdf | MOT: Masked Optimal Transport for Partial Domain Adaptation | As an important methodology to measure distribution discrepancy, optimal transport (OT) has been successfully applied to learn generalizable visual models under changing environments. However, there are still limitations, including strict prior assumption and implicit alignment, for current OT modeling in challenging real-world scenarios like partial domain adaptation, where the learned transport plan may be biased and negative transfer is inevitable. Thus, it is necessary to explore a more feasible OT methodology for real-world applications. In this work, we focus on the rigorous OT modeling for conditional distribution matching and label shift correction. A novel masked OT (MOT) methodology on conditional distributions is proposed by defining a mask operation with label information. Further, a relaxed and reweighting formulation is proposed to improve the robustness of OT in extreme scenarios. We prove the theoretical equivalence between conditional OT and MOT, which implies the well-defined MOT serves as a computation-friendly proxy. Extensive experiments validate the effectiveness of theoretical results and proposed model. | ['Chuan-Xian Ren', 'You-Wei Luo'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['partial-domain-adaptation'] | ['methodology'] | [ 5.38796186e-01 -1.89903662e-01 -3.41060817e-01 -5.10768056e-01
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adb4bcc5-fc61-4a65-a153-36ed956eae8a | an-end-to-end-document-level-neural-discourse | 2012.11169 | null | https://arxiv.org/abs/2012.11169v1 | https://arxiv.org/pdf/2012.11169v1.pdf | An End-to-End Document-Level Neural Discourse Parser Exploiting Multi-Granularity Representations | Document-level discourse parsing, in accordance with the Rhetorical Structure Theory (RST), remains notoriously challenging. Challenges include the deep structure of document-level discourse trees, the requirement of subtle semantic judgments, and the lack of large-scale training corpora. To address such challenges, we propose to exploit robust representations derived from multiple levels of granularity across syntax and semantics, and in turn incorporate such representations in an end-to-end encoder-decoder neural architecture for more resourceful discourse processing. In particular, we first use a pre-trained contextual language model that embodies high-order and long-range dependency to enable finer-grain semantic, syntactic, and organizational representations. We further encode such representations with boundary and hierarchical information to obtain more refined modeling for document-level discourse processing. Experimental results show that our parser achieves the state-of-the-art performance, approaching human-level performance on the benchmarked RST dataset. | ['Nancy F. Chen', 'Zhengyuan Liu', 'Ke Shi'] | 2020-12-21 | null | null | null | null | ['discourse-parsing'] | ['natural-language-processing'] | [ 5.37337184e-01 7.45517373e-01 -3.80478173e-01 -6.01455033e-01
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5d88b3f6-a940-4282-9272-df860cb192ef | temporal-kernel-consistency-for-blind-video | 2108.08305 | null | https://arxiv.org/abs/2108.08305v1 | https://arxiv.org/pdf/2108.08305v1.pdf | Temporal Kernel Consistency for Blind Video Super-Resolution | Deep learning-based blind super-resolution (SR) methods have recently achieved unprecedented performance in upscaling frames with unknown degradation. These models are able to accurately estimate the unknown downscaling kernel from a given low-resolution (LR) image in order to leverage the kernel during restoration. Although these approaches have largely been successful, they are predominantly image-based and therefore do not exploit the temporal properties of the kernels across multiple video frames. In this paper, we investigated the temporal properties of the kernels and highlighted its importance in the task of blind video super-resolution. Specifically, we measured the kernel temporal consistency of real-world videos and illustrated how the estimated kernels might change per frame in videos of varying dynamicity of the scene and its objects. With this new insight, we revisited previous popular video SR approaches, and showed that previous assumptions of using a fixed kernel throughout the restoration process can lead to visual artifacts when upscaling real-world videos. In order to counteract this, we tailored existing single-image and video SR techniques to leverage kernel consistency during both kernel estimation and video upscaling processes. Extensive experiments on synthetic and real-world videos show substantial restoration gains quantitatively and qualitatively, achieving the new state-of-the-art in blind video SR and underlining the potential of exploiting kernel temporal consistency. | ['Hongkai Wen', 'Nicholas D. Lane', 'Mohamed S. Abdelfattah', 'Royson Lee', 'Lichuan Xiang'] | 2021-08-18 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [ 1.86014235e-01 -5.53491831e-01 4.50599454e-02 5.00350185e-02
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ddd82d64-8aa0-4e16-acbf-ebce573de7fb | a-high-efficiency-model-indicating-the-role | 2304.00333 | null | https://arxiv.org/abs/2304.00333v1 | https://arxiv.org/pdf/2304.00333v1.pdf | A high-efficiency model indicating the role of inhibition in the resilience of neuronal networks to damage resulting from traumatic injury | Recent investigations of traumatic brain injuries have shown that these injuries can result in conformational changes at the level of individual neurons in the cerebral cortex. Focal axonal swelling is one consequence of such injuries and leads to a variable width along the cell axon. Simulations of the electrical properties of axons impacted in such a way show that this damage may have a nonlinear deleterious effect on spike-encoded signal transmission. The computational cost of these simulations complicates the investigation of the effects of such damage at a network level. We have developed an efficient algorithm that faithfully reproduces the spike train filtering properties seen in physical simulations. We use this algorithm to explore the impact of focal axonal swelling on small networks of integrate and fire neurons. We explore also the effects of architecture modifications to networks impacted in this manner. In all tested networks, our results indicate that the addition of presynaptic inhibitory neurons either increases or leaves unchanged the fidelity of the network's processing properties with respect to this damage. | ['Stanislav M. Mintchev', 'Brian L. Frost'] | 2023-04-01 | null | null | null | null | ['physical-simulations'] | ['miscellaneous'] | [ 5.13407946e-01 -2.69402027e-01 7.10122287e-01 -6.85646012e-03
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1b5d4010-a35d-4678-88ce-1eecce736036 | discourse-aware-neural-extractive-model-for | 1910.14142 | null | https://arxiv.org/abs/1910.14142v2 | https://arxiv.org/pdf/1910.14142v2.pdf | Discourse-Aware Neural Extractive Text Summarization | Recently BERT has been adopted for document encoding in state-of-the-art text summarization models. However, sentence-based extractive models often result in redundant or uninformative phrases in the extracted summaries. Also, long-range dependencies throughout a document are not well captured by BERT, which is pre-trained on sentence pairs instead of documents. To address these issues, we present a discourse-aware neural summarization model - DiscoBert. DiscoBert extracts sub-sentential discourse units (instead of sentences) as candidates for extractive selection on a finer granularity. To capture the long-range dependencies among discourse units, structural discourse graphs are constructed based on RST trees and coreference mentions, encoded with Graph Convolutional Networks. Experiments show that the proposed model outperforms state-of-the-art methods by a significant margin on popular summarization benchmarks compared to other BERT-base models. | ['Zhe Gan', 'Yu Cheng', 'Jingjing Liu', 'Jiacheng Xu'] | 2019-10-30 | discourse-aware-neural-extractive-text | https://aclanthology.org/2020.acl-main.451 | https://aclanthology.org/2020.acl-main.451.pdf | acl-2020-6 | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 4.75601196e-01 7.50844300e-01 -5.02696991e-01 -3.43452543e-01
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ba1c60a0-98e2-439e-b3eb-af23b10cabb6 | hyfactor-hydrogen-count-labelled-graph-based | null | null | https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/61aa38576d4e8f3bdba8aead/original/hy-factor-hydrogen-count-labelled-graph-based-defactorization-autoencoder.pdf | https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/61aa38576d4e8f3bdba8aead/original/hy-factor-hydrogen-count-labelled-graph-based-defactorization-autoencoder.pdf | HyFactor: Hydrogen-count labelled graph-based defactorization Autoencoder | Graph-based architectures are becoming increasingly popular as a tool for structure generation. Here, we introduce a novel open-source architecture HyFactor which is inspired by previously reported DEFactor architecture and based on the hydrogen labeled graphs. Since the original DEFactor code was not available, its new implementation (ReFactor) was prepared in this work for the benchmarking purpose. HyFactor demonstrates its high performance on the ZINC 250K MOSES and ChEMBL data set and in molecular generation tasks, it is considerably more effective than ReFactor. The code of HyFactor and all models obtained in this study are publicly available from our GitHub repository: https://github.com/Laboratoire-de-Chemoinformatique/hyfactor | ['Alexandre Varnek', 'Timur Madzhidov', 'Evgenii Ziaikin', 'Daniyar Mazitov', 'Arkadii Lin', 'Tagir Akhmetshin'] | 2021-12-06 | null | null | null | chemrxiv-2021-12 | ['graph-sampling'] | ['graphs'] | [ 2.14940608e-01 1.40541524e-01 -8.89894441e-02 -1.43962830e-01
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7bbcfaa8-fbf9-4b35-90b4-f2d7f01da5c4 | ad-pt-autonomous-driving-pre-training-with | 2306.00612 | null | https://arxiv.org/abs/2306.00612v2 | https://arxiv.org/pdf/2306.00612v2.pdf | AD-PT: Autonomous Driving Pre-Training with Large-scale Point Cloud Dataset | It is a long-term vision for Autonomous Driving (AD) community that the perception models can learn from a large-scale point cloud dataset, to obtain unified representations that can achieve promising results on different tasks or benchmarks. Previous works mainly focus on the self-supervised pre-training pipeline, meaning that they perform the pre-training and fine-tuning on the same benchmark, which is difficult to attain the performance scalability and cross-dataset application for the pre-training checkpoint. In this paper, for the first time, we are committed to building a large-scale pre-training point-cloud dataset with diverse data distribution, and meanwhile learning generalizable representations from such a diverse pre-training dataset. We formulate the point-cloud pre-training task as a semi-supervised problem, which leverages the few-shot labeled and massive unlabeled point-cloud data to generate the unified backbone representations that can be directly applied to many baseline models and benchmarks, decoupling the AD-related pre-training process and downstream fine-tuning task. During the period of backbone pre-training, by enhancing the scene- and instance-level distribution diversity and exploiting the backbone's ability to learn from unknown instances, we achieve significant performance gains on a series of downstream perception benchmarks including Waymo, nuScenes, and KITTI, under different baseline models like PV-RCNN++, SECOND, CenterPoint. | ['Yu Qiao', 'Yikang Li', 'Botian Shi', 'Tao Chen', 'Xiangchao Yan', 'Bo Zhang', 'Jiakang Yuan'] | 2023-06-01 | null | null | null | null | ['point-cloud-pre-training'] | ['computer-vision'] | [ 9.28124264e-02 -9.82763246e-02 -3.75619680e-01 -5.97837210e-01
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4366dfbd-8c3c-4d86-a1f0-76101632f1b1 | learning-relational-representations-by | null | null | https://aclanthology.org/N19-1327 | https://aclanthology.org/N19-1327.pdf | Learning Relational Representations by Analogy using Hierarchical Siamese Networks | We address relation extraction as an analogy problem by proposing a novel approach to learn representations of relations expressed by their textual mentions. In our assumption, if two pairs of entities belong to the same relation, then those two pairs are analogous. Following this idea, we collect a large set of analogous pairs by matching triples in knowledge bases with web-scale corpora through distant supervision. We leverage this dataset to train a hierarchical siamese network in order to learn entity-entity embeddings which encode relational information through the different linguistic paraphrasing expressing the same relation. We evaluate our model in a one-shot learning task by showing a promising generalization capability in order to classify unseen relation types, which makes this approach suitable to perform automatic knowledge base population with minimal supervision. Moreover, the model can be used to generate pre-trained embeddings which provide a valuable signal when integrated into an existing neural-based model by outperforming the state-of-the-art methods on a downstream relation extraction task. | ['Alfio Gliozzo', 'Michael Glass', 'Robert Farrell', 'Nicolas Fauceglia', 'Gaetano Rossiello'] | 2019-06-01 | null | https://openreview.net/forum?id=SkxE1b56TQ | https://openreview.net/pdf?id=SkxE1b56TQ | naacl-2019-6 | ['knowledge-base-population'] | ['natural-language-processing'] | [ 1.78652778e-01 8.77126455e-01 -4.77691948e-01 -2.67066002e-01
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7cc96841-ffe6-46b3-8c4e-85618e399dbd | evaluation-of-regularization-based-continual | 2304.13327 | null | https://arxiv.org/abs/2304.13327v1 | https://arxiv.org/pdf/2304.13327v1.pdf | Evaluation of Regularization-based Continual Learning Approaches: Application to HAR | Pervasive computing allows the provision of services in many important areas, including the relevant and dynamic field of health and well-being. In this domain, Human Activity Recognition (HAR) has gained a lot of attention in recent years. Current solutions rely on Machine Learning (ML) models and achieve impressive results. However, the evolution of these models remains difficult, as long as a complete retraining is not performed. To overcome this problem, the concept of Continual Learning is very promising today and, more particularly, the techniques based on regularization. These techniques are particularly interesting for their simplicity and their low cost. Initial studies have been conducted and have shown promising outcomes. However, they remain very specific and difficult to compare. In this paper, we provide a comprehensive comparison of three regularization-based methods that we adapted to the HAR domain, highlighting their strengths and limitations. Our experiments were conducted on the UCI HAR dataset and the results showed that no single technique outperformed all others in all scenarios considered. | ['Philippe Lalanda', 'Sandra Castellanos-Paez', 'Bonpagna Kann'] | 2023-04-26 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [ 1.46797419e-01 -1.50675341e-01 -3.83739978e-01 -1.53500706e-01
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a2d46f3b-8754-4077-80c3-353528579ee5 | neural-markov-logic-networks | 1905.13462 | null | https://arxiv.org/abs/1905.13462v3 | https://arxiv.org/pdf/1905.13462v3.pdf | Neural Markov Logic Networks | We introduce neural Markov logic networks (NMLNs), a statistical relational learning system that borrows ideas from Markov logic. Like Markov logic networks (MLNs), NMLNs are an exponential-family model for modelling distributions over possible worlds, but unlike MLNs, they do not rely on explicitly specified first-order logic rules. Instead, NMLNs learn an implicit representation of such rules as a neural network that acts as a potential function on fragments of the relational structure. Similarly to many neural symbolic methods, NMLNs can exploit embeddings of constants but, unlike them, NMLNs work well also in their absence. This is extremely important for predicting in settings other than the transductive one. We showcase the potential of NMLNs on knowledge-base completion, triple classification and on generation of molecular (graph) data. | ['Ondřej Kuželka', 'Giuseppe Marra'] | 2019-05-31 | neural-markov-logic-networks-1 | https://openreview.net/forum?id=SkeGvaEtPr | https://openreview.net/pdf?id=SkeGvaEtPr | iclr-2020-1 | ['triple-classification'] | ['graphs'] | [ 1.21912651e-01 1.06483781e+00 -6.81357861e-01 -4.27785814e-01
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316487f9-329e-47fa-a6b9-2c0e4a940578 | parallel-context-windows-improve-in-context | 2212.10947 | null | https://arxiv.org/abs/2212.10947v2 | https://arxiv.org/pdf/2212.10947v2.pdf | Parallel Context Windows for Large Language Models | When applied for processing long text, Large Language Models (LLMs) are limited by their context window. Existing efforts to address this limitation involve training specialized architectures, and cannot be easily applied to off-the-shelf LLMs. We present Parallel Context Windows (PCW), a method that alleviates the context window restriction for any off-the-shelf LLM without further training. The key to the approach is to carve a long context into chunks (``windows''), restrict the attention mechanism to apply only within each window, and re-use the positional embeddings across the windows. Our main results test the PCW approach on in-context learning with models that range in size between 750 million and 178 billion parameters, and show substantial improvements for tasks with diverse input and output spaces. We show additional benefits in other settings where long context windows may be beneficial: multi-hop questions and retrieval-augmented question answering with multiple retrieved documents. Our results highlight Parallel Context Windows as a promising method for applying off-the-shelf LLMs in a range of settings that require long text sequences. We make our code publicly available at https://github.com/ai21labs/parallel-context-windows. | ['Ehud Karpas', 'Omri Abend', 'Inbal Magar', 'Yoav Shoham', 'Kevin Leyton-Brown', 'Amnon Shashua', 'Ori Ram', 'Yonatan Belinkov', 'Yoav Levine', 'Nir Ratner'] | 2022-12-21 | null | null | null | null | ['2048'] | ['playing-games'] | [ 1.44111380e-01 -1.99558556e-01 -1.72842294e-01 -3.55271280e-01
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b03040ba-9f88-48ff-88f6-f4ec436a93c1 | optical-non-line-of-sight-physics-based-3d | 2003.14414 | null | https://arxiv.org/abs/2003.14414v1 | https://arxiv.org/pdf/2003.14414v1.pdf | Optical Non-Line-of-Sight Physics-based 3D Human Pose Estimation | We describe a method for 3D human pose estimation from transient images (i.e., a 3D spatio-temporal histogram of photons) acquired by an optical non-line-of-sight (NLOS) imaging system. Our method can perceive 3D human pose by `looking around corners' through the use of light indirectly reflected by the environment. We bring together a diverse set of technologies from NLOS imaging, human pose estimation and deep reinforcement learning to construct an end-to-end data processing pipeline that converts a raw stream of photon measurements into a full 3D human pose sequence estimate. Our contributions are the design of data representation process which includes (1) a learnable inverse point spread function (PSF) to convert raw transient images into a deep feature vector; (2) a neural humanoid control policy conditioned on the transient image feature and learned from interactions with a physics simulator; and (3) a data synthesis and augmentation strategy based on depth data that can be transferred to a real-world NLOS imaging system. Our preliminary experiments suggest that our method is able to generalize to real-world NLOS measurement to estimate physically-valid 3D human poses. | ['Kris Kitani', "Matthew O'Toole", 'Ye Yuan', 'Mariko Isogawa'] | 2020-03-31 | optical-non-line-of-sight-physics-based-3d-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Isogawa_Optical_Non-Line-of-Sight_Physics-Based_3D_Human_Pose_Estimation_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Isogawa_Optical_Non-Line-of-Sight_Physics-Based_3D_Human_Pose_Estimation_CVPR_2020_paper.pdf | cvpr-2020-6 | ['humanoid-control'] | ['robots'] | [ 1.49524704e-01 -1.65423681e-03 4.66054708e-01 -3.82562786e-01
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c5c8c0bf-8d2d-4b4e-9d3a-66e7ce613fd1 | exploring-the-viability-of-synthetic-query | 2305.11944 | null | https://arxiv.org/abs/2305.11944v2 | https://arxiv.org/pdf/2305.11944v2.pdf | Exploring the Viability of Synthetic Query Generation for Relevance Prediction | Query-document relevance prediction is a critical problem in Information Retrieval systems. This problem has increasingly been tackled using (pretrained) transformer-based models which are finetuned using large collections of labeled data. However, in specialized domains such as e-commerce and healthcare, the viability of this approach is limited by the dearth of large in-domain data. To address this paucity, recent methods leverage these powerful models to generate high-quality task and domain-specific synthetic data. Prior work has largely explored synthetic data generation or query generation (QGen) for Question-Answering (QA) and binary (yes/no) relevance prediction, where for instance, the QGen models are given a document, and trained to generate a query relevant to that document. However in many problems, we have a more fine-grained notion of relevance than a simple yes/no label. Thus, in this work, we conduct a detailed study into how QGen approaches can be leveraged for nuanced relevance prediction. We demonstrate that -- contrary to claims from prior works -- current QGen approaches fall short of the more conventional cross-domain transfer-learning approaches. Via empirical studies spanning 3 public e-commerce benchmarks, we identify new shortcomings of existing QGen approaches -- including their inability to distinguish between different grades of relevance. To address this, we introduce label-conditioned QGen models which incorporates knowledge about the different relevance. While our experiments demonstrate that these modifications help improve performance of QGen techniques, we also find that QGen approaches struggle to capture the full nuance of the relevance label space and as a result the generated queries are not faithful to the desired relevance label. | ['Marc Najork', 'Mike Bendersky', 'Kazuma Hashimoto', 'Krishna Srinivasan', 'Karthik Raman', 'Aditi Chaudhary'] | 2023-05-19 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 4.74484622e-01 2.07457334e-01 -7.28298068e-01 -4.23760325e-01
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1bc02c06-5550-4fe9-a402-996b76442184 | a-neuromorphic-hardware-architecture-using | 1507.05695 | null | http://arxiv.org/abs/1507.05695v1 | http://arxiv.org/pdf/1507.05695v1.pdf | A neuromorphic hardware architecture using the Neural Engineering Framework for pattern recognition | We present a hardware architecture that uses the Neural Engineering Framework
(NEF) to implement large-scale neural networks on Field Programmable Gate
Arrays (FPGAs) for performing pattern recognition in real time. NEF is a
framework that is capable of synthesising large-scale cognitive systems from
subnetworks. We will first present the architecture of the proposed neural
network implemented using fixed-point numbers and demonstrate a routine that
computes the decoding weights by using the online pseudoinverse update method
(OPIUM) in a parallel and distributed manner. The proposed system is
efficiently implemented on a compact digital neural core. This neural core
consists of 64 neurons that are instantiated by a single physical neuron using
a time-multiplexing approach. As a proof of concept, we combined 128 identical
neural cores together to build a handwritten digit recognition system using the
MNIST database and achieved a recognition rate of 96.55%. The system is
implemented on a state-of-the-art FPGA and can process 5.12 million digits per
second. The architecture is not limited to handwriting recognition, but is
generally applicable as an extremely fast pattern recognition processor for
various kinds of patterns such as speech and images. | ['Andre van Schaik', 'Runchun Wang', 'Tara Julia Hamilton', 'Jonathan Tapson', 'Chetan Singh Thakur'] | 2015-07-21 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 5.03998339e-01 -4.08140421e-01 3.29732776e-01 -2.73380131e-01
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207c5b2e-96ee-4314-ac27-142dbacf8f7f | does-the-magic-of-bert-apply-to-medical-code | 2103.06511 | null | https://arxiv.org/abs/2103.06511v2 | https://arxiv.org/pdf/2103.06511v2.pdf | Does the Magic of BERT Apply to Medical Code Assignment? A Quantitative Study | Unsupervised pretraining is an integral part of many natural language processing systems, and transfer learning with language models has achieved remarkable results in many downstream tasks. In the clinical application of medical code assignment, diagnosis and procedure codes are inferred from lengthy clinical notes such as hospital discharge summaries. However, it is not clear if pretrained models are useful for medical code prediction without further architecture engineering. This paper conducts a comprehensive quantitative analysis of various contextualized language models' performance, pretrained in different domains, for medical code assignment from clinical notes. We propose a hierarchical fine-tuning architecture to capture interactions between distant words and adopt label-wise attention to exploit label information. Contrary to current trends, we demonstrate that a carefully trained classical CNN outperforms attention-based models on a MIMIC-III subset with frequent codes. Our empirical findings suggest directions for improving the medical code assignment application. | ['Pekka Marttinen', 'Matti Hölttä', 'Shaoxiong Ji'] | 2021-03-11 | null | null | null | null | ['medical-code-prediction'] | ['medical'] | [ 4.15594667e-01 3.02156866e-01 -6.02670908e-01 -6.91871881e-01
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b0a5430a-e8de-4e59-b83f-10bf6c64639a | learning-review-representations-from-user-and | 1909.04455 | null | https://arxiv.org/abs/1909.04455v1 | https://arxiv.org/pdf/1909.04455v1.pdf | Learning review representations from user and product level information for spam detection | Opinion spam has become a widespread problem in social media, where hired spammers write deceptive reviews to promote or demote products to mislead the consumers for profit or fame. Existing works mainly focus on manually designing discrete textual or behavior features, which cannot capture complex semantics of reviews. Although recent works apply deep learning methods to learn review-level semantic features, their models ignore the impact of the user-level and product-level information on learning review semantics and the inherent user-review-product relationship information. In this paper, we propose a Hierarchical Fusion Attention Network (HFAN) to automatically learn the semantics of reviews from the user and product level. Specifically, we design a multi-attention unit to extract user(product)-related review information. Then, we use orthogonal decomposition and fusion attention to learn a user, review, and product representation from the review information. Finally, we take the review as a relation between user and product entity and apply TransH to jointly encode this relationship into review representation. Experimental results obtained more than 10\% absolute precision improvement over the state-of-the-art performances on four real-world datasets, which show the effectiveness and versatility of the model. | ['Shangwen Lv', 'Qianwen Ma', 'Chunyuan Yuan', 'Wei Zhou', 'Songlin Hu', 'Jizhong Han'] | 2019-09-10 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [-1.95257906e-02 -4.12626937e-02 -4.49112386e-01 -7.03763902e-01
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8eeb140d-0b8e-4657-8e5c-a318c6a2a558 | dek-forecaster-a-novel-deep-learning-model | 2306.03412 | null | https://arxiv.org/abs/2306.03412v1 | https://arxiv.org/pdf/2306.03412v1.pdf | DEK-Forecaster: A Novel Deep Learning Model Integrated with EMD-KNN for Traffic Prediction | Internet traffic volume estimation has a significant impact on the business policies of the ISP (Internet Service Provider) industry and business successions. Forecasting the internet traffic demand helps to shed light on the future traffic trend, which is often helpful for ISPs decision-making in network planning activities and investments. Besides, the capability to understand future trend contributes to managing regular and long-term operations. This study aims to predict the network traffic volume demand using deep sequence methods that incorporate Empirical Mode Decomposition (EMD) based noise reduction, Empirical rule based outlier detection, and $K$-Nearest Neighbour (KNN) based outlier mitigation. In contrast to the former studies, the proposed model does not rely on a particular EMD decomposed component called Intrinsic Mode Function (IMF) for signal denoising. In our proposed traffic prediction model, we used an average of all IMFs components for signal denoising. Moreover, the abnormal data points are replaced by $K$ nearest data points average, and the value for $K$ has been optimized based on the KNN regressor prediction error measured in Root Mean Squared Error (RMSE). Finally, we selected the best time-lagged feature subset for our prediction model based on AutoRegressive Integrated Moving Average (ARIMA) and Akaike Information Criterion (AIC) value. Our experiments are conducted on real-world internet traffic datasets from industry, and the proposed method is compared with various traditional deep sequence baseline models. Our results show that the proposed EMD-KNN integrated prediction models outperform comparative models. | ['Anwar Haque', 'Sudipto Baral', 'Sajal Saha'] | 2023-06-06 | null | null | null | null | ['outlier-detection', 'traffic-prediction'] | ['methodology', 'time-series'] | [-9.77953598e-02 -7.08480418e-01 -9.57228839e-02 7.64293130e-03
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b48c8f9b-f167-437d-908e-645445362d37 | practical-blind-membership-inference-attack | 2101.01341 | null | https://arxiv.org/abs/2101.01341v2 | https://arxiv.org/pdf/2101.01341v2.pdf | Practical Blind Membership Inference Attack via Differential Comparisons | Membership inference (MI) attacks affect user privacy by inferring whether given data samples have been used to train a target learning model, e.g., a deep neural network. There are two types of MI attacks in the literature, i.e., these with and without shadow models. The success of the former heavily depends on the quality of the shadow model, i.e., the transferability between the shadow and the target; the latter, given only blackbox probing access to the target model, cannot make an effective inference of unknowns, compared with MI attacks using shadow models, due to the insufficient number of qualified samples labeled with ground truth membership information. In this paper, we propose an MI attack, called BlindMI, which probes the target model and extracts membership semantics via a novel approach, called differential comparison. The high-level idea is that BlindMI first generates a dataset with nonmembers via transforming existing samples into new samples, and then differentially moves samples from a target dataset to the generated, non-member set in an iterative manner. If the differential move of a sample increases the set distance, BlindMI considers the sample as non-member and vice versa. BlindMI was evaluated by comparing it with state-of-the-art MI attack algorithms. Our evaluation shows that BlindMI improves F1-score by nearly 20% when compared to state-of-the-art on some datasets, such as Purchase-50 and Birds-200, in the blind setting where the adversary does not know the target model's architecture and the target dataset's ground truth labels. We also show that BlindMI can defeat state-of-the-art defenses. | ['Yinzhi Cao', 'Neil Zhenqiang Gong', 'Philippe Burlina', 'Haolin Yuan', 'Yuchen Yang', 'Bo Hui'] | 2021-01-05 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 3.20006996e-01 1.22494753e-02 -2.07838669e-01 -2.63687313e-01
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6d6643bf-7bc4-4ec6-9672-126fa5cf5db2 | fairness-continual-learning-approach-to | 2305.15700 | null | https://arxiv.org/abs/2305.15700v1 | https://arxiv.org/pdf/2305.15700v1.pdf | Fairness Continual Learning Approach to Semantic Scene Understanding in Open-World Environments | Continual semantic segmentation aims to learn new classes while maintaining the information from the previous classes. Although prior studies have shown impressive progress in recent years, the fairness concern in the continual semantic segmentation needs to be better addressed. Meanwhile, fairness is one of the most vital factors in deploying the deep learning model, especially in human-related or safety applications. In this paper, we present a novel Fairness Continual Learning approach to the semantic segmentation problem. In particular, under the fairness objective, a new fairness continual learning framework is proposed based on class distributions. Then, a novel Prototypical Contrastive Clustering loss is proposed to address the significant challenges in continual learning, i.e., catastrophic forgetting and background shift. Our proposed loss has also been proven as a novel, generalized learning paradigm of knowledge distillation commonly used in continual learning. Moreover, the proposed Conditional Structural Consistency loss further regularized the structural constraint of the predicted segmentation. Our proposed approach has achieved State-of-the-Art performance on three standard scene understanding benchmarks, i.e., ADE20K, Cityscapes, and Pascal VOC, and promoted the fairness of the segmentation model. | ['Khoa Luu', 'Bhiksha Raj', 'Hoang-Quan Nguyen', 'Thanh-Dat Truong'] | 2023-05-25 | null | null | null | null | ['continual-semantic-segmentation', 'scene-understanding'] | ['computer-vision', 'computer-vision'] | [ 1.48728788e-01 1.58119619e-01 -1.35494679e-01 -8.00118923e-01
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14cb0078-8555-4bd0-852d-aa063075fa4e | position-aware-structure-learning-for-graph | 2208.08302 | null | https://arxiv.org/abs/2208.08302v1 | https://arxiv.org/pdf/2208.08302v1.pdf | Position-aware Structure Learning for Graph Topology-imbalance by Relieving Under-reaching and Over-squashing | Topology-imbalance is a graph-specific imbalance problem caused by the uneven topology positions of labeled nodes, which significantly damages the performance of GNNs. What topology-imbalance means and how to measure its impact on graph learning remain under-explored. In this paper, we provide a new understanding of topology-imbalance from a global view of the supervision information distribution in terms of under-reaching and over-squashing, which motivates two quantitative metrics as measurements. In light of our analysis, we propose a novel position-aware graph structure learning framework named PASTEL, which directly optimizes the information propagation path and solves the topology-imbalance issue in essence. Our key insight is to enhance the connectivity of nodes within the same class for more supervision information, thereby relieving the under-reaching and over-squashing phenomena. Specifically, we design an anchor-based position encoding mechanism, which better incorporates relative topology position and enhances the intra-class inductive bias by maximizing the label influence. We further propose a class-wise conflict measure as the edge weights, which benefits the separation of different node classes. Extensive experiments demonstrate the superior potential and adaptability of PASTEL in enhancing GNNs' power in different data annotation scenarios. | ['Philip S. Yu', 'Qian Li', 'Cheng Ji', 'Hao Peng', 'Xingcheng Fu', 'Haonan Yuan', 'JianXin Li', 'Qingyun Sun'] | 2022-08-17 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [ 1.56238163e-02 1.96858078e-01 -6.57682836e-01 -5.15921891e-01
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e76d9dd1-7e35-4563-b744-575fe4cba35d | swin2sr-swinv2-transformer-for-compressed | 2209.11345 | null | https://arxiv.org/abs/2209.11345v1 | https://arxiv.org/pdf/2209.11345v1.pdf | Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration | Compression plays an important role on the efficient transmission and storage of images and videos through band-limited systems such as streaming services, virtual reality or videogames. However, compression unavoidably leads to artifacts and the loss of the original information, which may severely degrade the visual quality. For these reasons, quality enhancement of compressed images has become a popular research topic. While most state-of-the-art image restoration methods are based on convolutional neural networks, other transformers-based methods such as SwinIR, show impressive performance on these tasks. In this paper, we explore the novel Swin Transformer V2, to improve SwinIR for image super-resolution, and in particular, the compressed input scenario. Using this method we can tackle the major issues in training transformer vision models, such as training instability, resolution gaps between pre-training and fine-tuning, and hunger on data. We conduct experiments on three representative tasks: JPEG compression artifacts removal, image super-resolution (classical and lightweight), and compressed image super-resolution. Experimental results demonstrate that our method, Swin2SR, can improve the training convergence and performance of SwinIR, and is a top-5 solution at the "AIM 2022 Challenge on Super-Resolution of Compressed Image and Video". | ['Radu Timofte', 'Maxime Burchi', 'Ui-Jin Choi', 'Marcos V. Conde'] | 2022-09-22 | null | null | null | null | ['jpeg-artifact-correction'] | ['computer-vision'] | [ 6.19797647e-01 -4.88996923e-01 -1.84808314e-01 -4.19689491e-02
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64365054-9d0e-43fd-8c45-cba78dce2039 | czech-dataset-for-cross-lingual-subjectivity | 2204.13915 | null | https://arxiv.org/abs/2204.13915v1 | https://arxiv.org/pdf/2204.13915v1.pdf | Czech Dataset for Cross-lingual Subjectivity Classification | In this paper, we introduce a new Czech subjectivity dataset of 10k manually annotated subjective and objective sentences from movie reviews and descriptions. Our prime motivation is to provide a reliable dataset that can be used with the existing English dataset as a benchmark to test the ability of pre-trained multilingual models to transfer knowledge between Czech and English and vice versa. Two annotators annotated the dataset reaching 0.83 of the Cohen's \k{appa} inter-annotator agreement. To the best of our knowledge, this is the first subjectivity dataset for the Czech language. We also created an additional dataset that consists of 200k automatically labeled sentences. Both datasets are freely available for research purposes. Furthermore, we fine-tune five pre-trained BERT-like models to set a monolingual baseline for the new dataset and we achieve 93.56% of accuracy. We fine-tune models on the existing English dataset for which we obtained results that are on par with the current state-of-the-art results. Finally, we perform zero-shot cross-lingual subjectivity classification between Czech and English to verify the usability of our dataset as the cross-lingual benchmark. We compare and discuss the cross-lingual and monolingual results and the ability of multilingual models to transfer knowledge between languages. | ['Josef Steinberger', 'Pavel Přibáň'] | 2022-04-29 | null | https://aclanthology.org/2022.lrec-1.148 | https://aclanthology.org/2022.lrec-1.148.pdf | lrec-2022-6 | ['subjectivity-analysis'] | ['natural-language-processing'] | [-3.28127205e-01 2.16402620e-01 -1.74471036e-01 -5.35234928e-01
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7d5f7172-d19f-4e89-9bfa-fd32765a5613 | aware-aspect-based-sentiment-analysis-dataset | null | null | https://ieeexplore.ieee.org/document/9679823 | https://ieeexplore.ieee.org/document/9679823 | AWARE: Aspect-Based Sentiment Analysis Dataset of Apps Reviews for Requirements Elicitation | The smartphone apps market is growing rapidly which challenges apps owners to continue improving their products and to compete in the market. The analysis of users feedback is a key enabler for improvements as stakeholders can utilize it to gain a broad understanding of the successes and failures of their products as well as those of competitors. That leads to generating evidence-based requirements and enhancing the requirements elicitation activities. Aspect-Based Sentiment Analysis (ABSA) is a branch of Sentiment Analysis that identifies aspects and assigns a sentiment to each aspect. Having the aspect information adds a more accurate understanding of opinions and addresses the limited use of the overall sentiment. However, the ABSA task has not yet been investigated in the context of smartphone apps reviews and requirements elicitation.
In this paper, we introduce AWARE as a benchmark dataset of 11323 apps reviews that are annotated with aspect terms, categories, and sentiment. Reviews were collected from three domains: productivity, social networking, and games. We derived the aspect categories for each domain using content analysis and validated them with domain experts in terms of importance, comprehensiveness, overlapping, and granularity level. We crowdsourced the annotations of aspect categories and sentiment polarities and performed quality control procedures. The aspect terms were annotated using a partially automated Natural Language Processing (NLP) approach and validated by annotators, which resulted in 98% correct aspect terms. Lastly, we built machine learning baselines for three tasks, namely (i) aspect term extraction using a POS tagger, (ii) aspect category classification, and (iii) aspect sentiment classification, using both Support Vector Machine (SVM) and Multi-layer Perceptron (MLP) classifiers. | ['Malak Baslyman', 'Hamoud Aljamaan', 'Nouf Alturaief'] | 2021-11-19 | null | null | null | 2021-11-19-2021-11 | ['term-extraction', 'aspect-based-sentiment-analysis', 'aspect-category-polarity', 'aspect-category-detection'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 4.04766470e-01 2.09201038e-01 -5.79894125e-01 -5.45018435e-01
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e5129816-dc66-4107-b097-a43a0c42d883 | blindfold-baselines-for-embodied-qa | 1811.05013 | null | http://arxiv.org/abs/1811.05013v1 | http://arxiv.org/pdf/1811.05013v1.pdf | Blindfold Baselines for Embodied QA | We explore blindfold (question-only) baselines for Embodied Question
Answering. The EmbodiedQA task requires an agent to answer a question by
intelligently navigating in a simulated environment, gathering necessary visual
information only through first-person vision before finally answering.
Consequently, a blindfold baseline which ignores the environment and visual
information is a degenerate solution, yet we show through our experiments on
the EQAv1 dataset that a simple question-only baseline achieves
state-of-the-art results on the EmbodiedQA task in all cases except when the
agent is spawned extremely close to the object. | ['Hugo Larochelle', 'Eugene Belilovsky', 'Kyle Kastner', 'Ankesh Anand', 'Aaron Courville'] | 2018-11-12 | null | null | null | null | ['embodied-question-answering'] | ['computer-vision'] | [-1.59048170e-01 4.33716267e-01 7.52924502e-01 -2.02801794e-01
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f7b8cd71-7936-42d4-9051-acb63bad1406 | long-distance-dependencies-dont-have-to-be | null | null | https://aclanthology.org/P19-2012 | https://aclanthology.org/P19-2012.pdf | Long-Distance Dependencies Don't Have to Be Long: Simplifying through Provably (Approximately) Optimal Permutations | Neural models at the sentence level often operate on the constituent words/tokens in a way that encodes the inductive bias of processing the input in a similar fashion to how humans do. However, there is no guarantee that the standard ordering of words is computationally efficient or optimal. To help mitigate this, we consider a dependency parse as a proxy for the inter-word dependencies in a sentence and simplify the sentence with respect to combinatorial objectives imposed on the sentence-parse pair. The associated optimization results in permuted sentences that are provably (approximately) optimal with respect to minimizing dependency parse lengths and that are demonstrably simpler. We evaluate our general-purpose permutations within a fine-tuning schema for the downstream task of subjectivity analysis. Our fine-tuned baselines reflect a new state of the art for the SUBJ dataset and the permutations we introduce lead to further improvements with a 2.0{\%} increase in classification accuracy (absolute) and a 45{\%} reduction in classification error (relative) over the previous state of the art. | ['Rishi Bommasani'] | 2019-07-01 | null | null | null | acl-2019-7 | ['subjectivity-analysis'] | ['natural-language-processing'] | [ 5.36996901e-01 5.84440589e-01 -6.11900240e-02 -8.48801374e-01
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5f29cc75-d6cc-4d17-bf06-201724d68561 | propensity-scored-probabilistic-label-trees | 2110.10803 | null | https://arxiv.org/abs/2110.10803v1 | https://arxiv.org/pdf/2110.10803v1.pdf | Propensity-scored Probabilistic Label Trees | Extreme multi-label classification (XMLC) refers to the task of tagging instances with small subsets of relevant labels coming from an extremely large set of all possible labels. Recently, XMLC has been widely applied to diverse web applications such as automatic content labeling, online advertising, or recommendation systems. In such environments, label distribution is often highly imbalanced, consisting mostly of very rare tail labels, and relevant labels can be missing. As a remedy to these problems, the propensity model has been introduced and applied within several XMLC algorithms. In this work, we focus on the problem of optimal predictions under this model for probabilistic label trees, a popular approach for XMLC problems. We introduce an inference procedure, based on the $A^*$-search algorithm, that efficiently finds the optimal solution, assuming that all probabilities and propensities are known. We demonstrate the attractiveness of this approach in a wide empirical study on popular XMLC benchmark datasets. | ['Krzysztof Dembczyński', 'Rohit Babbar', 'Kalina Jasinska-Kobus', 'Marek Wydmuch'] | 2021-10-20 | null | null | null | null | ['extreme-multi-label-classification'] | ['methodology'] | [ 5.08622944e-01 1.10601686e-01 -6.89625978e-01 -6.75026000e-01
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429d2711-312d-41e8-a7bf-aa93229b2215 | multi-channel-weighted-nuclear-norm | 1705.09912 | null | http://arxiv.org/abs/1705.09912v2 | http://arxiv.org/pdf/1705.09912v2.pdf | Multi-channel Weighted Nuclear Norm Minimization for Real Color Image Denoising | Most of the existing denoising algorithms are developed for grayscale images,
while it is not a trivial work to extend them for color image denoising because
the noise statistics in R, G, B channels can be very different for real noisy
images. In this paper, we propose a multi-channel (MC) optimization model for
real color image denoising under the weighted nuclear norm minimization (WNNM)
framework. We concatenate the RGB patches to make use of the channel
redundancy, and introduce a weight matrix to balance the data fidelity of the
three channels in consideration of their different noise statistics. The
proposed MC-WNNM model does not have an analytical solution. We reformulate it
into a linear equality-constrained problem and solve it with the alternating
direction method of multipliers. Each alternative updating step has closed-form
solution and the convergence can be guaranteed. Extensive experiments on both
synthetic and real noisy image datasets demonstrate the superiority of the
proposed MC-WNNM over state-of-the-art denoising methods. | ['David Zhang', 'Xiangchu Feng', 'Jun Xu', 'Lei Zhang'] | 2017-05-28 | multi-channel-weighted-nuclear-norm-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Xu_Multi-Channel_Weighted_Nuclear_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Xu_Multi-Channel_Weighted_Nuclear_ICCV_2017_paper.pdf | iccv-2017-10 | ['color-image-denoising'] | ['computer-vision'] | [ 2.96499997e-01 -6.47162378e-01 4.60955977e-01 -2.38139808e-01
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041ec1f3-2bcc-4842-b7ca-88b16a010ad1 | information-recovery-in-shuffled-graphs-via | 1605.02315 | null | http://arxiv.org/abs/1605.02315v2 | http://arxiv.org/pdf/1605.02315v2.pdf | Information Recovery in Shuffled Graphs via Graph Matching | While many multiple graph inference methodologies operate under the implicit
assumption that an explicit vertex correspondence is known across the vertex
sets of the graphs, in practice these correspondences may only be partially or
errorfully known. Herein, we provide an information theoretic foundation for
understanding the practical impact that errorfully observed vertex
correspondences can have on subsequent inference, and the capacity of graph
matching methods to recover the lost vertex alignment and inferential
performance. Working in the correlated stochastic blockmodel setting, we
establish a duality between the loss of mutual information due to an errorfully
observed vertex correspondence and the ability of graph matching algorithms to
recover the true correspondence across graphs. In the process, we establish a
phase transition for graph matchability in terms of the correlation across
graphs, and we conjecture the analogous phase transition for the relative
information loss due to shuffling vertex labels. We demonstrate the practical
effect that graph shuffling---and matching---can have on subsequent inference,
with examples from two sample graph hypothesis testing and joint spectral graph
clustering. | ['Vince Lyzinski'] | 2016-05-08 | null | null | null | null | ['spectral-graph-clustering'] | ['graphs'] | [ 6.98363066e-01 4.84556854e-01 -2.46369913e-01 -2.57735610e-01
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adf07009-a048-49b7-b4c8-102fb455af96 | textworldexpress-simulating-text-games-at-one | 2208.01174 | null | https://arxiv.org/abs/2208.01174v2 | https://arxiv.org/pdf/2208.01174v2.pdf | TextWorldExpress: Simulating Text Games at One Million Steps Per Second | Text-based games offer a challenging test bed to evaluate virtual agents at language understanding, multi-step problem-solving, and common-sense reasoning. However, speed is a major limitation of current text-based games, capping at 300 steps per second, mainly due to the use of legacy tooling. In this work we present TextWorldExpress, a high-performance simulator that includes implementations of three common text game benchmarks that increases simulation throughput by approximately three orders of magnitude, reaching over one million steps per second on common desktop hardware. This significantly reduces experiment runtime, enabling billion-step-scale experiments in about one day. | ['Marc-Alexandre Côté', 'Peter A. Jansen'] | 2022-08-01 | null | null | null | null | ['text-based-games'] | ['playing-games'] | [-4.74932730e-01 7.38092288e-02 2.07434192e-01 9.93940830e-02
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ed9f4035-df8c-4c0b-8cba-10f58f75e3f8 | srel-severity-rating-ensemble-learning-for | 2304.10207 | null | https://arxiv.org/abs/2304.10207v2 | https://arxiv.org/pdf/2304.10207v2.pdf | Learning Graph Patterns of Reflection Coefficient for Non-destructive Diagnosis of Cu Interconnects | With the increasing operating frequencies and clock speeds in processors, interconnects affect both the reliability and performance of entire electronic systems. Fault detection and diagnosis of the interconnects are crucial for prognostics and health management (PHM) of electronics. However, traditional approaches using electrical signals as prognostic factors often face challenges in distinguishing defect root causes, necessitating additional destructive evaluations, and are prone to noise interference, leading to potential false alarms. To address these limitations, this paper introduces a novel approach for non-destructive detection and diagnosis of defects in Cu interconnects, offering early detection, enhanced diagnostic accuracy, and noise resilience. Our approach uniquely analyzes both the root cause and severity of interconnect defects by leveraging graph patterns of reflection coefficient, a technique distinct from traditional time series signal analysis. We experimentally demonstrate that the graph patterns possess the capability for fault diagnosis and serve as effective input data for learning algorithms. Additionally, we introduce a novel severity rating ensemble learning (SREL) approach, which significantly enhances diagnostic accuracy and noise robustness. Experimental results demonstrate that the proposed method outperforms conventional machine learning methods and multi-class convolutional neural networks (CNN), achieving a maximum accuracy of 99.3%, especially under elevated noise levels. | ['Sungho Suh', 'Haebom Lee', 'Tae Yeob Kang'] | 2023-04-20 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [ 1.51277278e-02 -4.91159827e-01 1.51397884e-01 2.89073642e-02
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033b59ed-f379-435c-a95f-c0e421b7e8c2 | savir-t-spatially-attentive-visual-reasoning | 2206.09265 | null | https://arxiv.org/abs/2206.09265v2 | https://arxiv.org/pdf/2206.09265v2.pdf | SAViR-T: Spatially Attentive Visual Reasoning with Transformers | We present a novel computational model, "SAViR-T", for the family of visual reasoning problems embodied in the Raven's Progressive Matrices (RPM). Our model considers explicit spatial semantics of visual elements within each image in the puzzle, encoded as spatio-visual tokens, and learns the intra-image as well as the inter-image token dependencies, highly relevant for the visual reasoning task. Token-wise relationship, modeled through a transformer-based SAViR-T architecture, extract group (row or column) driven representations by leveraging the group-rule coherence and use this as the inductive bias to extract the underlying rule representations in the top two row (or column) per token in the RPM. We use this relation representations to locate the correct choice image that completes the last row or column for the RPM. Extensive experiments across both synthetic RPM benchmarks, including RAVEN, I-RAVEN, RAVEN-FAIR, and PGM, and the natural image-based "V-PROM" demonstrate that SAViR-T sets a new state-of-the-art for visual reasoning, exceeding prior models' performance by a considerable margin. | ['Vladimir Pavlovic', 'Kalliopi Basioti', 'Pritish Sahu'] | 2022-06-18 | null | null | null | null | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [-1.23409882e-01 -8.48423690e-03 -3.92023511e-02 1.41474992e-01
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915c40f1-0275-4c7e-b18d-a08e87f8212a | a-survey-on-stance-detection-for-mis-and-1 | null | null | https://openreview.net/forum?id=eHMpE26Z2LC | https://openreview.net/pdf?id=eHMpE26Z2LC | A Survey on Stance Detection for Mis- and Disinformation Identification | Understanding attitudes expressed in texts, also known as stance detection, plays an important role in systems for detecting false information online, be it misinformation (unintentionally false) or disinformation (intentionally false information). Stance detection has been framed in different ways, including (a) as a component of fact-checking, rumour detection, and detecting previously fact-checked claims, or (b) as a task in its own right. While there have been prior efforts to contrast stance detection with other related tasks such as argumentation mining and sentiment analysis, there is no existing survey on examining the relationship between stance detection and mis- and disinformation detection. Here, we aim to bridge this gap by reviewing and analysing existing work in this area, with mis- and disinformation in focus, and discussing lessons learnt and future challenges. | ['Anonymous'] | 2021-12-17 | null | null | null | acl-arr-december-2022-12 | ['rumour-detection'] | ['natural-language-processing'] | [ 4.52227026e-01 8.91923726e-01 -5.24585843e-01 -2.02974036e-01
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91b3a58a-2724-49db-83eb-81f230be5aba | similarity-based-label-smoothing-for-dialogue | 2107.11481 | null | https://arxiv.org/abs/2107.11481v1 | https://arxiv.org/pdf/2107.11481v1.pdf | Similarity Based Label Smoothing For Dialogue Generation | Generative neural conversational systems are generally trained with the objective of minimizing the entropy loss between the training "hard" targets and the predicted logits. Often, performance gains and improved generalization can be achieved by using regularization techniques like label smoothing, which converts the training "hard" targets to "soft" targets. However, label smoothing enforces a data independent uniform distribution on the incorrect training targets, which leads to an incorrect assumption of equi-probable incorrect targets for each correct target. In this paper we propose and experiment with incorporating data dependent word similarity based weighing methods to transforms the uniform distribution of the incorrect target probabilities in label smoothing, to a more natural distribution based on semantics. We introduce hyperparameters to control the incorrect target distribution, and report significant performance gains over networks trained using standard label smoothing based loss, on two standard open domain dialogue corpora. | ['Rohini Srihari', 'Souvik Das', 'Sougata Saha'] | 2021-07-23 | null | null | null | null | ['word-similarity'] | ['natural-language-processing'] | [ 3.74708802e-01 8.13102305e-01 -1.52234465e-01 -1.09443057e+00
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19e9976f-6764-451c-a625-ed4f5ffad53f | shikeblcu-at-semeval-2020-task-2-an-external | null | null | https://aclanthology.org/2020.semeval-1.31 | https://aclanthology.org/2020.semeval-1.31.pdf | SHIKEBLCU at SemEval-2020 Task 2: An External Knowledge-enhanced Matrix for Multilingual and Cross-Lingual Lexical Entailment | Lexical entailment recognition plays an important role in tasks like Question Answering and Machine Translation. As important branches of lexical entailment, predicting multilingual and cross-lingual lexical entailment (LE) are two subtasks of SemEval2020 Task2. In previous monolingual LE studies, researchers leverage external linguistic constraints to transform word embeddings for LE relation. In our system, we expand the number of external constraints in multiple languages to obtain more specialised multilingual word embeddings. For the cross-lingual subtask, we apply a bilingual word embeddings mapping method in the model. The mapping method takes specialised embeddings as inputs and is able to retain the embeddings{'} LE features after operations. Our results for multilingual subtask are about 20{\%} and 10{\%} higher than the baseline in graded and binary prediction respectively. | ['Dong Yu', 'Xiangying Luo', 'Yuchen Fan', 'Shike Wang'] | 2020-12-01 | null | null | null | semeval-2020 | ['multilingual-word-embeddings'] | ['methodology'] | [-1.88376814e-01 1.82038635e-01 -5.97581983e-01 -5.94923198e-01
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01727ede-d94b-49da-80c6-be0f0fcac032 | augmenting-passage-representations-with-query | 2305.03950 | null | https://arxiv.org/abs/2305.03950v1 | https://arxiv.org/pdf/2305.03950v1.pdf | Augmenting Passage Representations with Query Generation for Enhanced Cross-Lingual Dense Retrieval | Effective cross-lingual dense retrieval methods that rely on multilingual pre-trained language models (PLMs) need to be trained to encompass both the relevance matching task and the cross-language alignment task. However, cross-lingual data for training is often scarcely available. In this paper, rather than using more cross-lingual data for training, we propose to use cross-lingual query generation to augment passage representations with queries in languages other than the original passage language. These augmented representations are used at inference time so that the representation can encode more information across the different target languages. Training of a cross-lingual query generator does not require additional training data to that used for the dense retriever. The query generator training is also effective because the pre-training task for the generator (T5 text-to-text training) is very similar to the fine-tuning task (generation of a query). The use of the generator does not increase query latency at inference and can be combined with any cross-lingual dense retrieval method. Results from experiments on a benchmark cross-lingual information retrieval dataset show that our approach can improve the effectiveness of existing cross-lingual dense retrieval methods. Implementation of our methods, along with all generated query files are made publicly available at https://github.com/ielab/xQG4xDR. | ['Guido Zuccon', 'Linjun Shou', 'Shengyao Zhuang'] | 2023-05-06 | null | null | null | null | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [-2.71322250e-01 -3.41604084e-01 -2.18360901e-01 -3.17427933e-01
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ab4a828d-f0bb-4e63-909f-7cc6229f9c9c | visual-causal-scene-refinement-for-video | 2305.04224 | null | https://arxiv.org/abs/2305.04224v1 | https://arxiv.org/pdf/2305.04224v1.pdf | Visual Causal Scene Refinement for Video Question Answering | Existing methods for video question answering (VideoQA) often suffer from spurious correlations between different modalities, leading to a failure in identifying the dominant visual evidence and the intended question. Moreover, these methods function as black boxes, making it difficult to interpret the visual scene during the QA process. In this paper, to discover critical video segments and frames that serve as the visual causal scene for generating reliable answers, we present a causal analysis of VideoQA and propose a framework for cross-modal causal relational reasoning, named Visual Causal Scene Refinement (VCSR). Particularly, a set of causal front-door intervention operations is introduced to explicitly find the visual causal scenes at both segment and frame levels. Our VCSR involves two essential modules: i) the Question-Guided Refiner (QGR) module, which refines consecutive video frames guided by the question semantics to obtain more representative segment features for causal front-door intervention; ii) the Causal Scene Separator (CSS) module, which discovers a collection of visual causal and non-causal scenes based on the visual-linguistic causal relevance and estimates the causal effect of the scene-separating intervention in a contrastive learning manner. Extensive experiments on the NExT-QA, Causal-VidQA, and MSRVTT-QA datasets demonstrate the superiority of our VCSR in discovering visual causal scene and achieving robust video question answering. | ['Liang Lin', 'Guanbin Li', 'Hong Yan', 'Yang Liu', 'Yushen Wei'] | 2023-05-07 | null | null | null | null | ['video-question-answering', 'relational-reasoning'] | ['computer-vision', 'natural-language-processing'] | [ 2.57841229e-01 -5.16053699e-02 -2.81298518e-01 -2.87304968e-01
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51f66896-7fbb-47b8-958d-a8ed4b7d5b87 | the-2018-shared-task-on-extrinsic-parser | null | null | https://aclanthology.org/K18-2002 | https://aclanthology.org/K18-2002.pdf | The 2018 Shared Task on Extrinsic Parser Evaluation: On the Downstream Utility of English Universal Dependency Parsers | We summarize empirical results and tentative conclusions from the Second Extrinsic Parser Evaluation Initiative (EPE 2018). We review the basic task setup, downstream applications involved, and end-to-end results for seventeen participating teams. Based on in-depth quantitative and qualitative analysis, we correlate intrinsic evaluation results at different layers of morph-syntactic analysis with observed downstream behavior. | ['Jari Bj{\\"o}rne', 'Lilja {\\O}vrelid', 'Stephan Oepen', 'Richard Johansson', 'Murhaf Fares'] | 2018-10-01 | null | null | null | conll-2018-10 | ['fine-grained-opinion-analysis'] | ['natural-language-processing'] | [ 2.28474457e-02 5.43218672e-01 5.74717969e-02 -8.83825362e-01
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bc5ed7f5-0de9-44d3-ae05-ade0cfb3ddf3 | contextualized-embeddings-for-connective | null | null | https://aclanthology.org/2020.codi-1.7 | https://aclanthology.org/2020.codi-1.7.pdf | Contextualized Embeddings for Connective Disambiguation in Shallow Discourse Parsing | This paper studies a novel model that simplifies the disambiguation of connectives for explicit discourse relations. We use a neural approach that integrates contextualized word embeddings and predicts whether a connective candidate is part of a discourse relation or not. We study the influence of those context-specific embeddings. Further, we show the benefit of training the tasks of connective disambiguation and sense classification together at the same time. The success of our approach is supported by state-of-the-art results. | ['Manfred Stede', 'René Knaebel'] | null | null | null | null | emnlp-codi-2020-11 | ['discourse-parsing'] | ['natural-language-processing'] | [ 6.88442960e-02 8.24905694e-01 -4.84892815e-01 -4.19216692e-01
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75249087-41a9-4358-bd08-aafd08e6a830 | video-corpus-moment-retrieval-with | 2105.06247 | null | https://arxiv.org/abs/2105.06247v1 | https://arxiv.org/pdf/2105.06247v1.pdf | Video Corpus Moment Retrieval with Contrastive Learning | Given a collection of untrimmed and unsegmented videos, video corpus moment retrieval (VCMR) is to retrieve a temporal moment (i.e., a fraction of a video) that semantically corresponds to a given text query. As video and text are from two distinct feature spaces, there are two general approaches to address VCMR: (i) to separately encode each modality representations, then align the two modality representations for query processing, and (ii) to adopt fine-grained cross-modal interaction to learn multi-modal representations for query processing. While the second approach often leads to better retrieval accuracy, the first approach is far more efficient. In this paper, we propose a Retrieval and Localization Network with Contrastive Learning (ReLoCLNet) for VCMR. We adopt the first approach and introduce two contrastive learning objectives to refine video encoder and text encoder to learn video and text representations separately but with better alignment for VCMR. The video contrastive learning (VideoCL) is to maximize mutual information between query and candidate video at video-level. The frame contrastive learning (FrameCL) aims to highlight the moment region corresponds to the query at frame-level, within a video. Experimental results show that, although ReLoCLNet encodes text and video separately for efficiency, its retrieval accuracy is comparable with baselines adopting cross-modal interaction learning. | ['Rick Siow Mong Goh', 'Joey Tianyi Zhou', 'Liangli Zhen', 'Guoshun Nan', 'Wei Jing', 'Aixin Sun', 'Hao Zhang'] | 2021-05-13 | null | null | null | null | ['moment-retrieval'] | ['computer-vision'] | [ 2.14865044e-01 -3.26508462e-01 -5.70306182e-01 6.27808347e-02
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0a1d911b-4fe1-44fe-8a53-5962d6fe0c41 | 3d-rotation-and-translation-for-hyperbolic | 2305.13015 | null | https://arxiv.org/abs/2305.13015v1 | https://arxiv.org/pdf/2305.13015v1.pdf | 3D Rotation and Translation for Hyperbolic Knowledge Graph Embedding | The main objective of Knowledge Graph (KG) embeddings is to learn low-dimensional representations of entities and relations, enabling the prediction of missing facts. A significant challenge in achieving better KG embeddings lies in capturing relation patterns, including symmetry, antisymmetry, inversion, commutative composition, non-commutative composition, hierarchy, and multiplicity. This study introduces a novel model called 3H-TH (3D Rotation and Translation in Hyperbolic space) that captures these relation patterns simultaneously. In contrast, previous attempts have not achieved satisfactory performance across all the mentioned properties at the same time. The experimental results demonstrate that the new model outperforms existing state-of-the-art models in terms of accuracy, hierarchy property, and other relation patterns in low-dimensional space, meanwhile performing similarly in high-dimensional space. | ['Hidetoshi Shimodaira', 'Yihua Zhu'] | 2023-05-22 | null | null | null | null | ['graph-embedding', 'knowledge-graph-embedding'] | ['graphs', 'graphs'] | [-6.27634108e-01 3.29778284e-01 -4.26777780e-01 -3.99468914e-02
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ede2e9a6-6495-4874-9a59-aa2e9c224b1e | delivering-inflated-explanations | 2306.15272 | null | https://arxiv.org/abs/2306.15272v1 | https://arxiv.org/pdf/2306.15272v1.pdf | Delivering Inflated Explanations | In the quest for Explainable Artificial Intelligence (XAI) one of the questions that frequently arises given a decision made by an AI system is, ``why was the decision made in this way?'' Formal approaches to explainability build a formal model of the AI system and use this to reason about the properties of the system. Given a set of feature values for an instance to be explained, and a resulting decision, a formal abductive explanation is a set of features, such that if they take the given value will always lead to the same decision. This explanation is useful, it shows that only some features were used in making the final decision. But it is narrow, it only shows that if the selected features take their given values the decision is unchanged. It's possible that some features may change values and still lead to the same decision. In this paper we formally define inflated explanations which is a set of features, and for each feature of set of values (always including the value of the instance being explained), such that the decision will remain unchanged. Inflated explanations are more informative than abductive explanations since e.g they allow us to see if the exact value of a feature is important, or it could be any nearby value. Overall they allow us to better understand the role of each feature in the decision. We show that we can compute inflated explanations for not that much greater cost than abductive explanations, and that we can extend duality results for abductive explanations also to inflated explanations. | ['Joao Marques-Silva', 'Peter Stuckey', 'Alexey Ignatiev', 'Yacine Izza'] | 2023-06-27 | null | null | null | null | ['explainable-artificial-intelligence'] | ['computer-vision'] | [ 4.35635775e-01 1.03837776e+00 -2.65470207e-01 -6.26994610e-01
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1a5eb765-db34-41b5-b360-9e2173aee452 | weakly-supervised-amodal-instance-1 | 2010.13175 | null | https://arxiv.org/abs/2010.13175v4 | https://arxiv.org/pdf/2010.13175v4.pdf | Amodal Segmentation through Out-of-Task and Out-of-Distribution Generalization with a Bayesian Model | Amodal completion is a visual task that humans perform easily but which is difficult for computer vision algorithms. The aim is to segment those object boundaries which are occluded and hence invisible. This task is particularly challenging for deep neural networks because data is difficult to obtain and annotate. Therefore, we formulate amodal segmentation as an out-of-task and out-of-distribution generalization problem. Specifically, we replace the fully connected classifier in neural networks with a Bayesian generative model of the neural network features. The model is trained from non-occluded images using bounding box annotations and class labels only, but is applied to generalize out-of-task to object segmentation and to generalize out-of-distribution to segment occluded objects. We demonstrate how such Bayesian models can naturally generalize beyond the training task labels when they learn a prior that models the object's background context and shape. Moreover, by leveraging an outlier process, Bayesian models can further generalize out-of-distribution to segment partially occluded objects and to predict their amodal object boundaries. Our algorithm outperforms alternative methods that use the same supervision by a large margin, and even outperforms methods where annotated amodal segmentations are used during training, when the amount of occlusion is large. Code is publicly available at https://github.com/YihongSun/Bayesian-Amodal. | ['Alan Yuille', 'Adam Kortylewski', 'Yihong Sun'] | 2020-10-25 | weakly-supervised-amodal-instance | http://openaccess.thecvf.com//content/CVPR2022/html/Sun_Amodal_Segmentation_Through_Out-of-Task_and_Out-of-Distribution_Generalization_With_a_Bayesian_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Sun_Amodal_Segmentation_Through_Out-of-Task_and_Out-of-Distribution_Generalization_With_a_Bayesian_CVPR_2022_paper.pdf | cvpr-2022-1 | ['amodal-instance-segmentation'] | ['computer-vision'] | [ 3.61004084e-01 5.73727310e-01 -2.93290913e-01 -5.66167712e-01
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074149dc-0d7f-40e0-a6b7-e280a6b19dd8 | frame-recurrent-video-inpainting-by-robust | 1905.02882 | null | https://arxiv.org/abs/1905.02882v1 | https://arxiv.org/pdf/1905.02882v1.pdf | Frame-Recurrent Video Inpainting by Robust Optical Flow Inference | In this paper, we present a new inpainting framework for recovering missing regions of video frames. Compared with image inpainting, performing this task on video presents new challenges such as how to preserving temporal consistency and spatial details, as well as how to handle arbitrary input video size and length fast and efficiently. Towards this end, we propose a novel deep learning architecture which incorporates ConvLSTM and optical flow for modeling the spatial-temporal consistency in videos. It also saves much computational resource such that our method can handle videos with larger frame size and arbitrary length streamingly in real-time. Furthermore, to generate an accurate optical flow from corrupted frames, we propose a robust flow generation module, where two sources of flows are fed and a flow blending network is trained to fuse them. We conduct extensive experiments to evaluate our method in various scenarios and different datasets, both qualitatively and quantitatively. The experimental results demonstrate the superior of our method compared with the state-of-the-art inpainting approaches. | ['Jiaming Liu', 'Chuan Wang', 'Yifan Ding', 'Haibin Huang', 'Jue Wang', 'Liqiang Wang'] | 2019-05-08 | null | null | null | null | ['video-inpainting'] | ['computer-vision'] | [-4.66106795e-02 -4.88119572e-01 -1.03371657e-01 -2.14616686e-01
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6a6a6cb3-f34e-4b69-9021-bfd11ed28d4a | learning-to-count-objects-in-images | null | null | http://papers.nips.cc/paper/4043-learning-to-count-objects-in-images | http://papers.nips.cc/paper/4043-learning-to-count-objects-in-images.pdf | Learning To Count Objects in Images | We propose a new supervised learning framework for visual object counting tasks, such as estimating the number of cells in a microscopic image or the number of humans in surveillance video frames. We focus on the practically-attractive case when the training images are annotated with dots (one dot per object). Our goal is to accurately estimate the count. However, we evade the hard task of learning to detect and localize individual object instances. Instead, we cast the problem as that of estimating an image density whose integral over any image region gives the count of objects within that region. Learning to infer such density can be formulated as a minimization of a regularized risk quadratic cost function. We introduce a new loss function, which is well-suited for such learning, and at the same time can be computed efficiently via a maximum subarray algorithm. The learning can then be posed as a convex quadratic program solvable with cutting-plane optimization. The proposed framework is very flexible as it can accept any domain-specific visual features. Once trained, our system provides accurate object counts and requires a very small time overhead over the feature extraction step, making it a good candidate for applications involving real-time processing or dealing with huge amount of visual data. | ['Andrew Zisserman', 'Victor Lempitsky'] | 2010-12-01 | null | null | null | neurips-2010-12 | ['object-counting'] | ['computer-vision'] | [ 2.29571730e-01 3.55573632e-02 -1.20450303e-01 -3.81438762e-01
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c5137eaa-0ea9-4721-bc71-93e18313f85b | alzheimer-disease-classification-through-asr | 2306.03443 | null | https://arxiv.org/abs/2306.03443v1 | https://arxiv.org/pdf/2306.03443v1.pdf | Alzheimer Disease Classification through ASR-based Transcriptions: Exploring the Impact of Punctuation and Pauses | Alzheimer's Disease (AD) is the world's leading neurodegenerative disease, which often results in communication difficulties. Analysing speech can serve as a diagnostic tool for identifying the condition. The recent ADReSS challenge provided a dataset for AD classification and highlighted the utility of manual transcriptions. In this study, we used the new state-of-the-art Automatic Speech Recognition (ASR) model Whisper to obtain the transcriptions, which also include automatic punctuation. The classification models achieved test accuracy scores of 0.854 and 0.833 combining the pretrained FastText word embeddings and recurrent neural networks on manual and ASR transcripts respectively. Additionally, we explored the influence of including pause information and punctuation in the transcriptions. We found that punctuation only yielded minor improvements in some cases, whereas pause encoding aided AD classification for both manual and ASR transcriptions across all approaches investigated. | ['Helmer Strik', 'Mariano Alcañiz', 'Javier Marín-Morales', 'Cristian Tejedor-Garcia', 'Simone Wills', 'Lucía Gómez-Zaragozá'] | 2023-06-06 | null | null | null | null | ['word-embeddings', 'automatic-speech-recognition'] | ['methodology', 'speech'] | [ 1.97748303e-01 6.76688924e-02 2.17840493e-01 -2.68576503e-01
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d9b542e4-2e88-47ca-947a-9340e65549e7 | parallel-and-flexible-sampling-from | 2105.08164 | null | https://arxiv.org/abs/2105.08164v2 | https://arxiv.org/pdf/2105.08164v2.pdf | Parallel and Flexible Sampling from Autoregressive Models via Langevin Dynamics | This paper introduces an alternative approach to sampling from autoregressive models. Autoregressive models are typically sampled sequentially, according to the transition dynamics defined by the model. Instead, we propose a sampling procedure that initializes a sequence with white noise and follows a Markov chain defined by Langevin dynamics on the global log-likelihood of the sequence. This approach parallelizes the sampling process and generalizes to conditional sampling. Using an autoregressive model as a Bayesian prior, we can steer the output of a generative model using a conditional likelihood or constraints. We apply these techniques to autoregressive models in the visual and audio domains, with competitive results for audio source separation, super-resolution, and inpainting. | ['John Thickstun', 'Vivek Jayaram'] | 2021-05-17 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 5.42739332e-01 7.61597753e-02 -1.14798851e-01 -3.71882886e-01
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30b22e61-7e09-4465-a9e2-6ec40accc5d4 | an-open-web-platform-for-rule-based-speech-to | null | null | https://aclanthology.org/P16-2027 | https://aclanthology.org/P16-2027.pdf | An Open Web Platform for Rule-Based Speech-to-Sign Translation | null | ['Nikos Tsourakis', 'Manny Rayner', 'Irene Strasly', 'Johanna Gerlach', 'Sarah Ebling', 'Pierrette Bouillon'] | 2016-08-01 | null | null | null | acl-2016-8 | ['sign-language-translation'] | ['computer-vision'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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53468341-02de-46b7-a41d-fd6fddd5eea5 | graph-attention-transformer-network-for-multi | 2203.04049 | null | https://arxiv.org/abs/2203.04049v1 | https://arxiv.org/pdf/2203.04049v1.pdf | Graph Attention Transformer Network for Multi-Label Image Classification | Multi-label classification aims to recognize multiple objects or attributes from images. However, it is challenging to learn from proper label graphs to effectively characterize such inter-label correlations or dependencies. Current methods often use the co-occurrence probability of labels based on the training set as the adjacency matrix to model this correlation, which is greatly limited by the dataset and affects the model's generalization ability. In this paper, we propose a Graph Attention Transformer Network (GATN), a general framework for multi-label image classification that can effectively mine complex inter-label relationships. First, we use the cosine similarity based on the label word embedding as the initial correlation matrix, which can represent rich semantic information. Subsequently, we design the graph attention transformer layer to transfer this adjacency matrix to adapt to the current domain. Our extensive experiments have demonstrated that our proposed methods can achieve state-of-the-art performance on three datasets. | ['Yong Rui', 'Jianping Fan', 'Xin Geng', 'Zhongchao shi', 'Yao Zhang', 'Shikai Chen', 'Jin Yuan'] | 2022-03-08 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 4.02597338e-01 -9.90399048e-02 -3.63223791e-01 -6.84149444e-01
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fa5c6906-a409-4af4-935e-af5120fe518b | revisiting-temporal-modeling-for-clip-based | 2301.11116 | null | https://arxiv.org/abs/2301.11116v1 | https://arxiv.org/pdf/2301.11116v1.pdf | Revisiting Temporal Modeling for CLIP-based Image-to-Video Knowledge Transferring | Image-text pretrained models, e.g., CLIP, have shown impressive general multi-modal knowledge learned from large-scale image-text data pairs, thus attracting increasing attention for their potential to improve visual representation learning in the video domain. In this paper, based on the CLIP model, we revisit temporal modeling in the context of image-to-video knowledge transferring, which is the key point for extending image-text pretrained models to the video domain. We find that current temporal modeling mechanisms are tailored to either high-level semantic-dominant tasks (e.g., retrieval) or low-level visual pattern-dominant tasks (e.g., recognition), and fail to work on the two cases simultaneously. The key difficulty lies in modeling temporal dependency while taking advantage of both high-level and low-level knowledge in CLIP model. To tackle this problem, we present Spatial-Temporal Auxiliary Network (STAN) -- a simple and effective temporal modeling mechanism extending CLIP model to diverse video tasks. Specifically, to realize both low-level and high-level knowledge transferring, STAN adopts a branch structure with decomposed spatial-temporal modules that enable multi-level CLIP features to be spatial-temporally contextualized. We evaluate our method on two representative video tasks: Video-Text Retrieval and Video Recognition. Extensive experiments demonstrate the superiority of our model over the state-of-the-art methods on various datasets, including MSR-VTT, DiDeMo, LSMDC, MSVD, Kinetics-400, and Something-Something-V2. Codes will be available at https://github.com/farewellthree/STAN | ['Thomas H. Li', 'Xinglong Wu', 'Jiashi Feng', 'Ge Li', 'Jingjia Huang', 'Ruyang Liu'] | 2023-01-26 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Liu_Revisiting_Temporal_Modeling_for_CLIP-Based_Image-to-Video_Knowledge_Transferring_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Liu_Revisiting_Temporal_Modeling_for_CLIP-Based_Image-to-Video_Knowledge_Transferring_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-recognition', 'video-text-retrieval', 'video-retrieval'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.77548363e-03 -7.32968748e-01 -4.80377108e-01 -2.54896045e-01
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93dffa8d-eacb-427f-ab75-9ca426a8cabb | n-grams-textrank-a-novel-domain-keyword | null | null | https://aclanthology.org/2020.icon-termtraction.3 | https://aclanthology.org/2020.icon-termtraction.3.pdf | N-Grams TextRank A Novel Domain Keyword Extraction Technique | The rapid growth of the internet has given us a wealth of information and data spread across the web. However, as the data begins to grow we simultaneously face the grave problem of an Information Explosion. An abundance of data can lead to large scale data management problems as well as the loss of the true meaning of the data. In this paper, we present an advanced domain specific keyword extraction algorithm in order to tackle this problem of paramount importance. Our algorithm is based on a modified version of TextRank algorithm - an algorithm based on PageRank to successfully determine the keywords from a domain specific document. Furthermore, this paper proposes a modification to the traditional TextRank algorithm that takes into account bigrams and trigrams and returns results with an extremely high precision. We observe how the precision and f1-score of this model outperforms other models in many domains and the recall can be easily increased by increasing the number of results without affecting the precision. We also discuss about the future work of extending the same algorithm to Indian languages. | ['Dipti Mishra Sharma', 'Manvith Reddy', 'Akshat Gahoi', 'Saransh Rajput'] | null | null | null | null | icon-2020-12 | ['keyword-extraction'] | ['natural-language-processing'] | [ 1.41437098e-01 -6.95173442e-02 -3.65063131e-01 -1.17581710e-01
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e83e53fd-64c8-49e7-a7b4-ad5db54a103d | does-data-repair-lead-to-fair-models-curating | 2110.10389 | null | https://arxiv.org/abs/2110.10389v1 | https://arxiv.org/pdf/2110.10389v1.pdf | Does Data Repair Lead to Fair Models? Curating Contextually Fair Data To Reduce Model Bias | Contextual information is a valuable cue for Deep Neural Networks (DNNs) to learn better representations and improve accuracy. However, co-occurrence bias in the training dataset may hamper a DNN model's generalizability to unseen scenarios in the real world. For example, in COCO, many object categories have a much higher co-occurrence with men compared to women, which can bias a DNN's prediction in favor of men. Recent works have focused on task-specific training strategies to handle bias in such scenarios, but fixing the available data is often ignored. In this paper, we propose a novel and more generic solution to address the contextual bias in the datasets by selecting a subset of the samples, which is fair in terms of the co-occurrence with various classes for a protected attribute. We introduce a data repair algorithm using the coefficient of variation, which can curate fair and contextually balanced data for a protected class(es). This helps in training a fair model irrespective of the task, architecture or training methodology. Our proposed solution is simple, effective, and can even be used in an active learning setting where the data labels are not present or being generated incrementally. We demonstrate the effectiveness of our algorithm for the task of object detection and multi-label image classification across different datasets. Through a series of experiments, we validate that curating contextually fair data helps make model predictions fair by balancing the true positive rate for the protected class across groups without compromising on the model's overall performance. | ['Chetan Arora', 'Saket Anand', 'Sumanyu Muku', 'Sharat Agarwal'] | 2021-10-20 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 4.12186295e-01 1.81831509e-01 -4.77651060e-01 -9.08375144e-01
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26fa5576-425b-4253-8da0-d45e6d4d4e7f | automatic-glossary-of-clinical-terminology-a | 2306.00665 | null | https://arxiv.org/abs/2306.00665v1 | https://arxiv.org/pdf/2306.00665v1.pdf | Automatic Glossary of Clinical Terminology: a Large-Scale Dictionary of Biomedical Definitions Generated from Ontological Knowledge | Background: More than 400,000 biomedical concepts and some of their relationships are contained in SnomedCT, a comprehensive biomedical ontology. However, their concept names are not always readily interpretable by non-experts, or patients looking at their own electronic health records (EHR). Clear definitions or descriptions in understandable language are often not available. Therefore, generating human-readable definitions for biomedical concepts might help make the information they encode more accessible and understandable to a wider public. Objective: In this article, we introduce the Automatic Glossary of Clinical Terminology (AGCT), a large-scale biomedical dictionary of clinical concepts generated using high-quality information extracted from the biomedical knowledge contained in SnomedCT. Methods: We generate a novel definition for every SnomedCT concept, after prompting the OpenAI Turbo model, a variant of GPT 3.5, using a high-quality verbalization of the SnomedCT relationships of the to-be-defined concept. A significant subset of the generated definitions was subsequently judged by NLP researchers with biomedical expertise on 5-point scales along the following three axes: factuality, insight, and fluency. Results: AGCT contains 422,070 computer-generated definitions for SnomedCT concepts, covering various domains such as diseases, procedures, drugs, and anatomy. The average length of the definitions is 49 words. The definitions were assigned average scores of over 4.5 out of 5 on all three axes, indicating a majority of factual, insightful, and fluent definitions. Conclusion: AGCT is a novel and valuable resource for biomedical tasks that require human-readable definitions for SnomedCT concepts. It can also serve as a base for developing robust biomedical retrieval models or other applications that leverage natural language understanding of biomedical knowledge. | ['Thomas Demeester', 'François Remy'] | 2023-06-01 | null | null | null | null | ['anatomy'] | ['miscellaneous'] | [ 1.22219168e-01 4.17650282e-01 -6.24095559e-01 -3.19661081e-01
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c157dacd-33cb-4bb6-a495-8955db1c09f0 | autonomous-intelligent-software-development | 2208.06393 | null | https://arxiv.org/abs/2208.06393v1 | https://arxiv.org/pdf/2208.06393v1.pdf | Autonomous Intelligent Software Development | We present an overview of the design and first proof-of-concept implementation for AIDA, an autonomous intelligent developer agent that develops software from scratch. AIDA takes a software requirements specification and uses reasoning over a semantic knowledge graph to interpret the requirements, then designs and writes software to satisfy them. AIDA uses both declarative and procedural knowledge in the core domains of data, algorithms, and code, plus some general knowledge. The reasoning codebase uses this knowledge to identify needed components, then designs and builds the necessary information structures around them that become the software. These structures, the motivating requirements, and the resulting source code itself are all new knowledge that are added to the knowledge graph, becoming available for future reasoning. In this way, AIDA also learns as she writes code and becomes more efficient when writing subsequent code. | ['Mark Alan Matties'] | 2022-08-12 | null | null | null | null | ['general-knowledge'] | ['miscellaneous'] | [-8.10470507e-02 9.29250479e-01 -1.38099179e-01 -4.91102099e-01
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4569fe8a-1e34-4f65-b379-9c63fb3048e5 | reinforcement-learning-with-action-free-pre | 2203.13880 | null | https://arxiv.org/abs/2203.13880v2 | https://arxiv.org/pdf/2203.13880v2.pdf | Reinforcement Learning with Action-Free Pre-Training from Videos | Recent unsupervised pre-training methods have shown to be effective on language and vision domains by learning useful representations for multiple downstream tasks. In this paper, we investigate if such unsupervised pre-training methods can also be effective for vision-based reinforcement learning (RL). To this end, we introduce a framework that learns representations useful for understanding the dynamics via generative pre-training on videos. Our framework consists of two phases: we pre-train an action-free latent video prediction model, and then utilize the pre-trained representations for efficiently learning action-conditional world models on unseen environments. To incorporate additional action inputs during fine-tuning, we introduce a new architecture that stacks an action-conditional latent prediction model on top of the pre-trained action-free prediction model. Moreover, for better exploration, we propose a video-based intrinsic bonus that leverages pre-trained representations. We demonstrate that our framework significantly improves both final performances and sample-efficiency of vision-based RL in a variety of manipulation and locomotion tasks. Code is available at https://github.com/younggyoseo/apv. | ['Pieter Abbeel', 'Stephen James', 'Kimin Lee', 'Younggyo Seo'] | 2022-03-25 | null | null | null | null | ['video-prediction', 'unsupervised-pre-training'] | ['computer-vision', 'methodology'] | [ 1.34067714e-01 1.17768399e-01 -2.67920136e-01 -2.78812140e-01
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37cfeea1-203f-4581-9efb-e5510205a881 | advanced-local-motion-patterns-for-macro-and | 1805.01951 | null | http://arxiv.org/abs/1805.01951v1 | http://arxiv.org/pdf/1805.01951v1.pdf | Advanced local motion patterns for macro and micro facial expression recognition | In this paper, we develop a new method that recognizes facial expressions, on
the basis of an innovative local motion patterns feature, with three main
contributions. The first one is the analysis of the face skin temporal
elasticity and face deformations during expression. The second one is a unified
approach for both macro and micro expression recognition. And, the third one is
the step forward towards in-the-wild expression recognition, dealing with
challenges such as various intensity and various expression activation
patterns, illumination variation and small head pose variations. Our method
outperforms state-of-the-art methods for micro expression recognition and
positions itself among top-rank state-of-the-art methods for macro expression
recognition. | ['C. Djeraba', 'B. Allaert', 'IM. Bilasco'] | 2018-05-04 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [ 1.01511911e-01 -2.29353756e-01 -2.38171205e-01 -6.69275641e-01
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bf97bfcd-cf4c-4489-8613-ac1cbf2f215a | reasoning-over-the-air-a-reasoning-based | 2306.11229 | null | https://arxiv.org/abs/2306.11229v1 | https://arxiv.org/pdf/2306.11229v1.pdf | Reasoning over the Air: A Reasoning-based Implicit Semantic-Aware Communication Framework | Semantic-aware communication is a novel paradigm that draws inspiration from human communication focusing on the delivery of the meaning of messages. It has attracted significant interest recently due to its potential to improve the efficiency and reliability of communication and enhance users' QoE. Most existing works focus on transmitting and delivering the explicit semantic meaning that can be directly identified from the source signal. This paper investigates the implicit semantic-aware communication in which the hidden information that cannot be directly observed from the source signal must be recognized and interpreted by the intended users. To this end, a novel implicit semantic-aware communication (iSAC) architecture is proposed for representing, communicating, and interpreting the implicit semantic meaning between source and destination users. A projection-based semantic encoder is proposed to convert the high-dimensional graphical representation of explicit semantics into a low-dimensional semantic constellation space for efficient physical channel transmission. To enable the destination user to learn and imitate the implicit semantic reasoning process of source user, a generative adversarial imitation learning-based solution, called G-RML, is proposed. Different from existing communication solutions, the source user in G-RML does not focus only on sending as much of the useful messages as possible; but, instead, it tries to guide the destination user to learn a reasoning mechanism to map any observed explicit semantics to the corresponding implicit semantics that are most relevant to the semantic meaning. Compared to the existing solutions, our proposed G-RML requires much less communication and computational resources and scales well to the scenarios involving the communication of rich semantic meanings consisting of a large number of concepts and relations. | ['Mehdi Bennis', 'Merouane Debbah', 'Walid Saad', 'H. Vincent Poor', 'Guangming Shi', 'Yingyu Li', 'Yiwei Liao', 'Yong Xiao'] | 2023-06-20 | null | null | null | null | ['imitation-learning'] | ['methodology'] | [ 6.73027992e-01 6.50577605e-01 -1.29083112e-01 -4.64141518e-01
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551d3f9d-2935-4da0-b5e8-51149a04085d | ueca-prompt-universal-prompt-for-emotion | null | null | https://aclanthology.org/2022.coling-1.613 | https://aclanthology.org/2022.coling-1.613.pdf | UECA-Prompt: Universal Prompt for Emotion Cause Analysis | Emotion cause analysis (ECA) aims to extract emotion clauses and find the corresponding cause of the emotion. Existing methods adopt fine-tuning paradigm to solve certain types of ECA tasks. These task-specific methods have a deficiency of universality. And the relations among multiple objectives in one task are not explicitly modeled. Moreover, the relative position information introduced in most existing methods may make the model suffer from dataset bias. To address the first two problems, this paper proposes a universal prompt tuning method to solve different ECA tasks in the unified framework. As for the third problem, this paper designs a directional constraint module and a sequential learning module to ease the bias. Considering the commonalities among different tasks, this paper proposes a cross-task training method to further explore the capability of the model. The experimental results show that our method achieves competitive performance on the ECA datasets. | ['Jiahai Wang', 'Zhaoyang Wang', 'Zizhen Zhang', 'Zhiyue Liu', 'Xiaopeng Zheng'] | null | null | null | null | coling-2022-10 | ['emotion-cause-pair-extraction', 'emotion-cause-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [-1.68632083e-02 -2.05506265e-01 -4.62742627e-01 -4.97381479e-01
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00df8cc1-04a5-431c-858d-42e8c136724f | how-much-does-input-data-type-impact-final | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Luo_How_Much_Does_Input_Data_Type_Impact_Final_Face_Model_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Luo_How_Much_Does_Input_Data_Type_Impact_Final_Face_Model_CVPR_2022_paper.pdf | How Much Does Input Data Type Impact Final Face Model Accuracy? | Face models are widely used in image processing and other domains. The input data to create a 3D face model ranges from accurate laser scans to simple 2D RGB photographs. These input data types are typically deficient either due to missing regions, or because they are under-constrained. As a result, reconstruction methods include embedded priors encoding the valid domain of faces. System designers must choose a source of input data and then choose a reconstruction method to obtain a usable 3D face. If a particular application domain requires accuracy X, which kinds of input data are suitable? Does the input data need to be 3D, or will 2D data suffice? This paper takes a step toward answering these questions using synthetic data. A ground truth dataset is used to analyze accuracy obtainable from 2D landmarks, 3D landmarks, low quality 3D, high quality 3D, texture color, normals, dense 2D image data, and when regions of the face are missing. Since the data is synthetic it can be analyzed both with and without measurement error. This idealized synthetic analysis is then compared to real results from several methods for constructing 3D faces from 2D photographs. The experimental results suggest that accuracy is severely limited when only 2D raw input data exists. | ['James Davis', 'Alex Pang', 'Minghao Liu', 'Eric Sandoval Ruezga', 'Akila de Silva', 'Issei Mori', 'Fahim Hasan Khan', 'Jiahao Luo'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['face-model'] | ['computer-vision'] | [ 1.51532546e-01 2.09391087e-01 7.97469448e-03 -8.16808641e-01
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8dd93199-9179-46dd-ad8d-fd520facae2e | knowledge-driven-encode-retrieve-paraphrase | 1903.10122 | null | http://arxiv.org/abs/1903.10122v1 | http://arxiv.org/pdf/1903.10122v1.pdf | Knowledge-driven Encode, Retrieve, Paraphrase for Medical Image Report Generation | Generating long and semantic-coherent reports to describe medical images
poses great challenges towards bridging visual and linguistic modalities,
incorporating medical domain knowledge, and generating realistic and accurate
descriptions. We propose a novel Knowledge-driven Encode, Retrieve, Paraphrase
(KERP) approach which reconciles traditional knowledge- and retrieval-based
methods with modern learning-based methods for accurate and robust medical
report generation. Specifically, KERP decomposes medical report generation into
explicit medical abnormality graph learning and subsequent natural language
modeling. KERP first employs an Encode module that transforms visual features
into a structured abnormality graph by incorporating prior medical knowledge;
then a Retrieve module that retrieves text templates based on the detected
abnormalities; and lastly, a Paraphrase module that rewrites the templates
according to specific cases. The core of KERP is a proposed generic
implementation unit---Graph Transformer (GTR) that dynamically transforms
high-level semantics between graph-structured data of multiple domains such as
knowledge graphs, images and sequences. Experiments show that the proposed
approach generates structured and robust reports supported with accurate
abnormality description and explainable attentive regions, achieving the
state-of-the-art results on two medical report benchmarks, with the best
medical abnormality and disease classification accuracy and improved human
evaluation performance. | ['Zhiting Hu', 'Xiaodan Liang', 'Christy Y. Li', 'Eric P. Xing'] | 2019-03-25 | null | null | null | null | ['medical-report-generation'] | ['medical'] | [ 3.68590534e-01 4.20101553e-01 -1.71859935e-01 -4.39029872e-01
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3f6d2503-1e77-4079-a51d-4fefcf24e2b1 | i2dformer-learning-image-to-document | 2209.10304 | null | https://arxiv.org/abs/2209.10304v1 | https://arxiv.org/pdf/2209.10304v1.pdf | I2DFormer: Learning Image to Document Attention for Zero-Shot Image Classification | Despite the tremendous progress in zero-shot learning(ZSL), the majority of existing methods still rely on human-annotated attributes, which are difficult to annotate and scale. An unsupervised alternative is to represent each class using the word embedding associated with its semantic class name. However, word embeddings extracted from pre-trained language models do not necessarily capture visual similarities, resulting in poor zero-shot performance. In this work, we argue that online textual documents, e.g., Wikipedia, contain rich visual descriptions about object classes, therefore can be used as powerful unsupervised side information for ZSL. To this end, we propose I2DFormer, a novel transformer-based ZSL framework that jointly learns to encode images and documents by aligning both modalities in a shared embedding space. In order to distill discriminative visual words from noisy documents, we introduce a new cross-modal attention module that learns fine-grained interactions between image patches and document words. Consequently, our I2DFormer not only learns highly discriminative document embeddings that capture visual similarities but also gains the ability to localize visually relevant words in image regions. Quantitatively, we demonstrate that our I2DFormer significantly outperforms previous unsupervised semantic embeddings under both zero-shot and generalized zero-shot learning settings on three public datasets. Qualitatively, we show that our method leads to highly interpretable results where document words can be grounded in the image regions. | ['Federico Tombari', 'Luc van Gool', 'Yongqin Xian', 'Muhammad Ferjad Naeem'] | 2022-09-21 | null | null | null | null | ['generalized-zero-shot-learning', 'generalized-zero-shot-learning'] | ['computer-vision', 'methodology'] | [-3.61790298e-03 -2.21347716e-02 -4.28984791e-01 -3.05804521e-01
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24326ece-fe6e-4190-b129-e7f66badf79a | an-aggregation-of-aggregation-methods-in | 2211.01256 | null | https://arxiv.org/abs/2211.01256v1 | https://arxiv.org/pdf/2211.01256v1.pdf | An Aggregation of Aggregation Methods in Computational Pathology | Image analysis and machine learning algorithms operating on multi-gigapixel whole-slide images (WSIs) often process a large number of tiles (sub-images) and require aggregating predictions from the tiles in order to predict WSI-level labels. In this paper, we present a review of existing literature on various types of aggregation methods with a view to help guide future research in the area of computational pathology (CPath). We propose a general CPath workflow with three pathways that consider multiple levels and types of data and the nature of computation to analyse WSIs for predictive modelling. We categorize aggregation methods according to the context and representation of the data, features of computational modules and CPath use cases. We compare and contrast different methods based on the principle of multiple instance learning, perhaps the most commonly used aggregation method, covering a wide range of CPath literature. To provide a fair comparison, we consider a specific WSI-level prediction task and compare various aggregation methods for that task. Finally, we conclude with a list of objectives and desirable attributes of aggregation methods in general, pros and cons of the various approaches, some recommendations and possible future directions. | ['Nasir Rajpoot', 'Mark Eastwood', 'Amina Asif', 'Hammam M. AlGhamdi', 'Ruoyu Wang', 'Robert Jewsbury', 'Mohsin Bilal'] | 2022-11-02 | null | null | null | null | ['multiple-instance-learning'] | ['methodology'] | [ 9.39860702e-01 2.52848476e-01 -3.74511212e-01 -1.44233674e-01
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b348282b-aef9-4599-816b-9c93156eca95 | paced-curriculum-distillation-with-prediction | 2302.01049 | null | https://arxiv.org/abs/2302.01049v1 | https://arxiv.org/pdf/2302.01049v1.pdf | Paced-Curriculum Distillation with Prediction and Label Uncertainty for Image Segmentation | Purpose: In curriculum learning, the idea is to train on easier samples first and gradually increase the difficulty, while in self-paced learning, a pacing function defines the speed to adapt the training progress. While both methods heavily rely on the ability to score the difficulty of data samples, an optimal scoring function is still under exploration. Methodology: Distillation is a knowledge transfer approach where a teacher network guides a student network by feeding a sequence of random samples. We argue that guiding student networks with an efficient curriculum strategy can improve model generalization and robustness. For this purpose, we design an uncertainty-based paced curriculum learning in self distillation for medical image segmentation. We fuse the prediction uncertainty and annotation boundary uncertainty to develop a novel paced-curriculum distillation (PCD). We utilize the teacher model to obtain prediction uncertainty and spatially varying label smoothing with Gaussian kernel to generate segmentation boundary uncertainty from the annotation. We also investigate the robustness of our method by applying various types and severity of image perturbation and corruption. Results: The proposed technique is validated on two medical datasets of breast ultrasound image segmentation and robotassisted surgical scene segmentation and achieved significantly better performance in terms of segmentation and robustness. Conclusion: P-CD improves the performance and obtains better generalization and robustness over the dataset shift. While curriculum learning requires extensive tuning of hyper-parameters for pacing function, the level of performance improvement suppresses this limitation. | ['Hongliang Ren', 'Ben Glocker', 'Bhavesh Gupta', 'V. K. Viekash', 'S. P. Sharan', 'Lalithkumar Seenivasan', 'Mobarakol Islam'] | 2023-02-02 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 3.88107359e-01 3.99696141e-01 -3.41593474e-01 -4.38342273e-01
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489a2fd6-16a3-4473-9526-3f069b689259 | relative-velocity-based-reward-functions-for | 2112.13984 | null | https://arxiv.org/abs/2112.13984v2 | https://arxiv.org/pdf/2112.13984v2.pdf | Relative velocity-based reward functions for crowd navigation of robots | The four-wheeled Mecanum robot is widely used in various industries due to its maneuverability and strong load capacity, which is suitable for performing precise transportation tasks in a narrow environment, but while the Mecanum wheel robot has mobility, it also consumes more energy than ordinary robots. The power consumed by the Mecanum wheel mobile robot varies enormously depending on their operating regimes and environments. Therefore, only knowing the working environment of the robot and the accurate power consumption model can we accurately predict the power consumption of the robot. In order to increase the appli-cable scenarios of energy consumption modeling for Mecanum wheel robots and improve the accuracy of energy consumption modeling, this paper focuses on various factors that affect the energy consumption of the Mecanum wheel robot, such as motor temperature, terrain, the center of gravity position, etc. The model is derived from the kinematic and kinetic model combined with electrical engineering and energy flow principles. The model has been simulated in MATLAB and experimentally validated with the four-wheeled Mecanum robot platform in our lab. Experimental results show that the model is 90% accurate. The results of energy consumption modeling can help robots to save energy by helping them to perform rational path planning and task planning. | ['Fei Li', 'Xiaoqing Yang'] | 2021-12-28 | null | null | null | null | ['electrical-engineering'] | ['miscellaneous'] | [-5.04937887e-01 4.38078120e-02 -5.88766694e-01 3.46591502e-01
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8a323983-5609-4fda-b34b-1ba824b498d4 | deep-attentive-survival-analysis-in-limit | 2306.05479 | null | https://arxiv.org/abs/2306.05479v1 | https://arxiv.org/pdf/2306.05479v1.pdf | Deep Attentive Survival Analysis in Limit Order Books: Estimating Fill Probabilities with Convolutional-Transformers | One of the key decisions in execution strategies is the choice between a passive (liquidity providing) or an aggressive (liquidity taking) order to execute a trade in a limit order book (LOB). Essential to this choice is the fill probability of a passive limit order placed in the LOB. This paper proposes a deep learning method to estimate the filltimes of limit orders posted in different levels of the LOB. We develop a novel model for survival analysis that maps time-varying features of the LOB to the distribution of filltimes of limit orders. Our method is based on a convolutional-Transformer encoder and a monotonic neural network decoder. We use proper scoring rules to compare our method with other approaches in survival analysis, and perform an interpretability analysis to understand the informativeness of features used to compute fill probabilities. Our method significantly outperforms those typically used in survival analysis literature. Finally, we carry out a statistical analysis of the fill probability of orders placed in the order book (e.g., within the bid-ask spread) for assets with different queue dynamics and trading activity. | ['Stefan Zohren', 'Fernando Moreno-Pino', 'Alvaro Cartea', 'Alvaro Arroyo'] | 2023-06-08 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [-5.87456286e-01 -3.22888464e-01 -2.65189439e-01 -4.87540990e-01
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e8d06ff6-0985-496f-b643-894a79c7956f | utilizing-lexical-similarity-to-enable-zero | 2305.05214 | null | https://arxiv.org/abs/2305.05214v1 | https://arxiv.org/pdf/2305.05214v1.pdf | Utilizing Lexical Similarity to Enable Zero-Shot Machine Translation for Extremely Low-resource Languages | We address the task of machine translation from an extremely low-resource language (LRL) to English using cross-lingual transfer from a closely related high-resource language (HRL). For many of these languages, no parallel corpora are available, even monolingual corpora are limited and representations in pre-trained sequence-to-sequence models are absent. These factors limit the benefits of cross-lingual transfer from shared embedding spaces in multilingual models. However, many extremely LRLs have a high level of lexical similarity with related HRLs. We utilize this property by injecting character and character-span noise into the training data of the HRL prior to learning the vocabulary. This serves as a regularizer which makes the model more robust to lexical divergences between the HRL and LRL and better facilitates cross-lingual transfer. On closely related HRL and LRL pairs from multiple language families, we observe that our method significantly outperforms the baseline MT as well as approaches proposed previously to address cross-lingual transfer between closely related languages. We also show that the proposed character-span noise injection performs better than the unigram-character noise injection. | ['Anoop Kunchukuttan', 'Maunendra Sankar Desarkar', 'Rahul Kejriwal', 'Kaushal Kumar Maurya'] | 2023-05-09 | null | null | null | null | ['zero-shot-machine-translation', 'cross-lingual-transfer'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.71735319e-02 -2.79017031e-01 -6.11282110e-01 -3.75328183e-01
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fdd6b35e-f0ab-400d-a813-f09812a7762e | the-robotic-surgery-procedural-framebank | null | null | https://aclanthology.org/2022.lrec-1.420 | https://aclanthology.org/2022.lrec-1.420.pdf | The Robotic Surgery Procedural Framebank | Robot-Assisted minimally invasive robotic surgery is the gold standard for the surgical treatment of many pathological conditions, and several manuals and academic papers describe how to perform these interventions. These high-quality, often peer-reviewed texts are the main study resource for medical personnel and consequently contain essential procedural domain-specific knowledge. The procedural knowledge therein described could be extracted, e.g., on the basis of semantic parsing models, and used to develop clinical decision support systems or even automation methods for some procedure’s steps. However, natural language understanding algorithms such as, for instance, semantic role labelers have lower efficacy and coverage issues when applied to domain others than those they are typically trained on (i.e., newswire text). To overcome this problem, starting from PropBank frames, we propose a new linguistic resource specific to the robotic-surgery domain, named Robotic Surgery Procedural Framebank (RSPF). We extract from robotic-surgical texts verbs and nouns that describe surgical actions and extend PropBank frames by adding any of new lemmas, frames or role sets required to cover missing lemmas, specific frames describing the surgical significance, or new semantic roles used in procedural surgical language. Our resource is publicly available and can be used to annotate corpora in the surgical domain to train and evaluate Semantic Role Labeling (SRL) systems in a challenging fine-grained domain setting. | ['Paolo Fiorini', 'Simone Paolo Ponzetto', 'Marco Rospocher', 'Marco Bombieri'] | null | null | null | null | lrec-2022-6 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 5.02995908e-01 9.81049359e-01 -9.72640574e-01 -3.42518985e-01
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