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1e88523a-13d3-4b4d-8854-e8999341204a | environment-invariant-linear-least-squares | 2303.03092 | null | https://arxiv.org/abs/2303.03092v1 | https://arxiv.org/pdf/2303.03092v1.pdf | Environment Invariant Linear Least Squares | This paper considers a multiple environments linear regression model in which data from multiple experimental settings are collected. The joint distribution of the response variable and covariate may vary across different environments, yet the conditional expectation of $y$ given the unknown set of important variables are invariant across environments. Such a statistical model is related to the problem of endogeneity, causal inference, and transfer learning. The motivation behind it is illustrated by how the goals of prediction and attribution are inherent in estimating the true parameter and the important variable set. We construct a novel {\it environment invariant linear least squares (EILLS)} objective function, a multiple-environment version of linear least squares that leverages the above conditional expectation invariance structure and heterogeneity among different environments to determine the true parameter. Our proposed method is applicable without any additional structural knowledge and can identify the true parameter under a near-minimal identification condition. We establish non-asymptotic $\ell_2$ error bounds on the estimation error for the EILLS estimator in the presence of spurious variables. Moreover, we further show that the EILLS estimator is able to eliminate all endogenous variables and the $\ell_0$ penalized EILLS estimator can achieve variable selection consistency in high-dimensional regimes. These non-asymptotic results demonstrate the sample efficiency of the EILLS estimator and its capability to circumvent the curse of endogeneity in an algorithmic manner without any prior structural knowledge. | ['Tong Zhang', 'Yihong Gu', 'Cong Fang', 'Jianqing Fan'] | 2023-03-06 | null | null | null | null | ['variable-selection'] | ['methodology'] | [ 2.57017076e-01 -2.02669248e-01 -5.80295444e-01 -3.12621534e-01
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12651707-e14d-4ac8-a9f5-bbcca864d0b0 | predicting-the-presence-of-reasoning-markers | null | null | https://aclanthology.org/2022.argmining-1.13 | https://aclanthology.org/2022.argmining-1.13.pdf | Predicting the Presence of Reasoning Markers in Argumentative Text | This paper proposes a novel task in Argument Mining, which we will refer to as Reasoning Marker Prediction. We reuse the popular Persuasive Essays Corpus (Stab and Gurevych, 2014). Instead of using this corpus for Argument Structure Parsing, we use a simple heuristic method to identify text spans which we can identify as reasoning markers. We propose baseline methods for predicting the presence of these reasoning markers automatically, and make a script to generate the data for the task publicly available. | ['Rob Gaizauskas', 'Jonathan Clayton'] | null | null | null | null | argmining-acl-2022-10 | ['argument-mining'] | ['natural-language-processing'] | [ 3.01096618e-01 9.57460999e-01 -6.22734845e-01 -1.42263785e-01
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d88c4aa3-fefc-48c9-9d19-2353cc9360a4 | nemo-learning-3d-neural-motion-fields-from | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_NeMo_Learning_3D_Neural_Motion_Fields_From_Multiple_Video_Instances_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_NeMo_Learning_3D_Neural_Motion_Fields_From_Multiple_Video_Instances_CVPR_2023_paper.pdf | NeMo: Learning 3D Neural Motion Fields From Multiple Video Instances of the Same Action | The task of reconstructing 3D human motion has wide-ranging applications. The gold standard Motion capture (MoCap) systems are accurate but inaccessible to the general public due to their cost, hardware, and space constraints. In contrast, monocular human mesh recovery (HMR) methods are much more accessible than MoCap as they take single-view videos as inputs. Replacing the multi-view MoCap systems with a monocular HMR method would break the current barriers to collecting accurate 3D motion thus making exciting applications like motion analysis and motion-driven animation accessible to the general public. However, the performance of existing HMR methods degrades when the video contains challenging and dynamic motion that is not in existing MoCap datasets used for training. This reduces its appeal as dynamic motion is frequently the target in 3D motion recovery in the aforementioned applications. Our study aims to bridge the gap between monocular HMR and multi-view MoCap systems by leveraging information shared across multiple video instances of the same action. We introduce the Neural Motion (NeMo) field. It is optimized to represent the underlying 3D motions across a set of videos of the same action. Empirically, we show that NeMo can recover 3D motion in sports using videos from the Penn Action dataset, where NeMo outperforms existing HMR methods in terms of 2D keypoint detection. To further validate NeMo using 3D metrics, we collected a small MoCap dataset mimicking actions in Penn Action, and show that NeMo achieves better 3D reconstruction compared to various baselines. | ['Serena Yeung', 'Karen Liu', 'Jeffrey Gu', 'João Pedro Araújo', 'Maria Xenochristou', 'Zhenzhen Weng', 'Kuan-Chieh Wang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['keypoint-detection', '3d-reconstruction', 'human-mesh-recovery'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.91563383e-01 -4.45666164e-01 -5.50278187e-01 2.69808918e-01
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235106ff-5246-4677-a5e2-2abb918cff02 | improving-word-translation-via-two-stage-1 | 2203.08307 | null | https://arxiv.org/abs/2203.08307v3 | https://arxiv.org/pdf/2203.08307v3.pdf | Improving Word Translation via Two-Stage Contrastive Learning | Word translation or bilingual lexicon induction (BLI) is a key cross-lingual task, aiming to bridge the lexical gap between different languages. In this work, we propose a robust and effective two-stage contrastive learning framework for the BLI task. At Stage C1, we propose to refine standard cross-lingual linear maps between static word embeddings (WEs) via a contrastive learning objective; we also show how to integrate it into the self-learning procedure for even more refined cross-lingual maps. In Stage C2, we conduct BLI-oriented contrastive fine-tuning of mBERT, unlocking its word translation capability. We also show that static WEs induced from the `C2-tuned' mBERT complement static WEs from Stage C1. Comprehensive experiments on standard BLI datasets for diverse languages and different experimental setups demonstrate substantial gains achieved by our framework. While the BLI method from Stage C1 already yields substantial gains over all state-of-the-art BLI methods in our comparison, even stronger improvements are met with the full two-stage framework: e.g., we report gains for 112/112 BLI setups, spanning 28 language pairs. | ['Ivan Vulić', 'Anna Korhonen', 'Nigel Collier', 'Fangyu Liu', 'Yaoyiran Li'] | 2022-03-15 | null | https://aclanthology.org/2022.acl-long.299 | https://aclanthology.org/2022.acl-long.299.pdf | acl-2022-5 | ['multilingual-word-embeddings', 'pretrained-multilingual-language-models', 'self-learning', 'multilingual-nlp'] | ['methodology', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 1.14365242e-01 -2.08823234e-01 -7.17695713e-01 -3.95036876e-01
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35186373-3599-4f93-949f-c6d9b8e0c297 | symbolic-music-structure-analysis-with-graph | 2303.13881 | null | https://arxiv.org/abs/2303.13881v1 | https://arxiv.org/pdf/2303.13881v1.pdf | Symbolic Music Structure Analysis with Graph Representations and Changepoint Detection Methods | Music Structure Analysis is an open research task in Music Information Retrieval (MIR). In the past, there have been several works that attempt to segment music into the audio and symbolic domains, however, the identification and segmentation of the music structure at different levels is still an open research problem in this area. In this work we propose three methods, two of which are novel graph-based algorithms that aim to segment symbolic music by its form or structure: Norm, G-PELT and G-Window. We performed an ablation study with two public datasets that have different forms or structures in order to compare such methods varying their parameter values and comparing the performance against different music styles. We have found that encoding symbolic music with graph representations and computing the novelty of Adjacency Matrices obtained from graphs represent the structure of symbolic music pieces well without the need to extract features from it. We are able to detect the boundaries with an online unsupervised changepoint detection method with a F_1 of 0.5640 for a 1 bar tolerance in one of the public datasets that we used for testing our methods. We also provide the performance results of the algorithms at different levels of structure, high, medium and low, to show how the parameters of the proposed methods have to be adjusted depending on the level. We added the best performing method with its parameters for each structure level to musicaiz, an open source python package, to facilitate the reproducibility and usability of this work. We hope that this methods could be used to improve other MIR tasks such as music generation with structure, music classification or key changes detection. | ['Jose R. Beltran', 'Sonia Rubio Llamas', 'Carlos Hernandez-Olivan'] | 2023-03-24 | null | null | null | null | ['music-generation', 'music-generation', 'music-classification', 'music-information-retrieval'] | ['audio', 'music', 'music', 'music'] | [ 4.26480472e-01 -2.07260996e-01 2.10056990e-01 1.29064038e-01
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9a5cfa19-d7e7-4e67-9854-a9116199226e | camera-pose-estimation-with-unknown-principal | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Larsson_Camera_Pose_Estimation_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Larsson_Camera_Pose_Estimation_CVPR_2018_paper.pdf | Camera Pose Estimation With Unknown Principal Point | To estimate the 6-DoF extrinsic pose of a pinhole camera with partially unknown intrinsic parameters is a critical sub-problem in structure-from-motion and camera localization. In most of existing camera pose estimation solvers, the principal point is assumed to be in the image center. Unfortunately, this assumption is not always true, especially for asymmetrically cropped images. In this paper, we develop the first exactly minimal solver for the case of unknown principal point and focal length by using four and a half point correspondences (P4.5Pfuv). We also present an extremely fast solver for the case of unknown aspect ratio (P5Pfuva). The new solvers outperform the previous state-of-the-art in terms of stability and speed. Finally, we explore the extremely challenging case of both unknown principal point and radial distortion, and develop the first practical non-minimal solver by using seven point correspondences (P7Pfruv). Experimental results on both simulated data and real Internet images demonstrate the usefulness of our new solvers. | ['Yinqiang Zheng', 'Viktor Larsson', 'Zuzana Kukelova'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['camera-localization'] | ['computer-vision'] | [ 1.24272667e-02 3.06046661e-02 6.91943839e-02 2.62101471e-01
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b53a89d3-3d7f-460c-ac5f-c54e9ff1f4a5 | rgb-d-saliency-detection-via-cascaded-mutual | 2109.07246 | null | https://arxiv.org/abs/2109.07246v2 | https://arxiv.org/pdf/2109.07246v2.pdf | RGB-D Saliency Detection via Cascaded Mutual Information Minimization | Existing RGB-D saliency detection models do not explicitly encourage RGB and depth to achieve effective multi-modal learning. In this paper, we introduce a novel multi-stage cascaded learning framework via mutual information minimization to "explicitly" model the multi-modal information between RGB image and depth data. Specifically, we first map the feature of each mode to a lower dimensional feature vector, and adopt mutual information minimization as a regularizer to reduce the redundancy between appearance features from RGB and geometric features from depth. We then perform multi-stage cascaded learning to impose the mutual information minimization constraint at every stage of the network. Extensive experiments on benchmark RGB-D saliency datasets illustrate the effectiveness of our framework. Further, to prosper the development of this field, we contribute the largest (7x larger than NJU2K) dataset, which contains 15,625 image pairs with high quality polygon-/scribble-/object-/instance-/rank-level annotations. Based on these rich labels, we additionally construct four new benchmarks with strong baselines and observe some interesting phenomena, which can motivate future model design. Source code and dataset are available at "https://github.com/JingZhang617/cascaded_rgbd_sod". | ['Ling Shao', 'Nick Barnes', 'Yiran Zhong', 'Xin Yu', 'Yuchao Dai', 'Deng-Ping Fan', 'Jing Zhang'] | 2021-09-15 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zhang_RGB-D_Saliency_Detection_via_Cascaded_Mutual_Information_Minimization_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zhang_RGB-D_Saliency_Detection_via_Cascaded_Mutual_Information_Minimization_ICCV_2021_paper.pdf | iccv-2021-1 | ['thermal-image-segmentation'] | ['computer-vision'] | [ 1.65325999e-02 7.77550042e-02 -3.90226752e-01 -6.22748494e-01
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47028e04-9c9d-413d-b917-a891c6b1c542 | 190600365 | 1906.00365 | null | https://arxiv.org/abs/1906.00365v1 | https://arxiv.org/pdf/1906.00365v1.pdf | User Profile Feature-Based Approach to Address the Cold Start Problem in Collaborative Filtering for Personalized Movie Recommendation | A huge amount of user generated content related to movies is created with the popularization of web 2.0. With these continues exponential growth of data, there is an inevitable need for recommender systems as people find it difficult to make informed and timely decisions. Movie recommendation systems assist users to find the next interest or the best recommendation. In this proposed approach the authors apply the relationship of user feature-scores derived from user-item interaction via ratings to optimize the prediction algorithm's input parameters used in the recommender system to improve the accuracy of predictions when there are less past user records. This addresses a major drawback in collaborative filtering, the cold start problem by showing an improvement of 8.4% compared to the base collaborative filtering algorithm. The user-feature generation and evaluation of the system is carried out using the 'MovieLens 100k dataset'. The proposed system can be generalized to other domains as well. | ['Supunmali Ahangama', 'Tharindu Ranasinghe', 'Lasitha Uyangoda'] | 2019-06-02 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [-1.69608086e-01 -2.92314500e-01 -1.02347648e-02 -6.61831737e-01
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e44504e1-e435-45a4-b45a-3a065ef719a1 | joint-extraction-of-entities-and-overlapping | null | null | https://www.aaai.org/ojs/index.php/AAAI/article/view/4591 | https://www.aaai.org/ojs/index.php/AAAI/article/view/4591/4469 | Joint extraction of entities and overlapping relations using position-attentive sequence labeling | Joint entity and relation extraction is to detect entity and relation using a single model. In this paper, we present a novel unified joint extraction model which directly tags entity and relation labels according to a query word position p, i.e., detecting entity at p, and identifying entities at other positions that have relationship with the former. To this end, we first design a tagging scheme to generate n tag sequences for an n-word sentence. Then a position-attention mechanism is introduced to produce different sentence representations for every query position to model these n tag sequences. In this way, our method can simultaneously extract all entities and their type, as well as all overlapping relations. Experiment results show that our framework performances significantly better on extracting overlapping relations as well as detecting long-range relation, and thus we achieve state-of-the-art performance on two public datasets. | ['Xinyan Xiao', 'Qiaoqiao She', 'Yajuan Lyu', 'Dai Dai', 'Shan Dou', 'Haifeng Wang'] | 2019-07-17 | null | null | null | aaai-2019-2019-7 | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [ 2.93632336e-02 4.08327430e-01 -3.29018742e-01 -3.30372870e-01
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f002935a-8b2c-441e-aa84-0b1381e0a47f | provably-scale-covariant-networks-from | 1903.00289 | null | https://arxiv.org/abs/1903.00289v2 | https://arxiv.org/pdf/1903.00289v2.pdf | Provably scale-covariant networks from oriented quasi quadrature measures in cascade | This article presents a continuous model for hierarchical networks based on a combination of mathematically derived models of receptive fields and biologically inspired computations. Based on a functional model of complex cells in terms of an oriented quasi quadrature combination of first- and second-order directional Gaussian derivatives, we couple such primitive computations in cascade over combinatorial expansions over image orientations. Scale-space properties of the computational primitives are analysed and it is shown that the resulting representation allows for provable scale and rotation covariance. A prototype application to texture analysis is developed and it is demonstrated that a simplified mean-reduced representation of the resulting QuasiQuadNet leads to promising experimental results on three texture datasets. | ['Tony Lindeberg'] | 2019-03-01 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 2.48399973e-01 2.32639849e-01 3.15886855e-01 -5.04759789e-01
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f9202821-162c-461a-b8de-9c2b2be79d55 | continuous-multimodal-emotion-recognition | 1709.05861 | null | http://arxiv.org/abs/1709.05861v2 | http://arxiv.org/pdf/1709.05861v2.pdf | Continuous Multimodal Emotion Recognition Approach for AVEC 2017 | This paper reports the analysis of audio and visual features in predicting
the continuous emotion dimensions under the seventh Audio/Visual Emotion
Challenge (AVEC 2017), which was done as part of a B.Tech. 2nd year internship
project. For visual features we used the HOG (Histogram of Gradients) features,
Fisher encodings of SIFT (Scale-Invariant Feature Transform) features based on
Gaussian mixture model (GMM) and some pretrained Convolutional Neural Network
layers as features; all these extracted for each video clip. For audio features
we used the Bag-of-audio-words (BoAW) representation of the LLDs (low-level
descriptors) generated by openXBOW provided by the organisers of the event.
Then we trained fully connected neural network regression model on the dataset
for all these different modalities. We applied multimodal fusion on the output
models to get the Concordance correlation coefficient on Development set as
well as Test set. | ['Abhinav Dhall', 'Narotam Singh', 'Nittin Singh'] | 2017-09-18 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [-9.42674875e-02 -1.72331125e-01 3.10922235e-01 -5.22341847e-01
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92f7695c-6871-45cf-9ae1-35c0c35c3d03 | deep-reinforcement-learning-for-chinese-zero | 1806.03711 | null | http://arxiv.org/abs/1806.03711v2 | http://arxiv.org/pdf/1806.03711v2.pdf | Deep Reinforcement Learning for Chinese Zero pronoun Resolution | Deep neural network models for Chinese zero pronoun resolution learn semantic
information for zero pronoun and candidate antecedents, but tend to be
short-sighted---they often make local decisions. They typically predict
coreference chains between the zero pronoun and one single candidate antecedent
one link at a time, while overlooking their long-term influence on future
decisions. Ideally, modeling useful information of preceding potential
antecedents is critical when later predicting zero pronoun-candidate antecedent
pairs. In this study, we show how to integrate local and global decision-making
by exploiting deep reinforcement learning models. With the help of the
reinforcement learning agent, our model learns the policy of selecting
antecedents in a sequential manner, where useful information provided by
earlier predicted antecedents could be utilized for making later coreference
decisions. Experimental results on OntoNotes 5.0 dataset show that our
technique surpasses the state-of-the-art models. | ['Wei-Nan Zhang', 'William Yang Wang', 'Ting Liu', 'Qingyu Yin', 'Yu Zhang'] | 2018-06-10 | deep-reinforcement-learning-for-chinese-zero-1 | https://aclanthology.org/P18-1053 | https://aclanthology.org/P18-1053.pdf | acl-2018-7 | ['chinese-zero-pronoun-resolution'] | ['natural-language-processing'] | [ 1.40165482e-02 5.76832771e-01 -5.69393337e-01 -6.52098298e-01
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589556db-a028-47cc-8445-49f507c1fda2 | comparison-of-automatic-prostate-zones | 2207.09483 | null | https://arxiv.org/abs/2207.09483v1 | https://arxiv.org/pdf/2207.09483v1.pdf | Comparison of automatic prostate zones segmentation models in MRI images using U-net-like architectures | Prostate cancer is the second-most frequently diagnosed cancer and the sixth leading cause of cancer death in males worldwide. The main problem that specialists face during the diagnosis of prostate cancer is the localization of Regions of Interest (ROI) containing a tumor tissue. Currently, the segmentation of this ROI in most cases is carried out manually by expert doctors, but the procedure is plagued with low detection rates (of about 27-44%) or overdiagnosis in some patients. Therefore, several research works have tackled the challenge of automatically segmenting and extracting features of the ROI from magnetic resonance images, as this process can greatly facilitate many diagnostic and therapeutic applications. However, the lack of clear prostate boundaries, the heterogeneity inherent to the prostate tissue, and the variety of prostate shapes makes this process very difficult to automate.In this work, six deep learning models were trained and analyzed with a dataset of MRI images obtained from the Centre Hospitalaire de Dijon and Universitat Politecnica de Catalunya. We carried out a comparison of multiple deep learning models (i.e. U-Net, Attention U-Net, Dense-UNet, Attention Dense-UNet, R2U-Net, and Attention R2U-Net) using categorical cross-entropy loss function. The analysis was performed using three metrics commonly used for image segmentation: Dice score, Jaccard index, and mean squared error. The model that give us the best result segmenting all the zones was R2U-Net, which achieved 0.869, 0.782, and 0.00013 for Dice, Jaccard and mean squared error, respectively. | ['Christian Mata', 'Gerardo Rodriguez-Hernandez', 'Miguel Gonzalez-Mendoza', 'Gilberto Ochoa-Ruiz', 'Pablo Cesar Quihui-Rubio'] | 2022-07-19 | null | null | null | null | ['prostate-zones-segmentation'] | ['computer-vision'] | [ 1.50304720e-01 3.95210028e-01 -2.19479203e-02 -2.55814135e-01
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e9c3eafd-bb60-4f17-a65a-50669d786f04 | irregular-change-detection-in-sparse-bi | 2306.15416 | null | https://arxiv.org/abs/2306.15416v2 | https://arxiv.org/pdf/2306.15416v2.pdf | Irregular Change Detection in Sparse Bi-Temporal Point Clouds using Learned Place Recognition Descriptors and Point-to-Voxel Comparison | Change detection and irregular object extraction in 3D point clouds is a challenging task that is of high importance not only for autonomous navigation but also for updating existing digital twin models of various industrial environments. This article proposes an innovative approach for change detection in 3D point clouds using deep learned place recognition descriptors and irregular object extraction based on voxel-to-point comparison. The proposed method first aligns the bi-temporal point clouds using a map-merging algorithm in order to establish a common coordinate frame. Then, it utilizes deep learning techniques to extract robust and discriminative features from the 3D point cloud scans, which are used to detect changes between consecutive point cloud frames and therefore find the changed areas. Finally, the altered areas are sampled and compared between the two time instances to extract any obstructions that caused the area to change. The proposed method was successfully evaluated in real-world field experiments, where it was able to detect different types of changes in 3D point clouds, such as object or muck-pile addition and displacement, showcasing the effectiveness of the approach. The results of this study demonstrate important implications for various applications, including safety and security monitoring in construction sites, mapping and exploration and suggests potential future research directions in this field. | ['George Nikolakopoulos', 'Anton Koval', 'Nikolaos Stathoulopoulos'] | 2023-06-27 | null | null | null | null | ['change-detection', 'autonomous-navigation'] | ['computer-vision', 'computer-vision'] | [ 2.02818349e-01 -4.51189965e-01 3.90908778e-01 -2.05510676e-01
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3f18eae5-eae5-47b9-b6cd-f8516aeae13a | designing-perceptual-puzzles-by | 2204.12301 | null | https://arxiv.org/abs/2204.12301v1 | https://arxiv.org/pdf/2204.12301v1.pdf | Designing Perceptual Puzzles by Differentiating Probabilistic Programs | We design new visual illusions by finding "adversarial examples" for principled models of human perception -- specifically, for probabilistic models, which treat vision as Bayesian inference. To perform this search efficiently, we design a differentiable probabilistic programming language, whose API exposes MCMC inference as a first-class differentiable function. We demonstrate our method by automatically creating illusions for three features of human vision: color constancy, size constancy, and face perception. | ['Jonathan Ragan-Kelley', 'Joshua Tenenbaum', 'Tzu-Mao Li', 'Kartik Chandra'] | 2022-04-26 | null | null | null | null | ['color-constancy', 'probabilistic-programming'] | ['computer-vision', 'methodology'] | [ 1.98594883e-01 4.91708100e-01 2.37256512e-01 -3.76317769e-01
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c8fc0a23-dba9-4a3f-ae21-96d58bb1302f | linkage-based-face-clustering-via-graph | 1903.11306 | null | http://arxiv.org/abs/1903.11306v3 | http://arxiv.org/pdf/1903.11306v3.pdf | Linkage Based Face Clustering via Graph Convolution Network | In this paper, we present an accurate and scalable approach to the face
clustering task. We aim at grouping a set of faces by their potential
identities. We formulate this task as a link prediction problem: a link exists
between two faces if they are of the same identity. The key idea is that we
find the local context in the feature space around an instance (face) contains
rich information about the linkage relationship between this instance and its
neighbors. By constructing sub-graphs around each instance as input data, which
depict the local context, we utilize the graph convolution network (GCN) to
perform reasoning and infer the likelihood of linkage between pairs in the
sub-graphs. Experiments show that our method is more robust to the complex
distribution of faces than conventional methods, yielding favorably comparable
results to state-of-the-art methods on standard face clustering benchmarks, and
is scalable to large datasets. Furthermore, we show that the proposed method
does not need the number of clusters as prior, is aware of noises and outliers,
and can be extended to a multi-view version for more accurate clustering
accuracy. | ['Ya-Li Li', 'Shengjin Wang', 'Zhongdao Wang', 'Liang Zheng'] | 2019-03-27 | linkage-based-face-clustering-via-graph-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Wang_Linkage_Based_Face_Clustering_via_Graph_Convolution_Network_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_Linkage_Based_Face_Clustering_via_Graph_Convolution_Network_CVPR_2019_paper.pdf | cvpr-2019-6 | ['face-clustering'] | ['computer-vision'] | [-2.37082034e-01 9.89723355e-02 -1.23675680e-02 -8.06358635e-01
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49601e95-6aca-490d-a6e1-57e7af6837ee | image-harmonization-by-matching-regional | 2204.04715 | null | https://arxiv.org/abs/2204.04715v1 | https://arxiv.org/pdf/2204.04715v1.pdf | Image Harmonization by Matching Regional References | To achieve visual consistency in composite images, recent image harmonization methods typically summarize the appearance pattern of global background and apply it to the global foreground without location discrepancy. However, for a real image, the appearances (illumination, color temperature, saturation, hue, texture, etc) of different regions can vary significantly. So previous methods, which transfer the appearance globally, are not optimal. Trying to solve this issue, we firstly match the contents between the foreground and background and then adaptively adjust every foreground location according to the appearance of its content-related background regions. Further, we design a residual reconstruction strategy, that uses the predicted residual to adjust the appearance, and the composite foreground to reserve the image details. Extensive experiments demonstrate the effectiveness of our method. The source code will be available publicly. | ['Chun-Le Guo', 'Zhi Chai', 'Ruiqi Wu', 'Zheng Lin', 'Zhao Zhang', 'Ziyue Zhu'] | 2022-04-10 | null | null | null | null | ['image-harmonization'] | ['computer-vision'] | [ 2.59303391e-01 -4.17783737e-01 -6.85486421e-02 -1.38144106e-01
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cac1aa60-1911-4902-a04a-8ca4f77adb29 | domain-relevant-embeddings-for-medical | 1910.04192 | null | https://arxiv.org/abs/1910.04192v2 | https://arxiv.org/pdf/1910.04192v2.pdf | Domain-Relevant Embeddings for Medical Question Similarity | The rate at which medical questions are asked online far exceeds the capacity of qualified people to answer them, and many of these questions are not unique. Identifying same-question pairs could enable questions to be answered more effectively. While many research efforts have focused on the problem of general question similarity for non-medical applications, these approaches do not generalize well to the medical domain, where medical expertise is often required to determine semantic similarity. In this paper, we show how a semi-supervised approach of pre-training a neural network on medical question-answer pairs is a particularly useful intermediate task for the ultimate goal of determining medical question similarity. While other pre-training tasks yield an accuracy below 78.7% on this task, our model achieves an accuracy of 82.6% with the same number of training examples, and an accuracy of 80.0% with a much smaller training set. | ['Clara McCreery', 'Xavier Amatriain', 'Anitha Kannan', 'Namit Katariya', 'Manish Chablani'] | 2019-10-09 | null | null | null | null | ['question-similarity'] | ['natural-language-processing'] | [ 2.74159372e-01 4.04698133e-01 -8.94259810e-02 -7.08255827e-01
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089afe1f-14e7-4f1e-afca-39f0805bf058 | clustering-similar-amendments-at-the-italian | null | null | https://aclanthology.org/2022.parlaclarin-1.7 | https://aclanthology.org/2022.parlaclarin-1.7.pdf | Clustering Similar Amendments at the Italian Senate | In this paper we describe an experiment for the application of text clustering techniques to dossiers of amendments to proposed legislation discussed in the Italian Senate. The aim is to assist the Senate staff in the detection of groups of amendments similar in their textual formulation in order to schedule their simultaneous voting. Experiments show that the exploitation (extraction, annotation and normalization) of domain features is crucial to improve the clustering performance in many problematic cases not properly dealt with by standard approaches. The similarity engine was implemented and integrated as an experimental feature in the internal application used for the management of amendments in the Senate Assembly and Committees. Thanks to the Open Data strategy pursued by the Senate for several years, all documents and data produced by the institution are publicly available for reuse in open formats. | ['Giuseppe Briotti', 'Roberto Battistoni', 'Carlo Marchetti', 'Tommaso Agnoloni'] | null | null | null | null | parlaclarin-lrec-2022-6 | ['text-clustering'] | ['natural-language-processing'] | [ 3.09934109e-01 3.72702591e-02 7.81332701e-02 -3.47822309e-01
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c67b333e-206c-4f29-bdc5-e9658d970460 | softgroup-for-3d-instance-segmentation-on | 2203.01509 | null | https://arxiv.org/abs/2203.01509v1 | https://arxiv.org/pdf/2203.01509v1.pdf | SoftGroup for 3D Instance Segmentation on Point Clouds | Existing state-of-the-art 3D instance segmentation methods perform semantic segmentation followed by grouping. The hard predictions are made when performing semantic segmentation such that each point is associated with a single class. However, the errors stemming from hard decision propagate into grouping that results in (1) low overlaps between the predicted instance with the ground truth and (2) substantial false positives. To address the aforementioned problems, this paper proposes a 3D instance segmentation method referred to as SoftGroup by performing bottom-up soft grouping followed by top-down refinement. SoftGroup allows each point to be associated with multiple classes to mitigate the problems stemming from semantic prediction errors and suppresses false positive instances by learning to categorize them as background. Experimental results on different datasets and multiple evaluation metrics demonstrate the efficacy of SoftGroup. Its performance surpasses the strongest prior method by a significant margin of +6.2% on the ScanNet v2 hidden test set and +6.8% on S3DIS Area 5 in terms of AP_50. SoftGroup is also fast, running at 345ms per scan with a single Titan X on ScanNet v2 dataset. The source code and trained models for both datasets are available at \url{https://github.com/thangvubk/SoftGroup.git}. | ['Chang D. Yoo', 'Xuan Thanh Nguyen', 'Tung M. Luu', 'Kookhoi Kim', 'Thang Vu'] | 2022-03-03 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Vu_SoftGroup_for_3D_Instance_Segmentation_on_Point_Clouds_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Vu_SoftGroup_for_3D_Instance_Segmentation_on_Point_Clouds_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-instance-segmentation-1'] | ['computer-vision'] | [ 3.49006534e-01 7.24383712e-01 -3.26196164e-01 -5.24301946e-01
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face111b-108a-4b18-8429-c634881bb6fa | multi-layered-graph-based-multi-document | 1405.7975 | null | http://arxiv.org/abs/1405.7975v1 | http://arxiv.org/pdf/1405.7975v1.pdf | Multi-layered graph-based multi-document summarization model | Multi-document summarization is a process of automatic generation of a
compressed version of the given collection of documents. Recently, the
graph-based models and ranking algorithms have been actively investigated by
the extractive document summarization community. While most work to date
focuses on homogeneous connecteness of sentences and heterogeneous connecteness
of documents and sentences (e.g. sentence similarity weighted by document
importance), in this paper we present a novel 3-layered graph model that
emphasizes not only sentence and document level relations but also the
influence of under sentence level relations (e.g. a part of sentence
similarity). | ['Ercan Canhasi'] | 2014-05-17 | null | null | null | null | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 5.38056970e-01 5.56025147e-01 -4.04701471e-01 -2.47628525e-01
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3058a2da-adda-4dc2-ac7a-cfef7e7fba20 | rapgen-an-approach-for-fixing-code | 2306.17077 | null | https://arxiv.org/abs/2306.17077v1 | https://arxiv.org/pdf/2306.17077v1.pdf | RAPGen: An Approach for Fixing Code Inefficiencies in Zero-Shot | Performance bugs are non-functional bugs that can even manifest in well-tested commercial products. Fixing these performance bugs is an important yet challenging problem. In this work, we address this challenge and present a new approach called Retrieval-Augmented Prompt Generation (RAPGen). Given a code snippet with a performance issue, RAPGen first retrieves a prompt instruction from a pre-constructed knowledge-base of previous performance bug fixes and then generates a prompt using the retrieved instruction. It then uses this prompt on a Large Language Model (such as Codex) in zero-shot to generate a fix. We compare our approach with the various prompt variations and state of the art methods in the task of performance bug fixing. Our evaluation shows that RAPGen can generate performance improvement suggestions equivalent or better than a developer in ~60% of the cases, getting ~39% of them verbatim, in an expert-verified dataset of past performance changes made by C# developers. | ['Neel Sundaresan', 'Roshanak Zilouchian Moghaddam', 'Spandan Garg'] | 2023-06-29 | null | null | null | null | ['retrieval'] | ['methodology'] | [-4.82602492e-02 -2.80122906e-02 -2.42337376e-01 -3.35309468e-02
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f6fcfad2-9ade-4dcb-85f5-96b3cb3a177b | a-report-on-the-complex-word-identification | 1804.09132 | null | http://arxiv.org/abs/1804.09132v1 | http://arxiv.org/pdf/1804.09132v1.pdf | A Report on the Complex Word Identification Shared Task 2018 | We report the findings of the second Complex Word Identification (CWI) shared
task organized as part of the BEA workshop co-located with NAACL-HLT'2018. The
second CWI shared task featured multilingual and multi-genre datasets divided
into four tracks: English monolingual, German monolingual, Spanish monolingual,
and a multilingual track with a French test set, and two tasks: binary
classification and probabilistic classification. A total of 12 teams submitted
their results in different task/track combinations and 11 of them wrote system
description papers that are referred to in this report and appear in the BEA
workshop proceedings. | ['Anaïs Tack', 'Sanja Štajner', 'Lucia Specia', 'Gustavo H. Paetzold', 'Seid Muhie Yimam', 'Marcos Zampieri', 'Chris Biemann', 'Shervin Malmasi'] | 2018-04-24 | a-report-on-the-complex-word-identification-1 | https://aclanthology.org/W18-0507 | https://aclanthology.org/W18-0507.pdf | ws-2018-6 | ['complex-word-identification'] | ['natural-language-processing'] | [-8.66821781e-02 -2.30413899e-01 -4.99563187e-01 -3.37359875e-01
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2530fffc-80ce-4e4b-af64-5bb60a2cf1cd | neural-network-interpretation-via-fine | 1805.08969 | null | https://arxiv.org/abs/1805.08969v3 | https://arxiv.org/pdf/1805.08969v3.pdf | Semantic Network Interpretation | Network interpretation as an effort to reveal the features learned by a network remains largely visualization-based. In this paper, our goal is to tackle semantic network interpretation at both filter and decision level. For filter-level interpretation, we represent the concepts a filter encodes with a probability distribution of visual attributes. The decision-level interpretation is achieved by textual summarization that generates an explanatory sentence containing clues behind a network's decision. A Bayesian inference algorithm is proposed to automatically associate filters and network decisions with visual attributes. Human study confirms that the semantic interpretation is a beneficial alternative or complement to visualization methods. We demonstrate the crucial role that semantic network interpretation can play in understanding a network's failure patterns. More importantly, semantic network interpretation enables a better understanding of the correlation between a model's performance and its distribution metrics like filter selectivity and concept sparseness. | ['Pei Guo', 'Ryan Farrell'] | 2018-05-23 | null | null | null | null | ['network-interpretation'] | ['computer-vision'] | [ 4.39850658e-01 4.62064952e-01 -1.10749997e-01 -3.02786589e-01
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3260490f-6c93-43a7-9e39-78bfdba3fa6b | identifying-shared-decodable-concepts-in-the | 2306.03375 | null | https://arxiv.org/abs/2306.03375v1 | https://arxiv.org/pdf/2306.03375v1.pdf | Identifying Shared Decodable Concepts in the Human Brain Using Image-Language Foundation Models | We introduce a method that takes advantage of high-quality pretrained multimodal representations to explore fine-grained semantic networks in the human brain. Previous studies have documented evidence of functional localization in the brain, with different anatomical regions preferentially activating for different types of sensory input. Many such localized structures are known, including the fusiform face area and parahippocampal place area. This raises the question of whether additional brain regions (or conjunctions of brain regions) are also specialized for other important semantic concepts. To identify such brain regions, we developed a data-driven approach to uncover visual concepts that are decodable from a massive functional magnetic resonance imaging (fMRI) dataset. Our analysis is broadly split into three sections. First, a fully connected neural network is trained to map brain responses to the outputs of an image-language foundation model, CLIP (Radford et al., 2021). Subsequently, a contrastive-learning dimensionality reduction method reveals the brain-decodable components of CLIP space. In the final section of our analysis, we localize shared decodable concepts in the brain using a voxel-masking optimization method to produce a shared decodable concept (SDC) space. The accuracy of our procedure is validated by comparing it to previous localization experiments that identify regions for faces, bodies, and places. In addition to these concepts, whose corresponding brain regions were already known, we localize novel concept representations which are shared across participants to other areas of the human brain. We also demonstrate how this method can be used to inspect fine-grained semantic networks for individual participants. We envisage that this extensible method can also be adapted to explore other questions at the intersection of AI and neuroscience. | ['Alona Fyshe', 'Joel Zylberberg', 'Alex Murphy', 'Cory Efird'] | 2023-06-06 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [ 3.84908676e-01 8.41061249e-02 2.89606035e-01 -4.35158432e-01
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b6834e1d-210c-413c-a167-d2468380eff0 | us-rule-discovering-utility-driven-sequential | 2111.15020 | null | https://arxiv.org/abs/2111.15020v1 | https://arxiv.org/pdf/2111.15020v1.pdf | US-Rule: Discovering Utility-driven Sequential Rules | Utility-driven mining is an important task in data science and has many applications in real life. High utility sequential pattern mining (HUSPM) is one kind of utility-driven mining. HUSPM aims to discover all sequential patterns with high utility. However, the existing algorithms of HUSPM can not provide an accurate probability to deal with some scenarios for prediction or recommendation. High-utility sequential rule mining (HUSRM) was proposed to discover all sequential rules with high utility and high confidence. There is only one algorithm proposed for HUSRM, which is not enough efficient. In this paper, we propose a faster algorithm, called US-Rule, to efficiently mine high-utility sequential rules. It utilizes rule estimated utility co-occurrence pruning strategy (REUCP) to avoid meaningless computation. To improve the efficiency on dense and long sequence datasets, four tighter upper bounds (LEEU, REEU, LERSU, RERSU) and their corresponding pruning strategies (LEEUP, REEUP, LERSUP, RERSUP) are proposed. Besides, US-Rule proposes rule estimated utility recomputing pruning strategy (REURP) to deal with sparse datasets. At last, a large number of experiments on different datasets compared to the state-of-the-art algorithm demonstrate that US-Rule can achieve better performance in terms of execution time, memory consumption and scalability. | ['Philip S. Yu', 'Jian Weng', 'Wensheng Gan', 'Gengsen Huang'] | 2021-11-29 | null | null | null | null | ['sequential-pattern-mining'] | ['natural-language-processing'] | [ 2.08129793e-01 -2.53520519e-01 -5.05024552e-01 -2.10809305e-01
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8b7b04bb-c299-4568-8311-9c5685de9cc8 | vision-transformer-with-attention-map | 2306.10875 | null | https://arxiv.org/abs/2306.10875v1 | https://arxiv.org/pdf/2306.10875v1.pdf | Vision Transformer with Attention Map Hallucination and FFN Compaction | Vision Transformer(ViT) is now dominating many vision tasks. The drawback of quadratic complexity of its token-wise multi-head self-attention (MHSA), is extensively addressed via either token sparsification or dimension reduction (in spatial or channel). However, the therein redundancy of MHSA is usually overlooked and so is the feed-forward network (FFN). To this end, we propose attention map hallucination and FFN compaction to fill in the blank. Specifically, we observe similar attention maps exist in vanilla ViT and propose to hallucinate half of the attention maps from the rest with much cheaper operations, which is called hallucinated-MHSA (hMHSA). As for FFN, we factorize its hidden-to-output projection matrix and leverage the re-parameterization technique to strengthen its capability, making it compact-FFN (cFFN). With our proposed modules, a 10$\%$-20$\%$ reduction of floating point operations (FLOPs) and parameters (Params) is achieved for various ViT-based backbones, including straight (DeiT), hybrid (NextViT) and hierarchical (PVT) structures, meanwhile, the performances are quite competitive. | ['Jingdong Wang', 'Fu Li', 'Dongliang He', 'Zhichao Zhou', 'Haiyang Xu'] | 2023-06-19 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [ 1.68779984e-01 4.14051861e-01 4.50846761e-01 3.15871951e-03
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1d701d68-34ab-4f1d-99d3-040fd5f43520 | simulated-car-racing-championship-competition | 1304.1672 | null | http://arxiv.org/abs/1304.1672v2 | http://arxiv.org/pdf/1304.1672v2.pdf | Simulated Car Racing Championship: Competition Software Manual | This manual describes the competition software for the Simulated Car Racing
Championship, an international competition held at major conferences in the
field of Evolutionary Computation and in the field of Computational
Intelligence and Games. It provides an overview of the architecture, the
instructions to install the software and to run the simple drivers provided in
the package, the description of the sensors and the actuators. | ['Luigi Cardamone', 'Pier Luca Lanzi', 'Daniele Loiacono'] | 2013-04-05 | null | null | null | null | ['carracing-v0'] | ['playing-games'] | [-1.32426605e-01 5.85816056e-02 1.52242426e-02 -1.20753773e-01
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a5b8707e-4433-4071-92c0-1c7184786e88 | compmix-a-benchmark-for-heterogeneous | 2306.12235 | null | https://arxiv.org/abs/2306.12235v2 | https://arxiv.org/pdf/2306.12235v2.pdf | CompMix: A Benchmark for Heterogeneous Question Answering | Fact-centric question answering (QA) often requires access to multiple, heterogeneous, information sources. By jointly considering several sources like a knowledge base (KB), a text collection, and tables from the web, QA systems can enhance their answer coverage and confidence. However, existing QA benchmarks are mostly constructed with a single source of knowledge in mind. This limits capabilities of these benchmarks to fairly evaluate QA systems that can tap into more than one information repository. To bridge this gap, we release CompMix, a crowdsourced QA benchmark which naturally demands the integration of a mixture of input sources. CompMix has a total of 9,410 questions, and features several complex intents like joins and temporal conditions. Evaluation of a range of QA systems on CompMix highlights the need for further research on leveraging information from heterogeneous sources. | ['Gerhard Weikum', 'Rishiraj Saha Roy', 'Philipp Christmann'] | 2023-06-21 | null | null | null | null | ['question-answering'] | ['natural-language-processing'] | [-4.98011649e-01 3.44948441e-01 -2.42616013e-01 -4.57925171e-01
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6e87c896-39d2-4f7e-af3c-46ddb6c1f60b | metalogic-logical-reasoning-explanations-with | 2210.12487 | null | https://arxiv.org/abs/2210.12487v1 | https://arxiv.org/pdf/2210.12487v1.pdf | MetaLogic: Logical Reasoning Explanations with Fine-Grained Structure | In this paper, we propose a comprehensive benchmark to investigate models' logical reasoning capabilities in complex real-life scenarios. Current explanation datasets often employ synthetic data with simple reasoning structures. Therefore, it cannot express more complex reasoning processes, such as the rebuttal to a reasoning step and the degree of certainty of the evidence. To this end, we propose a comprehensive logical reasoning explanation form. Based on the multi-hop chain of reasoning, the explanation form includes three main components: (1) The condition of rebuttal that the reasoning node can be challenged; (2) Logical formulae that uncover the internal texture of reasoning nodes; (3) Reasoning strength indicated by degrees of certainty. The fine-grained structure conforms to the real logical reasoning scenario, better fitting the human cognitive process but, simultaneously, is more challenging for the current models. We evaluate the current best models' performance on this new explanation form. The experimental results show that generating reasoning graphs remains a challenging task for current models, even with the help of giant pre-trained language models. | ['Dong Yu', 'ChangShui Zhang', 'Xiaodan Liang', 'Ruixin Hong', 'Hongming Zhang', 'Yinya Huang'] | 2022-10-22 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [ 2.12016031e-01 1.04878104e+00 -6.63380027e-02 -3.49291772e-01
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d091cedc-74e9-4ea8-8924-c396627d62c8 | exploration-by-distributional-reinforcement | 1805.01907 | null | http://arxiv.org/abs/1805.01907v2 | http://arxiv.org/pdf/1805.01907v2.pdf | Exploration by Distributional Reinforcement Learning | We propose a framework based on distributional reinforcement learning and
recent attempts to combine Bayesian parameter updates with deep reinforcement
learning. We show that our proposed framework conceptually unifies multiple
previous methods in exploration. We also derive a practical algorithm that
achieves efficient exploration on challenging control tasks. | ['Yunhao Tang', 'Shipra Agrawal'] | 2018-05-04 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-2.35521615e-01 -2.81808600e-02 -6.34778082e-01 -2.17642069e-01
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a06061a0-bdbc-4d51-a821-89f9af09a8a2 | boundless-generative-adversarial-networks-for | 1908.07007 | null | https://arxiv.org/abs/1908.07007v1 | https://arxiv.org/pdf/1908.07007v1.pdf | Boundless: Generative Adversarial Networks for Image Extension | Image extension models have broad applications in image editing, computational photography and computer graphics. While image inpainting has been extensively studied in the literature, it is challenging to directly apply the state-of-the-art inpainting methods to image extension as they tend to generate blurry or repetitive pixels with inconsistent semantics. We introduce semantic conditioning to the discriminator of a generative adversarial network (GAN), and achieve strong results on image extension with coherent semantics and visually pleasing colors and textures. We also show promising results in extreme extensions, such as panorama generation. | ['William T. Freeman', 'Aaron Sarna', 'Aaron Maschinot', 'Piotr Teterwak', 'David Belanger', 'Ce Liu', 'Dilip Krishnan'] | 2019-08-19 | boundless-generative-adversarial-networks-for-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Teterwak_Boundless_Generative_Adversarial_Networks_for_Image_Extension_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Teterwak_Boundless_Generative_Adversarial_Networks_for_Image_Extension_ICCV_2019_paper.pdf | iccv-2019-10 | ['uncropping'] | ['computer-vision'] | [ 9.89076257e-01 2.11149633e-01 5.44907898e-02 -2.90540397e-01
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4e4465c2-c1dc-46a1-8a2d-6ac630345d06 | customizing-general-purpose-foundation-models | 2306.05642 | null | https://arxiv.org/abs/2306.05642v1 | https://arxiv.org/pdf/2306.05642v1.pdf | Customizing General-Purpose Foundation Models for Medical Report Generation | Medical caption prediction which can be regarded as a task of medical report generation (MRG), requires the automatic generation of coherent and accurate captions for the given medical images. However, the scarcity of labelled medical image-report pairs presents great challenges in the development of deep and large-scale neural networks capable of harnessing the potential artificial general intelligence power like large language models (LLMs). In this work, we propose customizing off-the-shelf general-purpose large-scale pre-trained models, i.e., foundation models (FMs), in computer vision and natural language processing with a specific focus on medical report generation. Specifically, following BLIP-2, a state-of-the-art vision-language pre-training approach, we introduce our encoder-decoder-based MRG model. This model utilizes a lightweight query Transformer to connect two FMs: the giant vision Transformer EVA-ViT-g and a bilingual LLM trained to align with human intentions (referred to as ChatGLM-6B). Furthermore, we conduct ablative experiments on the trainable components of the model to identify the crucial factors for effective transfer learning. Our findings demonstrate that unfreezing EVA-ViT-g to learn medical image representations, followed by parameter-efficient training of ChatGLM-6B to capture the writing styles of medical reports, is essential for achieving optimal results. Our best attempt (PCLmed Team) achieved the 4th and the 2nd, respectively, out of 13 participating teams, based on the BERTScore and ROUGE-1 metrics, in the ImageCLEFmedical Caption 2023 Caption Prediction Task competition. | ['Tong Zhang', 'Yuexian Zou', 'Asif Raza', 'Bang Yang'] | 2023-06-09 | null | null | null | null | ['medical-report-generation'] | ['medical'] | [ 5.00299931e-01 7.92505801e-01 5.19399047e-02 -4.03311312e-01
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1cc9f574-9e52-4fda-9b38-968f95a92595 | ssim-a-deep-learning-approach-for-recovering | null | null | https://ieeexplore.ieee.org/document/8681112 | https://www.ivivan.com/papers/IOTJ2019.pdf | SSIM -A Deep Learning Approach for Recovering Missing Time Series Sensor Data | Missing data are unavoidable in wireless sensor networks, due to issues such as network communication outage, sensor maintenance or failure, etc. Although a plethora of methods have been proposed for imputing sensor data, limitations still exist. Firstly, most methods give poor estimates when a consecutive number of data are missing. Secondly, some methods reconstruct missing data based on other parameters monitored simultaneously. When all the data are missing, these methods are no longer effective. Thirdly, the performance of deep learning methods relies highly on a massive number of training data. Moreover in many scenarios, it is difficult to obtain large volumes of data from wireless sensor networks. Hence, we propose a new sequence-to-sequence imputation model (SSIM) for recovering missing data in wireless sensor networks. The SSIM uses the state-of-the-art sequence-to-sequence deep learning architecture, and the Long Short Term Memory Network is chosen to utilize both the past and future information for a given time. Moreover, a variable-length sliding window algorithm is developed to generate a large number of training samples so the SSIM can be trained with small data sets. We evaluate the SSIM by using real-world time series data from a water quality monitoring network. Compared to methods like ARIMA, Seasonal ARIMA, Matrix Factorization, Multivariate Imputation by Chained Equations and Expectation Maximization, the proposed SSIM achieves up to 69.2%, 70.3%, 98.3% and 76% improvements in terms of the RMSE, MAE, MAPE and SMAPE respectively, when recovering missing data sequences of three different lengths. The SSIM is therefore a promising approach for data quality control in wireless sensor networks. | ['Xiang ; Peter', 'Thorburn ; Wei', 'Zhang ; Peter', 'Yi-Fan', 'Fitch'] | 2019-04-03 | null | null | null | ieee-internet-of-things-journal-2019-4 | ['small-data'] | ['computer-vision'] | [ 7.16644645e-01 -3.83917809e-01 -2.54614472e-01 -4.42623198e-01
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2efd2e73-5619-4e49-a301-8f84514f8f03 | gpt-too-a-language-model-first-approach-for | 2005.09123 | null | https://arxiv.org/abs/2005.09123v2 | https://arxiv.org/pdf/2005.09123v2.pdf | GPT-too: A language-model-first approach for AMR-to-text generation | Meaning Representations (AMRs) are broad-coverage sentence-level semantic graphs. Existing approaches to generating text from AMR have focused on training sequence-to-sequence or graph-to-sequence models on AMR annotated data only. In this paper, we propose an alternative approach that combines a strong pre-trained language model with cycle consistency-based re-scoring. Despite the simplicity of the approach, our experimental results show these models outperform all previous techniques on the English LDC2017T10dataset, including the recent use of transformer architectures. In addition to the standard evaluation metrics, we provide human evaluation experiments that further substantiate the strength of our approach. | ['Md. Arafat Sultan', 'Young-suk Lee', 'Salim Roukos', 'Ramon Fernandez Astudillo', 'Manuel Mager', 'Tahira Naseem', 'Radu Florian'] | 2020-05-18 | gpt-too-a-language-model-first-approach-for-1 | https://aclanthology.org/2020.acl-main.167 | https://aclanthology.org/2020.acl-main.167.pdf | acl-2020-6 | ['graph-to-sequence'] | ['natural-language-processing'] | [ 6.65357828e-01 6.73784733e-01 -2.31850237e-01 -2.86174238e-01
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3cf33623-90fb-453a-8176-2a0bd89045a1 | you-are-what-you-talk-about-inducing | 2302.00493 | null | https://arxiv.org/abs/2302.00493v1 | https://arxiv.org/pdf/2302.00493v1.pdf | You Are What You Talk About: Inducing Evaluative Topics for Personality Analysis | Expressing attitude or stance toward entities and concepts is an integral part of human behavior and personality. Recently, evaluative language data has become more accessible with social media's rapid growth, enabling large-scale opinion analysis. However, surprisingly little research examines the relationship between personality and evaluative language. To bridge this gap, we introduce the notion of evaluative topics, obtained by applying topic models to pre-filtered evaluative text from social media. We then link evaluative topics to individual text authors to build their evaluative profiles. We apply evaluative profiling to Reddit comments labeled with personality scores and conduct an exploratory study on the relationship between evaluative topics and Big Five personality facets, aiming for a more interpretable, facet-level analysis. Finally, we validate our approach by observing correlations consistent with prior research in personality psychology. | ['Jan Šnajder', 'Iva Vukojević', 'Josip Jukić'] | 2023-02-01 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [-5.53682804e-01 4.98340607e-01 -4.60134357e-01 -5.46273887e-01
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0ba836ec-41fb-456f-ac1d-de0514b14abe | watch-or-listen-robust-audio-visual-speech | 2303.08536 | null | https://arxiv.org/abs/2303.08536v2 | https://arxiv.org/pdf/2303.08536v2.pdf | Watch or Listen: Robust Audio-Visual Speech Recognition with Visual Corruption Modeling and Reliability Scoring | This paper deals with Audio-Visual Speech Recognition (AVSR) under multimodal input corruption situations where audio inputs and visual inputs are both corrupted, which is not well addressed in previous research directions. Previous studies have focused on how to complement the corrupted audio inputs with the clean visual inputs with the assumption of the availability of clean visual inputs. However, in real life, clean visual inputs are not always accessible and can even be corrupted by occluded lip regions or noises. Thus, we firstly analyze that the previous AVSR models are not indeed robust to the corruption of multimodal input streams, the audio and the visual inputs, compared to uni-modal models. Then, we design multimodal input corruption modeling to develop robust AVSR models. Lastly, we propose a novel AVSR framework, namely Audio-Visual Reliability Scoring module (AV-RelScore), that is robust to the corrupted multimodal inputs. The AV-RelScore can determine which input modal stream is reliable or not for the prediction and also can exploit the more reliable streams in prediction. The effectiveness of the proposed method is evaluated with comprehensive experiments on popular benchmark databases, LRS2 and LRS3. We also show that the reliability scores obtained by AV-RelScore well reflect the degree of corruption and make the proposed model focus on the reliable multimodal representations. | ['Yong Man Ro', 'Jeongsoo Choi', 'Minsu Kim', 'Joanna Hong'] | 2023-03-15 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Hong_Watch_or_Listen_Robust_Audio-Visual_Speech_Recognition_With_Visual_Corruption_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Hong_Watch_or_Listen_Robust_Audio-Visual_Speech_Recognition_With_Visual_Corruption_CVPR_2023_paper.pdf | cvpr-2023-1 | ['audio-visual-speech-recognition'] | ['speech'] | [ 1.65537104e-01 -1.56543046e-01 2.39132270e-02 -8.45573917e-02
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74e31854-4f1d-4bc1-862f-ebebed627b75 | addressing-the-issue-of-stochastic | 2211.08669 | null | https://arxiv.org/abs/2211.08669v1 | https://arxiv.org/pdf/2211.08669v1.pdf | Addressing the issue of stochastic environments and local decision-making in multi-objective reinforcement learning | Multi-objective reinforcement learning (MORL) is a relatively new field which builds on conventional Reinforcement Learning (RL) to solve multi-objective problems. One of common algorithm is to extend scalar value Q-learning by using vector Q values in combination with a utility function, which captures the user's preference for action selection. This study follows on prior works, and focuses on what factors influence the frequency with which value-based MORL Q-learning algorithms learn the optimal policy for an environment with stochastic state transitions in scenarios where the goal is to maximise the Scalarised Expected Return (SER) - that is, to maximise the average outcome over multiple runs rather than the outcome within each individual episode. The analysis of the interaction between stochastic environment and MORL Q-learning algorithms run on a simple Multi-objective Markov decision process (MOMDP) Space Traders problem with different variant versions. The empirical evaluations show that well designed reward signal can improve the performance of the original baseline algorithm, however it is still not enough to address more general environment. A variant of MORL Q-Learning incorporating global statistics is shown to outperform the baseline method in original Space Traders problem, but remains below 100 percent effectiveness in finding the find desired SER-optimal policy at the end of training. On the other hand, Option learning is guarantied to converge to desired SER-optimal policy but it is not able to scale up to solve more complex problem in real-life. The main contribution of this thesis is to identify the extent to which the issue of noisy Q-value estimates impacts on the ability to learn optimal policies under the combination of stochastic environments, non-linear utility and a constant learning rate. | ['Kewen Ding'] | 2022-11-16 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [-8.33221078e-02 -3.00021730e-02 -3.60878170e-01 1.44078389e-01
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6c830322-728c-4672-8617-a3ca1d5ff6e0 | quantum-split-neural-network-learning-using | 2211.06524 | null | https://arxiv.org/abs/2211.06524v2 | https://arxiv.org/pdf/2211.06524v2.pdf | Quantum Split Neural Network Learning using Cross-Channel Pooling | In recent years, the field of quantum science has attracted significant interest across various disciplines, including quantum machine learning, quantum communication, and quantum computing. Among these emerging areas, quantum federated learning (QFL) has gained particular attention due to the integration of quantum neural networks (QNNs) with traditional federated learning (FL) techniques. In this study, a novel approach entitled quantum split learning (QSL) is presented, which represents an advanced extension of classical split learning. Previous research in classical computing has demonstrated numerous advantages of split learning, such as accelerated convergence, reduced communication costs, and enhanced privacy protection. To maximize the potential of QSL, cross-channel pooling is introduced, a technique that capitalizes on the distinctive properties of quantum state tomography facilitated by QNNs. Through rigorous numerical analysis, evidence is provided that QSL not only achieves a 1.64\% higher top-1 accuracy compared to QFL but also demonstrates robust privacy preservation in the context of the MNIST classification task. | ['Joongheon Kim', 'Hankyul Baek', 'Won Joon Yun'] | 2022-11-12 | null | null | null | null | ['quantum-state-tomography'] | ['medical'] | [ 2.69968718e-01 -1.22307226e-01 -2.07400367e-01 -2.88307160e-01
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07b5959a-2206-42c2-91e3-9f2123cfba83 | supporting-future-electrical-utilities-using | 2303.00428 | null | https://arxiv.org/abs/2303.00428v1 | https://arxiv.org/pdf/2303.00428v1.pdf | Supporting Future Electrical Utilities: Using Deep Learning Methods in EMS and DMS Algorithms | Electrical power systems are increasing in size, complexity, as well as dynamics due to the growing integration of renewable energy resources, which have sporadic power generation. This necessitates the development of near real-time power system algorithms, demanding lower computational complexity regarding the power system size. Considering the growing trend in the collection of historical measurement data and recent advances in the rapidly developing deep learning field, the main goal of this paper is to provide a review of recent deep learning-based power system monitoring and optimization algorithms. Electrical utilities can benefit from this review by re-implementing or enhancing the algorithms traditionally used in energy management systems (EMS) and distribution management systems (DMS). | ['Dejan Vukobratovic', 'Dragisa Miskovic', 'Mile Mitrovic', 'Gorana Gojic', 'Ognjen Kundacina'] | 2023-03-01 | null | null | null | null | ['energy-management'] | ['time-series'] | [-4.32482034e-01 -3.25058490e-01 -7.42318183e-02 1.80148594e-02
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91bed09f-5aeb-429c-98f1-2b1be7ba651e | transcribing-educational-videos-using-whisper | 2307.03200 | null | https://arxiv.org/abs/2307.03200v1 | https://arxiv.org/pdf/2307.03200v1.pdf | Transcribing Educational Videos Using Whisper: A preliminary study on using AI for transcribing educational videos | Videos are increasingly being used for e-learning, and transcripts are vital to enhance the learning experience. The costs and delays of generating transcripts can be alleviated by automatic speech recognition (ASR) systems. In this article, we quantify the transcripts generated by whisper for 25 educational videos and identify some open avenues of research when leveraging ASR for transcribing educational videos. | ['Ashwin Rao'] | 2023-07-04 | null | null | null | null | ['speech-recognition', 'automatic-speech-recognition'] | ['speech', 'speech'] | [ 2.87892759e-01 1.52478755e-01 -1.14430889e-01 -2.76233792e-01
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06ccf98f-0488-4807-a414-3fbd3434a44e | investigating-emergent-goal-like-behaviour-in | 2305.07970 | null | https://arxiv.org/abs/2305.07970v1 | https://arxiv.org/pdf/2305.07970v1.pdf | Investigating Emergent Goal-Like Behaviour in Large Language Models Using Experimental Economics | In this study, we investigate the capacity of large language models (LLMs), specifically GPT-3.5, to operationalise natural language descriptions of cooperative, competitive, altruistic, and self-interested behavior in social dilemmas. Our focus is on the iterated Prisoner's Dilemma, a classic example of a non-zero-sum interaction, but our broader research program encompasses a range of experimental economics scenarios, including the ultimatum game, dictator game, and public goods game. Using a within-subject experimental design, we instantiated LLM-generated agents with various prompts that conveyed different cooperative and competitive stances. We then assessed the agents' level of cooperation in the iterated Prisoner's Dilemma, taking into account their responsiveness to the cooperative or defection actions of their partners. Our results provide evidence that LLMs can translate natural language descriptions of altruism and selfishness into appropriate behaviour to some extent, but exhibit limitations in adapting their behavior based on conditioned reciprocity. The observed pattern of increased cooperation with defectors and decreased cooperation with cooperators highlights potential constraints in the LLM's ability to generalize its knowledge about human behavior in social dilemmas. We call upon the research community to further explore the factors contributing to the emergent behavior of LLM-generated agents in a wider array of social dilemmas, examining the impact of model architecture, training parameters, and various partner strategies on agent behavior. As more advanced LLMs like GPT-4 become available, it is crucial to investigate whether they exhibit similar limitations or are capable of more nuanced cooperative behaviors, ultimately fostering the development of AI systems that better align with human values and social norms. | ['Yvan I. Russell', 'Steve Phelps'] | 2023-05-13 | null | null | null | null | ['experimental-design'] | ['methodology'] | [-1.43336564e-01 4.16159302e-01 1.63433269e-01 -2.65183479e-01
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7361ce0d-a14b-4fd1-83f4-86986ba6b037 | one-model-to-edit-them-all-free-form-text | 2210.07883 | null | https://arxiv.org/abs/2210.07883v2 | https://arxiv.org/pdf/2210.07883v2.pdf | One Model to Edit Them All: Free-Form Text-Driven Image Manipulation with Semantic Modulations | Free-form text prompts allow users to describe their intentions during image manipulation conveniently. Based on the visual latent space of StyleGAN[21] and text embedding space of CLIP[34], studies focus on how to map these two latent spaces for text-driven attribute manipulations. Currently, the latent mapping between these two spaces is empirically designed and confines that each manipulation model can only handle one fixed text prompt. In this paper, we propose a method named Free-Form CLIP (FFCLIP), aiming to establish an automatic latent mapping so that one manipulation model handles free-form text prompts. Our FFCLIP has a cross-modality semantic modulation module containing semantic alignment and injection. The semantic alignment performs the automatic latent mapping via linear transformations with a cross attention mechanism. After alignment, we inject semantics from text prompt embeddings to the StyleGAN latent space. For one type of image (e.g., `human portrait'), one FFCLIP model can be learned to handle free-form text prompts. Meanwhile, we observe that although each training text prompt only contains a single semantic meaning, FFCLIP can leverage text prompts with multiple semantic meanings for image manipulation. In the experiments, we evaluate FFCLIP on three types of images (i.e., `human portraits', `cars', and `churches'). Both visual and numerical results show that FFCLIP effectively produces semantically accurate and visually realistic images. Project page: https://github.com/KumapowerLIU/FFCLIP. | ['Ziyang Yuan', 'Jue Wang', 'Qifeng Chen', 'Chun Yuan', 'Xintong Han', 'Yibing Song', 'Hongyu Liu', 'Yiming Zhu'] | 2022-10-14 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [ 4.49286014e-01 -1.58427916e-02 -4.25952584e-01 -2.63186902e-01
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ddcfbd04-38bb-4d44-8742-45439427ebbe | training-compact-models-for-low-resource | 1910.06294 | null | https://arxiv.org/abs/1910.06294v2 | https://arxiv.org/pdf/1910.06294v2.pdf | Training Compact Models for Low Resource Entity Tagging using Pre-trained Language Models | Training models on low-resource named entity recognition tasks has been shown to be a challenge, especially in industrial applications where deploying updated models is a continuous effort and crucial for business operations. In such cases there is often an abundance of unlabeled data, while labeled data is scarce or unavailable. Pre-trained language models trained to extract contextual features from text were shown to improve many natural language processing (NLP) tasks, including scarcely labeled tasks, by leveraging transfer learning. However, such models impose a heavy memory and computational burden, making it a challenge to train and deploy such models for inference use. In this work-in-progress we combined the effectiveness of transfer learning provided by pre-trained masked language models with a semi-supervised approach to train a fast and compact model using labeled and unlabeled examples. Preliminary evaluations show that the compact models can achieve competitive accuracy with 36x compression rate when compared with a state-of-the-art pre-trained language model, and run significantly faster in inference, allowing deployment of such models in production environments or on edge devices. | ['Shira Guskin', 'Peter Izsak', 'Moshe Wasserblat'] | 2019-10-14 | null | null | null | null | ['low-resource-named-entity-recognition'] | ['natural-language-processing'] | [ 3.85774463e-01 2.25728020e-01 -5.12272477e-01 -6.99079990e-01
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da835cdb-4d51-48f2-81e1-7065554b2ccd | variational-learning-across-domains-with-1 | null | null | https://openreview.net/forum?id=ryfwFJWioX | https://openreview.net/pdf?id=ryfwFJWioX | Variational learning across domains with triplet information | The work investigates deep generative models, which allow us to use training data from one domain to build a model for another domain. We propose the Variational Bi-domain Triplet Autoencoder (VBTA) that learns a joint distribution of objects from different domains. We extend the VBTAs objective function by the relative constraints or triplets that sampled from the shared latent space across domains. In other words, we combine the deep generative models with a metric learning ideas in order to improve the final objective with the triplets information. The performance of the VBTA model is demonstrated on different tasks: image-to-image translation, bi-directional image generation and cross-lingual document classification. | ['Anonymous'] | 2018-10-22 | null | null | null | null | ['cross-lingual-document-classification'] | ['natural-language-processing'] | [ 1.62170902e-02 -1.27881914e-01 -5.30292392e-02 -6.56424165e-01
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c4fec90b-c980-443d-99a3-cd8e5f222bcd | phonetic-spelling-algorithm-implementations | null | null | https://www.jstatsoft.org/article/view/v095i08 | https://www.jstatsoft.org/index.php/jss/article/view/v095i08/v95i08.pdf | Phonetic Spelling Algorithm Implementations for R | The phonics package provides several functions for indexing words by their English language pronunciation. Over nearly one hundred years, many different algorithms have been developed to support word and name indexing. From Soundex, developed in the early 20th century and predating the digital computer, through to modern digital phonetic algorithms like Phonex, the phonics package provides support for more than a dozen methods. Together, these provide phonetic algorithms appropriate for use in name indexing and name matching across a variety of English language use cases. | ['James P. Howard II'] | 2020-10-07 | null | null | null | journal-of-statistical-software-2020-10 | ['record-linking'] | ['natural-language-processing'] | [-2.88998604e-01 -6.99209869e-01 -5.47235370e-01 -3.99255008e-01
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856afeca-91e2-43cc-814f-d7320c46b81b | a-probabilistic-generative-model-for-tracking | 2302.08673 | null | https://arxiv.org/abs/2302.08673v1 | https://arxiv.org/pdf/2302.08673v1.pdf | A Probabilistic Generative Model for Tracking Multi-Knowledge Concept Mastery Probability | Knowledge tracing aims to track students' knowledge status over time to predict students' future performance accurately. Markov chain-based knowledge tracking (MCKT) models can track knowledge concept mastery probability over time. However, as the number of tracked knowledge concepts increases, the time complexity of MCKT predicting student performance increases exponentially (also called explaining away problem. In addition, the existing MCKT models only consider the relationship between students' knowledge status and problems when modeling students' responses but ignore the relationship between knowledge concepts in the same problem. To address these challenges, we propose an inTerpretable pRobAbilistiC gEnerative moDel (TRACED), which can track students' numerous knowledge concepts mastery probabilities over time. To solve \emph{explain away problem}, we design Long and Short-Term Memory (LSTM)-based networks to approximate the posterior distribution, predict students' future performance, and propose a heuristic algorithm to train LSTMs and probabilistic graphical model jointly. To better model students' exercise responses, we proposed a logarithmic linear model with three interactive strategies, which models students' exercise responses by considering the relationship among students' knowledge status, knowledge concept, and problems. We conduct experiments with four real-world datasets in three knowledge-driven tasks. The experimental results show that TRACED outperforms existing knowledge tracing methods in predicting students' future performance and can learn the relationship among students, knowledge concepts, and problems from students' exercise sequences. We also conduct several case studies. The case studies show that TRACED exhibits excellent interpretability and thus has the potential for personalized automatic feedback in the real-world educational environment. | ['Ge Yu', 'Minghe Yu', 'Fan Li', 'Tiancheng Zhang', 'Hengyu Liu'] | 2023-02-17 | null | null | null | null | ['knowledge-tracing'] | ['miscellaneous'] | [-5.24857976e-02 -1.58378884e-01 -4.92662400e-01 -4.12084430e-01
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9db35c05-9218-41e1-a786-ecf4e3d86b62 | the-treasure-beneath-multiple-annotations-an | 2303.11828 | null | https://arxiv.org/abs/2303.11828v1 | https://arxiv.org/pdf/2303.11828v1.pdf | The Treasure Beneath Multiple Annotations: An Uncertainty-aware Edge Detector | Deep learning-based edge detectors heavily rely on pixel-wise labels which are often provided by multiple annotators. Existing methods fuse multiple annotations using a simple voting process, ignoring the inherent ambiguity of edges and labeling bias of annotators. In this paper, we propose a novel uncertainty-aware edge detector (UAED), which employs uncertainty to investigate the subjectivity and ambiguity of diverse annotations. Specifically, we first convert the deterministic label space into a learnable Gaussian distribution, whose variance measures the degree of ambiguity among different annotations. Then we regard the learned variance as the estimated uncertainty of the predicted edge maps, and pixels with higher uncertainty are likely to be hard samples for edge detection. Therefore we design an adaptive weighting loss to emphasize the learning from those pixels with high uncertainty, which helps the network to gradually concentrate on the important pixels. UAED can be combined with various encoder-decoder backbones, and the extensive experiments demonstrate that UAED achieves superior performance consistently across multiple edge detection benchmarks. The source code is available at \url{https://github.com/ZhouCX117/UAED} | ['Haibin Ling', 'Li Huang', 'Qingji Guan', 'Mengyang Pu', 'Yaping Huang', 'Caixia Zhou'] | 2023-03-21 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhou_The_Treasure_Beneath_Multiple_Annotations_An_Uncertainty-Aware_Edge_Detector_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhou_The_Treasure_Beneath_Multiple_Annotations_An_Uncertainty-Aware_Edge_Detector_CVPR_2023_paper.pdf | cvpr-2023-1 | ['edge-detection'] | ['computer-vision'] | [-1.82365745e-01 3.15677702e-01 -2.85544157e-01 -6.26869440e-01
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7136f607-7626-4c51-885f-c6391839fffe | dilated-recurrent-neural-networks | 1710.02224 | null | http://arxiv.org/abs/1710.02224v3 | http://arxiv.org/pdf/1710.02224v3.pdf | Dilated Recurrent Neural Networks | Learning with recurrent neural networks (RNNs) on long sequences is a
notoriously difficult task. There are three major challenges: 1) complex
dependencies, 2) vanishing and exploding gradients, and 3) efficient
parallelization. In this paper, we introduce a simple yet effective RNN
connection structure, the DilatedRNN, which simultaneously tackles all of these
challenges. The proposed architecture is characterized by multi-resolution
dilated recurrent skip connections and can be combined flexibly with diverse
RNN cells. Moreover, the DilatedRNN reduces the number of parameters needed and
enhances training efficiency significantly, while matching state-of-the-art
performance (even with standard RNN cells) in tasks involving very long-term
dependencies. To provide a theory-based quantification of the architecture's
advantages, we introduce a memory capacity measure, the mean recurrent length,
which is more suitable for RNNs with long skip connections than existing
measures. We rigorously prove the advantages of the DilatedRNN over other
recurrent neural architectures. The code for our method is publicly available
at https://github.com/code-terminator/DilatedRNN | ['Mark Hasegawa-Johnson', 'Wei Tan', 'Shiyu Chang', 'Michael Witbrock', 'Yang Zhang', 'Xiaoxiao Guo', 'Wei Han', 'Xiaodong Cui', 'Thomas S. Huang', 'Mo Yu'] | 2017-10-05 | dilated-recurrent-neural-networks-1 | http://papers.nips.cc/paper/6613-dilated-recurrent-neural-networks | http://papers.nips.cc/paper/6613-dilated-recurrent-neural-networks.pdf | neurips-2017-12 | ['sequential-image-classification'] | ['computer-vision'] | [ 7.77909830e-02 -1.80521056e-01 -1.33041620e-01 -1.96349416e-02
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0185683b-1ce1-4557-8b84-276a8485b5de | a-large-cross-modal-video-retrieval-dataset | 2305.03347 | null | https://arxiv.org/abs/2305.03347v1 | https://arxiv.org/pdf/2305.03347v1.pdf | A Large Cross-Modal Video Retrieval Dataset with Reading Comprehension | Most existing cross-modal language-to-video retrieval (VR) research focuses on single-modal input from video, i.e., visual representation, while the text is omnipresent in human environments and frequently critical to understand video. To study how to retrieve video with both modal inputs, i.e., visual and text semantic representations, we first introduce a large-scale and cross-modal Video Retrieval dataset with text reading comprehension, TextVR, which contains 42.2k sentence queries for 10.5k videos of 8 scenario domains, i.e., Street View (indoor), Street View (outdoor), Games, Sports, Driving, Activity, TV Show, and Cooking. The proposed TextVR requires one unified cross-modal model to recognize and comprehend texts, relate them to the visual context, and decide what text semantic information is vital for the video retrieval task. Besides, we present a detailed analysis of TextVR compared to the existing datasets and design a novel multimodal video retrieval baseline for the text-based video retrieval task. The dataset analysis and extensive experiments show that our TextVR benchmark provides many new technical challenges and insights from previous datasets for the video-and-language community. The project website and GitHub repo can be found at https://sites.google.com/view/loveucvpr23/guest-track and https://github.com/callsys/TextVR, respectively. | ['Xiang Bai', 'Mike Zheng Shou', 'Hong Zhou', 'Jiahong Li', 'Zhuang Li', 'Yuzhong Zhao', 'Weijia Wu'] | 2023-05-05 | null | null | null | null | ['video-retrieval', 'reading-comprehension'] | ['computer-vision', 'natural-language-processing'] | [ 2.44038757e-02 -7.00558364e-01 -4.33060467e-01 -8.38616639e-02
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5f155859-6241-4612-96aa-4c816b79fd59 | pre-training-and-fine-tuning-neural-topic | null | null | https://openreview.net/forum?id=8ds7iPDUrls | https://openreview.net/pdf?id=8ds7iPDUrls | Pre-training and Fine-tuning Neural Topic Model: A Simple yet Effective Approach to Incorporating External Knowledge | Recent years have witnessed growing interests in incorporating external knowledge such as pre-trained word embeddings (PWEs) or pre-trained language models (PLMs) into neural topic modeling. However, we found that employing PWEs and PLMs for topic modeling only achieved limited performance improvements but with huge computational overhead. In this paper, we propose a novel strategy to incorporate external knowledge into neural topic modeling where the neural topic model is pre-trained on a large corpus and then fine-tuned on the target dataset. Experiments have been conducted on three datasets and results show that the proposed approach significantly outperforms both current state-of-the-art neural topic models and some topic modeling approaches enhanced with PWEs or PLMs. Moreover, further study shows that the proposed approach greatly reduces the need for the huge size of training data. | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['topic-models'] | ['natural-language-processing'] | [-1.30689442e-01 3.81725281e-01 -3.36550266e-01 -3.89748454e-01
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1ddc8c4f-3eae-4509-8c27-57c1dfa68c6d | does-image-anonymization-impact-computer | 2306.05135 | null | https://arxiv.org/abs/2306.05135v1 | https://arxiv.org/pdf/2306.05135v1.pdf | Does Image Anonymization Impact Computer Vision Training? | Image anonymization is widely adapted in practice to comply with privacy regulations in many regions. However, anonymization often degrades the quality of the data, reducing its utility for computer vision development. In this paper, we investigate the impact of image anonymization for training computer vision models on key computer vision tasks (detection, instance segmentation, and pose estimation). Specifically, we benchmark the recognition drop on common detection datasets, where we evaluate both traditional and realistic anonymization for faces and full bodies. Our comprehensive experiments reflect that traditional image anonymization substantially impacts final model performance, particularly when anonymizing the full body. Furthermore, we find that realistic anonymization can mitigate this decrease in performance, where our experiments reflect a minimal performance drop for face anonymization. Our study demonstrates that realistic anonymization can enable privacy-preserving computer vision development with minimal performance degradation across a range of important computer vision benchmarks. | ['Frank Lindseth', 'Håkon Hukkelås'] | 2023-06-08 | null | null | null | null | ['pose-estimation', 'face-anonymization'] | ['computer-vision', 'computer-vision'] | [ 3.45560402e-01 1.60224095e-01 -6.28427416e-02 -5.31976223e-01
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5b1470cd-736a-4a1a-bedd-d8be3ee26f73 | applications-of-gaussian-processes-at-extreme | 2303.14291 | null | https://arxiv.org/abs/2303.14291v1 | https://arxiv.org/pdf/2303.14291v1.pdf | Applications of Gaussian Processes at Extreme Lengthscales: From Molecules to Black Holes | In many areas of the observational and experimental sciences data is scarce. Data observation in high-energy astrophysics is disrupted by celestial occlusions and limited telescope time while data derived from laboratory experiments in synthetic chemistry and materials science is time and cost-intensive to collect. On the other hand, knowledge about the data-generation mechanism is often available in the sciences, such as the measurement error of a piece of laboratory apparatus. Both characteristics, small data and knowledge of the underlying physics, make Gaussian processes (GPs) ideal candidates for fitting such datasets. GPs can make predictions with consideration of uncertainty, for example in the virtual screening of molecules and materials, and can also make inferences about incomplete data such as the latent emission signature from a black hole accretion disc. Furthermore, GPs are currently the workhorse model for Bayesian optimisation, a methodology foreseen to be a guide for laboratory experiments in scientific discovery campaigns. The first contribution of this thesis is to use GP modelling to reason about the latent emission signature from the Seyfert galaxy Markarian 335, and by extension, to reason about the applicability of various theoretical models of black hole accretion discs. The second contribution is to extend the GP framework to molecular and chemical reaction representations and to provide an open-source software library to enable the framework to be used by scientists. The third contribution is to leverage GPs to discover novel and performant photoswitch molecules. The fourth contribution is to introduce a Bayesian optimisation scheme capable of modelling aleatoric uncertainty to facilitate the identification of material compositions that possess intrinsic robustness to large scale fabrication processes. | ['Ryan-Rhys Griffiths'] | 2023-03-24 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 2.95974821e-01 1.96124800e-02 -1.48655381e-02 2.14183778e-01
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c586b4f5-497f-435c-8797-33693a9ddcef | point-cloud-completion-by-skip-attention | 2005.03871 | null | https://arxiv.org/abs/2005.03871v2 | https://arxiv.org/pdf/2005.03871v2.pdf | Point Cloud Completion by Skip-attention Network with Hierarchical Folding | Point cloud completion aims to infer the complete geometries for missing regions of 3D objects from incomplete ones. Previous methods usually predict the complete point cloud based on the global shape representation extracted from the incomplete input. However, the global representation often suffers from the information loss of structure details on local regions of incomplete point cloud. To address this problem, we propose Skip-Attention Network (SA-Net) for 3D point cloud completion. Our main contributions lie in the following two-folds. First, we propose a skip-attention mechanism to effectively exploit the local structure details of incomplete point clouds during the inference of missing parts. The skip-attention mechanism selectively conveys geometric information from the local regions of incomplete point clouds for the generation of complete ones at different resolutions, where the skip-attention reveals the completion process in an interpretable way. Second, in order to fully utilize the selected geometric information encoded by skip-attention mechanism at different resolutions, we propose a novel structure-preserving decoder with hierarchical folding for complete shape generation. The hierarchical folding preserves the structure of complete point cloud generated in upper layer by progressively detailing the local regions, using the skip-attentioned geometry at the same resolution. We conduct comprehensive experiments on ShapeNet and KITTI datasets, which demonstrate that the proposed SA-Net outperforms the state-of-the-art point cloud completion methods. | ['Yu-Shen Liu', 'Zhizhong Han', 'Tianyang Li', 'Xin Wen'] | 2020-05-08 | point-cloud-completion-by-skip-attention-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Wen_Point_Cloud_Completion_by_Skip-Attention_Network_With_Hierarchical_Folding_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Wen_Point_Cloud_Completion_by_Skip-Attention_Network_With_Hierarchical_Folding_CVPR_2020_paper.pdf | cvpr-2020-6 | ['point-cloud-completion'] | ['computer-vision'] | [-7.70788640e-02 2.52421737e-01 8.63375142e-02 -3.85947317e-01
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c292e42e-5e1c-4165-8e4f-072ddde98b63 | maximizing-classification-accuracy-in-native | null | null | https://aclanthology.org/W13-1714 | https://aclanthology.org/W13-1714.pdf | Maximizing Classification Accuracy in Native Language Identification | null | ['Yves Bestgen', 'Steve Pepper', 'Scott Jarvis'] | 2013-06-01 | null | null | null | ws-2013-6 | ['native-language-identification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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084b2b31-99e6-45e7-8046-6f99dfbff110 | automated-detection-and-diagnosis-of-diabetic | 2107.00115 | null | https://arxiv.org/abs/2107.00115v1 | https://arxiv.org/pdf/2107.00115v1.pdf | Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey | Diabetic Retinopathy (DR) is a leading cause of vision loss in the world,. In the past few Diabetic Retinopathy (DR) is a leading cause of vision loss in the world. In the past few years, Artificial Intelligence (AI) based approaches have been used to detect and grade DR. Early detection enables appropriate treatment and thus prevents vision loss, Both fundus and optical coherence tomography (OCT) images are used to image the retina. With deep learning/machine learning apprroaches it is possible to extract features from the images and detect the presence of DR. Multiple strategies are implemented to detect and grade the presence of DR using classification, segmentation, and hybrid techniques. This review covers the literature dealing with AI approaches to DR that have been published in the open literature over a five year span (2016-2021). In addition a comprehensive list of available DR datasets is reported. Both the PICO (P-patient, I-intervention, C-control O-outcome) and Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA)2009 search strategies were employed. We summarize a total of 114 published articles which conformed to the scope of the review. In addition a list of 43 major datasets is presented. | ['J. Jothi Balaji', 'Arya Sarkar', 'Hoda Kherdfallah', 'Vasudevan Lakshminarayanan'] | 2021-06-30 | null | null | null | null | ['pico'] | ['natural-language-processing'] | [ 1.50105506e-01 -1.73629761e-01 -5.09114802e-01 -1.11789323e-01
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89c96dba-eb5e-47fb-80d6-78b7e0a19799 | assemblenet-searching-for-multi-stream-neural | 1905.13209 | null | https://arxiv.org/abs/1905.13209v4 | https://arxiv.org/pdf/1905.13209v4.pdf | AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures | Learning to represent videos is a very challenging task both algorithmically and computationally. Standard video CNN architectures have been designed by directly extending architectures devised for image understanding to include the time dimension, using modules such as 3D convolutions, or by using two-stream design to capture both appearance and motion in videos. We interpret a video CNN as a collection of multi-stream convolutional blocks connected to each other, and propose the approach of automatically finding neural architectures with better connectivity and spatio-temporal interactions for video understanding. This is done by evolving a population of overly-connected architectures guided by connection weight learning. Architectures combining representations that abstract different input types (i.e., RGB and optical flow) at multiple temporal resolutions are searched for, allowing different types or sources of information to interact with each other. Our method, referred to as AssembleNet, outperforms prior approaches on public video datasets, in some cases by a great margin. We obtain 58.6% mAP on Charades and 34.27% accuracy on Moments-in-Time. | ['Mingxing Tan', 'Michael S. Ryoo', 'Anelia Angelova', 'AJ Piergiovanni'] | 2019-05-30 | assemblenet-searching-for-multi-stream-neural-1 | https://openreview.net/forum?id=SJgMK64Ywr | https://openreview.net/pdf?id=SJgMK64Ywr | iclr-2020-1 | ['multimodal-activity-recognition'] | ['computer-vision'] | [-1.13475785e-01 -1.06100067e-01 -5.77137507e-02 -3.60426009e-01
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1ff85bd7-26ff-4945-9611-be7ac305c15a | veda-uneven-light-image-enhancement-via-a | 2305.16072 | null | https://arxiv.org/abs/2305.16072v1 | https://arxiv.org/pdf/2305.16072v1.pdf | VEDA: Uneven light image enhancement via a vision-based exploratory data analysis model | Uneven light image enhancement is a highly demanded task in many industrial image processing applications. Many existing enhancement methods using physical lighting models or deep-learning techniques often lead to unnatural results. This is mainly because: 1) the assumptions and priors made by the physical lighting model (PLM) based approaches are often violated in most natural scenes, and 2) the training datasets or loss functions used by deep-learning technique based methods cannot handle the various lighting scenarios in the real world well. In this paper, we propose a novel vision-based exploratory data analysis model (VEDA) for uneven light image enhancement. Our method is conceptually simple yet effective. A given image is first decomposed into a contrast image that preserves most of the perceptually important scene details, and a residual image that preserves the lighting variations. After achieving this decomposition at multiple scales using a retinal model that simulates the neuron response to light, the enhanced result at each scale can be obtained by manipulating the two images and recombining them. Then, a weighted averaging strategy based on the residual image is designed to obtain the output image by combining enhanced results at multiple scales. A similar weighting strategy can also be leveraged to reconcile noise suppression and detail preservation. Extensive experiments on different image datasets demonstrate that the proposed method can achieve competitive results in its simplicity and effectiveness compared with state-of-the-art methods. It does not require any explicit assumptions and priors about the scene imaging process, nor iteratively solving any optimization functions or any learning procedures. | ['Qingsong Zhu', 'Zhenming Peng', 'Shuhang Wang', 'Tian Pu'] | 2023-05-25 | null | null | null | null | ['image-enhancement'] | ['computer-vision'] | [ 5.65455496e-01 -5.26201189e-01 2.44092569e-01 -1.10236220e-01
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8c7d8724-1553-43fe-8634-18317690a370 | semi-supervised-hyperspectral-image-1 | null | null | https://ieeexplore.ieee.org/abstract/document/9849704 | https://ieeexplore.ieee.org/abstract/document/9849704 | Semi-Supervised Hyperspectral Image Classification Using a Probabilistic Pseudo-Label Generation Framework | Deep neural networks (DNNs) show impressive performance for hyperspectral image (HSI) classification when abundant labeled samples are available. The problem is that HSI sample annotation is extremely costly and the budget for this task is usually limited. To reduce the reliance on labeled samples, deep semi-supervised learning (SSL), which jointly learns from labeled and unlabeled samples, has been introduced in the literature. However, learning robust and discriminative features from unlabeled data is a challenging task due to various noise effects and ambiguity of unlabeled samples. As a result, recent advances are constrained, mainly in the pre-training or warm-up stage. In this paper, we propose a deep probabilistic framework to generate reliable pseudo labels to explicitly learn discriminative features from unlabeled samples. The generated pseudo labels of our proposed framework can be fed to various DNNs to improve their generalization capacity. Our proposed framework takes only 10 labeled samples per class to represent the label set as an uncertainty-aware distribution in the latent space. The pseudo labels are then generated for those unlabeled samples whose feature values match the distribution with high probability. By performing extensive experiments on four publicly available datasets, we show that our framework can generate reliable pseudo labels to significantly improve the generalization capacity of several state-of-the-art DNNs. In addition, we introduce a new DNN for HSI classification that demonstrates outstanding accuracy results in comparison with its rivals. | ['Azam Asilian Bidgoli', 'Pedram Ghamisi', 'Shahryar Rahnamayan', 'Majid Seydgar'] | 2022-08-05 | null | null | null | journal-2022-8 | ['semi-supervised-image-classification'] | ['computer-vision'] | [ 4.93526518e-01 -2.02880085e-01 -3.56484056e-01 -8.62385035e-01
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780daa83-8d2a-44ae-b0ad-52d7b0eb00b9 | semi-supervised-video-salient-object | 1908.04051 | null | https://arxiv.org/abs/1908.04051v2 | https://arxiv.org/pdf/1908.04051v2.pdf | Semi-Supervised Video Salient Object Detection Using Pseudo-Labels | Deep learning-based video salient object detection has recently achieved great success with its performance significantly outperforming any other unsupervised methods. However, existing data-driven approaches heavily rely on a large quantity of pixel-wise annotated video frames to deliver such promising results. In this paper, we address the semi-supervised video salient object detection task using pseudo-labels. Specifically, we present an effective video saliency detector that consists of a spatial refinement network and a spatiotemporal module. Based on the same refinement network and motion information in terms of optical flow, we further propose a novel method for generating pixel-level pseudo-labels from sparsely annotated frames. By utilizing the generated pseudo-labels together with a part of manual annotations, our video saliency detector learns spatial and temporal cues for both contrast inference and coherence enhancement, thus producing accurate saliency maps. Experimental results demonstrate that our proposed semi-supervised method even greatly outperforms all the state-of-the-art fully supervised methods across three public benchmarks of VOS, DAVIS, and FBMS. | ['Pengxiang Yan', 'Liang Lin', 'Chuan Wang', 'Tianshui Chen', 'Guanbin Li', 'Zhen Li', 'Yuan Xie'] | 2019-08-12 | semi-supervised-video-salient-object-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Yan_Semi-Supervised_Video_Salient_Object_Detection_Using_Pseudo-Labels_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Yan_Semi-Supervised_Video_Salient_Object_Detection_Using_Pseudo-Labels_ICCV_2019_paper.pdf | iccv-2019-10 | ['video-salient-object-detection', 'unsupervised-video-object-segmentation'] | ['computer-vision', 'computer-vision'] | [ 4.54015285e-01 -5.15209958e-02 -4.10163701e-01 -3.54146749e-01
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3d3db3a4-c299-472f-851f-ec94933e04aa | an-open-access-database-for-the-evaluation-of | null | null | https://pubmed.ncbi.nlm.nih.gov/30708353/ | https://www.researchgate.net/publication/330815510_An_open_access_database_for_the_evaluation_of_respiratory_sound_classification_algorithms | An open access database for the evaluation of respiratory sound classification algorithms | ###Objective: Over the last few decades, there has been significant interest in the automatic analysis of respiratory sounds. However, currently there are no publicly available large databases with which new algorithms can be evaluated and compared. Further developments in the field are dependent on the creation of such databases.
###Approach: This paper describes a public respiratory sound database, which was compiled for an international competition, the first scientific challenge of the IFMBE's International Conference on Biomedical and Health Informatics. The database includes 920 recordings acquired from 126 participants and two sets of annotations. One set contains 6898 annotated respiratory cycles, some including crackles, wheezes, or a combination of both, and some with no adventitious respiratory sounds. In the other set, precise locations of 10 775 events of crackles and wheezes were annotated.
###Main results: The best system that participated in the challenge achieved an average score of 52.5% with the respiratory cycle annotations and an average score of 91.2% with the event annotations.
###Significance: The creation and public release of this database will be useful to the research community and could bring attention to the respiratory sound classification problem. | ['Paulo de Carvalho', 'Ioanna Chouvarda', 'Rui Pedro Paiva', 'Nicos Maglaveras', 'Alda Marques', 'Cristina Jácome', 'Ana Oliveira', 'Pantelis Natsiavas', 'Evangelos Kaimakamis', 'Eleni Perantoni', 'Ioannis M Vogiatzis', 'Tatjana L Turukalo', 'Nikša Jakovljevic', 'Yasemin P Kahya', 'Sezer Ulukaya', 'Gorkem Serbes', 'Luís Mendes', 'Dimitris Filos', 'Bruno M Rocha'] | 2019-05-22 | null | null | null | physiological-measurement-2019-5 | ['sound-classification'] | ['audio'] | [ 2.32327193e-01 -1.76103801e-01 2.14567125e-01 -1.53167263e-01
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90463757-e915-4cb6-a1e6-3d9572b01146 | image-fine-grained-inpainting | 2002.02609 | null | https://arxiv.org/abs/2002.02609v2 | https://arxiv.org/pdf/2002.02609v2.pdf | Image Fine-grained Inpainting | Image inpainting techniques have shown promising improvement with the assistance of generative adversarial networks (GANs) recently. However, most of them often suffered from completed results with unreasonable structure or blurriness. To mitigate this problem, in this paper, we present a one-stage model that utilizes dense combinations of dilated convolutions to obtain larger and more effective receptive fields. Benefited from the property of this network, we can more easily recover large regions in an incomplete image. To better train this efficient generator, except for frequently-used VGG feature matching loss, we design a novel self-guided regression loss for concentrating on uncertain areas and enhancing the semantic details. Besides, we devise a geometrical alignment constraint item to compensate for the pixel-based distance between prediction features and ground-truth ones. We also employ a discriminator with local and global branches to ensure local-global contents consistency. To further improve the quality of generated images, discriminator feature matching on the local branch is introduced, which dynamically minimizes the similarity of intermediate features between synthetic and ground-truth patches. Extensive experiments on several public datasets demonstrate that our approach outperforms current state-of-the-art methods. Code is available at https://github.com/Zheng222/DMFN. | ['Zheng Hui', 'Xiumei Wang', 'Jie Li', 'Xinbo Gao'] | 2020-02-07 | null | null | null | null | ['fine-grained-image-inpainting', 'facial-inpainting'] | ['computer-vision', 'computer-vision'] | [ 2.63675660e-01 2.31441632e-01 2.00932682e-03 -3.29019099e-01
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92776f7a-64d9-431b-9a3e-8e68b146f52d | semantic-distance-a-new-metric-for-asr | 2104.02138 | null | https://arxiv.org/abs/2104.02138v1 | https://arxiv.org/pdf/2104.02138v1.pdf | Semantic Distance: A New Metric for ASR Performance Analysis Towards Spoken Language Understanding | Word Error Rate (WER) has been the predominant metric used to evaluate the performance of automatic speech recognition (ASR) systems. However, WER is sometimes not a good indicator for downstream Natural Language Understanding (NLU) tasks, such as intent recognition, slot filling, and semantic parsing in task-oriented dialog systems. This is because WER takes into consideration only literal correctness instead of semantic correctness, the latter of which is typically more important for these downstream tasks. In this study, we propose a novel Semantic Distance (SemDist) measure as an alternative evaluation metric for ASR systems to address this issue. We define SemDist as the distance between a reference and hypothesis pair in a sentence-level embedding space. To represent the reference and hypothesis as a sentence embedding, we exploit RoBERTa, a state-of-the-art pre-trained deep contextualized language model based on the transformer architecture. We demonstrate the effectiveness of our proposed metric on various downstream tasks, including intent recognition, semantic parsing, and named entity recognition. | ['Michael L. Seltzer', 'Ozlem Kalinli', 'Christian Fuegen', 'Ching-Feng Yeh', 'Duc Le', 'Abhinav Arora', 'Suyoun Kim'] | 2021-04-05 | null | null | null | null | ['intent-recognition'] | ['natural-language-processing'] | [ 3.62785906e-01 2.51695782e-01 6.86413795e-02 -8.62029314e-01
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1d197899-fdec-420e-8178-8eb52b8763ab | anomaly-segmentation-for-high-resolution | 2301.13422 | null | https://arxiv.org/abs/2301.13422v2 | https://arxiv.org/pdf/2301.13422v2.pdf | Anomaly Segmentation for High-Resolution Remote Sensing Images Based on Pixel Descriptors | Anomaly segmentation in high spatial resolution (HSR) remote sensing imagery is aimed at segmenting anomaly patterns of the earth deviating from normal patterns, which plays an important role in various Earth vision applications. However, it is a challenging task due to the complex distribution and the irregular shapes of objects, and the lack of abnormal samples. To tackle these problems, an anomaly segmentation model based on pixel descriptors (ASD) is proposed for anomaly segmentation in HSR imagery. Specifically, deep one-class classification is introduced for anomaly segmentation in the feature space with discriminative pixel descriptors. The ASD model incorporates the data argument for generating virtual ab-normal samples, which can force the pixel descriptors to be compact for normal data and meanwhile to be diverse to avoid the model collapse problems when only positive samples participated in the training. In addition, the ASD introduced a multi-level and multi-scale feature extraction strategy for learning the low-level and semantic information to make the pixel descriptors feature-rich. The proposed ASD model was validated using four HSR datasets and compared with the recent state-of-the-art models, showing its potential value in Earth vision applications. | ['Yanfei Zhong', 'Shaoyu Wang', 'Hengwei Zhao', 'Xinyu Wang', 'Jingtao Li'] | 2023-01-31 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [ 3.54937732e-01 -2.95122743e-01 3.89034420e-01 -3.28371584e-01
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eade902e-8145-4a4e-8903-a56350869b0f | connecting-the-dots-document-level-neural | 1909.00228 | null | https://arxiv.org/abs/1909.00228v1 | https://arxiv.org/pdf/1909.00228v1.pdf | Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented Graphs | Document-level relation extraction is a complex human process that requires logical inference to extract relationships between named entities in text. Existing approaches use graph-based neural models with words as nodes and edges as relations between them, to encode relations across sentences. These models are node-based, i.e., they form pair representations based solely on the two target node representations. However, entity relations can be better expressed through unique edge representations formed as paths between nodes. We thus propose an edge-oriented graph neural model for document-level relation extraction. The model utilises different types of nodes and edges to create a document-level graph. An inference mechanism on the graph edges enables to learn intra- and inter-sentence relations using multi-instance learning internally. Experiments on two document-level biomedical datasets for chemical-disease and gene-disease associations show the usefulness of the proposed edge-oriented approach. | ['Makoto Miwa', 'Fenia Christopoulou', 'Sophia Ananiadou'] | 2019-08-31 | connecting-the-dots-document-level-neural-1 | https://aclanthology.org/D19-1498 | https://aclanthology.org/D19-1498.pdf | ijcnlp-2019-11 | ['document-level-relation-extraction'] | ['natural-language-processing'] | [ 4.82413113e-01 6.93094015e-01 -5.66811144e-01 -6.44688725e-01
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fe005c5a-b738-4785-8849-1b2515e7bce0 | incremental-embedding-for-temporal-networks | 1904.03423 | null | https://arxiv.org/abs/1904.03423v2 | https://arxiv.org/pdf/1904.03423v2.pdf | FILDNE: A Framework for Incremental Learning of Dynamic Networks Embeddings | Representation learning on graphs has emerged as a powerful mechanism to automate feature vector generation for downstream machine learning tasks. The advances in representation on graphs have centered on both homogeneous and heterogeneous graphs, where the latter presenting the challenges associated with multi-typed nodes and/or edges. In this paper, we consider the additional challenge of evolving graphs. We ask the question of whether the advances in representation learning for static graphs can be leveraged for dynamic graphs and how? It is important to be able to incorporate those advances to maximize the utility and generalization of methods. To that end, we propose the Framework for Incremental Learning of Dynamic Networks Embedding (FILDNE), which can utilize any existing static representation learning method for learning node embeddings, while keeping the computational costs low. FILDNE integrates the feature vectors computed using the standard methods over different timesteps into a single representation by developing a convex combination function and alignment mechanism. Experimental results on several downstream tasks, over seven real-world data sets, show that FILDNE is able to reduce memory and computational time costs while providing competitive quality measure gains with respect to the contemporary methods for representation learning on dynamic graphs. | ['Tomasz Kajdanowicz', 'Maciej Falkiewicz', 'Piotr Bielak', 'Nitesh V. Chawla', 'Kamil Tagowski'] | 2019-04-06 | null | null | null | null | ['dynamic-graph-embedding'] | ['graphs'] | [ 2.76023954e-01 2.82772332e-01 -3.10153931e-01 -8.72352067e-03
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8a4c6711-a321-48b0-a6cf-2ffcc1510bad | event-driven-learning-of-systematic | 2010.15586 | null | https://arxiv.org/abs/2010.15586v1 | https://arxiv.org/pdf/2010.15586v1.pdf | Event-Driven Learning of Systematic Behaviours in Stock Markets | It is reported that financial news, especially financial events expressed in news, provide information to investors' long/short decisions and influence the movements of stock markets. Motivated by this, we leverage financial event streams to train a classification neural network that detects latent event-stock linkages and stock markets' systematic behaviours in the U.S. stock market. Our proposed pipeline includes (1) a combined event extraction method that utilizes Open Information Extraction and neural co-reference resolution, (2) a BERT/ALBERT enhanced representation of events, and (3) an extended hierarchical attention network that includes attentions on event, news and temporal levels. Our pipeline achieves significantly better accuracies and higher simulated annualized returns than state-of-the-art models when being applied to predicting Standard\&Poor 500, Dow Jones, Nasdaq indices and 10 individual stocks. | ['Xianchao Wu'] | 2020-10-23 | null | https://aclanthology.org/2020.findings-emnlp.220 | https://aclanthology.org/2020.findings-emnlp.220.pdf | findings-of-the-association-for-computational | ['open-information-extraction'] | ['natural-language-processing'] | [-7.69098938e-01 -1.35220379e-01 -4.32089597e-01 -2.77610689e-01
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766e7649-07c2-4213-b775-7b6de6c922ad | power-bundle-adjustment-for-large-scale-3d | 2204.12834 | null | https://arxiv.org/abs/2204.12834v4 | https://arxiv.org/pdf/2204.12834v4.pdf | Power Bundle Adjustment for Large-Scale 3D Reconstruction | We introduce Power Bundle Adjustment as an expansion type algorithm for solving large-scale bundle adjustment problems. It is based on the power series expansion of the inverse Schur complement and constitutes a new family of solvers that we call inverse expansion methods. We theoretically justify the use of power series and we prove the convergence of our approach. Using the real-world BAL dataset we show that the proposed solver challenges the state-of-the-art iterative methods and significantly accelerates the solution of the normal equation, even for reaching a very high accuracy. This easy-to-implement solver can also complement a recently presented distributed bundle adjustment framework. We demonstrate that employing the proposed Power Bundle Adjustment as a sub-problem solver significantly improves speed and accuracy of the distributed optimization. | ['Tin Chon Chan', 'Daniel Cremers', 'Nikolaus Demmel', 'Simon Weber'] | 2022-04-27 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Weber_Power_Bundle_Adjustment_for_Large-Scale_3D_Reconstruction_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Weber_Power_Bundle_Adjustment_for_Large-Scale_3D_Reconstruction_CVPR_2023_paper.pdf | cvpr-2023-1 | ['distributed-optimization'] | ['methodology'] | [-3.25832009e-01 1.28141074e-02 5.10903656e-01 1.04439020e-01
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aacf351d-ff8e-4c2f-a09d-25c9307455ff | hyper-connected-transformer-network-for-co | 2210.15808 | null | https://arxiv.org/abs/2210.15808v1 | https://arxiv.org/pdf/2210.15808v1.pdf | Hyper-Connected Transformer Network for Co-Learning Multi-Modality PET-CT Features | [18F]-Fluorodeoxyglucose (FDG) positron emission tomography - computed tomography (PET-CT) has become the imaging modality of choice for diagnosing many cancers. Co-learning complementary PET-CT imaging features is a fundamental requirement for automatic tumor segmentation and for developing computer aided cancer diagnosis systems. We propose a hyper-connected transformer (HCT) network that integrates a transformer network (TN) with a hyper connected fusion for multi-modality PET-CT images. The TN was leveraged for its ability to provide global dependencies in image feature learning, which was achieved by using image patch embeddings with a self-attention mechanism to capture image-wide contextual information. We extended the single-modality definition of TN with multiple TN based branches to separately extract image features. We introduced a hyper connected fusion to fuse the contextual and complementary image features across multiple transformers in an iterative manner. Our results with two non-small cell lung cancer and soft-tissue sarcoma datasets show that HCT achieved better performance in segmentation accuracy when compared to state-of-the-art methods. We also show that HCT produces consistent performance across various image fusion strategies and network backbones. | ['Jinman Kim', 'Michael Fulham', 'David Dagan Feng', 'Shaoli Song', 'Qiufang Liu', 'Xiaohang Fu', 'Lei Bi'] | 2022-10-28 | null | null | null | null | ['tumor-segmentation'] | ['computer-vision'] | [ 5.05593359e-01 2.29513958e-01 -6.47617459e-01 -4.42304194e-01
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8efe0fa5-ef5f-4315-af58-0ccd2c7f3a20 | decoupled-pyramid-correlation-network-for | 2205.13199 | null | https://arxiv.org/abs/2205.13199v1 | https://arxiv.org/pdf/2205.13199v1.pdf | Decoupled Pyramid Correlation Network for Liver Tumor Segmentation from CT images | Purpose: Automated liver tumor segmentation from Computed Tomography (CT) images is a necessary prerequisite in the interventions of hepatic abnormalities and surgery planning. However, accurate liver tumor segmentation remains challenging due to the large variability of tumor sizes and inhomogeneous texture. Recent advances based on Fully Convolutional Network (FCN) for medical image segmentation drew on the success of learning discriminative pyramid features. In this paper, we propose a Decoupled Pyramid Correlation Network (DPC-Net) that exploits attention mechanisms to fully leverage both low- and high-level features embedded in FCN to segment liver tumor. Methods: We first design a powerful Pyramid Feature Encoder (PFE) to extract multi-level features from input images. Then we decouple the characteristics of features concerning spatial dimension (i.e., height, width, depth) and semantic dimension (i.e., channel). On top of that, we present two types of attention modules, Spatial Correlation (SpaCor) and Semantic Correlation (SemCor) modules, to recursively measure the correlation of multi-level features. The former selectively emphasizes global semantic information in low-level features with the guidance of high-level ones. The latter adaptively enhance spatial details in high-level features with the guidance of low-level ones. Results: We evaluate the DPC-Net on MICCAI 2017 LiTS Liver Tumor Segmentation (LiTS) challenge dataset. Dice Similarity Coefficient (DSC) and Average Symmetric Surface Distance (ASSD) are employed for evaluation. The proposed method obtains a DSC of 76.4% and an ASSD of 0.838 mm for liver tumor segmentation, outperforming the state-of-the-art methods. It also achieves a competitive results with a DSC of 96.0% and an ASSD of 1.636 mm for liver segmentation. | ['Zhiqiang He', 'Yang Zhang', 'Zhongchao shi', 'Cheng Zhong', 'Siyun Wang', 'Jiang Tian', 'Yang Liu', 'Jiawei Yang', 'Yao Zhang'] | 2022-05-26 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [-1.36206537e-01 -6.90484270e-02 -1.85658768e-01 -2.93717474e-01
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5f4e8583-2fab-4d64-b2b5-84dbc0bc1c7c | privileged-prior-information-distillation-for | 2211.14036 | null | https://arxiv.org/abs/2211.14036v1 | https://arxiv.org/pdf/2211.14036v1.pdf | Privileged Prior Information Distillation for Image Matting | Performance of trimap-free image matting methods is limited when trying to decouple the deterministic and undetermined regions, especially in the scenes where foregrounds are semantically ambiguous, chromaless, or high transmittance. In this paper, we propose a novel framework named Privileged Prior Information Distillation for Image Matting (PPID-IM) that can effectively transfer privileged prior environment-aware information to improve the performance of students in solving hard foregrounds. The prior information of trimap regulates only the teacher model during the training stage, while not being fed into the student network during actual inference. In order to achieve effective privileged cross-modality (i.e. trimap and RGB) information distillation, we introduce a Cross-Level Semantic Distillation (CLSD) module that reinforces the trimap-free students with more knowledgeable semantic representations and environment-aware information. We also propose an Attention-Guided Local Distillation module that efficiently transfers privileged local attributes from the trimap-based teacher to trimap-free students for the guidance of local-region optimization. Extensive experiments demonstrate the effectiveness and superiority of our PPID framework on the task of image matting. In addition, our trimap-free IndexNet-PPID surpasses the other competing state-of-the-art methods by a large margin, especially in scenarios with chromaless, weak texture, or irregular objects. | ['Yong Tang', 'Chuang Zhang', 'Ming Wu', 'Xin Huang', 'Han Huang', 'Cheng Lu', 'Bo Xu', 'Jiake Xie', 'Cheng Lyu'] | 2022-11-25 | null | null | null | null | ['image-matting'] | ['computer-vision'] | [ 3.05586159e-01 2.47781873e-01 -1.96370572e-01 -4.82993513e-01
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32c9d61c-a1de-4229-9f02-c0dc61c5e7ca | lore-logical-location-regression-network-for | 2303.03730 | null | https://arxiv.org/abs/2303.03730v1 | https://arxiv.org/pdf/2303.03730v1.pdf | LORE: Logical Location Regression Network for Table Structure Recognition | Table structure recognition (TSR) aims at extracting tables in images into machine-understandable formats. Recent methods solve this problem by predicting the adjacency relations of detected cell boxes, or learning to generate the corresponding markup sequences from the table images. However, they either count on additional heuristic rules to recover the table structures, or require a huge amount of training data and time-consuming sequential decoders. In this paper, we propose an alternative paradigm. We model TSR as a logical location regression problem and propose a new TSR framework called LORE, standing for LOgical location REgression network, which for the first time combines logical location regression together with spatial location regression of table cells. Our proposed LORE is conceptually simpler, easier to train and more accurate than previous TSR models of other paradigms. Experiments on standard benchmarks demonstrate that LORE consistently outperforms prior arts. Code is available at https:// github.com/AlibabaResearch/AdvancedLiterateMachinery/tree/main/DocumentUnderstanding/LORE-TSR. | ['Zhi Yu', 'Cong Yao', 'Liangcheng Li', 'Qi Zheng', 'Jiajun Bu', 'Rujiao Long', 'Feiyu Gao', 'Hangdi Xing'] | 2023-03-07 | null | null | null | null | ['table-recognition'] | ['computer-vision'] | [ 2.71891475e-01 9.44864526e-02 -6.05445623e-01 -3.14339608e-01
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4bea12d9-94fd-4a38-afc9-141f5c60b20b | elaborative-simplification-as-implicit | 2305.10387 | null | https://arxiv.org/abs/2305.10387v1 | https://arxiv.org/pdf/2305.10387v1.pdf | Elaborative Simplification as Implicit Questions Under Discussion | Automated text simplification, a technique useful for making text more accessible to people such as children and emergent bilinguals, is often thought of as a monolingual translation task from complex sentences to simplified sentences using encoder-decoder models. This view fails to account for elaborative simplification, where new information is added into the simplified text. This paper proposes to view elaborative simplification through the lens of the Question Under Discussion (QUD) framework, providing a robust way to investigate what writers elaborate upon, how they elaborate, and how elaborations fit into the discourse context by viewing elaborations as explicit answers to implicit questions. We introduce ElabQUD, consisting of 1.3K elaborations accompanied with implicit QUDs, to study these phenomena. We show that explicitly modeling QUD (via question generation) not only provides essential understanding of elaborative simplification and how the elaborations connect with the rest of the discourse, but also substantially improves the quality of elaboration generation. | ['Junyi Jessy Li', 'Kyle Mahowald', 'William Sheffield', 'Yating Wu'] | 2023-05-17 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [ 3.29427302e-01 8.79911721e-01 1.67777818e-02 -4.02910620e-01
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4f5abead-ad56-43bc-b3a8-eb49f77483e8 | natural-language-to-code-translation-with | 2204.11454 | null | https://arxiv.org/abs/2204.11454v2 | https://arxiv.org/pdf/2204.11454v2.pdf | Natural Language to Code Translation with Execution | Generative models of code, pretrained on large corpora of programs, have shown great success in translating natural language to code (Chen et al., 2021; Austin et al., 2021; Li et al., 2022, inter alia). While these models do not explicitly incorporate program semantics (i.e., execution results) during training, they are able to generate correct solutions for many problems. However, choosing a single correct program from a generated set for each problem remains challenging. In this work, we introduce execution result--based minimum Bayes risk decoding (MBR-EXEC) for program selection and show that it improves the few-shot performance of pretrained code models on natural-language-to-code tasks. We select output programs from a generated candidate set by marginalizing over program implementations that share the same semantics. Because exact equivalence is intractable, we execute each program on a small number of test inputs to approximate semantic equivalence. Across datasets, execution or simulated execution significantly outperforms the methods that do not involve program semantics. We find that MBR-EXEC consistently improves over all execution-unaware selection methods, suggesting it as an effective approach for natural language to code translation. We open-source our code at github.com/facebookresearch/mbr-exec and data at dl.fbaipublicfiles.com/mbr-exec/mbr-exec-release.zip | ['Sida I. Wang', 'Luke Zettlemoyer', 'Marjan Ghazvininejad', 'Daniel Fried', 'Freda Shi'] | 2022-04-25 | null | null | null | null | ['code-translation'] | ['computer-code'] | [ 1.16012603e-01 -1.97160035e-01 -6.66205347e-01 -5.09634614e-01
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1829e20d-7c17-4dd2-acde-e23e6525a767 | long-term-person-re-identification-with | null | null | https://dl.acm.org/doi/abs/10.1145/3503161.3548327 | https://dl.acm.org/doi/pdf/10.1145/3503161.3548327 | Long-Term Person Re-identification with Dramatic Appearance Change: Algorithm and Benchmark | For person re-identification (Re-ID) task, most of previous studies assumed that the pedestrians do not change their appearances. The works on cross-appearance Re-ID, including datasets and algorithms, are still few. Therefore, this paper contributes a cross-season appearance change Re-ID dataset, namely NKUP+, including more than 300 IDs from surveillance videos over 10 months, to support the studies of the cross-appearance Re-ID. In addition, we propose a network named M2Net, which integrates multi-modality features from the RGB images, contour images and human parsing images. By ignoring irrelevant misleading information for cross-appearance retrieval in RGB images, M2Net can learn features that are robust to appearance changes. Meanwhile, we propose a sampling strategy called RAS to contain a variety of appearances in one batch. And appearance loss and multi-appearance loss are designed to guide the network to learn both same-appearance and cross-appearance features. Finally, we evaluated our method on NKUP+/PRCC/DeepChange datasets, and the results showed that, compared with the baseline, our method renders significant improvement, leading to the state-of-the-art performance over other methods. Our dataset is available at https://github.com/nkicsl/NKUP-dataset. | ['Kai Wang', 'Yanfeng Jiang', 'Tao Li', 'Zhi Ma', 'Mengmeng Liu'] | 2022-10-01 | null | null | null | mm-22-proceedings-of-the-30th-acm | ['person-re-identification', 'human-parsing'] | ['computer-vision', 'computer-vision'] | [-2.68645734e-01 -6.81099296e-01 1.34987205e-01 -5.80559433e-01
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aa3d1a4b-ed69-4b11-854e-304d6d2e7ae9 | joint-transmission-map-estimation-and | 1708.00581 | null | http://arxiv.org/abs/1708.00581v2 | http://arxiv.org/pdf/1708.00581v2.pdf | Joint Transmission Map Estimation and Dehazing using Deep Networks | Single image haze removal is an extremely challenging problem due to its
inherent ill-posed nature. Several prior-based and learning-based methods have
been proposed in the literature to solve this problem and they have achieved
superior results. However, most of the existing methods assume constant
atmospheric light model and tend to follow a two-step procedure involving
prior-based methods for estimating transmission map followed by calculation of
dehazed image using the closed form solution. In this paper, we relax the
constant atmospheric light assumption and propose a novel unified single image
dehazing network that jointly estimates the transmission map and performs
dehazing. In other words, our new approach provides an end-to-end learning
framework, where the inherent transmission map and dehazed result are learned
directly from the loss function. Extensive experiments on synthetic and real
datasets with challenging hazy images demonstrate that the proposed method
achieves significant improvements over the state-of-the-art methods. | ['He Zhang', 'Vishal M. Patel', 'Vishwanath Sindagi'] | 2017-08-02 | null | null | null | null | ['single-image-haze-removal'] | ['computer-vision'] | [ 3.72236252e-01 -4.14577395e-01 4.89424825e-01 -3.79551560e-01
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551fba41-d403-4efd-9e93-44405a21ed34 | cross-corpora-language-recognition-a | 2105.04639 | null | https://arxiv.org/abs/2105.04639v2 | https://arxiv.org/pdf/2105.04639v2.pdf | Cross-Corpora Language Recognition: A Preliminary Investigation with Indian Languages | In this paper, we conduct one of the very first studies for cross-corpora performance evaluation in the spoken language identification (LID) problem. Cross-corpora evaluation was not explored much in LID research, especially for the Indian languages. We have selected three Indian spoken language corpora: IIITH-ILSC, LDC South Asian, and IITKGP-MLILSC. For each of the corpus, LID systems are trained on the state-of-the-art time-delay neural network (TDNN) based architecture with MFCC features. We observe that the LID performance degrades drastically for cross-corpora evaluation. For example, the system trained on the IIITH-ILSC corpus shows an average EER of 11.80 % and 43.34 % when evaluated with the same corpora and LDC South Asian corpora, respectively. Our preliminary analysis shows the significant differences among these corpora in terms of mismatch in the long-term average spectrum (LTAS) and signal-to-noise ratio (SNR). Subsequently, we apply different feature level compensation methods to reduce the cross-corpora acoustic mismatch. Our results indicate that these feature normalization schemes can help to achieve promising LID performance on cross-corpora experiments. | ['Md Sahidullah', 'Goutam Saha', 'Spandan Dey'] | 2021-05-10 | null | null | null | null | ['spoken-language-identification'] | ['speech'] | [-3.96177173e-01 -4.00264621e-01 1.82056472e-01 -1.60846576e-01
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d49fb7c4-fd67-44b0-a6c7-b393e9db20fe | audio-diffusion-model-for-speech-synthesis-a | 2303.13336 | null | https://arxiv.org/abs/2303.13336v2 | https://arxiv.org/pdf/2303.13336v2.pdf | A Survey on Audio Diffusion Models: Text To Speech Synthesis and Enhancement in Generative AI | Generative AI has demonstrated impressive performance in various fields, among which speech synthesis is an interesting direction. With the diffusion model as the most popular generative model, numerous works have attempted two active tasks: text to speech and speech enhancement. This work conducts a survey on audio diffusion model, which is complementary to existing surveys that either lack the recent progress of diffusion-based speech synthesis or highlight an overall picture of applying diffusion model in multiple fields. Specifically, this work first briefly introduces the background of audio and diffusion model. As for the text-to-speech task, we divide the methods into three categories based on the stage where diffusion model is adopted: acoustic model, vocoder and end-to-end framework. Moreover, we categorize various speech enhancement tasks by either certain signals are removed or added into the input speech. Comparisons of experimental results and discussions are also covered in this survey. | ['In So Kweon', 'Sung-Ho Bae', 'Maryam Qamar', 'Mengchun Zhang', 'Sheng Zheng', 'Chaoning Zhang', 'Chenshuang Zhang'] | 2023-03-23 | null | null | null | null | ['text-to-speech-synthesis', 'speech-enhancement', 'speech-synthesis'] | ['speech', 'speech', 'speech'] | [ 4.05373096e-01 2.74869919e-01 1.12126440e-01 -2.10810438e-01
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b21237a5-6b58-4738-ae6e-537dc262b2bb | classification-of-fib-sem-tomography-images | 2207.14114 | null | https://arxiv.org/abs/2207.14114v1 | https://arxiv.org/pdf/2207.14114v1.pdf | Classification of FIB/SEM-tomography images for highly porous multiphase materials using random forest classifiers | FIB/SEM tomography represents an indispensable tool for the characterization of three-dimensional nanostructures in battery research and many other fields. However, contrast and 3D classification/reconstruction problems occur in many cases, which strongly limits the applicability of the technique especially on porous materials, like those used for electrode materials in batteries or fuel cells. Distinguishing the different components like active Li storage particles and carbon/binder materials is difficult and often prevents a reliable quantitative analysis of image data, or may even lead to wrong conclusions about structure-property relationships. In this contribution, we present a novel approach for data classification in three-dimensional image data obtained by FIB/SEM tomography and its applications to NMC battery electrode materials. We use two different image signals, namely the signal of the angled SE2 chamber detector and the Inlens detector signal, combine both signals and train a random forest, i.e. a particular machine learning algorithm. We demonstrate that this approach can overcome current limitations of existing techniques suitable for multi-phase measurements and that it allows for quantitative data reconstruction even where current state-of the art techniques fail, or demand for large training sets. This approach may yield as guideline for future research using FIB/SEM tomography. | ['Ingo Manke', 'John Banhart', 'Volker Schmidt', 'Joachim R. Binder', 'Nicole Bohn', 'Amalia Wagner', 'Matthias Neumann', 'André Hilger', 'Markus Osenberg'] | 2022-07-28 | null | null | null | null | ['3d-classification'] | ['computer-vision'] | [ 4.70075697e-01 -4.84799862e-01 1.21300146e-01 -1.81237943e-02
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0853a49f-ce0b-4778-8029-379f9ec1d57f | mrnet-a-multi-scale-residual-network-for-eeg | 2101.02538 | null | https://arxiv.org/abs/2101.02538v1 | https://arxiv.org/pdf/2101.02538v1.pdf | MRNet: a Multi-scale Residual Network for EEG-based Sleep Staging | Sleep staging based on electroencephalogram (EEG) plays an important role in the clinical diagnosis and treatment of sleep disorders. In order to emancipate human experts from heavy labeling work, deep neural networks have been employed to formulate automated sleep staging systems recently. However, EEG signals lose considerable detailed information in network propagation, which affects the representation of deep features. To address this problem, we propose a new framework, called MRNet, for data-driven sleep staging by integrating a multi-scale feature fusion model and a Markov-based sequential correction algorithm. The backbone of MRNet is a residual block-based network, which performs as a feature extractor.Then the fusion model constructs a feature pyramid by concatenating the outputs from the different depths of the backbone, which can help the network better comprehend the signals in different scales. The Markov-based sequential correction algorithm is designed to reduce the output jitters generated by the classifier. The algorithm depends on a prior stage distribution associated with the sleep stage transition rule and the Markov chain. Experiment results demonstrate the competitive performance of our proposed approach on both accuracy and F1 score (e.g., 85.14% Acc and 78.91% F1 score on Sleep-EDFx, and 87.59% Acc and 79.62% F1 score on Sleep-EDF). | ['Xue Jiang'] | 2021-01-07 | null | null | null | null | ['sleep-staging', 'eeg-based-sleep-staging'] | ['medical', 'time-series'] | [ 2.55913883e-02 -3.32120717e-01 -3.26994546e-02 -6.30888402e-01
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20a701f4-778c-476d-ba2e-93eb6b96c775 | hgcc-enhancing-hyperbolic-graph-convolution | 2304.02961 | null | https://arxiv.org/abs/2304.02961v1 | https://arxiv.org/pdf/2304.02961v1.pdf | HGCC: Enhancing Hyperbolic Graph Convolution Networks on Heterogeneous Collaborative Graph for Recommendation | Due to the naturally power-law distributed nature of user-item interaction data in recommendation tasks, hyperbolic space modeling has recently been introduced into collaborative filtering methods. Among them, hyperbolic GCN combines the advantages of GCN and hyperbolic space and achieves a surprising performance. However, these methods only partially exploit the nature of hyperbolic space in their designs due to completely random embedding initialization and an inaccurate tangent space aggregation. In addition, the data used in these works mainly focus on user-item interaction data only, which further limits the performance of the models. In this paper, we propose a hyperbolic GCN collaborative filtering model, HGCC, which improves the existing hyperbolic GCN structure for collaborative filtering and incorporates side information. It keeps the long-tailed nature of the collaborative graph by adding power law prior to node embedding initialization; then, it aggregates neighbors directly in multiple hyperbolic spaces through the gyromidpoint method to obtain more accurate computation results; finally, the gate fusion with prior is used to fuse multiple embeddings of one node from different hyperbolic space automatically. Experimental results on four real datasets show that our model is highly competitive and outperforms leading baselines, including hyperbolic GCNs. Further experiments validate the efficacy of our proposed approach and give a further explanation by the learned embedding. | ['Ning Wu', 'Lu Zhang'] | 2023-04-06 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [-6.82961166e-01 -1.06301658e-01 -4.92990576e-03 -2.11655721e-01
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2120dd1f-8543-4e0a-89e1-9b899577637b | tropical-cyclone-track-forecasting-using | 1910.10566 | null | https://arxiv.org/abs/1910.10566v2 | https://arxiv.org/pdf/1910.10566v2.pdf | Tropical Cyclone Track Forecasting using Fused Deep Learning from Aligned Reanalysis Data | The forecast of tropical cyclone trajectories is crucial for the protection of people and property. Although forecast dynamical models can provide high-precision short-term forecasts, they are computationally demanding, and current statistical forecasting models have much room for improvement given that the database of past hurricanes is constantly growing. Machine learning methods, that can capture non-linearities and complex relations, have only been scarcely tested for this application. We propose a neural network model fusing past trajectory data and reanalysis atmospheric images (wind and pressure 3D fields). We use a moving frame of reference that follows the storm center for the 24h tracking forecast. The network is trained to estimate the longitude and latitude displacement of tropical cyclones and depressions from a large database from both hemispheres (more than 3000 storms since 1979, sampled at a 6 hour frequency). The advantage of the fused network is demonstrated and a comparison with current forecast models shows that deep learning methods could provide a valuable and complementary prediction. Moreover, our method can give a forecast for a new storm in a few seconds, which is an important asset for real-time forecasts compared to traditional forecasts. | ['Balázs Kégl', 'Christina Kumler-Bonfanti', 'Sophie Giffard-Roisin', 'Guillaume Charpiat', 'Mo Yang', 'Claire Monteleoni'] | 2019-10-23 | null | null | null | null | ['tropical-cyclone-track-forecasting'] | ['time-series'] | [-5.59458971e-01 -6.84114814e-01 -1.32291481e-01 -7.25187719e-01
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67d781a6-9c29-4808-8aec-4fd6e3b67d75 | music-source-separation-in-the-waveform-1 | 1911.13254 | null | https://arxiv.org/abs/1911.13254v2 | https://arxiv.org/pdf/1911.13254v2.pdf | Music Source Separation in the Waveform Domain | Source separation for music is the task of isolating contributions, or stems, from different instruments recorded individually and arranged together to form a song. Such components include voice, bass, drums and any other accompaniments.Contrarily to many audio synthesis tasks where the best performances are achieved by models that directly generate the waveform, the state-of-the-art in source separation for music is to compute masks on the magnitude spectrum. In this paper, we compare two waveform domain architectures. We first adapt Conv-Tasnet, initially developed for speech source separation,to the task of music source separation. While Conv-Tasnet beats many existing spectrogram-domain methods, it suffersfrom significant artifacts, as shown by human evaluations. We propose instead Demucs, a novel waveform-to-waveform model,with a U-Net structure and bidirectional LSTM.Experiments on the MusDB dataset show that, with proper data augmentation, Demucs beats allexisting state-of-the-art architectures, including Conv-Tasnet, with 6.3 SDR on average, (and up to 6.8 with 150 extra training songs, even surpassing the IRM oracle for the bass source).Using recent development in model quantization, Demucs can be compressed down to 120MBwithout any loss of accuracy.We also provide human evaluations, showing that Demucs benefit from a large advantagein terms of the naturalness of the audio. However, it suffers from some bleeding,especially between the vocals and other source. | ['Léon Bottou', 'Nicolas Usunier', 'Alexandre Défossez', 'Francis Bach'] | 2019-11-27 | null | https://openreview.net/forum?id=HJx7uJStPH | https://openreview.net/pdf?id=HJx7uJStPH | null | ['audio-generation', 'music-source-separation'] | ['audio', 'music'] | [ 3.68188143e-01 -2.73865908e-01 1.72348514e-01 1.13613106e-01
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a1098ef2-89df-4e66-8ce5-95643236b473 | world-from-blur | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Qiu_World_From_Blur_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Qiu_World_From_Blur_CVPR_2019_paper.pdf | World From Blur | What can we tell from a single motion-blurred image? We show in this paper that a 3D scene can be revealed. Unlike prior methods that focus on producing a deblurred image, we propose to estimate and take advantage of the hidden message of a blurred image, the relative motion trajectory, to restore the 3D scene collapsed during the exposure process. To this end, we train a deep network that jointly predicts the motion trajectory, the deblurred image, and the depth one, all of which in turn form a collaborative and self-supervised cycle that supervise one another to reproduce the input blurred image, enabling plausible 3D scene reconstruction from a single blurred image. We test the proposed model on several large-scale datasets we constructed based on benchmarks, as well as real-world blurred images, and show that it yields very encouraging quantitative and qualitative results.
| [' Dacheng Tao', ' Stephen J. Maybank', ' Xinchao Wang', 'Jiayan Qiu'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['3d-scene-reconstruction'] | ['computer-vision'] | [ 4.55785453e-01 2.33147591e-02 4.58748102e-01 -3.79677534e-01
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44d7b0d8-15f4-4378-b480-6d645f37f0cf | fisher-discriminant-triplet-and-contrastive | 2004.04674 | null | https://arxiv.org/abs/2004.04674v1 | https://arxiv.org/pdf/2004.04674v1.pdf | Fisher Discriminant Triplet and Contrastive Losses for Training Siamese Networks | Siamese neural network is a very powerful architecture for both feature extraction and metric learning. It usually consists of several networks that share weights. The Siamese concept is topology-agnostic and can use any neural network as its backbone. The two most popular loss functions for training these networks are the triplet and contrastive loss functions. In this paper, we propose two novel loss functions, named Fisher Discriminant Triplet (FDT) and Fisher Discriminant Contrastive (FDC). The former uses anchor-neighbor-distant triplets while the latter utilizes pairs of anchor-neighbor and anchor-distant samples. The FDT and FDC loss functions are designed based on the statistical formulation of the Fisher Discriminant Analysis (FDA), which is a linear subspace learning method. Our experiments on the MNIST and two challenging and publicly available histopathology datasets show the effectiveness of the proposed loss functions. | ['Sobhan Shafiei', 'Milad Sikaroudi', 'Fakhri Karray', 'Mark Crowley', 'H. R. Tizhoosh', 'Benyamin Ghojogh'] | 2020-04-05 | null | null | null | null | ['histopathological-image-classification', 'classification-of-breast-cancer-histology'] | ['medical', 'medical'] | [-2.99518127e-02 -4.94538903e-01 -2.14579403e-01 -6.73768759e-01
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72d6ba9c-bf35-4577-8155-2556e562eeb9 | a-named-entity-recognition-corpus-for | null | null | https://aclanthology.org/2022.lrec-1.385 | https://aclanthology.org/2022.lrec-1.385.pdf | A Named Entity Recognition Corpus for Vietnamese Biomedical Texts to Support Tuberculosis Treatment | Named Entity Recognition (NER) is an important task in information extraction. However, due to the lack of labelled corpora, biomedical NER has scarcely been studied in Vietnamese compared to English. To address this situation, we have constructed VietBioNER, a labelled NER corpus of Vietnamese academic biomedical text. The corpus focuses specifically on supporting tuberculosis surveillance, and was constructed by collecting scientific papers and grey literature related to tuberculosis symptoms and diagnostics. We manually annotated a small set of the collected documents with five categories of named entities: Organisation, Location, Date and Time, Symptom and Disease, and Diagnostic Procedure. Inter-annotator agreement ranges from 70.59% and 95.89% F-score according to entity category. In this paper, we make available two splits of the corpus, corresponding to traditional supervised learning and few-shot learning settings. We also provide baseline results for both of these settings, in addition to a dictionary-based approach, as a means to stimulate further research into Vietnamese biomedical NER. Although supervised methods produce results that are far superior to the other two approaches, the fact that even one-shot learning can outperform the dictionary-based method provides evidence that further research into few-shot learning on this text type would be worthwhile. | ['Nhung Nguyen', 'Phuong N.V Nguyen', 'Uyen Phan'] | null | null | null | null | lrec-2022-6 | ['one-shot-learning'] | ['methodology'] | [ 1.62738830e-01 1.73616812e-01 -3.29238653e-01 -7.15663955e-02
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2774b0bc-317b-41cd-a1f1-67b596c02811 | constrained-policy-optimization-for | 2209.08429 | null | https://arxiv.org/abs/2209.08429v1 | https://arxiv.org/pdf/2209.08429v1.pdf | Constrained Policy Optimization for Controlled Self-Learning in Conversational AI Systems | Recently, self-learning methods based on user satisfaction metrics and contextual bandits have shown promising results to enable consistent improvements in conversational AI systems. However, directly targeting such metrics by off-policy bandit learning objectives often increases the risk of making abrupt policy changes that break the current user experience. In this study, we introduce a scalable framework for supporting fine-grained exploration targets for individual domains via user-defined constraints. For example, we may want to ensure fewer policy deviations in business-critical domains such as shopping, while allocating more exploration budget to domains such as music. Furthermore, we present a novel meta-gradient learning approach that is scalable and practical to address this problem. The proposed method adjusts constraint violation penalty terms adaptively through a meta objective that encourages balanced constraint satisfaction across domains. We conduct extensive experiments using data from a real-world conversational AI on a set of realistic constraint benchmarks. Based on the experimental results, we demonstrate that the proposed approach is capable of achieving the best balance between the policy value and constraint satisfaction rate. | ['Sungjin Lee', 'Mohammad Kachuee'] | 2022-09-17 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [ 9.77820829e-02 1.42513111e-01 -8.71586919e-01 -5.25117218e-01
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454922b4-2b71-4030-889e-8d00f87873be | data-driven-input-reconstruction-and | 2203.02827 | null | https://arxiv.org/abs/2203.02827v1 | https://arxiv.org/pdf/2203.02827v1.pdf | Data-driven input reconstruction and experimental validation | This paper addresses a data-driven input reconstruction problem based on Willems' Fundamental Lemma in which unknown input estimators (UIEs) are constructed directly from historical I/O data. Given only output measurements, the inputs are estimated by the UIE, which is shown to asymptotically converge to the true input without knowing the initial conditions. Both open-loop and closed-loop UIEs are developed based on Lyapunov conditions and the Luenberger-observer-type feedback, whose convergence properties are studied. An experimental study is presented demonstrating the efficacy of the closed-loop UIE for estimating the occupancy of a building on the EPFL campus via measured carbon dioxide levels. | ['Colin N. Jones', 'Yingzhao Lian', 'Jicheng Shi'] | 2022-03-05 | null | null | null | null | ['uie'] | ['computer-vision'] | [ 3.16988200e-01 2.57173449e-01 -3.62379640e-01 2.19415978e-01
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29795d9b-ed3e-4ce0-9b75-34d0c6ad603f | convkn-at-semeval-2016-task-3-answer-and | null | null | https://aclanthology.org/S16-1138 | https://aclanthology.org/S16-1138.pdf | ConvKN at SemEval-2016 Task 3: Answer and Question Selection for Question Answering on Arabic and English Fora | null | ['Kateryna Tymoshenko', 'ro', "Alberto Barr{\\'o}n-Cede{\\~n}o", 'Giovanni Da San Martino', 'Shafiq Joty', 'Antonio Uva', 'Salvatore Romeo', 'Fahad Al Obaidli', 'Daniele Bonadiman', 'Aless Moschitti'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['question-selection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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7977cdc4-ed8d-4ba8-a16e-f10fd9c1036f | onix-an-x-ray-deep-learning-tool-for-3d | 2203.00682 | null | https://arxiv.org/abs/2203.00682v1 | https://arxiv.org/pdf/2203.00682v1.pdf | ONIX: an X-ray deep-learning tool for 3D reconstructions from sparse views | Three-dimensional (3D) X-ray imaging techniques like tomography and confocal microscopy are crucial for academic and industrial applications. These approaches access 3D information by scanning the sample with respect to the X-ray source. However, the scanning process limits the temporal resolution when studying dynamics and is not feasible for some applications, such as surgical guidance in medical applications. Alternatives to obtaining 3D information when scanning is not possible are X-ray stereoscopy and multi-projection imaging. However, these approaches suffer from limited volumetric information as they only acquire a small number of views or projections compared to traditional 3D scanning techniques. Here, we present ONIX (Optimized Neural Implicit X-ray imaging), a deep-learning algorithm capable of retrieving 3D objects with arbitrary large resolution from only a set of sparse projections. ONIX, although it does not have access to any volumetric information, outperforms current 3D reconstruction approaches because it includes the physics of image formation with X-rays, and it generalizes across different experiments over similar samples to overcome the limited volumetric information provided by sparse views. We demonstrate the capabilities of ONIX compared to state-of-the-art tomographic reconstruction algorithms by applying it to simulated and experimental datasets, where a maximum of eight projections are acquired. We anticipate that ONIX will become a crucial tool for the X-ray community by i) enabling the study of fast dynamics not possible today when implemented together with X-ray multi-projection imaging, and ii) enhancing the volumetric information and capabilities of X-ray stereoscopic imaging in medical applications. | ['Pablo Villanueva-Perez', 'Tobias Ritschel', 'Zisheng Yao', 'Yuhe Zhang'] | 2022-03-01 | null | null | null | null | ['3d-object-reconstruction'] | ['computer-vision'] | [ 3.14084888e-01 -3.53918254e-01 -4.76962999e-02 -7.75730312e-02
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fd81fa74-d3c8-45ea-9159-ea0614bb97cd | treeprompt-learning-to-compose-tree-prompts | 2305.11497 | null | https://arxiv.org/abs/2305.11497v1 | https://arxiv.org/pdf/2305.11497v1.pdf | TreePrompt: Learning to Compose Tree Prompts for Explainable Visual Grounding | Prompt tuning has achieved great success in transferring the knowledge from large pretrained vision-language models into downstream tasks, and has dominated the performance on visual grounding (VG). However, almost all existing prompt tuning paradigms suffer from poor interpretability. In this paper, we argue that their poor interpretability is attributed to the holistic prompt generation and inference process. By "holistic", we mean that they usually directly learn a set of vectors as the prompt (i.e., prompt generation), and use the learned global prompt to augment the textual input for the VG model (i.e., prompt inference). To this end, we propose a new prompt construction paradigm with explicit explainable ability, named TreePrompt. Specifically, we first deconstruct a complex sentence into a tree, that is consistent with human reasoning. Then, following the syntax tree, we compose a structured prompt in a bottom-up manner. Thanks to this step-by-step prompt construction process, each intermediate prompt (i.e., tree node) permits us to understand the reasoning process. Extensive ablations on various backbones and benchmarks consistently demonstrate the effectiveness and interpretability of our TreePrompt. | ['Long Chen', 'Jian Shao', 'Lei Chen', 'Jun Xiao', 'Chenchi Zhang'] | 2023-05-19 | null | null | null | null | ['visual-grounding'] | ['computer-vision'] | [ 3.93701404e-01 4.09188747e-01 -9.43460390e-02 -5.46837151e-01
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b75a1181-3dac-4bd1-86c1-43d2c453cad2 | comprehensive-review-of-deep-learning-based | 2203.03311 | null | https://arxiv.org/abs/2203.03311v3 | https://arxiv.org/pdf/2203.03311v3.pdf | Comprehensive Review of Deep Learning-Based 3D Point Cloud Completion Processing and Analysis | Point cloud completion is a generation and estimation issue derived from the partial point clouds, which plays a vital role in the applications in 3D computer vision. The progress of deep learning (DL) has impressively improved the capability and robustness of point cloud completion. However, the quality of completed point clouds is still needed to be further enhanced to meet the practical utilization. Therefore, this work aims to conduct a comprehensive survey on various methods, including point-based, convolution-based, graph-based, and generative model-based approaches, etc. And this survey summarizes the comparisons among these methods to provoke further research insights. Besides, this review sums up the commonly used datasets and illustrates the applications of point cloud completion. Eventually, we also discussed possible research trends in this promptly expanding field. | ['Lipeng Ma', 'Xing Hu', 'Tao Ma', 'Yikang Li', 'Zhijun Li', 'Wenming Chen', 'Weidong Yang', 'Ben Fei'] | 2022-03-07 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [-1.97905988e-01 -4.24199760e-01 1.69316754e-01 -1.75452024e-01
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d8985245-6c20-4277-b6e6-bd7bc36ff185 | docee-a-large-scale-and-fine-grained-1 | null | null | https://aclanthology.org/2022.naacl-main.291 | https://aclanthology.org/2022.naacl-main.291.pdf | DocEE: A Large-Scale and Fine-grained Benchmark for Document-level Event Extraction | Event extraction aims to identify an event and then extract the arguments participating in the event. Despite the great success in sentence-level event extraction, events are more naturally presented in the form of documents, with event arguments scattered in multiple sentences. However, a major barrier to promote document-level event extraction has been the lack of large-scale and practical training and evaluation datasets. In this paper, we present DocEE, a new document-level event extraction dataset including 27,000+ events, 180,000+ arguments. We highlight three features: large-scale manual annotations, fine-grained argument types and application-oriented settings. Experiments show that there is still a big gap between state-of-the-art models and human beings (41% Vs 85% in F1 score), indicating that DocEE is an open issue. DocEE is now available at https://github.com/tongmeihan1995/DocEE.git. | ['Juanzi Li', 'Lei Hou', 'Siyu Chen', 'Jiangqi Zhu', 'Yixin Cao', 'Meihuan Han', 'Shuai Wang', 'Bin Xu', 'Meihan Tong'] | null | null | null | null | naacl-2022-7 | ['document-level-event-extraction'] | ['natural-language-processing'] | [ 1.47176608e-01 4.30063963e-01 -1.60223231e-01 -2.46437684e-01
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d29b4cbb-209c-4702-98b7-6ac7fab8e8b1 | drug-response-prediction-by-inferring-pathway | 1606.03623 | null | http://arxiv.org/abs/1606.03623v1 | http://arxiv.org/pdf/1606.03623v1.pdf | Drug response prediction by inferring pathway-response associations with Kernelized Bayesian Matrix Factorization | A key goal of computational personalized medicine is to systematically
utilize genomic and other molecular features of samples to predict drug
responses for a previously unseen sample. Such predictions are valuable for
developing hypotheses for selecting therapies tailored for individual patients.
This is especially valuable in oncology, where molecular and genetic
heterogeneity of the cells has a major impact on the response. However, the
prediction task is extremely challenging, raising the need for methods that can
effectively model and predict drug responses. In this study, we propose a novel
formulation of multi-task matrix factorization that allows selective data
integration for predicting drug responses. To solve the modeling task, we
extend the state-of-the-art kernelized Bayesian matrix factorization (KBMF)
method with component-wise multiple kernel learning. In addition, our approach
exploits the known pathway information in a novel and biologically meaningful
fashion to learn the drug response associations. Our method quantitatively
outperforms the state of the art on predicting drug responses in two publicly
available cancer data sets as well as on a synthetic data set. In addition, we
validated our model predictions with lab experiments using an in-house cancer
cell line panel. We finally show the practical applicability of the proposed
method by utilizing prior knowledge to infer pathway-drug response
associations, opening up the opportunity for elucidating drug action
mechanisms. We demonstrate that pathway-response associations can be learned by
the proposed model for the well known EGFR and MEK inhibitors. | ['Olli Kallioniemi', 'Astrid Murumägi', 'Disha Malani', 'Muhammad Ammad-Ud-Din', 'Suleiman A. Khan', 'Tero Aittokallio', 'Samuel Kaski'] | 2016-06-11 | null | null | null | null | ['drug-response-prediction'] | ['medical'] | [ 5.34084201e-01 -4.75777239e-01 -7.42661655e-01 -1.04756676e-01
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c26b6de7-be92-4916-a89d-d253acb2ced3 | dias-a-comprehensive-benchmark-for-dsa | 2306.12153 | null | https://arxiv.org/abs/2306.12153v1 | https://arxiv.org/pdf/2306.12153v1.pdf | DIAS: A Comprehensive Benchmark for DSA-sequence Intracranial Artery Segmentation | Automatic segmentation of the intracranial artery (IA) in digital subtraction angiography (DSA) sequence is an essential step in diagnosing IA-related diseases and guiding neuro-interventional surgery. However, the lack of publicly available datasets has impeded research in this area. In this paper, we release DIAS, an IA segmentation dataset, consisting of 120 DSA sequences from intracranial interventional therapy. In addition to pixel-wise annotations, this dataset provides two types of scribble annotations for weakly supervised IA segmentation research. We present a comprehensive benchmark for evaluating the performance of this challenging dataset by utilizing fully-, weakly-, and semi-supervised learning approaches. Specifically, we propose a method that incorporates a dimensionality reduction module into a 2D/3D model to achieve vessel segmentation in DSA sequences. For weakly-supervised learning, we propose a scribble learning-based image segmentation framework, SSCR, which comprises scribble supervision and consistency regularization. Furthermore, we introduce a random patch-based self-training framework that utilizes unlabeled DSA sequences to improve segmentation performance. Our extensive experiments on the DIAS dataset demonstrate the effectiveness of these methods as potential baselines for future research and clinical applications. | ['Ruisheng Su', 'Yiming Deng', 'Feng Gao', 'Huihua Yang', 'Xipeng Pan', 'Wenyi Zhao', 'Haoyuan Li', 'Weijin Xu', 'Lemeng Wang', 'Tong Tian', 'Wentao Liu'] | 2023-06-21 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [ 2.13762045e-01 -8.15511248e-06 -4.14767414e-01 -6.15039051e-01
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0de46beb-8b62-49c4-9fb7-59c0015e9ddc | predicting-user-engagement-in-twitter-with | 1412.7990 | null | http://arxiv.org/abs/1412.7990v1 | http://arxiv.org/pdf/1412.7990v1.pdf | Predicting User Engagement in Twitter with Collaborative Ranking | Collaborative Filtering (CF) is a core component of popular web-based
services such as Amazon, YouTube, Netflix, and Twitter. Most applications use
CF to recommend a small set of items to the user. For instance, YouTube
presents to a user a list of top-n videos she would likely watch next based on
her rating and viewing history. Current methods of CF evaluation have been
focused on assessing the quality of a predicted rating or the ranking
performance for top-n recommended items. However, restricting the recommender
system evaluation to these two aspects is rather limiting and neglects other
dimensions that could better characterize a well-perceived recommendation. In
this paper, instead of optimizing rating or top-n recommendation, we focus on
the task of predicting which items generate the highest user engagement. In
particular, we use Twitter as our testbed and cast the problem as a
Collaborative Ranking task where the rich features extracted from the metadata
of the tweets help to complement the transaction information limited to user
ids, item ids, ratings and timestamps. We learn a scoring function that
directly optimizes the user engagement in terms of nDCG@10 on the predicted
ranking. Experiments conducted on an extended version of the MovieTweetings
dataset, released as part of the RecSys Challenge 2014, show the effectiveness
of our approach. | ['Michele Berlingerio', 'Ernesto Diaz-Aviles', 'Yiannis Gkoufas', 'Stefano Braghin', 'Fabio Pinelli', 'Hoang Thanh Lam', 'Francesco Calabrese'] | 2014-12-26 | null | null | null | null | ['collaborative-ranking'] | ['graphs'] | [-2.90290236e-01 -4.40021753e-01 -6.58664882e-01 -6.16400123e-01
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e961d9e9-33f0-4f5d-bba2-e55d31cfe308 | edgenet-semantic-scene-completion-from-rgb-d | 1908.02893 | null | https://arxiv.org/abs/1908.02893v2 | https://arxiv.org/pdf/1908.02893v2.pdf | EdgeNet: Semantic Scene Completion from a Single RGB-D Image | Semantic scene completion is the task of predicting a complete 3D representation of volumetric occupancy with corresponding semantic labels for a scene from a single point of view. Previous works on Semantic Scene Completion from RGB-D data used either only depth or depth with colour by projecting the 2D image into the 3D volume resulting in a sparse data representation. In this work, we present a new strategy to encode colour information in 3D space using edge detection and flipped truncated signed distance. We also present EdgeNet, a new end-to-end neural network architecture capable of handling features generated from the fusion of depth and edge information. Experimental results show improvement of 6.9% over the state-of-the-art result on real data, for end-to-end approaches. | ['Hansung Kim', 'Aloisio Dourado', 'Adrian Hilton', 'Teofilo Emidio de Campos'] | 2019-08-08 | null | null | null | null | ['3d-semantic-scene-completion'] | ['computer-vision'] | [ 6.31518781e-01 2.49686658e-01 3.40321034e-01 -7.24912465e-01
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e5920979-c738-41e6-a900-e71e3450c643 | 3d-shape-synthesis-for-conceptual-design-and | 1904.07964 | null | http://arxiv.org/abs/1904.07964v1 | http://arxiv.org/pdf/1904.07964v1.pdf | 3D Shape Synthesis for Conceptual Design and Optimization Using Variational Autoencoders | We propose a data-driven 3D shape design method that can learn a generative
model from a corpus of existing designs, and use this model to produce a wide
range of new designs. The approach learns an encoding of the samples in the
training corpus using an unsupervised variational autoencoder-decoder
architecture, without the need for an explicit parametric representation of the
original designs. To facilitate the generation of smooth final surfaces, we
develop a 3D shape representation based on a distance transformation of the
original 3D data, rather than using the commonly utilized binary voxel
representation. Once established, the generator maps the latent space
representations to the high-dimensional distance transformation fields, which
are then automatically surfaced to produce 3D representations amenable to
physics simulations or other objective function evaluation modules. We
demonstrate our approach for the computational design of gliders that are
optimized to attain prescribed performance scores. Our results show that when
combined with genetic optimization, the proposed approach can generate a rich
set of candidate concept designs that achieve prescribed functional goals, even
when the original dataset has only a few or no solutions that achieve these
goals. | ['Zhangsihao Yang', 'Tomotake Furuhata', 'Soji Yamakawa', 'Levent Burak Kara', 'Suyash Nigam', 'Haoliang Jiang', 'Wentai Zhang', 'Kenji Shimada'] | 2019-04-16 | null | null | null | null | ['3d-shape-representation'] | ['computer-vision'] | [ 1.69202328e-01 3.51803780e-01 1.18842155e-01 -1.52660102e-01
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257e02d2-28b9-46e2-b96a-9b6281ca09b2 | aaformer-auto-aligned-transformer-for-person | 2104.00921 | null | https://arxiv.org/abs/2104.00921v2 | https://arxiv.org/pdf/2104.00921v2.pdf | AAformer: Auto-Aligned Transformer for Person Re-Identification | In person re-identification, extracting part-level features from person images has been verified to be crucial. Most of existing CNN-based methods only locate the human parts coarsely, or rely on pre-trained human parsing models and fail in locating the identifiable non-human parts (e.g., knapsack). In this paper, we introduce an alignment scheme in Transformer architecture for the first time and propose the Auto-Aligned Transformer (AAformer) to automatically locate both the human parts and non-human ones at patch-level. We introduce the "part tokens", which are learnable vectors, to extract part features in Transformer. A part token only interacts with a local subset of patches in self-attention and learns to be the part representation. To adaptively group the image patches into different subsets, we design the Auto-Alignment. Auto-Alignment employs a fast variant of Optimal Transport algorithm to online cluster the patch embeddings into several groups with the part tokens as their prototypes. We harmoniously integrate the part alignment into the self-attention and the output part tokens can be directly used for retrieval. Extensive experiments validate the effectiveness of part tokens and the superiority of AAformer over various state-of-the-art methods. | ['Ming Tang', 'Jinqiao Wang', 'Jing Liu', 'Honglin Qiao', 'Gaopan Huang', 'YaoWei Wang', 'Shiliang Zhang', 'Haiyun Guo', 'Kuan Zhu'] | 2021-04-02 | null | null | null | null | ['human-parsing'] | ['computer-vision'] | [-1.60831556e-01 3.25094648e-02 4.15906869e-02 -2.99550980e-01
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00ed8731-c79d-422f-a6c2-3161a8869d9f | safari-sparsity-enabled-federated-learning | 2204.02321 | null | https://arxiv.org/abs/2204.02321v1 | https://arxiv.org/pdf/2204.02321v1.pdf | SAFARI: Sparsity enabled Federated Learning with Limited and Unreliable Communications | Federated learning (FL) enables edge devices to collaboratively learn a model in a distributed fashion. Many existing researches have focused on improving communication efficiency of high-dimensional models and addressing bias caused by local updates. However, most of FL algorithms are either based on reliable communications or assume fixed and known unreliability characteristics. In practice, networks could suffer from dynamic channel conditions and non-deterministic disruptions, with time-varying and unknown characteristics. To this end, in this paper we propose a sparsity enabled FL framework with both communication efficiency and bias reduction, termed as SAFARI. It makes novel use of a similarity among client models to rectify and compensate for bias that is resulted from unreliable communications. More precisely, sparse learning is implemented on local clients to mitigate communication overhead, while to cope with unreliable communications, a similarity-based compensation method is proposed to provide surrogates for missing model updates. We analyze SAFARI under bounded dissimilarity and with respect to sparse models. It is demonstrated that SAFARI under unreliable communications is guaranteed to converge at the same rate as the standard FedAvg with perfect communications. Implementations and evaluations on CIFAR-10 dataset validate the effectiveness of SAFARI by showing that it can achieve the same convergence speed and accuracy as FedAvg with perfect communications, with up to 80% of the model weights being pruned and a high percentage of client updates missing in each round. | ['Xiao-Ping Zhang', 'Tian Lan', 'Wenbo Ding', 'Yang Liu', 'Le Liang', 'Meilin Yang', 'Zihao Zhao', 'Yuzhu Mao'] | 2022-04-05 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [-1.96961358e-01 -4.75159995e-02 -3.13230157e-01 -2.87780225e-01
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4.98452991e-01 -6.24765038e-01 -7.30498195e-01 -2.23286703e-01] | [5.946261405944824, 5.679646968841553] |
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