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
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0f595064-8130-4483-8a03-45cd918ec1df | xdgan-multi-modal-3d-shape-generation-in-2d | 2210.03007 | null | https://arxiv.org/abs/2210.03007v1 | https://arxiv.org/pdf/2210.03007v1.pdf | XDGAN: Multi-Modal 3D Shape Generation in 2D Space | Generative models for 2D images has recently seen tremendous progress in quality, resolution and speed as a result of the efficiency of 2D convolutional architectures. However it is difficult to extend this progress into the 3D domain since most current 3D representations rely on custom network components. This paper a... | ['Maria Shugrina', 'Sanja Fidler', 'André Knörig', 'Alara Dirik', 'Hassan Abu Alhaija'] | 2022-10-06 | null | null | null | null | ['3d-shape-generation'] | ['computer-vision'] | [ 2.48519301e-01 3.04219753e-01 3.65182966e-01 -1.32284477e-01
-5.20489931e-01 -9.40563679e-01 9.04000401e-01 -4.99051005e-01
3.52573186e-01 3.83233845e-01 1.14626199e-01 -4.50288624e-01
3.45178008e-01 -1.17394996e+00 -8.61786604e-01 -3.68930221e-01
1.86272055e-01 7.33082294e-01 -1.12660699e-01 -3.77930343... | [9.049298286437988, -3.5601699352264404] |
35cbfe8a-64c8-4e75-b684-141e5ebb35a9 | sparse-pairwise-re-ranking-with-pre-trained | 2207.0447 | null | https://arxiv.org/abs/2207.04470v1 | https://arxiv.org/pdf/2207.04470v1.pdf | Sparse Pairwise Re-ranking with Pre-trained Transformers | Pairwise re-ranking models predict which of two documents is more relevant to a query and then aggregate a final ranking from such preferences. This is often more effective than pointwise re-ranking models that directly predict a relevance value for each document. However, the high inference overhead of pairwise models... | ['Martin Potthast', 'Matthias Hagen', 'Maik Fröbe', 'Lukas Gienapp'] | 2022-07-10 | null | null | null | null | ['passage-ranking'] | ['natural-language-processing'] | [ 9.33806002e-02 -8.98553804e-02 -4.21756417e-01 -6.48451507e-01
-1.62317204e+00 -9.74264383e-01 7.41921365e-01 7.22739697e-01
-7.17685938e-01 1.00070107e+00 4.18032348e-01 -2.44334966e-01
-8.68849456e-01 -7.16770589e-01 -4.13997889e-01 -3.86329740e-01
-2.28225186e-01 9.28278029e-01 3.17032248e-01 -1.51282474... | [11.419478416442871, 7.526092052459717] |
3c481f72-9f37-435e-b707-6f41386b59b2 | dominating-set-database-selection-for-visual | 2303.05123 | null | https://arxiv.org/abs/2303.05123v1 | https://arxiv.org/pdf/2303.05123v1.pdf | Dominating Set Database Selection for Visual Place Recognition | This paper presents an approach for creating a visual place recognition (VPR) database for localization in indoor environments from RGBD scanning sequences. The proposed approach is formulated as a minimization problem in terms of dominating set algorithm for graph, constructed from spatial information, and referred as... | ['Gonzalo Ferrer', 'Rahim Tariverdizadeh', 'Fakhriddin Tojiboev', 'Timofei Pushkin', 'Ivan Moskalenko', 'Anastasiia Kornilova'] | 2023-03-09 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [ 3.58265162e-01 -2.95471340e-01 1.11629412e-01 -6.72110200e-01
-9.82062936e-01 -7.69956708e-01 2.41168350e-01 3.18262607e-01
-6.15361333e-01 5.65801919e-01 -6.17535599e-02 -2.86312521e-01
-6.36511594e-02 -8.00297797e-01 -1.03663063e+00 -5.90033889e-01
-2.09182546e-01 5.17374754e-01 3.20986599e-01 -1.81471050... | [7.486589431762695, -2.022987127304077] |
f15c9bfc-540f-43c7-bfc9-09d1297061d8 | learning-word-embeddings-from-the-portuguese | 1709.00947 | null | http://arxiv.org/abs/1709.00947v1 | http://arxiv.org/pdf/1709.00947v1.pdf | Learning Word Embeddings from the Portuguese Twitter Stream: A Study of some Practical Aspects | This paper describes a preliminary study for producing and distributing a
large-scale database of embeddings from the Portuguese Twitter stream. We start
by experimenting with a relatively small sample and focusing on three
challenges: volume of training data, vocabulary size and intrinsic evaluation
metrics. Using a s... | ['Eugénio Oliveira', 'Luís Sarmento', 'Pedro Saleiro', 'Eduarda Mendes Rodrigues', 'Carlos Soares'] | 2017-09-04 | null | null | null | null | ['learning-word-embeddings', '2048'] | ['methodology', 'playing-games'] | [-2.48319298e-01 -2.11284831e-01 1.01722389e-01 -4.19005066e-01
-7.70699501e-01 -6.85806632e-01 8.40716243e-01 8.75463009e-01
-1.19433475e+00 7.13980854e-01 8.08389038e-02 -3.07446539e-01
8.37976672e-03 -8.15813243e-01 -2.62348443e-01 -2.81380147e-01
-5.41652977e-01 5.34248292e-01 3.34404320e-01 -2.64223129... | [10.598845481872559, 8.61349105834961] |
d283e74a-a062-4aad-bdc0-6db0b0cf22a9 | fast-and-unsupervised-action-boundary | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Du_Fast_and_Unsupervised_Action_Boundary_Detection_for_Action_Segmentation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Du_Fast_and_Unsupervised_Action_Boundary_Detection_for_Action_Segmentation_CVPR_2022_paper.pdf | Fast and Unsupervised Action Boundary Detection for Action Segmentation | To deal with the great number of untrimmed videos produced every day, we propose an efficient unsupervised action segmentation method by detecting boundaries, named action boundary detection (ABD). In particular, the proposed method has the following advantages: no training stage and low-latency inference. To detec... | ['Qing Wang', 'Guoqing Zhou', 'Xue Wang', 'Zexing Du'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['boundary-detection', 'action-segmentation'] | ['computer-vision', 'computer-vision'] | [ 5.41750491e-01 -2.24313155e-01 -2.63297856e-01 -1.78181276e-01
-6.45305872e-01 -3.03378582e-01 4.09474611e-01 1.34995300e-02
-5.47401786e-01 3.88546288e-01 1.12905502e-01 1.12243667e-01
-1.49644792e-01 -5.14081895e-01 -3.66002470e-01 -8.14086914e-01
1.73370183e-01 1.25998974e-01 7.89493740e-01 1.97917044... | [8.52652645111084, 0.38884034752845764] |
a0f7932e-ef94-40b5-babb-4529bf24d830 | fully-automated-and-standardized-segmentation | 2008.02251 | null | https://arxiv.org/abs/2008.02251v1 | https://arxiv.org/pdf/2008.02251v1.pdf | Fully Automated and Standardized Segmentation of Adipose Tissue Compartments by Deep Learning in Three-dimensional Whole-body MRI of Epidemiological Cohort Studies | Purpose: To enable fast and reliable assessment of subcutaneous and visceral adipose tissue compartments derived from whole-body MRI. Methods: Quantification and localization of different adipose tissue compartments from whole-body MR images is of high interest to examine metabolic conditions. For correct identificatio... | ['Jürgen Machann', 'Fabian Bamberg', 'Hans-Ulrich Häring', 'Martin Schwartz', 'Thomas Küstner', 'Sergios Gatidis', 'Konstantin Nikolaou', 'Fritz Schick', 'Tobias Hepp', 'Marc Fischer', 'Andreas Fritsche', 'Bin Yang'] | 2020-08-05 | null | null | null | null | ['unet-segmentation'] | ['computer-vision'] | [ 2.00662240e-01 -1.63468823e-01 -3.44493896e-01 -5.24659634e-01
-6.74117267e-01 -4.88781452e-01 1.93193648e-02 5.46289802e-01
-5.07987618e-01 6.43679202e-01 -7.92071074e-02 -2.86926448e-01
-2.14308724e-01 -6.41089678e-01 -3.29032332e-01 -5.37085176e-01
-9.12487268e-01 1.06552482e+00 1.31125674e-01 4.72716063... | [14.289719581604004, -2.529683828353882] |
2dc83534-e70d-4bf4-a3e2-074db14f6ab5 | spherical-fourier-neural-operators-learning | 2306.03838 | null | https://arxiv.org/abs/2306.03838v1 | https://arxiv.org/pdf/2306.03838v1.pdf | Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere | Fourier Neural Operators (FNOs) have proven to be an efficient and effective method for resolution-independent operator learning in a broad variety of application areas across scientific machine learning. A key reason for their success is their ability to accurately model long-range dependencies in spatio-temporal data... | ['Anima Anandkumar', 'Karthik Kashinath', 'Maximilian Baust', 'Jaideep Pathak', 'Christian Hundt', 'Thorsten Kurth', 'Boris Bonev'] | 2023-06-06 | null | null | null | null | ['operator-learning'] | ['miscellaneous'] | [ 2.66541876e-02 -6.02275848e-01 5.64391494e-01 -1.30141333e-01
-1.91873550e-01 -6.20178521e-01 7.24732935e-01 -8.32407922e-02
-4.75885957e-01 9.62726116e-01 -5.26331551e-02 -6.39477968e-01
-3.31199408e-01 -7.63580143e-01 -8.68315876e-01 -8.78475249e-01
-7.00267971e-01 1.11054525e-01 1.74247310e-01 -2.43233293... | [6.578958034515381, 3.3266279697418213] |
49e43879-9a96-42d3-b889-78e73044ba71 | spatio-temporal-structure-consistency-for | 2303.01707 | null | https://arxiv.org/abs/2303.01707v1 | https://arxiv.org/pdf/2303.01707v1.pdf | Spatio-Temporal Structure Consistency for Semi-supervised Medical Image Classification | Intelligent medical diagnosis has shown remarkable progress based on the large-scale datasets with precise annotations. However, fewer labeled images are available due to significantly expensive cost for annotating data by experts. To fully exploit the easily available unlabeled data, we propose a novel Spatio-Temporal... | ['Li Liu', 'Lei Liu', 'Wentao Lei'] | 2023-03-03 | null | null | null | null | ['medical-diagnosis', 'semi-supervised-medical-image-classification'] | ['medical', 'medical'] | [ 1.43355027e-01 9.06296447e-02 -1.57026112e-01 -4.58425760e-01
-8.50437582e-01 -3.47705632e-01 2.55417019e-01 4.80191469e-01
1.16503946e-01 4.41929936e-01 4.13441479e-01 -7.22095668e-02
-5.68951070e-01 -2.98043847e-01 -3.95692438e-01 -9.52228844e-01
-4.05302286e-01 3.06813031e-01 3.26454222e-01 1.53183296... | [14.806594848632812, -2.0544650554656982] |
190ecbc5-2407-43bc-b402-24348924ddc8 | pose-constraints-for-consistent-self | 2304.08916 | null | https://arxiv.org/abs/2304.08916v1 | https://arxiv.org/pdf/2304.08916v1.pdf | Pose Constraints for Consistent Self-supervised Monocular Depth and Ego-motion | Self-supervised monocular depth estimation approaches suffer not only from scale ambiguity but also infer temporally inconsistent depth maps w.r.t. scale. While disambiguating scale during training is not possible without some kind of ground truth supervision, having scale consistent depth predictions would make it pos... | ['Zeeshan Khan Suri'] | 2023-04-18 | null | null | null | null | ['motion-prediction', 'monocular-depth-estimation'] | ['computer-vision', 'computer-vision'] | [ 1.69750139e-01 2.46244684e-01 -1.21208332e-01 -8.17014754e-01
-5.68371415e-01 -7.30016232e-01 6.53577864e-01 6.26437217e-02
-5.93023121e-01 8.35024774e-01 1.82500437e-01 -5.58059253e-02
1.34829938e-01 -9.04659986e-01 -7.44011283e-01 -4.37320679e-01
9.52955633e-02 4.41876680e-01 6.66454792e-01 1.96353719... | [8.67723274230957, -2.4013452529907227] |
0687df05-4f42-4ac7-881a-d47d265f6dcb | blocks2world-controlling-realistic-scenes | 2307.03847 | null | https://arxiv.org/abs/2307.03847v1 | https://arxiv.org/pdf/2307.03847v1.pdf | Blocks2World: Controlling Realistic Scenes with Editable Primitives | We present Blocks2World, a novel method for 3D scene rendering and editing that leverages a two-step process: convex decomposition of images and conditioned synthesis. Our technique begins by extracting 3D parallelepipeds from various objects in a given scene using convex decomposition, thus obtaining a primitive repre... | ['David Forsyth', 'Anand Bhattad', 'Rahul Vasanth', 'Seemandhar Jain', 'Vaibhav Vavilala'] | 2023-07-07 | null | null | null | null | ['data-augmentation'] | ['methodology'] | [ 8.56156111e-01 2.08040431e-01 2.61973351e-01 -2.86377668e-01
-4.96640533e-01 -8.92308891e-01 9.78672564e-01 -1.27084360e-01
7.84197450e-02 2.17038468e-01 2.18049601e-01 -2.82551497e-01
3.98310483e-01 -9.53530252e-01 -9.47239041e-01 -5.85634887e-01
-1.46942586e-02 8.84132564e-01 -4.20800932e-02 -1.04714006... | [9.184714317321777, -3.2304763793945312] |
ca8cf5b7-3c3c-49ca-8959-8e154a3e3b91 | bokehornot-transforming-bokeh-effect-with | 2306.04032 | null | https://arxiv.org/abs/2306.04032v1 | https://arxiv.org/pdf/2306.04032v1.pdf | BokehOrNot: Transforming Bokeh Effect with Image Transformer and Lens Metadata Embedding | Bokeh effect is an optical phenomenon that offers a pleasant visual experience, typically generated by high-end cameras with wide aperture lenses. The task of bokeh effect transformation aims to produce a desired effect in one set of lenses and apertures based on another combination. Current models are limited in their... | ['Siyuan Lai', 'Wenyi Lian', 'Zhihao Yang'] | 2023-06-06 | null | null | null | null | ['image-restoration'] | ['computer-vision'] | [ 2.26381049e-01 -3.59123766e-01 4.64734375e-01 -1.20716304e-01
-3.17971289e-01 -6.13148272e-01 8.79294813e-01 -3.94892454e-01
-2.24119257e-02 6.87151790e-01 6.03849232e-01 -1.77916497e-01
-2.41321743e-01 -5.75442255e-01 -7.50178814e-01 -6.10567153e-01
2.36919105e-01 -2.08822399e-01 3.19466591e-01 -1.89909622... | [10.76163387298584, -2.2976503372192383] |
aa1eec38-2bbe-4ec7-a52b-3913fbe8b2ca | a-method-for-discovering-novel-classes-in | 2209.01217 | null | https://arxiv.org/abs/2209.01217v3 | https://arxiv.org/pdf/2209.01217v3.pdf | A Method for Discovering Novel Classes in Tabular Data | In Novel Class Discovery (NCD), the goal is to find new classes in an unlabeled set given a labeled set of known but different classes. While NCD has recently gained attention from the community, no framework has yet been proposed for heterogeneous tabular data, despite being a very common representation of data. In th... | ['Vincent Lemaire', 'Alexandre Reiffers-Masson', 'Sandrine Vaton', 'Stéphane Gosselin', 'Joachim Flocon-Cholet', 'Colin Troisemaine'] | 2022-09-02 | null | null | null | null | ['novel-class-discovery', 'novel-class-discovery'] | ['computer-vision', 'methodology'] | [ 4.22993958e-01 -2.95835696e-02 -5.81419051e-01 -6.71646416e-01
-1.22718310e+00 -7.90409863e-01 3.96194547e-01 3.05928648e-01
-6.00068383e-02 1.18613482e+00 -1.91467345e-01 -1.80554911e-01
-2.93511003e-01 -6.52283907e-01 -5.77628613e-01 -9.63753045e-01
1.09572962e-01 1.19448411e+00 8.77899006e-02 3.05862337... | [9.58808422088623, 3.077209711074829] |
8bdc6466-a4ef-48a4-b839-d544f4184e67 | open-vocabulary-multi-label-classification | 2207.01887 | null | https://arxiv.org/abs/2207.01887v2 | https://arxiv.org/pdf/2207.01887v2.pdf | Open-Vocabulary Multi-Label Classification via Multi-Modal Knowledge Transfer | Real-world recognition system often encounters the challenge of unseen labels. To identify such unseen labels, multi-label zero-shot learning (ML-ZSL) focuses on transferring knowledge by a pre-trained textual label embedding (e.g., GloVe). However, such methods only exploit single-modal knowledge from a language model... | ['Shu-Tao Xia', 'Bo Ren', 'Ruizhi Qiao', 'Tao Dai', 'Taian Guo', 'Sunan He'] | 2022-07-05 | null | null | null | null | ['multi-label-zero-shot-learning'] | ['computer-vision'] | [ 2.69848257e-01 -3.56462419e-01 -3.96536738e-01 -4.48755980e-01
-1.09040749e+00 -5.07251918e-01 7.53127158e-01 3.30293477e-02
-3.71736646e-01 2.09058434e-01 -1.57213017e-01 1.06170245e-01
2.49265969e-01 -6.00821257e-01 -7.38695681e-01 -7.39764452e-01
5.92869103e-01 3.42076719e-01 1.13139138e-01 1.05244480... | [10.379364013671875, 2.0862927436828613] |
69254aa0-051a-4e8e-8cc4-4ef239142396 | conformal-prediction-for-text-infilling-and | 2111.02592 | null | https://arxiv.org/abs/2111.02592v1 | https://arxiv.org/pdf/2111.02592v1.pdf | Conformal prediction for text infilling and part-of-speech prediction | Modern machine learning algorithms are capable of providing remarkably accurate point-predictions; however, questions remain about their statistical reliability. Unlike conventional machine learning methods, conformal prediction algorithms return confidence sets (i.e., set-valued predictions) that correspond to a given... | ['Jonathan P Williams', 'Emiliano Planchon', 'Maxwell Lovig', 'Carolina Kapper', 'Jack Ferrell', 'Jing Ding', 'Neil Dey'] | 2021-11-04 | null | null | null | null | ['text-infilling'] | ['natural-language-processing'] | [ 4.34880674e-01 5.14601350e-01 -2.61182666e-01 -6.87011242e-01
-1.41433501e+00 -2.61183023e-01 5.38817883e-01 3.61865461e-01
-1.75573900e-01 8.41387331e-01 3.46365005e-01 -6.56728745e-01
-1.05396494e-01 -7.38322854e-01 -8.64404559e-01 -4.38927799e-01
-3.67929667e-01 6.01345420e-01 4.26651627e-01 -4.75213937... | [11.941207885742188, 9.093732833862305] |
30453fd9-b54e-4d74-962c-d11a8e1b1364 | mvimgnet-a-large-scale-dataset-of-multi-view | 2303.06042 | null | https://arxiv.org/abs/2303.06042v1 | https://arxiv.org/pdf/2303.06042v1.pdf | MVImgNet: A Large-scale Dataset of Multi-view Images | Being data-driven is one of the most iconic properties of deep learning algorithms. The birth of ImageNet drives a remarkable trend of "learning from large-scale data" in computer vision. Pretraining on ImageNet to obtain rich universal representations has been manifested to benefit various 2D visual tasks, and becomes... | ['Xiaoguang Han', 'Shuguang Cui', 'GuanYing Chen', 'Tianyou Liang', 'Zhangyang Xiong', 'Chenming Zhu', 'Zizheng Yan', 'Yushuang Wu', 'Chongjie Ye', 'Haolin Liu', 'Yidan Zhang', 'Mutian Xu', 'Xianggang Yu'] | 2023-03-10 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yu_MVImgNet_A_Large-Scale_Dataset_of_Multi-View_Images_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yu_MVImgNet_A_Large-Scale_Dataset_of_Multi-View_Images_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-object-classification'] | ['computer-vision'] | [-0.11874713 -0.17988297 -0.11320394 -0.39356568 -0.49641985 -0.7257856
0.6775549 -0.46322247 -0.06587857 0.08496366 0.10527376 -0.10206644
-0.05390731 -0.8192262 -1.098685 -0.836285 0.07403466 0.5477881
0.12538946 -0.35228264 0.12690566 0.8562807 -1.8455703 0.37886658
0.2693822 1.2252151 0.61... | [8.153735160827637, -3.4066545963287354] |
a1db2729-8268-457e-842e-84d2b63c755f | gosum-extractive-summarization-of-long | 2211.10247 | null | https://arxiv.org/abs/2211.10247v2 | https://arxiv.org/pdf/2211.10247v2.pdf | GoSum: Extractive Summarization of Long Documents by Reinforcement Learning and Graph Organized discourse state | Extracting summaries from long documents can be regarded as sentence classification using the structural information of the documents. How to use such structural information to summarize a document is challenging. In this paper, we propose GoSum, a novel graph and reinforcement learning based extractive model for long-... | ['Shanfeng Zhu', 'Hong Zhou', 'Xiaodi Huang', 'Junyi Bian'] | 2022-11-18 | null | null | null | null | ['sentence-classification', 'extractive-summarization', 'document-summarization'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 3.30007285e-01 9.00676370e-01 -7.01865613e-01 -1.44906342e-01
-9.00768042e-01 -5.38855791e-01 6.09455287e-01 7.47261405e-01
-2.14795679e-01 1.13324523e+00 1.15111923e+00 -9.70541984e-02
-1.83763262e-02 -5.18611670e-01 -1.05507255e+00 -4.34066594e-01
-4.46505658e-02 2.70502359e-01 1.85657933e-01 -3.05774689... | [12.514756202697754, 9.499940872192383] |
be29e395-c4cf-4a00-a4c1-948600bbb0c5 | applying-multilingual-and-monolingual | null | null | https://aclanthology.org/2020.vardial-1.18 | https://aclanthology.org/2020.vardial-1.18.pdf | Applying Multilingual and Monolingual Transformer-Based Models for Dialect Identification | We study the ability of large fine-tuned transformer models to solve a binary classification task of dialect identification, with a special interest in comparing the performance of multilingual to monolingual ones. The corpus analyzed contains Romanian and Moldavian samples from the news domain, as well as tweets for a... | ['Vlad Ștefănescu', 'Cristian Popa'] | null | null | null | null | vardial-coling-2020-12 | ['dialect-identification'] | ['natural-language-processing'] | [-4.20320958e-01 -9.19242278e-02 -3.97502393e-01 -5.26254773e-01
-9.33640003e-01 -8.66952300e-01 9.14920509e-01 1.25206783e-01
-5.23017228e-01 6.72825575e-01 4.30042028e-01 -6.34048700e-01
-2.45238289e-01 -4.98951763e-01 -5.01009107e-01 -6.61180735e-01
4.93287109e-02 1.11991286e+00 7.37659112e-02 -7.14003742... | [10.236717224121094, 10.656421661376953] |
482bec83-32f7-47d5-ad9a-89bd46732afc | end-to-end-sound-source-separation | 1811.0185 | null | https://arxiv.org/abs/1811.01850v2 | https://arxiv.org/pdf/1811.01850v2.pdf | End-to-End Sound Source Separation Conditioned On Instrument Labels | Can we perform an end-to-end music source separation with a variable number of sources using a deep learning model? We present an extension of the Wave-U-Net model which allows end-to-end monaural source separation with a non-fixed number of sources. Furthermore, we propose multiplicative conditioning with instrument l... | ['Olga Slizovskaia', 'Gloria Haro', 'Emilia Gomez', 'Leo Kim'] | 2018-11-05 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 2.90963873e-02 -2.72947192e-01 4.28183377e-01 -1.64768219e-01
-1.43907070e+00 -7.82183468e-01 2.42359638e-01 6.08774275e-02
-2.25536749e-01 5.70680380e-01 5.90288579e-01 6.48809597e-02
-3.85288894e-01 -2.73331881e-01 -7.06908882e-01 -6.81797683e-01
-2.45312303e-01 1.59624606e-01 1.40373483e-01 -1.93894550... | [15.460905075073242, 5.590144157409668] |
e304f0d6-2e1d-48db-b2e0-2de65cbc71a3 | exemplar-based-face-parsing | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Smith_Exemplar-Based_Face_Parsing_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Smith_Exemplar-Based_Face_Parsing_2013_CVPR_paper.pdf | Exemplar-Based Face Parsing | In this work, we propose an exemplar-based face image segmentation algorithm. We take inspiration from previous works on image parsing for general scenes. Our approach assumes a database of exemplar face images, each of which is associated with a hand-labeled segmentation map. Given a test image, our algorithm first se... | ['Brandon M. Smith', 'Li Zhang', 'Jianchao Yang', 'Zhe Lin', 'Jonathan Brandt'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['face-parsing'] | ['computer-vision'] | [ 8.57306063e-01 2.86919296e-01 -5.31711355e-02 -9.00983155e-01
-8.53607416e-01 -7.60055602e-01 4.83417839e-01 -2.62033314e-01
-3.37132841e-01 3.26890886e-01 -1.66733146e-01 1.25006571e-01
1.34455357e-02 -7.02380002e-01 -7.90561140e-01 -6.27062142e-01
1.33204937e-01 1.11777270e+00 2.64710665e-01 9.20668542... | [13.424643516540527, 0.588017463684082] |
d69a8fcd-9630-44d4-8bfa-d294fff99536 | ca-spacenet-counterfactual-analysis-for-6d | 2207.07869 | null | https://arxiv.org/abs/2207.07869v1 | https://arxiv.org/pdf/2207.07869v1.pdf | CA-SpaceNet: Counterfactual Analysis for 6D Pose Estimation in Space | Reliable and stable 6D pose estimation of uncooperative space objects plays an essential role in on-orbit servicing and debris removal missions. Considering that the pose estimator is sensitive to background interference, this paper proposes a counterfactual analysis framework named CASpaceNet to complete robust 6D pos... | ['Lihua Zhang', 'Chixiao Chen', 'Peng Zhai', 'Liuzhen Su', 'Dingkang Yang', 'Bo Jiao', 'Shuaibing Wang', 'Shunli Wang'] | 2022-07-16 | null | null | null | null | ['6d-pose-estimation-1'] | ['computer-vision'] | [ 2.67013222e-01 9.47725326e-02 -8.76120403e-02 -1.54321909e-01
-2.90689290e-01 -5.41707039e-01 6.43691838e-01 -3.49370629e-01
-3.69959474e-01 1.06779742e+00 1.96275622e-01 -6.86073482e-01
-6.06312573e-01 -7.45953619e-01 -7.26148427e-01 -8.74516368e-01
-4.03712958e-01 7.75414109e-02 -1.06126722e-02 -2.13616073... | [7.7578277587890625, -1.4606894254684448] |
febed65e-54e2-4ab0-8829-b0b227327451 | end-to-end-attention-based-large-vocabulary | 1508.04395 | null | http://arxiv.org/abs/1508.04395v2 | http://arxiv.org/pdf/1508.04395v2.pdf | End-to-End Attention-based Large Vocabulary Speech Recognition | Many of the current state-of-the-art Large Vocabulary Continuous Speech
Recognition Systems (LVCSR) are hybrids of neural networks and Hidden Markov
Models (HMMs). Most of these systems contain separate components that deal with
the acoustic modelling, language modelling and sequence decoding. We
investigate a more dir... | ['Philemon Brakel', 'Jan Chorowski', 'Yoshua Bengio', 'Dmitriy Serdyuk', 'Dzmitry Bahdanau'] | 2015-08-18 | null | null | null | null | ['acoustic-modelling'] | ['speech'] | [ 7.22189009e-01 5.18501103e-02 -2.42124721e-01 -4.48343515e-01
-8.46862555e-01 -2.48763517e-01 6.38867319e-01 -1.01512708e-01
-7.40942001e-01 5.92810333e-01 3.39125276e-01 -6.07362747e-01
6.01716816e-01 -5.14440536e-01 -5.92420399e-01 -8.30806077e-01
2.09158167e-01 4.80075210e-01 5.87174237e-01 5.20966388... | [14.437909126281738, 6.867190837860107] |
eb458e48-0279-467e-9f0a-b49e577874d5 | a-locally-linear-procedure-for-word | null | null | https://aclanthology.org/2020.coling-main.528 | https://aclanthology.org/2020.coling-main.528.pdf | A Locally Linear Procedure for Word Translation | Learning a mapping between word embeddings of two languages given a dictionary is an important problem with several applications. A common mapping approach is using an orthogonal matrix. The Orthogonal Procrustes Analysis (PA) algorithm can be applied to find the optimal orthogonal matrix. This solution restricts the e... | ['Jacob Goldberger', 'Hagai Taitelbaum', 'Soham Dan'] | 2020-12-01 | null | null | null | coling-2020-8 | ['word-similarity'] | ['natural-language-processing'] | [ 2.42928881e-02 -3.22362959e-01 -5.47096848e-01 -1.86063871e-01
-7.37205386e-01 -8.54602933e-01 7.30108440e-01 -9.56007242e-02
-5.47171116e-01 4.60774183e-01 3.80912423e-01 -5.73939621e-01
6.78276718e-02 -5.36351025e-01 -5.48539162e-01 -4.53801036e-01
2.16790766e-01 6.66208744e-01 -4.60609309e-02 -5.10594964... | [11.111481666564941, 10.119620323181152] |
6dd58f2f-2716-41a7-88e0-fdce11ed13f5 | a-survey-of-pansharpening-methods-with-a-new | 1606.05703 | null | http://arxiv.org/abs/1606.05703v1 | http://arxiv.org/pdf/1606.05703v1.pdf | A Survey of Pansharpening Methods with A New Band-Decoupled Variational Model | Most satellites decouple the acquisition of a panchromatic image at high
spatial resolution from the acquisition of a multispectral image at lower
spatial resolution. Pansharpening is a fusion technique used to increase the
spatial resolution of the multispectral data while simultaneously preserving
its spectral inform... | ['Gwendoline Blanchet', 'Bartomeu Coll', 'Catalina Sbert', 'Antoni Buades', 'Joan Duran'] | 2016-06-17 | null | null | null | null | ['pansharpening'] | ['computer-vision'] | [ 8.58585536e-01 -5.93872190e-01 5.56112565e-02 -1.43228799e-01
-7.62658596e-01 -8.30585599e-01 4.84662473e-01 2.17710212e-01
-6.11556351e-01 7.37290859e-01 -1.53196946e-01 -8.86554047e-02
-4.62974787e-01 -1.05557823e+00 -2.85533994e-01 -1.29754639e+00
4.15049493e-01 -2.75396612e-02 -5.30465282e-02 -2.12626308... | [10.110269546508789, -2.1105642318725586] |
70635493-9980-4df3-bc9c-4bbdc4d15081 | context-endcoding-for-neural-network-based | 1910.10798 | null | https://arxiv.org/abs/1910.10798v1 | https://arxiv.org/pdf/1910.10798v1.pdf | Context-endcoding for neural network based skull stripping in magnetic resonance imaging | Skull stripping is usually the first step for most brain analysisprocess in magnetic resonance images. A lot of deep learn-ing neural network based methods have been developed toachieve higher accuracy. Since the 3D deep learning modelssuffer from high computational cost and are subject to GPUmemory limit challenge, a ... | ['Yong Fan', 'Yuemeng Li', 'Borui Xiao', 'Zhen Liu'] | 2019-10-23 | null | null | null | null | ['skull-stripping'] | ['medical'] | [-6.71983957e-02 -7.17039853e-02 9.74544659e-02 -5.26547968e-01
-6.58092856e-01 1.16231143e-01 2.76944220e-01 3.90809588e-02
-6.11896694e-01 7.17987776e-01 6.63300529e-02 -2.33112127e-01
-2.37358555e-01 -7.50100791e-01 -6.61383927e-01 -8.30766261e-01
-3.75757933e-01 4.08491939e-01 3.46432626e-01 9.35525633... | [14.32426929473877, -2.3324880599975586] |
a124e002-eb5a-47c4-afc0-f9285392ea77 | rankdnn-learning-to-rank-for-few-shot | 2211.1532 | null | https://arxiv.org/abs/2211.15320v2 | https://arxiv.org/pdf/2211.15320v2.pdf | RankDNN: Learning to Rank for Few-shot Learning | This paper introduces a new few-shot learning pipeline that casts relevance ranking for image retrieval as binary ranking relation classification. In comparison to image classification, ranking relation classification is sample efficient and domain agnostic. Besides, it provides a new perspective on few-shot learning a... | ['Wenqiang Zhang', 'Yizhou Yu', 'Weifeng Ge', 'Yanwei Fu', 'Xujun Wei', 'Hongtong Gong', 'Qianyu Guo'] | 2022-11-28 | null | null | null | null | ['relation-classification'] | ['natural-language-processing'] | [ 2.06744745e-01 -2.77380854e-01 -5.89450657e-01 -4.60349768e-01
-1.07537007e+00 -1.41675817e-02 8.69385242e-01 5.64993657e-02
-4.23784941e-01 2.63110191e-01 2.48050630e-01 1.66138168e-02
-3.41103077e-01 -9.77834702e-01 -5.77479720e-01 -5.06284595e-01
-2.85070315e-02 5.43760955e-01 5.92582047e-01 -4.87186223... | [9.923490524291992, 2.6753828525543213] |
e3a758d1-60b6-4c09-96c3-902abd98fd1a | learning-transferable-features-for-point | null | null | http://proceedings.neurips.cc/paper/2021/hash/b3b25a26a0828ea5d48d8f8aa0d6f9af-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/b3b25a26a0828ea5d48d8f8aa0d6f9af-Paper.pdf | Learning Transferable Features for Point Cloud Detection via 3D Contrastive Co-training | Most existing point cloud detection models require large-scale, densely annotated datasets. They typically underperform in domain adaptation settings, due to geometry shifts caused by different physical environments or LiDAR sensor configurations. Therefore, it is challenging but valuable to learn transferable features... | ['Chao Ma', 'Zhen Yang', 'Chaoqiang Ye', 'Hang Xu', 'Yunbo Wang', 'Chunwei Wang', 'Zeng Yihan'] | 2021-12-01 | null | https://openreview.net/forum?id=iH1_KBzbwQq | https://openreview.net/pdf?id=iH1_KBzbwQq | neurips-2021-12 | ['cloud-detection'] | ['computer-vision'] | [ 5.61599843e-02 -1.79949626e-01 -1.49671584e-01 -4.74365860e-01
-9.81571138e-01 -8.74318838e-01 8.77547920e-01 -6.59851506e-02
-2.64540076e-01 5.34310460e-01 -3.07386398e-01 -8.46793503e-02
1.53114095e-01 -7.99917340e-01 -1.20134330e+00 -4.74127322e-01
2.08946690e-01 9.68860865e-01 4.91750240e-01 -1.66757137... | [7.996267795562744, -2.8491005897521973] |
518b77b4-60ab-4d1f-a52c-f266316f6d61 | multilingual-word-sense-disambiguation-with-1 | 2210.07447 | null | https://arxiv.org/abs/2210.07447v1 | https://arxiv.org/pdf/2210.07447v1.pdf | Multilingual Word Sense Disambiguation with Unified Sense Representation | As a key natural language processing (NLP) task, word sense disambiguation (WSD) evaluates how well NLP models can understand the lexical semantics of words under specific contexts. Benefited from the large-scale annotation, current WSD systems have achieved impressive performances in English by combining supervised le... | ['Tong Zhang', 'Yangqiu Song', 'Hongming Zhang', 'Ying Su'] | 2022-10-14 | multilingual-word-sense-disambiguation-with | https://aclanthology.org/2022.coling-1.368 | https://aclanthology.org/2022.coling-1.368.pdf | coling-2022-10 | ['word-sense-disambiguation'] | ['natural-language-processing'] | [-1.61457360e-01 8.92008021e-02 -5.56239307e-01 -2.80717760e-01
-7.10943401e-01 -8.01961184e-01 5.80407739e-01 7.32325137e-01
-8.37782919e-01 9.63761449e-01 6.34090483e-01 -2.18903467e-01
-3.23789008e-03 -7.02569425e-01 -1.72703609e-01 -1.72181800e-01
2.85710931e-01 6.25542164e-01 2.82830089e-01 -7.83301234... | [10.411925315856934, 9.465141296386719] |
895e7143-c2eb-457d-9817-bc6db7892fdc | augmented-transformers-with-adaptive-n-grams | 2302.14261 | null | https://arxiv.org/abs/2302.14261v1 | https://arxiv.org/pdf/2302.14261v1.pdf | Augmented Transformers with Adaptive n-grams Embedding for Multilingual Scene Text Recognition | While vision transformers have been highly successful in improving the performance in image-based tasks, not much work has been reported on applying transformers to multilingual scene text recognition due to the complexities in the visual appearance of multilingual texts. To fill the gap, this paper proposes an augment... | ['Yaochu Jin', 'Zhihang Fang', 'Xueming Yan'] | 2023-02-28 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 1.49168223e-01 -4.33383256e-01 1.81795269e-01 -2.30488136e-01
-8.86996388e-01 -4.78692770e-01 9.68742192e-01 -1.86664134e-01
-6.42836392e-01 2.47106016e-01 5.02290487e-01 -3.20710957e-01
2.41733566e-01 -2.77642757e-01 -5.43573737e-01 -6.99064195e-01
3.59995574e-01 3.01567614e-01 4.38740142e-02 -3.80385250... | [11.732962608337402, 1.9959323406219482] |
6b517b8f-b7d9-4c56-adfc-a2029273f33d | spatio-temporal-perception-distortion-trade | 2307.01556 | null | https://arxiv.org/abs/2307.01556v1 | https://arxiv.org/pdf/2307.01556v1.pdf | Spatio-Temporal Perception-Distortion Trade-off in Learned Video SR | Perception-distortion trade-off is well-understood for single-image super-resolution. However, its extension to video super-resolution (VSR) is not straightforward, since popular perceptual measures only evaluate naturalness of spatial textures and do not take naturalness of flow (temporal coherence) into account. To t... | ['A. Murat Tekalp', 'Nasrin Rahimi'] | 2023-07-04 | null | null | null | null | ['image-super-resolution', 'video-super-resolution', 'optical-flow-estimation', 'super-resolution'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 3.83435935e-01 -2.40722701e-01 -1.87075630e-01 -3.33925545e-01
-1.01966485e-01 -3.74949545e-01 6.00715518e-01 -1.96926057e-01
-4.70705256e-02 6.61313474e-01 4.67595607e-01 -1.31915063e-01
-2.37958372e-01 -1.00097907e+00 -5.05088508e-01 -4.40693259e-01
-2.82497853e-01 -5.34439325e-01 6.44493520e-01 -4.05175269... | [11.167256355285645, -1.855332374572754] |
4010558d-df8e-4867-9723-f0d32c904ff3 | a-strong-baseline-for-batch-imitation | 2302.02788 | null | https://arxiv.org/abs/2302.02788v1 | https://arxiv.org/pdf/2302.02788v1.pdf | A Strong Baseline for Batch Imitation Learning | Imitation of expert behaviour is a highly desirable and safe approach to the problem of sequential decision making. We provide an easy-to-implement, novel algorithm for imitation learning under a strict data paradigm, in which the agent must learn solely from data collected a priori. This paradigm allows our algorithm ... | ['Kamil Ciosek', 'Zhenwen Dai', 'Lucas Maystre', 'Matthew Smith'] | 2023-02-06 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [ 2.78526157e-01 3.35151464e-01 -4.81973976e-01 1.16295867e-01
-7.97112048e-01 -8.18376660e-01 6.84400439e-01 -5.96036054e-02
-9.11661088e-01 1.03218281e+00 -3.23647350e-01 -6.30937934e-01
-3.92126471e-01 -3.89544666e-01 -8.06237817e-01 -8.93847644e-01
-3.95203412e-01 5.45670986e-01 1.41954839e-01 -2.95435250... | [4.093694686889648, 2.078648567199707] |
63cbd75b-e62d-4add-a8ca-67afef2280a1 | the-massively-multilingual-natural-language | 2212.06346 | null | https://arxiv.org/abs/2212.06346v1 | https://arxiv.org/pdf/2212.06346v1.pdf | The Massively Multilingual Natural Language Understanding 2022 (MMNLU-22) Workshop and Competition | Despite recent progress in Natural Language Understanding (NLU), the creation of multilingual NLU systems remains a challenge. It is common to have NLU systems limited to a subset of languages due to lack of available data. They also often vary widely in performance. We launch a three-phase approach to address the limi... | ['Kay Rottmann', 'Jack FitzGerald', 'Charith Peris', 'Christopher Hench'] | 2022-12-13 | null | null | null | null | ['intent-classification', 'slot-filling'] | ['natural-language-processing', 'natural-language-processing'] | [-2.87831515e-01 1.25126019e-01 -5.72985053e-01 -3.37731689e-01
-1.39965355e+00 -1.05312991e+00 7.04936028e-01 2.44631603e-01
-5.45189679e-01 9.90074813e-01 6.03602946e-01 -8.35084796e-01
1.51399001e-01 -3.70375723e-01 -5.23530245e-01 2.66344190e-01
3.46189648e-01 1.29065561e+00 1.07460894e-01 -5.22449017... | [12.055871963500977, 8.670328140258789] |
4f7335dd-77a9-4f9e-b2e9-e1225de44a60 | inception-architecture-and-residual | 1912.04619 | null | https://arxiv.org/abs/1912.04619v1 | https://arxiv.org/pdf/1912.04619v1.pdf | Inception Architecture and Residual Connections in Classification of Breast Cancer Histology Images | This paper presents results of applying Inception v4 deep convolutional neural network to ICIAR-2018 Breast Cancer Classification Grand Challenge, part a. The Challenge task is to classify breast cancer biopsy results, presented in form of hematoxylin and eosin stained images. Breast cancer classification is of primary... | ['Hyongsuk Kim', 'Dinar Akhmetzanov', 'Denis Tarasov', 'Mohammad Ibrahim Sarker'] | 2019-12-10 | null | null | null | null | ['classification-of-breast-cancer-histology'] | ['medical'] | [ 3.22882414e-01 9.41610485e-02 -4.44360554e-01 -5.32855690e-01
-8.75994503e-01 -4.03564841e-01 3.71250272e-01 6.26516938e-01
-6.99631631e-01 7.57437587e-01 -1.91386312e-01 -7.41078019e-01
-3.60202566e-02 -4.68411028e-01 -4.27454293e-01 -8.48927379e-01
-1.95785210e-01 5.84136128e-01 -1.96702391e-01 3.53104658... | [15.18423843383789, -2.9473013877868652] |
ec475d40-9771-43a9-8b9a-cf0fc45ff4f6 | dip-learning-discriminative-implicit-parts | 2212.13906 | null | https://arxiv.org/abs/2212.13906v2 | https://arxiv.org/pdf/2212.13906v2.pdf | DiP: Learning Discriminative Implicit Parts for Person Re-Identification | In person re-identification (ReID) tasks, many works explore the learning of part features to improve the performance over global image features. Existing methods explicitly extract part features by either using a hand-designed image division or keypoints obtained with external visual systems. In this work, we propose ... | ['Lin Ma', 'Yujie Zhong', 'Siyu Chen', 'Dengjie Li'] | 2022-12-24 | null | null | null | null | ['person-re-identification'] | ['computer-vision'] | [-1.25731677e-01 1.17867455e-01 -3.95938307e-01 -6.23817742e-01
-2.61994392e-01 -4.40251797e-01 7.88657784e-01 -1.17510103e-01
-2.80492991e-01 4.85814661e-01 4.85364676e-01 3.90192777e-01
-9.11165550e-02 -5.77444434e-01 -7.66473114e-01 -7.39551961e-01
5.11126406e-02 3.12811643e-01 1.47246525e-01 -2.62346059... | [14.675703048706055, 0.9196473360061646] |
dd79e1f1-6638-484e-999f-6bc97f66d802 | transint-embedding-implication-rules-in-1 | 2007.00271 | null | https://arxiv.org/abs/2007.00271v1 | https://arxiv.org/pdf/2007.00271v1.pdf | TransINT: Embedding Implication Rules in Knowledge Graphs with Isomorphic Intersections of Linear Subspaces | Knowledge Graphs (KG), composed of entities and relations, provide a structured representation of knowledge. For easy access to statistical approaches on relational data, multiple methods to embed a KG into f(KG) $\in$ R^d have been introduced. We propose TransINT, a novel and interpretable KG embedding method that iso... | ['So Yeon Min', 'Preethi Raghavan', 'Peter Szolovits'] | 2020-07-01 | null | https://openreview.net/forum?id=shkmWLRBXH | https://openreview.net/pdf?id=shkmWLRBXH | akbc-2020-6 | ['triple-classification'] | ['graphs'] | [ 7.47553706e-02 8.08072567e-01 -7.26618409e-01 -5.14883339e-01
3.31778228e-01 -6.21245205e-01 5.19408762e-01 5.79645574e-01
1.12590296e-02 6.44686460e-01 4.31972980e-01 -5.20935774e-01
-8.86372447e-01 -1.36535609e+00 -6.66124165e-01 -4.35318589e-01
-6.10633552e-01 7.10505247e-01 1.12364680e-01 -5.08741379... | [8.808566093444824, 7.775623798370361] |
06ffb00f-3c46-40b6-8719-307064865b13 | sejarah-dan-perkembangan-teknik-natural | 2304.02746 | null | https://arxiv.org/abs/2304.02746v1 | https://arxiv.org/pdf/2304.02746v1.pdf | Sejarah dan Perkembangan Teknik Natural Language Processing (NLP) Bahasa Indonesia: Tinjauan tentang sejarah, perkembangan teknologi, dan aplikasi NLP dalam bahasa Indonesia | This study provides an overview of the history of the development of Natural Language Processing (NLP) in the context of the Indonesian language, with a focus on the basic technologies, methods, and practical applications that have been developed. This review covers developments in basic NLP technologies such as stemmi... | ['Mukhlis Amien'] | 2023-03-28 | null | null | null | null | ['part-of-speech-tagging'] | ['natural-language-processing'] | [ 1.63883239e-01 -2.01536432e-01 -6.10189855e-01 -3.40288401e-01
-7.38106251e-01 -9.84553516e-01 4.29806948e-01 7.63967454e-01
-5.59956849e-01 6.76417053e-01 5.85954785e-01 -8.11604440e-01
3.60575542e-02 -5.83443999e-01 -2.25264709e-02 -5.34746647e-01
4.11512971e-01 4.85233873e-01 -2.37288520e-01 -3.36894929... | [10.491177558898926, 10.084372520446777] |
e5e2a2ac-c719-4c62-9a8c-8f70ef0cd4ff | feature-augmented-hybrid-cnn-for-stress | 2108.03166 | null | https://arxiv.org/abs/2108.03166v1 | https://arxiv.org/pdf/2108.03166v1.pdf | Feature Augmented Hybrid CNN for Stress Recognition Using Wrist-based Photoplethysmography Sensor | Stress is a physiological state that hampers mental health and has serious consequences to physical health. Moreover, the COVID-19 pandemic has increased stress levels among people across the globe. Therefore, continuous monitoring and detection of stress are necessary. The recent advances in wearable devices have allo... | ['Mohammad Abdullah Al Faruque', 'Peter Tseng', 'Abel Jimenez', 'Manik Dautta', 'Luke Chen', 'Nafiul Rashid'] | 2021-08-02 | null | null | null | null | ['photoplethysmography-ppg'] | ['medical'] | [-6.74192607e-02 -2.29789868e-01 -9.94283035e-02 -3.65639478e-01
-6.83268607e-02 -9.16817635e-02 -6.75843284e-02 4.73747998e-01
-6.96980417e-01 8.47071588e-01 8.95048752e-02 -1.78674698e-01
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-2.27419078e-01 -1.95203170e-01 -1.85182989e-01 -1.84763283... | [13.684279441833496, 3.0836682319641113] |
ef1482a9-f5be-455d-a922-4b47acb56a05 | reliable-and-interpretable-personalized | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Qin_Reliable_and_Interpretable_Personalized_Federated_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Qin_Reliable_and_Interpretable_Personalized_Federated_Learning_CVPR_2023_paper.pdf | Reliable and Interpretable Personalized Federated Learning | Federated learning can coordinate multiple users to participate in data training while ensuring data privacy. The collaboration of multiple agents allows for a natural connection between federated learning and collective intelligence. When there are large differences in data distribution among clients, it is crucia... | ['QinGhua Hu', 'Yahong Han', 'Qilong Wang', 'Liu Yang', 'Zixuan Qin'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['personalized-federated-learning'] | ['methodology'] | [-7.34845996e-01 2.23246679e-01 -4.88352269e-01 -6.11852229e-01
-4.45475310e-01 -4.50412273e-01 3.51441592e-01 4.03179564e-02
-1.34418994e-01 7.00165212e-01 3.66307199e-01 1.85649842e-01
-6.35735214e-01 -9.69996929e-01 -2.53522754e-01 -9.92522418e-01
-6.49947748e-02 6.15070879e-01 -2.37564057e-01 2.84741580... | [5.827223300933838, 6.319437503814697] |
6fcf354c-e286-4813-a21e-32d19f9b2085 | joint-radar-communication-waveform-design | 2006.16096 | null | https://arxiv.org/abs/2006.16096v2 | https://arxiv.org/pdf/2006.16096v2.pdf | Joint Radar-Communication Waveform Design Based on Composite Modulation | Joint radar-communication (JRC) waveform can be used for simultaneous radar detection and communication in the same frequency band. However, radar detection processing requires the prior knowledge of the waveform including the embedded information for matched filtering. To remove this requirement, we propose a unimodul... | ['Jiazhi Ma', 'Longfei Shi', 'Fulai Wang', 'Yuan Quan'] | 2020-06-29 | null | null | null | null | ['joint-radar-communication'] | ['robots'] | [ 6.61118388e-01 -4.80891794e-01 -8.20690393e-02 -1.07280217e-01
-3.84693265e-01 -4.37587261e-01 3.78531814e-01 -3.24468702e-01
-3.44131619e-01 6.70158565e-01 9.29176584e-02 -3.20643455e-01
-4.77034569e-01 -8.18723142e-01 1.05146438e-01 -1.06980896e+00
-2.51798034e-01 -2.50026435e-01 1.78200915e-01 -2.31303409... | [6.401474952697754, 1.2432429790496826] |
3ecb5408-9934-455c-8493-b1ee4ec524cc | identification-of-causal-relationship-between | 2307.01389 | null | https://arxiv.org/abs/2307.01389v1 | https://arxiv.org/pdf/2307.01389v1.pdf | Identification of Causal Relationship between Amyloid-beta Accumulation and Alzheimer's Disease Progression via Counterfactual Inference | Alzheimer's disease (AD) is a neurodegenerative disorder that is beginning with amyloidosis, followed by neuronal loss and deterioration in structure, function, and cognition. The accumulation of amyloid-beta in the brain, measured through 18F-florbetapir (AV45) positron emission tomography (PET) imaging, has been wide... | ['Xiang Li', 'Tianming Liu', 'Sheng Li', 'Quanzheng Li', 'Manhua Liu', 'Xingyu Gao', 'Fan Zhang', 'Jorge Sepulcre', 'Ibai Diez', 'Dajiang Zhu', 'Lin Zhao', 'Lu Zhang', 'Qing Li', 'Mengxuan Hu', 'Haixing Dai'] | 2023-07-03 | null | null | null | null | ['causal-inference', 'counterfactual-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous', 'miscellaneous'] | [ 1.75860941e-01 -3.60858887e-01 -2.55253106e-01 -4.53997880e-01
-2.96028495e-01 -1.84624299e-01 2.57005960e-01 2.28078857e-01
-4.30049151e-01 1.32169139e+00 4.00279969e-01 -3.34900677e-01
-8.59143138e-02 -9.06559944e-01 -6.27420902e-01 -5.76375067e-01
-6.78445160e-01 8.06261957e-01 1.50424898e-01 1.09913692... | [14.086749076843262, -1.7748231887817383] |
f9fdc818-098c-4767-8a36-1402cb87d88b | generative-tertiary-structure-based-rna | 2301.10774 | null | https://arxiv.org/abs/2301.10774v2 | https://arxiv.org/pdf/2301.10774v2.pdf | Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design | While artificial intelligence has made remarkable strides in revealing the relationship between biological macromolecules' primary sequence and tertiary structure, designing RNA sequences based on specified tertiary structures remains challenging. Though existing approaches in protein design have thoroughly explored st... | ['Hanqun Cao', 'Zhangyang Gao', 'Yijie Zhang', 'Stan Z. Li', 'Cheng Tan'] | 2023-01-25 | null | null | null | null | ['protein-design'] | ['medical'] | [ 4.09474701e-01 -8.22007209e-02 -2.08429605e-01 -5.33252478e-01
-7.42574990e-01 -8.54256630e-01 5.04719794e-01 1.42865241e-01
-3.64440233e-02 9.80476499e-01 5.26839018e-01 -6.05201423e-01
-1.57539830e-01 -5.65347195e-01 -8.03310990e-01 -1.05388868e+00
-2.84889787e-02 4.78901744e-01 -3.70504230e-01 -1.06294572... | [4.770307540893555, 5.6258544921875] |
cbea4c4c-7c0e-4752-8529-8b111ac50592 | detect-faces-efficiently-a-survey-and | 2112.01787 | null | https://arxiv.org/abs/2112.01787v1 | https://arxiv.org/pdf/2112.01787v1.pdf | Detect Faces Efficiently: A Survey and Evaluations | Face detection is to search all the possible regions for faces in images and locate the faces if there are any. Many applications including face recognition, facial expression recognition, face tracking and head-pose estimation assume that both the location and the size of faces are known in the image. In recent decade... | ['JianGuo Zhang', 'Yan-ran Li', 'Hanyang Peng', 'Shiqi Yu', 'Yuantao Feng'] | 2021-12-03 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [-4.59260076e-01 -6.73837245e-01 6.00141147e-03 -6.21711969e-01
-1.98991492e-01 -5.27948081e-01 2.67609358e-01 -2.65681446e-01
-4.06779647e-01 3.29015881e-01 -3.16710025e-01 2.25150019e-01
7.87222907e-02 -4.19696033e-01 -4.13085282e-01 -7.02785552e-01
-2.99579889e-01 4.20141548e-01 2.55705733e-02 -4.28120568... | [13.332460403442383, 0.7453262805938721] |
71800755-6bca-436e-afdd-1c75763eec7e | self-supervised-and-supervised-joint-training-1 | 2106.0406 | null | https://arxiv.org/abs/2106.04060v1 | https://arxiv.org/pdf/2106.04060v1.pdf | Self-supervised and Supervised Joint Training for Resource-rich Machine Translation | Self-supervised pre-training of text representations has been successfully applied to low-resource Neural Machine Translation (NMT). However, it usually fails to achieve notable gains on resource-rich NMT. In this paper, we propose a joint training approach, $F_2$-XEnDec, to combine self-supervised and supervised learn... | ['Wolfgang Macherey', 'Lu Jiang', 'Wei Wang', 'Yong Cheng'] | 2021-06-08 | self-supervised-and-supervised-joint-training | https://openreview.net/forum?id=1yDrpckYHnN | https://openreview.net/pdf?id=1yDrpckYHnN | null | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 6.27221107e-01 3.94930281e-02 -5.78674257e-01 -4.11105603e-01
-1.63129199e+00 -3.73257875e-01 7.32330799e-01 -2.95530111e-01
-4.32300955e-01 1.09723687e+00 3.46069217e-01 -7.34561801e-01
5.51972210e-01 -1.49734840e-01 -1.36860514e+00 -3.04549545e-01
2.86240608e-01 8.49843264e-01 -5.90260744e-01 -4.92546260... | [11.62243938446045, 10.2316255569458] |
733a4892-d820-47bb-84eb-8426736b87e4 | multilingual-dependency-parsing-for-low | null | null | https://aclanthology.org/L18-1352 | https://aclanthology.org/L18-1352.pdf | Multilingual Dependency Parsing for Low-Resource Languages: Case Studies on North Saami and Komi-Zyrian | null | ['KyungTae Lim', 'Niko Partanen', 'Thierry Poibeau'] | 2018-05-01 | multilingual-dependency-parsing-for-low-1 | https://aclanthology.org/L18-1352 | https://aclanthology.org/L18-1352.pdf | lrec-2018-5 | ['multilingual-word-embeddings'] | ['methodology'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.2896013259887695, 3.764153003692627] |
90aa2168-dea8-4980-897d-37bfe52f356e | grill-grounded-vision-language-pre-training | 2305.14676 | null | https://arxiv.org/abs/2305.14676v1 | https://arxiv.org/pdf/2305.14676v1.pdf | GRILL: Grounded Vision-language Pre-training via Aligning Text and Image Regions | Generalization to unseen tasks is an important ability for few-shot learners to achieve better zero-/few-shot performance on diverse tasks. However, such generalization to vision-language tasks including grounding and generation tasks has been under-explored; existing few-shot VL models struggle to handle tasks that in... | ['Xiang Ren', 'Damien Jose', 'Ahmed Hassan Awadallah', 'Weizhu Chen', 'Yelong Shen', 'Yu Cheng', 'Subhabrata Mukherjee', 'Woojeong Jin'] | 2023-05-24 | null | null | null | null | ['visual-commonsense-reasoning'] | ['reasoning'] | [ 2.32767493e-01 3.50933731e-01 -1.85678035e-01 -2.65278757e-01
-1.01343966e+00 -3.97139192e-01 9.13899183e-01 2.02002451e-01
-2.06255466e-01 5.81172407e-01 1.04522996e-01 -4.00371164e-01
1.89082444e-01 -7.36377001e-01 -1.08082116e+00 -1.87414378e-01
3.16800117e-01 6.81483388e-01 6.65518224e-01 -5.75025797... | [10.751593589782715, 1.7021682262420654] |
5bf74334-0af4-49e2-bf0c-cbd0892dfd2d | 2305-14656 | 2305.14656 | null | https://arxiv.org/abs/2305.14656v1 | https://arxiv.org/pdf/2305.14656v1.pdf | RSRM: Reinforcement Symbolic Regression Machine | In nature, the behaviors of many complex systems can be described by parsimonious math equations. Automatically distilling these equations from limited data is cast as a symbolic regression process which hitherto remains a grand challenge. Keen efforts in recent years have been placed on tackling this issue and demonst... | ['Hao Sun', 'Yang Liu', 'Yilong Xu'] | 2023-05-24 | null | null | null | null | ['q-learning'] | ['methodology'] | [ 2.77988076e-01 1.31867141e-01 -3.69873106e-01 -1.81872457e-01
-9.29479301e-01 -4.57914501e-01 3.50548834e-01 -1.19412929e-01
-1.74193494e-02 8.72291863e-01 -4.61122602e-01 -7.24660516e-01
-3.79565984e-01 -7.31959999e-01 -7.73876309e-01 -3.95540476e-01
-1.92205772e-01 8.59593451e-01 1.79277241e-01 -6.03174686... | [8.500164031982422, 6.880970478057861] |
3aea8da3-674c-460a-ba0f-aef274f71731 | punctuation-prediction-in-spontaneous | 2004.05985 | null | https://arxiv.org/abs/2004.05985v1 | https://arxiv.org/pdf/2004.05985v1.pdf | Punctuation Prediction in Spontaneous Conversations: Can We Mitigate ASR Errors with Retrofitted Word Embeddings? | Automatic Speech Recognition (ASR) systems introduce word errors, which often confuse punctuation prediction models, turning punctuation restoration into a challenging task. These errors usually take the form of homonyms. We show how retrofitting of the word embeddings on the domain-specific data can mitigate ASR error... | ['Piotr .Zelasko', 'Mikołaj Morzy', 'Łukasz Augustyniak', 'Yishay Carmiel', 'Jan Mizgajski', 'Adrian Szymczak', 'Piotr Szymanski', 'Najim Dehak'] | 2020-04-13 | null | null | null | null | ['punctuation-restoration'] | ['natural-language-processing'] | [ 2.81205505e-01 3.90420943e-01 1.25665769e-01 -2.98242629e-01
-8.10636044e-01 -4.84154642e-01 5.27142823e-01 5.42186737e-01
-8.42850864e-01 5.57839572e-01 5.52151918e-01 -6.98432207e-01
4.49419282e-02 -3.24913502e-01 -5.08879244e-01 -2.94739157e-01
1.88605189e-01 4.22900975e-01 2.25779846e-01 -4.53281194... | [14.231868743896484, 7.081413269042969] |
c96ebd61-18be-4a9f-ac37-b462dc0320fe | u-cam-visual-explanation-using-uncertainty | 1908.06306 | null | https://arxiv.org/abs/1908.06306v4 | https://arxiv.org/pdf/1908.06306v4.pdf | U-CAM: Visual Explanation using Uncertainty based Class Activation Maps | Understanding and explaining deep learning models is an imperative task. Towards this, we propose a method that obtains gradient-based certainty estimates that also provide visual attention maps. Particularly, we solve for visual question answering task. We incorporate modern probabilistic deep learning methods that we... | ['Vinay P. Namboodiri', 'Shivansh Patel', 'Mayank Lunayach', 'Badri N. Patro'] | 2019-08-17 | u-cam-visual-explanation-using-uncertainty-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Patro_U-CAM_Visual_Explanation_Using_Uncertainty_Based_Class_Activation_Maps_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Patro_U-CAM_Visual_Explanation_Using_Uncertainty_Based_Class_Activation_Maps_ICCV_2019_paper.pdf | iccv-2019-10 | ['probabilistic-deep-learning'] | ['computer-vision'] | [-2.94998318e-01 4.29750621e-01 -1.78235173e-01 -5.81536293e-01
-1.10699594e+00 -5.52250862e-01 7.79523075e-01 2.47661173e-01
-1.92096934e-01 6.43584967e-01 2.41047576e-01 -4.66763526e-01
-1.07385732e-01 -6.95731699e-01 -1.01253760e+00 -3.38428527e-01
1.92376867e-01 5.27712345e-01 2.53894508e-01 1.54309928... | [10.826393127441406, 1.768477439880371] |
5d3368ed-7714-41dc-a3de-69811511a530 | quantifying-the-controllability-of-coarsely | null | null | https://openreview.net/forum?id=okmZ6-zU6Lz | https://openreview.net/pdf?id=okmZ6-zU6Lz | Quantifying the Controllability of Coarsely Characterized Networked Dynamical Systems | We study the controllability of large-scale networked dynamical systems when complete knowledge of network structure is unavailable. In particular, we establish the power of learning community-based representations to understand the ability of a group of control nodes to steer the network to a target state. We are moti... | ['Stark Draper', 'Gautam Dasarathy', 'Rajasekhar Anguluri', 'Nafiseh Ghoroghchian'] | 2021-09-29 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 2.53320038e-01 3.90498668e-01 1.19352527e-01 4.05595243e-01
1.93048432e-01 -9.48381305e-01 7.24131405e-01 1.85852423e-01
1.05485119e-01 9.42457974e-01 9.12237242e-02 -1.53927565e-01
-6.41753912e-01 -9.53848004e-01 -6.90957546e-01 -9.49186623e-01
-5.87540984e-01 5.68085611e-01 3.03423516e-02 -5.95937848... | [6.7026519775390625, 4.947437763214111] |
2074f0be-0954-4d55-9486-4d8f104f6057 | a-positive-feedback-method-based-on-f-measure | 2304.14619 | null | https://arxiv.org/abs/2304.14619v1 | https://arxiv.org/pdf/2304.14619v1.pdf | A positive feedback method based on F-measure value for Salient Object Detection | The majority of current salient object detection (SOD) models are focused on designing a series of decoders based on fully convolutional networks (FCNs) or Transformer architectures and integrating them in a skillful manner. These models have achieved remarkable high performance and made significant contributions to th... | ['Yunchao Xu', 'Dongping Zhang', 'Chen Pan', 'Chao Dai', 'Ailing Pan'] | 2023-04-28 | null | null | null | null | ['saliency-prediction', 'salient-object-detection-1'] | ['computer-vision', 'computer-vision'] | [ 4.40337449e-01 1.26765087e-01 -1.17413029e-01 -1.29086122e-01
-3.94436419e-01 2.16550697e-02 3.92633051e-01 2.42096279e-02
-3.73721123e-01 5.19326150e-01 1.16337016e-01 -1.54754534e-01
1.30122259e-01 -7.31179476e-01 -8.71445596e-01 -3.77511531e-01
1.12282977e-01 -7.40616173e-02 1.21269834e+00 -3.44652236... | [9.721364974975586, -0.3883533775806427] |
72b7feca-cffa-44a9-a266-6de248b8e916 | one-class-classification-robust-to-geometric | null | null | https://openreview.net/forum?id=oY7La6DBTLx | https://openreview.net/pdf?id=oY7La6DBTLx | One-class Classification Robust to Geometric Transformation | Recent studies on one-class classification have achieved a remarkable performance, by employing the self-supervised classifier that predicts the geometric transformation applied to in-class images. However, they cannot identify in-class images at all when the input images are geometrically-transformed (e.g., rotated im... | ['Hwanjo Yu', 'SeongKu Kang', 'Dongha Lee', 'Hyunjun Ju'] | 2021-01-01 | null | null | null | null | ['one-class-classifier'] | ['methodology'] | [ 4.25899595e-01 4.38292883e-02 -2.72372276e-01 -7.22512364e-01
-3.65951329e-01 -6.47086263e-01 5.99741101e-01 1.19093195e-01
-3.51294279e-01 3.42271656e-01 -4.95743990e-01 -2.39878982e-01
-2.15861991e-01 -8.34205747e-01 -6.53218687e-01 -7.63274074e-01
1.69930249e-01 4.68732417e-01 3.50218177e-01 9.29220617... | [9.70783805847168, 2.6112921237945557] |
5b96c07f-270d-4d49-be94-db4bd119a1ae | illuminati-towards-explaining-graph-neural | 2303.14836 | null | https://arxiv.org/abs/2303.14836v1 | https://arxiv.org/pdf/2303.14836v1.pdf | Illuminati: Towards Explaining Graph Neural Networks for Cybersecurity Analysis | Graph neural networks (GNNs) have been utilized to create multi-layer graph models for a number of cybersecurity applications from fraud detection to software vulnerability analysis. Unfortunately, like traditional neural networks, GNNs also suffer from a lack of transparency, that is, it is challenging to interpret th... | ['H. Howie Huang', 'Yuede Ji', 'Haoyu He'] | 2023-03-26 | null | null | null | null | ['fraud-detection', 'vulnerability-detection'] | ['miscellaneous', 'miscellaneous'] | [ 3.95856090e-02 7.79381931e-01 -3.75645429e-01 -4.33942564e-02
1.37012631e-01 -5.64419627e-01 1.30737662e-01 1.87341273e-01
6.20737612e-01 1.12970658e-01 6.05524965e-02 -1.14498723e+00
-3.69530648e-01 -9.11080420e-01 -6.77326024e-01 -3.98689806e-02
-3.07746112e-01 9.89888385e-02 9.03349072e-02 -3.48777324... | [7.000202178955078, 7.2494072914123535] |
a7a06c47-b8c5-439c-959c-890cc0acd46f | crosspoint-self-supervised-cross-modal | 2203.0068 | null | https://arxiv.org/abs/2203.00680v3 | https://arxiv.org/pdf/2203.00680v3.pdf | CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding | Manual annotation of large-scale point cloud dataset for varying tasks such as 3D object classification, segmentation and detection is often laborious owing to the irregular structure of point clouds. Self-supervised learning, which operates without any human labeling, is a promising approach to address this issue. We ... | ['Ranga Rodrigo', 'Kanchana Thilakarathna', 'Amaya Dharmasiri', 'Dinithi Dissanayake', 'Isuru Dissanayake', 'Mohamed Afham'] | 2022-03-01 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Afham_CrossPoint_Self-Supervised_Cross-Modal_Contrastive_Learning_for_3D_Point_Cloud_Understanding_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Afham_CrossPoint_Self-Supervised_Cross-Modal_Contrastive_Learning_for_3D_Point_Cloud_Understanding_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-object-classification', '3d-point-cloud-linear-classification'] | ['computer-vision', 'computer-vision'] | [-5.35481330e-03 2.09674295e-02 -3.87572765e-01 -4.58380938e-01
-8.66409659e-01 -8.87697637e-01 7.13710904e-01 2.99232334e-01
-8.19406807e-02 -3.80032584e-02 -3.97798270e-01 -7.04575628e-02
4.42302004e-02 -5.90434253e-01 -9.17556465e-01 -4.72608745e-01
-3.36932614e-02 7.87487209e-01 4.81423855e-01 6.05810732... | [7.999567031860352, -3.164469003677368] |
eb5885ea-b3cb-47f6-8dcd-136c0ab67c7c | uscore-an-effective-approach-to-fully | 2202.10062 | null | https://arxiv.org/abs/2202.10062v3 | https://arxiv.org/pdf/2202.10062v3.pdf | USCORE: An Effective Approach to Fully Unsupervised Evaluation Metrics for Machine Translation | The vast majority of evaluation metrics for machine translation are supervised, i.e., (i) are trained on human scores, (ii) assume the existence of reference translations, or (iii) leverage parallel data. This hinders their applicability to cases where such supervision signals are not available. In this work, we develo... | ['Steffen Eger', 'Jonas Belouadi'] | 2022-02-21 | null | null | null | null | ['parallel-corpus-mining'] | ['natural-language-processing'] | [ 3.58050466e-01 -6.27827644e-02 -4.74068761e-01 -3.73595774e-01
-1.15996099e+00 -1.00093806e+00 8.61371279e-01 2.09186345e-01
-5.70498109e-01 7.54383922e-01 5.06734490e-01 -7.20117807e-01
9.06799734e-02 -4.22483861e-01 -6.02468431e-01 -3.66718262e-01
3.44467163e-01 6.75382137e-01 -1.07280120e-01 -2.42068216... | [11.574405670166016, 10.334409713745117] |
dd0684da-8322-4275-9269-20dd8341b545 | sedroid-a-robust-android-malware-detector | 1909.03837 | null | https://arxiv.org/abs/1909.03837v1 | https://arxiv.org/pdf/1909.03837v1.pdf | SEdroid: A Robust Android Malware Detector using Selective Ensemble Learning | For the dramatic increase of Android malware and low efficiency of manual check process, deep learning methods started to be an auxiliary means for Android malware detection these years. However, these models are highly dependent on the quality of datasets, and perform unsatisfactory results when the quality of trainin... | ['Qi Jing', 'Jianbo Gao', 'Ji Wang'] | 2019-09-06 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [ 3.55863832e-02 -3.69557351e-01 -3.30155253e-01 4.96124364e-02
-3.33002776e-01 -4.21935678e-01 4.50166434e-01 -1.55453950e-01
-1.82166725e-01 6.44126654e-01 -2.90054142e-01 -4.69486892e-01
-2.40398690e-01 -7.82446802e-01 -5.03719270e-01 -6.41717851e-01
-4.18787003e-02 2.25036666e-01 3.62037629e-01 -2.89098740... | [14.422179222106934, 9.679648399353027] |
4e3c79eb-b4ec-4704-941c-8d5858b2e2a3 | mixed-curvature-multi-relational-graph-neural | null | null | https://dl.acm.org/doi/abs/10.1145/3442381.3450118 | https://dl.acm.org/doi/pdf/10.1145/3442381.3450118 | Mixed-Curvature Multi-Relational Graph Neural Network for Knowledge Graph Completion | Knowledge graphs (KGs) have gradually become valuable assets for many AI applications. In a KG, a node denotes an entity, and an edge (or link) denotes a relationship between the entities represented by the nodes. Knowledge graph completion infers and predicts missing edges in a KG automatically. Knowledge graph embedd... | ['Shen Wang∗'] | 2021-04-19 | null | null | null | www-2021-4 | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-3.24622631e-01 4.75262791e-01 -2.42079452e-01 -1.46615639e-01
-2.92763561e-01 -8.04374874e-01 2.35201985e-01 1.52869314e-01
1.94068909e-01 1.96738541e-01 3.08329135e-01 -2.76490778e-01
-4.21695054e-01 -1.13742757e+00 -9.10320699e-01 -6.49222791e-01
-2.05653667e-01 5.14930665e-01 1.26486957e-01 -2.61362374... | [8.62592887878418, 7.771095275878906] |
5b2b747a-8273-4d42-ae3f-37ddfe1c5036 | units-unsupervised-intermediate-training | 2205.04683 | null | https://arxiv.org/abs/2205.04683v1 | https://arxiv.org/pdf/2205.04683v1.pdf | UNITS: Unsupervised Intermediate Training Stage for Scene Text Detection | Recent scene text detection methods are almost based on deep learning and data-driven. Synthetic data is commonly adopted for pre-training due to expensive annotation cost. However, there are obvious domain discrepancies between synthetic data and real-world data. It may lead to sub-optimal performance to directly adop... | ['Weiping Wang', 'Enze Xie', 'Xugong Qin', 'Yu Zhou', 'Youhui Guo'] | 2022-05-10 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 6.02373481e-01 -3.84519547e-02 9.39389765e-02 -7.47218668e-01
-5.46014190e-01 -3.38130593e-01 7.42128789e-01 2.29600027e-01
-7.61143029e-01 5.18303990e-01 1.69047683e-01 -3.40822220e-01
4.03391987e-01 -1.05031800e+00 -8.59416723e-01 -6.29133999e-01
6.19725943e-01 5.64457655e-01 7.02042699e-01 -1.40740082... | [11.847878456115723, 2.109696626663208] |
8b56d6e3-353e-43f0-9d3a-86c2f13b1e3c | an-efficient-and-straightforward-online | 2306.12574 | null | https://arxiv.org/abs/2306.12574v1 | https://arxiv.org/pdf/2306.12574v1.pdf | An efficient and straightforward online quantization method for a data stream through remove-birth updating | The growth of network-connected devices is creating an explosion of data, known as big data, and posing significant challenges to efficient data analysis. This data is generated continuously, creating a dynamic flow known as a data stream. The characteristics of a data stream may change dynamically, and this change is ... | ['Kazuhisa Fujita'] | 2023-06-21 | null | null | null | null | ['quantization'] | ['methodology'] | [ 1.30964160e-01 -4.64455009e-01 -3.72130811e-01 -4.68481988e-01
1.85102060e-01 -3.23926419e-01 9.02800858e-02 7.19426394e-01
-3.03505927e-01 8.62036645e-01 3.15501764e-02 3.06209087e-01
2.96514877e-03 -1.02046835e+00 -3.09609026e-01 -6.33400798e-01
-3.61385942e-01 2.38517672e-01 4.59354162e-01 -1.30910203... | [7.450951099395752, 2.9285542964935303] |
a566c217-faf0-40ee-ad24-f797d04dea47 | fully-automated-binary-pattern-extraction-for | 2205.0384 | null | https://arxiv.org/abs/2205.03840v1 | https://arxiv.org/pdf/2205.03840v1.pdf | Fully Automated Binary Pattern Extraction For Finger Vein Identification using Double Optimization Stages-Based Unsupervised Learning Approach | Today, finger vein identification is gaining popularity as a potential biometric identification framework solution. Machine learning-based unsupervised, supervised, and deep learning algorithms have had a significant influence on finger vein detection and recognition at the moment. Deep learning, on the other hand, nec... | ['Adil Al-Azzawi', 'Ali Salah Hameed'] | 2022-05-08 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [ 3.12492609e-01 -3.32575113e-01 1.58915587e-03 -4.96950179e-01
3.40472907e-02 -5.99745274e-01 3.54222387e-01 -4.91755940e-02
-5.46858668e-01 5.11410594e-01 -3.06228399e-01 -1.64243713e-01
-2.78382868e-01 -8.40298414e-01 1.68724254e-01 -8.89536262e-01
4.03412640e-01 4.77727681e-01 -1.99801117e-01 3.40512574... | [13.050249099731445, 1.0255918502807617] |
4990e440-bb76-4252-b005-d45be34d74e2 | imputing-knowledge-tracing-data-with-subject | 2302.1291 | null | https://arxiv.org/abs/2302.12910v1 | https://arxiv.org/pdf/2302.12910v1.pdf | Imputing Knowledge Tracing Data with Subject-Based Training via LSTM Variational Autoencoders Frameworks | The issue of missing data poses a great challenge on boosting performance and application of deep learning models in the {\em Knowledge Tracing} (KT) problem. However, there has been the lack of understanding on the issue in the literature. %are not sufficient studies tackling this problem. In this work, to address thi... | ['Dongwon Lee', 'Jia Tracy Shen'] | 2023-02-24 | null | null | null | null | ['knowledge-tracing'] | ['miscellaneous'] | [-1.81123778e-01 7.76156560e-02 -1.25737667e-01 -3.84024978e-01
-8.27838659e-01 -2.44242251e-01 4.15161341e-01 -2.50431687e-01
-2.84381241e-01 9.42428827e-01 1.76915541e-01 -3.71470690e-01
-2.97690719e-01 -1.09667361e+00 -1.10583639e+00 -7.21557081e-01
5.77454329e-01 4.76258010e-01 -3.39436829e-01 -3.63823846... | [10.479349136352539, 5.402845859527588] |
87bf50b4-aa52-4395-b374-031991d29285 | percqa-persian-community-question-answering | 2112.13238 | null | https://arxiv.org/abs/2112.13238v1 | https://arxiv.org/pdf/2112.13238v1.pdf | PerCQA: Persian Community Question Answering Dataset | Community Question Answering (CQA) forums provide answers for many real-life questions. Thanks to the large size, these forums are very popular among machine learning researchers. Automatic answer selection, answer ranking, question retrieval, expert finding, and fact-checking are example learning tasks performed using... | ['Hesham Faili', 'Yadollah Yaghoobzadeh', 'Naghme Jamali'] | 2021-12-25 | null | https://aclanthology.org/2022.lrec-1.654 | https://aclanthology.org/2022.lrec-1.654.pdf | lrec-2022-6 | ['answer-selection'] | ['natural-language-processing'] | [-4.28092808e-01 1.11063376e-01 2.69858539e-01 -2.47760937e-01
-1.70709693e+00 -1.08122575e+00 5.67424178e-01 6.60224557e-01
-7.21287668e-01 8.13405573e-01 4.89690095e-01 -5.53431690e-01
-2.11170956e-01 -8.10205698e-01 -1.46972656e-01 3.08837481e-02
2.27954075e-01 1.08684206e+00 9.00153577e-01 -6.43924415... | [11.390298843383789, 8.002192497253418] |
36fd92d1-ebc9-4b85-9d37-51d08d7a2cf1 | towards-future-directions-in-data-integrative | 2205.13088 | null | https://arxiv.org/abs/2205.13088v1 | https://arxiv.org/pdf/2205.13088v1.pdf | Towards future directions in data-integrative supervised prediction of human aging-related genes | Identification of human genes involved in the aging process is critical due to the incidence of many diseases with age. A state-of-the-art approach for this purpose infers a weighted dynamic aging-specific subnetwork by mapping gene expression (GE) levels at different ages onto the protein-protein interaction network (... | ['Tijana Milenković', 'Khalique Newaz', 'Qi Li'] | 2022-05-26 | null | null | null | null | ['data-integration', 'human-aging'] | ['knowledge-base', 'miscellaneous'] | [ 4.03700799e-01 -1.80170480e-02 -2.00322270e-01 -1.88662931e-01
-2.30360657e-01 -3.56686711e-01 -9.54499617e-02 5.42509556e-01
-2.42090896e-01 1.23520601e+00 3.12465906e-01 -5.32243371e-01
-3.88890177e-01 -9.01985347e-01 -7.30198085e-01 -5.88047624e-01
-5.70973575e-01 4.36689466e-01 -1.15183461e-02 -1.60509765... | [6.5778632164001465, 5.503453731536865] |
5a93cec6-79e4-4f79-91dd-e6c37a3a9731 | forensic-similarity-for-digital-images | 1902.04684 | null | https://arxiv.org/abs/1902.04684v2 | https://arxiv.org/pdf/1902.04684v2.pdf | Forensic Similarity for Digital Images | In this paper we introduce a new digital image forensics approach called forensic similarity, which determines whether two image patches contain the same forensic trace or different forensic traces. One benefit of this approach is that prior knowledge, e.g. training samples, of a forensic trace are not required to make... | ['Owen Mayer', 'Matthew C. Stamm'] | 2019-02-13 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 2.67680228e-01 -4.41351354e-01 3.53489608e-01 -3.21810156e-01
-7.94295192e-01 -6.63718283e-01 6.10989571e-01 1.65516570e-01
-3.50921571e-01 2.44364426e-01 -4.63795304e-01 -3.67607474e-01
6.50481284e-02 -6.83225274e-01 -1.01478362e+00 -5.84165692e-01
2.94124167e-02 2.89029390e-01 3.19881052e-01 2.69628823... | [12.35909652709961, 0.9864934682846069] |
fb37c75f-aa0e-43f6-9f33-2bc0a0e9cc1a | visually-informed-binaural-audio-generation | 2104.06162 | null | https://arxiv.org/abs/2104.06162v1 | https://arxiv.org/pdf/2104.06162v1.pdf | Visually Informed Binaural Audio Generation without Binaural Audios | Stereophonic audio, especially binaural audio, plays an essential role in immersive viewing environments. Recent research has explored generating visually guided stereophonic audios supervised by multi-channel audio collections. However, due to the requirement of professional recording devices, existing datasets are li... | ['Dahua Lin', 'Xiaogang Wang', 'Bo Dai', 'Ziwei Liu', 'Hang Zhou', 'Xudong Xu'] | 2021-04-13 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Xu_Visually_Informed_Binaural_Audio_Generation_without_Binaural_Audios_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Xu_Visually_Informed_Binaural_Audio_Generation_without_Binaural_Audios_CVPR_2021_paper.pdf | cvpr-2021-1 | ['audio-generation'] | ['audio'] | [ 9.65171978e-02 -4.85871464e-01 5.64409256e-01 -1.06094278e-01
-1.40717399e+00 -7.70886779e-01 3.18845570e-01 2.31699683e-02
3.13397758e-02 4.87474412e-01 5.63816011e-01 2.37423833e-02
6.37566522e-02 -5.36127150e-01 -8.63597572e-01 -6.95873201e-01
1.78512275e-01 3.20839696e-02 2.91363239e-01 -1.27784505... | [14.950034141540527, 5.078827381134033] |
5dfcf918-789d-495d-8c4d-edd83c5e8b7b | soundify-matching-sound-effects-to-video | 2112.09726 | null | https://arxiv.org/abs/2112.09726v1 | https://arxiv.org/pdf/2112.09726v1.pdf | Soundify: Matching Sound Effects to Video | In the art of video editing, sound is really half the story. A skilled video editor overlays sounds, such as effects and ambients, over footage to add character to an object or immerse the viewer within a space. However, through formative interviews with professional video editors, we found that this process can be ext... | ['Nikolas Martelaro', 'Yining Shi', 'Cristóbal Valenzuela', 'Anastasis Germanidis', 'David Chuan-En Lin'] | 2021-12-17 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 3.46038371e-01 -4.06234264e-01 2.51792818e-01 -1.70341939e-01
-9.99740839e-01 -7.40206599e-01 2.87137389e-01 -2.80011833e-01
1.88101754e-02 2.02010199e-01 3.43232960e-01 -3.25397402e-01
2.42074415e-01 -5.12805760e-01 -9.16873157e-01 -2.24600006e-02
-8.97149295e-02 -1.60058022e-01 3.55849624e-01 2.11021397... | [15.583098411560059, 5.344087600708008] |
49053721-3b19-45c5-844a-f6b5a3d19798 | bayesian-optimisation-with-formal-guarantees | 2106.06067 | null | https://arxiv.org/abs/2106.06067v1 | https://arxiv.org/pdf/2106.06067v1.pdf | Bayesian Optimisation with Formal Guarantees | Application domains of Bayesian optimization include optimizing black-box functions or very complex functions. The functions we are interested in describe complex real-world systems applied in industrial settings. Even though they do have explicit representations, standard optimization techniques fail to provide valida... | ['Konstantin Korovin', 'Zurab Khasidashvili', 'Franz Brauße'] | 2021-06-10 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [-1.14823533e-02 2.15457648e-01 -3.58490705e-01 -5.47242999e-01
-4.55552548e-01 -5.64123452e-01 2.82050937e-01 -1.11888967e-01
-1.11261029e-02 1.18013418e+00 -5.43457031e-01 -6.94187820e-01
-8.29977155e-01 -4.69987541e-01 -5.77547431e-01 -8.51616800e-01
-3.40121120e-01 6.28705978e-01 9.91906077e-02 6.03981465... | [5.937675952911377, 3.5864436626434326] |
1188b774-a4ac-48fa-91a9-49689951dfe9 | persistent-homology-meets-object-unity-object | 2305.03815 | null | https://arxiv.org/abs/2305.03815v1 | https://arxiv.org/pdf/2305.03815v1.pdf | Persistent Homology Meets Object Unity: Object Recognition in Clutter | Recognition of occluded objects in unseen and unstructured indoor environments is a challenging problem for mobile robots. To address this challenge, we propose a new descriptor, TOPS, for point clouds generated from depth images and an accompanying recognition framework, THOR, inspired by human reasoning. The descript... | ['Ashis G. Banerjee', 'Ekta U. Samani'] | 2023-05-05 | null | null | null | null | ['unity', 'object-recognition'] | ['computer-vision', 'computer-vision'] | [ 1.80874884e-01 -2.90254075e-02 1.71927556e-01 -2.71256387e-01
-4.05495197e-01 -5.26299000e-01 8.13872337e-01 -3.41292769e-02
-2.71805644e-01 5.86159408e-01 -1.25851125e-01 -1.53885871e-01
-4.35701698e-01 -7.85190046e-01 -9.23015952e-01 -3.08532953e-01
-3.83661568e-01 1.05325687e+00 6.22167885e-01 -4.96488184... | [7.486422061920166, -2.4709291458129883] |
55958e8a-8ef4-41c9-b56c-f8a4b79ce351 | how-deep-learning-sees-the-world-a-survey-on | 2305.10862 | null | https://arxiv.org/abs/2305.10862v1 | https://arxiv.org/pdf/2305.10862v1.pdf | How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses | Deep Learning is currently used to perform multiple tasks, such as object recognition, face recognition, and natural language processing. However, Deep Neural Networks (DNNs) are vulnerable to perturbations that alter the network prediction (adversarial examples), raising concerns regarding its usage in critical areas,... | ['Pedro R. M. Inácio', 'Hugo Proença', 'Tiago Roxo', 'Joana C. Costa'] | 2023-05-18 | null | null | null | null | ['face-recognition', 'object-recognition'] | ['computer-vision', 'computer-vision'] | [ 2.31513157e-01 6.97250068e-02 6.73335344e-02 -1.63154587e-01
9.58739743e-02 -9.17136669e-01 8.38917255e-01 -2.52499372e-01
-6.00999117e-01 5.33430636e-01 4.31452692e-02 -5.01787722e-01
3.40740681e-02 -7.20310390e-01 -8.42226982e-01 -7.77239978e-01
-2.60475904e-01 5.06971292e-02 1.98674753e-01 -5.05861163... | [5.574195384979248, 7.8567423820495605] |
cfecc962-03d8-4ab7-9049-17cd6078d7ed | a-comprehensive-assessment-of-dialog | 2106.03706 | null | https://arxiv.org/abs/2106.03706v4 | https://arxiv.org/pdf/2106.03706v4.pdf | A Comprehensive Assessment of Dialog Evaluation Metrics | Automatic evaluation metrics are a crucial component of dialog systems research. Standard language evaluation metrics are known to be ineffective for evaluating dialog. As such, recent research has proposed a number of novel, dialog-specific metrics that correlate better with human judgements. Due to the fast pace of r... | ['Shikib Mehri', 'Maxine Eskenazi', 'Yi-Ting Yeh'] | 2021-06-07 | null | https://aclanthology.org/2021.eancs-1.3 | https://aclanthology.org/2021.eancs-1.3.pdf | eancs-2021-11 | ['dialogue-evaluation'] | ['natural-language-processing'] | [-2.99290746e-01 1.91782981e-01 -3.54951695e-02 -6.20333493e-01
-6.89650476e-01 -9.77699101e-01 1.11852717e+00 5.37689328e-01
-5.50239503e-01 8.44125867e-01 7.59279490e-01 -2.42482230e-01
-3.95758003e-01 -4.91307288e-01 5.59088290e-01 -2.66853064e-01
2.85146207e-01 9.33816791e-01 2.26120502e-01 -8.74168813... | [12.911811828613281, 8.047504425048828] |
f2a2a8da-7e05-45d2-ae73-95df4e7b6169 | solving-constraint-satisfaction-problems-with | 1801.04515 | null | http://arxiv.org/abs/1801.04515v1 | http://arxiv.org/pdf/1801.04515v1.pdf | Solving constraint-satisfaction problems with distributed neocortical-like neuronal networks | Finding actions that satisfy the constraints imposed by both external inputs
and internal representations is central to decision making. We demonstrate that
some important classes of constraint satisfaction problems (CSPs) can be solved
by networks composed of homogeneous cooperative-competitive modules that have
conne... | [] | 2018-01-14 | null | null | null | null | ['mathematical-proofs'] | ['miscellaneous'] | [ 5.06294668e-01 5.45410216e-01 -1.31728753e-01 1.21482097e-01
4.34457123e-01 -1.07708108e+00 2.31344134e-01 -3.62671256e-01
-3.45445931e-01 6.12290382e-01 -2.34485134e-01 -1.44973844e-01
-7.59248674e-01 -5.89426816e-01 -6.56428814e-01 -1.00190377e+00
-6.44042313e-01 4.97308940e-01 2.49710634e-01 -3.64750832... | [8.221549987792969, 3.2975027561187744] |
8d01ec99-7b0f-4732-a11a-8831ae349bf4 | spontaneous-facial-micro-expression-1 | 1904.0139 | null | http://arxiv.org/abs/1904.01390v1 | http://arxiv.org/pdf/1904.01390v1.pdf | Spontaneous Facial Micro-Expression Recognition using 3D Spatiotemporal Convolutional Neural Networks | Facial expression recognition in videos is an active area of research in
computer vision. However, fake facial expressions are difficult to be
recognized even by humans. On the other hand, facial micro-expressions
generally represent the actual emotion of a person, as it is a spontaneous
reaction expressed through huma... | ['Shiv Ram Dubey', 'Snehasis Mukherjee', 'Sai Prasanna Teja Reddy', 'Surya Teja Karri'] | 2019-03-27 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [-1.00015469e-01 -1.99908197e-01 -1.51648283e-01 -5.43988049e-01
-1.59827113e-01 -4.41052653e-02 6.59121811e-01 -4.20504093e-01
-4.51502860e-01 5.87068260e-01 -1.90887854e-01 3.85792106e-01
2.88681656e-01 -4.51592416e-01 -5.36170661e-01 -1.07203555e+00
6.75276816e-02 -1.84167832e-01 -2.35532060e-01 -4.01279420... | [13.632052421569824, 1.7909804582595825] |
db6dedac-28c2-41b3-af58-3426da5feb76 | open-vocabulary-object-detection-using | 2011.10678 | null | https://arxiv.org/abs/2011.10678v2 | https://arxiv.org/pdf/2011.10678v2.pdf | Open-Vocabulary Object Detection Using Captions | Despite the remarkable accuracy of deep neural networks in object detection, they are costly to train and scale due to supervision requirements. Particularly, learning more object categories typically requires proportionally more bounding box annotations. Weakly supervised and zero-shot learning techniques have been ex... | ['Shih-Fu Chang', 'Derek Hao Hu', 'Kevin Dela Rosa', 'Alireza Zareian'] | 2020-11-20 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Zareian_Open-Vocabulary_Object_Detection_Using_Captions_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Zareian_Open-Vocabulary_Object_Detection_Using_Captions_CVPR_2021_paper.pdf | cvpr-2021-1 | ['open-vocabulary-object-detection', 'open-vocabulary-attribute-detection'] | ['computer-vision', 'computer-vision'] | [ 6.01072796e-02 2.90166996e-02 -3.43412936e-01 -3.83286178e-01
-9.50678170e-01 -5.56339920e-01 4.34179723e-01 3.87168139e-01
-5.25426984e-01 3.25700969e-01 -2.69018173e-01 7.11412579e-02
2.44780853e-01 -7.69792616e-01 -8.42207074e-01 -5.17397761e-01
1.10084951e-01 5.78492224e-01 9.46323812e-01 -6.08631521... | [9.424413681030273, 1.4535902738571167] |
f9dd3ce2-a141-4fa1-a140-d10a179ba3ed | large-scale-bidirectional-training-for-zero | 2211.06774 | null | https://arxiv.org/abs/2211.06774v2 | https://arxiv.org/pdf/2211.06774v2.pdf | Large-Scale Bidirectional Training for Zero-Shot Image Captioning | When trained on large-scale datasets, image captioning models can understand the content of images from a general domain but often fail to generate accurate, detailed captions. To improve performance, pretraining-and-finetuning has been a key strategy for image captioning. However, we find that large-scale bidirectiona... | ['Seung Hwan Kim', 'Alessandra Sala', 'Sihaeng Lee', 'Sangyun Kim', 'Pyunghwan Ahn', 'Mark Marsden', 'TaeHoon Kim'] | 2022-11-13 | null | null | null | null | ['keyword-extraction'] | ['natural-language-processing'] | [ 7.62487710e-01 2.37091735e-01 -7.07545459e-01 -4.75505739e-01
-1.44571328e+00 -4.72759008e-01 7.44175553e-01 -2.98592150e-01
-2.73674786e-01 7.08118618e-01 4.32017237e-01 -2.91954190e-01
4.16424036e-01 -5.51210940e-01 -1.24359524e+00 -3.08821112e-01
3.89966339e-01 4.29169357e-01 2.43507829e-02 -1.52893320... | [11.024300575256348, 1.0418976545333862] |
469813ae-eb55-4c2a-a8d0-7b79959bafbe | inout-diverse-image-outpainting-via-gan | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Cheng_InOut_Diverse_Image_Outpainting_via_GAN_Inversion_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Cheng_InOut_Diverse_Image_Outpainting_via_GAN_Inversion_CVPR_2022_paper.pdf | InOut: Diverse Image Outpainting via GAN Inversion | Image outpainting seeks for a semantically consistent extension of the input image beyond its available content. Compared to inpainting --- filling in missing pixels in a way coherent with the neighboring pixels --- outpainting can be achieved in more diverse ways since the problem is less constrained by the surrou... | ['Ming-Hsuan Yang', 'Sergey Tulyakov', 'Jian Ren', 'Hsin-Ying Lee', 'Chieh Hubert Lin', 'Yen-Chi Cheng'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['image-outpainting'] | ['computer-vision'] | [ 8.41682196e-01 4.09821779e-01 -1.64931417e-01 3.26729612e-04
-7.71660507e-01 -6.39320970e-01 3.23419511e-01 -4.97924715e-01
4.62306328e-02 1.12516594e+00 1.37758434e-01 1.66615233e-01
3.50528687e-01 -9.52586472e-01 -1.05303895e+00 -8.89669240e-01
4.72537845e-01 1.74890533e-01 -2.46765360e-01 -1.77813247... | [11.61185073852539, -0.8205229043960571] |
d0ca35c6-07a1-4b47-bef8-79a2b1b8a07d | the-newsbridge-telecom-sudparis-voxceleb | 2301.07491 | null | https://arxiv.org/abs/2301.07491v1 | https://arxiv.org/pdf/2301.07491v1.pdf | The Newsbridge -Telecom SudParis VoxCeleb Speaker Recognition Challenge 2022 System Description | We describe the system used by our team for the VoxCeleb Speaker Recognition Challenge 2022 (VoxSRC 2022) in the speaker diarization track. Our solution was designed around a new combination of voice activity detection algorithms that uses the strengths of several systems. We introduce a novel multi stream approach wit... | ['Frédéric Petitpont', 'Jérôme Boudy', 'Yannis Tevissen'] | 2023-01-17 | null | null | null | null | ['activity-detection', 'speaker-recognition'] | ['computer-vision', 'speech'] | [-2.16390546e-02 2.09336728e-01 1.62082911e-01 -2.06823140e-01
-1.03791738e+00 -5.76483011e-01 1.06545162e+00 -8.16658959e-02
-4.69249785e-01 1.92000553e-01 6.84090614e-01 -1.24355748e-01
1.97547987e-01 -4.38357182e-02 8.31276178e-02 -7.49706984e-01
-2.07882524e-01 3.96402925e-01 2.69575208e-01 -3.95475864... | [14.418034553527832, 5.942387104034424] |
21ef9a46-7276-4574-b22f-50b88c08524e | joint-channel-estimation-and-turbo | 2305.09226 | null | https://arxiv.org/abs/2305.09226v1 | https://arxiv.org/pdf/2305.09226v1.pdf | Joint Channel Estimation and Turbo Equalization of Single-Carrier Systems over Time-Varying Channels | Block transmission systems have been proven successful over frequency-selective channels. For time-varying channel such as in high-speed mobile communication and underwater communication, existing equalizers assume that channels over different data frames are independent. However, the real-world channels over different... | ['Yan Wei', 'Fengzhong Qu', 'Zhipeng Li', 'Xingbin Tu', 'Minhao Zhang', 'Yifan Wang'] | 2023-05-16 | null | null | null | null | ['compressive-sensing'] | ['computer-vision'] | [ 6.86387777e-01 4.32900339e-02 1.18806869e-01 -6.69779480e-02
-7.10449576e-01 -1.92709938e-01 7.82785937e-02 -1.09972522e-01
-6.51763439e-01 8.35794985e-01 1.91396102e-01 -1.73520863e-01
-2.38588735e-01 -4.65680748e-01 -9.62484241e-01 -1.04382348e+00
-8.94811809e-01 -3.79190683e-01 -1.29385635e-01 -1.99892804... | [6.473203182220459, 1.3386143445968628] |
36f67b94-6614-4b1e-9e82-3befe87762e2 | eye-gaze-estimation-model-analysis | 2207.14373 | null | https://arxiv.org/abs/2207.14373v1 | https://arxiv.org/pdf/2207.14373v1.pdf | Eye Gaze Estimation Model Analysis | We explore techniques for eye gaze estimation using machine learning. Eye gaze estimation is a common problem for various behavior analysis and human-computer interfaces. The purpose of this work is to discuss various model types for eye gaze estimation and present the results from predicting gaze direction using eye l... | ['Ayush Kumar', 'Aveena Kottwani'] | 2022-07-28 | null | null | null | null | ['gaze-estimation'] | ['computer-vision'] | [-5.65228611e-03 -1.00775383e-01 -1.29785314e-01 -6.32450163e-01
2.70645488e-02 -1.19357325e-01 1.01517871e-01 -3.19208890e-01
-4.40047413e-01 7.57203579e-01 -2.41136253e-01 -1.00491270e-01
9.78614390e-02 2.72355348e-01 -4.78231311e-01 -5.57830691e-01
1.01043634e-01 -1.86662719e-01 2.65857100e-01 3.44172902... | [14.09528636932373, 0.12305130064487457] |
994d5778-d2a4-4dc9-80ee-9e852089fcca | a-label-aware-autoregressive-framework-for | null | null | https://aclanthology.org/2022.findings-naacl.171 | https://aclanthology.org/2022.findings-naacl.171.pdf | A Label-Aware Autoregressive Framework for Cross-Domain NER | Cross-domain named entity recognition (NER) aims to borrow the entity information from the source domain to help the entity recognition in the target domain with limited labeled data. Despite the promising performance of existing approaches, most of them focus on reducing the discrepancy of token representation between... | ['Tsung-Hui Chang', 'Xiang Wan', 'Dan Guo', 'He Zhao', 'Jinpeng Hu'] | null | null | null | null | findings-naacl-2022-7 | ['cross-domain-named-entity-recognition'] | ['natural-language-processing'] | [ 1.42357824e-02 -1.13834582e-01 -2.84201056e-01 -6.37309611e-01
-7.54315376e-01 -6.38121963e-01 3.67737353e-01 9.53035057e-02
-6.43883049e-01 6.06402695e-01 3.57115269e-01 4.34341058e-02
2.43292242e-01 -7.81888664e-01 -4.63722020e-01 -6.04547858e-01
3.61681908e-01 1.94619119e-01 1.59316123e-01 1.54693297... | [9.819731712341309, 9.548460960388184] |
ac8c2867-9283-4160-94e3-4b5b16c69c63 | guiding-pretraining-in-reinforcement-learning | 2302.06692 | null | https://arxiv.org/abs/2302.06692v1 | https://arxiv.org/pdf/2302.06692v1.pdf | Guiding Pretraining in Reinforcement Learning with Large Language Models | Reinforcement learning algorithms typically struggle in the absence of a dense, well-shaped reward function. Intrinsically motivated exploration methods address this limitation by rewarding agents for visiting novel states or transitions, but these methods offer limited benefits in large environments where most discove... | ['Jacob Andreas', 'Abhishek Gupta', 'Pieter Abbeel', 'Trevor Darrell', 'Cédric Colas', 'Zihan Wang', 'Olivia Watkins', 'Yuqing Du'] | 2023-02-13 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [-1.59494355e-01 4.96859908e-01 -4.23208565e-01 -5.54983318e-02
-6.69497013e-01 -7.56286919e-01 9.98347223e-01 3.26651067e-01
-9.11989093e-01 1.08344316e+00 4.21394557e-01 -3.58006865e-01
-1.04970478e-01 -4.54133451e-01 -6.93473399e-01 -4.55942631e-01
-4.50791210e-01 8.17937434e-01 2.44701847e-01 -6.03949785... | [3.9800546169281006, 1.4874266386032104] |
120f7bd9-68e2-420c-a9ec-292f65670c6d | heartbeat-classification-in-wearables-using | 1908.06865 | null | https://arxiv.org/abs/1908.06865v1 | https://arxiv.org/pdf/1908.06865v1.pdf | Heartbeat Classification in Wearables Using Multi-layer Perceptron and Time-Frequency Joint Distribution of ECG | Heartbeat classification using electrocardiogram (ECG) data is a vital assistive technology for wearable health solutions. We propose heartbeat feature classification based on a novel sparse representation using time-frequency joint distribution of ECG. Fundamental to this is a multi-layer perceptron, which incorporate... | ['Siebren Schaafsma', 'Francky Catthoor', 'Anup Das'] | 2019-08-13 | null | null | null | null | ['heartbeat-classification'] | ['medical'] | [ 5.70361257e-01 -1.97702814e-02 -1.07618272e-01 -3.42816263e-01
-6.18755221e-01 -4.05607074e-02 -2.43368655e-01 4.50445652e-01
-1.55067354e-01 7.99005389e-01 2.69781172e-01 -5.53914048e-02
-4.70011771e-01 -4.32908416e-01 5.57407252e-02 -6.09369755e-01
-5.50358236e-01 1.78650022e-01 -4.56235617e-01 2.06870824... | [14.265707015991211, 3.2596347332000732] |
f3fe6c9f-f2ed-48cb-b21b-927a4776ff8e | self-attention-convlstm-for-spatiotemporal | null | null | https://ojs.aaai.org//index.php/AAAI/article/view/6819 | https://ojs.aaai.org/index.php/AAAI/article/view/6819/6673 | Self-Attention ConvLSTM for Spatiotemporal Prediction | Spatiotemporal prediction is challenging due to the complex dynamic motion and appearance changes. Existing work concentrates on embedding additional cells into the standard ConvLSTM to memorize spatial appearances during the prediction. These models always rely on the convolution layers to capture the spatial dependen... | ['Chun Yuan', 'Yangyang Cheng', 'Zhuobin Zheng', 'Maomao Li', 'Zhihui Lin'] | 2020-04-03 | null | null | null | aaai-2020-4 | ['video-prediction'] | ['computer-vision'] | [-1.08691953e-01 -6.82326019e-01 -2.52042562e-01 -5.75340867e-01
-3.06554586e-01 5.78454472e-02 4.60454494e-01 -1.20265409e-01
-5.53012729e-01 7.60872602e-01 2.84317821e-01 -7.13889822e-02
1.27142891e-02 -9.75248218e-01 -8.70156050e-01 -7.31959939e-01
-1.18149593e-01 -1.98993489e-01 9.87148583e-01 -7.49276429... | [8.844596862792969, 0.3916183114051819] |
65fbc964-0f7b-4b4d-9928-9807e8c1872e | unleashing-infinite-length-input-capacity-for | 2304.13343 | null | https://arxiv.org/abs/2304.13343v1 | https://arxiv.org/pdf/2304.13343v1.pdf | Unleashing Infinite-Length Input Capacity for Large-scale Language Models with Self-Controlled Memory System | Large-scale Language Models (LLMs) are constrained by their inability to process lengthy inputs. To address this limitation, we propose the Self-Controlled Memory (SCM) system to unleash infinite-length input capacity for large-scale language models. Our SCM system is composed of three key modules: the language model a... | ['Zhoujun Li', 'Zejun Ma', 'Lu Lu', 'Peihao Wu', 'Shuangzhi Wu', 'Hui Huang', 'Bing Wang', 'Xinnian Liang'] | 2023-04-26 | null | null | null | null | ['document-summarization'] | ['natural-language-processing'] | [ 1.76926497e-02 3.06666911e-01 -1.85264558e-01 -2.21549034e-01
-1.23044920e+00 -7.40459383e-01 8.11217844e-01 3.59622017e-02
-4.17562217e-01 9.48567212e-01 5.39592624e-01 -4.92055625e-01
1.79889545e-01 -7.08871007e-01 -3.85742038e-01 -1.64821863e-01
1.40738845e-01 9.41038251e-01 4.39452738e-01 -3.93969774... | [12.288188934326172, 8.561023712158203] |
5905305e-d833-48a1-97b5-a40e1cc14c02 | efficient-global-point-cloud-alignment-using | 1603.04868 | null | http://arxiv.org/abs/1603.04868v3 | http://arxiv.org/pdf/1603.04868v3.pdf | Efficient Global Point Cloud Alignment using Bayesian Nonparametric Mixtures | Point cloud alignment is a common problem in computer vision and robotics,
with applications ranging from 3D object recognition to reconstruction. We
propose a novel approach to the alignment problem that utilizes Bayesian
nonparametrics to describe the point cloud and surface normal densities, and
branch and bound (BB... | ['John W. Fisher III', 'Trevor Campbell', 'Julian Straub', 'Jonathan P. How'] | 2016-03-15 | efficient-global-point-cloud-alignment-using-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Straub_Efficient_Global_Point_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Straub_Efficient_Global_Point_CVPR_2017_paper.pdf | cvpr-2017-7 | ['3d-object-recognition'] | ['computer-vision'] | [-8.28618333e-02 -2.64883161e-01 -1.65003851e-01 -3.28270420e-02
-7.01484144e-01 -4.18094695e-01 4.97970909e-01 1.55336484e-01
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-2.87433177e-01 -4.81478840e-01 -1.14267039e+00 -7.53400743e-01
8.68350416e-02 1.10417283e+00 1.85698301e-01 3.97926092... | [7.718327045440674, -2.7936601638793945] |
3b389ec5-2d31-4ac4-adcf-7a51f5ac9d6e | popdx-an-automated-framework-for-patient | 2208.11223 | null | https://arxiv.org/abs/2208.11223v2 | https://arxiv.org/pdf/2208.11223v2.pdf | POPDx: An Automated Framework for Patient Phenotyping across 392,246 Individuals in the UK Biobank Study | Objective For the UK Biobank standardized phenotype codes are associated with patients who have been hospitalized but are missing for many patients who have been treated exclusively in an outpatient setting. We describe a method for phenotype recognition that imputes phenotype codes for all UK Biobank participants. Mat... | ['Russ B. Altman', 'Sheng Wang', 'Lu Yang'] | 2022-08-23 | null | null | null | null | ['patient-phenotyping'] | ['medical'] | [ 2.89134920e-01 3.44417766e-02 -2.65340745e-01 -5.47246456e-01
-1.33847594e+00 -4.28968132e-01 -2.03442108e-02 9.07884479e-01
-1.34370282e-01 1.23136103e+00 5.26873827e-01 -8.39490294e-02
-3.45150590e-01 -6.31191909e-01 -4.56996024e-01 -6.55337214e-01
-3.54661494e-01 1.25366831e+00 -7.05426395e-01 5.21011055... | [6.363039970397949, 5.788107872009277] |
e2e1ab1a-0118-421b-ad93-016a090d93af | soda10m-towards-large-scale-object-detection | 2106.11118 | null | https://arxiv.org/abs/2106.11118v3 | https://arxiv.org/pdf/2106.11118v3.pdf | SODA10M: A Large-Scale 2D Self/Semi-Supervised Object Detection Dataset for Autonomous Driving | Aiming at facilitating a real-world, ever-evolving and scalable autonomous driving system, we present a large-scale dataset for standardizing the evaluation of different self-supervised and semi-supervised approaches by learning from raw data, which is the first and largest dataset to date. Existing autonomous driving ... | ['Jiageng Mao', 'Xiaodan Liang', 'Chunjing Xu', 'Zhenguo Li', 'Wei zhang', 'Chaoqiang Ye', 'Lanqing Hong', 'Kai Chen', 'Hang Xu', 'Xiwen Liang', 'Jianhua Han'] | 2021-06-21 | null | null | null | null | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 1.78101566e-02 -3.20344828e-02 -3.72992247e-01 -6.76930904e-01
-6.37231171e-01 -6.19902134e-01 7.01225281e-01 -2.07235888e-01
-4.73814040e-01 4.40909743e-01 -2.69775897e-01 -4.50121343e-01
2.43287086e-01 -6.78679943e-01 -8.20501864e-01 -7.09192455e-01
1.24756463e-01 5.03616869e-01 7.20219493e-01 -3.80473822... | [8.189563751220703, -1.5717099905014038] |
b9843a9d-caca-4b09-8a6c-362c1f9cddd9 | towards-on-device-domain-adaptation-for-noise | null | null | https://ieeexplore.ieee.org/document/9869990 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9869990 | Towards On-device Domain Adaptation for Noise-Robust Keyword Spotting | The accuracy of a keyword spotting model deployed on embedded devices often degrades when the system is exposed to real environments with significant noise. In this paper, we explore a methodology for tailoring a model to on-site noises through on-device domain adaptation, while accounting for the edge computing-associ... | ['Luca Benini', 'Miguel de Prado', 'Manuele Rusci', 'Lukas Cavigelli', 'Cristian Cioflan'] | 2022-06-13 | null | null | null | ieee-international-conference-on-artificial | ['keyword-spotting'] | ['speech'] | [ 3.70848000e-01 -2.32146502e-01 5.86572364e-02 -1.86210230e-01
-1.04815555e+00 -5.54174304e-01 9.46973488e-02 1.60363406e-01
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1.13728922e-02 -6.18939757e-01 -6.69127405e-01 -5.01620591e-01
1.75903440e-01 2.40203500e-01 4.60567504e-01 1.09379232... | [14.336380004882812, 5.779839515686035] |
5af53eb3-c8a4-494a-af56-f27911bcb69e | visualize-before-you-write-imagination-guided | 2210.03765 | null | https://arxiv.org/abs/2210.03765v4 | https://arxiv.org/pdf/2210.03765v4.pdf | Visualize Before You Write: Imagination-Guided Open-Ended Text Generation | Recent advances in text-to-image synthesis make it possible to visualize machine imaginations for a given context. On the other hand, when generating text, human writers are gifted at creative visualization, which enhances their writings by forming imaginations as blueprints before putting down the stories in words. In... | ['William Yang Wang', 'Miguel Eckstein', 'Xin Eric Wang', 'Wenda Xu', 'Yujie Lu', 'An Yan', 'Wanrong Zhu'] | 2022-10-07 | null | null | null | null | ['story-generation', 'concept-to-text-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.44143414e-01 3.15599889e-01 1.45287409e-01 -1.98393866e-01
-1.14587247e-01 -5.73217571e-01 1.23914289e+00 7.82286301e-02
2.28465840e-01 6.37764931e-01 9.23000574e-01 -1.48684829e-01
2.30449095e-01 -8.82123649e-01 -3.93052131e-01 -2.44714245e-01
5.67317426e-01 3.85464579e-01 -2.74743885e-01 -3.74697775... | [11.224645614624023, 0.8984536528587341] |
80d3bb52-c893-41f3-ba10-8aa7a0d4786c | unsupervised-domain-adaptation-for-plant | 2009.01081 | null | https://arxiv.org/abs/2009.01081v1 | https://arxiv.org/pdf/2009.01081v1.pdf | Unsupervised Domain Adaptation For Plant Organ Counting | Supervised learning is often used to count objects in images, but for counting small, densely located objects, the required image annotations are burdensome to collect. Counting plant organs for image-based plant phenotyping falls within this category. Object counting in plant images is further challenged by having pla... | ['Ian Stavness', 'Jordan Ubbens', 'Tewodros Ayalew'] | 2020-09-02 | null | null | null | null | ['plant-phenotyping', 'object-counting'] | ['computer-vision', 'computer-vision'] | [ 6.15470588e-01 -3.93587708e-01 2.29459517e-02 -1.74461097e-01
-5.47802925e-01 -1.29980350e+00 3.76493961e-01 5.42685390e-01
-4.11678672e-01 7.21485913e-01 -6.51718378e-01 -3.99014890e-01
2.08409980e-01 -8.59358191e-01 -9.11237717e-01 -5.90668797e-01
1.87698796e-01 8.73999596e-01 2.99942702e-01 3.37298214... | [9.110036849975586, -1.4454361200332642] |
a2f04094-f7f3-4218-9246-b437d979ce18 | a-word-is-worth-a-thousand-dollars-1 | null | null | https://openreview.net/forum?id=l_Wlug2cgDi | https://openreview.net/pdf?id=l_Wlug2cgDi | A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Prediction | More and more investors and machine learning models rely on social media (e.g., Twitter and Reddit) to gather information and predict movements stock prices. Although text-based models are known to be vulnerable to adversarial attacks, whether stock prediction models have similar vulnerability given necessary constrain... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['stock-prediction'] | ['time-series'] | [-3.54390353e-01 2.05348864e-01 -3.53663713e-02 -4.14142087e-02
-4.84738380e-01 -1.16022158e+00 9.14335191e-01 5.11343814e-02
-2.17688292e-01 9.67444718e-01 9.51511115e-02 -4.01354164e-01
3.48408312e-01 -1.33774543e+00 -7.04907060e-01 -1.80854559e-01
-4.59700346e-01 6.32568002e-01 3.56678933e-01 -6.58090711... | [5.696815013885498, 7.625125408172607] |
60b56159-ca6e-46cb-bc4a-629b660057ad | towards-an-automatic-turing-test-learning-to | 1708.07149 | null | http://arxiv.org/abs/1708.07149v2 | http://arxiv.org/pdf/1708.07149v2.pdf | Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses | Automatically evaluating the quality of dialogue responses for unstructured
domains is a challenging problem. Unfortunately, existing automatic evaluation
metrics are biased and correlate very poorly with human judgements of response
quality. Yet having an accurate automatic evaluation procedure is crucial for
dialogue... | ['Nicolas Angelard-Gontier', 'Michael Noseworthy', 'Ryan Lowe', 'Yoshua Bengio', 'Joelle Pineau', 'Iulian V. Serban'] | 2017-08-23 | towards-an-automatic-turing-test-learning-to-1 | https://aclanthology.org/P17-1103 | https://aclanthology.org/P17-1103.pdf | acl-2017-7 | ['dialogue-evaluation'] | ['natural-language-processing'] | [ 2.41189420e-01 5.42324901e-01 1.51548445e-01 -8.63124073e-01
-1.11522222e+00 -8.20142031e-01 7.39910603e-01 4.72500026e-01
-6.00518763e-01 1.09303808e+00 6.22084320e-01 -3.12713891e-01
1.60816059e-01 -5.84702194e-01 9.56970528e-02 3.39499675e-02
2.70685732e-01 1.01243210e+00 1.78935498e-01 -7.84446836... | [12.818556785583496, 8.102625846862793] |
3b6ca6c8-1386-4776-952e-1a9269b5c353 | learning-word-embeddings-for-low-resource | null | null | https://aclanthology.org/N18-1093 | https://aclanthology.org/N18-1093.pdf | Learning Word Embeddings for Low-Resource Languages by PU Learning | Word embedding is a key component in many downstream applications in processing natural languages. Existing approaches often assume the existence of a large collection of text for learning effective word embedding. However, such a corpus may not be available for some low-resource languages. In this paper, we study how ... | ['Kai-Wei Chang', 'Cho-Jui Hsieh', 'Hsiang-Fu Yu', 'Chao Jiang'] | 2018-06-01 | null | null | null | naacl-2018-6 | ['learning-word-embeddings'] | ['methodology'] | [-4.62344056e-03 1.52748972e-01 -6.52858198e-01 -2.36444652e-01
-8.56235385e-01 -5.42435288e-01 6.50882602e-01 5.03691137e-01
-8.36862624e-01 7.80734420e-01 5.06534517e-01 -6.06858015e-01
2.64454156e-01 -7.94310331e-01 -5.33502817e-01 -5.43348730e-01
-1.04370743e-01 3.49842846e-01 -6.97217211e-02 -1.98372722... | [10.479339599609375, 8.660893440246582] |
ad403577-f001-4ef2-8786-955fae5ce3d4 | kekulescope-improved-prediction-of-cancer | 1811.09036 | null | https://arxiv.org/abs/1811.09036v2 | https://arxiv.org/pdf/1811.09036v2.pdf | KekuleScope: prediction of cancer cell line sensitivity and compound potency using convolutional neural networks trained on compound images | The application of convolutional neural networks (ConvNets) to harness high-content screening images or 2D compound representations is gaining increasing attention in drug discovery. However, existing applications often require large data sets for training, or sophisticated pretraining schemes. Here, we show using 33 I... | ['Isidro Cortes Ciriano', 'Andreas Bender'] | 2018-11-22 | null | null | null | null | ['prediction-of-cancer-cell-line-sensitivity'] | ['medical'] | [ 7.89999783e-01 -2.95028742e-02 -3.84408444e-01 -1.99212208e-01
-9.17485952e-01 -8.38582397e-01 8.04959953e-01 3.74697536e-01
-6.20702922e-01 1.12400675e+00 -2.36654997e-01 -5.61688840e-01
-2.76938170e-01 -6.91679299e-01 -1.00238657e+00 -8.91216457e-01
-2.50531048e-01 4.67055053e-01 3.36254954e-01 -1.02456547... | [5.13463020324707, 5.794782638549805] |
11a609df-acf8-4f47-bb46-8232ed31bcf7 | bird-species-categorization-using-pose | 1406.2952 | null | http://arxiv.org/abs/1406.2952v1 | http://arxiv.org/pdf/1406.2952v1.pdf | Bird Species Categorization Using Pose Normalized Deep Convolutional Nets | We propose an architecture for fine-grained visual categorization that
approaches expert human performance in the classification of bird species. Our
architecture first computes an estimate of the object's pose; this is used to
compute local image features which are, in turn, used for classification. The
features are c... | ['Grant van Horn', 'Serge Belongie', 'Steve Branson', 'Pietro Perona'] | 2014-06-11 | null | null | null | null | ['fine-grained-visual-categorization'] | ['computer-vision'] | [-1.40126079e-01 -5.81583738e-01 -1.13844229e-02 -6.84206903e-01
-8.51869285e-02 -1.12667775e+00 7.70641267e-01 5.07782400e-01
-9.30719435e-01 -7.86147416e-02 2.10567072e-01 1.31149039e-01
-3.61545593e-01 -8.35295320e-01 -6.09323859e-01 -5.41632950e-01
-4.34716433e-01 3.84415537e-01 3.27410907e-01 -3.56357217... | [9.76188850402832, 2.2365758419036865] |
67fceedf-f9dd-4583-a662-f67bea6c2a4e | multimodal-analysis-of-the-predictability-of | 2108.05762 | null | https://arxiv.org/abs/2108.05762v3 | https://arxiv.org/pdf/2108.05762v3.pdf | Multimodal analysis of the predictability of hand-gesture properties | Embodied conversational agents benefit from being able to accompany their speech with gestures. Although many data-driven approaches to gesture generation have been proposed in recent years, it is still unclear whether such systems can consistently generate gestures that convey meaning. We investigate which gesture pro... | ['Gustav Eje Henter', 'Hedvig Kjellström', 'Michael Neff', 'Rajmund Nagy', 'Taras Kucherenko'] | 2021-08-12 | null | null | null | null | ['gesture-generation'] | ['robots'] | [ 3.68261307e-01 2.97675818e-01 -1.44456804e-01 -6.90363050e-01
-6.50314927e-01 -6.11588120e-01 1.36943758e+00 -1.10565342e-01
-4.61474597e-01 6.00901365e-01 1.09432149e+00 2.90373545e-02
4.64407913e-02 -4.36434090e-01 -3.10518861e-01 -8.10424805e-01
-4.70188022e-01 5.23260772e-01 -1.26108631e-01 -4.80785847... | [5.613048076629639, -0.10880644619464874] |
b5407982-1750-4c45-93fe-58304ee6bf49 | a-categorized-reflection-removal-dataset-with | 2108.0338 | null | https://arxiv.org/abs/2108.03380v1 | https://arxiv.org/pdf/2108.03380v1.pdf | A Categorized Reflection Removal Dataset with Diverse Real-world Scenes | Due to the lack of a large-scale reflection removal dataset with diverse real-world scenes, many existing reflection removal methods are trained on synthetic data plus a small amount of real-world data, which makes it difficult to evaluate the strengths or weaknesses of different reflection removal methods thoroughly. ... | ['Qifeng Chen', 'Qiong Yan', 'Wenxiu Sun', 'Yankun Zhao', 'Chenyang Qi', 'Xuhua Huang', 'Chenyang Lei'] | 2021-08-07 | null | null | null | null | ['reflection-removal'] | ['computer-vision'] | [ 4.17715758e-01 -5.59399784e-01 4.88171279e-01 -1.03610598e-01
-6.39841199e-01 -2.85615861e-01 4.68658537e-01 -6.32914841e-01
7.08016679e-02 4.34751630e-01 5.36828279e-01 -2.09231645e-01
1.88543975e-01 -7.78551698e-01 -5.42035043e-01 -9.03823495e-01
2.37729326e-01 -3.00476998e-01 4.26967412e-01 -5.35179496... | [10.559151649475098, -2.7723824977874756] |
2143956c-3c37-4831-beef-107f5b8c5da2 | deep-aggregation-of-regional-convolutional | 1909.0942 | null | https://arxiv.org/abs/1909.09420v2 | https://arxiv.org/pdf/1909.09420v2.pdf | Deep Aggregation of Regional Convolutional Activations for Content Based Image Retrieval | One of the key challenges of deep learning based image retrieval remains in aggregating convolutional activations into one highly representative feature vector. Ideally, this descriptor should encode semantic, spatial and low level information. Even though off-the-shelf pre-trained neural networks can already produce g... | ['Konstantin Schall', 'Nico Hezel', 'Kai Uwe Barthel', 'Klaus Jung'] | 2019-09-20 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [-5.13405427e-02 -4.28943515e-01 -2.78682075e-02 -6.31336033e-01
-1.46890712e+00 -3.58306915e-01 8.17618370e-01 5.09890616e-01
-7.91844785e-01 5.23734510e-01 1.30811691e-01 3.71836483e-01
-3.51683646e-01 -9.26450193e-01 -7.13611722e-01 -7.56345212e-01
-1.92791641e-01 2.82501519e-01 1.79969355e-01 -2.98959047... | [10.60317611694336, 0.6659647822380066] |
9be43d4a-66bb-481f-a70b-0e28b282904b | meta-modeling-game-for-deriving-theoretical | 1810.10535 | null | http://arxiv.org/abs/1810.10535v1 | http://arxiv.org/pdf/1810.10535v1.pdf | Meta-modeling game for deriving theoretical-consistent, micro-structural-based traction-separation laws via deep reinforcement learning | This paper presents a new meta-modeling framework to employ deep
reinforcement learning (DRL) to generate mechanical constitutive models for
interfaces. The constitutive models are conceptualized as information flow in
directed graphs. The process of writing constitutive models are simplified as a
sequence of forming g... | ['WaiChing Sun', 'Kun Wang'] | 2018-10-24 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [ 1.03339382e-01 6.05513692e-01 -4.72748168e-02 2.73943275e-01
-4.71263319e-01 -5.02026640e-02 4.74760085e-01 1.52699694e-01
-9.09283385e-02 1.04096949e+00 -2.47633398e-01 -1.69578210e-01
-5.58983028e-01 -1.18282807e+00 -9.53845799e-01 -8.28572094e-01
-2.28698120e-01 8.52090657e-01 3.11097145e-01 -6.75007045... | [5.899358749389648, 3.372332811355591] |
a1691b32-bcce-4681-ace5-3c21ff840d39 | maskfusion-real-time-recognition-tracking-and | 1804.09194 | null | http://arxiv.org/abs/1804.09194v2 | http://arxiv.org/pdf/1804.09194v2.pdf | MaskFusion: Real-Time Recognition, Tracking and Reconstruction of Multiple Moving Objects | We present MaskFusion, a real-time, object-aware, semantic and dynamic RGB-D
SLAM system that goes beyond traditional systems which output a purely
geometric map of a static scene. MaskFusion recognizes, segments and assigns
semantic class labels to different objects in the scene, while tracking and
reconstructing them... | ['Martin Rünz', 'Maud Buffier', 'Lourdes Agapito'] | 2018-04-24 | null | null | null | null | ['object-slam', 'semantic-slam'] | ['computer-vision', 'computer-vision'] | [ 4.23204213e-01 2.74620801e-01 -9.52632225e-04 -6.30192280e-01
-6.61144197e-01 -8.32489729e-01 4.89945650e-01 3.43281105e-02
-2.20829666e-01 2.87904650e-01 -1.67676106e-01 -4.86660749e-02
4.64312918e-02 -7.99510479e-01 -8.61703098e-01 -2.21341774e-01
4.56566960e-02 1.29880250e+00 1.03577673e+00 2.18479671... | [7.334827899932861, -2.3053507804870605] |
e9355ae4-4f41-4398-ab3e-195daab246ac | activity-recognition-using-st-gcn-with-3d | null | null | https://doi.org/10.1145/3341162.3345581 | http://delivery.acm.org/10.1145/3350000/3345581/p689-cao.pdf | Activity recognition using ST-GCN with 3D motion data | For the Nurse Care Activity Recognition Challenge, an activity recognition algorithm was developed by Team TDU-DSML. A spatial-temporal graph convolutional network (ST-GCN) was applied to process 3D motion capture data included in the challenge dataset. Time-series data was divided into 20-second segments with a 10-sec... | ['Xin Cao', 'Masaki Shuzo', 'Wataru Kudo', 'Chihiro Ito', 'Eisaku Maeda'] | 2019-09-13 | null | null | null | ubicompiswc-19-adjunct-2019-9 | ['multimodal-activity-recognition'] | ['computer-vision'] | [ 4.67517942e-01 3.68760437e-01 -2.81658679e-01 -5.04913807e-01
-7.66807437e-01 -6.93601444e-02 3.02907117e-02 3.68728667e-01
-3.94965678e-01 4.41256195e-01 5.20641804e-01 -3.87428880e-01
-2.95943081e-01 -4.31309521e-01 -3.67070407e-01 -3.08798075e-01
-6.24655366e-01 1.92001104e-01 9.15334672e-02 2.67246306... | [14.046417236328125, -3.377955436706543] |
3cb546b8-72c9-4677-8cd1-23c1c7d4b043 | using-web-co-occurrence-statistics-for | 1312.5697 | null | http://arxiv.org/abs/1312.5697v2 | http://arxiv.org/pdf/1312.5697v2.pdf | Using Web Co-occurrence Statistics for Improving Image Categorization | Object recognition and localization are important tasks in computer vision.
The focus of this work is the incorporation of contextual information in order
to improve object recognition and localization. For instance, it is natural to
expect not to see an elephant to appear in the middle of an ocean. We consider
a simpl... | ['Dumitru Erhan', 'Samy Bengio', 'Jonathon Shlens', 'Eugene Ie', 'Yoram Singer', 'Quoc Le', 'Jeff Dean', 'Andrew Rabinovich'] | 2013-12-19 | null | null | null | null | ['image-categorization'] | ['computer-vision'] | [ 2.04664871e-01 -3.63177210e-01 -3.85684706e-03 -3.88336599e-01
-7.44365513e-01 -7.80574441e-01 1.06846964e+00 4.34986740e-01
-8.09527397e-01 4.90778565e-01 2.57915020e-01 -4.02944535e-01
1.20621547e-01 -7.44451046e-01 -1.13396049e+00 -6.85014606e-01
1.55567884e-01 3.20259690e-01 7.88662955e-02 1.95447996... | [9.917229652404785, 1.7579662799835205] |
a56e4b07-3b2a-4400-90a8-33815f2715d0 | learning-expressive-prompting-with-residuals | 2303.15591 | null | https://arxiv.org/abs/2303.15591v1 | https://arxiv.org/pdf/2303.15591v1.pdf | Learning Expressive Prompting With Residuals for Vision Transformers | Prompt learning is an efficient approach to adapt transformers by inserting learnable set of parameters into the input and intermediate representations of a pre-trained model. In this work, we present Expressive Prompts with Residuals (EXPRES) which modifies the prompt learning paradigm specifically for effective adapt... | ['Ashwin Swaminathan', 'Avinash Ravichandran', 'Yonatan Dukler', 'Rajshekhar Das'] | 2023-03-27 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Das_Learning_Expressive_Prompting_With_Residuals_for_Vision_Transformers_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Das_Learning_Expressive_Prompting_With_Residuals_for_Vision_Transformers_CVPR_2023_paper.pdf | cvpr-2023-1 | ['visual-prompting'] | ['computer-vision'] | [ 4.41516489e-01 4.18864906e-01 3.37817520e-02 -2.58777022e-01
-7.76139796e-01 -7.18530774e-01 9.56749260e-01 -1.31655991e-01
-5.04007399e-01 3.03188711e-01 5.22804797e-01 -3.06316942e-01
8.76994357e-02 -5.14230728e-01 -1.08133912e+00 -7.19270349e-01
3.36849302e-01 2.25462288e-01 6.43360078e-01 -2.80310690... | [10.097867965698242, 1.9561079740524292] |
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