paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
8513bdb4-ca94-489a-9466-a15e1ab557cb | group-sparse-matrix-factorization-for | 2104.08928 | null | https://arxiv.org/abs/2104.08928v2 | https://arxiv.org/pdf/2104.08928v2.pdf | Group-Sparse Matrix Factorization for Transfer Learning of Word Embeddings | Unstructured text provides decision-makers with a rich data source in many domains, ranging from product reviews in retailing to nursing notes in healthcare. To leverage this information, words are typically translated into word embeddings -- vectors that encode the semantic relationships between words -- through unsup... | ['Osbert Bastani', 'Hamsa Bastani', 'Xuanyi Zhao', 'Kan Xu'] | 2021-04-18 | null | null | null | null | ['learning-word-embeddings'] | ['methodology'] | [ 2.75134385e-01 2.54670829e-01 -5.73451161e-01 -4.70798403e-01
-6.95138454e-01 -6.72892153e-01 2.32663676e-01 7.32923329e-01
-8.32991540e-01 8.16943467e-01 6.55387163e-01 -3.17495793e-01
-1.17403544e-01 -8.79639804e-01 -6.57165647e-01 -5.20816267e-01
-8.08009803e-02 7.08433986e-01 -2.82312125e-01 -2.58441150... | [10.386984825134277, 8.658019065856934] |
7ec13636-e40d-4089-9f95-5ab368b408ef | one-step-multi-view-clustering-with-diverse | 2306.05437 | null | https://arxiv.org/abs/2306.05437v2 | https://arxiv.org/pdf/2306.05437v2.pdf | One-step Multi-view Clustering with Diverse Representation | Multi-view clustering has attracted broad attention due to its capacity to utilize consistent and complementary information among views. Although tremendous progress has been made recently, most existing methods undergo high complexity, preventing them from being applied to large-scale tasks. Multi-view clustering via ... | ['Li Shen', 'Tianjiao Wan', 'Yi Wen', 'Siwei Wang', 'Xinwang Liu', 'En Zhu', 'Jiyuan Liu', 'Xinhang Wan'] | 2023-06-08 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [-1.36701405e-01 -6.17515326e-01 -3.74977112e-01 -3.60180438e-01
-8.20376039e-01 -6.18011057e-01 3.37013602e-01 -2.44153947e-01
1.61528122e-02 1.62613690e-01 3.82398903e-01 2.08040327e-01
-3.59618813e-01 -5.23087382e-01 -2.14040503e-01 -1.11797857e+00
4.18526411e-01 3.67253006e-01 -2.06145555e-01 1.38576522... | [8.298360824584961, 4.626137733459473] |
ddc5fb40-f8bb-41a2-8430-a07a3b48922b | flexible-framework-for-audio-reconstruction | 2004.11162 | null | http://arxiv.org/abs/2004.11162v2 | http://arxiv.org/pdf/2004.11162v2.pdf | Flexible framework for audio reconstruction | The paper presents a unified, flexible framework for the tasks of audio
inpainting, declipping, and dequantization. The concept is further extended to
cover analogous degradation models in a transformed domain, e.g. quantization
of the signal's time-frequency coefficients. The task of reconstructing an
audio signal fro... | [] | 2020-07-29 | null | null | null | null | ['audio-inpainting'] | ['audio'] | [ 9.03475463e-01 1.23554930e-01 1.74192682e-01 -1.44173384e-01
-1.20470440e+00 -4.83723193e-01 2.01561809e-01 -2.53849000e-01
-1.82944477e-01 9.16230142e-01 4.83688742e-01 4.31092493e-02
-2.28124559e-01 -8.15418512e-02 -2.63999075e-01 -8.24095845e-01
-2.27529332e-01 -1.59966037e-01 -1.30020067e-01 -1.28217936... | [15.441868782043457, 5.677613735198975] |
f73e43d5-2a56-4f99-a1e2-8faae73f33e9 | boundary-preserved-deep-denoising-of-the | 1904.06329 | null | http://arxiv.org/abs/1904.06329v2 | http://arxiv.org/pdf/1904.06329v2.pdf | Boundary-Preserved Deep Denoising of the Stochastic Resonance Enhanced Multiphoton Images | As the rapid growth of high-speed and deep-tissue imaging in biomedical
research, it is urgent to find a robust and effective denoising method to
retain morphological features for further texture analysis and segmentation.
Conventional denoising filters and models can easily suppress perturbative
noises in high contras... | ['Tzu-Ming Liu', 'Tzung-Dau Wang', 'Lun-Zhang Guo', 'Sheng-Yong Niu', 'Yue Li', 'Yu Tsao'] | 2019-04-12 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 1.93102121e-01 -5.84687769e-01 6.23911619e-01 -9.68465358e-02
-6.58486903e-01 -1.72404975e-01 1.10505760e-01 2.98790067e-01
-6.72040582e-01 8.41847539e-01 -1.39804900e-01 1.28109768e-01
-1.14582963e-01 -7.96886921e-01 -5.09944022e-01 -1.55169332e+00
3.60116363e-01 5.70471101e-02 4.70434546e-01 -1.96118072... | [13.07038688659668, -2.593780517578125] |
1c28703c-1da1-401a-8a79-fd06459443d1 | analyzing-multi-task-learning-for-abstractive | 2210.14606 | null | https://arxiv.org/abs/2210.14606v2 | https://arxiv.org/pdf/2210.14606v2.pdf | Analyzing Multi-Task Learning for Abstractive Text Summarization | Despite the recent success of multi-task learning and pre-finetuning for natural language understanding, few works have studied the effects of task families on abstractive text summarization. Task families are a form of task grouping during the pre-finetuning stage to learn common skills, such as reading comprehension.... | ['Bela Gipp', 'Terry Ruas', 'Jan Philip Wahle', 'Frederic Kirstein'] | 2022-10-26 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 6.46406829e-01 1.62835181e-01 -3.81183326e-01 -3.85041952e-01
-7.69106150e-01 -7.31042385e-01 7.98202574e-01 6.87908709e-01
-7.03108370e-01 6.75850749e-01 9.31544483e-01 -5.89202464e-01
-2.41472900e-01 -4.73360687e-01 -6.47429585e-01 -1.59287557e-01
3.57064724e-01 2.17195317e-01 1.25373483e-01 -3.47542703... | [12.259114265441895, 9.297122955322266] |
5a215277-a8cc-4ec9-98b0-3884f241a41a | view-invariant-probabilistic-embedding-for | 1912.01001 | null | https://arxiv.org/abs/1912.01001v4 | https://arxiv.org/pdf/1912.01001v4.pdf | View-Invariant Probabilistic Embedding for Human Pose | Depictions of similar human body configurations can vary with changing viewpoints. Using only 2D information, we would like to enable vision algorithms to recognize similarity in human body poses across multiple views. This ability is useful for analyzing body movements and human behaviors in images and videos. In this... | ['Liang-Chieh Chen', 'Jiaping Zhao', 'Jennifer J. Sun', 'Florian Schroff', 'Ting Liu', 'Hartwig Adam'] | 2019-12-02 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2905_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500052.pdf | eccv-2020-8 | ['pose-retrieval', 'video-alignment'] | ['computer-vision', 'computer-vision'] | [-2.38315433e-01 -2.09371418e-01 -4.11178946e-01 -2.60406852e-01
-5.27776122e-01 -5.86168647e-01 5.45404494e-01 -2.17267200e-01
-1.95133671e-01 3.40907097e-01 6.73432291e-01 2.63798028e-01
5.23332730e-02 -2.57174313e-01 -6.67026281e-01 -3.14376622e-01
-1.45946518e-01 5.72903275e-01 1.07992060e-01 7.68302828... | [7.184626579284668, -0.6414316892623901] |
806fa490-3554-46b2-9d72-7511e784ed35 | medlatin1-and-medlatin2-two-datasets-for-the | 2006.12289 | null | https://arxiv.org/abs/2006.12289v2 | https://arxiv.org/pdf/2006.12289v2.pdf | MedLatinEpi and MedLatinLit: Two Datasets for the Computational Authorship Analysis of Medieval Latin Texts | We present and make available MedLatinEpi and MedLatinLit, two datasets of medieval Latin texts to be used in research on computational authorship analysis. MedLatinEpi and MedLatinLit consist of 294 and 30 curated texts, respectively, labelled by author; MedLatinEpi texts are of epistolary nature, while MedLatinLit te... | ['Mirko Tavoni', 'Alejandro Moreo', 'Silvia Corbara', 'Fabrizio Sebastiani'] | 2020-06-22 | null | null | null | null | ['authorship-verification'] | ['natural-language-processing'] | [-2.34883223e-02 1.19772039e-01 -3.95361632e-01 -2.37300783e-01
-2.44739547e-01 -1.06337678e+00 1.17897284e+00 4.88430947e-01
-5.59874594e-01 9.34237480e-01 3.09073150e-01 -5.09183645e-01
-1.57744866e-02 -4.31603402e-01 -3.59793246e-01 -3.49318177e-01
5.28995097e-01 1.04830706e+00 -1.42957747e-01 -5.18752560... | [9.563549995422363, 10.570634841918945] |
6b494ab9-368e-4149-b6fd-84e206525798 | turl-table-understanding-through | 2006.14806 | null | https://arxiv.org/abs/2006.14806v2 | https://arxiv.org/pdf/2006.14806v2.pdf | TURL: Table Understanding through Representation Learning | Relational tables on the Web store a vast amount of knowledge. Owing to the wealth of such tables, there has been tremendous progress on a variety of tasks in the area of table understanding. However, existing work generally relies on heavily-engineered task-specific features and model architectures. In this paper, we ... | ['Xiang Deng', 'Alyssa Lees', 'You Wu', 'Huan Sun', 'Cong Yu'] | 2020-06-26 | null | null | null | null | ['table-annotation', 'table-annotation', 'column-type-annotation', 'cell-entity-annotation', 'columns-property-annotation'] | ['knowledge-base', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 1.11531444e-01 4.93525803e-01 -5.18340588e-01 -6.49158120e-01
-1.01937532e+00 -6.65919721e-01 4.73988652e-01 6.47385478e-01
1.12258121e-01 7.39734888e-01 4.76867795e-01 -5.35251617e-01
7.00143129e-02 -1.22616231e+00 -1.28608227e+00 -1.76070128e-02
8.00512824e-03 9.83812749e-01 2.62815952e-01 -5.18415451... | [9.556639671325684, 7.882285118103027] |
6d4eafca-e6e3-4774-b3e3-03b4dbceedc7 | dynamic-contrastive-distillation-for-image | 2207.01426 | null | https://arxiv.org/abs/2207.01426v1 | https://arxiv.org/pdf/2207.01426v1.pdf | Dynamic Contrastive Distillation for Image-Text Retrieval | Although the vision-and-language pretraining (VLP) equipped cross-modal image-text retrieval (ITR) has achieved remarkable progress in the past two years, it suffers from a major drawback: the ever-increasing size of VLP models restricts its deployment to real-world search scenarios (where the high latency is unaccepta... | ['DaCheng Tao', 'Li Shen', 'Yang Liu', 'Meng Fang', 'Shuhan Qi', 'Liang Ding', 'Jun Rao'] | 2022-07-04 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [-0.05796929 -0.48444325 -0.22110352 -0.27489424 -1.1423652 -0.52051276
0.37745512 -0.03944975 -0.8537331 0.24200277 -0.23558281 -0.31611317
-0.29378945 -0.6972364 -0.655511 -0.7423844 0.4302176 0.7894943
0.2988333 -0.24610098 0.16183321 0.39865834 -1.8157021 0.2090088
1.286368 1.0939223 0.47... | [10.654106140136719, 1.4410185813903809] |
56c47426-b2a8-4d1c-8b7b-2b658533f09f | natural-image-stitching-using-depth-maps | 2202.06276 | null | https://arxiv.org/abs/2202.06276v2 | https://arxiv.org/pdf/2202.06276v2.pdf | Natural Image Stitching Using Depth Maps | Natural image stitching (NIS) aims to create one natural-looking mosaic from two overlapping images that capture the same 3D scene from different viewing positions. Challenges inevitably arise when the scene is non-planar and the camera baseline is wide, since parallax becomes not negligible in such cases. In this pape... | ['Nan Li', 'Tianli Liao'] | 2022-02-13 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [ 5.92367887e-01 -6.79602921e-02 2.86659479e-01 -6.57713711e-02
-3.46507937e-01 -5.95362902e-01 4.00463492e-01 -4.58411723e-01
1.77264120e-02 4.09643710e-01 1.03956409e-01 1.49791017e-01
-7.58818984e-02 -6.00312352e-01 -8.55992675e-01 -7.22692966e-01
4.05962467e-01 3.07885349e-01 2.16029942e-01 -1.37374014... | [9.27246379852295, -2.403306722640991] |
9f16be85-7dac-4a2c-80aa-e635beea5f82 | aerosense-a-self-sustainable-and-long-range | 2205.11902 | null | https://arxiv.org/abs/2205.11902v1 | https://arxiv.org/pdf/2205.11902v1.pdf | Aerosense: A Self-Sustainable And Long-Range Bluetooth Wireless Sensor Node for Aerodynamic and Aeroacoustic Monitoring on Wind Turbines | This paper presents a low-power, self-sustainable, and modular wireless sensor node for aerodynamic and acoustic measurements on wind turbines and other industrial structures. It includes 40 high-accuracy barometers, 10 microphones, 5 differential pressure sensors, and implements a lossy and a lossless on-board data co... | ['Michele Magno', 'Luca Benini', 'Raphael Fischer', 'Weikang Kong', 'Hanna Müller', 'Tommaso Polonelli'] | 2022-05-24 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 1.30000085e-01 5.98415025e-02 1.47136509e-01 2.93315858e-01
-1.18409708e-01 -5.23389220e-01 -3.51951867e-01 3.57336402e-01
-2.84668207e-01 8.34387004e-01 -3.08402628e-01 -3.80146593e-01
-2.65170395e-01 -1.30313230e+00 -1.43347338e-01 -1.15168226e+00
-6.58336520e-01 -1.85006648e-01 2.53173172e-01 2.62454808... | [6.289802074432373, 1.3925830125808716] |
d459612f-6320-4d49-8a94-00bc6e98dfda | on-the-efficiency-of-integrating-self | 2204.00352 | null | https://arxiv.org/abs/2204.00352v3 | https://arxiv.org/pdf/2204.00352v3.pdf | On the Efficiency of Integrating Self-supervised Learning and Meta-learning for User-defined Few-shot Keyword Spotting | User-defined keyword spotting is a task to detect new spoken terms defined by users. This can be viewed as a few-shot learning problem since it is unreasonable for users to define their desired keywords by providing many examples. To solve this problem, previous works try to incorporate self-supervised learning models ... | ['Yuan-Kuei Wu', 'Chia-Ping Chen', 'Hung-Yi Lee', 'Yu-Pao Tsai', 'Zhi-Sheng Chen', 'Wei-Tsung Kao'] | 2022-04-01 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 9.11102518e-02 -1.03828616e-01 -7.39674509e-01 -3.33495647e-01
-8.07600677e-01 -4.62776244e-01 8.22146773e-01 3.15735281e-01
-5.16406715e-01 7.70079911e-01 2.79917449e-01 -1.16624148e-03
-4.82010067e-01 -8.18511963e-01 -2.31082663e-01 -3.29149574e-01
-1.25095651e-01 6.39197230e-01 5.66554129e-01 -6.28204286... | [10.21362018585205, 3.5487568378448486] |
f7ed796c-cd66-4f51-bcd8-18bd1b109587 | holistic-recognition-of-low-quality-license | null | null | https://ieeexplore.ieee.org/abstract/document/8078501 | https://sci-hub.tw/https://ieeexplore.ieee.org/abstract/document/8078501 | Holistic Recognition of Low Quality License Plates by CNN using Track Annotated Data | This work is focused on recognition of license plates in low resolution and low quality images. We present a methodology for collection of real world (non-synthetic) dataset of low quality license plate images with ground truth transcriptions. Our approach to the license plate recognition is based on a Convolutional Ne... | ['Pavel Zemˇc´ık', 'Luk´aˇs Marˇs´ık', 'Roman Jur´anek', 'Jakub ˇSpaˇnhel', 'Adam Herout', 'Jakub Sochor'] | 2017-10-23 | null | null | null | ieee-international-conference-on-advanced | ['license-plate-recognition'] | ['computer-vision'] | [ 3.25022966e-01 -5.31450927e-01 2.19221905e-01 -4.99381632e-01
-1.43674111e+00 -1.13122988e+00 5.27581036e-01 -1.01227164e+00
-1.77654371e-01 6.61032498e-01 -1.17417186e-01 -1.42882243e-01
2.14817435e-01 -9.07824636e-01 -8.93995166e-01 -4.70306814e-01
5.43187082e-01 5.33380389e-01 4.25558031e-01 -1.60620958... | [9.835884094238281, -4.942375659942627] |
8c93cc1f-3d93-4f8a-a1aa-9a1823f09a8c | xcon-learning-with-experts-for-fine-grained | 2208.01898 | null | https://arxiv.org/abs/2208.01898v1 | https://arxiv.org/pdf/2208.01898v1.pdf | XCon: Learning with Experts for Fine-grained Category Discovery | We address the problem of generalized category discovery (GCD) in this paper, i.e. clustering the unlabeled images leveraging the information from a set of seen classes, where the unlabeled images could contain both seen classes and unseen classes. The seen classes can be seen as an implicit criterion of classes, which... | ['Bingchen Zhao', 'Siwei Yang', 'Zhongkai Zhao', 'Yixin Fei'] | 2022-08-03 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 3.12013954e-01 8.02830160e-02 -2.50131339e-01 -6.73200130e-01
-6.66438401e-01 -7.85849273e-01 6.03753328e-01 6.15799762e-02
-2.14284182e-01 4.84995693e-01 -6.58671111e-02 6.61833957e-03
-6.11834109e-01 -5.55669188e-01 -4.50195163e-01 -1.11262393e+00
-9.40774530e-02 6.72236443e-01 2.13227242e-01 1.52304903... | [9.608814239501953, 2.931100606918335] |
657f9ba5-c896-4ac7-ba8d-fc8a6180bbbe | dragon-decentralized-fault-tolerance-in-edge | 2208.07658 | null | https://arxiv.org/abs/2208.07658v1 | https://arxiv.org/pdf/2208.07658v1.pdf | DRAGON: Decentralized Fault Tolerance in Edge Federations | Edge Federation is a new computing paradigm that seamlessly interconnects the resources of multiple edge service providers. A key challenge in such systems is the deployment of latency-critical and AI based resource-intensive applications in constrained devices. To address this challenge, we propose a novel memory-effi... | ['Nicholas R. Jennings', 'Giuliano Casale', 'Shreshth Tuli'] | 2022-08-16 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [-5.54318666e-01 -4.87142771e-01 -3.18341047e-01 -1.43729657e-01
-5.73831916e-01 -2.97683120e-01 1.14763714e-02 -1.75033152e-01
2.60535389e-01 8.27587008e-01 -2.29538128e-01 -5.17528594e-01
-2.52102882e-01 -9.60604429e-01 -7.76899636e-01 -6.52411819e-01
-1.51300535e-01 7.05905497e-01 -1.82032451e-01 1.45346403... | [7.020698547363281, 2.8956105709075928] |
e4e768ac-6435-4772-983f-ff725086e4d9 | deep-learning-for-3d-point-clouds-a-survey | 1912.12033 | null | https://arxiv.org/abs/1912.12033v2 | https://arxiv.org/pdf/1912.12033v2.pdf | Deep Learning for 3D Point Clouds: A Survey | Point cloud learning has lately attracted increasing attention due to its wide applications in many areas, such as computer vision, autonomous driving, and robotics. As a dominating technique in AI, deep learning has been successfully used to solve various 2D vision problems. However, deep learning on point clouds is s... | ['Hao liu', 'Mohammed Bennamoun', 'Hanyun Wang', 'Li Liu', 'Yulan Guo', 'Qingyong Hu'] | 2019-12-27 | null | null | null | null | ['3d-shape-retrieval'] | ['computer-vision'] | [-2.14557782e-01 -4.85969901e-01 -1.06425159e-01 -4.05990094e-01
-4.85140681e-01 -5.71472347e-01 6.31551087e-01 1.40944064e-01
-2.17724741e-01 -4.68791090e-03 -6.76016927e-01 -4.42177236e-01
1.19473405e-01 -6.65023208e-01 -7.04802752e-01 -5.56455016e-01
-2.07330808e-01 7.39467323e-01 2.14522108e-01 -5.58085814... | [8.001296997070312, -3.3458476066589355] |
0d78b945-ee7f-4eb8-8682-529b34b48330 | spin-qudit-tomography | 2012.06464 | null | https://arxiv.org/abs/2012.06464v2 | https://arxiv.org/pdf/2012.06464v2.pdf | Spin qudit tomography and state reconstruction error | We consider the task of performing quantum state tomography on a $d$-level spin qudit, using only measurements of spin projection onto different quantization axes. After introducing a basis of operators closely related to the spherical harmonics, which obey the rotational symmetries of spin qudits, we map our quantum t... | ['Ana Maria Rey', 'Diego Barberena', 'Michael A. Perlin'] | 2020-12-11 | null | null | null | null | ['quantum-state-tomography'] | ['medical'] | [ 3.15827608e-01 3.00080210e-01 1.10599868e-01 -3.13775897e-01
-1.03505969e+00 -5.14324844e-01 4.54976916e-01 -4.89503145e-01
-6.08511567e-01 8.14289749e-01 -1.01737402e-01 -4.54473615e-01
-2.14993164e-01 -9.49647665e-01 -5.35011470e-01 -8.32605481e-01
-2.16216385e-01 8.67226362e-01 5.28150909e-02 -5.01308031... | [5.739383697509766, 4.840928554534912] |
df1970ed-4e68-42b3-bcdd-1ba4726a5263 | private-training-set-inspection-in-mlaas | 2305.09058 | null | https://arxiv.org/abs/2305.09058v1 | https://arxiv.org/pdf/2305.09058v1.pdf | Private Training Set Inspection in MLaaS | Machine Learning as a Service (MLaaS) is a popular cloud-based solution for customers who aim to use an ML model but lack training data, computation resources, or expertise in ML. In this case, the training datasets are typically a private possession of the ML or data companies and are inaccessible to the customers, bu... | ['Po-Yu Chen', 'Tongtong Xu', 'Mingxue Xu'] | 2023-05-15 | null | null | null | null | ['multiple-instance-learning'] | ['methodology'] | [-1.78215176e-01 -4.06772010e-02 -7.33704865e-01 -7.49185741e-01
-9.75387931e-01 -4.67531443e-01 3.69372725e-01 4.39191163e-01
-1.78681627e-01 9.61326063e-01 -6.57390594e-01 -5.44917941e-01
-2.50745386e-01 -7.80024230e-01 -4.58027035e-01 -6.44127011e-01
2.64274299e-01 9.02369499e-01 -1.15460463e-01 -5.12339845... | [9.17146110534668, 4.40355110168457] |
9322baa5-5712-49d7-bba3-74150ef4bb4c | a-closed-form-solution-to-photorealistic | 1802.06474 | null | http://arxiv.org/abs/1802.06474v5 | http://arxiv.org/pdf/1802.06474v5.pdf | A Closed-form Solution to Photorealistic Image Stylization | Photorealistic image stylization concerns transferring style of a reference
photo to a content photo with the constraint that the stylized photo should
remain photorealistic. While several photorealistic image stylization methods
exist, they tend to generate spatially inconsistent stylizations with
noticeable artifacts... | ['Ming-Hsuan Yang', 'Xueting Li', 'Ming-Yu Liu', 'Jan Kautz', 'Yijun Li'] | 2018-02-19 | a-closed-form-solution-to-photorealistic-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Yijun_Li_A_Closed-form_Solution_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Yijun_Li_A_Closed-form_Solution_ECCV_2018_paper.pdf | eccv-2018-9 | ['image-stylization'] | ['computer-vision'] | [ 1.12835430e-01 2.21213847e-02 1.45623863e-01 -1.24804892e-01
-4.53657120e-01 -7.28911102e-01 6.90973103e-01 -3.26674521e-01
-5.94801269e-02 7.04633057e-01 2.52671480e-01 -1.81548908e-01
5.54717183e-01 -4.67522591e-01 -7.27047920e-01 -6.08076036e-01
7.35525131e-01 3.38944085e-02 2.97660917e-01 1.89922929... | [11.371572494506836, -0.7642155289649963] |
16d66928-eae4-4198-b455-7d5cd0498562 | v2x-seq-a-large-scale-sequential-dataset-for | 2305.05938 | null | https://arxiv.org/abs/2305.05938v1 | https://arxiv.org/pdf/2305.05938v1.pdf | V2X-Seq: A Large-Scale Sequential Dataset for Vehicle-Infrastructure Cooperative Perception and Forecasting | Utilizing infrastructure and vehicle-side information to track and forecast the behaviors of surrounding traffic participants can significantly improve decision-making and safety in autonomous driving. However, the lack of real-world sequential datasets limits research in this area. To address this issue, we introduce ... | ['Zaiqing Nie', 'Ping Luo', 'Jirui Yuan', 'Juan Song', 'Ning Sun', 'Yifeng Pan', 'Yifeng Shi', 'Xin Hao', 'Xu Gao', 'Yingjuan Tang', 'Zhenwei Yang', 'Hongzhi Ruan', 'Wenxian Yang', 'Haibao Yu'] | 2023-05-10 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yu_V2X-Seq_A_Large-Scale_Sequential_Dataset_for_Vehicle-Infrastructure_Cooperative_Perception_and_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yu_V2X-Seq_A_Large-Scale_Sequential_Dataset_for_Vehicle-Infrastructure_Cooperative_Perception_and_CVPR_2023_paper.pdf | cvpr-2023-1 | ['trajectory-forecasting'] | ['computer-vision'] | [-4.18909252e-01 -2.38509208e-01 -3.11652660e-01 -6.87529862e-01
-6.65324926e-01 -5.66401064e-01 6.94883823e-01 -8.36526901e-02
-3.41263972e-02 5.10136485e-01 1.24300972e-01 -6.90095186e-01
-5.07387407e-02 -7.47168779e-01 -7.01318264e-01 -3.82501245e-01
-9.14719254e-02 2.59889483e-01 7.02582717e-01 -4.40607518... | [5.942317962646484, 0.9478492736816406] |
5290cf92-4f03-48e8-b2ba-8e4396daf2a5 | toward-compact-parameter-representations-for | 2111.1032 | null | https://arxiv.org/abs/2111.10320v1 | https://arxiv.org/pdf/2111.10320v1.pdf | Toward Compact Parameter Representations for Architecture-Agnostic Neural Network Compression | This paper investigates deep neural network (DNN) compression from the perspective of compactly representing and storing trained parameters. We explore the previously overlooked opportunity of cross-layer architecture-agnostic representation sharing for DNN parameters. To do this, we decouple feedforward parameters fro... | ['Matei Zaharia', 'Hui Guan', 'Xiao Liu', 'Lijun Zhang', 'Wenlong Zhao', 'Yuezhou Sun'] | 2021-11-19 | null | null | null | null | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 3.42054963e-01 1.10592775e-01 -2.57691324e-01 -5.73987186e-01
-5.81027091e-01 -3.28947425e-01 1.97581843e-01 -2.13455036e-01
-1.04155815e+00 6.60005212e-01 -1.18062412e-02 -5.09828627e-01
-2.84060121e-01 -6.68426216e-01 -6.73626959e-01 -4.53177392e-01
-1.12077683e-01 3.39588523e-01 2.10007951e-02 5.78140244... | [8.592135429382324, 3.113121271133423] |
d3c2a6ff-7cb2-472e-b684-2848c7fd3182 | a-coarse-to-fine-approach-for-dynamic-to | null | null | https://www.sciencedirect.com/science/article/pii/S0031320321005537 | https://www.sciencedirect.com/science/article/pii/S0031320321005537/pdf | A coarse-to-fine approach for dynamic-to-static image translation | Dynamic-to-static image translation aims to convert the dynamic scene into static so that dynamic elements are eliminated from the image. Recent works typically see the problem as an image-to-image translation task, and perform the learned feature mapping over the whole dynamic image to synthesize the static image, whi... | ['Changyin Sun', 'Lin Wu', 'Teng Wang'] | 2021-10-25 | null | null | null | pattern-recognition-2021-10 | ['image-to-image-translation', 'image-inpainting', 'visual-place-recognition', 'image-to-image-translation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'miscellaneous'] | [ 6.24002695e-01 1.13850988e-01 -1.67054400e-01 -3.97669017e-01
-7.97806561e-01 -4.07218665e-01 5.01500249e-01 -5.74485779e-01
-5.35703972e-02 6.89591765e-01 4.23383683e-01 1.79057837e-01
3.08169693e-01 -8.68742585e-01 -1.19780231e+00 -9.63683367e-01
5.61765313e-01 1.30000249e-01 4.20413315e-01 -3.12004417... | [11.271093368530273, -1.2609461545944214] |
23bfbf4b-9293-4a0d-8e75-ccd2eb5b6bc3 | spatial-temporal-transformer-for-dynamic | 2107.12309 | null | https://arxiv.org/abs/2107.12309v2 | https://arxiv.org/pdf/2107.12309v2.pdf | Spatial-Temporal Transformer for Dynamic Scene Graph Generation | Dynamic scene graph generation aims at generating a scene graph of the given video. Compared to the task of scene graph generation from images, it is more challenging because of the dynamic relationships between objects and the temporal dependencies between frames allowing for a richer semantic interpretation. In this ... | ['Bodo Rosenhahn', 'Michael Ying Yang', 'Hanno Ackermann', 'Wentong Liao', 'Yuren Cong'] | 2021-07-26 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Cong_Spatial-Temporal_Transformer_for_Dynamic_Scene_Graph_Generation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Cong_Spatial-Temporal_Transformer_for_Dynamic_Scene_Graph_Generation_ICCV_2021_paper.pdf | iccv-2021-1 | ['video-visual-relation-detection', 'visual-relationship-detection'] | ['computer-vision', 'computer-vision'] | [ 3.35325152e-01 1.62379533e-01 1.00375257e-01 -3.03636491e-01
-1.59377784e-01 -3.75808269e-01 6.37561977e-01 -5.89214452e-02
-2.15676799e-01 4.99798566e-01 3.49034876e-01 -1.72705159e-01
9.28730518e-02 -7.80139446e-01 -9.10529494e-01 -6.39213800e-01
-4.61504422e-02 -4.44190167e-02 5.50719798e-01 -2.37219557... | [9.181679725646973, 0.7255865931510925] |
973d83c4-bfb4-4fbd-9387-fc2cfe45899b | recognizing-abnormal-heart-sounds-using-deep | 1707.04642 | null | http://arxiv.org/abs/1707.04642v2 | http://arxiv.org/pdf/1707.04642v2.pdf | Recognizing Abnormal Heart Sounds Using Deep Learning | The work presented here applies deep learning to the task of automated
cardiac auscultation, i.e. recognizing abnormalities in heart sounds. We
describe an automated heart sound classification algorithm that combines the
use of time-frequency heat map representations with a deep convolutional neural
network (CNN). Give... | ['Kumar Sricharan', 'Anurag Ganguli', 'Ion Matei', 'Saigopal Nelaturi', 'Jonathan Rubin', 'Rui Abreu'] | 2017-07-14 | null | null | null | null | ['sound-classification'] | ['audio'] | [ 2.17823595e-01 2.14926973e-01 5.10219455e-01 -3.18844765e-01
-1.21256256e+00 -5.12659669e-01 -4.68933098e-02 3.47165257e-01
-4.22474325e-01 5.09914279e-01 1.22764789e-01 -6.54726982e-01
-2.62937754e-01 -3.76957238e-01 -2.92752713e-01 -5.01369596e-01
-4.80444670e-01 3.88274282e-01 -1.45366201e-02 1.11255124... | [14.339444160461426, 3.3163557052612305] |
c3205798-563e-49db-8a95-8bee955bb108 | deep-temporal-graph-clustering | 2305.10738 | null | https://arxiv.org/abs/2305.10738v1 | https://arxiv.org/pdf/2305.10738v1.pdf | Deep Temporal Graph Clustering | Deep graph clustering has recently received significant attention due to its ability to enhance the representation learning capabilities of models in unsupervised scenarios. Nevertheless, deep clustering for temporal graphs, which could capture crucial dynamic interaction information, has not been fully explored. It me... | ['Xinwang Liu', 'Sihang Zhou', 'Siwei Wang', 'Ke Liang', 'Yue Liu', 'Meng Liu'] | 2023-05-18 | null | null | null | null | ['graph-clustering', 'deep-clustering', 'deep-clustering'] | ['graphs', 'miscellaneous', 'natural-language-processing'] | [-2.00577110e-01 -2.64091015e-01 -2.01905996e-01 -1.35724872e-01
-8.85394141e-02 -6.08488739e-01 4.37370926e-01 3.12575728e-01
-1.35784522e-01 1.17201813e-01 3.25024687e-02 -4.10974950e-01
-5.83563268e-01 -7.62014389e-01 -3.28761190e-01 -8.79151881e-01
-6.94716573e-01 4.92446423e-01 4.93110418e-01 -3.92018110... | [7.258851528167725, 5.996743202209473] |
c8221e35-ef3c-4caa-8338-7544f5503cf4 | semantic-parsing-of-interpage-relations | 2205.1353 | null | https://arxiv.org/abs/2205.13530v1 | https://arxiv.org/pdf/2205.13530v1.pdf | Semantic Parsing of Interpage Relations | Page-level analysis of documents has been a topic of interest in digitization efforts, and multimodal approaches have been applied to both classification and page stream segmentation. In this work, we focus on capturing finer semantic relations between pages of a multi-page document. To this end, we formalize the task ... | ['Onur Deniz', 'Mehmet Yasin Akpınar', 'Berke Oral', 'Mehmet Arif Demirtaş'] | 2022-05-26 | null | null | null | null | ['page-stream-segmentation'] | ['natural-language-processing'] | [ 3.73090655e-01 2.23718345e-01 -3.70469987e-01 -4.29324150e-01
-1.23820794e+00 -7.33986318e-01 6.83373094e-01 6.37921810e-01
-3.64107311e-01 3.20643693e-01 3.88308138e-01 -2.57143438e-01
-6.81563979e-03 -5.14746428e-01 -6.80253148e-01 -3.57022464e-01
-7.59077221e-02 5.44864178e-01 3.27487320e-01 2.11123437... | [11.610614776611328, 2.769895315170288] |
f0f47ca0-d7b3-420b-89e2-76860b718371 | sleepposenet-multi-view-learning-for-sleep | 2005.02176 | null | https://arxiv.org/abs/2005.02176v2 | https://arxiv.org/pdf/2005.02176v2.pdf | SleepPoseNet: Multi-View Learning for Sleep Postural Transition Recognition Using UWB | Recognizing movements during sleep is crucial for the monitoring of patients with sleep disorders, and the utilization of ultra-wideband (UWB) radar for the classification of human sleep postures has not been explored widely. This study investigates the performance of an off-the-shelf single antenna UWB in a novel appl... | ['Theerawit Wilaiprasitporn', 'Subhas Chandra Mukhopadhyay', 'Nattee Niparnan', 'Supasorn Suwajanakorn', 'Nakorn Kumchaiseemak', 'Theerasarn Pianpanit', 'Pitsharponrn Leelaarporn', 'Payongkit Lakhan', 'Patchanon Warin', 'Maytus Piriyajitakonkij'] | 2020-05-02 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [ 3.4552169e-01 -1.9534017e-01 8.0644295e-02 -4.8591748e-01
-4.0964890e-01 1.9607385e-01 -2.3044689e-02 -3.7952083e-01
-5.5471987e-01 7.5662869e-01 1.8635295e-02 -1.3238871e-01
-4.2391101e-01 -5.7883376e-01 -1.1966287e-01 -1.0587217e+00
-4.0157327e-01 -6.9864161e-02 -1.9777445e-01 -3.8086978e-01
-1.5373376e-02... | [13.53908634185791, 3.483686923980713] |
589ad364-a184-4ff7-a4fb-958b09da3929 | when-crowd-meets-persona-creating-a-large | 2304.0035 | null | https://arxiv.org/abs/2304.00350v1 | https://arxiv.org/pdf/2304.00350v1.pdf | When Crowd Meets Persona: Creating a Large-Scale Open-Domain Persona Dialogue Corpus | Building a natural language dataset requires caution since word semantics is vulnerable to subtle text change or the definition of the annotated concept. Such a tendency can be seen in generative tasks like question-answering and dialogue generation and also in tasks that create a categorization-based corpus, like topi... | ['Nam Soo Kim', 'Sowon Hahn', 'Moosung Kim', 'Sangah Park', 'JiHwan Kim', 'Seoyeon Bae', 'Yoon Kyung Lee', 'Won Ik Cho'] | 2023-04-01 | null | null | null | null | ['dialogue-generation', 'dialogue-generation'] | ['natural-language-processing', 'speech'] | [ 2.66593188e-01 8.40401709e-01 2.76872665e-01 -6.26524150e-01
-6.65601730e-01 -9.35567081e-01 1.10249615e+00 4.14960980e-01
-6.49033785e-01 1.18702137e+00 8.25240254e-01 -7.52643645e-02
3.03825915e-01 -7.81626582e-01 -4.54840302e-01 -5.77421010e-01
4.47058678e-01 9.16575611e-01 1.44789904e-01 -3.60121846... | [12.757132530212402, 8.024104118347168] |
39d4c8f0-5dd3-45c1-85bd-48ff75d63f57 | flexer-flexible-entity-resolution-for | 2209.07569 | null | https://arxiv.org/abs/2209.07569v2 | https://arxiv.org/pdf/2209.07569v2.pdf | FlexER: Flexible Entity Resolution for Multiple Intents | Entity resolution, a longstanding problem of data cleaning and integration, aims at identifying data records that represent the same real-world entity. Existing approaches treat entity resolution as a universal task, assuming the existence of a single interpretation of a real-world entity and focusing only on finding m... | ['Avigdor Gal', 'Roee Shraga', 'Bar Genossar'] | 2022-08-23 | null | null | null | null | ['entity-resolution'] | ['natural-language-processing'] | [ 4.11519945e-01 5.22375643e-01 -4.50873703e-01 -3.93990755e-01
-1.04411840e+00 -5.01180887e-01 3.68739069e-01 8.44679594e-01
-1.99501947e-01 9.39252913e-01 2.31683373e-01 -1.10202909e-01
-5.32527506e-01 -1.20970619e+00 -1.08895886e+00 -1.85778618e-01
-2.06784174e-01 1.11156464e+00 -1.82617083e-02 -4.66786057... | [9.348283767700195, 8.40921401977539] |
b896a300-e1cf-42a8-aab3-a0a7f1b1b758 | a-survey-of-risk-aware-multi-armed-bandits | 2205.05843 | null | https://arxiv.org/abs/2205.05843v1 | https://arxiv.org/pdf/2205.05843v1.pdf | A Survey of Risk-Aware Multi-Armed Bandits | In several applications such as clinical trials and financial portfolio optimization, the expected value (or the average reward) does not satisfactorily capture the merits of a drug or a portfolio. In such applications, risk plays a crucial role, and a risk-aware performance measure is preferable, so as to capture loss... | ['Krishna Jagannathan', 'Prashanth L. A.', 'Vincent Y. F. Tan'] | 2022-05-12 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [ 2.91309118e-01 2.96596229e-01 -9.85805333e-01 -1.44612446e-01
-8.96196127e-01 -6.99833393e-01 3.03897243e-02 4.41381454e-01
-5.25518119e-01 1.03017223e+00 2.55345464e-01 -7.21244216e-01
-9.67787802e-01 -6.79012299e-01 -4.52255636e-01 -8.35485399e-01
-3.17123443e-01 4.37386602e-01 -5.18259883e-01 9.58226398... | [4.556595802307129, 3.305640459060669] |
f2e41e6a-0710-4ce4-b277-802ac7286bde | spoken-pass-phrase-verification-in-the-i | 1809.11068 | null | http://arxiv.org/abs/1809.11068v1 | http://arxiv.org/pdf/1809.11068v1.pdf | Spoken Pass-Phrase Verification in the i-vector Space | The task of spoken pass-phrase verification is to decide whether a test
utterance contains the same phrase as given enrollment utterances. Beside other
applications, pass-phrase verification can complement an independent speaker
verification subsystem in text-dependent speaker verification. It can also be
used for live... | ['Jan Cernocky', 'Hossein Zeinali', 'Lukas Burget', 'Hossein Sameti'] | 2018-09-28 | null | null | null | null | ['text-dependent-speaker-verification'] | ['speech'] | [ 2.46959493e-01 -1.69217125e-01 -1.67324334e-01 -8.85880291e-01
-1.30562949e+00 -7.08824039e-01 4.76323336e-01 2.67529190e-01
-4.51246232e-01 4.53161925e-01 4.34360594e-01 -7.22475529e-01
6.63101450e-02 -1.86586305e-01 -2.37214103e-01 -7.41502643e-01
8.49264637e-02 5.09992957e-01 6.39806837e-02 -3.96552563... | [14.341604232788086, 6.0935211181640625] |
94bba269-a19e-4423-95eb-261d0124fc30 | rocnet-recursive-octree-network-for-efficient | 2008.03875 | null | https://arxiv.org/abs/2008.03875v1 | https://arxiv.org/pdf/2008.03875v1.pdf | RocNet: Recursive Octree Network for Efficient 3D Deep Representation | We introduce a deep recursive octree network for the compression of 3D voxel data. Our network compresses a voxel grid of any size down to a very small latent space in an autoencoder-like network. We show results for compressing 32, 64 and 128 grids down to just 80 floats in the latent space. We demonstrate the effecti... | ['Juncheng Liu', 'Steven Mills', 'Brendan McCane'] | 2020-08-10 | null | null | null | null | ['3d-shape-retrieval'] | ['computer-vision'] | [-6.20391183e-02 1.52746692e-01 2.13240594e-01 -4.78075355e-01
-7.23382056e-01 -1.08419945e-02 4.04166400e-01 1.47555888e-01
-3.43501449e-01 3.96392226e-01 5.96327424e-01 -2.47942179e-01
-1.86192706e-01 -1.44323647e+00 -8.59932601e-01 -6.05674505e-01
-5.28945923e-01 1.12181079e+00 9.66333225e-02 2.66176134... | [8.512042045593262, -3.646287202835083] |
fab4107f-934c-4a2e-9f88-8a7869a908ae | anti-exploration-by-random-network | 2301.13616 | null | https://arxiv.org/abs/2301.13616v2 | https://arxiv.org/pdf/2301.13616v2.pdf | Anti-Exploration by Random Network Distillation | Despite the success of Random Network Distillation (RND) in various domains, it was shown as not discriminative enough to be used as an uncertainty estimator for penalizing out-of-distribution actions in offline reinforcement learning. In this paper, we revisit these results and show that, with a naive choice of condit... | ['Sergey Kolesnikov', 'Denis Tarasov', 'Vladislav Kurenkov', 'Alexander Nikulin'] | 2023-01-31 | null | null | null | null | ['d4rl'] | ['robots'] | [ 2.48108253e-01 3.31229478e-01 -3.68324444e-02 -2.51322627e-01
-7.93793201e-01 -6.29510164e-01 9.28550422e-01 -2.61764750e-02
-8.52615476e-01 1.37417817e+00 -1.90239865e-02 -5.05321145e-01
-4.44264442e-01 -3.87471944e-01 -6.53591156e-01 -1.03017867e+00
-2.45736033e-01 4.87780601e-01 -1.13134846e-01 -2.13508070... | [4.161418437957764, 2.3536648750305176] |
500566d7-9e93-4786-b54d-26b96b9b1268 | face-presentation-attack-detection | 2212.0368 | null | https://arxiv.org/abs/2212.03680v1 | https://arxiv.org/pdf/2212.03680v1.pdf | Face Presentation Attack Detection | Face recognition technology has been widely used in daily interactive applications such as checking-in and mobile payment due to its convenience and high accuracy. However, its vulnerability to presentation attacks (PAs) limits its reliable use in ultra-secure applicational scenarios. A presentation attack is first def... | ['Zhen Lei', 'Chenxu Zhao', 'Zitong Yu'] | 2022-12-07 | null | null | null | null | ['face-presentation-attack-detection', 'face-anti-spoofing'] | ['computer-vision', 'computer-vision'] | [ 0.699676 -0.12883623 0.07982825 -0.14368641 -0.07816652 -0.95875365
0.7166943 -0.2400868 -0.02447005 0.3801353 -0.21925238 -0.64875853
-0.04899801 -0.5525583 -0.2129689 -0.83132017 -0.04061539 -0.32384518
0.12842086 -0.06652182 0.3227649 1.1154596 -1.620072 0.3623388
0.18522315 1.1156361 -0.5... | [13.12984848022461, 1.1315404176712036] |
5edda055-0c3c-48d4-ac3e-f637a45fb814 | nonlinear-hyperspectral-unmixing-with-robust | 1401.5649 | null | http://arxiv.org/abs/1401.5649v2 | http://arxiv.org/pdf/1401.5649v2.pdf | Nonlinear hyperspectral unmixing with robust nonnegative matrix factorization | This paper introduces a robust mixing model to describe hyperspectral data
resulting from the mixture of several pure spectral signatures. This new model
not only generalizes the commonly used linear mixing model, but also allows for
possible nonlinear effects to be easily handled, relying on mild assumptions
regarding... | ['Nicolas Dobigeon', 'Cédric Févotte'] | 2014-01-22 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 4.84287411e-01 -5.26402593e-01 -9.03677046e-02 -3.47340941e-01
-4.34054226e-01 -7.17730284e-01 6.30539894e-01 -1.92192912e-01
-3.54434192e-01 8.34831357e-01 -1.23628870e-01 -1.55362427e-01
-7.18947709e-01 -4.40258563e-01 -2.83235282e-01 -1.25806451e+00
-2.09713921e-01 1.93811640e-01 -3.24525803e-01 -1.48282468... | [10.03104305267334, -2.0573439598083496] |
2f0b3d0c-08b3-4698-89ce-21e4120704c8 | semantic-aligned-fusion-transformer-for-one | 2203.09093 | null | https://arxiv.org/abs/2203.09093v2 | https://arxiv.org/pdf/2203.09093v2.pdf | Semantic-aligned Fusion Transformer for One-shot Object Detection | One-shot object detection aims at detecting novel objects according to merely one given instance. With extreme data scarcity, current approaches explore various feature fusions to obtain directly transferable meta-knowledge. Yet, their performances are often unsatisfactory. In this paper, we attribute this to inappropr... | ['Yan Lu', 'Xun Guo', 'Yizhou Zhao'] | 2022-03-17 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhao_Semantic-Aligned_Fusion_Transformer_for_One-Shot_Object_Detection_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhao_Semantic-Aligned_Fusion_Transformer_for_One-Shot_Object_Detection_CVPR_2022_paper.pdf | cvpr-2022-1 | ['one-shot-object-detection'] | ['computer-vision'] | [ 3.40251476e-01 -2.69559711e-01 -1.52681470e-01 -5.42372108e-01
-1.09778392e+00 -2.93170899e-01 6.22959077e-01 4.55525443e-02
-1.37373403e-01 3.40961814e-01 1.80267945e-01 1.62334159e-01
-1.80406556e-01 -6.30421042e-01 -7.41474092e-01 -6.09523833e-01
3.44206601e-01 -1.06567398e-01 7.59115517e-01 -2.17059717... | [9.692594528198242, 0.19946053624153137] |
19ea3a7a-0583-44c8-a3f4-7c846bc93157 | meshrir-a-dataset-of-room-impulse-responses | 2106.10801 | null | https://arxiv.org/abs/2106.10801v2 | https://arxiv.org/pdf/2106.10801v2.pdf | MeshRIR: A Dataset of Room Impulse Responses on Meshed Grid Points For Evaluating Sound Field Analysis and Synthesis Methods | A new impulse response (IR) dataset called "MeshRIR" is introduced. Currently available datasets usually include IRs at an array of microphones from several source positions under various room conditions, which are basically designed for evaluating speech enhancement and distant speech recognition methods. On the other... | ['Jesper Brunnström', 'Natsuki Ueno', 'Takumi Abe', 'Keisuke Kimura', 'Tomoya Nishida', 'Shoichi Koyama'] | 2021-06-21 | null | null | null | null | ['room-impulse-response', 'distant-speech-recognition'] | ['audio', 'speech'] | [-6.19929889e-03 -4.79422480e-01 3.76948714e-01 -7.11227655e-02
-1.41722691e+00 -5.20067453e-01 4.30068016e-01 -5.36089838e-02
-2.56780952e-01 6.15314603e-01 4.23422098e-01 -1.86930448e-01
-3.45281750e-01 -7.67596364e-01 -4.52001214e-01 -1.01318359e+00
-1.27630904e-01 1.11172989e-01 1.18461370e-01 -4.96021397... | [15.122405052185059, 5.791810512542725] |
13f82878-570c-412a-95ea-fcc7b6af7a9a | bayesian-inverse-contextual-reasoning-for | 2306.06403 | null | https://arxiv.org/abs/2306.06403v1 | https://arxiv.org/pdf/2306.06403v1.pdf | Bayesian Inverse Contextual Reasoning for Heterogeneous Semantics-Native Communication | This work deals with the heterogeneous semantic-native communication (SNC) problem. When agents do not share the same communication context, the effectiveness of contextual reasoning (CR) is compromised calling for agents to infer other agents' context. This article proposes a novel framework for solving the inverse pr... | ['Wan Choi', 'Mehdi Bennis', 'Yoonseong Kang', 'Hyowoon Seo'] | 2023-06-10 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [ 2.43400425e-01 2.79910207e-01 -3.50806504e-01 -3.20851296e-01
-8.44348252e-01 -4.29502070e-01 1.10021555e+00 -2.08799727e-02
-4.91601408e-01 9.20581698e-01 4.31674510e-01 -5.41529655e-01
-1.13571629e-01 -9.76799846e-01 -4.89789754e-01 -5.83751619e-01
1.40044034e-01 6.80704057e-01 3.84874523e-01 1.27877071... | [4.4556379318237305, 2.4382593631744385] |
2125d999-501f-4224-ab3d-1a38267062fb | graph-attention-network-for-camera | 2209.15056 | null | https://arxiv.org/abs/2209.15056v1 | https://arxiv.org/pdf/2209.15056v1.pdf | Graph Attention Network for Camera Relocalization on Dynamic Scenes | We devise a graph attention network-based approach for learning a scene triangle mesh representation in order to estimate an image camera position in a dynamic environment. Previous approaches built a scene-dependent model that explicitly or implicitly embeds the structure of the scene. They use convolution neural netw... | ['Riadh Ksantini', 'Mohamed Bouguessa', 'Mohamed Amine Ouali'] | 2022-09-29 | null | null | null | null | ['camera-relocalization'] | ['computer-vision'] | [ 1.35834485e-01 -1.53274253e-01 3.08642406e-02 -5.08653462e-01
-5.09891629e-01 -5.83638072e-01 4.15053189e-01 -4.37049307e-02
-4.01517153e-01 -5.94082512e-02 -1.08387165e-01 -2.80273050e-01
2.24313308e-02 -1.01413596e+00 -1.22057819e+00 -2.88069636e-01
2.85862416e-01 6.95601642e-01 1.64456904e-01 -1.64449632... | [7.7256855964660645, -2.287092924118042] |
152fb007-121c-4f41-8b2f-27da0648926c | multiphase-level-set-loss-for-semi-supervised | 1904.02872 | null | https://arxiv.org/abs/1904.02872v2 | https://arxiv.org/pdf/1904.02872v2.pdf | Mumford-Shah Loss Functional for Image Segmentation with Deep Learning | Recent state-of-the-art image segmentation algorithms are mostly based on deep neural networks, thanks to their high performance and fast computation time. However, these methods are usually trained in a supervised manner, which requires large number of high quality ground-truth segmentation masks. On the other hand, c... | ['Boah Kim', 'Jong Chul Ye'] | 2019-04-05 | null | null | null | null | ['unsupervised-semantic-segmentation'] | ['computer-vision'] | [ 4.17125612e-01 2.91771919e-01 -2.91603923e-01 -8.32383096e-01
-6.46198690e-01 -2.64336795e-01 1.60338104e-01 3.95806022e-02
-7.45602012e-01 6.87013268e-01 -3.63903433e-01 -2.09801272e-02
-3.89699684e-03 -9.57605124e-01 -8.46438825e-01 -8.06639493e-01
4.09917504e-01 5.32753050e-01 5.12828171e-01 -7.01781735... | [14.447525024414062, -2.0694518089294434] |
8f5d3df3-908a-4283-9d14-f85714be85a8 | an-ensemble-of-visnet-transformer-m-and | 2211.12791 | null | https://arxiv.org/abs/2211.12791v1 | https://arxiv.org/pdf/2211.12791v1.pdf | An ensemble of VisNet, Transformer-M, and pretraining models for molecular property prediction in OGB Large-Scale Challenge @ NeurIPS 2022 | In the technical report, we provide our solution for OGB-LSC 2022 Graph Regression Task. The target of this task is to predict the quantum chemical property, HOMO-LUMO gap for a given molecule on PCQM4Mv2 dataset. In the competition, we designed two kinds of models: Transformer-M-ViSNet which is an geometry-enhanced gr... | ['Tie-Yan Liu', 'Bin Shao', 'Xinheng He', 'Zun Wang', 'Tong Wang', 'Shaoning Li', 'Yusong Wang'] | 2022-11-23 | null | null | null | null | ['graph-regression', 'molecular-property-prediction'] | ['graphs', 'miscellaneous'] | [ 1.97135687e-01 3.64052981e-01 -2.39842728e-01 -3.76008786e-02
-5.68500459e-01 -4.20386732e-01 1.99431852e-01 3.90230417e-01
-2.68000960e-01 1.34844136e+00 2.17392445e-02 -8.52738380e-01
1.43601418e-01 -7.66348720e-01 -1.03345704e+00 -8.05411279e-01
-3.73328507e-01 4.81057405e-01 -5.24971671e-02 -3.93583089... | [5.195156574249268, 5.864029407501221] |
58c34c9d-38be-4e30-ad1d-95c52d7bc940 | orrn-an-ode-based-recursive-registration | 2305.14673 | null | https://arxiv.org/abs/2305.14673v2 | https://arxiv.org/pdf/2305.14673v2.pdf | ORRN: An ODE-based Recursive Registration Network for Deformable Respiratory Motion Estimation with Lung 4DCT Images | Deformable Image Registration (DIR) plays a significant role in quantifying deformation in medical data. Recent Deep Learning methods have shown promising accuracy and speedup for registering a pair of medical images. However, in 4D (3D + time) medical data, organ motion, such as respiratory motion and heart beating, c... | ['Fei Liu', 'Michael Yip', 'Dimitri Schreiber', 'Shan Lin', 'Xiao Liang'] | 2023-05-24 | null | null | null | null | ['image-registration', 'motion-estimation', 'motion-planning'] | ['computer-vision', 'computer-vision', 'robots'] | [ 8.45699236e-02 1.94025904e-01 -2.95375675e-01 -1.18884243e-01
-9.66640592e-01 -7.20292926e-01 3.05342764e-01 1.20667212e-01
-5.70878208e-01 4.11370486e-01 4.70446721e-02 -4.66216177e-01
-3.73728245e-01 -5.00654459e-01 -5.42150855e-01 -9.39406693e-01
-2.69198775e-01 9.21354175e-01 2.56429821e-01 1.07046187... | [13.925491333007812, -2.586927652359009] |
f346a90a-1c2f-48ae-ae6d-38d67fbb5098 | elixir-a-system-to-enhance-data-quality-for | 2212.04061 | null | https://arxiv.org/abs/2212.04061v1 | https://arxiv.org/pdf/2212.04061v1.pdf | Elixir: A system to enhance data quality for multiple analytics on a video stream | IoT sensors, especially video cameras, are ubiquitously deployed around the world to perform a variety of computer vision tasks in several verticals including retail, healthcare, safety and security, transportation, manufacturing, etc. To amortize their high deployment effort and cost, it is desirable to perform multip... | ['Srimat T. Chakradhar', 'Y. Charlie Hu', 'Oliver Po', 'Murugan Sankaradas', 'Giuseppe Coviello', 'Kunal Rao', 'Sibendu Paul'] | 2022-12-08 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [-1.96577087e-02 -2.27503777e-01 -7.06533156e-03 2.53716372e-02
-8.27312291e-01 -6.81693316e-01 1.61145091e-01 -2.63468713e-01
-5.71094453e-01 5.39667189e-01 -1.66503862e-01 -7.05831647e-02
-8.10184702e-02 -7.80745924e-01 -9.55392182e-01 -8.14892530e-01
-1.08773828e-01 2.00918213e-01 3.82268488e-01 1.73304826... | [8.240739822387695, -0.9315669536590576] |
7fcf9071-2d7d-4eb5-8ec4-a8405bc621d5 | fast-disparity-estimation-from-a-single | 2209.11342 | null | https://arxiv.org/abs/2209.11342v1 | https://arxiv.org/pdf/2209.11342v1.pdf | Fast Disparity Estimation from a Single Compressed Light Field Measurement | The abundant spatial and angular information from light fields has allowed the development of multiple disparity estimation approaches. However, the acquisition of light fields requires high storage and processing cost, limiting the use of this technology in practical applications. To overcome these drawbacks, the comp... | ['Henry Arguello', 'Edwin Vargas', 'Emmanuel Martinez'] | 2022-09-22 | null | null | null | null | ['disparity-estimation'] | ['computer-vision'] | [ 6.44778490e-01 -4.11955506e-01 3.46925646e-01 -4.32158142e-01
-4.35391754e-01 -5.62855899e-02 3.24942261e-01 -1.64370060e-01
-7.70099819e-01 9.05720890e-01 -3.78671825e-01 -1.26746133e-01
5.10017425e-02 -7.76720464e-01 -7.77018845e-01 -8.55026603e-01
3.79578114e-01 8.58299993e-03 1.13810204e-01 2.57244468... | [9.734971046447754, -2.6083598136901855] |
bf62c3b3-e9e6-4ce9-8799-da86dbfed0b8 | effective-medical-code-prediction-via-label | 2305.05162 | null | https://arxiv.org/abs/2305.05162v1 | https://arxiv.org/pdf/2305.05162v1.pdf | Effective Medical Code Prediction via Label Internal Alignment | The clinical notes are usually typed into the system by physicians. They are typically required to be marked by standard medical codes, and each code represents a diagnosis or medical treatment procedure. Annotating these notes is time consuming and prone to error. In this paper, we proposed a multi-view attention base... | ['Guodong Liu'] | 2023-05-09 | null | null | null | null | ['medical-code-prediction'] | ['medical'] | [ 7.46886358e-02 8.42721537e-02 -3.33092600e-01 -5.87584853e-01
-8.12823176e-01 -5.42975485e-01 6.19864240e-02 7.85613000e-01
-2.32316822e-01 3.19025844e-01 5.56543350e-01 -2.82888889e-01
-1.01142302e-01 -5.00898302e-01 -1.03743844e-01 -4.41579491e-01
2.06867427e-01 7.30975330e-01 -1.38545737e-01 1.96253538... | [7.997981071472168, 6.834123611450195] |
9a66758b-0443-430f-84a8-a99f1b7e36de | target-speaker-verification-with-selective | 2103.16269 | null | https://arxiv.org/abs/2103.16269v2 | https://arxiv.org/pdf/2103.16269v2.pdf | Target Speaker Verification with Selective Auditory Attention for Single and Multi-talker Speech | Speaker verification has been studied mostly under the single-talker condition. It is adversely affected in the presence of interference speakers. Inspired by the study on target speaker extraction, e.g., SpEx, we propose a unified speaker verification framework for both single- and multi-talker speech, that is able to... | ['Haizhou Li', 'Jibin Wu', 'Wei Rao', 'Chenglin Xu'] | 2021-03-30 | null | null | null | null | ['target-speaker-extraction'] | ['audio'] | [ 1.22373864e-01 -4.48625647e-02 -3.65684368e-02 -5.44191301e-01
-1.65102673e+00 -5.00902176e-01 4.88389432e-01 -2.26149619e-01
-2.44474709e-01 1.20916590e-01 3.52441192e-01 -3.38110030e-01
2.17926502e-01 5.78221492e-02 -3.72959435e-01 -1.02481961e+00
1.28474429e-01 2.01336041e-01 -2.25999936e-01 -2.83017248... | [14.376219749450684, 6.0920891761779785] |
50ec9deb-f314-40f7-895e-9c3af0ae8135 | turning-a-clip-model-into-a-scene-text | 2302.14338 | null | https://arxiv.org/abs/2302.14338v3 | https://arxiv.org/pdf/2302.14338v3.pdf | Turning a CLIP Model into a Scene Text Detector | The recent large-scale Contrastive Language-Image Pretraining (CLIP) model has shown great potential in various downstream tasks via leveraging the pretrained vision and language knowledge. Scene text, which contains rich textual and visual information, has an inherent connection with a model like CLIP. Recently, pretr... | ['Xiang Bai', 'Bo Ren', 'Deqiang Jiang', 'Wei Hua', 'Yuliang Liu', 'Wenwen Yu'] | 2023-02-28 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yu_Turning_a_CLIP_Model_Into_a_Scene_Text_Detector_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yu_Turning_a_CLIP_Model_Into_a_Scene_Text_Detector_CVPR_2023_paper.pdf | cvpr-2023-1 | ['scene-text-detection'] | ['computer-vision'] | [ 2.68955588e-01 -4.37723011e-01 -1.46815836e-01 -1.58074886e-01
-6.34122491e-01 -2.89555818e-01 9.05060470e-01 -3.95196714e-02
-5.32320559e-01 2.19573602e-01 3.62347126e-01 -2.88687825e-01
5.80263257e-01 -6.14134490e-01 -6.40489459e-01 -5.69645703e-01
7.09394336e-01 5.44103533e-02 5.52170634e-01 -1.68789223... | [11.752411842346191, 2.1353819370269775] |
e2ddd03c-59c8-44ce-86cc-d0e6da0e2ba3 | splenomegaly-segmentation-on-multi-modal-mri | 1811.04045 | null | http://arxiv.org/abs/1811.04045v1 | http://arxiv.org/pdf/1811.04045v1.pdf | Splenomegaly Segmentation on Multi-modal MRI using Deep Convolutional Networks | The findings of splenomegaly, abnormal enlargement of the spleen, is a
non-invasive clinical biomarker for liver and spleen disease. Automated
segmentation methods are essential to efficiently quantify splenomegaly from
clinically acquired abdominal magnetic resonance imaging (MRI) scans. However,
the task is challengi... | ['Prasanna Parvathaneni', 'Camilo Bermudez', 'Michael R. Savona', 'Hyeonsoo Moon', 'Yuankai Huo', 'Tamara K. Moyo', 'Zhoubing Xu', 'Shunxing Bao', 'Richard G. Abramson', 'Bennett A. Landman', 'Albert Assad'] | 2018-11-09 | null | null | null | null | ['splenomegaly-segmentation-on-multi-modal-mri'] | ['medical'] | [ 5.27053559e-03 -1.25111446e-01 2.09222972e-01 -4.77557212e-01
-6.13083422e-01 -4.06224936e-01 5.19350588e-01 1.02178734e-02
-2.08388850e-01 5.71653843e-01 3.44257683e-01 6.17133453e-02
-3.26587185e-02 -5.46901882e-01 -2.43555456e-01 -8.83110523e-01
-6.77929878e-01 1.04051971e+00 1.74693689e-01 7.22853988... | [14.362770080566406, -2.4911022186279297] |
d2987e2e-79a8-4f5b-8348-6de7314c0f3e | neural-person-search-machines | 1707.06777 | null | http://arxiv.org/abs/1707.06777v1 | http://arxiv.org/pdf/1707.06777v1.pdf | Neural Person Search Machines | We investigate the problem of person search in the wild in this work. Instead
of comparing the query against all candidate regions generated in a query-blind
manner, we propose to recursively shrink the search area from the whole image
till achieving precise localization of the target person, by fully exploiting
inform... | ['Shuicheng Yan', 'Karlekar Jayashree', 'Jianguo Jiang', 'Meibin Qi', 'Hao Liu', 'Zequn Jie', 'Jiashi Feng', 'Bo Zhao'] | 2017-07-21 | neural-person-search-machines-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Liu_Neural_Person_Search_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Liu_Neural_Person_Search_ICCV_2017_paper.pdf | iccv-2017-10 | ['person-search'] | ['computer-vision'] | [ 1.31793767e-01 6.29625050e-03 -8.69012922e-02 -2.09108680e-01
-9.40014243e-01 -4.20754761e-01 5.71620405e-01 6.97008744e-02
-8.49743783e-01 4.45386559e-01 3.19507778e-01 1.84097901e-01
-7.65509009e-02 -6.88585520e-01 -5.48621237e-01 -5.37461519e-01
6.09854013e-02 9.60846186e-01 5.22540033e-01 -2.17612609... | [14.821783065795898, 0.8245379328727722] |
21e9d2ef-9b01-4b50-ad96-1a43a2f68530 | estimating-relative-diffusion-from-3d-micro | 2208.03337 | null | https://arxiv.org/abs/2208.03337v1 | https://arxiv.org/pdf/2208.03337v1.pdf | Estimating relative diffusion from 3D micro-CT images using CNNs | In the past several years, convolutional neural networks (CNNs) have proven their capability to predict characteristic quantities in porous media research directly from pore-space geometries. Due to the frequently observed significant reduction in computation time in comparison to classical computational methods, bulk ... | ['Nadja Ray', 'Andreas Meier', 'Fabian Woller', 'Florian Frank', 'Stephan Gärttner'] | 2022-08-04 | null | null | null | null | ['parameter-prediction'] | ['miscellaneous'] | [-1.06115244e-01 -1.17894515e-01 6.93882033e-02 2.35153958e-01
-3.08483720e-01 -2.71578014e-01 7.42305458e-01 8.59886825e-01
-7.81672299e-01 9.54160869e-01 5.31566888e-02 -5.92018306e-01
-4.37003195e-01 -1.31759834e+00 -6.20601892e-01 -1.09937048e+00
-4.57097054e-01 7.14628339e-01 3.74751687e-01 -4.25779283... | [6.42698335647583, 3.379267454147339] |
126841ae-781b-420f-85f6-74e51bfbdf75 | phase-only-image-based-kernel-estimation-for-1 | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Pan_Phase-Only_Image_Based_Kernel_Estimation_for_Single_Image_Blind_Deblurring_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Pan_Phase-Only_Image_Based_Kernel_Estimation_for_Single_Image_Blind_Deblurring_CVPR_2019_paper.pdf | Phase-Only Image Based Kernel Estimation for Single Image Blind Deblurring | The image motion blurring process is generally modelled as the convolution of a blur kernel with a latent image. Therefore, the estimation of the blur kernel is essentially important for blind image deblurring. Unlike existing approaches which focus on approaching the problem by enforcing various priors on the blur ker... | [' Yuchao Dai', ' Miaomiao Liu', ' Richard Hartley', 'Liyuan Pan'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['single-image-blind-deblurring', 'blind-image-deblurring'] | ['computer-vision', 'computer-vision'] | [ 2.80356228e-01 -5.70119441e-01 3.51794690e-01 -7.13368282e-02
-3.97973627e-01 -5.63938916e-01 5.81900835e-01 -5.79769075e-01
-3.16781968e-01 8.48869562e-01 5.63281953e-01 9.96266156e-02
-2.92483687e-01 -2.35295549e-01 -5.59171617e-01 -1.05198801e+00
4.55357768e-02 -2.47440517e-01 1.09354340e-01 2.44661555... | [11.591912269592285, -2.729902982711792] |
dc0cd569-8a95-46a4-84aa-3c391f82e58d | self-attention-on-multi-shifted-windows-for | 2207.04403 | null | https://arxiv.org/abs/2207.04403v1 | https://arxiv.org/pdf/2207.04403v1.pdf | Self-attention on Multi-Shifted Windows for Scene Segmentation | Scene segmentation in images is a fundamental yet challenging problem in visual content understanding, which is to learn a model to assign every image pixel to a categorical label. One of the challenges for this learning task is to consider the spatial and semantic relationships to obtain descriptive feature representa... | ['Qiang Wu', 'Jian Zhang', 'Zhibin Li', 'Litao Yu'] | 2022-07-10 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 5.52903593e-01 -1.53186157e-01 -1.58625200e-01 -7.92515934e-01
-4.90306556e-01 -4.85115707e-01 4.99714822e-01 1.11767583e-01
-4.93512183e-01 3.31850260e-01 4.94332798e-02 -3.27229232e-01
-8.48442465e-02 -8.88409138e-01 -9.28720713e-01 -5.38702905e-01
1.55639380e-01 1.76920667e-01 6.38562024e-01 -6.42656311... | [9.609635353088379, 0.37397605180740356] |
ce7915b5-7cb5-4bbe-899b-04d2d024e994 | investigation-of-multilingual-neural-machine | null | null | https://aclanthology.org/2022.wat-1.9 | https://aclanthology.org/2022.wat-1.9.pdf | Investigation of Multilingual Neural Machine Translation for Indian Languages | In the domain of natural language processing, machine translation is a well-defined task where one natural language is automatically translated to another natural language. The deep learning-based approach of machine translation, known as neural machine translation attains remarkable translational performance. However,... | ['Sivaji Bandyopadhyay', 'Partha Pakray', 'Riyanka Manna', 'Sahinur Rahman Laskar'] | null | null | null | null | wat-2022-10 | ['transliteration'] | ['natural-language-processing'] | [ 2.43111491e-01 -8.56902003e-02 -3.05272788e-01 -4.58972871e-01
-1.39352584e+00 -8.51688147e-01 8.01221311e-01 -2.61026263e-01
-5.77422500e-01 1.34724391e+00 2.32949466e-01 -9.25008416e-01
4.37452316e-01 -7.09319711e-01 -9.91299391e-01 -3.30094516e-01
5.97130001e-01 9.16145384e-01 -4.92727935e-01 -5.81992149... | [11.549581527709961, 10.368590354919434] |
66fc5bdc-7e21-4e01-800a-bfbd51f2d2a5 | group-activity-recognition-by-using-effective | null | null | https://ieeexplore.ieee.org/abstract/document/9031366 | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9031366 | Group Activity Recognition by Using Effective Multiple Modality Relation Representation With Temporal-Spatial Attention | Group activity recognition has received a great deal of interest because of its broader applications in sports analysis, autonomous vehicles, CCTV surveillance systems and video summarization systems. Most existing methods typically use appearance features and they seldom consider underlying interaction information. In... | ['AND XU LIU', 'Dong Wang', 'Meng Jian', 'Lifang Wu', 'HENG FU', 'Dezhong Xu'] | 2020-03-10 | null | null | null | ieee-access-2020-3 | ['group-activity-recognition'] | ['computer-vision'] | [ 1.75308064e-01 -6.12457335e-01 -4.50539798e-01 -3.02734494e-01
-3.21871877e-01 -1.31450649e-02 4.97093886e-01 9.34142023e-02
-4.93093550e-01 4.94356245e-01 4.41119611e-01 2.46559769e-01
-2.75236815e-01 -8.91088903e-01 -5.19560158e-01 -9.27676320e-01
-7.10935891e-02 -7.51721784e-02 5.89583158e-01 -9.93023440... | [8.401277542114258, 0.6018940210342407] |
b5464ef4-3250-4fb2-8f51-15a6dadffed3 | monocular-3d-object-detection-with-depth-from | 2207.12988 | null | https://arxiv.org/abs/2207.12988v2 | https://arxiv.org/pdf/2207.12988v2.pdf | Monocular 3D Object Detection with Depth from Motion | Perceiving 3D objects from monocular inputs is crucial for robotic systems, given its economy compared to multi-sensor settings. It is notably difficult as a single image can not provide any clues for predicting absolute depth values. Motivated by binocular methods for 3D object detection, we take advantage of the stro... | ['Dahua Lin', 'Jiangmiao Pang', 'Tai Wang'] | 2022-07-26 | null | null | null | null | ['monocular-3d-object-detection'] | ['computer-vision'] | [ 1.13272488e-01 -9.53487083e-02 -9.18877050e-02 -2.24828377e-01
-6.67957842e-01 -7.62801468e-01 4.50886488e-01 -4.23003286e-01
-3.35052580e-01 3.65546316e-01 3.54850069e-02 -1.72543049e-01
4.09514271e-03 -5.23093402e-01 -8.56730700e-01 -7.01133192e-01
3.68224025e-01 3.03645372e-01 4.58772510e-01 8.62306803... | [8.227338790893555, -2.501697301864624] |
87fa3d8d-7b97-41ce-80ea-789e5e1ae48c | protein-secondary-structure-prediction-using-1 | 1611.01503 | null | http://arxiv.org/abs/1611.01503v1 | http://arxiv.org/pdf/1611.01503v1.pdf | Protein Secondary Structure Prediction Using Deep Multi-scale Convolutional Neural Networks and Next-Step Conditioning | Recently developed deep learning techniques have significantly improved the
accuracy of various speech and image recognition systems. In this paper we
adapt some of these techniques for protein secondary structure prediction. We
first train a series of deep neural networks to predict eight-class secondary
structure lab... | ['Akosua Busia', 'Navdeep Jaitly', 'Jasmine Collins'] | 2016-11-04 | null | null | null | null | ['protein-secondary-structure-prediction'] | ['medical'] | [ 6.61387682e-01 2.35930234e-01 2.13315114e-02 -8.64762306e-01
-8.57495487e-01 -4.22398150e-01 3.93940389e-01 1.12900734e-01
-8.37509990e-01 9.71540570e-01 1.35690182e-01 -8.13591242e-01
2.98533291e-01 -3.51368457e-01 -9.59570587e-01 -7.70804167e-01
-9.68505964e-02 3.24275970e-01 1.55532330e-01 -2.81283468... | [4.726266384124756, 5.642025947570801] |
8904091f-11eb-44a5-855a-856535a3957a | benchmarking-of-lightweight-deep-learning | 2110.1227 | null | https://arxiv.org/abs/2110.12270v1 | https://arxiv.org/pdf/2110.12270v1.pdf | Benchmarking of Lightweight Deep Learning Architectures for Skin Cancer Classification using ISIC 2017 Dataset | Skin cancer is one of the deadly types of cancer and is common in the world. Recently, there has been a huge jump in the rate of people getting skin cancer. For this reason, the number of studies on skin cancer classification with deep learning are increasing day by day. For the growth of work in this area, the Interna... | ['Huseyin Uvet', 'Mehmet Erhan Guvenilir', 'Yegor Samoylenko', 'Mucahit Kalebasi', 'Abdurrahim Yilmaz'] | 2021-10-23 | null | null | null | null | ['skin-cancer-classification'] | ['medical'] | [-8.57034847e-02 -3.43618751e-01 -1.92219764e-01 -4.79832590e-02
-7.61634290e-01 -3.05409223e-01 4.49458390e-01 7.14868456e-02
-7.64280438e-01 8.50733995e-01 1.33935332e-01 -1.23715661e-01
7.91039690e-02 -7.97430098e-01 -2.10074455e-01 -8.93695712e-01
9.47824419e-02 -1.40679345e-01 4.54555601e-01 -1.07543282... | [15.66567325592041, -2.972818613052368] |
980ba6b8-fac0-475f-923b-c785bb521163 | 3d-reconstruction-from-public-webcams | 2108.09476 | null | https://arxiv.org/abs/2108.09476v2 | https://arxiv.org/pdf/2108.09476v2.pdf | 3D Reconstruction from public webcams | We investigate the possibility of 3D scene reconstruction from two or more overlapping webcam streams. A large, and growing, number of webcams observe places of interest and are publicly accessible. The question naturally arises: can we make use of this free data source for 3D computer vision? It turns out that the tas... | ['Cenek Albl', 'Konrad Schindler', 'Tianyu Wu'] | 2021-08-21 | null | null | null | null | ['3d-scene-reconstruction'] | ['computer-vision'] | [ 2.05739364e-01 -2.24663451e-01 2.03471050e-01 -2.62756824e-01
-5.98673522e-01 -1.20802069e+00 7.84568548e-01 -1.73425287e-01
-2.29627877e-01 1.81190461e-01 1.10636735e-02 -1.10910840e-01
-4.70692944e-03 -2.90537179e-01 -8.85004580e-01 -5.83749712e-01
1.99036807e-01 9.00805891e-01 8.34991932e-01 -5.44210970... | [8.25352954864502, -2.4404947757720947] |
6bdc1c27-8373-4014-aed9-ff4f0409e8a3 | content-differences-in-syntactic-and-semantic-1 | null | null | https://aclanthology.org/N19-1047 | https://aclanthology.org/N19-1047.pdf | Content Differences in Syntactic and Semantic Representation | Syntactic analysis plays an important role in semantic parsing, but the nature of this role remains a topic of ongoing debate. The debate has been constrained by the scarcity of empirical comparative studies between syntactic and semantic schemes, which hinders the development of parsing methods informed by the details... | ['Ari Rappoport', 'Daniel Hershcovich', 'Omri Abend'] | 2019-06-01 | null | null | null | naacl-2019-6 | ['ucca-parsing'] | ['natural-language-processing'] | [ 4.56424534e-01 4.52247530e-01 -1.60346314e-01 -6.76682949e-01
-8.53257418e-01 -9.31946695e-01 7.37341106e-01 5.25854945e-01
-3.54470611e-01 6.41316652e-01 5.86423874e-01 -6.63869083e-01
-2.76978642e-01 -8.22937548e-01 -4.37930375e-01 -6.65395677e-01
2.02411398e-01 3.78688723e-01 6.39218867e-01 -3.98274213... | [10.335277557373047, 9.475459098815918] |
a7a95292-9b35-44dd-ba31-840285f6874b | improving-negation-detection-with-negation | null | null | https://openreview.net/forum?id=kVeV2zg8EV | https://openreview.net/pdf?id=kVeV2zg8EV | Improving negation detection with negation-focused pre-training | Negation is a common linguistic feature that is crucial in many language understanding tasks, yet it remains a hard problem due to diversity in its expression in different types of text. Recent works show that state-of-the-art NLP models underperform on samples containing negation in various tasks, and that negation de... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['negation-detection'] | ['natural-language-processing'] | [ 3.82071733e-02 -1.15917273e-01 -6.20656133e-01 -6.95269287e-01
-4.66875583e-01 -8.05512846e-01 6.69807076e-01 4.35609937e-01
-7.58888602e-01 1.17562973e+00 2.54980057e-01 -4.15429980e-01
4.73540008e-01 -8.78712654e-01 -6.97036386e-01 -7.63496831e-02
2.25125059e-01 3.98132712e-01 3.09667140e-01 -9.16181087... | [10.431546211242676, 9.250840187072754] |
28b43352-ac2d-4740-b584-4269950ca745 | people-penguins-and-petri-dishes-adapting | 1711.05586 | null | http://arxiv.org/abs/1711.05586v1 | http://arxiv.org/pdf/1711.05586v1.pdf | People, Penguins and Petri Dishes: Adapting Object Counting Models To New Visual Domains And Object Types Without Forgetting | In this paper we propose a technique to adapt a convolutional neural network
(CNN) based object counter to additional visual domains and object types while
still preserving the original counting function. Domain-specific normalisation
and scaling operators are trained to allow the model to adjust to the
statistical dis... | ["Noel E. O'Connor", 'Kevin McGuinness', 'Ciara E. Keogh', 'Suzanne Little', 'Mark Marsden'] | 2017-11-15 | people-penguins-and-petri-dishes-adapting-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Marsden_People_Penguins_and_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Marsden_People_Penguins_and_CVPR_2018_paper.pdf | cvpr-2018-6 | ['object-counting'] | ['computer-vision'] | [ 1.40086859e-01 -3.70260507e-01 1.39993086e-01 -3.17643881e-02
-3.07725042e-01 -2.41248712e-01 9.49907541e-01 7.46604502e-01
-1.24325037e+00 1.00939703e+00 -2.38143638e-01 -1.23114534e-01
3.88816953e-01 -7.08922327e-01 -4.22020823e-01 -6.65529788e-01
4.38903719e-02 9.18257535e-01 5.16154230e-01 8.24865922... | [14.774142265319824, -3.2057323455810547] |
f092c586-a32c-4569-812b-2236a7b5e811 | hidden-state-approximation-in-recurrent | 2212.09008 | null | https://arxiv.org/abs/2212.09008v1 | https://arxiv.org/pdf/2212.09008v1.pdf | Hidden State Approximation in Recurrent Neural Networks Using Continuous Particle Filtering | Using historical data to predict future events has many applications in the real world, such as stock price prediction; the robot localization. In the past decades, the Convolutional long short-term memory (LSTM) networks have achieved extraordinary success with sequential data in the related field. However, traditiona... | ['Dexun Li'] | 2022-12-18 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-2.45812893e-01 -8.36540163e-02 -4.49799180e-01 -2.39857629e-01
-1.10471174e-02 1.99344307e-01 8.01744580e-01 -1.79211333e-01
-5.77781200e-01 8.54687095e-01 3.91635954e-01 -2.72927016e-01
-6.19109422e-02 -1.06067550e+00 -8.34894001e-01 -7.53547728e-01
-1.70190230e-01 3.87340337e-01 3.41920137e-01 -1.61452472... | [7.029153823852539, 3.4575259685516357] |
438f0700-4db4-4152-8430-c1260acd316d | dynamic-texture-recognition-using-pdv-hashing | 2111.12315 | null | https://arxiv.org/abs/2111.12315v2 | https://arxiv.org/pdf/2111.12315v2.pdf | Dynamic Texture Recognition using PDV Hashing and Dictionary Learning on Multi-scale Volume Local Binary Pattern | Spatial-temporal local binary pattern (STLBP) has been widely used in dynamic texture recognition. STLBP often encounters the high-dimension problem as its dimension increases exponentially, so that STLBP could only utilize a small neighborhood. To tackle this problem, we propose a method for dynamic texture recognitio... | ['Jiawei Li', 'Heng Yu', 'Jianfeng Ren', 'Ruxin Ding'] | 2021-11-24 | null | null | null | null | ['dynamic-texture-recognition'] | ['computer-vision'] | [-2.00040434e-02 -8.53553951e-01 -3.90561342e-01 -1.16419960e-02
-7.55188465e-01 -6.86862320e-02 1.47686556e-01 9.55497772e-02
-2.66577780e-01 5.25247276e-01 1.44837955e-02 6.39374405e-02
-2.01514930e-01 -1.04812455e+00 -4.44568038e-01 -1.34248078e+00
-1.71523318e-01 2.12030217e-01 9.15929794e-01 -6.32140264... | [10.562355995178223, -0.30651214718818665] |
582908ad-a758-4bc9-bbdd-c0cd1871d610 | a-versatile-scene-model-with-differentiable | 1602.03725 | null | http://arxiv.org/abs/1602.03725v1 | http://arxiv.org/pdf/1602.03725v1.pdf | A Versatile Scene Model with Differentiable Visibility Applied to Generative Pose Estimation | Generative reconstruction methods compute the 3D configuration (such as pose
and/or geometry) of a shape by optimizing the overlap of the projected 3D shape
model with images. Proper handling of occlusions is a big challenge, since the
visibility function that indicates if a surface point is seen from a camera can
ofte... | ['Christian Theobalt', 'Hans-Peter Seidel', 'Nadia Robertini', 'Helge Rhodin', 'Christian Richardt'] | 2016-02-11 | a-versatile-scene-model-with-differentiable-1 | http://openaccess.thecvf.com/content_iccv_2015/html/Rhodin_A_Versatile_Scene_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Rhodin_A_Versatile_Scene_ICCV_2015_paper.pdf | iccv-2015-12 | ['occlusion-handling'] | ['computer-vision'] | [ 2.56012797e-01 -3.75967077e-03 3.24913770e-01 -8.70492458e-02
-6.10346079e-01 -6.36551380e-01 4.82119888e-01 -1.80189759e-01
-9.28157195e-02 5.41807473e-01 -2.70056933e-01 7.59434998e-02
-1.95275843e-02 -7.82907963e-01 -8.65602851e-01 -8.50611210e-01
2.70987064e-01 9.17180359e-01 2.42806047e-01 1.35062099... | [9.456892967224121, -2.961338520050049] |
68ebd322-e759-4916-8462-88734bc46f99 | faking-fake-news-for-real-fake-news-detection | 2203.05386 | null | https://arxiv.org/abs/2203.05386v2 | https://arxiv.org/pdf/2203.05386v2.pdf | Faking Fake News for Real Fake News Detection: Propaganda-loaded Training Data Generation | Despite recent advances in detecting fake news generated by neural models, their results are not readily applicable to effective detection of human-written disinformation. What limits the successful transfer between them is the sizable gap between machine-generated fake news and human-authored ones, including the notab... | ['Heng Ji', 'Yejin Choi', 'Preslav Nakov', 'Kathleen McKeown', 'Kung-Hsiang Huang'] | 2022-03-10 | null | null | null | null | ['news-generation'] | ['natural-language-processing'] | [ 9.22466218e-02 3.99102926e-01 -4.06409442e-01 -1.42693684e-01
-5.57354927e-01 -7.88293362e-01 1.16437697e+00 8.59130099e-02
-2.07409739e-01 7.33772337e-01 6.54422462e-01 -5.65645278e-01
5.73121309e-01 -7.81770349e-01 -7.94468224e-01 -2.47476995e-01
2.18909591e-01 3.83726269e-01 1.77753732e-01 -6.44342124... | [8.189767837524414, 10.170778274536133] |
5c2d8b09-63b2-48e3-99b9-eaf6c1c49f8a | recomif-reading-comprehension-based-multi | null | null | https://www.sciencedirect.com/science/article/abs/pii/S1566253523001057?via%3Dihub | https://www.sciencedirect.com/science/article/abs/pii/S1566253523001057?via%3Dihub | ReCoMIF: Reading comprehension based multi-source information fusion network for Chinese spoken language understanding | Spoken language understanding (SLU) plays a crucial role in the performance of dialogue systems. It usually includes slot filling and intent detection (SFID) tasks aiming at semantic parsing of utterances. At present, researchers focus mainly on English SLU tasks, while such investigations on Chinese utterances are not... | ['Yun Pan', 'Ye Wang', 'Bo Jiang', 'Bi Chen', 'Hua Zhang', 'Xiawen Song', 'Xiaohui Jia', 'Bo Xie'] | 2023-08-01 | null | null | null | journal-2023-8 | ['spoken-language-understanding', 'reading-comprehension', 'semantic-parsing', 'intent-detection', 'slot-filling', 'spoken-language-understanding'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'speech'] | [ 4.15544599e-01 5.97571731e-01 -1.62701175e-01 -5.44012666e-01
-1.13156235e+00 -1.55165359e-01 5.80551922e-01 1.60606161e-01
-6.03764594e-01 7.61522532e-01 9.40914214e-01 -5.56516469e-01
2.16354236e-01 -6.02765858e-01 -1.99037582e-01 -3.87694597e-01
4.92127568e-01 4.04276341e-01 4.15561587e-01 -6.01037323... | [12.604483604431152, 7.4475178718566895] |
1486e59b-73ce-4a1c-b463-de3cad82f42e | 3d-reconstruction-through-fusion-of-cross | 2106.14306 | null | https://arxiv.org/abs/2106.14306v1 | https://arxiv.org/pdf/2106.14306v1.pdf | 3D Reconstruction through Fusion of Cross-View Images | 3D recovery from multi-stereo and stereo images, as an important application of the image-based perspective geometry, serves many applications in computer vision, remote sensing and Geomatics. In this chapter, the authors utilize the imaging geometry and present approaches that perform 3D reconstruction from cross-view... | ['Mostafa Elhashash', 'Xiao Ling', 'Shuang Song', 'Rongjun Qin'] | 2021-06-27 | null | null | null | null | ['point-cloud-generation'] | ['computer-vision'] | [ 3.07640612e-01 -4.26724076e-01 6.61629617e-01 -3.11608642e-01
-7.89171934e-01 -6.59154832e-01 7.10208118e-01 -3.36466432e-01
-2.25325331e-01 3.02674413e-01 2.33113356e-02 -2.49628797e-01
-1.83908939e-01 -1.23035157e+00 -7.57058322e-01 -4.85112101e-01
3.63491289e-02 8.15191805e-01 4.68601584e-01 -5.89221060... | [8.545418739318848, -2.62018084526062] |
7ad302e8-67aa-4a0e-86fe-927228073d78 | constrained-structured-regression-with | 1511.07497 | null | http://arxiv.org/abs/1511.07497v1 | http://arxiv.org/pdf/1511.07497v1.pdf | Constrained Structured Regression with Convolutional Neural Networks | Convolutional Neural Networks (CNNs) have recently emerged as the dominant
model in computer vision. If provided with enough training data, they predict
almost any visual quantity. In a discrete setting, such as classification, CNNs
are not only able to predict a label but often predict a confidence in the form
of a pr... | ['Trevor Darrell', 'Philipp Krähenbühl', 'Deepak Pathak', 'Stella X. Yu'] | 2015-11-23 | null | null | null | null | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 3.03212821e-01 3.03294539e-01 -4.04271752e-01 -4.24395710e-01
-3.49987626e-01 -5.00270724e-01 6.56976521e-01 2.90644854e-01
-5.09711623e-01 6.71415567e-01 -3.44929397e-01 -8.54938757e-04
-7.43611902e-02 -8.58902335e-01 -1.10241234e+00 -9.62746441e-01
2.69168496e-01 4.50061560e-01 4.62082438e-02 2.64090568... | [9.726974487304688, 0.7985353469848633] |
956c22de-6adc-4fae-82a1-684b44c268c1 | click-here-human-localized-keypoints-as | 1703.09859 | null | http://arxiv.org/abs/1703.09859v2 | http://arxiv.org/pdf/1703.09859v2.pdf | Click Here: Human-Localized Keypoints as Guidance for Viewpoint Estimation | We motivate and address a human-in-the-loop variant of the monocular
viewpoint estimation task in which the location and class of one semantic
object keypoint is available at test time. In order to leverage the keypoint
information, we devise a Convolutional Neural Network called Click-Here CNN
(CH-CNN) that integrates... | ['Ryan Szeto', 'Jason J. Corso'] | 2017-03-29 | click-here-human-localized-keypoints-as-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Szeto_Click_Here_Human-Localized_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Szeto_Click_Here_Human-Localized_ICCV_2017_paper.pdf | iccv-2017-10 | ['viewpoint-estimation'] | ['computer-vision'] | [-6.15192652e-02 6.62211627e-02 -2.12452725e-01 -7.68615186e-01
-8.13335538e-01 -5.93654335e-01 6.04340613e-01 2.46006083e-02
-3.44113618e-01 3.47256005e-01 1.53028360e-02 -1.98796913e-01
3.03964406e-01 -7.81215370e-01 -1.24545348e+00 -1.58161610e-01
2.17112228e-01 5.29821217e-01 4.15333331e-01 2.12612912... | [7.626429080963135, -2.59311580657959] |
5c82cbbd-f690-473b-b30d-dc3742935962 | device-tuning-for-multi-task-large-model | 2302.1082 | null | https://arxiv.org/abs/2302.10820v1 | https://arxiv.org/pdf/2302.10820v1.pdf | Device Tuning for Multi-Task Large Model | Unsupervised pre-training approaches have achieved great success in many fields such as Computer Vision (CV), Natural Language Processing (NLP) and so on. However, compared to typical deep learning models, pre-training or even fine-tuning the state-of-the-art self-attention models is extremely expensive, as they requir... | ['Yinsi Zhou', 'Xuanchen Hou', 'Penghao Jiang'] | 2023-02-21 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [ 2.95936763e-01 -4.71968859e-01 -2.45895162e-01 -3.32977623e-01
-4.83933479e-01 -1.29435226e-01 2.57773370e-01 -8.90804827e-02
-1.07109714e-02 4.80390489e-01 6.29576817e-02 -1.98069125e-01
-1.46993801e-01 -6.23368382e-01 -5.81092834e-01 -5.73994279e-01
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1f23f771-d8da-4724-b38f-7b026aa00394 | temporal-action-proposal-generation-with | 2105.12043 | null | https://arxiv.org/abs/2105.12043v1 | https://arxiv.org/pdf/2105.12043v1.pdf | Temporal Action Proposal Generation with Transformers | Transformer networks are effective at modeling long-range contextual information and have recently demonstrated exemplary performance in the natural language processing domain. Conventionally, the temporal action proposal generation (TAPG) task is divided into two main sub-tasks: boundary prediction and proposal confid... | ['Hujie Huang', 'Hongxun Yao', 'Wenhao Wu', 'Haosen Yang', 'Lining Wang'] | 2021-05-25 | null | null | null | null | ['temporal-action-proposal-generation'] | ['computer-vision'] | [ 2.49654770e-01 3.75997201e-02 -6.88494146e-01 -4.23905820e-01
-1.08769119e+00 7.55951703e-02 7.84379661e-01 1.75260544e-01
-3.52298945e-01 7.69216895e-01 7.06778765e-01 1.16088636e-01
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-2.82733232e-01 3.55340511e-01 9.21009362e-01 -2.79657338... | [8.398733139038086, 0.4901648461818695] |
593c6b13-d515-4059-b4d7-169d220e62fd | gradient-domain-image-reconstruction | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Shibata_Gradient-Domain_Image_Reconstruction_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Shibata_Gradient-Domain_Image_Reconstruction_CVPR_2016_paper.pdf | Gradient-Domain Image Reconstruction Framework With Intensity-Range and Base-Structure Constraints | This paper presents a novel unified gradient-domain image reconstruction framework with intensity-range constraint and base-structure constraint. The existing method for manipulating base structures and detailed textures are classifiable into two major approaches: i) gradient-domain and ii) layer-decomposition. To gene... | ['Masatoshi Okutomi', 'Masayuki Tanaka', 'Takashi Shibata'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['tone-mapping'] | ['computer-vision'] | [ 6.66912258e-01 -2.79794812e-01 -1.09961659e-01 -1.83035120e-01
-1.32989064e-01 -1.70779079e-01 1.94786072e-01 -3.63431007e-01
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4.55407977e-01 -6.64576411e-01 5.25002420e-01 -3.26662034... | [10.97653579711914, -2.3432939052581787] |
c591a93e-1031-4642-8234-3793d198889b | illumination-insensitive-binary-descriptor | 2305.07943 | null | https://arxiv.org/abs/2305.07943v1 | https://arxiv.org/pdf/2305.07943v1.pdf | Illumination-insensitive Binary Descriptor for Visual Measurement Based on Local Inter-patch Invariance | Binary feature descriptors have been widely used in various visual measurement tasks, particularly those with limited computing resources and storage capacities. Existing binary descriptors may not perform well for long-term visual measurement tasks due to their sensitivity to illumination variations. It can be observe... | ['Ce Zhu', 'Yipeng Liu', 'Xun Zhang', 'Yingjie Zhou', 'Xinyu Lin'] | 2023-05-13 | null | null | null | null | ['visual-localization'] | ['computer-vision'] | [ 2.53271490e-01 -7.07070351e-01 -3.05122703e-01 -3.87632340e-01
-6.32627547e-01 -4.18398470e-01 5.49468577e-01 3.09620619e-01
-3.85777622e-01 7.13948011e-01 -2.33279541e-01 1.63623765e-01
-5.07800221e-01 -1.03664911e+00 -3.15555364e-01 -9.79002833e-01
7.22440192e-03 5.14787696e-02 5.81851065e-01 -2.05044642... | [10.44772720336914, -0.34803661704063416] |
3153da2b-7417-4a1d-9acb-1d8a45be9e8f | latent-dictionary-learning-for-sparse | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Yang_Latent_Dictionary_Learning_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Yang_Latent_Dictionary_Learning_2014_CVPR_paper.pdf | Latent Dictionary Learning for Sparse Representation based Classification | Dictionary learning (DL) for sparse coding has shown promising results in classification tasks, while how to adaptively build the relationship between dictionary atoms and class labels is still an important open question. The existing dictionary learning approaches simply fix a dictionary atom to b... | ['Luc van Gool', 'Dengxin Dai', 'Lilin Shen', 'Meng Yang'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['sparse-representation-based-classification'] | ['computer-vision'] | [ 2.47670919e-01 -4.80396627e-03 -7.06409633e-01 -5.12323558e-01
-4.28861022e-01 -2.46954978e-01 3.25959027e-01 2.65608191e-01
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-1.41188011e-01 -7.31743813e-01 -2.04989001e-01 -1.11850357e+00
1.55383244e-01 5.89398146e-01 -3.73736858e-01 3.02267391... | [12.407349586486816, 0.40259262919425964] |
7def38f3-a982-4494-819b-4f7b10b18ffc | is-domain-adaptation-worth-your-investment | null | null | https://aclanthology.org/2021.econlp-1.5 | https://aclanthology.org/2021.econlp-1.5.pdf | Is Domain Adaptation Worth Your Investment? Comparing BERT and FinBERT on Financial Tasks | With the recent rise in popularity of Transformer models in Natural Language Processing, research efforts have been dedicated to the development of domain-adapted versions of BERT-like architectures. In this study, we focus on FinBERT, a Transformer model trained on text from the financial domain. By comparing its perf... | ['Chu-Ren Huang', 'Yu-Yin Hsu', 'Emmanuele Chersoni', 'Bo Peng'] | null | null | null | null | emnlp-econlp-2021-11 | ['continual-pretraining'] | ['methodology'] | [-1.81215256e-01 1.28311083e-01 1.84298396e-01 -5.84812105e-01
-4.68221933e-01 -5.27312994e-01 1.06890821e+00 1.75322413e-01
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1.50207132e-01 7.87195504e-01 4.02404010e-01 -6.05306745... | [10.667414665222168, 8.851781845092773] |
59df0fda-b690-43ff-9eb7-b3be4f985204 | investigating-the-decoders-of-maximum | 2003.03716 | null | https://arxiv.org/abs/2003.03716v1 | https://arxiv.org/pdf/2003.03716v1.pdf | Investigating the Decoders of Maximum Likelihood Sequence Models: A Look-ahead Approach | We demonstrate how we can practically incorporate multi-step future information into a decoder of maximum likelihood sequence models. We propose a "k-step look-ahead" module to consider the likelihood information of a rollout up to k steps. Unlike other approaches that need to train another value network to evaluate th... | ['Yen-Ling Kuo', 'Yu-Siang Wang', 'Boris Katz'] | 2020-03-08 | null | null | null | null | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 3.81403089e-01 1.19096830e-01 -1.36547700e-01 -5.13709307e-01
-1.32134235e+00 -6.53972447e-01 6.38286948e-01 -1.36542618e-01
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1.71834454e-01 -3.30805004e-01 -1.13036454e+00 -5.31533122e-01
3.53817821e-01 5.92554986e-01 2.38279045e-01 -1.60514846... | [11.648837089538574, 10.249390602111816] |
918b516f-5887-4b5a-af1d-948d26e84dce | multimodal-neural-machine-translation-system | null | null | https://aclanthology.org/2021.mmtlrl-1.6 | https://aclanthology.org/2021.mmtlrl-1.6.pdf | Multimodal Neural Machine Translation System for English to Bengali | Multimodal Machine Translation (MMT) systems utilize additional information from other modalities beyond text to improve the quality of machine translation (MT). The additional modality is typically in the form of images. Despite proven advantages, it is indeed difficult to develop an MMT system for various languages p... | ['Petr Motlicek', 'Satya Ranjan Dash', 'Arghyadeep Sen', 'Ketan Kotwal', 'Satya Prakash Biswal', 'Subhadarshi Panda', 'Shantipriya Parida'] | null | null | null | null | mmtlrl-ranlp-2021-9 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 3.44245315e-01 -1.83951244e-01 -2.17574555e-02 -2.01930791e-01
-1.42605746e+00 -9.01263535e-01 1.09239793e+00 -2.74011511e-02
-5.70256829e-01 8.12967122e-01 -2.68737786e-02 -4.72588927e-01
4.09948230e-01 -2.88988441e-01 -8.18386853e-01 -6.25092864e-01
6.28096879e-01 8.70316207e-01 -3.67005542e-02 -3.96618277... | [11.456869125366211, 1.4955536127090454] |
5da41bf1-881f-4d8a-8af9-ba631048bf03 | interpretable-multi-objective-reinforcement | 1809.08343 | null | http://arxiv.org/abs/1809.08343v1 | http://arxiv.org/pdf/1809.08343v1.pdf | Interpretable Multi-Objective Reinforcement Learning through Policy Orchestration | Autonomous cyber-physical agents and systems play an increasingly large role
in our lives. To ensure that agents behave in ways aligned with the values of
the societies in which they operate, we must develop techniques that allow
these agents to not only maximize their reward in an environment, but also to
learn and fo... | ['Moninder Singh', 'Nicholas Mattei', 'Kush Varshney', 'Francesca Rossi', 'Ritesh Noothigattu', 'Rachita Chandra', 'Djallel Bouneffouf', 'Piyush Madan', 'Murray Campbell'] | 2018-09-21 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [ 7.91962147e-02 1.49139091e-01 -3.28843415e-01 -2.20616803e-01
-1.31747603e-01 -7.22724438e-01 7.52286196e-01 -8.16165507e-02
-9.23556745e-01 1.09546113e+00 -5.59213124e-02 -2.94455022e-01
-5.16712904e-01 -8.37069929e-01 -6.32840037e-01 -8.68662417e-01
-1.65245339e-01 8.14040303e-01 1.47079691e-01 -2.88210213... | [4.307559967041016, 2.0051205158233643] |
b2708e67-87b0-40e4-827b-dfb72f1b0085 | massively-parallel-reweighted-wake-sleep | 2305.11022 | null | https://arxiv.org/abs/2305.11022v1 | https://arxiv.org/pdf/2305.11022v1.pdf | Massively Parallel Reweighted Wake-Sleep | Reweighted wake-sleep (RWS) is a machine learning method for performing Bayesian inference in a very general class of models. RWS draws $K$ samples from an underlying approximate posterior, then uses importance weighting to provide a better estimate of the true posterior. RWS then updates its approximate posterior towa... | ['Laurence Aitchison', 'Gavin Leech', 'Thomas Heap'] | 2023-05-18 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [-1.14627756e-01 2.86368668e-01 -1.96536943e-01 -5.02273619e-01
-1.32946396e+00 -3.72994810e-01 5.05378544e-01 3.62482294e-02
-4.10860866e-01 9.97238338e-01 1.62184685e-01 -3.35959196e-01
-1.78528965e-01 -8.66888940e-01 -6.30155087e-01 -7.69436717e-01
-2.37213135e-01 7.81826317e-01 4.00862582e-02 3.45578730... | [7.019908905029297, 4.026686668395996] |
4f2010e2-0357-453e-a8df-71465e32d034 | counterfactual-explanations-for-machine-1 | 2010.10596 | null | https://arxiv.org/abs/2010.10596v3 | https://arxiv.org/pdf/2010.10596v3.pdf | Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review | Machine learning plays a role in many deployed decision systems, often in ways that are difficult or impossible to understand by human stakeholders. Explaining, in a human-understandable way, the relationship between the input and output of machine learning models is essential to the development of trustworthy machine ... | ['Chirag Shah', 'John P. Dickerson', 'Keegan E. Hines', 'Minh Hoang', 'Varich Boonsanong', 'Sahil Verma'] | 2020-10-20 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 4.50993001e-01 1.02078152e+00 -7.64527142e-01 -6.28760397e-01
-1.86086088e-01 -5.10930598e-01 9.84579265e-01 9.47894678e-02
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-5.07025421e-01 -5.71503460e-01 -8.11554611e-01 -3.06447268e-01
9.60928351e-02 3.53559762e-01 -6.06048226e-01 -6.10230630... | [8.775961875915527, 5.742010593414307] |
f9c320c0-8ff6-452b-a1a1-8ca25aa72090 | abstractive-approaches-to-multidocument | null | null | https://aclanthology.org/2022.sdp-1.24 | https://aclanthology.org/2022.sdp-1.24.pdf | Abstractive Approaches To Multidocument Summarization Of Medical Literature Reviews | Text summarization has been a trending domain of research in NLP in the past few decades. The medical domain is no exception to the same. Medical documents often contain a lot of jargon pertaining to certain domains, and performing an abstractive summarization on the same remains a challenge. This paper presents a summ... | ['Dipali Dattatray Kadam', 'Onkar Rupesh Litake', 'Aditya Vyankatesh Mandke', 'Aditya Jagdish Vyawahare', 'Rahul Tangsali'] | null | null | null | null | sdp-coling-2022-10 | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 2.14016080e-01 3.47338021e-01 -4.16144371e-01 -2.24138498e-01
-1.47953844e+00 -6.60904765e-01 6.80099607e-01 8.52599800e-01
-4.78300154e-01 1.25472856e+00 1.03259766e+00 -4.19830412e-01
-3.83869678e-01 -9.84624773e-02 -4.32108074e-01 -3.98569614e-01
2.02439025e-01 7.23216653e-01 -3.00159659e-02 -2.04536766... | [12.376495361328125, 9.568678855895996] |
26242d73-1491-4d13-95bc-a58d86bd8070 | pefat-boosting-semi-supervised-medical-image | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zeng_PEFAT_Boosting_Semi-Supervised_Medical_Image_Classification_via_Pseudo-Loss_Estimation_and_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zeng_PEFAT_Boosting_Semi-Supervised_Medical_Image_Classification_via_Pseudo-Loss_Estimation_and_CVPR_2023_paper.pdf | PEFAT: Boosting Semi-Supervised Medical Image Classification via Pseudo-Loss Estimation and Feature Adversarial Training | Pseudo-labeling approaches have been proven beneficial for semi-supervised learning (SSL) schemes in computer vision and medical imaging. Most works are dedicated to finding samples with high-confidence pseudo-labels from the perspective of model predicted probability. Whereas this way may lead to the inclusion of ... | ['Yong Xia', 'Zilin Lu', 'Yutong Xie', 'Qingjie Zeng'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['semi-supervised-medical-image-classification'] | ['medical'] | [ 4.91007119e-01 3.54446352e-01 -2.58354247e-01 -6.64128840e-01
-1.26368201e+00 -2.21654117e-01 2.80305892e-01 7.68908411e-02
-3.49321008e-01 9.11137819e-01 -2.21870974e-01 1.61698777e-02
-5.76510951e-02 -5.95604658e-01 -7.89600134e-01 -1.13222456e+00
3.07464868e-01 2.44353935e-01 5.06230779e-02 3.87997955... | [14.682416915893555, -2.1153132915496826] |
49e2b9ca-cbf4-4dd8-9425-546ff0ea6268 | fast-inference-for-quantile-regression-with | 2209.14502 | null | https://arxiv.org/abs/2209.14502v4 | https://arxiv.org/pdf/2209.14502v4.pdf | Fast Inference for Quantile Regression with Tens of Millions of Observations | Big data analytics has opened new avenues in economic research, but the challenge of analyzing datasets with tens of millions of observations is substantial. Conventional econometric methods based on extreme estimators require large amounts of computing resources and memory, which are often not readily available. In th... | ['Youngki Shin', 'Myung Hwan Seo', 'Yuan Liao', 'Sokbae Lee'] | 2022-09-29 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [-3.61409664e-01 -3.01984191e-01 -3.15229595e-01 -3.70189279e-01
-9.12310243e-01 -4.66536254e-01 3.02368224e-01 3.54499251e-01
-6.36619449e-01 1.27280402e+00 1.49506658e-01 -7.58690238e-01
-2.98307925e-01 -1.07445681e+00 -7.31256247e-01 -7.08025336e-01
-1.60489142e-01 2.63144225e-01 -5.31953096e-01 1.16248038... | [7.0985331535339355, 4.451025009155273] |
d20e68a0-8d27-4ef5-a2cd-b9559f311f43 | rule-based-event-extraction-for-artificial | null | null | https://aclanthology.org/2022.pandl-1.9 | https://aclanthology.org/2022.pandl-1.9.pdf | Rule Based Event Extraction for Artificial Social Intelligence | Natural language (as opposed to structured communication modes such as Morse code) is by far the most common mode of communication between humans, and can thus provide significant insight into both individual mental states and interpersonal dynamics. As part of DARPA’s Artificial Social Intelligence for Successful Team... | ['Rebecca Sharp', 'Adarsh Pyarelal', 'Chen Chen', 'Yuwei Wang', 'Remo Nitschke'] | null | null | null | null | pandl-coling-2022-10 | ['event-extraction'] | ['natural-language-processing'] | [ 1.32719144e-01 5.55223227e-01 1.73975125e-01 -4.60099459e-01
-3.96766394e-01 -3.98335159e-01 7.88616896e-01 7.77322590e-01
-5.86499512e-01 8.37304413e-01 3.95487070e-01 -2.90524364e-01
-3.74252260e-01 -9.69531536e-01 -2.66701192e-01 -1.79286018e-01
-5.62251031e-01 1.03489709e+00 2.30766565e-01 -8.83117139... | [4.093210697174072, 1.4680894613265991] |
fec17c6c-387e-49f9-8fb3-a0e7430d7c1c | feddwa-personalized-federated-learning-with | 2305.06124 | null | https://arxiv.org/abs/2305.06124v2 | https://arxiv.org/pdf/2305.06124v2.pdf | FedDWA: Personalized Federated Learning with Online Weight Adjustment | Different from conventional federated learning, personalized federated learning (PFL) is able to train a customized model for each individual client according to its unique requirement. The mainstream approach is to adopt a kind of weighted aggregation method to generate personalized models, in which weights are determ... | ['Di wu', 'Yipeng Zhou', 'Miao Hu', 'Jinyu Chen', 'Jiang Wu', 'Jiahao Liu'] | 2023-05-10 | null | null | null | null | ['personalized-federated-learning'] | ['methodology'] | [-3.04214478e-01 -1.56629264e-01 -2.41255626e-01 -7.89084315e-01
-7.44327486e-01 -4.74690169e-01 2.47634858e-01 -2.35195965e-01
-1.07497722e-01 3.63121033e-01 7.24812448e-02 -1.65698588e-01
-3.00773621e-01 -9.89604533e-01 -5.07937133e-01 -8.83374870e-01
1.64160758e-01 4.78220999e-01 1.61932573e-01 1.78984791... | [5.837118148803711, 6.319024085998535] |
47ff2e79-4ee9-4d36-8520-5d6a7a933aff | hierarchical-dialogue-understanding-with-1 | 2305.00262 | null | https://arxiv.org/abs/2305.00262v1 | https://arxiv.org/pdf/2305.00262v1.pdf | Hierarchical Dialogue Understanding with Special Tokens and Turn-level Attention | Compared with standard text, understanding dialogue is more challenging for machines as the dynamic and unexpected semantic changes in each turn. To model such inconsistent semantics, we propose a simple but effective Hierarchical Dialogue Understanding model, HiDialog. Specifically, we first insert multiple special to... | ['Yang You', 'Fuzhao Xue', 'Heng Zhang', 'Jian Zhang', 'Xiao Liu'] | 2023-04-29 | hierarchical-dialogue-understanding-with | https://openreview.net/forum?id=Peb3QdR8zzP | https://openreview.net/pdf?id=Peb3QdR8zzP | tiny-papers-iclr-2023-5 | ['dialogue-understanding', 'dialogue-act-classification'] | ['natural-language-processing', 'natural-language-processing'] | [-2.09244207e-01 7.40240097e-01 -3.16588223e-01 -6.67362571e-01
-3.85457516e-01 -5.98908663e-01 9.12018239e-01 2.60071635e-01
-2.38180071e-01 7.24029124e-01 8.11625421e-01 -2.53808439e-01
4.68707621e-01 -5.30566692e-01 -7.71058723e-02 -1.99569892e-02
1.38954446e-01 7.16783583e-01 1.21768015e-02 -6.98600769... | [12.524874687194824, 7.948256969451904] |
00bcd24f-5659-4412-adda-524c5d5efcf2 | eegdenoisenet-a-benchmark-dataset-for-deep | 2009.11662 | null | https://arxiv.org/abs/2009.11662v4 | https://arxiv.org/pdf/2009.11662v4.pdf | EEGdenoiseNet: A benchmark dataset for end-to-end deep learning solutions of EEG denoising | Deep learning networks are increasingly attracting attention in various fields, including electroencephalography (EEG) signal processing. These models provided comparable performance with that of traditional techniques. At present, however, lacks of well-structured and standardized datasets with specific benchmark limi... | ['Quanying Liu', 'Zherui Li', 'Dante Mantini', 'Chen Wei', 'Mingqi Zhao', 'Haoming Zhang'] | 2020-09-24 | null | null | null | null | ['eeg-denoising'] | ['methodology'] | [-5.95568717e-02 -4.32065606e-01 8.64515364e-01 -4.62656885e-01
-6.76162481e-01 -8.95869508e-02 6.84625804e-02 -1.58058196e-01
-3.83361667e-01 9.27934945e-01 1.64149836e-01 -4.75408211e-02
-2.82365650e-01 -3.63563210e-01 -6.21656895e-01 -9.77825105e-01
-3.86351347e-01 -1.59732237e-01 -4.45423007e-01 -3.15450847... | [13.139178276062012, 3.4141664505004883] |
43676f40-3891-4815-9bff-ceb292d40e93 | adaptive-incomplete-multi-view-learning-via | 2208.0371 | null | https://arxiv.org/abs/2208.03710v1 | https://arxiv.org/pdf/2208.03710v1.pdf | Adaptive incomplete multi-view learning via tensor graph completion | With the advancement of the data acquisition techniques, multi-view learning has become a hot topic. Some multi-view learning methods assume that the multi-view data is complete, which means that all instances are present, but this too ideal. Certain tensor-based methods for handing incomplete multi-view data have emer... | ['Xiaohong Chen', 'Heng Zhang'] | 2022-08-07 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [-3.63938302e-01 -4.25459176e-01 -2.79899091e-01 -1.91956416e-01
-4.61490870e-01 -2.18892083e-01 1.83954075e-01 -2.66651243e-01
-6.56380579e-02 4.21181798e-01 3.64872396e-01 3.84108096e-01
-4.71227735e-01 -6.72129452e-01 -4.50973541e-01 -8.95235062e-01
1.12652265e-01 4.20988470e-01 7.08973110e-02 -1.33610606... | [8.270506858825684, 4.612700939178467] |
446dd062-e191-40a1-80bb-9e0dc5026ca0 | sketch2cloth-sketch-based-3d-garment | 2303.00167 | null | https://arxiv.org/abs/2303.00167v1 | https://arxiv.org/pdf/2303.00167v1.pdf | Sketch2Cloth: Sketch-based 3D Garment Generation with Unsigned Distance Fields | 3D model reconstruction from a single image has achieved great progress with the recent deep generative models. However, the conventional reconstruction approaches with template mesh deformation and implicit fields have difficulty in reconstructing non-watertight 3D mesh models, such as garments. In contrast to image-b... | ['Kazunori Miyata', 'Haoran Xie', 'Yi He'] | 2023-03-01 | null | null | null | null | ['model-editing'] | ['natural-language-processing'] | [ 1.39807105e-01 7.41996467e-02 2.00223044e-01 -6.70691878e-02
-3.78307521e-01 -5.41534960e-01 6.29167914e-01 -6.21146858e-01
4.47265089e-01 4.26658601e-01 -3.03807650e-02 2.74187643e-02
-2.77340990e-02 -1.24272835e+00 -7.89220035e-01 -1.00162238e-01
4.16098893e-01 8.65891457e-01 -2.15027668e-02 -2.91744620... | [9.102856636047363, -3.5187559127807617] |
fc9ed768-b627-488d-9f27-96ef86a0b310 | partition-hypergraphs-with-embeddings | 1909.04016 | null | https://arxiv.org/abs/1909.04016v5 | https://arxiv.org/pdf/1909.04016v5.pdf | Hypergraph Partitioning With Embeddings | Problems in scientific computing, such as distributing large sparse matrix operations, have analogous formulations as hypergraph partitioning problems. A hypergraph is a generalization of a traditional graph wherein "hyperedges" may connect any number of nodes. As a result, hypergraph partitioning is an NP-Hard problem... | ['Justin Sybrandt', 'Ruslan Shaydulin', 'Ilya Safro'] | 2019-09-09 | null | null | null | null | ['hypergraph-partitioning'] | ['graphs'] | [ 4.34948765e-02 3.43362182e-01 -1.08487323e-01 1.71250980e-02
-4.56467271e-01 -8.24342370e-01 2.21479803e-01 7.86746800e-01
1.52861234e-02 7.31091857e-01 1.04668206e-02 -2.17862293e-01
-5.59390366e-01 -1.18947935e+00 -7.90028512e-01 -6.60258591e-01
-3.58994275e-01 1.04611826e+00 3.05543870e-01 1.39523959... | [7.052209854125977, 5.211737155914307] |
76653b7f-b211-4392-86d1-b57d32451bfb | meemi-finding-the-middle-ground-in-cross | 1910.07221 | null | https://arxiv.org/abs/1910.07221v4 | https://arxiv.org/pdf/1910.07221v4.pdf | Meemi: A Simple Method for Post-processing and Integrating Cross-lingual Word Embeddings | Word embeddings have become a standard resource in the toolset of any Natural Language Processing practitioner. While monolingual word embeddings encode information about words in the context of a particular language, cross-lingual embeddings define a multilingual space where word embeddings from two or more languages ... | ['Steven Schockaert', 'Luis Espinosa-Anke', 'Jose Camacho-Collados', 'Yerai Doval'] | 2019-10-16 | null | null | null | null | ['cross-lingual-natural-language-inference', 'hypernym-discovery'] | ['natural-language-processing', 'natural-language-processing'] | [-2.16825426e-01 -3.15848351e-01 -4.75186318e-01 -2.25680545e-01
-6.89654768e-01 -1.02921712e+00 8.81339550e-01 7.70665288e-01
-9.05247569e-01 3.63492668e-01 4.76675302e-01 -4.58199650e-01
1.34943798e-01 -7.84501195e-01 -5.70026338e-01 -4.86469597e-01
2.67509192e-01 6.21708393e-01 -1.09122276e-01 -5.49852490... | [11.011564254760742, 9.931183815002441] |
0b2f3b41-b80f-4946-9b37-dede3c4bf65e | quantifying-sample-anonymity-in-score-based | 2306.01363 | null | https://arxiv.org/abs/2306.01363v1 | https://arxiv.org/pdf/2306.01363v1.pdf | Quantifying Sample Anonymity in Score-Based Generative Models with Adversarial Fingerprinting | Recent advances in score-based generative models have led to a huge spike in the development of downstream applications using generative models ranging from data augmentation over image and video generation to anomaly detection. Despite publicly available trained models, their potential to be used for privacy preservin... | ['Bernhard Kainz', 'Mischa Dombrowski'] | 2023-06-02 | null | null | null | null | ['video-generation'] | ['computer-vision'] | [ 9.31934953e-01 7.14122057e-01 -9.03137997e-02 -1.82822183e-01
-8.86861742e-01 -9.52548444e-01 4.25656706e-01 2.85129324e-02
-3.12099904e-01 7.63974011e-01 1.29142076e-01 -3.42530966e-01
2.06097569e-02 -8.92141700e-01 -9.53987598e-01 -8.69700015e-01
-3.68619747e-02 3.17396194e-01 -3.50025713e-01 4.00428206... | [6.129668712615967, 6.875210762023926] |
eaa3b048-5cd6-481e-b29a-fdde2cfab70e | parallelised-diffeomorphic-sampling-based | 2108.11775 | null | https://arxiv.org/abs/2108.11775v2 | https://arxiv.org/pdf/2108.11775v2.pdf | Parallelised Diffeomorphic Sampling-based Motion Planning | We propose Parallelised Diffeomorphic Sampling-based Motion Planning (PDMP). PDMP is a novel parallelised framework that uses bijective and differentiable mappings, or diffeomorphisms, to transform sampling distributions of sampling-based motion planners, in a manner akin to normalising flows. Unlike normalising flow m... | ['Fabio Ramos', 'Tucker Hermans', 'Weiming Zhi', 'Tin Lai'] | 2021-08-26 | null | null | null | null | ['normalising-flows'] | ['methodology'] | [ 2.52315462e-01 5.64428151e-01 -2.80519724e-01 7.88838118e-02
-5.11132598e-01 -5.52284598e-01 1.00109625e+00 -1.18071891e-01
-5.54504752e-01 8.02661657e-01 7.25782156e-01 -2.54257202e-01
-4.63831753e-01 -1.28745508e+00 -7.21850276e-01 -6.56226575e-01
-4.73610222e-01 7.41350353e-01 2.35395178e-01 -5.22917628... | [4.67917013168335, 0.9662708640098572] |
09b75fb3-da5f-4dbd-ad9a-58f11ffa747f | weight-poisoning-attacks-on-pre-trained | 2004.0666 | null | https://arxiv.org/abs/2004.06660v1 | https://arxiv.org/pdf/2004.06660v1.pdf | Weight Poisoning Attacks on Pre-trained Models | Recently, NLP has seen a surge in the usage of large pre-trained models. Users download weights of models pre-trained on large datasets, then fine-tune the weights on a task of their choice. This raises the question of whether downloading untrusted pre-trained weights can pose a security threat. In this paper, we show ... | ['Paul Michel', 'Keita Kurita', 'Graham Neubig'] | 2020-04-14 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [ 2.27823630e-01 -2.79673543e-02 -1.89944841e-02 -6.40351847e-02
-7.16665149e-01 -1.41887963e+00 4.18092042e-01 1.99591160e-01
-6.38477981e-01 5.89182436e-01 -2.40873083e-01 -6.67208552e-01
3.20599556e-01 -7.55394638e-01 -1.12195659e+00 -7.89371908e-01
-1.47302628e-01 7.46620968e-02 2.68951088e-01 -7.68701285... | [5.913322448730469, 7.7172369956970215] |
29cec251-3895-40d5-9577-cc3efacc6a31 | keyphrase-generation-with-correlation | 1808.07185 | null | http://arxiv.org/abs/1808.07185v1 | http://arxiv.org/pdf/1808.07185v1.pdf | Keyphrase Generation with Correlation Constraints | In this paper, we study automatic keyphrase generation. Although conventional
approaches to this task show promising results, they neglect correlation among
keyphrases, resulting in duplication and coverage issues. To solve these
problems, we propose a new sequence-to-sequence architecture for keyphrase
generation name... | ['Xiao-Ming Zhang', 'Zhoujun Li', 'Yu Wu', 'Jun Chen', 'Zhao Yan'] | 2018-08-22 | keyphrase-generation-with-correlation-1 | https://aclanthology.org/D18-1439 | https://aclanthology.org/D18-1439.pdf | emnlp-2018-10 | ['keyphrase-generation'] | ['natural-language-processing'] | [ 3.55304062e-01 -4.99421328e-01 -3.93842995e-01 1.42276406e-01
-8.90247703e-01 -8.05139720e-01 7.52551317e-01 6.86308682e-01
-4.80592757e-01 1.05316651e+00 7.36274064e-01 -1.89383850e-01
1.83797941e-01 -8.88746321e-01 -4.64188159e-01 -4.37390268e-01
3.50968003e-01 1.25888050e-01 5.13404727e-01 -3.93117368... | [12.286810874938965, 8.900248527526855] |
1a7a6c4b-acc2-476b-b9d5-ece4a6e85b0f | neural-additive-models-for-nowcasting | 2205.1002 | null | https://arxiv.org/abs/2205.10020v1 | https://arxiv.org/pdf/2205.10020v1.pdf | Neural Additive Models for Nowcasting | Deep neural networks (DNNs) are one of the most highlighted methods in machine learning. However, as DNNs are black-box models, they lack explanatory power for their predictions. Recently, neural additive models (NAMs) have been proposed to provide this power while maintaining high prediction performance. In this paper... | ['Dongil Kim', 'Wonkeun Jo'] | 2022-05-20 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 1.80196106e-01 1.84043720e-01 -3.29538882e-01 -5.84649265e-01
-3.63290370e-01 -1.85926619e-03 8.34648490e-01 1.45023856e-02
2.60643847e-02 9.49278712e-01 2.70950735e-01 -5.52896082e-01
-5.88283181e-01 -6.84430420e-01 -7.32645869e-01 -7.58630991e-01
-1.49421155e-01 3.92697960e-01 -5.38820177e-02 -2.43765593... | [7.014723300933838, 3.0870344638824463] |
7aa5201f-78ea-4c26-b036-89a5438d3be1 | an-end-to-end-goal-oriented-dialog-system | 1803.02279 | null | http://arxiv.org/abs/1803.02279v2 | http://arxiv.org/pdf/1803.02279v2.pdf | An End-to-End Goal-Oriented Dialog System with a Generative Natural Language Response Generation | Recently advancements in deep learning allowed the development of end-to-end
trained goal-oriented dialog systems. Although these systems already achieve
good performance, some simplifications limit their usage in real-life
scenarios.
In this work, we address two of these limitations: ignoring positional
information ... | ['Jan Niehues', 'Stefan Constantin', 'Alex Waibel'] | 2018-03-06 | null | null | null | null | ['goal-oriented-dialog'] | ['natural-language-processing'] | [ 6.84290826e-02 4.39251572e-01 3.68731171e-01 -8.32097530e-01
-4.80474949e-01 -6.79428697e-01 7.32264161e-01 1.13486812e-01
-7.32945800e-01 1.00324094e+00 3.06632519e-01 -3.74783128e-01
1.84912977e-04 -8.68274033e-01 -1.86631992e-01 -1.55224234e-01
2.18279883e-01 9.79491055e-01 4.19713914e-01 -6.57507122... | [12.868128776550293, 7.925685882568359] |
32a2b673-2238-409a-aecf-28a58221feaa | adaptive-superpixel-for-active-learning-in | 2303.16817 | null | https://arxiv.org/abs/2303.16817v1 | https://arxiv.org/pdf/2303.16817v1.pdf | Adaptive Superpixel for Active Learning in Semantic Segmentation | Learning semantic segmentation requires pixel-wise annotations, which can be time-consuming and expensive. To reduce the annotation cost, we propose a superpixel-based active learning (AL) framework, which collects a dominant label per superpixel instead. To be specific, it consists of adaptive superpixel and sieving m... | ['Jungseul Ok', 'Suha Kwak', 'Sehyun Hwang', 'Minhyeon Oh', 'Hoyoung Kim'] | 2023-03-29 | null | null | null | null | ['superpixels'] | ['computer-vision'] | [ 3.70753020e-01 3.10488850e-01 -2.09952235e-01 -6.65492773e-01
-1.18608308e+00 -8.16002131e-01 6.65951595e-02 1.93237424e-01
-7.54876792e-01 8.54141831e-01 -4.84495282e-01 1.42363990e-02
3.49404454e-01 -9.07835424e-01 -8.40435624e-01 -8.57933402e-01
3.88723731e-01 3.04683268e-01 9.04978096e-01 4.06475514... | [9.486536026000977, 0.6836819648742676] |
6edb98c2-1bfb-43ad-acd4-d0062f0bdf7e | improving-prosody-for-cross-speaker-style | 2303.07711 | null | https://arxiv.org/abs/2303.07711v1 | https://arxiv.org/pdf/2303.07711v1.pdf | Improving Prosody for Cross-Speaker Style Transfer by Semi-Supervised Style Extractor and Hierarchical Modeling in Speech Synthesis | Cross-speaker style transfer in speech synthesis aims at transferring a style from source speaker to synthesized speech of a target speaker's timbre. In most previous methods, the synthesized fine-grained prosody features often represent the source speaker's average style, similar to the one-to-many problem(i.e., multi... | ['Zhongyuan Wang', 'Xiaorui Wang', 'Ying Zhang', 'Hao Che', 'Peng Yang', 'Chunyu Qiang'] | 2023-03-14 | null | null | null | null | ['prosody-prediction', 'speech-synthesis'] | ['natural-language-processing', 'speech'] | [ 3.24113011e-01 -1.71533480e-01 -3.90767813e-01 -6.45854115e-01
-9.93169487e-01 -6.81339264e-01 2.48799920e-01 -3.30328405e-01
-3.82541269e-02 5.66447258e-01 6.17920339e-01 1.16487443e-01
4.14679378e-01 -3.78225982e-01 -4.36114639e-01 -7.34804153e-01
4.70600754e-01 8.01423043e-02 -2.54589081e-01 -3.49346101... | [14.967974662780762, 6.545884609222412] |
9ad6a790-fd37-43ca-92a0-b7ee071346a8 | n-ltp-a-open-source-neural-chinese-language | 2009.11616 | null | https://arxiv.org/abs/2009.11616v4 | https://arxiv.org/pdf/2009.11616v4.pdf | N-LTP: An Open-source Neural Language Technology Platform for Chinese | We introduce \texttt{N-LTP}, an open-source neural language technology platform supporting six fundamental Chinese NLP tasks: {lexical analysis} (Chinese word segmentation, part-of-speech tagging, and named entity recognition), {syntactic parsing} (dependency parsing), and {semantic parsing} (semantic dependency parsin... | ['Ting Liu', 'Wanxiang Che', 'Yunlong Feng', 'Libo Qin'] | 2020-09-24 | null | https://aclanthology.org/2021.emnlp-demo.6 | https://aclanthology.org/2021.emnlp-demo.6.pdf | emnlp-acl-2021-11 | ['lexical-analysis', 'semantic-dependency-parsing'] | ['natural-language-processing', 'natural-language-processing'] | [-7.37029761e-02 2.28998348e-01 -9.91785526e-02 -6.30659878e-01
-9.58458543e-01 -8.18675220e-01 1.80755481e-01 1.89854831e-01
-6.41569018e-01 6.77243114e-01 1.78410143e-01 -7.12180853e-01
3.13050508e-01 -5.46878219e-01 -6.43722415e-01 -5.37159920e-01
4.00698483e-01 4.67113912e-01 3.11501950e-01 -1.01079404... | [10.426592826843262, 9.886438369750977] |
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