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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
a619186f-3d2f-4151-89c7-678e2fd771b2 | bidirectional-projection-network-for-cross | 2103.14326 | null | https://arxiv.org/abs/2103.14326v1 | https://arxiv.org/pdf/2103.14326v1.pdf | Bidirectional Projection Network for Cross Dimension Scene Understanding | 2D image representations are in regular grids and can be processed efficiently, whereas 3D point clouds are unordered and scattered in 3D space. The information inside these two visual domains is well complementary, e.g., 2D images have fine-grained texture while 3D point clouds contain plentiful geometry information. ... | ['Tien-Tsin Wong', 'Jiaya Jia', 'Li Jiang', 'Hengshuang Zhao', 'WenBo Hu'] | 2021-03-26 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Hu_Bidirectional_Projection_Network_for_Cross_Dimension_Scene_Understanding_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Hu_Bidirectional_Projection_Network_for_Cross_Dimension_Scene_Understanding_CVPR_2021_paper.pdf | cvpr-2021-1 | ['scene-recognition', '2d-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [-7.83015490e-02 2.26489395e-01 -1.55910298e-01 -3.95440131e-01
-1.99279994e-01 -7.22021401e-01 5.77621698e-01 -3.68342996e-02
-1.04404390e-02 -1.24283038e-01 -2.74242103e-01 -5.20073593e-01
-7.83746876e-03 -1.01113415e+00 -6.62484288e-01 -4.12383705e-01
1.90068007e-01 7.86071181e-01 7.32624888e-01 -3.04396391... | [8.006296157836914, -3.26090145111084] |
23754b08-0e83-4bd1-ac5c-f64556da6a16 | federated-nonconvex-sparse-learning | 2101.00052 | null | https://arxiv.org/abs/2101.00052v1 | https://arxiv.org/pdf/2101.00052v1.pdf | Federated Nonconvex Sparse Learning | Nonconvex sparse learning plays an essential role in many areas, such as signal processing and deep network compression. Iterative hard thresholding (IHT) methods are the state-of-the-art for nonconvex sparse learning due to their capability of recovering true support and scalability with large datasets. Theoretical an... | ['Jinbo Bi', 'Tan Zhu', 'Guannan Liang', 'Qianqian Tong'] | 2020-12-31 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [ 3.80360223e-02 1.47894949e-01 -1.79834664e-01 -3.63400169e-02
-8.99170518e-01 -2.54119009e-01 -8.17563161e-02 2.29714327e-02
-2.63352185e-01 8.42591345e-01 2.12874219e-01 -6.41936436e-02
-2.95021385e-01 -5.87046444e-01 -1.05850291e+00 -9.53948617e-01
-4.96614486e-01 5.53584874e-01 2.45819278e-02 3.25907290... | [6.27708625793457, 5.023930549621582] |
b62a2974-3717-4e1d-9b99-6bed5b7d2a35 | effective-cross-lingual-transfer-of-neural | 1905.05475 | null | https://arxiv.org/abs/1905.05475v2 | https://arxiv.org/pdf/1905.05475v2.pdf | Effective Cross-lingual Transfer of Neural Machine Translation Models without Shared Vocabularies | Transfer learning or multilingual model is essential for low-resource neural machine translation (NMT), but the applicability is limited to cognate languages by sharing their vocabularies. This paper shows effective techniques to transfer a pre-trained NMT model to a new, unrelated language without shared vocabularies.... | ['Yunsu Kim', 'Yingbo Gao', 'Hermann Ney'] | 2019-05-14 | effective-cross-lingual-transfer-of-neural-1 | https://aclanthology.org/P19-1120 | https://aclanthology.org/P19-1120.pdf | acl-2019-7 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 2.66188651e-01 1.61219507e-01 -4.42925245e-01 -3.75318229e-01
-1.37682498e+00 -8.86450768e-01 6.03289008e-01 -4.67202872e-01
-7.90103495e-01 1.31102216e+00 3.93616170e-01 -7.86256492e-01
6.17500603e-01 -5.89273036e-01 -1.10081446e+00 -3.11793357e-01
4.75997835e-01 8.55762005e-01 -3.95404458e-01 -6.45615220... | [11.642223358154297, 10.167377471923828] |
7c742759-5356-4b1c-9429-07058f386211 | towards-interpreting-vulnerability-of-multi | 2211.17071 | null | https://arxiv.org/abs/2211.17071v3 | https://arxiv.org/pdf/2211.17071v3.pdf | Interpreting Vulnerabilities of Multi-Instance Learning to Adversarial Perturbations | Multi-Instance Learning (MIL) is a recent machine learning paradigm which is immensely useful in various real-life applications, like image analysis, video anomaly detection, text classification, etc. It is well known that most of the existing machine learning classifiers are highly vulnerable to adversarial perturbati... | ['Avik Ranjan Adhikary', 'Xue-Mei Cao', 'Mei Yang', 'Zhengchun Zhou', 'Hua Meng', 'Yu-Xuan Zhang'] | 2022-11-30 | null | null | null | null | ['video-anomaly-detection', 'multiple-instance-learning'] | ['computer-vision', 'methodology'] | [ 1.06640540e-01 -9.87173617e-02 5.56669151e-03 3.05201914e-02
-4.27556187e-01 -5.74208856e-01 4.12854761e-01 2.66702771e-01
-1.39881521e-01 1.03415120e+00 -1.66899785e-01 -3.39691520e-01
-8.50193948e-02 -9.15986359e-01 -1.06186044e+00 -1.01843619e+00
-3.76428366e-02 3.09724480e-01 3.16584021e-01 -4.67412263... | [5.642446517944336, 7.852972507476807] |
3f992123-8e46-4663-819d-94aeaa5663a7 | negation-detection-in-clinical-reports | null | null | https://aclanthology.org/W16-5113 | https://aclanthology.org/W16-5113.pdf | Negation Detection in Clinical Reports Written in German | An important subtask in clinical text mining tries to identify whether a clinical finding is expressed as present, absent or unsure in a text. This work presents a system for detecting mentions of clinical findings that are negated or just speculated. The system has been applied to two different types of German clinica... | ['Danilo Schmidt', '', 'Rol Roller', 'Hans Uszkoreit', 'Feiyu Xu', 'Klemens Budde', 'Viviana Cotik'] | 2016-12-01 | null | null | null | ws-2016-12 | ['negation-detection'] | ['natural-language-processing'] | [ 1.36152178e-01 5.72116137e-01 -5.72527409e-01 -4.19416845e-01
-5.83565891e-01 -4.03431445e-01 5.44227004e-01 1.16215277e+00
-5.83923519e-01 1.09115243e+00 4.73239630e-01 -7.99419761e-01
-9.28322151e-02 -6.02599084e-01 -2.68462420e-01 -1.99594945e-01
-1.56603143e-01 4.73995388e-01 3.46704900e-01 -2.57384598... | [8.444260597229004, 8.817804336547852] |
5f634655-abb9-4482-bf1f-5518cdf553ad | building-sequential-inference-models-for-end | 1812.00686 | null | http://arxiv.org/abs/1812.00686v1 | http://arxiv.org/pdf/1812.00686v1.pdf | Building Sequential Inference Models for End-to-End Response Selection | This paper presents an end-to-end response selection model for Track 1 of the
7th Dialogue System Technology Challenges (DSTC7). This task focuses on
selecting the correct next utterance from a set of candidates given a partial
conversation. We propose an end-to-end neural network based on enhanced
sequential inference... | ['Yu-Ping Ruan', 'Zhen-Hua Ling', 'Jia-Chen Gu', 'Quan Liu'] | 2018-12-03 | null | null | null | null | ['conversational-response-selection'] | ['natural-language-processing'] | [ 3.19176883e-01 4.17858630e-01 4.83647585e-02 -7.74476707e-01
-7.17237592e-01 -1.30615115e-01 7.04721212e-01 1.33393453e-02
-8.34960043e-01 6.30256057e-01 8.84286940e-01 -8.19549188e-02
6.52418509e-02 -4.82114464e-01 -1.27473166e-02 -3.75888109e-01
2.01622874e-01 4.18427378e-01 3.18589538e-01 -7.33225346... | [12.637394905090332, 7.813736915588379] |
d645b311-a7e7-403b-b889-c8b82d045cf0 | blind-face-restoration-via-deep-multi-scale | 2008.00418 | null | https://arxiv.org/abs/2008.00418v1 | https://arxiv.org/pdf/2008.00418v1.pdf | Blind Face Restoration via Deep Multi-scale Component Dictionaries | Recent reference-based face restoration methods have received considerable attention due to their great capability in recovering high-frequency details on real low-quality images. However, most of these methods require a high-quality reference image of the same identity, making them only applicable in limited scenes. T... | ['WangMeng Zuo', 'Shangchen Zhou', 'Xianhui Lin', 'Lei Zhang', 'Xiaoming Li', 'Chaofeng Chen'] | 2020-08-02 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/806_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123540375.pdf | eccv-2020-8 | ['blind-face-restoration'] | ['computer-vision'] | [ 1.48222670e-01 -3.25119168e-01 5.94855361e-02 -3.81143779e-01
-7.83149898e-01 -2.87239343e-01 4.11000937e-01 -5.34320712e-01
1.35175928e-01 5.38327694e-01 5.03111899e-01 1.90569371e-01
-2.23608688e-01 -7.08509922e-01 -5.22820115e-01 -1.01855302e+00
3.09824228e-01 -2.58740336e-01 -3.73249441e-01 -2.88953990... | [12.820121765136719, -0.02125396952033043] |
43b236fd-c60c-4faf-b0bf-d8d4c930ce10 | nnunet-raspp-for-retinal-oct-fluid-detection | 2302.13195 | null | https://arxiv.org/abs/2302.13195v1 | https://arxiv.org/pdf/2302.13195v1.pdf | nnUNet RASPP for Retinal OCT Fluid Detection, Segmentation and Generalisation over Variations of Data Sources | Retinal Optical Coherence Tomography (OCT), a noninvasive cross-sectional scan of the eye with qualitative 3D visualization of the retinal anatomy is use to study the retinal structure and the presence of pathogens. The advent of the retinal OCT has transformed ophthalmology and it is currently paramount for the diagno... | ['Yongmin Li', 'Zidong Wang', 'Alina Miron', 'Nchongmaje Ndipenoch'] | 2023-02-25 | null | null | null | null | ['anatomy'] | ['miscellaneous'] | [ 7.21824169e-02 -2.00290307e-01 3.91358018e-01 7.86707699e-02
-2.74295419e-01 -5.25076509e-01 1.46646649e-01 -1.96385667e-01
-3.65291297e-01 7.58334816e-01 1.03925928e-01 -5.91365814e-01
-2.43781552e-01 -2.67530531e-01 -2.95695394e-01 -5.79789937e-01
-2.56503195e-01 3.45527440e-01 5.30307591e-01 4.48057443... | [15.81814956665039, -3.997189998626709] |
91044228-cf70-4b18-946a-22a2872d5cb0 | cross-modal-ranking-with-soft-consistency-and | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Chenglong_Li_Cross-Modal_Ranking_with_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Chenglong_Li_Cross-Modal_Ranking_with_ECCV_2018_paper.pdf | Cross-Modal Ranking with Soft Consistency and Noisy Labels for Robust RGB-T Tracking | Due to the complementary benefits of visible (RGB) and thermal infrared (T) data, RGB-T object tracking attracts more and more attention recently for boosting the performance under adverse illumination conditions. Existing RGB-T tracking methods usually localize a target object with a bounding box, in which the tracker... | ['Yan Huang', 'Chengli Zhu', 'Liang Wang', 'Jin Tang', 'Chenglong Li'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['rgb-t-tracking'] | ['computer-vision'] | [ 1.09543651e-01 -6.08508170e-01 -2.62029678e-01 -2.69998997e-01
-8.82784307e-01 -2.22918004e-01 1.86118677e-01 -2.19797164e-01
-2.91318804e-01 3.29237819e-01 -1.84369653e-01 1.26646578e-01
-1.14302583e-01 -4.76896346e-01 -6.31538749e-01 -1.06066704e+00
5.22893488e-01 -5.86129874e-02 5.16549349e-01 8.19281414... | [6.378810882568359, -2.184385061264038] |
6c6a86bc-e8b4-4044-a951-d28f6ab4b304 | large-scale-semi-supervised-learning-via | 1912.02233 | null | https://arxiv.org/abs/1912.02233v1 | https://arxiv.org/pdf/1912.02233v1.pdf | Large-Scale Semi-Supervised Learning via Graph Structure Learning over High-Dense Points | We focus on developing a novel scalable graph-based semi-supervised learning (SSL) method for a small number of labeled data and a large amount of unlabeled data. Due to the lack of labeled data and the availability of large-scale unlabeled data, existing SSL methods usually encounter either suboptimal performance beca... | ['Tieyong Zeng', 'Raymond Chan', 'Zitong Wang', 'Li Wang'] | 2019-12-04 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [ 1.36394918e-01 3.27527106e-01 -4.56866086e-01 -4.66912419e-01
-6.62729084e-01 -6.01560235e-01 2.96382844e-01 3.84220988e-01
-6.61560819e-02 7.34418094e-01 -2.11465627e-01 -2.55390465e-01
-2.01316461e-01 -9.20897007e-01 -6.34961784e-01 -7.12475061e-01
-2.01955773e-02 8.07711065e-01 2.79007435e-01 1.48383409... | [7.412568092346191, 6.128377914428711] |
99523fec-58f8-4360-aa44-8b13965205e9 | monitoring-of-perception-systems | 2205.10906 | null | https://arxiv.org/abs/2205.10906v1 | https://arxiv.org/pdf/2205.10906v1.pdf | Monitoring of Perception Systems: Deterministic, Probabilistic, and Learning-based Fault Detection and Identification | This paper investigates runtime monitoring of perception systems. Perception is a critical component of high-integrity applications of robotics and autonomous systems, such as self-driving cars. In these applications, failure of perception systems may put human life at risk, and a broad adoption of these technologies r... | ['Luca Carlone', 'Heath Nilsen', 'Pasquale Antonante'] | 2022-05-22 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [ 4.62386794e-02 2.96544224e-01 8.03442150e-02 -3.88077348e-01
-4.19710129e-01 -2.65181839e-01 7.05422461e-01 3.62218052e-01
-1.64829105e-01 2.67705768e-01 -6.04079664e-01 -7.97363043e-01
-2.09837198e-01 -8.41337144e-01 -9.86389220e-01 -4.31042552e-01
-3.99838895e-01 2.97037095e-01 1.18958950e+00 -4.74347174... | [5.426877021789551, 1.4375098943710327] |
11a4fcdd-a77e-4fe6-a921-491616d229bc | minmax-radon-barcodes-for-medical-image | 1610.00318 | null | http://arxiv.org/abs/1610.00318v1 | http://arxiv.org/pdf/1610.00318v1.pdf | MinMax Radon Barcodes for Medical Image Retrieval | Content-based medical image retrieval can support diagnostic decisions by
clinical experts. Examining similar images may provide clues to the expert to
remove uncertainties in his/her final diagnosis. Beyond conventional feature
descriptors, binary features in different ways have been recently proposed to
encode the im... | ['Varun Chaudhari', 'Tahmid Mehdi', 'H. R. Tizhoosh', 'Hanson Lo', 'Shujin Zhu'] | 2016-10-02 | null | null | null | null | ['medical-image-retrieval', 'medical-image-retrieval'] | ['computer-vision', 'medical'] | [ 4.11502481e-01 -2.04498265e-02 -4.35804218e-01 -3.83282661e-01
-1.66824758e+00 -3.35245222e-01 4.98167574e-01 7.99483061e-01
-7.97608495e-01 5.76224327e-01 4.14069206e-01 -3.73764604e-01
-8.08037996e-01 -8.00263226e-01 -1.41516224e-01 -9.77619529e-01
1.71246231e-01 4.34533417e-01 2.82064706e-01 5.63383028... | [14.291685104370117, -1.4673207998275757] |
aec08f57-9303-4a35-8a4b-f00dda8fc165 | robust-and-efficient-fault-diagnosis-of-mm | 2306.04360 | null | https://arxiv.org/abs/2306.04360v1 | https://arxiv.org/pdf/2306.04360v1.pdf | Robust and Efficient Fault Diagnosis of mm-Wave Active Phased Arrays using Baseband Signal | One key communication block in 5G and 6G radios is the active phased array (APA). To ensure reliable operation, efficient and timely fault diagnosis of APAs on-site is crucial. To date, fault diagnosis has relied on measurement of frequency domain radiation patterns using costly equipment and multiple strictly controll... | ['Gert F. Pedersen', 'Ming Shen', 'Yingzeng Yin', 'Jian Ren', 'Changbin Xue', 'Yufeng Zhang', 'Martin H. Nielsen'] | 2023-06-07 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [-1.69468850e-01 -9.01512653e-02 1.45780072e-01 7.44381279e-04
-6.00629628e-01 -4.76083666e-01 -9.18553621e-02 2.35605404e-01
2.95175105e-01 6.29272223e-01 -5.54340541e-01 -5.07354081e-01
-8.35635245e-01 -8.51927221e-01 -2.33671695e-01 -1.01357746e+00
-4.28664058e-01 4.14654553e-01 -1.74873948e-01 1.90540746... | [6.398812770843506, 1.316398024559021] |
90eeeb11-bf58-49a1-81b4-52185e320233 | aligning-sequence-reads-clone-sequences-and | 1303.3997 | null | http://arxiv.org/abs/1303.3997v2 | http://arxiv.org/pdf/1303.3997v2.pdf | Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM | Summary: BWA-MEM is a new alignment algorithm for aligning sequence reads or
long query sequences against a large reference genome such as human. It
automatically chooses between local and end-to-end alignments, supports
paired-end reads and performs chimeric alignment. The algorithm is robust to
sequencing errors and ... | ['Heng Li'] | 2013-03-16 | null | null | null | null | ['3d-object-reconstruction-from-a-single-image'] | ['computer-vision'] | [ 5.80386579e-01 -5.64438164e-01 -4.43085968e-01 -3.47090721e-01
-1.17217636e+00 -1.17189300e+00 2.56697148e-01 3.28325748e-01
-2.92169750e-01 1.13496125e+00 2.78435349e-01 -6.61317110e-01
1.33251594e-02 -8.13162029e-01 -5.89550734e-01 -6.84082925e-01
2.65840411e-01 8.85500014e-01 3.45289499e-01 -5.44774607... | [4.877599716186523, 5.172602653503418] |
5306a947-2a8f-4cd4-92f5-05fd2f1249b2 | a-new-citation-recommendation-strategy-based | 2009.08948 | null | https://arxiv.org/abs/2009.08948v2 | https://arxiv.org/pdf/2009.08948v2.pdf | A New Citation Recommendation Strategy Based on Term Functions in Related Studies Section | Purpose: Researchers frequently encounter the following problems when writing scientific articles: (1) Selecting appropriate citations to support the research idea is challenging. (2) The literature review is not conducted extensively, which leads to working on a research problem that others have well addressed. This s... | ['Haihua Chen'] | 2020-09-11 | null | null | null | null | ['review-generation'] | ['natural-language-processing'] | [-2.28990152e-01 -1.85869500e-01 -8.05200934e-01 7.14230016e-02
-5.66541672e-01 -5.65728188e-01 5.87792695e-01 1.61135912e-01
-3.19001138e-01 8.18763256e-01 5.12117743e-01 -7.71430910e-01
-6.74450874e-01 -7.49302328e-01 -5.42991340e-01 -4.90503639e-01
6.83046043e-01 1.38705865e-01 -2.41510302e-01 3.46872360... | [9.774740219116211, 8.355619430541992] |
e65d42a4-f1bd-4831-8b51-6624e1c06052 | progressive-temporal-feature-alignment | 2104.03507 | null | https://arxiv.org/abs/2104.03507v1 | https://arxiv.org/pdf/2104.03507v1.pdf | Progressive Temporal Feature Alignment Network for Video Inpainting | Video inpainting aims to fill spatio-temporal "corrupted" regions with plausible content. To achieve this goal, it is necessary to find correspondences from neighbouring frames to faithfully hallucinate the unknown content. Current methods achieve this goal through attention, flow-based warping, or 3D temporal convolut... | ['Yong Jae Lee', 'Ding Liu', 'Linjie Yang', 'Xueyan Zou'] | 2021-04-08 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Zou_Progressive_Temporal_Feature_Alignment_Network_for_Video_Inpainting_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Zou_Progressive_Temporal_Feature_Alignment_Network_for_Video_Inpainting_CVPR_2021_paper.pdf | cvpr-2021-1 | ['video-inpainting'] | ['computer-vision'] | [ 4.09221239e-02 -3.92683953e-01 -6.01922832e-02 -1.51313573e-01
-5.38708806e-01 -3.60617012e-01 5.13655245e-01 -3.51846516e-01
-1.49474382e-01 9.38436389e-01 6.32397890e-01 1.23386979e-01
5.98848946e-02 -5.80127001e-01 -8.47049892e-01 -4.20564860e-01
-8.98377821e-02 -1.91758528e-01 1.48860380e-01 6.07221760... | [10.795790672302246, -1.3191653490066528] |
06d7cf2a-1f73-4d4c-aa2e-35c9fd65f63e | assessing-the-performance-of-automated | 2207.01302 | null | https://arxiv.org/abs/2207.01302v1 | https://arxiv.org/pdf/2207.01302v1.pdf | Assessing the Performance of Automated Prediction and Ranking of Patient Age from Chest X-rays Against Clinicians | Understanding the internal physiological changes accompanying the aging process is an important aspect of medical image interpretation, with the expected changes acting as a baseline when reporting abnormal findings. Deep learning has recently been demonstrated to allow the accurate estimation of patient age from chest... | ['Giovanni Montana', 'Vicky Goh', 'Charles Hutchinson', 'Ashik Amlani', 'Keerthini Muthuswamy', 'Matthew MacPherson'] | 2022-07-04 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [ 4.44867134e-01 6.41894162e-01 7.30365366e-02 -6.86855614e-01
-1.05366898e+00 -2.18772978e-01 6.57523036e-01 5.86252928e-01
-9.13123548e-01 6.13982260e-01 6.15399003e-01 -4.82382298e-01
-2.72197992e-01 -7.53464282e-01 -6.52127922e-01 -6.45477653e-01
-4.24618274e-01 1.03237355e+00 -2.85101142e-02 3.00873429... | [15.20938777923584, -1.961327314376831] |
edf82d86-b320-41c7-b0cc-504aeaefcfad | machine-learned-solutions-for-three-stages-of | null | null | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3168309/ | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3168309/pdf/amiajnl-2011-000150.pdf | Machine-learned solutions for three stages of clinical information extraction: the state of the art at i2b2 2010 | Objective: As clinical text mining continues to mature, its
potential as an enabling technology for innovations in
patient care and clinical research is becoming a reality. A
critical part of that process is rigid benchmark testing of
natural language processing methods on realistic clinical
narrative. In this pap... | ['Svetlana Kiritchenko', 'Xiaodan Zhu', 'Joel Martin', 'Berry de Bruijn', 'Colin Cherry'] | 2011-05-12 | null | null | null | jamia-2011-5 | ['clinical-concept-extraction'] | ['medical'] | [ 4.40100074e-01 2.95129776e-01 -4.96312886e-01 -2.77426004e-01
-1.30414057e+00 -5.17028332e-01 5.89292049e-01 1.14803338e+00
-6.51986361e-01 9.51255679e-01 4.95683879e-01 -6.82765067e-01
-5.23272157e-01 -4.10873055e-01 -1.82843938e-01 -3.32648724e-01
-3.87431145e-01 6.06274724e-01 -8.58802125e-02 8.61473903... | [8.45052433013916, 8.598287582397461] |
255605b1-1e6d-4255-bd22-8a2abbd3202f | mmm-multi-stage-multi-task-learning-for-multi | 1910.00458 | null | https://arxiv.org/abs/1910.00458v2 | https://arxiv.org/pdf/1910.00458v2.pdf | MMM: Multi-stage Multi-task Learning for Multi-choice Reading Comprehension | Machine Reading Comprehension (MRC) for question answering (QA), which aims to answer a question given the relevant context passages, is an important way to test the ability of intelligence systems to understand human language. Multiple-Choice QA (MCQA) is one of the most difficult tasks in MRC because it often require... | ['Dilek Hakkani-Tur', 'Jiun-Yu Kao', 'Shuyang Gao', 'Tagyoung Chung', 'Di Jin'] | 2019-10-01 | null | null | null | null | ['multiple-choice-qa'] | ['natural-language-processing'] | [ 5.47683835e-01 5.98083287e-02 3.62390429e-01 -5.36243796e-01
-1.35038340e+00 -5.98724365e-01 3.85496944e-01 5.87408364e-01
-5.53879201e-01 6.76018059e-01 4.41024452e-01 -7.98199415e-01
-1.13841124e-01 -9.75344896e-01 -8.31239283e-01 -9.88869071e-02
3.88726711e-01 6.73646927e-01 5.09590685e-01 -6.67551100... | [11.273195266723633, 8.088088989257812] |
71942676-4794-442e-826d-cf23aab1ed7b | community-detection-via-hashtag-graphs-for | 2111.10401 | null | https://arxiv.org/abs/2111.10401v1 | https://arxiv.org/pdf/2111.10401v1.pdf | Community-Detection via Hashtag-Graphs for Semi-Supervised NMF Topic Models | Extracting topics from large collections of unstructured text-documents has become a central task in current NLP applications and algorithms like NMF, LDA as well as their generalizations are the well-established current state of the art. However, especially when it comes to short text documents like Tweets, these appr... | ['Benjamin Säfken', 'Christoph Weisser', 'Anton Thielmann', 'Mattias Luber'] | 2021-11-17 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [ 6.41335100e-02 3.12180132e-01 3.51358801e-02 -2.17368871e-01
-6.25641763e-01 -5.31742632e-01 1.00347841e+00 9.36918497e-01
-3.95528585e-01 7.40259826e-01 3.38377774e-01 -1.44013032e-01
-2.88617283e-01 -9.72389638e-01 -2.10305944e-01 -7.41525233e-01
-3.79639804e-01 8.44947398e-01 2.41165802e-01 -1.36739969... | [10.30681324005127, 7.32743501663208] |
e1bed9cd-21fd-4c14-a5b2-61aec5719cbb | panonet3d-combining-semantic-and-geometric | 2012.09418 | null | https://arxiv.org/abs/2012.09418v1 | https://arxiv.org/pdf/2012.09418v1.pdf | PanoNet3D: Combining Semantic and Geometric Understanding for LiDARPoint Cloud Detection | Visual data in autonomous driving perception, such as camera image and LiDAR point cloud, can be interpreted as a mixture of two aspects: semantic feature and geometric structure. Semantics come from the appearance and context of objects to the sensor, while geometric structure is the actual 3D shape of point clouds. M... | ['Martial Hebert', 'David Held', 'Jianren Wang', 'Xia Chen'] | 2020-12-17 | null | null | null | null | ['cloud-detection'] | ['computer-vision'] | [ 1.32353038e-01 -3.23455542e-01 -1.56671777e-01 -7.56399810e-01
-3.26876909e-01 -8.06663752e-01 1.00575185e+00 3.42372656e-01
-3.83307964e-01 -9.21811461e-02 -2.97724903e-01 -2.90184796e-01
-9.37488899e-02 -9.80074048e-01 -8.90273035e-01 -4.02533591e-01
3.59878018e-02 7.91118681e-01 6.76873326e-01 -4.84989166... | [7.809700965881348, -2.6803269386291504] |
70f6dca3-74e2-4d5a-a9fc-abb88f4da870 | a-new-k-means-grey-wolf-algorithm-for | 2103.05760 | null | https://arxiv.org/abs/2103.05760v1 | https://arxiv.org/pdf/2103.05760v1.pdf | A New K means Grey Wolf Algorithm for Engineering Problems | Purpose: The development of metaheuristic algorithms has increased by researchers to use them extensively in the field of business, science, and engineering. One of the common metaheuristic optimization algorithms is called Grey Wolf Optimization (GWO). The algorithm works based on imitation of the wolves' searching an... | ['Nebojsa Bacanin', 'Abeer Alsadoon', 'Tarik A. Rashid', 'Zrar Kh. Abdul', 'Hardi M. Mohammed'] | 2021-02-27 | null | null | null | null | ['metaheuristic-optimization'] | ['methodology'] | [-3.48156840e-01 -3.78290981e-01 2.24704280e-01 5.75457871e-01
2.41025418e-01 -9.26437825e-02 2.05655590e-01 1.60286412e-01
-6.36499345e-01 7.92250812e-01 -2.33080033e-02 -2.47590557e-01
-9.25951660e-01 -9.62156892e-01 -1.71756208e-01 -1.20606089e+00
-2.25920781e-01 3.82428855e-01 1.94770351e-01 -9.23420370... | [5.589442729949951, 3.4477434158325195] |
33fa3623-79f2-4842-8aac-ba00f4339459 | deepasl-enabling-ubiquitous-and-non-intrusive | 1802.07584 | null | http://arxiv.org/abs/1802.07584v3 | http://arxiv.org/pdf/1802.07584v3.pdf | DeepASL: Enabling Ubiquitous and Non-Intrusive Word and Sentence-Level Sign Language Translation | There is an undeniable communication barrier between deaf people and people
with normal hearing ability. Although innovations in sign language translation
technology aim to tear down this communication barrier, the majority of
existing sign language translation systems are either intrusive or constrained
by resolution ... | ['Mi Zhang', 'Biyi Fang', 'Jillian Co'] | 2018-02-21 | null | null | null | null | ['sign-language-translation'] | ['computer-vision'] | [ 2.91809469e-01 -1.84196949e-01 -2.70366490e-01 -4.14644033e-02
-1.35939670e+00 -4.83176976e-01 2.77368426e-01 -6.11251175e-01
-5.71774304e-01 6.30928636e-01 7.76758671e-01 -4.30092901e-01
3.13691229e-01 -5.40714741e-01 -5.24624228e-01 -6.62876308e-01
5.90165794e-01 -3.12739760e-02 1.62585810e-01 -2.85539687... | [9.121747016906738, -6.435465335845947] |
32ea0d3e-2772-4de3-aae9-9b22c5b16b47 | decipherment-for-adversarial-offensive | null | null | https://aclanthology.org/W18-5119 | https://aclanthology.org/W18-5119.pdf | Decipherment for Adversarial Offensive Language Detection | Automated filters are commonly used by online services to stop users from sending age-inappropriate, bullying messages, or asking others to expose personal information. Previous work has focused on rules or classifiers to detect and filter offensive messages, but these are vulnerable to cleverly disguised plaintext and... | ['Nishant Kambhatla', 'Anoop Sarkar', 'Zhelun Wu'] | 2018-10-01 | null | null | null | ws-2018-10 | ['decipherment'] | ['natural-language-processing'] | [ 5.03153145e-01 5.47184683e-02 -2.55753428e-01 -2.70573944e-01
-3.13112140e-01 -1.41498995e+00 9.67537045e-01 2.47542903e-01
-3.36547494e-01 6.94243908e-01 1.24153875e-01 -6.13542616e-01
2.43125185e-01 -8.95465434e-01 -3.35702151e-01 -4.45177406e-01
-3.06040287e-01 -2.36819729e-01 5.51476516e-02 -3.97341311... | [5.888779163360596, 7.869454860687256] |
22e6463b-b366-44a1-a67e-43702aaf9bc6 | learning-physically-realizable-skills-for | 2212.02094 | null | https://arxiv.org/abs/2212.02094v2 | https://arxiv.org/pdf/2212.02094v2.pdf | Learning Physically Realizable Skills for Online Packing of General 3D Shapes | We study the problem of learning online packing skills for irregular 3D shapes, which is arguably the most challenging setting of bin packing problems. The goal is to consecutively move a sequence of 3D objects with arbitrary shapes into a designated container with only partial observations of the object sequence. Mean... | ['Kai Xu', 'Yang Yu', 'Zherong Pan', 'Hang Zhao'] | 2022-12-05 | null | null | null | null | ['action-generation'] | ['computer-vision'] | [-1.99012950e-01 -3.35638933e-02 -1.43909216e-01 5.32773212e-02
-7.18288064e-01 -8.54554415e-01 1.90014720e-01 7.30769634e-01
-5.49727023e-01 6.90719783e-01 -1.51241913e-01 -8.14914167e-01
-1.39098853e-01 -9.19733107e-01 -1.39279997e+00 -8.07854354e-01
-5.07544518e-01 1.46868026e+00 1.15272067e-01 -1.39918178... | [4.981522083282471, 2.6872220039367676] |
fae34351-c4db-432c-9c0c-90b689088b9e | glocal-glocalized-curriculum-aided-learning | 2204.06835 | null | https://arxiv.org/abs/2204.06835v1 | https://arxiv.org/pdf/2204.06835v1.pdf | GloCAL: Glocalized Curriculum-Aided Learning of Multiple Tasks with Application to Robotic Grasping | The domain of robotics is challenging to apply deep reinforcement learning due to the need for large amounts of data and for ensuring safety during learning. Curriculum learning has shown good performance in terms of sample- efficient deep learning. In this paper, we propose an algorithm (named GloCAL) that creates a c... | ['Keng Peng Tee', 'Domenico Campolo', 'Cihan Acar', 'Anil Kurkcu'] | 2022-04-14 | null | null | null | null | ['robotic-grasping'] | ['robots'] | [-1.15198798e-01 6.04212806e-02 -6.91314414e-02 -1.87833428e-01
-7.29802787e-01 -6.88194752e-01 2.58942902e-01 4.34944242e-01
-6.00854456e-01 7.32420027e-01 -3.38369161e-01 1.08569197e-01
-5.52467167e-01 -6.22470140e-01 -1.01541746e+00 -1.14775074e+00
-5.31019330e-01 9.44233537e-01 2.11311519e-01 1.90113634... | [4.264570236206055, 1.230695366859436] |
3f07324e-846f-409d-9eed-7474500e35f5 | shas-approaching-optimal-segmentation-for-end | 2202.04774 | null | https://arxiv.org/abs/2202.04774v3 | https://arxiv.org/pdf/2202.04774v3.pdf | SHAS: Approaching optimal Segmentation for End-to-End Speech Translation | Speech translation models are unable to directly process long audios, like TED talks, which have to be split into shorter segments. Speech translation datasets provide manual segmentations of the audios, which are not available in real-world scenarios, and existing segmentation methods usually significantly reduce tran... | ['Marta R. Costa-jussà', 'José A. R. Fonollosa', 'Gerard I. Gállego', 'Ioannis Tsiamas'] | 2022-02-09 | null | null | null | null | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 3.77129287e-01 2.93116480e-01 -3.19171190e-01 -4.92935538e-01
-1.60984635e+00 -7.43747294e-01 3.17321956e-01 -9.08883382e-03
-3.60714704e-01 5.53337693e-01 3.01779330e-01 -5.09434044e-01
4.15147156e-01 -4.79627877e-01 -6.58588588e-01 -6.28877938e-01
4.66898918e-01 9.80273902e-01 5.06196260e-01 -9.62752849... | [14.589102745056152, 6.841160774230957] |
1c854101-4ad3-473d-8df1-04ee56dea067 | long-term-blood-pressure-prediction-with-deep | 1705.04524 | null | http://arxiv.org/abs/1705.04524v3 | http://arxiv.org/pdf/1705.04524v3.pdf | Long-term Blood Pressure Prediction with Deep Recurrent Neural Networks | Existing methods for arterial blood pressure (BP) estimation directly map the
input physiological signals to output BP values without explicitly modeling the
underlying temporal dependencies in BP dynamics. As a result, these models
suffer from accuracy decay over a long time and thus require frequent
calibration. In t... | ['Yuan-Ting Zhang', 'Xiao-Rong Ding', 'Ni Zhao', 'Fen Miao', 'Peng Su', 'Jing Liu'] | 2017-05-12 | null | null | null | null | ['blood-pressure-estimation', 'photoplethysmography-ppg', 'electrocardiography-ecg'] | ['medical', 'medical', 'methodology'] | [-8.10487345e-02 -3.07954222e-01 6.54007792e-02 -6.92002177e-01
-5.08476608e-02 -1.79977745e-01 -2.34978385e-02 1.30129397e-01
-2.13012651e-01 1.28490090e+00 2.00258017e-01 -6.84090316e-01
-1.46284059e-01 -8.96678448e-01 -5.28887570e-01 -5.39931297e-01
-5.45683444e-01 1.64636280e-02 1.35921270e-01 -2.48433098... | [14.106114387512207, 3.0116355419158936] |
f200cb83-14e9-44cb-8831-c617306e375d | flock-defending-malicious-behaviors-in | 2211.04344 | null | https://arxiv.org/abs/2211.04344v1 | https://arxiv.org/pdf/2211.04344v1.pdf | FLock: Defending Malicious Behaviors in Federated Learning with Blockchain | Federated learning (FL) is a promising way to allow multiple data owners (clients) to collaboratively train machine learning models without compromising data privacy. Yet, existing FL solutions usually rely on a centralized aggregator for model weight aggregation, while assuming clients are honest. Even if data privacy... | ['Shuhao Zheng', 'Shuoying Zhang', 'Zhipeng Wang', 'Jiahao Sun', 'Nanqing Dong'] | 2022-11-05 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [-6.01826668e-01 3.29336703e-01 -3.00777227e-01 -3.33789676e-01
-8.76226544e-01 -1.09568012e+00 6.12065732e-01 1.68577015e-01
-1.72588378e-01 8.20675015e-01 -2.36375540e-01 -2.41616398e-01
4.53198142e-02 -7.01666415e-01 -8.52754653e-01 -1.12044740e+00
-7.25026429e-02 6.43276095e-01 1.25271708e-01 3.44079167... | [5.8483405113220215, 6.6025390625] |
abb4c0c1-be41-4dfe-81b1-6464bcbc0f96 | augment-and-criticize-exploring-informative | 2303.11243 | null | https://arxiv.org/abs/2303.11243v1 | https://arxiv.org/pdf/2303.11243v1.pdf | Augment and Criticize: Exploring Informative Samples for Semi-Supervised Monocular 3D Object Detection | In this paper, we improve the challenging monocular 3D object detection problem with a general semi-supervised framework. Specifically, having observed that the bottleneck of this task lies in lacking reliable and informative samples to train the detector, we introduce a novel, simple, yet effective `Augment and Critic... | ['Junjun Jiang', 'Xianming Liu', 'Ke Wang', 'Yuan He', 'Heng Fan', 'Zhipeng Zhang', 'Zhenyu Li'] | 2023-03-20 | null | null | null | null | ['monocular-3d-object-detection', 'pseudo-label'] | ['computer-vision', 'miscellaneous'] | [ 1.79922506e-01 5.94249554e-02 -1.04159005e-01 -2.60778844e-01
-8.64381194e-01 -8.10064793e-01 7.27531672e-01 -2.60406554e-01
-3.59460890e-01 5.13247311e-01 -3.01235288e-01 -1.73677444e-01
3.23512316e-01 -2.33741865e-01 -8.69188786e-01 -9.49177384e-01
8.96751136e-02 4.63513047e-01 5.70576310e-01 2.26893008... | [9.1622953414917, 1.2399746179580688] |
9498628c-d370-40a2-aab6-662a8a7f3b78 | improving-face-recognition-by-clustering | 2007.06995 | null | https://arxiv.org/abs/2007.06995v2 | https://arxiv.org/pdf/2007.06995v2.pdf | Improving Face Recognition by Clustering Unlabeled Faces in the Wild | While deep face recognition has benefited significantly from large-scale labeled data, current research is focused on leveraging unlabeled data to further boost performance, reducing the cost of human annotation. Prior work has mostly been in controlled settings, where the labeled and unlabeled data sets have no overla... | ['Manmohan Chandraker', 'Erik Learned-Miller', 'Kihyuk Sohn', 'Aruni RoyChowdhury', 'Xiang Yu'] | 2020-07-14 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/4531_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123690120.pdf | eccv-2020-8 | ['face-clustering'] | ['computer-vision'] | [ 2.43294522e-01 3.68001238e-02 -1.73575401e-01 -8.58743370e-01
-9.48372602e-01 -5.97733200e-01 3.51571411e-01 -8.52643847e-02
-3.63745898e-01 5.89096367e-01 3.10033914e-02 -2.69997921e-02
9.78690013e-03 -3.06948215e-01 -5.08772731e-01 -9.41752374e-01
1.35660961e-01 4.77052093e-01 -4.25458431e-01 2.80054569... | [13.467323303222656, 1.0567272901535034] |
1c621c78-b72a-420f-9026-c6d3c221d8e4 | irit-at-trac-2018 | null | null | https://aclanthology.org/W18-4403 | https://aclanthology.org/W18-4403.pdf | IRIT at TRAC 2018 | This paper describes the participation of the IRIT team to the TRAC 2018 shared task on Aggression Identification and more precisely to the shared task in English language. The three following methods have been used: a) a combination of machine learning techniques that relies on a set of features and document/text vect... | ['Josiane Mothe', 'Rami', 'Faneva risoa'] | 2018-08-01 | null | null | null | coling-2018-8 | ['aggression-identification'] | ['natural-language-processing'] | [-3.20235103e-01 -2.08287790e-01 -2.06602402e-02 -1.61020875e-01
-6.02100134e-01 -1.11914642e-01 9.49546576e-01 1.26346081e-01
-1.16064394e+00 7.90394247e-01 5.79420745e-01 -2.74533778e-01
-5.72541356e-01 -5.70581913e-01 -2.00818330e-01 -2.97197580e-01
-1.16669804e-01 5.09593785e-01 -7.45549947e-02 -5.02287209... | [8.806180000305176, 10.729721069335938] |
bdfcb860-079b-45dc-a4ac-2a8360bc1f9f | penet-a-joint-panoptic-edge-detection-network | 2303.08848 | null | https://arxiv.org/abs/2303.08848v1 | https://arxiv.org/pdf/2303.08848v1.pdf | PENet: A Joint Panoptic Edge Detection Network | In recent years, compact and efficient scene understanding representations have gained popularity in increasing situational awareness and autonomy of robotic systems. In this work, we illustrate the concept of a panoptic edge segmentation and propose PENet, a novel detection network called that combines semantic edge d... | ['Giuseppe Loianno', 'Yang Zhou'] | 2023-03-15 | null | null | null | null | ['edge-detection'] | ['computer-vision'] | [ 5.59513494e-02 1.36482060e-01 -2.80982435e-01 -4.28351760e-01
-1.27102062e-01 -2.88203239e-01 8.29906821e-01 2.77944624e-01
-3.41158032e-01 4.99929637e-01 -1.20988078e-02 5.53908795e-02
-4.54419613e-01 -8.97640884e-01 -5.80773115e-01 -2.54204839e-01
-3.89465898e-01 2.92104900e-01 5.25899410e-01 -1.57161906... | [8.138388633728027, -2.658522367477417] |
3a744a3b-a4a6-4df2-b6db-2f3d64d15c2d | planning-automated-driving-with-accident | 2301.10892 | null | https://arxiv.org/abs/2301.10892v1 | https://arxiv.org/pdf/2301.10892v1.pdf | Planning Automated Driving with Accident Experience Referencing and Common-sense Inferencing | Although a typical autopilot system far surpasses humans in term of sensing accuracy, performance stability and response agility, such a system is still far behind humans in the wisdom of understanding an unfamiliar environment with creativity, adaptivity and resiliency. Current AD brains are basically expert systems f... | ['Boqi Li', 'Hong Wang', 'Guoxi Chen', 'Ji Li', 'Shaobo Qiu'] | 2023-01-26 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [-1.25866815e-01 4.66728568e-01 1.46337897e-01 -6.58505619e-01
-8.73766169e-02 -5.55337965e-01 7.46144593e-01 1.31448343e-01
-3.55822831e-01 4.45165455e-01 4.32987094e-01 -9.89730358e-01
-7.75485694e-01 -8.62871408e-01 -4.07648832e-01 -3.16868246e-01
3.50331631e-03 4.96054024e-01 1.28074870e-01 -9.95308995... | [5.178920745849609, 1.258935809135437] |
338fecf0-5784-4e3b-acf5-ae8ee6336bac | depth-estimation-maps-of-lidar-and-stereo | 2212.11741 | null | https://arxiv.org/abs/2212.11741v1 | https://arxiv.org/pdf/2212.11741v1.pdf | Depth Estimation maps of lidar and stereo images | This paper as technology report is focusing on evaluation and performance about depth estimations based on lidar data and stereo images(front left and front right). The lidar 3d cloud data and stereo images are provided by ford. In addition, this paper also will explain some details about optimization for depth estimat... | ['Luoyu Chen', 'Fei Wu'] | 2022-12-22 | null | null | null | null | ['stereo-depth-estimation'] | ['computer-vision'] | [-2.79758759e-02 -1.05403617e-01 -4.38559204e-02 -6.22277081e-01
-3.47516507e-01 -2.43589029e-01 4.59531367e-01 9.95714664e-02
-4.11290318e-01 8.37648630e-01 -6.69461265e-02 -4.51439828e-01
-1.67301297e-01 -1.14210951e+00 -2.68120319e-01 -5.76084733e-01
-1.70246035e-01 9.11576927e-01 1.56096548e-01 -6.05429187... | [7.898592948913574, -2.539938449859619] |
c744022b-f9e7-48b0-b86a-031474c114de | liquid-theory-analogy-of-direct-coupling | 1510.06794 | null | http://arxiv.org/abs/1510.06794v2 | http://arxiv.org/pdf/1510.06794v2.pdf | Liquid-theory analogy of direct-coupling analysis of multiple-sequence alignment and its implications for protein structure prediction | The direct-coupling analysis is a powerful method for protein contact
prediction, and enables us to extract "direct" correlations between distant
sites that are latent in "indirect" correlations observed in a protein
multiple-sequence alignment. I show that the direct correlation can be obtained
by using a formulation ... | [] | 2015-11-10 | null | null | null | null | ['multiple-sequence-alignment'] | ['medical'] | [ 4.62307781e-01 -1.23065757e-02 -4.88701254e-01 -4.07927901e-01
-4.08482254e-01 -6.26951396e-01 4.55096871e-01 2.86821157e-01
-2.16337532e-01 1.23526120e+00 3.43219563e-02 -6.47079825e-01
-4.57508117e-01 -5.09043813e-01 -7.66525745e-01 -1.28623319e+00
-4.75037217e-01 6.17275894e-01 4.16805446e-01 -4.20859993... | [4.744943618774414, 5.308642387390137] |
abea32d8-91d0-4e02-8b9b-65da10e54dd4 | analyzing-bias-in-diffusion-based-face | 2305.06402 | null | https://arxiv.org/abs/2305.06402v1 | https://arxiv.org/pdf/2305.06402v1.pdf | Analyzing Bias in Diffusion-based Face Generation Models | Diffusion models are becoming increasingly popular in synthetic data generation and image editing applications. However, these models can amplify existing biases and propagate them to downstream applications. Therefore, it is crucial to understand the sources of bias in their outputs. In this paper, we investigate the ... | ['Vishal M. Patel', 'Malsha V. Perera'] | 2023-05-10 | null | null | null | null | ['face-generation', 'synthetic-data-generation', 'synthetic-data-generation'] | ['computer-vision', 'medical', 'miscellaneous'] | [ 2.26555496e-01 2.63811141e-01 -2.86314130e-01 -2.95489579e-01
-1.33489579e-01 -6.16319597e-01 9.69524920e-01 -1.44987628e-01
-8.99428278e-02 9.53833997e-01 5.72751701e-01 -1.13378651e-01
3.57389867e-01 -1.13485682e+00 -5.82388639e-01 -6.10146761e-01
5.66671193e-01 1.10572159e-01 -5.51577151e-01 -2.16102898... | [12.765375137329102, 0.9297983646392822] |
7b9adc22-5605-4983-9b24-755dcb22dc13 | gate-time-extraction-of-temporal-expressions | null | null | https://aclanthology.org/L16-1587 | https://aclanthology.org/L16-1587.pdf | GATE-Time: Extraction of Temporal Expressions and Events | GATE is a widely used open-source solution for text processing with a large user community. It contains components for several natural language processing tasks. However, temporal information extraction functionality within GATE has been rather limited so far, despite being a prerequisite for many application scenarios... | ['Jannik Str{\\"o}tgen', 'Mark A. Greenwood', 'Manuel Jung', 'Leon Derczynski', 'Diana Maynard'] | 2016-05-01 | gate-time-extraction-of-temporal-expressions-1 | https://aclanthology.org/L16-1587 | https://aclanthology.org/L16-1587.pdf | lrec-2016-5 | ['temporal-information-extraction', 'temporal-tagging'] | ['natural-language-processing', 'natural-language-processing'] | [-9.21355784e-02 -1.62636265e-01 -2.36890495e-01 -3.33725184e-01
-4.82016116e-01 -8.40300620e-01 1.00317872e+00 7.75258958e-01
-8.59296441e-01 5.71746886e-01 2.89558113e-01 -2.41127118e-01
-3.89679104e-01 -7.78731346e-01 6.14421591e-02 -3.87262762e-01
-3.98122281e-01 4.92813170e-01 7.83306718e-01 -3.64706546... | [9.228060722351074, 9.252634048461914] |
3de0ac32-c0d6-4629-8351-42fe73686cb3 | deep-single-image-deraining-via-estimating | 1906.09433 | null | https://arxiv.org/abs/1906.09433v1 | https://arxiv.org/pdf/1906.09433v1.pdf | Deep Single Image Deraining Via Estimating Transmission and Atmospheric Light in rainy Scenes | Rain removal in images/videos is still an important task in computer vision field and attracting attentions of more and more people. Traditional methods always utilize some incomplete priors or filters (e.g. guided filter) to remove rain effect. Deep learning gives more probabilities to better solve this task. However,... | ['Qinfeng Shi', 'Chao Ma', 'Yinglong Wang', 'Ehsan Abbasnejad', 'Bing Zeng', 'Xiaoping Ma'] | 2019-06-22 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [ 2.21306175e-01 -5.63005030e-01 4.59018558e-01 -6.98246181e-01
-3.96725163e-02 -1.49880096e-01 -5.22574969e-02 -5.60791433e-01
-5.12736499e-01 1.05548847e+00 -2.39097983e-01 -1.78360835e-01
3.01191628e-01 -1.15031493e+00 -6.64760530e-01 -1.26990306e+00
1.30408555e-01 -2.85987146e-02 2.76127994e-01 -3.08377385... | [10.9114408493042, -3.2586185932159424] |
4d4ff259-4978-4b8e-b900-de99a9a54545 | lmbot-distilling-graph-knowledge-into | 2306.17408 | null | https://arxiv.org/abs/2306.17408v2 | https://arxiv.org/pdf/2306.17408v2.pdf | LMBot: Distilling Graph Knowledge into Language Model for Graph-less Deployment in Twitter Bot Detection | As malicious actors employ increasingly advanced and widespread bots to disseminate misinformation and manipulate public opinion, the detection of Twitter bots has become a crucial task. Though graph-based Twitter bot detection methods achieve state-of-the-art performance, we find that their inference depends on the ne... | ['Minnan Luo', 'Qinghua Zheng', 'Hongrui Wang', 'Zifeng Zhu', 'Zhenyu Lei', 'Zhaoxuan Tan', 'Zijian Cai'] | 2023-06-30 | null | null | null | null | ['twitter-bot-detection', 'misinformation'] | ['miscellaneous', 'miscellaneous'] | [-3.11618716e-01 -1.16813526e-01 -5.47190845e-01 2.98216581e-01
-1.31431624e-01 -8.97081077e-01 5.99851191e-01 1.39953271e-01
-5.83700180e-01 3.23909342e-01 -3.37820888e-01 -6.49711251e-01
3.55101317e-01 -1.16016769e+00 -4.36914146e-01 -4.49315786e-01
-7.97824040e-02 6.28879011e-01 7.89486349e-01 -4.24153596... | [8.072781562805176, 10.110623359680176] |
a17bf800-b330-4bb6-ae5d-2597b34888f6 | heuristic-dropout-an-efficient-regularization | null | null | https://ieeexplore.ieee.org/abstract/document/9747409 | https://ieeexplore.ieee.org/abstract/document/9747409 | Heuristic Dropout: An Efficient Regularization Method for Medical Image Segmentation Models | For medical image segmentation in a real scenario, the amount of accurate annotation data at the pixel level is typically small, which tends to cause an overfitting problem. This manuscript goes deep into the research of the Dropout algorithm, which is commonly used in neural networks to alleviate the overfitting probl... | ['Chun Yuan', 'Linmi Tao', 'Ruiyang Liu', 'Dachuan Shi'] | 2022-05-23 | null | null | null | international-conference-on-acoustics-speech | ['medical-image-segmentation'] | ['medical'] | [ 3.38661492e-01 1.69608489e-01 -4.23392683e-01 -4.80715841e-01
-6.68775380e-01 1.60983086e-01 -4.79155779e-01 -7.30104297e-02
-8.78754735e-01 7.38570571e-01 -1.14286125e-01 -1.24991789e-01
3.40625234e-02 -5.34724295e-01 -6.14928424e-01 -9.16575015e-01
4.03690904e-01 -1.06466182e-01 1.32082552e-01 3.53650331... | [14.625158309936523, -2.3288018703460693] |
982b0907-349a-4828-ab2b-98d95284fd00 | on-the-effectiveness-of-regularization | 2006.05336 | null | https://arxiv.org/abs/2006.05336v1 | https://arxiv.org/pdf/2006.05336v1.pdf | On the Effectiveness of Regularization Against Membership Inference Attacks | Deep learning models often raise privacy concerns as they leak information about their training data. This enables an adversary to determine whether a data point was in a model's training set by conducting a membership inference attack (MIA). Prior work has conjectured that regularization techniques, which combat overf... | ['Tudor Dumitras', 'Sanghyun Hong', 'Yigitcan Kaya'] | 2020-06-09 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 4.58703667e-01 4.18987274e-01 -5.24114072e-02 -3.97149980e-01
-1.12570679e+00 -1.16834104e+00 5.34281850e-01 2.47171775e-01
-5.25423110e-01 6.59591138e-01 6.42837510e-02 -9.03780937e-01
5.40818423e-02 -7.87817717e-01 -1.03910875e+00 -8.16659510e-01
-2.69418210e-01 -3.18456650e-01 -6.51242882e-02 2.16264799... | [5.953433036804199, 7.099343776702881] |
5f1ed51f-8a5c-4abe-a7ac-f6afd3d31dcf | investigating-certain-choices-of-cnn | 2212.01235 | null | https://arxiv.org/abs/2212.01235v1 | https://arxiv.org/pdf/2212.01235v1.pdf | Investigating certain choices of CNN configurations for brain lesion segmentation | Brain tumor imaging has been part of the clinical routine for many years to perform non-invasive detection and grading of tumors. Tumor segmentation is a crucial step for managing primary brain tumors because it allows a volumetric analysis to have a longitudinal follow-up of tumor growth or shrinkage to monitor diseas... | ['Michel Koole', 'Karolien Goffin', 'Stefan Sunaert', 'Henri Vandermeulen', 'Ahmed Radwan', 'Masoomeh Rahimpour'] | 2022-12-02 | null | null | null | null | ['tumor-segmentation', 'brain-tumor-segmentation'] | ['computer-vision', 'medical'] | [-5.40134273e-02 1.32962540e-02 -7.80700073e-02 -3.07467431e-01
-4.42248106e-01 -2.20303223e-01 4.49265003e-01 4.04840380e-01
-9.71668661e-01 5.97393215e-01 -1.02658391e-01 -4.21935767e-01
-6.40826076e-02 -7.21840262e-01 -2.33123392e-01 -9.75233376e-01
-9.39996019e-02 6.06437325e-01 2.78653294e-01 -6.50590211... | [14.610426902770996, -2.5136966705322266] |
725f3318-5342-4b03-bde6-93075f074227 | data-efficient-contrastive-self-supervised | 2302.09195 | null | https://arxiv.org/abs/2302.09195v4 | https://arxiv.org/pdf/2302.09195v4.pdf | Data-Efficient Contrastive Self-supervised Learning: Most Beneficial Examples for Supervised Learning Contribute the Least | Self-supervised learning (SSL) learns high-quality representations from large pools of unlabeled training data. As datasets grow larger, it becomes crucial to identify the examples that contribute the most to learning such representations. This enables efficient SSL by reducing the volume of data required. Nevertheless... | ['Baharan Mirzasoleiman', 'Siddharth Joshi'] | 2023-02-18 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 1.42587647e-01 2.99974293e-01 -2.53807873e-01 -4.17529911e-01
-9.17315006e-01 -7.39535034e-01 5.76456606e-01 2.94823796e-01
-6.43906116e-01 1.03602219e+00 1.55383479e-02 -6.09347299e-02
-1.40764996e-01 -5.40554643e-01 -1.11123776e+00 -5.97839952e-01
-2.18369678e-01 4.39114243e-01 2.28423655e-01 -8.59173760... | [9.47859001159668, 3.029827356338501] |
244bee68-da71-45d4-bcf4-9095542aa2d7 | model-based-constrained-mdp-for-budget | 2303.01049 | null | https://arxiv.org/abs/2303.01049v1 | https://arxiv.org/pdf/2303.01049v1.pdf | Model-based Constrained MDP for Budget Allocation in Sequential Incentive Marketing | Sequential incentive marketing is an important approach for online businesses to acquire customers, increase loyalty and boost sales. How to effectively allocate the incentives so as to maximize the return (e.g., business objectives) under the budget constraint, however, is less studied in the literature. This problem ... | ['Shuang Yang', 'Jun Zhu', 'Yuanbo Chen', 'Lei Lv', 'Zaifan Jiang', 'Le Guo', 'Shuai Xiao'] | 2023-03-02 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [ 2.33830363e-01 3.43800068e-01 -9.34217632e-01 -1.28547028e-01
-5.59829831e-01 -4.58070129e-01 -4.99750338e-02 6.63092658e-02
-4.47276413e-01 1.01484823e+00 -1.11521212e-02 -6.44053519e-01
-5.69379568e-01 -8.69747639e-01 -9.50450599e-01 -8.75800610e-01
-1.55927718e-01 6.80861354e-01 -4.26544219e-01 3.75274345... | [4.52748966217041, 2.9324777126312256] |
bb910c4a-a1dc-4f7d-9c00-3a5a9add7cf9 | optimal-spatial-signal-design-for-mmwave | 2105.07664 | null | https://arxiv.org/abs/2105.07664v4 | https://arxiv.org/pdf/2105.07664v4.pdf | Optimal Spatial Signal Design for mmWave Positioning under Imperfect Synchronization | We consider the problem of spatial signal design for multipath-assisted mmWave positioning under limited prior knowledge on the user's location and clock bias. We propose an optimal robust design and, based on the low-dimensional precoder structure under perfect prior knowledge, a codebook-based heuristic design with o... | ['Henk Wymeersch', 'Gonzalo Seco-Granados', 'Florent Munier', 'Fan Jiang', 'Musa Furkan Keskin'] | 2021-05-17 | null | null | null | null | ['robust-design'] | ['miscellaneous'] | [-1.87866375e-01 9.13032517e-02 -1.96130082e-01 -3.14116597e-01
-9.91732538e-01 -6.56976104e-01 2.08048239e-01 -2.27386370e-01
-2.03151405e-01 7.95110106e-01 2.59475797e-01 -8.91654015e-01
-7.37261295e-01 -3.98239464e-01 -2.17019126e-01 -1.08883965e+00
-2.83856660e-01 2.27891296e-01 -3.00429285e-01 -5.19289263... | [6.271188259124756, 1.2144765853881836] |
9c4812df-82ef-4fa7-b84d-05dd1afb72a8 | jrdb-pose-a-large-scale-dataset-for-multi | 2210.11940 | null | https://arxiv.org/abs/2210.11940v2 | https://arxiv.org/pdf/2210.11940v2.pdf | JRDB-Pose: A Large-scale Dataset for Multi-Person Pose Estimation and Tracking | Autonomous robotic systems operating in human environments must understand their surroundings to make accurate and safe decisions. In crowded human scenes with close-up human-robot interaction and robot navigation, a deep understanding requires reasoning about human motion and body dynamics over time with human body po... | ['Hamid Rezatofighi', 'Jianfei Cai', 'Duy Tho Le', 'Edward Vendrow'] | 2022-10-20 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Vendrow_JRDB-Pose_A_Large-Scale_Dataset_for_Multi-Person_Pose_Estimation_and_Tracking_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Vendrow_JRDB-Pose_A_Large-Scale_Dataset_for_Multi-Person_Pose_Estimation_and_Tracking_CVPR_2023_paper.pdf | cvpr-2023-1 | ['multi-person-pose-estimation', 'multi-person-pose-estimation-and-tracking', 'social-navigation'] | ['computer-vision', 'computer-vision', 'robots'] | [-4.89957124e-01 -9.56488773e-02 7.40966648e-02 -3.93205762e-01
-5.06253839e-01 -5.45773625e-01 3.84158522e-01 -3.81930172e-01
-6.32595062e-01 7.10663736e-01 3.67075533e-01 3.47205788e-01
1.46839499e-01 -2.79859304e-01 -6.79501951e-01 -3.06823879e-01
-3.70334685e-01 1.06332541e+00 5.00535548e-01 -3.48569989... | [7.033779621124268, -0.8377019762992859] |
dae2ea5a-fc13-476f-beb4-150aef405324 | context-modulated-dynamic-networks-for-actor | null | null | https://ojs.aaai.org//index.php/AAAI/article/view/6895 | https://ojs.aaai.org/index.php/AAAI/article/view/6895/6749 | Context Modulated Dynamic Networks for Actor and Action Video Segmentation with Language Queries | Actor and action video segmentation with language queries aims to segment out the expression referred objects in the video. This process requires comprehensive language reasoning and fine-grained video understanding. Previous methods mainly leverage dynamic convolutional networks to match visual and semantic representa... | ['Yi Yang', 'Fan Ma', 'Cheng Deng', 'Hao Wang'] | 2020-04-03 | null | null | null | null | ['referring-expression-segmentation'] | ['computer-vision'] | [ 1.83290645e-01 -3.58684182e-01 -4.55142379e-01 -5.03048956e-01
-6.13330901e-01 -4.71242249e-01 6.03722572e-01 -2.88025558e-01
-5.21779597e-01 4.65958863e-01 4.92732763e-01 -1.52144611e-01
2.46469840e-01 -5.18711567e-01 -8.94547999e-01 -4.31436419e-01
2.55003214e-01 -4.43837382e-02 7.34901011e-01 -8.70790258... | [9.827776908874512, 0.6541498899459839] |
1b7a4dca-1ad8-44bd-af8e-57a1c6e684aa | remax-relaxing-for-better-training-on | 2306.17319 | null | https://arxiv.org/abs/2306.17319v1 | https://arxiv.org/pdf/2306.17319v1.pdf | ReMaX: Relaxing for Better Training on Efficient Panoptic Segmentation | This paper presents a new mechanism to facilitate the training of mask transformers for efficient panoptic segmentation, democratizing its deployment. We observe that due to its high complexity, the training objective of panoptic segmentation will inevitably lead to much higher false positive penalization. Such unbalan... | ['Liang-Chieh Chen', 'Philip Torr', 'Andrew Howard', 'Qihang Yu', 'Weijun Wang', 'Shuyang Sun'] | 2023-06-29 | null | null | null | null | ['panoptic-segmentation'] | ['computer-vision'] | [-6.03816565e-03 1.72872826e-01 -4.13158149e-01 -5.09386122e-01
-8.74307334e-01 -7.56403625e-01 3.28466326e-01 -2.07320184e-01
-2.80278146e-01 5.65222502e-01 -2.16284409e-01 -7.69006252e-01
1.45978883e-01 -6.00478709e-01 -5.89462101e-01 -6.23228729e-01
-3.70985448e-01 9.27111506e-01 3.41288269e-01 2.07094941... | [9.465005874633789, 0.11435875296592712] |
613aba6c-5cc6-4655-8211-ed635da4cd0d | towards-single-integrated-spoofing-aware | 2305.19051 | null | https://arxiv.org/abs/2305.19051v2 | https://arxiv.org/pdf/2305.19051v2.pdf | Towards single integrated spoofing-aware speaker verification embeddings | This study aims to develop a single integrated spoofing-aware speaker verification (SASV) embeddings that satisfy two aspects. First, rejecting non-target speakers' input as well as target speakers' spoofed inputs should be addressed. Second, competitive performance should be demonstrated compared to the fusion of auto... | ['Jee-weon Jung', 'Nam Soo Kim', 'Tomi Kinnunen', 'Nicholas Evans', 'Junichi Yamagishi', 'Kong Aik Lee', 'Massimiliano Todisco', 'Min Hyun Han', 'Myeonghun Jeong', 'Md Sahidullah', 'Xuechen Liu', 'Xin Wang', 'Hemlata Tak', 'Hye-jin Shim', 'Sung Hwan Mun'] | 2023-05-30 | null | null | null | null | ['speaker-verification'] | ['speech'] | [ 3.19254369e-01 8.66810232e-03 -1.16924606e-01 -4.23190266e-01
-9.66400623e-01 -7.20461011e-01 8.45148265e-01 -4.85575013e-02
-3.97967011e-01 2.26451173e-01 4.57510054e-01 -7.56634593e-01
3.51547420e-01 -1.75080135e-01 -4.67814058e-01 -5.49960673e-01
2.09300909e-02 6.70696720e-02 1.65961444e-01 -3.02285403... | [14.095584869384766, 5.8846001625061035] |
c04e2080-945b-4308-8254-e83930743185 | attentive-neural-network-for-named-entity | 1810.13097 | null | https://arxiv.org/abs/1810.13097v2 | https://arxiv.org/pdf/1810.13097v2.pdf | Attentive Neural Network for Named Entity Recognition in Vietnamese | We propose an attentive neural network for the task of named entity recognition in Vietnamese. The proposed attentive neural model makes use of character-based language models and word embeddings to encode words as vector representations. A neural network architecture of encoder, attention, and decoder layers is then u... | ['Cam-Tu Nguyen', 'Kim Anh Nguyen', 'Ngan Dong'] | 2018-10-31 | null | null | null | null | ['named-entity-recognition-in-vietnamese'] | ['natural-language-processing'] | [-1.82377413e-01 1.01129532e-01 -1.51781529e-01 -7.57028222e-01
-2.62779653e-01 -1.17398545e-01 5.80761969e-01 6.51111752e-02
-1.19261110e+00 7.12276161e-01 6.02095127e-01 -1.91888392e-01
4.25087839e-01 -8.94427001e-01 -4.63179916e-01 -2.70175159e-01
-3.01707357e-01 3.70654553e-01 -5.51665016e-02 -2.27550834... | [9.784268379211426, 9.607739448547363] |
e49b33a3-1c3e-4c41-ac31-a370a36e6f6e | enriching-existing-conversational-emotion | 1912.00819 | null | https://arxiv.org/abs/1912.00819v3 | https://arxiv.org/pdf/1912.00819v3.pdf | EDA: Enriching Emotional Dialogue Acts using an Ensemble of Neural Annotators | The recognition of emotion and dialogue acts enriches conversational analysis and help to build natural dialogue systems. Emotion interpretation makes us understand feelings and dialogue acts reflect the intentions and performative functions in the utterances. However, most of the textual and multi-modal conversational... | ['Chandrakant Bothe', 'Cornelius Weber', 'Sven Magg', 'Stefan Wermter'] | 2019-12-02 | eda-enriching-emotional-dialogue-acts-using | https://aclanthology.org/2020.lrec-1.78 | https://aclanthology.org/2020.lrec-1.78.pdf | lrec-2020-5 | ['emotional-dialogue-acts', 'dialogue-act-classification'] | ['natural-language-processing', 'natural-language-processing'] | [ 6.18295260e-02 5.57715535e-01 1.00077894e-02 -8.27758253e-01
-6.25640675e-02 -6.83087885e-01 8.68548334e-01 1.72042698e-01
-4.00626808e-01 8.76030684e-01 9.59465981e-01 1.75380707e-01
4.02244389e-01 -5.77473402e-01 2.84525841e-01 -4.03977871e-01
1.30136624e-01 6.19256258e-01 -5.16456306e-01 -7.16215253... | [12.983922958374023, 6.352423191070557] |
6947c9d1-7068-4a58-bab4-f1e9dbdb5ad9 | exploring-the-application-of-large-scale-pre | 2306.09008 | null | https://arxiv.org/abs/2306.09008v1 | https://arxiv.org/pdf/2306.09008v1.pdf | Exploring the Application of Large-scale Pre-trained Models on Adverse Weather Removal | Image restoration under adverse weather conditions (e.g., rain, snow and haze) is a fundamental computer vision problem and has important indications for various downstream applications. Different from early methods that are specially designed for specific type of weather, most recent works tend to remove various adver... | ['Nenghai Yu', 'Jieping Ye', 'Le Lu', 'Qi Chu', 'Qiankun Liu', 'Yue Wu', 'Zhentao Tan'] | 2023-06-15 | null | null | null | null | ['image-restoration'] | ['computer-vision'] | [ 2.82373190e-01 -3.11993420e-01 1.10060588e-01 -5.77368915e-01
-6.17253780e-01 -2.88217098e-01 4.71069127e-01 -3.18484247e-01
-1.44621432e-01 6.69349194e-01 5.92708707e-01 -2.56691277e-01
-1.57606855e-01 -8.39739859e-01 -9.01590407e-01 -1.11919069e+00
-4.33020443e-02 -4.33579206e-01 2.68988550e-01 -4.15175796... | [10.993401527404785, -3.0468790531158447] |
366ada70-6e3f-4dce-ae8a-ce63298ffcdf | deletion-robust-submodular-maximization-data | null | null | https://icml.cc/Conferences/2017/Schedule?showEvent=698 | http://proceedings.mlr.press/v70/mirzasoleiman17a/mirzasoleiman17a.pdf | Deletion-Robust Submodular Maximization: Data Summarization with "the Right to be Forgotten" |
How can we summarize a dynamic data stream when elements selected for the summary can be deleted at any time? This is an important challenge in online services, where the users generating the data may decide to exercise their right to restrict the service provider from using (part of) their data due to privacy con... | ['Baharan Mirzasoleiman', 'Andreas Krause', 'Amin Karbasi'] | 2017-08-01 | null | null | null | icml-2017-8 | ['data-summarization'] | ['miscellaneous'] | [ 4.45647240e-01 2.53687263e-03 -3.58323097e-01 -5.01111209e-01
-1.16998172e+00 -8.30965817e-01 4.02149111e-02 5.27871847e-01
-3.86251181e-01 5.69390714e-01 5.97012043e-01 -2.23976910e-01
-3.00333530e-01 -4.15257812e-01 -1.00727534e+00 -6.63248718e-01
-7.80855596e-01 3.58056694e-01 1.22137286e-01 -1.51413843... | [6.578424453735352, 5.006245136260986] |
6d752dfe-97c5-48cc-91ca-1c33f8045dd3 | the-dota-2-bot-competition | 2103.02943 | null | https://arxiv.org/abs/2103.02943v1 | https://arxiv.org/pdf/2103.02943v1.pdf | The Dota 2 Bot Competition | Multiplayer Online Battle Area (MOBA) games are a recent huge success both in the video game industry and the international eSports scene. These games encourage team coordination and cooperation, short and long-term planning, within a real-time combined action and strategy gameplay. Artificial Intelligence and Computat... | ['Tobias Mahlmann', 'Jose M. Font'] | 2021-03-04 | null | null | null | null | ['dota-2'] | ['playing-games'] | [-2.41563454e-01 2.56691426e-02 1.61943734e-01 1.87127993e-01
-2.27195203e-01 -6.67394102e-01 5.99138916e-01 -2.58032262e-01
-8.00387263e-01 6.71897590e-01 -2.80466139e-01 -5.29589236e-01
-6.05768740e-01 -1.08553326e+00 -1.82925284e-01 -2.52428949e-01
-2.34763101e-01 1.09397864e+00 7.11830437e-01 -1.26183379... | [3.462144136428833, 1.4778019189834595] |
7c75f70a-3b46-4da3-84d4-90d38c855865 | novel-modelling-strategies-for-high-frequency | 2212.00148 | null | https://arxiv.org/abs/2212.00148v1 | https://arxiv.org/pdf/2212.00148v1.pdf | Novel Modelling Strategies for High-frequency Stock Trading Data | Full electronic automation in stock exchanges has recently become popular, generating high-frequency intraday data and motivating the development of near real-time price forecasting methods. Machine learning algorithms are widely applied to mid-price stock predictions. Processing raw data as inputs for prediction model... | ['Li Xing', 'Ke Xu', 'Yuying Huang', 'Xuekui Zhang'] | 2022-11-30 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [-4.65666920e-01 -2.25718454e-01 -1.42052963e-01 -4.13361102e-01
-2.77802378e-01 -6.10707700e-01 6.57206953e-01 1.75483927e-01
-2.08975911e-01 1.00527442e+00 -1.78736031e-01 -6.02820814e-01
2.27788538e-02 -1.14383113e+00 -5.52825689e-01 -4.70403135e-01
-3.04904848e-01 1.57835931e-01 1.30035460e-01 -3.25040489... | [4.56086540222168, 4.176708221435547] |
b91e6a00-35ae-459f-9404-de0b6337ca8d | attention-based-residual-autoencoder-for | null | null | https://link.springer.com/article/10.1007/s10489-022-03613-1 | https://link.springer.com/content/pdf/10.1007/s10489-022-03613-1.pdf | Attention-based residual autoencoder for video anomaly detection | Automatic anomaly detection is a crucial task in video surveillance system intensively used for public safety and others. The present system adopts a spatial branch and a temporal branch in a unified network that exploits both spatial and temporal information effectively. The network has a residual autoencoder architec... | ['Yong-Guk Kim', 'Viet-Tuan Le'] | 2022-05-25 | null | null | null | applied-intelligence-2022-5 | ['anomaly-detection-in-surveillance-videos', 'anomaly-detection-in-surveillance-videos'] | ['computer-vision', 'methodology'] | [-1.28465250e-01 -3.09392333e-01 -9.83524993e-02 -1.99896842e-01
-3.25606346e-01 7.30885640e-02 4.75912958e-01 -2.51870304e-01
-7.17683613e-01 4.19387996e-01 2.76345581e-01 -3.22881460e-01
3.21776628e-01 -5.09164393e-01 -7.25826979e-01 -9.83865440e-01
-2.77485490e-01 -4.68319505e-01 6.10091448e-01 -1.20037302... | [7.891859531402588, 1.5032962560653687] |
aeacff06-9abe-4aa6-81f9-08621301eb48 | cctc-a-cross-sentence-chinese-text-correction | null | null | https://aclanthology.org/2022.coling-1.294 | https://aclanthology.org/2022.coling-1.294.pdf | CCTC: A Cross-Sentence Chinese Text Correction Dataset for Native Speakers | The Chinese text correction (CTC) focuses on detecting and correcting Chinese spelling errors and grammatical errors. Most existing datasets of Chinese spelling check (CSC) and Chinese grammatical error correction (GEC) are focused on a single sentence written by Chinese-as-a-second-language (CSL) learners. We find tha... | ['Guoping Hu', 'Zhigang Chen', 'Wanxiang Che', 'Dayong Wu', 'Xingyi Duan', 'Baoxin Wang'] | null | null | null | null | coling-2022-10 | ['grammatical-error-correction'] | ['natural-language-processing'] | [ 1.96047336e-01 -3.10769588e-01 2.01446995e-01 -4.65151280e-01
-9.40754354e-01 -4.20291185e-01 2.37132698e-01 5.19102156e-01
-7.78862596e-01 8.42664480e-01 4.68858570e-01 -6.68117106e-01
5.92559457e-01 -3.81225526e-01 -6.41456068e-01 -1.04127973e-01
4.83129203e-01 3.47498834e-01 4.42503780e-01 -5.50879955... | [11.022405624389648, 10.742475509643555] |
2340fa81-cfcd-4069-bd51-5042c70dc9cf | partially-does-it-towards-scene-level-fg-sbir | 2203.14804 | null | https://arxiv.org/abs/2203.14804v1 | https://arxiv.org/pdf/2203.14804v1.pdf | Partially Does It: Towards Scene-Level FG-SBIR with Partial Input | We scrutinise an important observation plaguing scene-level sketch research -- that a significant portion of scene sketches are "partial". A quick pilot study reveals: (i) a scene sketch does not necessarily contain all objects in the corresponding photo, due to the subjective holistic interpretation of scenes, (ii) th... | ['Yi-Zhe Song', 'Tao Xiang', 'Aneeshan Sain', 'Viswanatha Reddy Gajjala', 'Ayan Kumar Bhunia', 'Pinaki Nath Chowdhury'] | 2022-03-28 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Chowdhury_Partially_Does_It_Towards_Scene-Level_FG-SBIR_With_Partial_Input_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Chowdhury_Partially_Does_It_Towards_Scene-Level_FG-SBIR_With_Partial_Input_CVPR_2022_paper.pdf | cvpr-2022-1 | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 4.19639319e-01 -3.40170711e-01 -8.58693868e-02 -1.43593967e-01
-5.51655591e-01 -8.43330145e-01 9.38162327e-01 1.40293851e-01
1.40473843e-01 2.83339828e-01 4.94063735e-01 -2.76430044e-02
-2.95080006e-01 -7.68948555e-01 -7.55666614e-01 -4.71704930e-01
1.19204886e-01 3.50041509e-01 4.19226438e-01 -2.48582542... | [11.642879486083984, 0.6004000306129456] |
f33657ff-c309-4e24-8515-27a9e42c6331 | neca-network-embedded-deep-representation | 2205.12752 | null | https://arxiv.org/abs/2205.12752v1 | https://arxiv.org/pdf/2205.12752v1.pdf | NECA: Network-Embedded Deep Representation Learning for Categorical Data | We propose NECA, a deep representation learning method for categorical data. Built upon the foundations of network embedding and deep unsupervised representation learning, NECA deeply embeds the intrinsic relationship among attribute values and explicitly expresses data objects with numeric vector representations. Desi... | ['Wenjun Zhou', 'Sen Wu', 'Xiaonan Gao'] | 2022-05-25 | null | null | null | null | ['network-embedding'] | ['methodology'] | [-2.40178913e-01 2.93953985e-01 -6.46844029e-01 -9.57543492e-01
-4.16596308e-02 -2.03115627e-01 3.07911426e-01 7.75700688e-01
-5.52444980e-02 3.68851602e-01 7.88668811e-01 -4.94556129e-01
-6.07524872e-01 -1.35480237e+00 -2.95910597e-01 -6.40044332e-01
-6.18249714e-01 3.58050168e-01 -8.41317177e-01 -7.43259415... | [7.122446060180664, 6.311608791351318] |
1f267fb1-b5c7-4e10-8ad4-33e7c59ddda5 | visual-text-correction | 1801.01967 | null | http://arxiv.org/abs/1801.01967v3 | http://arxiv.org/pdf/1801.01967v3.pdf | Visual Text Correction | Videos, images, and sentences are mediums that can express the same
semantics. One can imagine a picture by reading a sentence or can describe a
scene with some words. However, even small changes in a sentence can cause a
significant semantic inconsistency with the corresponding video/image. For
example, by changing th... | ['Amir Mazaheri', 'Mubarak Shah'] | 2018-01-06 | visual-text-correction-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Amir_Mazaheri_Visual_Text_Correction_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Amir_Mazaheri_Visual_Text_Correction_ECCV_2018_paper.pdf | eccv-2018-9 | ['visual-text-correction'] | ['computer-vision'] | [ 3.96942735e-01 -2.93286771e-01 2.20106430e-02 -5.29600263e-01
-4.77115393e-01 -3.86601865e-01 3.97341162e-01 2.25169510e-01
-2.05899492e-01 5.28866708e-01 3.91299784e-01 1.63271856e-02
4.92862016e-01 -3.39112759e-01 -1.17582142e+00 -4.77360308e-01
4.96205658e-01 -1.90918744e-01 2.66225517e-01 -3.71854715... | [10.269360542297363, 0.8659043312072754] |
5152230a-731a-460d-baad-0388c8871e11 | structural-imbalance-aware-graph-augmentation | 2303.13757 | null | https://arxiv.org/abs/2303.13757v1 | https://arxiv.org/pdf/2303.13757v1.pdf | Structural Imbalance Aware Graph Augmentation Learning | Graph machine learning (GML) has made great progress in node classification, link prediction, graph classification and so on. However, graphs in reality are often structurally imbalanced, that is, only a few hub nodes have a denser local structure and higher influence. The imbalance may compromise the robustness of exi... | ['Zheng Liu', 'Kejia-Chen', 'Zulong Liu'] | 2023-03-24 | null | null | null | null | ['graph-classification'] | ['graphs'] | [-1.08658098e-01 6.10267997e-01 -6.94952250e-01 1.03182912e-01
-1.09563150e-01 -2.32399791e-01 4.34772462e-01 4.23740566e-01
2.26832017e-01 8.60000670e-01 1.46247506e-01 -4.04161602e-01
-1.83645487e-02 -1.16610992e+00 -2.99956679e-01 -8.93292367e-01
-5.02740204e-01 7.51937270e-01 5.07603884e-01 -9.22376513... | [7.197645664215088, 6.141395568847656] |
7c74530b-695c-4468-bb98-223c1c7db0a0 | look-listen-and-learn | 1705.08168 | null | http://arxiv.org/abs/1705.08168v2 | http://arxiv.org/pdf/1705.08168v2.pdf | Look, Listen and Learn | We consider the question: what can be learnt by looking at and listening to a
large number of unlabelled videos? There is a valuable, but so far untapped,
source of information contained in the video itself -- the correspondence
between the visual and the audio streams, and we introduce a novel
"Audio-Visual Correspond... | ['Relja Arandjelović', 'Andrew Zisserman'] | 2017-05-23 | look-listen-and-learn-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Arandjelovic_Look_Listen_and_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Arandjelovic_Look_Listen_and_ICCV_2017_paper.pdf | iccv-2017-10 | ['sound-classification'] | ['audio'] | [ 4.23551083e-01 -1.39476344e-01 -9.39619541e-02 -3.54435742e-01
-1.04200399e+00 -6.63420200e-01 7.24777460e-01 2.45099328e-02
-2.68133491e-01 4.12629664e-01 3.39860260e-01 2.03582674e-01
4.58298549e-02 -3.45949292e-01 -1.05620265e+00 -6.36838257e-01
-3.60187441e-01 3.51577222e-01 2.56284654e-01 4.91645634... | [9.947213172912598, 1.1065850257873535] |
216c02d5-3fca-471e-9008-363442485f27 | robust-automatic-monocular-vehicle-speed | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Revaud_Robust_Automatic_Monocular_Vehicle_Speed_Estimation_for_Traffic_Surveillance_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Revaud_Robust_Automatic_Monocular_Vehicle_Speed_Estimation_for_Traffic_Surveillance_ICCV_2021_paper.pdf | Robust Automatic Monocular Vehicle Speed Estimation for Traffic Surveillance | Even though CCTV cameras are widely deployed for traffic surveillance and have therefore the potential of becoming cheap automated sensors for traffic speed analysis, their large-scale usage toward this goal has not been reported yet. A key difficulty lies in fact in the camera calibration phase. Existing state-of-... | ['Martin Humenberger', 'Jerome Revaud'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['vehicle-speed-estimation'] | ['computer-vision'] | [ 2.23494977e-01 -3.61310512e-01 -3.13925266e-01 -3.13497543e-01
-8.39065790e-01 -6.20091379e-01 4.33154851e-01 -3.67027104e-01
-3.99807692e-01 4.47964281e-01 -3.54782432e-01 -4.96200353e-01
2.81460404e-01 -6.14327729e-01 -7.82038391e-01 -6.68221295e-01
5.09726286e-01 5.80797613e-01 7.54335821e-01 -2.94375625... | [8.058159828186035, -1.1637208461761475] |
a5f4d128-800a-47b5-826c-72d5b6f2f6cf | the-statistical-benefits-of-quantile-temporal | 2305.18388 | null | https://arxiv.org/abs/2305.18388v1 | https://arxiv.org/pdf/2305.18388v1.pdf | The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation | We study the problem of temporal-difference-based policy evaluation in reinforcement learning. In particular, we analyse the use of a distributional reinforcement learning algorithm, quantile temporal-difference learning (QTD), for this task. We reach the surprising conclusion that even if a practitioner has no interes... | ['Will Dabney', 'Marc G. Bellemare', 'Rémi Munos', 'Clare Lyle', 'Yunhao Tang', 'Mark Rowland'] | 2023-05-28 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-3.70735347e-01 2.59972721e-01 -6.08623445e-01 -1.91799477e-01
-1.16668522e+00 -6.47305846e-01 6.56371117e-01 3.93941134e-01
-8.82676780e-01 1.45031679e+00 2.24406257e-01 -9.62021470e-01
-5.20121753e-01 -8.26198936e-01 -6.79285586e-01 -7.63938904e-01
-4.94751185e-01 7.07785845e-01 9.71794128e-04 -1.67815834... | [4.187106132507324, 2.6112005710601807] |
a2b67e79-3418-4579-a56a-dcdb673f34f2 | posegu-3d-human-pose-estimation-with-novel | 2207.03618 | null | https://arxiv.org/abs/2207.03618v1 | https://arxiv.org/pdf/2207.03618v1.pdf | PoseGU: 3D Human Pose Estimation with Novel Human Pose Generator and Unbiased Learning | 3D pose estimation has recently gained substantial interests in computer vision domain. Existing 3D pose estimation methods have a strong reliance on large size well-annotated 3D pose datasets, and they suffer poor model generalization on unseen poses due to limited diversity of 3D poses in training sets. In this work,... | ['Gengfa Fang', 'Linchao Zhu', 'Haiyan Lu', 'Shannan Guan'] | 2022-07-07 | null | null | null | null | ['3d-pose-estimation'] | ['computer-vision'] | [ 5.08320518e-02 2.76167989e-01 -2.90224761e-01 -2.64260709e-01
-1.03083110e+00 -7.15403318e-01 6.39997065e-01 -4.10363942e-01
-3.81484747e-01 1.04766536e+00 2.57446647e-01 2.14203432e-01
4.63268906e-02 -3.88580918e-01 -8.73896360e-01 -4.63354379e-01
-2.18836322e-01 9.21807408e-01 -2.38791792e-04 -8.94988477... | [6.993582725524902, -1.012322187423706] |
4a7abdaa-3a8d-4e9d-8170-c8c7063e1717 | deep-reinforcement-learning-for-programming | 1801.10467 | null | http://arxiv.org/abs/1801.10467v1 | http://arxiv.org/pdf/1801.10467v1.pdf | Deep Reinforcement Learning for Programming Language Correction | Novice programmers often struggle with the formal syntax of programming
languages. To assist them, we design a novel programming language correction
framework amenable to reinforcement learning. The framework allows an agent to
mimic human actions for text navigation and editing. We demonstrate that the
agent can be tr... | ['Rahul Gupta', 'Aditya Kanade', 'Shirish Shevade'] | 2018-01-31 | null | null | null | null | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [ 2.81663626e-01 2.14495867e-01 -4.28264648e-01 -2.98017174e-01
-3.96076351e-01 -9.08964813e-01 2.35232338e-01 5.02917469e-01
-6.14956200e-01 2.85115600e-01 -3.91965508e-01 -1.18478251e+00
3.62959415e-01 -8.14288616e-01 -1.35179007e+00 3.59381020e-01
1.78086892e-01 2.75050044e-01 1.26753435e-01 -6.50359154... | [8.17969036102295, 7.627690315246582] |
4714530d-3762-44b2-a328-1ab1b73bc98c | discovering-playing-patterns-time-series | 1710.02268 | null | http://arxiv.org/abs/1710.02268v1 | http://arxiv.org/pdf/1710.02268v1.pdf | Discovering Playing Patterns: Time Series Clustering of Free-To-Play Game Data | The classification of time series data is a challenge common to all
data-driven fields. However, there is no agreement about which are the most
efficient techniques to group unlabeled time-ordered data. This is because a
successful classification of time series patterns depends on the goal and the
domain of interest, i... | ['África Periáñez', 'Anna Guitart', 'Alain Saas'] | 2017-10-06 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [-3.96828294e-01 -6.57197297e-01 -1.09479874e-01 2.51855962e-02
-1.64731786e-01 -9.01632309e-01 3.07637215e-01 5.42775750e-01
-3.69368643e-01 1.83548465e-01 3.17030512e-02 -3.19115102e-01
-8.28385830e-01 -8.91422510e-01 2.63110735e-02 -6.83047295e-01
-6.62077546e-01 6.07529759e-01 6.00241780e-01 -5.10429740... | [7.235499382019043, 3.3399460315704346] |
d9fcbfa3-332a-4ffc-ac57-a9ee6bc4404a | flow-guided-transformer-for-video-inpainting | 2208.06768 | null | https://arxiv.org/abs/2208.06768v1 | https://arxiv.org/pdf/2208.06768v1.pdf | Flow-Guided Transformer for Video Inpainting | We propose a flow-guided transformer, which innovatively leverage the motion discrepancy exposed by optical flows to instruct the attention retrieval in transformer for high fidelity video inpainting. More specially, we design a novel flow completion network to complete the corrupted flows by exploiting the relevant fl... | ['Dong Liu', 'Jingjing Fu', 'Kaidong Zhang'] | 2022-08-14 | null | null | null | null | ['video-inpainting'] | ['computer-vision'] | [-2.26883620e-01 -3.03109318e-01 -2.05836505e-01 -1.42698184e-01
-5.27965188e-01 -5.16497850e-01 4.01337475e-01 -3.61065626e-01
-2.16852248e-01 6.20659053e-01 7.43880033e-01 -9.95746627e-02
-1.81140110e-01 -8.06285203e-01 -6.72287464e-01 -6.09705806e-01
1.24475271e-01 -3.30335259e-01 4.34975177e-01 9.74010825... | [10.72584342956543, -1.4096647500991821] |
83e1214f-b0a9-428e-8509-b4dff1556633 | youtube-vos-sequence-to-sequence-video-object | 1809.00461 | null | http://arxiv.org/abs/1809.00461v1 | http://arxiv.org/pdf/1809.00461v1.pdf | YouTube-VOS: Sequence-to-Sequence Video Object Segmentation | Learning long-term spatial-temporal features are critical for many video
analysis tasks. However, existing video segmentation methods predominantly rely
on static image segmentation techniques, and methods capturing temporal
dependency for segmentation have to depend on pretrained optical flow models,
leading to subopt... | ['Yuchen Liang', 'Jianchao Yang', 'Brian Price', 'Linjie Yang', 'Dingcheng Yue', 'Scott Cohen', 'Ning Xu', 'Yuchen Fan', 'Thomas Huang'] | 2018-09-03 | youtube-vos-sequence-to-sequence-video-object-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Ning_Xu_YouTube-VOS_Sequence-to-Sequence_Video_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Ning_Xu_YouTube-VOS_Sequence-to-Sequence_Video_ECCV_2018_paper.pdf | eccv-2018-9 | ['one-shot-visual-object-segmentation'] | ['computer-vision'] | [ 1.80854984e-02 -4.71314818e-01 -7.60351002e-01 -1.23899877e-01
-6.47893667e-01 -7.08297968e-01 2.55903602e-01 -4.05032635e-01
-6.42590880e-01 4.26965237e-01 6.32853247e-03 -1.97042763e-01
1.65996999e-01 -2.80932456e-01 -9.05481577e-01 -4.07779902e-01
-3.06205124e-01 7.09171370e-02 9.10781741e-01 1.11593716... | [9.184981346130371, 0.06069455295801163] |
07594955-7fbc-4e66-ab65-9df45f2279de | framm-fair-ranking-with-missing-modalities | 2305.19407 | null | https://arxiv.org/abs/2305.19407v1 | https://arxiv.org/pdf/2305.19407v1.pdf | FRAMM: Fair Ranking with Missing Modalities for Clinical Trial Site Selection | Despite many efforts to address the disparities, the underrepresentation of gender, racial, and ethnic minorities in clinical trials remains a problem and undermines the efficacy of treatments on minorities. This paper focuses on the trial site selection task and proposes FRAMM, a deep reinforcement learning framework ... | ['Jimeng Sun', 'Cao Xiao', 'Lucas Glass', 'Brandon Theodorou'] | 2023-05-30 | null | null | null | null | ['imputation', 'imputation', 'imputation'] | ['computer-vision', 'miscellaneous', 'time-series'] | [ 5.67076430e-02 3.33458424e-01 -1.13642478e+00 -5.78776896e-01
-1.40790915e+00 -3.53395313e-01 3.17221314e-01 5.39504945e-01
-7.29192972e-01 1.02878714e+00 9.45941746e-01 -8.71738195e-01
-1.22373961e-01 -6.73746288e-01 -8.36731374e-01 -3.94602895e-01
1.41078457e-01 5.74269056e-01 -9.20641482e-01 1.10446267... | [15.001489639282227, -2.056020736694336] |
2e4ab606-e4ca-459f-8d5a-b43b3ee04cee | a-hierarchical-approach-for-visual | 1909.12401 | null | https://arxiv.org/abs/1909.12401v1 | https://arxiv.org/pdf/1909.12401v1.pdf | A Hierarchical Approach for Visual Storytelling Using Image Description | One of the primary challenges of visual storytelling is developing techniques that can maintain the context of the story over long event sequences to generate human-like stories. In this paper, we propose a hierarchical deep learning architecture based on encoder-decoder networks to address this problem. To better help... | ['Ryan Gaines', 'Sagar Gandhi', 'Md Sultan Al Nahian', 'Brent Harrison', 'Tasmia Tasrin'] | 2019-09-26 | null | null | null | null | ['visual-storytelling'] | ['natural-language-processing'] | [-6.67359727e-03 2.26842090e-01 1.43540785e-01 -3.34254682e-01
-6.73068464e-01 -4.47714537e-01 1.10766518e+00 -1.30631775e-01
4.36106212e-02 8.08051229e-01 8.63183200e-01 -4.72902358e-02
3.91894400e-01 -8.22753489e-01 -1.01060760e+00 -2.63847500e-01
8.70284513e-02 2.21234411e-01 3.80529016e-01 -3.28364432... | [11.180729866027832, 0.7322661876678467] |
691b4687-4230-4621-8711-add5c7c7323d | is-ai-model-interpretable-to-combat-with | 2010.02006 | null | https://arxiv.org/abs/2010.02006v7 | https://arxiv.org/pdf/2010.02006v7.pdf | Interpretable Machine Learning for COVID-19: An Empirical Study on Severity Prediction Task | The black-box nature of machine learning models hinders the deployment of some high-accuracy models in medical diagnosis. It is risky to put one's life in the hands of models that medical researchers do not fully understand. However, through model interpretation, black-box models can promptly reveal significant biomark... | ['Xiangfei Chai', 'Sumi Helal', 'Kunwei Li', 'Jian Chen', 'Bei Liu', 'Yayuan Gen', 'Shaolin Li', 'Dingchang Zheng', 'Jiangtao Wang', 'Wenjie Ruan', 'Han Wu'] | 2020-09-30 | null | null | null | null | ['severity-prediction'] | ['computer-vision'] | [ 3.21645200e-01 -1.32013634e-01 -4.42613631e-01 -2.51766354e-01
-3.18241984e-01 -3.64225090e-01 2.23374873e-01 6.40955031e-01
-2.52032518e-01 9.35017645e-01 4.10000086e-01 -9.41223323e-01
-4.41434920e-01 -5.71469128e-01 -4.20602620e-01 -5.04407287e-01
-5.89619696e-01 9.20526743e-01 -5.89410841e-01 2.37304017... | [7.950652599334717, 5.931516170501709] |
471558ec-8ddc-4140-b005-6adc8e2008fd | improving-semantic-dependency-parsing-with | null | null | https://aclanthology.org/W19-6202 | https://aclanthology.org/W19-6202.pdf | Improving Semantic Dependency Parsing with Syntactic Features | We extend a state-of-the-art deep neural architecture for semantic dependency parsing with features defined over syntactic dependency trees. Our empirical results show that only gold-standard syntactic information leads to consistent improvements in semantic parsing accuracy, and that the magnitude of these improvement... | ['Robin Kurtz', 'Marco Kuhlmann', 'Daniel Roxbo'] | 2019-09-01 | null | null | null | ws-2019-9 | ['semantic-dependency-parsing'] | ['natural-language-processing'] | [-1.20076761e-01 5.17225742e-01 -1.55918181e-01 -9.66864109e-01
-7.44844735e-01 -6.17725790e-01 4.66223001e-01 2.76418716e-01
-4.88630444e-01 6.62789822e-01 6.10222399e-01 -5.07304847e-01
6.08765893e-03 -8.81214619e-01 -5.82727551e-01 -2.18604341e-01
5.86687997e-02 4.48512495e-01 2.59837121e-01 -4.68095660... | [10.482059478759766, 9.34813117980957] |
ae36aa6d-1433-4b10-be40-34e1bbb0852d | learning-semi-supervised-gaussian-mixture | 2305.06144 | null | https://arxiv.org/abs/2305.06144v1 | https://arxiv.org/pdf/2305.06144v1.pdf | Learning Semi-supervised Gaussian Mixture Models for Generalized Category Discovery | In this paper, we address the problem of generalized category discovery (GCD), \ie, given a set of images where part of them are labelled and the rest are not, the task is to automatically cluster the images in the unlabelled data, leveraging the information from the labelled data, while the unlabelled data contain ima... | ['Kai Han', 'Xin Wen', 'Bingchen Zhao'] | 2023-05-10 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [ 5.69848716e-01 2.92003721e-01 -3.03805709e-01 -4.52636868e-01
-6.38231695e-01 -6.54028654e-01 7.44315743e-01 2.46371686e-01
-4.69309449e-01 5.15782833e-01 -3.41967046e-01 -1.00169197e-01
-3.16626489e-01 -6.16029501e-01 -5.87165236e-01 -1.27949107e+00
2.19694585e-01 1.15266037e+00 2.24340603e-01 3.37490350... | [9.53947925567627, 2.929332971572876] |
dba54e19-38bf-4c0a-a42d-8b1d199c7630 | variational-context-deformable-convnets-for | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Xiong_Variational_Context-Deformable_ConvNets_for_Indoor_Scene_Parsing_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Xiong_Variational_Context-Deformable_ConvNets_for_Indoor_Scene_Parsing_CVPR_2020_paper.pdf | Variational Context-Deformable ConvNets for Indoor Scene Parsing | Context information is critical for image semantic segmentation. Especially in indoor scenes, the large variation of object scales makes spatial-context an important factor for improving the segmentation performance. Thus, in this paper, we propose a novel variational context-deformable (VCD) module to learn adaptive r... | [' Qi Wang', ' Nianhui Guo', ' Yuan Yuan', 'Zhitong Xiong'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['scene-parsing'] | ['computer-vision'] | [ 7.87780210e-02 -7.67026693e-02 -1.14697509e-01 -6.57118499e-01
-3.30332667e-01 -5.06073236e-01 4.27564234e-01 5.92336915e-02
-5.28315067e-01 2.84475863e-01 3.83490287e-02 5.10618463e-02
-3.35549027e-01 -1.04199171e+00 -6.97587550e-01 -9.30040896e-01
1.26718596e-01 -4.09845822e-02 8.74641836e-01 -2.95354966... | [9.356664657592773, -0.9767367839813232] |
531bb5a0-1bf0-4eb7-9e25-9b61f984a64f | lifelong-change-detection-continuous-domain | 2306.16086 | null | https://arxiv.org/abs/2306.16086v1 | https://arxiv.org/pdf/2306.16086v1.pdf | Lifelong Change Detection: Continuous Domain Adaptation for Small Object Change Detection in Every Robot Navigation | The recently emerging research area in robotics, ground view change detection, suffers from its ill-posed-ness because of visual uncertainty combined with complex nonlinear perspective projection. To regularize the ill-posed-ness, the commonly applied supervised learning methods (e.g., CSCD-Net) rely on manually annota... | ['Yoshimasa Nakamura', 'Kanji Tanaka', 'Koji Takeda'] | 2023-06-28 | null | null | null | null | ['change-detection', 'robot-navigation'] | ['computer-vision', 'robots'] | [ 4.50938165e-01 2.36120254e-01 6.61130846e-02 -2.59249806e-01
-3.13254833e-01 -4.64201093e-01 6.96576834e-01 -1.51513368e-01
-4.92278099e-01 8.15770090e-01 -3.25215787e-01 1.77489698e-01
-1.76544175e-01 -3.49250793e-01 -7.77292490e-01 -6.13601327e-01
1.18233375e-01 4.77435827e-01 6.84942901e-01 -2.77558148... | [7.6480631828308105, -2.423809766769409] |
0c986cdd-0ddc-43cc-9392-a1680f969714 | a-challenging-benchmark-of-anime-style | 2204.14034 | null | https://arxiv.org/abs/2204.14034v1 | https://arxiv.org/pdf/2204.14034v1.pdf | A Challenging Benchmark of Anime Style Recognition | Given two images of different anime roles, anime style recognition (ASR) aims to learn abstract painting style to determine whether the two images are from the same work, which is an interesting but challenging problem. Unlike biometric recognition, such as face recognition, iris recognition, and person re-identificati... | ['Huanqiang Zeng', 'Jianqing Zhu', 'Tianchen Chen', 'Xiao Yang', 'Kailin Lyu', 'Shengtao Guo', 'Haotang Li'] | 2022-04-29 | null | null | null | null | ['art-analysis'] | ['computer-vision'] | [ 0.1969417 -0.5608254 0.1942494 -0.4957109 -0.5241314 -0.6595017
0.6804162 -0.6340627 -0.25762466 0.559555 0.2113811 0.333817
0.05849309 -0.6960617 -0.6553064 -0.5520225 0.43603775 0.5267313
-0.19815005 -0.39986387 0.17303157 0.49172196 -1.5304401 0.40061748
0.51427066 1.0598004 -0.1978... | [14.622374534606934, 0.9457252621650696] |
fc560f6a-8260-4454-831d-f057791a8d77 | do-ssl-models-have-deja-vu-a-case-of | 2304.13850 | null | https://arxiv.org/abs/2304.13850v2 | https://arxiv.org/pdf/2304.13850v2.pdf | Do SSL Models Have Déjà Vu? A Case of Unintended Memorization in Self-supervised Learning | Self-supervised learning (SSL) algorithms can produce useful image representations by learning to associate different parts of natural images with one another. However, when taken to the extreme, SSL models can unintendedly memorize specific parts in individual training samples rather than learning semantically meaning... | ['Casey Meehan', 'Kamalika Chaudhuri', 'Pascal Vincent', 'Florian Bordes', 'Chuan Guo'] | 2023-04-26 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 8.07562113e-01 5.52765906e-01 -1.40212610e-01 -2.43972048e-01
-5.78518331e-01 -7.07915246e-01 5.21013021e-01 5.18164814e-01
-5.72713390e-02 7.36208141e-01 8.57244655e-02 -4.49772954e-01
-1.65514082e-01 -8.62611055e-01 -1.35536361e+00 -6.52677476e-01
-1.16989061e-01 -1.53684646e-01 -9.47829112e-02 3.51836294... | [10.035548210144043, 2.2451930046081543] |
ab5be222-ae90-4bb2-9f22-b05884b0e301 | architecture-and-evolution-of-semantic | 1908.04911 | null | https://arxiv.org/abs/1908.04911v2 | https://arxiv.org/pdf/1908.04911v2.pdf | Architecture and evolution of semantic networks in mathematics texts | Knowledge is a network of interconnected concepts. Yet, precisely how the topological structure of knowledge constrains its acquisition remains unknown, hampering the development of learning enhancement strategies. Here we study the topological structure of semantic networks reflecting mathematical concepts and their r... | ['Nicolas H. Christianson', 'Ann Sizemore Blevins', 'Danielle S. Bassett'] | 2019-08-14 | null | null | null | null | ['logical-sequence'] | ['reasoning'] | [-2.24504158e-01 3.15071404e-01 -1.60268500e-01 -2.67051551e-02
5.06013691e-01 -1.17754447e+00 5.69306970e-01 6.21649504e-01
1.43485382e-01 2.75820583e-01 6.47363245e-01 -8.40169430e-01
-1.15948665e+00 -1.22930098e+00 -8.03446949e-01 -1.21245362e-01
-3.21072549e-01 2.06188902e-01 5.27668536e-01 -6.08040750... | [9.543160438537598, 7.167332172393799] |
6c092d45-b1db-4153-ae62-1ca2e09b4acd | backtracking-regression-forests-for-accurate | 1710.07965 | null | http://arxiv.org/abs/1710.07965v1 | http://arxiv.org/pdf/1710.07965v1.pdf | Backtracking Regression Forests for Accurate Camera Relocalization | Camera relocalization plays a vital role in many robotics and computer vision
tasks, such as global localization, recovery from tracking failure, and loop
closure detection. Recent random forests based methods directly predict 3D
world locations for 2D image locations to guide the camera pose optimization.
During train... | ['Clarence W. de Silva', 'Julien Valentin', 'James J. Little', 'Lili Meng', 'Frederick Tung', 'Jianhui Chen'] | 2017-10-22 | null | null | null | null | ['camera-relocalization', 'loop-closure-detection'] | ['computer-vision', 'computer-vision'] | [ 5.80801927e-02 -3.52911443e-01 -3.63456398e-01 -4.06206548e-01
-5.89635849e-01 -5.51743746e-01 3.67815256e-01 -1.24436013e-01
-4.39068317e-01 8.95947039e-01 -1.50101900e-01 -4.20440286e-01
2.93728244e-02 -6.63241684e-01 -8.66625845e-01 -7.07834959e-01
3.58025879e-02 4.16417778e-01 6.50740385e-01 1.51460826... | [7.548652172088623, -2.3766424655914307] |
7cd12a08-ff4b-45e5-beb9-604480865b1b | divide-conquer-and-combine-mixture-of | 2306.00434 | null | https://arxiv.org/abs/2306.00434v1 | https://arxiv.org/pdf/2306.00434v1.pdf | Divide, Conquer, and Combine: Mixture of Semantic-Independent Experts for Zero-Shot Dialogue State Tracking | Zero-shot transfer learning for Dialogue State Tracking (DST) helps to handle a variety of task-oriented dialogue domains without the cost of collecting in-domain data. Existing works mainly study common data- or model-level augmentation methods to enhance the generalization but fail to effectively decouple the semanti... | ['Li Guo', 'DaCheng Tao', 'Shi Wang', 'Zheng Lin', 'Yibing Zhan', 'Yanan Cao', 'Liang Ding', 'Qingyue Wang'] | 2023-06-01 | null | null | null | null | ['dialogue-state-tracking'] | ['natural-language-processing'] | [ 1.82751077e-03 4.22904998e-01 -3.62677574e-01 -5.62622607e-01
-8.91907275e-01 -6.65761411e-01 7.31099546e-01 -1.77264571e-01
-3.86254609e-01 7.74792135e-01 5.41774988e-01 -3.61367464e-01
1.01197250e-01 -4.66053277e-01 -2.30104387e-01 -5.48115432e-01
3.04290950e-01 1.00451350e+00 3.83959442e-01 -6.44738376... | [12.760828971862793, 7.916084289550781] |
2b895baa-cd7f-4e2d-ae11-06dc2a4a7bb2 | risk-averse-model-uncertainty-for | 2301.12593 | null | https://arxiv.org/abs/2301.12593v1 | https://arxiv.org/pdf/2301.12593v1.pdf | Risk-Averse Model Uncertainty for Distributionally Robust Safe Reinforcement Learning | Many real-world domains require safe decision making in the presence of uncertainty. In this work, we propose a deep reinforcement learning framework for approaching this important problem. We consider a risk-averse perspective towards model uncertainty through the use of coherent distortion risk measures, and we show ... | ['Mouhacine Benosman', 'James Queeney'] | 2023-01-30 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [-6.76561818e-02 5.55851579e-01 -1.27335802e-01 -2.43215114e-01
-1.35064280e+00 -6.13237381e-01 5.83706498e-01 2.58415490e-01
-7.09579945e-01 1.11053860e+00 -1.17604606e-01 -4.79230374e-01
-5.77254236e-01 -7.54225254e-01 -1.02098489e+00 -6.04665220e-01
-4.41928804e-01 3.90515924e-01 -3.64413075e-02 -8.38427246... | [4.472442150115967, 2.3362348079681396] |
eae68543-7524-4ffd-a498-fcc611945083 | learning-structural-graph-layouts-and-3d | 1907.03387 | null | https://arxiv.org/abs/1907.03387v2 | https://arxiv.org/pdf/1907.03387v2.pdf | Learning Structural Graph Layouts and 3D Shapes for Long Span Bridges 3D Reconstruction | A learning-based 3D reconstruction method for long-span bridges is proposed in this paper. 3D reconstruction generates a 3D computer model of a real object or scene from images, it involves many stages and open problems. Existing point-based methods focus on generating 3D point clouds and their reconstructed polygonal ... | ['Yong Huang', 'Fangqiao Hu', 'Jin Zhao', 'Hui Li'] | 2019-07-08 | null | null | null | null | ['generating-3d-point-clouds'] | ['computer-vision'] | [-4.39377911e-02 3.28540444e-01 3.81718099e-01 -1.00445282e-02
-4.94560122e-01 -3.39045376e-01 2.99503595e-01 2.30938762e-01
3.21565956e-01 6.82476103e-01 -3.17010075e-01 -3.28481823e-01
-6.89357102e-01 -1.15938044e+00 -8.48360956e-01 -2.21723244e-01
-5.09137213e-01 1.25490904e+00 6.30766988e-01 -5.04566491... | [8.59327507019043, -3.1007919311523438] |
db2b923d-e013-4a56-b7ef-849af8f5fd81 | a-survey-on-complex-knowledge-base-question | 2105.11644 | null | https://arxiv.org/abs/2105.11644v1 | https://arxiv.org/pdf/2105.11644v1.pdf | A Survey on Complex Knowledge Base Question Answering: Methods, Challenges and Solutions | Knowledge base question answering (KBQA) aims to answer a question over a knowledge base (KB). Recently, a large number of studies focus on semantically or syntactically complicated questions. In this paper, we elaborately summarize the typical challenges and solutions for complex KBQA. We begin with introducing the ba... | ['Ji-Rong Wen', 'Wayne Xin Zhao', 'Jing Jiang', 'Jinhao Jiang', 'Gaole He', 'Yunshi Lan'] | 2021-05-25 | null | null | null | null | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-3.16170424e-01 5.16983747e-01 -4.07648832e-02 -4.74842548e-01
-1.61299765e+00 -8.15262437e-01 5.72950915e-02 2.69751787e-01
-2.82099038e-01 1.14854240e+00 4.58591402e-01 -4.62607831e-01
-2.16989413e-01 -1.14088929e+00 -5.28633177e-01 -1.52325898e-01
4.13562864e-01 8.41950953e-01 8.42131853e-01 -8.47586632... | [10.637533187866211, 7.952310085296631] |
fdcb3eba-3663-49a9-8ffa-bd9af61cc1ad | semi-supervised-medical-image-segmentation | 1911.01218 | null | https://arxiv.org/abs/1911.01218v1 | https://arxiv.org/pdf/1911.01218v1.pdf | Semi-Supervised Medical Image Segmentation via Learning Consistency under Transformations | The scarcity of labeled data often limits the application of supervised deep learning techniques for medical image segmentation. This has motivated the development of semi-supervised techniques that learn from a mixture of labeled and unlabeled images. In this paper, we propose a novel semi-supervised method that, in a... | ['Gerda Bortsova', 'Florian Dubost', 'Marleen de Bruijne', 'Laurens Hogeweg', 'Ioannis Katramados'] | 2019-11-04 | null | null | null | null | ['semi-supervised-medical-image-segmentation'] | ['computer-vision'] | [ 4.55595851e-01 6.47335291e-01 -4.11681950e-01 -1.03127444e+00
-1.25164986e+00 -4.51531023e-01 2.60586053e-01 1.74257115e-01
-7.25507081e-01 6.55364037e-01 5.22105359e-02 -1.86028674e-01
1.50733665e-01 -5.13395429e-01 -8.02274287e-01 -6.89787507e-01
3.11892390e-01 9.18135703e-01 2.09386215e-01 1.86496258... | [14.648211479187012, -2.2642641067504883] |
d739795e-a90d-49bd-b51c-c1802cb98337 | multi-view-representation-learning-from | 2210.15429 | null | https://arxiv.org/abs/2210.15429v1 | https://arxiv.org/pdf/2210.15429v1.pdf | Multi-view Representation Learning from Malware to Defend Against Adversarial Variants | Deep learning-based adversarial malware detectors have yielded promising results in detecting never-before-seen malware executables without relying on expensive dynamic behavior analysis and sandbox. Despite their abilities, these detectors have been shown to be vulnerable to adversarial malware variants - meticulously... | ['Hsinchun Chen', 'Xin Li', 'Weifeng Li', 'MohammadReza Ebrahimi', 'James Lee Hu'] | 2022-10-25 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [ 1.31282717e-01 -4.41000789e-01 -2.33737320e-01 1.61766022e-01
-2.86337078e-01 -1.49443948e+00 7.93965101e-01 -5.25802635e-02
1.41655132e-01 2.68349677e-01 -2.85181016e-01 -8.80755544e-01
5.00656009e-01 -9.35785353e-01 -8.50854635e-01 -7.30176032e-01
-3.38036090e-01 2.64028192e-01 4.15188164e-01 -4.03673142... | [14.385711669921875, 9.658913612365723] |
5ab3ea30-1855-445d-aedc-cd673b166082 | global-and-local-consistent-age-generative | 1801.08390 | null | http://arxiv.org/abs/1801.08390v1 | http://arxiv.org/pdf/1801.08390v1.pdf | Global and Local Consistent Age Generative Adversarial Networks | Age progression/regression is a challenging task due to the complicated and
non-linear transformation in human aging process. Many researches have shown
that both global and local facial features are essential for face
representation, but previous GAN based methods mainly focused on the global
feature in age synthesis.... | ['Pei-Pei Li', 'Zhenan Sun', 'Yibo Hu', 'Qi Li', 'Ran He'] | 2018-01-25 | null | null | null | null | ['human-aging'] | ['miscellaneous'] | [-2.39090435e-02 -6.71008974e-02 7.84779787e-02 -3.65419835e-01
-2.20853955e-01 6.13784194e-02 2.49376893e-01 -5.23497522e-01
-1.79582328e-01 8.98471594e-01 2.95035481e-01 5.21734953e-01
3.08614314e-01 -9.40573573e-01 -5.30352235e-01 -1.10647976e+00
2.47638166e-01 -7.78766423e-02 -1.55610397e-01 -1.69232577... | [13.16911506652832, 0.4411361813545227] |
d65c289f-6f64-4454-9f5e-4283870ae385 | a-deep-behavior-path-matching-network-for | 2302.00302 | null | https://arxiv.org/abs/2302.00302v1 | https://arxiv.org/pdf/2302.00302v1.pdf | A Deep Behavior Path Matching Network for Click-Through Rate Prediction | User behaviors on an e-commerce app not only contain different kinds of feedback on items but also sometimes imply the cognitive clue of the user's decision-making. For understanding the psychological procedure behind user decisions, we present the behavior path and propose to match the user's current behavior path wit... | ['Dong Wang', 'Xingxing Wang', 'Yongkang Wang', 'Beihong Jin', 'Shuli Wang', 'Yimin Lv', 'Yapeng Zhang', 'Yisong Yu', 'Jian Dong'] | 2023-02-01 | null | null | null | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [-1.29225478e-01 -4.08573359e-01 -8.60957980e-01 -7.50938296e-01
-1.89181328e-01 -2.88283467e-01 -2.67248660e-01 3.10405433e-01
-3.34651142e-01 -1.75895289e-01 4.24472153e-01 -6.25750244e-01
-1.36693537e-01 -8.83748114e-01 -6.44469321e-01 4.31009382e-02
-2.04680189e-01 1.71488211e-01 -1.15336098e-01 -4.33012515... | [10.097901344299316, 5.603891372680664] |
d652a418-80e9-4570-b01c-aac088598a4f | electricity-an-efficient-transformer-for-non | null | null | https://www.mdpi.com/1424-8220/22/8/2926 | https://www.mdpi.com/1424-8220/22/8/2926 | ELECTRIcity: An Efficient Transformer for Non-Intrusive Load Monitoring | Non-Intrusive Load Monitoring (NILM) describes the process of inferring the consumption pattern of appliances by only having access to the aggregated household signal. Sequence-to-sequence deep learning models have been firmly established as state-of-the-art approaches for NILM, in an attempt to identify the pattern of... | ['Nikolaos Doulamis', 'Anastasios Doulamis', 'Maria Kaselimi', 'Stavros Sykiotis'] | 2022-04-11 | electricity-an-efficient-transformer-for-non-1 | https://www.mdpi.com/1424-8220/22/8/2926/htm | https://www.mdpi.com/1424-8220/22/8/2926/pdf?version=1649762674 | mdpi-sensors-2022-4 | ['non-intrusive-load-monitoring', 'unsupervised-pre-training', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'methodology', 'miscellaneous', 'time-series'] | [ 2.26670057e-01 -1.14752017e-02 -4.98847514e-02 -3.86448115e-01
-6.29438877e-01 -3.71289521e-01 6.54337645e-01 -1.78681031e-01
5.76404184e-02 3.52704406e-01 1.69627979e-01 -1.45731226e-01
-9.01196525e-02 -7.39827573e-01 -4.42278624e-01 -7.66890168e-01
3.11570559e-02 3.78990114e-01 -3.94806504e-01 -1.27531260... | [16.070533752441406, 7.584036350250244] |
bdb072c4-43c7-4e5f-b27f-a5d3039f9c99 | automatic-cell-counting-in-flourescent | 2103.01141 | null | https://arxiv.org/abs/2103.01141v1 | https://arxiv.org/pdf/2103.01141v1.pdf | Automatic Cell Counting in Flourescent Microscopy Using Deep Learning | Counting cells in fluorescent microscopy is a tedious, time-consuming task that researchers have to accomplish to assess the effects of different experimental conditions on biological structures of interest. Although such objects are generally easy to identify, the process of manually annotating cells is sometimes subj... | ['A. Zoccoli', 'L. Rinaldi', 'M. Luppi', 'M. Dalla', 'L. Clissa', 'R. Morelli'] | 2021-02-24 | null | null | null | null | ['automatic-cell-counting'] | ['miscellaneous'] | [ 3.83265078e-01 7.35601410e-02 4.19004768e-01 -2.15580419e-01
-5.80025315e-01 -7.19909906e-01 3.94224524e-01 6.79194808e-01
-1.11551404e+00 1.02282107e+00 -4.40938950e-01 -1.42196015e-01
8.60973969e-02 -4.83261615e-01 -7.82663226e-01 -1.05052614e+00
1.05625808e-01 3.30271155e-01 3.66872638e-01 3.01944017... | [14.670273780822754, -3.1581003665924072] |
191f5348-1f6b-404f-83d4-aab416adac75 | deep-view-sensitive-pedestrian-attribute | 1707.06089 | null | http://arxiv.org/abs/1707.06089v1 | http://arxiv.org/pdf/1707.06089v1.pdf | Deep View-Sensitive Pedestrian Attribute Inference in an end-to-end Model | Pedestrian attribute inference is a demanding problem in visual surveillance
that can facilitate person retrieval, search and indexing. To exploit semantic
relations between attributes, recent research treats it as a multi-label image
classification task. The visual cues hinting at attributes can be strongly
localized ... | ['Yan Wang', 'Rainer Stiefelhagen', 'M. Saquib Sarfraz', 'Arne Schumann'] | 2017-07-19 | null | null | null | null | ['person-retrieval', 'multi-label-image-classification'] | ['computer-vision', 'computer-vision'] | [ 1.26690581e-01 -1.10842278e-02 -3.52953792e-01 -9.55432951e-01
-7.63849974e-01 -5.87701321e-01 1.03467786e+00 2.62901366e-01
-3.01994652e-01 7.86489964e-01 4.11231846e-01 3.60980660e-01
-6.40879497e-02 -5.50066769e-01 -8.12461793e-01 -6.89842641e-01
1.21728063e-01 1.14775956e+00 2.43763149e-01 4.07640301... | [14.360862731933594, 0.9878052473068237] |
149d5ff3-612b-49e6-9233-6cac0edc1652 | comparison-of-short-text-sentiment-analysis | null | null | https://aclanthology.org/W17-1411 | https://aclanthology.org/W17-1411.pdf | Comparison of Short-Text Sentiment Analysis Methods for Croatian | We focus on the task of supervised sentiment classification of short and informal texts in Croatian, using two simple yet effective methods: word embeddings and string kernels. We investigate whether word embeddings offer any advantage over corpus- and preprocessing-free string kernels, and how these compare to bag-of-... | ['Jan {\\v{S}}najder', 'Leon Rotim'] | 2017-04-01 | null | null | null | ws-2017-4 | ['stock-price-prediction'] | ['time-series'] | [-1.90408528e-01 -3.97706389e-01 -2.96122640e-01 -3.16707641e-01
-8.89685333e-01 -8.30611527e-01 9.63448346e-01 8.70952785e-01
-1.22421896e+00 4.68684107e-01 7.41893649e-01 -7.09397554e-01
-8.54159892e-02 -8.75436246e-01 -3.72751392e-02 -6.81415915e-01
-3.54767106e-02 1.13838494e-01 -3.30777131e-02 -4.28125054... | [10.484956741333008, 8.72552490234375] |
0bb67ba9-e245-4d1c-bb26-fa7a586ee782 | evaluation-phonemic-transcription-of-low | null | null | https://aclanthology.org/L18-1530 | https://aclanthology.org/L18-1530.pdf | Evaluation Phonemic Transcription of Low-Resource Tonal Languages for Language Documentation | null | ['Alexis Michaud', 'Steven Bird', 'Hilaria Cruz', 'Trevor Cohn', 'Oliver Adams', 'Graham Neubig'] | 2018-05-01 | evaluation-phonemic-transcription-of-low-1 | https://aclanthology.org/L18-1530 | https://aclanthology.org/L18-1530.pdf | lrec-2018-5 | ['acoustic-modelling'] | ['speech'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.327335357666016, 3.7193233966827393] |
fe5ba9f3-5f0f-4ad1-a5af-b631736dc58a | non-linear-prediction-with-lstm-recurrent | null | null | https://ieeexplore.ieee.org/abstract/document/7280757 | https://ieeexplore.ieee.org/abstract/document/7280757 | Non-linear prediction with LSTM recurrent neural networks for acoustic novelty detection | Acoustic novelty detection aims at identifying abnormal/novel acoustic signals which differ from the reference/normal data that the system was trained with. In this paper we present a novel approach based on non-linear predictive denoising autoencoders. In our approach, auditory spectral features of the next short-term... | ['Erik Marchi ; Fabio Vesperini ; Felix Weninger ; Florian Eyben ; Stefano Squartini ; Björn Schuller'] | 2015-10-01 | null | null | null | 2015-international-joint-conference-on-neural | ['acoustic-novelty-detection'] | ['audio'] | [ 3.41564745e-01 3.12529095e-02 7.40362465e-01 -2.57167280e-01
-1.01319826e+00 -4.98357825e-02 3.25803399e-01 1.63290009e-01
-4.11930352e-01 5.19646108e-01 4.10121650e-01 3.05236578e-01
1.71038643e-01 -6.23095155e-01 -9.61188793e-01 -6.62507892e-01
-3.24328631e-01 -5.55502139e-02 3.72491241e-01 -1.34923220... | [15.307180404663086, 5.584121227264404] |
c0ef951c-5fb4-4564-972a-a31dc89299d2 | the-exploitation-of-multiple-feature | 2112.07940 | null | https://arxiv.org/abs/2112.07940v1 | https://arxiv.org/pdf/2112.07940v1.pdf | The exploitation of Multiple Feature Extraction Techniques for Speaker Identification in Emotional States under Disguised Voices | Due to improvements in artificial intelligence, speaker identification (SI) technologies have brought a great direction and are now widely used in a variety of sectors. One of the most important components of SI is feature extraction, which has a substantial impact on the SI process and performance. As a result, numero... | ['Ali Bou Nassif', 'Ismail Shahin', 'Noor Ahmad Al Hindawi'] | 2021-12-15 | null | null | null | null | ['speaker-identification'] | ['speech'] | [ 4.76749055e-02 -4.83793139e-01 2.45601614e-03 -1.36770919e-01
-4.27973986e-01 -5.63351274e-01 5.34905553e-01 -7.71863712e-03
-2.46597633e-01 6.22592092e-01 4.84822154e-01 2.72888411e-02
-1.28960848e-01 -2.39003390e-01 3.19451213e-01 -8.75917673e-01
-2.10009560e-01 -4.10084546e-01 -1.21793382e-01 -3.59351039... | [14.703463554382324, 5.909375190734863] |
01f21440-5c1d-4fac-9e5f-7144f1f94e10 | rnn-sm-fast-steganalysis-of-voip-streams | null | null | https://github.com/fjxmlzn/RNN-SM | http://www.andrew.cmu.edu/user/zinanl/publications/rnn-sm.pdf | RNN-SM: Fast Steganalysis of VoIP Streams Using Recurrent Neural Network | Quantization index modulation (QIM) steganography makes it possible to hide secret information in voice-over IP (VoIP) streams, which could be utilized by unauthorized entities to set up covert channels for malicious purposes. Detecting short QIM steganography samples, as is required by real circumstances, remains an u... | ['Yongfeng Huang', 'Jilong Wang', 'Zinan Lin'] | 2018-02-15 | null | null | null | ieee-transactions-on-information-forensics | ['steganalysis'] | ['computer-vision'] | [ 6.73633099e-01 8.36603194e-02 -3.95406723e-01 3.71294141e-01
-7.20044434e-01 -1.90516040e-01 2.52567798e-01 -7.64182031e-01
2.56181248e-02 2.80078679e-01 -1.17888898e-01 -7.66431034e-01
3.52304727e-01 -6.16941690e-01 -3.58917445e-01 -8.35414052e-01
-3.63514572e-01 6.24190420e-02 2.42991909e-01 -2.42018521... | [4.299074172973633, 8.055582046508789] |
39d51d4c-3750-4133-a029-ac0ecf260751 | jointly-modeling-motion-and-appearance-cues | 2007.02041 | null | https://arxiv.org/abs/2007.02041v1 | https://arxiv.org/pdf/2007.02041v1.pdf | Jointly Modeling Motion and Appearance Cues for Robust RGB-T Tracking | In this study, we propose a novel RGB-T tracking framework by jointly modeling both appearance and motion cues. First, to obtain a robust appearance model, we develop a novel late fusion method to infer the fusion weight maps of both RGB and thermal (T) modalities. The fusion weights are determined by using offline-tra... | ['Dong Wang', 'Xiaoyun Yang', 'Pengyu Zhang', 'Jie Zhao', 'Huchuan Lu'] | 2020-07-04 | null | null | null | null | ['rgb-t-tracking'] | ['computer-vision'] | [-3.02392486e-02 -6.29387498e-01 -4.54173833e-02 -3.06853354e-01
-7.20589697e-01 -7.25219131e-01 5.08240998e-01 -6.16793096e-01
-4.50904518e-01 2.98239499e-01 -2.53340125e-01 1.24459714e-01
2.74687618e-01 -8.60632956e-02 -5.77821434e-01 -9.95848894e-01
5.44175267e-01 -1.70624942e-01 4.57106918e-01 2.05837652... | [6.389822006225586, -2.1567201614379883] |
5e61bf67-18c8-4b7d-9534-5e4414696882 | exploring-intermediate-representation-for | 2011.08464 | null | https://arxiv.org/abs/2011.08464v5 | https://arxiv.org/pdf/2011.08464v5.pdf | Exploring intermediate representation for monocular vehicle pose estimation | We present a new learning-based framework to recover vehicle pose in SO(3) from a single RGB image. In contrast to previous works that map from local appearance to observation angles, we explore a progressive approach by extracting meaningful Intermediate Geometrical Representations (IGRs) to estimate egocentric vehicl... | ['Kwang-Ting Cheng', 'Hongyang Li', 'Zengqiang Yan', 'Shichao Li'] | 2020-11-17 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Li_Exploring_intermediate_representation_for_monocular_vehicle_pose_estimation_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Li_Exploring_intermediate_representation_for_monocular_vehicle_pose_estimation_CVPR_2021_paper.pdf | cvpr-2021-1 | ['vehicle-pose-estimation'] | ['computer-vision'] | [ 1.66297168e-01 2.58595735e-01 -3.19044948e-01 -6.72270954e-01
-9.00902748e-01 -8.02934647e-01 8.37801218e-01 -4.50174332e-01
-4.13452923e-01 4.90210652e-01 3.58614214e-02 -3.63772959e-01
4.60794330e-01 -7.21803844e-01 -1.11223924e+00 -5.85694671e-01
3.27303439e-01 6.41599357e-01 1.70239553e-01 -1.38920829... | [7.836111068725586, -2.411095142364502] |
248d349c-5ebb-4213-bcfc-66cabbe41a28 | finding-the-smallest-or-largest-element-of-a | 2210.11413 | null | https://arxiv.org/abs/2210.11413v2 | https://arxiv.org/pdf/2210.11413v2.pdf | Finding the smallest or largest element of a tensor from its low-rank factors | We consider the problem of finding the smallest or largest entry of a tensor of order $N$ that is specified via its rank decomposition. Stated in a different way, we are given $N$ sets of $R$-dimensional vectors and we wish to select one vector from each set such that the sum of the Hadamard product of the selected vec... | ['Aritra Konar', 'Paris Karakasis', 'Nicholas D. Sidiropoulos'] | 2022-10-16 | null | null | null | null | ['graph-mining'] | ['graphs'] | [-7.20187724e-02 -6.33323342e-02 -2.81037629e-01 -2.49437302e-01
-6.55354798e-01 -7.05267251e-01 2.54549146e-01 2.22775936e-01
-3.10382307e-01 3.73066813e-01 2.81136841e-01 -4.68973935e-01
-7.49773979e-01 -6.64475024e-01 -4.96190548e-01 -5.66553473e-01
-9.80891287e-01 7.79459298e-01 -3.29385996e-01 -3.56531620... | [6.926605224609375, 4.669559478759766] |
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