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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
5d9ff70d-ff3a-4ee6-96b1-3fb14e1f0a9d | yolo2u-net-detection-guided-3d-instance | 2207.06215 | null | https://arxiv.org/abs/2207.06215v1 | https://arxiv.org/pdf/2207.06215v1.pdf | YOLO2U-Net: Detection-Guided 3D Instance Segmentation for Microscopy | Microscopy imaging techniques are instrumental for characterization and analysis of biological structures. As these techniques typically render 3D visualization of cells by stacking 2D projections, issues such as out-of-plane excitation and low resolution in the $z$-axis may pose challenges (even for human experts) to ... | ['David Solecki', 'Abbas Shirinifard', 'Derek C. Ros', 'Amirkoushyar Ziabari'] | 2022-07-13 | null | null | null | null | ['3d-instance-segmentation-1'] | ['computer-vision'] | [ 9.30761024e-02 -6.45925552e-02 6.30174279e-01 -1.20238908e-01
-5.61487734e-01 -5.17644703e-01 1.90926671e-01 2.86080688e-01
-4.98693734e-01 7.74571240e-01 -4.86007035e-01 -9.49063525e-02
2.41146013e-01 -5.03395677e-01 -5.12732506e-01 -1.04643357e+00
-1.12286225e-01 7.49711871e-01 3.86562765e-01 1.94564372... | [14.443215370178223, -3.1395487785339355] |
f7e6ba7d-1ac5-428f-ab92-cef6b7a109b9 | move-as-you-like-image-animation-in-e | 2112.13647 | null | https://arxiv.org/abs/2112.13647v1 | https://arxiv.org/pdf/2112.13647v1.pdf | Move As You Like: Image Animation in E-Commerce Scenario | Creative image animations are attractive in e-commerce applications, where motion transfer is one of the import ways to generate animations from static images. However, existing methods rarely transfer motion to objects other than human body or human face, and even fewer apply motion transfer in practical scenarios. In... | ['Lixin Duan', 'Wen Li', 'Yuning Jiang', 'Tiezheng Ge', 'Jiale Tao', 'Biao Wang', 'Borun Xu'] | 2021-12-19 | null | null | null | null | ['image-animation'] | ['computer-vision'] | [-4.24414217e-01 -2.71180630e-01 7.60039091e-02 6.42966032e-02
9.93225351e-02 -5.24678409e-01 4.04101998e-01 -7.84537733e-01
-4.13050316e-02 8.35412562e-01 -9.48991533e-03 -2.15795696e-01
3.68908167e-01 -8.49216580e-01 -5.14183700e-01 -5.02953947e-01
-1.22229263e-01 1.76409170e-01 3.94931585e-01 -4.93271112... | [10.859989166259766, -0.7282785177230835] |
0da39448-565a-4ac4-8d15-a5ddabbed56a | hierarchical-dynamic-filtering-network-for | 2007.06227 | null | https://arxiv.org/abs/2007.06227v3 | https://arxiv.org/pdf/2007.06227v3.pdf | Hierarchical Dynamic Filtering Network for RGB-D Salient Object Detection | The main purpose of RGB-D salient object detection (SOD) is how to better integrate and utilize cross-modal fusion information. In this paper, we explore these issues from a new perspective. We integrate the features of different modalities through densely connected structures and use their mixed features to generate d... | ['Youwei Pang', 'Lihe Zhang', 'Huchuan Lu', 'Xiaoqi Zhao'] | 2020-07-13 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/4959_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123700239.pdf | eccv-2020-8 | ['rgb-d-salient-object-detection', 'thermal-image-segmentation'] | ['computer-vision', 'computer-vision'] | [-3.78330536e-02 -3.74732345e-01 -1.70235254e-03 -4.39927936e-01
-6.67730391e-01 -1.74836919e-01 3.85985345e-01 -9.97509658e-02
-3.12815249e-01 6.54860437e-01 4.31130469e-01 1.73173532e-01
-2.27607340e-02 -7.29346514e-01 -6.45040631e-01 -6.84855938e-01
4.00440633e-01 -4.03624207e-01 9.04671848e-01 -3.12651724... | [9.697980880737305, -0.8338647484779358] |
97ad0800-0712-438a-a894-260742b4e14f | counterfactual-inference-in-duration-models | 1902.08502 | null | http://arxiv.org/abs/1902.08502v1 | http://arxiv.org/pdf/1902.08502v1.pdf | Counterfactual Inference in Duration Models with Random Censoring | We propose a counterfactual Kaplan-Meier estimator that incorporates
exogenous covariates and unobserved heterogeneity of unrestricted
dimensionality in duration models with random censoring. Under some regularity
conditions, we establish the joint weak convergence of the proposed
counterfactual estimator and the uncon... | [] | 2019-02-22 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [-1.83948740e-01 1.10603869e-01 -8.92396331e-01 -3.54592592e-01
-9.03982997e-01 -2.41538584e-01 4.05383140e-01 3.22674438e-02
-6.11079752e-01 1.71082985e+00 4.92459744e-01 -6.55806303e-01
-2.52241284e-01 -8.00235987e-01 -5.12431264e-01 -3.84814948e-01
-4.89279121e-01 1.47302315e-01 -4.08921838e-01 4.59540278... | [7.959960460662842, 5.197795391082764] |
20af043f-e54c-45cf-99c1-5a3239836049 | machine-translation-with-weakly-paired-1 | null | null | https://aclanthology.org/D19-1446 | https://aclanthology.org/D19-1446.pdf | Machine Translation With Weakly Paired Documents | Neural machine translation, which achieves near human-level performance in some languages, strongly relies on the large amounts of parallel sentences, which hinders its applicability to low-resource language pairs. Recent works explore the possibility of unsupervised machine translation with monolingual data only, lead... | ['Jian-Huang Lai', 'Tie-Yan Liu', 'Tao Qin', 'Jinhua Zhu', 'Fei Gao', 'Di He', 'Lijun Wu'] | 2019-11-01 | null | null | null | ijcnlp-2019-11 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 1.08087458e-01 -9.58122611e-02 -6.16531789e-01 -3.05194408e-01
-1.32337677e+00 -8.94794822e-01 9.69346881e-01 2.65473928e-02
-7.24266768e-01 1.26625681e+00 2.09769726e-01 -6.77318096e-01
1.71990737e-01 -6.50023639e-01 -1.00059676e+00 -6.47140622e-01
4.27837074e-01 8.05145025e-01 -1.61345720e-01 -4.85994071... | [11.561674118041992, 10.318110466003418] |
54c9751f-69e4-4124-9d26-a2f5f1e09673 | doad-decoupled-one-stage-action-detection | 2304.00254 | null | https://arxiv.org/abs/2304.00254v2 | https://arxiv.org/pdf/2304.00254v2.pdf | DOAD: Decoupled One Stage Action Detection Network | Localizing people and recognizing their actions from videos is a challenging task towards high-level video understanding. Existing methods are mostly two-stage based, with one stage for person bounding box generation and the other stage for action recognition. However, such two-stage methods are generally with low effi... | ['Mike Zheng Show', 'Jiashi Feng', 'Fan Wang', 'Pichao Wang', 'Shuning Chang'] | 2023-04-01 | null | null | null | null | ['action-recognition-in-videos', 'video-understanding'] | ['computer-vision', 'computer-vision'] | [ 2.61747718e-01 -3.81769300e-01 -1.58312708e-01 -1.88118115e-01
-5.94881296e-01 -2.70226926e-01 9.14564192e-01 -7.57224709e-02
-6.07847393e-01 4.01744068e-01 6.15484655e-01 3.68087031e-02
1.76674098e-01 -5.41770816e-01 -4.58290726e-01 -7.90377975e-01
9.14664865e-02 2.55916059e-01 5.11053860e-01 -5.00722080... | [8.204155921936035, 0.5377448797225952] |
7e2d6e9c-9b43-4de6-b6c9-5bc6622ee90e | an-artificial-neural-network-functionalized | 2205.10118 | null | https://arxiv.org/abs/2205.10118v1 | https://arxiv.org/pdf/2205.10118v1.pdf | An Artificial Neural Network Functionalized by Evolution | The topology of artificial neural networks has a significant effect on their performance. Characterizing efficient topology is a field of promising research in Artificial Intelligence. However, it is not a trivial task and it is mainly experimented on through convolutional neural networks. We propose a hybrid model whi... | ['Geoffroy Berthelot', 'Avner Bar-Hen', 'Fabien Furfaro'] | 2022-05-16 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [-2.90429771e-01 -2.19560936e-01 -5.93706220e-02 -3.19125921e-01
1.06762934e+00 -2.09912956e-01 7.39541650e-01 9.98054892e-02
-5.56701779e-01 6.30428195e-01 -7.41864145e-02 -1.42840311e-01
-5.39934099e-01 -1.11254573e+00 -6.18482947e-01 -7.82872260e-01
-5.32280862e-01 3.97015959e-01 3.57328445e-01 -6.97166979... | [8.262261390686035, 3.159212112426758] |
d680a63d-1925-418c-bfba-5ee66721fd8c | morphological-disambiguation-from-stemming | 2011.05504 | null | https://arxiv.org/abs/2011.05504v1 | https://arxiv.org/pdf/2011.05504v1.pdf | Morphological Disambiguation from Stemming Data | Morphological analysis and disambiguation is an important task and a crucial preprocessing step in natural language processing of morphologically rich languages. Kinyarwanda, a morphologically rich language, currently lacks tools for automated morphological analysis. While linguistically curated finite state tools can ... | ['Antoine Nzeyimana'] | 2020-11-11 | null | https://aclanthology.org/2020.coling-main.409 | https://aclanthology.org/2020.coling-main.409.pdf | coling-2020-8 | ['morphological-disambiguation'] | ['natural-language-processing'] | [ 3.86421680e-02 -5.13724983e-01 -1.05902873e-01 -2.14451328e-01
-6.15541577e-01 -1.25583160e+00 2.00837493e-01 7.34320402e-01
-8.62336457e-01 6.29985809e-01 3.40583652e-01 -7.42461503e-01
-2.64335126e-01 -8.68802607e-01 3.82355670e-03 -4.45967406e-01
1.31574243e-01 3.92833829e-01 -1.52955219e-01 -4.81400609... | [10.410239219665527, 10.100655555725098] |
f93fa78d-74d6-4fc5-aacf-b75bf6abf8d2 | conceptual-cognitive-maps-formation-with | 2307.01577 | null | https://arxiv.org/abs/2307.01577v1 | https://arxiv.org/pdf/2307.01577v1.pdf | Conceptual Cognitive Maps Formation with Neural Successor Networks and Word Embeddings | The human brain possesses the extraordinary capability to contextualize the information it receives from our environment. The entorhinal-hippocampal plays a critical role in this function, as it is deeply engaged in memory processing and constructing cognitive maps using place and grid cells. Comprehending and leveragi... | ['Patrick Krauss', 'Andreas Maier', 'Achim Schilling', 'Paul Stoewer'] | 2023-07-04 | null | null | null | null | ['word-embeddings'] | ['methodology'] | [ 3.24151888e-02 1.18083939e-01 1.52868360e-01 -1.24584645e-01
-3.31537239e-02 -6.03347778e-01 1.06720614e+00 8.01305950e-01
-5.74299276e-01 3.36059988e-01 8.21853101e-01 -2.57230937e-01
-5.20902097e-01 -1.22878122e+00 -4.02930915e-01 -5.71035445e-01
-3.08983058e-01 5.64753413e-01 4.78131622e-01 -5.62116683... | [10.599699974060059, 2.381809949874878] |
a6ec2097-58ae-4b9e-b716-1555648beea1 | tuning-free-plug-and-play-hyperspectral-image | 2211.15307 | null | https://arxiv.org/abs/2211.15307v2 | https://arxiv.org/pdf/2211.15307v2.pdf | Tuning-free Plug-and-Play Hyperspectral Image Deconvolution with Deep Priors | Deconvolution is a widely used strategy to mitigate the blurring and noisy degradation of hyperspectral images~(HSI) generated by the acquisition devices. This issue is usually addressed by solving an ill-posed inverse problem. While investigating proper image priors can enhance the deconvolution performance, it is not... | ['Cédric Richard', 'Jie Chen', 'Xiuheng Wang'] | 2022-11-28 | null | null | null | null | ['image-deconvolution'] | ['computer-vision'] | [ 0.3860587 -0.49093235 0.39198965 -0.20951803 -0.6081874 -0.3630014
0.17625694 -0.65835184 -0.4316466 0.8157039 0.1338201 -0.24655177
-0.3628284 -0.4292173 -0.33002958 -1.2655188 0.36415634 -0.13636656
-0.16703553 -0.04611519 0.26118186 0.5003949 -1.2025503 -0.23954155
1.5907981 0.9046377 0.7... | [11.218779563903809, -2.4727227687835693] |
1d56f060-1379-4e88-b13b-4259ccf76a42 | clipter-looking-at-the-bigger-picture-in | 2301.07464 | null | https://arxiv.org/abs/2301.07464v1 | https://arxiv.org/pdf/2301.07464v1.pdf | CLIPTER: Looking at the Bigger Picture in Scene Text Recognition | Understanding the scene is often essential for reading text in real-world scenarios. However, current scene text recognizers operate on cropped text images, unaware of the bigger picture. In this work, we harness the representative power of recent vision-language models, such as CLIP, to provide the crop-based recogniz... | ['Ron Litman', 'Shai Mazor', 'Royee Tichauer', 'Oren Nuriel', 'Roy Ganz', 'Alona Golts', 'David Bensaïd', 'Aviad Aberdam'] | 2023-01-18 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 7.95559108e-01 -5.94160020e-01 -1.30946651e-01 -2.51658618e-01
-1.02377939e+00 -9.02771473e-01 1.03997815e+00 1.23943113e-01
-4.41138208e-01 -3.66359204e-02 4.84210998e-01 -4.06180292e-01
4.14295912e-01 -4.39401239e-01 -8.62167537e-01 -6.35497034e-01
6.28692508e-01 3.33665103e-01 -3.94906811e-02 -2.42329329... | [11.794608116149902, 2.1817545890808105] |
9ec42af3-996b-474e-a763-d0acfab1f24a | 3d-skeleton-based-few-shot-action-recognition | 2112.12668 | null | https://arxiv.org/abs/2112.12668v1 | https://arxiv.org/pdf/2112.12668v1.pdf | 3D Skeleton-based Few-shot Action Recognition with JEANIE is not so Naïve | In this paper, we propose a Few-shot Learning pipeline for 3D skeleton-based action recognition by Joint tEmporal and cAmera viewpoiNt alIgnmEnt (JEANIE). To factor out misalignment between query and support sequences of 3D body joints, we propose an advanced variant of Dynamic Time Warping which jointly models each sm... | ['Piotr Koniusz', 'Jun Liu', 'Lei Wang'] | 2021-12-23 | null | null | null | null | ['few-shot-action-recognition'] | ['computer-vision'] | [ 3.08124781e-01 -2.87883207e-02 -4.13442105e-01 -1.92351595e-01
-6.97494507e-01 -1.67328522e-01 6.40521824e-01 -4.99917001e-01
-3.80794823e-01 1.98067650e-01 5.61939955e-01 3.32429379e-01
8.80493373e-02 -1.12892605e-01 -7.85890639e-01 -5.28447032e-01
-3.90818655e-01 4.10805613e-01 5.63294709e-01 -1.30906463... | [7.743653297424316, 0.14527355134487152] |
007c8436-f305-431e-b664-dd94b5100b9a | cdvae-co-embedding-deep-variational-auto | 1612.00132 | null | http://arxiv.org/abs/1612.00132v2 | http://arxiv.org/pdf/1612.00132v2.pdf | CDVAE: Co-embedding Deep Variational Auto Encoder for Conditional Variational Generation | Problems such as predicting a new shading field (Y) for an image (X) are
ambiguous: many very distinct solutions are good. Representing this ambiguity
requires building a conditional model P(Y|X) of the prediction, conditioned on
the image. Such a model is difficult to train, because we do not usually have
training dat... | ['Aditya Deshpande', 'Jiajun Lu', 'David Forsyth'] | 2016-12-01 | null | null | null | null | ['image-relighting'] | ['computer-vision'] | [ 7.12877631e-01 3.44672143e-01 4.10153657e-01 -9.59070325e-01
-1.19482422e+00 -5.53436339e-01 5.28304279e-01 -4.74500656e-02
2.19079889e-02 7.14421213e-01 2.26451442e-01 -1.26410127e-01
5.78154266e-01 -5.72442770e-01 -1.11079097e+00 -6.19313776e-01
1.17890246e-01 4.53556895e-01 2.49949917e-01 -1.69351041... | [9.910144805908203, -2.8825721740722656] |
ac6761ab-555a-4c1a-8cac-3baadfbcc9ec | reverberation-as-supervision-for-speech | 2211.08303 | null | https://arxiv.org/abs/2211.08303v1 | https://arxiv.org/pdf/2211.08303v1.pdf | Reverberation as Supervision for Speech Separation | This paper proposes reverberation as supervision (RAS), a novel unsupervised loss function for single-channel reverberant speech separation. Prior methods for unsupervised separation required the synthesis of mixtures of mixtures or assumed the existence of a teacher model, making them difficult to consider as potentia... | ['Jonathan Le Roux', 'Aswin Shanmugam Subramanian', 'Gordon Wichern', 'Christoph Boeddeker', 'Rohith Aralikatti'] | 2022-11-15 | null | null | null | null | ['speech-separation'] | ['speech'] | [ 3.12386960e-01 2.44694874e-01 4.10398692e-01 -1.58038855e-01
-9.98724997e-01 -6.30997837e-01 2.83582360e-01 -1.06510095e-01
-3.42191905e-01 6.34630263e-01 4.21490997e-01 -5.49315810e-01
-1.53222233e-01 -1.29173309e-01 -9.19574380e-01 -1.20151579e+00
-1.39199570e-01 1.72634318e-01 -9.90167186e-02 -4.53200266... | [15.174395561218262, 5.743254661560059] |
ed2c2d18-7248-444f-bf9b-41c5c3a44013 | accidental-learners-spoken-language | 2211.05103 | null | https://arxiv.org/abs/2211.05103v2 | https://arxiv.org/pdf/2211.05103v2.pdf | Accidental Learners: Spoken Language Identification in Multilingual Self-Supervised Models | In this paper, we extend previous self-supervised approaches for language identification by experimenting with Conformer based architecture in a multilingual pre-training paradigm. We find that pre-trained speech models optimally encode language discriminatory information in lower layers. Further, we demonstrate that t... | ['Boris Ginsburg', 'Samuel Kriman', 'Krishna C. Puvvada', 'Fei Jia', 'Travis M. Bartley'] | 2022-11-09 | null | null | null | null | ['spoken-language-identification'] | ['speech'] | [-2.59055138e-01 -3.31090838e-01 -1.74766779e-01 -7.35993028e-01
-1.05885804e+00 -9.56388474e-01 5.68195283e-01 -2.08924990e-02
-8.91565084e-01 1.21734485e-01 2.84321815e-01 -5.05942643e-01
4.62988198e-01 -2.42900372e-01 -5.33672810e-01 -2.45699435e-01
-1.76670969e-01 7.38564789e-01 -1.19847178e-01 -1.36192024... | [14.192110061645508, 6.626827716827393] |
9689b8a2-6c1c-4110-894b-9e36554fa697 | uncertainty-aware-multiple-instance-learning-1 | 2302.03116 | null | https://arxiv.org/abs/2302.03116v1 | https://arxiv.org/pdf/2302.03116v1.pdf | Uncertainty-Aware Multiple-Instance Learning for Reliable Classification: Application to Optical Coherence Tomography | Deep learning classification models for medical image analysis often perform well on data from scanners that were used during training. However, when these models are applied to data from different vendors, their performance tends to drop substantially. Artifacts that only occur within scans from specific scanners are ... | ['Clara I. Sánchez', 'Caroline C. W. Klaver', 'Carel B. Hoyng', 'Bram van Ginneken', 'Coen de Vente'] | 2023-02-06 | null | null | null | null | ['multiple-instance-learning'] | ['methodology'] | [ 1.11428164e-01 9.93919745e-02 1.34122089e-01 -6.42157972e-01
-1.06306839e+00 -2.74348944e-01 1.12402029e-01 3.70253384e-01
-5.52469850e-01 7.46091783e-01 -2.66377449e-01 -4.19334143e-01
-4.80582803e-01 -7.32681811e-01 -9.56140220e-01 -7.29617655e-01
-1.26599580e-01 5.44485748e-01 1.83391571e-01 5.47242939... | [15.017261505126953, -2.435399055480957] |
2aed6653-8b1c-46f3-860a-9a78f67e1652 | usd-unknown-sensitive-detector-empowered-by | 2306.02275 | null | https://arxiv.org/abs/2306.02275v1 | https://arxiv.org/pdf/2306.02275v1.pdf | USD: Unknown Sensitive Detector Empowered by Decoupled Objectness and Segment Anything Model | Open World Object Detection (OWOD) is a novel and challenging computer vision task that enables object detection with the ability to detect unknown objects. Existing methods typically estimate the object likelihood with an additional objectness branch, but ignore the conflict in learning objectness and classification b... | ['Siqi Wang', 'Yusong Tan', 'Wei Chen', 'Yulin He'] | 2023-06-04 | null | null | null | null | ['open-world-object-detection'] | ['computer-vision'] | [ 1.75511956e-01 1.19539171e-01 -1.18713170e-01 -2.84371585e-01
-8.12409759e-01 -3.38409215e-01 4.88273203e-01 -8.32384601e-02
-6.41528249e-01 5.15406072e-01 -3.07423115e-01 -4.32350636e-02
2.03631058e-01 -5.55938125e-01 -9.10897493e-01 -7.27678299e-01
3.32498014e-01 1.86557204e-01 9.58096862e-01 1.52078778... | [9.304661750793457, 1.2957957983016968] |
cf1309ae-a6f6-412b-b313-c19e6f44121d | duth-at-semeval-2017-task-4-a-voting | null | null | https://aclanthology.org/S17-2117 | https://aclanthology.org/S17-2117.pdf | DUTH at SemEval-2017 Task 4: A Voting Classification Approach for Twitter Sentiment Analysis | This report describes our participation to SemEval-2017 Task 4: Sentiment Analysis in Twitter, specifically in subtasks A, B, and C. The approach for text sentiment classification is based on a Majority Vote scheme and combined supervised machine learning methods with classical linguistic resources, including bag-of-wo... | ['Symeon Symeonidis', 'John Kordonis', 'Dimitrios Effrosynidis', 'Avi Arampatzis'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-3.88724469e-02 1.31878699e-03 -4.28026199e-01 -9.21228349e-01
-7.09612072e-01 -7.42296934e-01 9.45391417e-01 8.66314054e-01
-8.08796108e-01 6.79063380e-01 6.86983705e-01 -2.59091377e-01
4.87059057e-01 -5.89184761e-01 7.68177509e-02 -3.89548451e-01
3.74471396e-01 4.16191906e-01 -2.29510441e-01 -9.91673589... | [11.196707725524902, 6.923301696777344] |
f19510e4-7ea0-4f97-a0aa-f21c5e9dd3f7 | on-the-convergence-of-policy-gradient-in | 2212.10439 | null | https://arxiv.org/abs/2212.10439v2 | https://arxiv.org/pdf/2212.10439v2.pdf | Policy Gradient in Robust MDPs with Global Convergence Guarantee | Robust Markov decision processes (RMDPs) provide a promising framework for computing reliable policies in the face of model errors. Many successful reinforcement learning algorithms build on variations of policy-gradient methods, but adapting these methods to RMDPs has been challenging. As a result, the applicability o... | ['Marek Petrik', 'Chin Pang Ho', 'Qiuhao Wang'] | 2022-12-20 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [-2.39517599e-01 8.19378256e-05 -5.61324179e-01 3.94077115e-02
-1.12014163e+00 -5.12168646e-01 6.89340949e-01 -5.28927632e-02
-5.39381504e-01 1.47511113e+00 3.74162234e-02 -7.74387956e-01
-2.06330582e-01 -4.59519356e-01 -7.19762683e-01 -7.79196024e-01
-2.07702860e-01 5.60951948e-01 3.60267401e-01 1.81740150... | [4.156149864196777, 2.3501391410827637] |
855f4ff2-e04d-4a22-b9c1-975d0ef385c7 | hard-sample-guided-hybrid-contrast-learning | 2109.12333 | null | https://arxiv.org/abs/2109.12333v2 | https://arxiv.org/pdf/2109.12333v2.pdf | Hard-sample Guided Hybrid Contrast Learning for Unsupervised Person Re-Identification | Unsupervised person re-identification (Re-ID) is a promising and very challenging research problem in computer vision. Learning robust and discriminative features with unlabeled data is of central importance to Re-ID. Recently, more attention has been paid to unsupervised Re-ID algorithms based on clustered pseudo-labe... | ['Gang He', 'Chuang Zhu', 'Zheng Hu'] | 2021-09-25 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [-2.27114990e-01 -2.86780626e-01 -3.35509151e-01 -6.43335521e-01
-5.64249694e-01 -3.28799099e-01 7.89510608e-01 2.04227164e-01
-7.77419567e-01 6.33857667e-01 1.89415082e-01 4.13616240e-01
-2.90524326e-02 -5.03056705e-01 -4.89191562e-01 -7.36423254e-01
2.22461417e-01 7.69352376e-01 1.15303788e-02 1.40871212... | [14.803421974182129, 1.049684762954712] |
44db4a75-e6fc-407d-bcf1-26023fe35d38 | learning-reward-machines-for-partially | null | null | http://papers.nips.cc/paper/9685-learning-reward-machines-for-partially-observable-reinforcement-learning | http://papers.nips.cc/paper/9685-learning-reward-machines-for-partially-observable-reinforcement-learning.pdf | Learning Reward Machines for Partially Observable Reinforcement Learning | Reward Machines (RMs), originally proposed for specifying problems in Reinforcement Learning (RL), provide a structured, automata-based representation of a reward function that allows an agent to decompose problems into subproblems that can be efficiently learned using off-policy learning. Here we show that RMs can be ... | ['Sheila McIlraith', 'Rodrigo Toro Icarte', 'Rick Valenzano', 'Ethan Waldie', 'Toryn Klassen', 'Margarita Castro'] | 2019-12-01 | null | null | null | neurips-2019-12 | ['problem-decomposition'] | ['miscellaneous'] | [ 5.08078523e-02 6.88476324e-01 -5.87325633e-01 1.78210080e-01
-9.73969221e-01 -8.02157283e-01 6.82098985e-01 -6.98535815e-02
-5.94264627e-01 1.25479555e+00 2.69576907e-01 -5.04249573e-01
-3.22878599e-01 -4.47246134e-01 -7.32434332e-01 -7.55623996e-01
-4.55501944e-01 7.50255644e-01 -5.05255647e-02 -2.51919866... | [4.100226879119873, 1.7518945932388306] |
29a21968-c0a1-4a77-8418-5ebf2730871e | mobile-end-tone-mapping-based-on-integral | 2102.01289 | null | https://arxiv.org/abs/2102.01289v1 | https://arxiv.org/pdf/2102.01289v1.pdf | Mobile-end Tone Mapping based on Integral Image and Integral Histogram | Wide dynamic range (WDR) image tone mapping is in high demand in many applications like film production, security monitoring, and photography. It is especially crucial for mobile devices because most of the images taken today are from mobile phones, hence such technology is highly demanded in the consumer market of mob... | ['Orly Yadid-Pecht', 'Ulian Shahnovich', 'Ziyi Liu', 'Mengchen Lin', 'Jie Yang'] | 2021-02-02 | null | null | null | null | ['tone-mapping'] | ['computer-vision'] | [ 6.09686196e-01 -7.46083140e-01 -1.06618360e-01 -8.49372447e-02
-5.22774041e-01 -2.07626224e-01 2.02486575e-01 -1.58339813e-01
-5.37733555e-01 3.54279011e-01 -1.28872797e-01 -5.42125762e-01
-9.83994827e-02 -1.28845537e+00 -2.61848360e-01 -4.95289087e-01
5.36935806e-01 -1.51514769e-01 8.25292170e-01 -4.27707583... | [10.882229804992676, -2.449770212173462] |
ec707aa0-93b2-4125-844a-b59fb9a64ab7 | moving-object-detection-by-detecting | 1109.0882 | null | http://arxiv.org/abs/1109.0882v2 | http://arxiv.org/pdf/1109.0882v2.pdf | Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation | Object detection is a fundamental step for automated video analysis in many
vision applications. Object detection in a video is usually performed by object
detectors or background subtraction techniques. Often, an object detector
requires manually labeled examples to train a binary classifier, while
background subtract... | ['Can Yang', 'Xiaowei Zhou', 'Weichuan Yu'] | 2011-09-05 | null | null | null | null | ['moving-object-detection'] | ['computer-vision'] | [ 5.60330093e-01 -8.92546177e-01 -6.04828000e-02 -1.68555863e-02
-5.17227232e-01 -3.49082023e-01 4.32515293e-01 -8.40867087e-02
-6.06304049e-01 6.24099433e-01 -2.03207597e-01 -1.75742701e-01
3.52656096e-01 -3.80108535e-01 -4.99538571e-01 -1.14622176e+00
1.47532389e-01 6.03801571e-02 8.12524796e-01 1.87408596... | [8.985371589660645, -0.7897384762763977] |
4f36f9e1-7821-4356-be66-43bb97d49e5f | neural-affine-grayscale-image-denoising | 1709.05672 | null | http://arxiv.org/abs/1709.05672v1 | http://arxiv.org/pdf/1709.05672v1.pdf | Neural Affine Grayscale Image Denoising | We propose a new grayscale image denoiser, dubbed as Neural Affine Image
Denoiser (Neural AIDE), which utilizes neural network in a novel way. Unlike
other neural network based image denoising methods, which typically apply
simple supervised learning to learn a mapping from a noisy patch to a clean
patch, we formulate ... | ['Sungmin Cha', 'Taesup Moon'] | 2017-09-17 | null | null | null | null | ['grayscale-image-denoising'] | ['computer-vision'] | [ 6.51201367e-01 -9.03035104e-02 2.46801645e-01 -6.72627747e-01
-1.05977118e+00 -2.81083107e-01 3.15714121e-01 -2.17827678e-01
-5.39082527e-01 5.85881174e-01 6.61919340e-02 3.79234105e-02
-3.55487108e-01 -9.46601331e-01 -1.01099086e+00 -1.22253871e+00
3.14853400e-01 3.73649597e-03 -1.30749360e-01 -3.36999655... | [11.550800323486328, -2.3362417221069336] |
b833dce8-4cbe-4a81-9beb-df8fb588ff2c | improving-generalizability-in-implicitly | null | null | https://openreview.net/forum?id=AfBuiCDtF_0 | https://openreview.net/pdf?id=AfBuiCDtF_0 | Improving Generalizability in Implicitly Abusive Language Detection with Concept Activation Vectors | Robustness of machine learning models on ever-changing real-world data is critical, especially for applications affecting human well-being such as content moderation. New kinds of abusive language continually emerge in online discussions in response to current events (e.g., COVID-19), and the deployed abuse detection s... | ['Anonymous'] | 2021-11-16 | null | https://openreview.net/forum?id=eRRLIv7DrOX | https://openreview.net/pdf?id=eRRLIv7DrOX | acl-arr-september-2021-9 | ['abusive-language', 'abuse-detection'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.14016545e-02 2.48382818e-02 -4.22458351e-01 -4.73256648e-01
-4.36682254e-01 -7.49853015e-01 5.37315309e-01 4.39218074e-01
-4.10234839e-01 9.70649779e-01 3.32223296e-01 -4.51348513e-01
8.10517184e-03 -3.62596810e-01 -3.84075791e-01 -3.62331063e-01
-4.49509323e-02 4.16867733e-01 -8.44686478e-02 -4.51781660... | [8.716962814331055, 10.48544979095459] |
17f17a91-9b4d-4aa9-8ccc-51937c45a4bc | self-supervised-co-training-for-video | 2010.09709 | null | https://arxiv.org/abs/2010.09709v2 | https://arxiv.org/pdf/2010.09709v2.pdf | Self-supervised Co-training for Video Representation Learning | The objective of this paper is visual-only self-supervised video representation learning. We make the following contributions: (i) we investigate the benefit of adding semantic-class positives to instance-based Info Noise Contrastive Estimation (InfoNCE) training, showing that this form of supervised contrastive learni... | ['Andrew Zisserman', 'Weidi Xie', 'Tengda Han'] | 2020-10-19 | null | http://proceedings.neurips.cc/paper/2020/hash/3def184ad8f4755ff269862ea77393dd-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/3def184ad8f4755ff269862ea77393dd-Paper.pdf | neurips-2020-12 | ['self-supervised-action-recognition'] | ['computer-vision'] | [ 6.63439929e-01 9.46843401e-02 -3.05542380e-01 -3.47710937e-01
-1.02901196e+00 -4.26005095e-01 8.54305029e-01 4.51030433e-02
-4.58865702e-01 8.21572423e-01 3.01307201e-01 2.46788993e-01
-8.54789242e-02 -4.72469240e-01 -9.40108955e-01 -7.29736030e-01
-6.39698282e-02 2.27624685e-01 2.64805138e-01 6.65888637... | [8.815120697021484, 0.851734459400177] |
0f285daf-b882-4df2-a4dd-cc4ca687449a | on-the-generalisation-capabilities-of-fisher | 2103.01721 | null | https://arxiv.org/abs/2103.01721v1 | https://arxiv.org/pdf/2103.01721v1.pdf | On the Generalisation Capabilities of Fisher Vector based Face Presentation Attack Detection | In the last decades, the broad development experienced by biometric systems has unveiled several threats which may decrease their trustworthiness. Those are attack presentations which can be easily carried out by a non-authorised subject to gain access to the biometric system. In order to mitigate those security concer... | ['Christoph Busch', 'Marta Gomez-Barrero', 'Lázaro J. González-Soler'] | 2021-03-02 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 4.25762028e-01 -2.21821472e-01 1.96446389e-01 -2.76552171e-01
-5.75868011e-01 -7.31969416e-01 8.60292494e-01 2.23566025e-01
-5.70693254e-01 5.95032334e-01 -3.82845253e-01 -2.57712603e-03
-2.89271414e-01 -6.66427612e-01 -4.45733786e-01 -8.31560910e-01
-3.05256724e-01 2.78552949e-01 1.62550788e-02 -1.87647447... | [13.068305969238281, 1.0729204416275024] |
e87be0b5-6fb5-47e1-96f9-ac0890998b0d | motion-compensated-three-dimensional | 2204.12882 | null | https://arxiv.org/abs/2204.12882v1 | https://arxiv.org/pdf/2204.12882v1.pdf | Motion Compensated Three-Dimensional Frequency Selective Extrapolation for Improved Error Concealment in Video Communication | During transmission of video data over error-prone channels the risk of getting severe image distortions due to transmission errors is ubiquitous. To deal with image distortions at decoder side, error concealment is applied. This article presents Motion Compensated Three-Dimensional Frequency Selective Extrapolation, a... | ['André Kaup', 'Jürgen Seiler'] | 2022-04-27 | null | null | null | null | ['motion-compensation'] | ['computer-vision'] | [ 8.26890111e-01 -5.13902418e-02 2.02268854e-01 1.27816960e-01
-6.13403916e-01 -1.55623525e-01 5.08568227e-01 3.28394949e-01
-5.19847631e-01 8.99722219e-01 5.03673494e-01 -4.04897988e-01
-3.70252728e-02 -5.90339303e-01 -6.97838724e-01 -7.15858281e-01
-4.02060151e-01 -5.33679366e-01 2.05708608e-01 8.64819288... | [11.32832145690918, -2.2104036808013916] |
cea292fa-e394-40a1-b91b-0b1a3110c312 | a-stacked-deep-convolutional-neural-network | 2111.12689 | null | https://arxiv.org/abs/2111.12689v1 | https://arxiv.org/pdf/2111.12689v1.pdf | A stacked deep convolutional neural network to predict the remaining useful life of a turbofan engine | This paper presents the data-driven techniques and methodologies used to predict the remaining useful life (RUL) of a fleet of aircraft engines that can suffer failures of diverse nature. The solution presented is based on two Deep Convolutional Neural Networks (DCNN) stacked in two levels. The first DCNN is used to ex... | ['Joaquin Borrego-Diaz', 'Juan Galan-Paez', 'David Solis-Martin'] | 2021-11-24 | null | null | null | null | ['remaining-useful-lifetime-estimation'] | ['time-series'] | [-8.29327293e-03 -2.27412969e-01 1.64289534e-01 -4.74752992e-01
-2.11850017e-01 -6.75808564e-02 4.19323146e-01 3.33499573e-02
-3.84992421e-01 9.19803977e-01 -6.56506270e-02 -4.14897174e-01
-1.10538208e+00 -8.09131324e-01 -4.02012259e-01 -8.90564322e-01
-4.04321164e-01 6.17849648e-01 4.75473814e-02 -1.39671803... | [6.726168632507324, 2.4764046669006348] |
75bd02a6-2204-415e-88d8-ba6041d4ced7 | where-are-the-facts-searching-for-fact | 2010.03159 | null | https://arxiv.org/abs/2010.03159v1 | https://arxiv.org/pdf/2010.03159v1.pdf | Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News | Although many fact-checking systems have been developed in academia and industry, fake news is still proliferating on social media. These systems mostly focus on fact-checking but usually neglect online users who are the main drivers of the spread of misinformation. How can we use fact-checked information to improve us... | ['Kyumin Lee', 'Nguyen Vo'] | 2020-10-07 | null | https://aclanthology.org/2020.emnlp-main.621 | https://aclanthology.org/2020.emnlp-main.621.pdf | emnlp-2020-11 | ['image-similarity-search', 'ad-hoc-information-retrieval'] | ['computer-vision', 'natural-language-processing'] | [-2.62456447e-01 2.22346276e-01 -6.19247019e-01 2.13050917e-01
-4.90333736e-01 -8.81631196e-01 8.68848205e-01 3.38571757e-01
-1.58721313e-01 6.93120420e-01 3.11868578e-01 -3.76812786e-01
5.29954791e-01 -1.16789150e+00 -6.85977519e-01 -1.04135402e-01
3.76739442e-01 1.89622551e-01 8.33110809e-01 -6.30891323... | [8.136796951293945, 10.247846603393555] |
548759a8-9444-4e10-8948-cd8db3d38c2e | on-robust-face-recognition-via-sparse | 1303.01624 | null | http://arxiv.org/abs/1303.1624v1 | http://arxiv.org/pdf/1303.1624v1.pdf | On Robust Face Recognition via Sparse Encoding: the Good, the Bad, and the Ugly | In the field of face recognition, Sparse Representation (SR) has received
considerable attention during the past few years. Most of the relevant
literature focuses on holistic descriptors in closed-set identification
applications. The underlying assumption in SR-based methods is that each class
in the gallery has suffi... | ['Conrad Sanderson', 'Yongkang Wong', 'Mehrtash T. Harandi'] | 2013-03-07 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 6.06864870e-01 -2.47448027e-01 1.21062417e-02 -3.33043724e-01
-7.86109686e-01 -3.16817552e-01 5.97630858e-01 -2.36381948e-01
1.14309601e-01 5.91574132e-01 -3.99469733e-02 2.68049985e-01
-5.17289162e-01 -6.09531760e-01 -3.59156698e-01 -1.13109720e+00
9.11651701e-02 1.62308916e-01 -2.67019391e-01 -7.32211173... | [12.873790740966797, 0.540584146976471] |
11536c1d-4f13-402e-8fe4-1a1f8c61f46c | learning-spatially-variant-map-models-for-non | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Dong_Learning_Spatially-Variant_MAP_Models_for_Non-Blind_Image_Deblurring_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Dong_Learning_Spatially-Variant_MAP_Models_for_Non-Blind_Image_Deblurring_CVPR_2021_paper.pdf | Learning Spatially-Variant MAP Models for Non-Blind Image Deblurring | The classical maximum a-posteriori (MAP) framework for non-blind image deblurring requires defining suitable data and regularization terms, whose interplay yields the desired clear image through optimization. The vast majority of prior work focuses on advancing one of these two crucial ingredients, while keeping th... | ['Bernt Schiele', 'Stefan Roth', 'Jiangxin Dong'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['blind-image-deblurring'] | ['computer-vision'] | [ 3.89799178e-01 -2.87111253e-01 -1.14907816e-01 -2.78670251e-01
-7.45893061e-01 -3.94158900e-01 7.01088488e-01 -2.40691289e-01
-4.72190946e-01 6.65422320e-01 4.88933980e-01 2.07036138e-02
-4.04867023e-01 -4.61206734e-01 -4.36822683e-01 -1.20370400e+00
-2.11082511e-02 -6.38187900e-02 4.66327146e-02 -9.27326381... | [11.562132835388184, -2.5125339031219482] |
49e31c4e-8ec5-4c93-8ac3-11bcf85ac1a6 | hierarchical-and-progressive-image-matting | 2210.06906 | null | https://arxiv.org/abs/2210.06906v1 | https://arxiv.org/pdf/2210.06906v1.pdf | Hierarchical and Progressive Image Matting | Most matting researches resort to advanced semantics to achieve high-quality alpha mattes, and direct low-level features combination is usually explored to complement alpha details. However, we argue that appearance-agnostic integration can only provide biased foreground details and alpha mattes require different-level... | ['Xin Yang', 'Guofeng Zhang', 'Qiang Cai', 'Yuxin Wang', 'Ziqi Wei', 'Yuhao Liu', 'Yu Qiao'] | 2022-10-13 | null | null | null | null | ['image-matting'] | ['computer-vision'] | [ 4.81903464e-01 -1.42429560e-01 1.27197787e-01 -4.01615441e-01
-5.08784592e-01 -2.14704096e-01 2.78863043e-01 -4.45236176e-01
-1.55612528e-01 5.27559936e-01 -9.40825045e-02 -2.06178233e-01
3.00039649e-01 -8.78687561e-01 -1.02325332e+00 -9.23054934e-01
3.03500503e-01 3.05295885e-01 7.67265260e-01 -4.55783755... | [10.622556686401367, -0.9487459063529968] |
c5f3f26c-962e-4136-a821-0a7b40e05663 | boosting-multitask-learning-on-graphs-through | 2306.14009 | null | https://arxiv.org/abs/2306.14009v1 | https://arxiv.org/pdf/2306.14009v1.pdf | Boosting Multitask Learning on Graphs through Higher-Order Task Affinities | Predicting node labels on a given graph is a widely studied problem with many applications, including community detection and molecular graph prediction. This paper considers predicting multiple node labeling functions on graphs simultaneously and revisits this problem from a multitask learning perspective. For a concr... | ['Hongyang R. Zhang', 'Aneesh Sharma', 'Haotian Ju', 'Dongyue Li'] | 2023-06-24 | null | null | null | null | ['node-classification', 'community-detection'] | ['graphs', 'graphs'] | [ 5.19554794e-01 -6.17736131e-02 -3.30738842e-01 -1.26329795e-01
-5.85140824e-01 -7.12772727e-01 3.43964458e-01 4.96747673e-01
-3.28648686e-01 5.55945814e-01 -9.38323140e-02 -4.72792357e-01
-3.86495799e-01 -4.54501510e-01 -8.59129429e-01 -8.34680915e-01
-5.40211260e-01 9.18935239e-01 4.95719254e-01 1.94130793... | [7.180486679077148, 5.435795307159424] |
8915797a-3044-4dce-ad94-87eed0cbad9a | blood-oxygen-saturation-estimation-from | 2212.07116 | null | https://arxiv.org/abs/2212.07116v2 | https://arxiv.org/pdf/2212.07116v2.pdf | Blood Oxygen Saturation Estimation from Facial Video via DC and AC components of Spatio-temporal Map | Peripheral blood oxygen saturation (SpO2), an indicator of oxygen levels in the blood, is one of the most important physiological parameters. Although SpO2 is usually measured using a pulse oximeter, non-contact SpO2 estimation methods from facial or hand videos have been attracting attention in recent years. In this p... | ['Hitoshi Imaoka', 'Yoshifumi Onishi', 'Yusuke Akamatsu'] | 2022-12-14 | null | null | null | null | ['spo2-estimation'] | ['medical'] | [-1.20710187e-01 -2.66435534e-01 1.65079862e-01 -5.56492269e-01
9.82001275e-02 1.27740040e-01 -1.06166549e-01 -3.16430479e-01
-4.29562390e-01 6.69865668e-01 2.19548434e-01 1.89988166e-02
1.85344413e-01 -5.39489865e-01 -1.30673856e-01 -8.74003112e-01
-1.76906288e-02 -6.44798458e-01 1.34822473e-01 1.28702790... | [13.899896621704102, 2.766494035720825] |
5211d304-4503-45c9-810f-a2f447f92137 | pnlp-mixer-an-efficient-all-mlp-architecture | 2202.04350 | null | https://arxiv.org/abs/2202.04350v2 | https://arxiv.org/pdf/2202.04350v2.pdf | pNLP-Mixer: an Efficient all-MLP Architecture for Language | Large pre-trained language models based on transformer architecture have drastically changed the natural language processing (NLP) landscape. However, deploying those models for on-device applications in constrained devices such as smart watches is completely impractical due to their size and inference cost. As an alte... | ['Diego Antognini', 'Peter Staar', 'Damian Pascual', 'Francesco Fusco'] | 2022-02-09 | null | null | null | null | ['slot-filling'] | ['natural-language-processing'] | [ 2.90638059e-01 5.52915990e-01 -5.96311271e-01 -3.64015669e-01
-1.45379889e+00 -5.06347120e-01 2.30821788e-01 6.09784126e-02
-4.77150142e-01 3.52392137e-01 3.88954401e-01 -7.46115923e-01
5.71532190e-01 -6.67641699e-01 -8.39148462e-01 -1.71694741e-01
4.49218839e-01 7.69996166e-01 2.77359307e-01 2.51433942... | [8.774942398071289, 3.72241473197937] |
9167449a-ae86-472e-b6f9-3fb7d5214676 | approximate-spectral-clustering-with | 2302.11297 | null | https://arxiv.org/abs/2302.11297v1 | https://arxiv.org/pdf/2302.11297v1.pdf | Approximate spectral clustering with eigenvector selection and self-tuned $k$ | The recently emerged spectral clustering surpasses conventional clustering methods by detecting clusters of any shape without the convexity assumption. Unfortunately, with a computational complexity of $O(n^3)$, it was infeasible for multiple real applications, where $n$ could be large. This stimulates researchers to p... | ['Masahiro Takatsuka', 'Mashaan Alshammari'] | 2023-02-22 | null | null | null | null | ['graph-clustering', 'spectral-graph-clustering', 'graph-partitioning'] | ['graphs', 'graphs', 'graphs'] | [-8.12590420e-02 -1.33755267e-01 2.12050945e-01 6.25537485e-02
-4.04012114e-01 -6.55570924e-01 4.52843681e-02 1.02687150e-01
-3.44338477e-01 4.61797118e-01 -2.75573462e-01 -2.60273933e-01
-5.59670687e-01 -7.66834199e-01 -3.34524632e-01 -1.08643401e+00
-3.39134544e-01 4.95007008e-01 7.17553049e-02 1.49739712... | [7.541796684265137, 4.687739849090576] |
1acdae83-85c6-4832-9c0c-d8e590f890ed | object-detection-based-inspection-of-power | 2212.11017 | null | https://arxiv.org/abs/2212.11017v1 | https://arxiv.org/pdf/2212.11017v1.pdf | Object detection-based inspection of power line insulators: Incipient fault detection in the low data-regime | Deep learning-based object detection is a powerful approach for detecting faulty insulators in power lines. This involves training an object detection model from scratch, or fine tuning a model that is pre-trained on benchmark computer vision datasets. This approach works well with a large number of insulator images, b... | ['Giovanni Sansavini', 'Etienne Auger', 'Blazhe Gjorgiev', 'Mohammad Hossein Saadat', 'Laya Das'] | 2022-12-21 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [ 2.55640268e-01 -3.42397869e-01 2.99693704e-01 -1.19824700e-01
-8.04526269e-01 -7.56388128e-01 2.45577008e-01 1.16599716e-01
1.94754049e-01 2.34973729e-01 -5.77383280e-01 -4.56230164e-01
-2.07459539e-01 -7.98399031e-01 -6.77163661e-01 -8.44740450e-01
-1.44395307e-01 3.81441742e-01 4.94427711e-01 -1.47674382... | [7.420025825500488, 1.9078953266143799] |
8a7a165c-ed14-4e0d-9461-079478cb6b89 | can-rumour-stance-alone-predict-veracity | null | null | https://aclanthology.org/C18-1284 | https://aclanthology.org/C18-1284.pdf | Can Rumour Stance Alone Predict Veracity? | Prior manual studies of rumours suggested that crowd stance can give insights into the actual rumour veracity. Even though numerous studies of automatic veracity classification of social media rumours have been carried out, none explored the effectiveness of leveraging crowd stance to determine veracity. We use stance ... | ['Sebastian Dungs', 'Kalina Bontcheva', 'Ahmet Aker', 'Norbert Fuhr'] | 2018-08-01 | can-rumour-stance-alone-predict-veracity-1 | https://aclanthology.org/C18-1284 | https://aclanthology.org/C18-1284.pdf | coling-2018-8 | ['rumour-detection'] | ['natural-language-processing'] | [-4.77171749e-01 3.59026939e-01 -4.35600877e-01 -2.33220756e-01
-2.34630153e-01 -1.17989138e-01 1.23860705e+00 4.66751009e-01
-3.61140490e-01 8.40692163e-01 6.27814770e-01 -4.94486719e-01
6.03188038e-01 -6.54812038e-01 -1.19240144e-02 -3.57350111e-01
-2.01532498e-01 4.61537510e-01 2.74916112e-01 -4.91797894... | [8.263701438903809, 10.117100715637207] |
71789437-961d-4d05-8ce4-75c828f1cee7 | vehicle-interfacing-neural-network-verifiers | 2202.05207 | null | https://arxiv.org/abs/2202.05207v1 | https://arxiv.org/pdf/2202.05207v1.pdf | Vehicle: Interfacing Neural Network Verifiers with Interactive Theorem Provers | Verification of neural networks is currently a hot topic in automated theorem proving. Progress has been rapid and there are now a wide range of tools available that can verify properties of networks with hundreds of thousands of nodes. In theory this opens the door to the verification of larger control systems that ma... | ['Ekaterina Komendantskya', 'Luca Arnaboldi', 'Robert Atkey', 'Wen Kokke', 'Matthew L. Daggitt'] | 2022-02-10 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 3.14689666e-01 7.56681085e-01 -3.95869836e-02 -1.67333037e-01
5.84406639e-03 -9.37615573e-01 5.67729354e-01 -3.87518466e-01
-4.09427620e-02 9.60586786e-01 -8.42010498e-01 -1.34731233e+00
-3.02696675e-01 -9.72782195e-01 -1.15216815e+00 -3.22810471e-01
-6.91925049e-01 2.10693240e-01 6.78220034e-01 -3.59040260... | [6.412506103515625, 7.4565253257751465] |
28ab2c40-29b1-4770-962f-56c33af019db | toplight-lightweight-neural-networks-with | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yu_TOPLight_Lightweight_Neural_Networks_With_Task-Oriented_Pretraining_for_Visible-Infrared_Recognition_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yu_TOPLight_Lightweight_Neural_Networks_With_Task-Oriented_Pretraining_for_Visible-Infrared_Recognition_CVPR_2023_paper.pdf | TOPLight: Lightweight Neural Networks With Task-Oriented Pretraining for Visible-Infrared Recognition | Visible-infrared recognition (VI recognition) is a challenging task due to the enormous visual difference across heterogeneous images. Most existing works achieve promising results by transfer learning, such as pretraining on the ImageNet, based on advanced neural architectures like ResNet and ViT. However, such me... | ['Wei Peng', 'Xu Cheng', 'Hao Yu'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['person-re-identification', 'face-recognition'] | ['computer-vision', 'computer-vision'] | [ 4.60231006e-01 -3.45720172e-01 -2.12571118e-02 -3.76079768e-01
-1.30549893e-01 -1.92528903e-01 6.43743277e-01 -7.54023850e-01
-4.61375237e-01 5.77287674e-01 9.37718451e-02 -5.16306907e-02
-1.86960429e-01 -5.34620583e-01 -6.05303586e-01 -8.84957612e-01
3.92833740e-01 9.77118090e-02 -1.87232673e-01 -2.02842161... | [14.656471252441406, 0.9393826723098755] |
d0763971-cc7a-4737-a56f-547d3ebd6c18 | learning-programs-by-combining-programs | 2206.01614 | null | https://arxiv.org/abs/2206.01614v2 | https://arxiv.org/pdf/2206.01614v2.pdf | Learning programs by combining programs | The goal of inductive logic programming is to induce a logic program (a set of logical rules) that generalises training examples. Inducing programs with many rules and literals is a major challenge. To tackle this challenge, we introduce an approach where we learn small non-separable programs and combine them. We imple... | ['Céline Hocquette', 'Andrew Cropper'] | 2022-06-01 | null | null | null | null | ['program-synthesis', 'inductive-logic-programming'] | ['computer-code', 'methodology'] | [ 4.91474658e-01 4.41451609e-01 -5.64840555e-01 -5.32268226e-01
-8.10805202e-01 -8.90680015e-01 3.67438793e-01 9.08019114e-03
2.22923551e-02 9.57639039e-01 -3.87823910e-01 -8.36132228e-01
-3.27115357e-01 -1.31803954e+00 -1.05157721e+00 -3.77839953e-02
-1.99607238e-01 8.37881923e-01 5.48542619e-01 -1.80677578... | [8.717412948608398, 7.151403427124023] |
e9929964-1972-4d8c-9b75-5f5bd5488ba4 | recognizing-scenes-from-novel-viewpoints | 2112.01520 | null | https://arxiv.org/abs/2112.01520v1 | https://arxiv.org/pdf/2112.01520v1.pdf | Recognizing Scenes from Novel Viewpoints | Humans can perceive scenes in 3D from a handful of 2D views. For AI agents, the ability to recognize a scene from any viewpoint given only a few images enables them to efficiently interact with the scene and its objects. In this work, we attempt to endow machines with this ability. We propose a model which takes as inp... | ['Georgia Gkioxari', 'David F. Fouhey', 'Justin Johnson', 'Devendra Singh Chaplot', 'Nikhila Ravi', 'Alexander Kirillov', 'Shengyi Qian'] | 2021-12-02 | null | null | null | null | ['scene-recognition'] | ['computer-vision'] | [ 2.46050254e-01 8.43147561e-02 3.37861001e-01 -7.15822756e-01
-1.94505721e-01 -1.22474754e+00 5.61333954e-01 -1.49362355e-01
-1.15003526e-01 5.96484775e-03 -1.94495097e-01 -7.74173364e-02
3.28531891e-01 -6.96623147e-01 -8.80606949e-01 -1.74783304e-01
2.23798633e-01 7.93066621e-01 3.93627584e-01 -1.30266905... | [8.365771293640137, -2.971015453338623] |
0e688b28-9894-4c6d-b1a2-ef1908bfb464 | an-implicit-alignment-for-video-super | 2305.00163 | null | https://arxiv.org/abs/2305.00163v1 | https://arxiv.org/pdf/2305.00163v1.pdf | An Implicit Alignment for Video Super-Resolution | Video super-resolution commonly uses a frame-wise alignment to support the propagation of information over time. The role of alignment is well-studied for low-level enhancement in video, but existing works have overlooked one critical step -- re-sampling. Most works, regardless of how they compensate for motion between... | ['Angela Yao', 'Michael Bi Mi', 'Xin Wang', 'Ziwei Yu', 'Kai Xu'] | 2023-04-29 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [ 4.19197649e-01 -2.80172914e-01 -2.52558351e-01 -3.43025327e-01
-5.21523178e-01 -1.96572408e-01 5.78238606e-01 -1.56492114e-01
-3.15542459e-01 7.81710565e-01 6.97045863e-01 2.20968515e-01
-4.73808311e-02 -9.30186033e-01 -8.06686044e-01 -7.78909147e-01
-1.23105623e-01 -5.79726160e-01 5.15185893e-01 -3.23015988... | [11.049919128417969, -1.8806318044662476] |
cf7003d9-6ff0-44ba-af55-508aeec1c8d9 | teamwork-is-not-always-good-an-empirical | 2305.16559 | null | https://arxiv.org/abs/2305.16559v1 | https://arxiv.org/pdf/2305.16559v1.pdf | Teamwork Is Not Always Good: An Empirical Study of Classifier Drift in Class-incremental Information Extraction | Class-incremental learning (CIL) aims to develop a learning system that can continually learn new classes from a data stream without forgetting previously learned classes. When learning classes incrementally, the classifier must be constantly updated to incorporate new classes, and the drift in decision boundary may le... | ['Lifu Huang', 'Minqian Liu'] | 2023-05-26 | null | null | null | null | ['class-incremental-learning', 'incremental-learning'] | ['computer-vision', 'methodology'] | [ 4.59419042e-01 -2.40872856e-02 -4.42573667e-01 -6.21186852e-01
-5.18576026e-01 -5.74388385e-01 3.07634443e-01 3.19996327e-01
-4.21044737e-01 1.08134520e+00 -3.84976298e-01 -1.95157304e-01
-1.30896643e-01 -6.44084513e-01 -8.23265195e-01 -8.43537867e-01
-3.09156954e-01 4.30035442e-01 4.84565020e-01 -9.28630028... | [9.86865520477295, 3.393603563308716] |
d1cabad5-36af-4e94-8751-3d5610f263ed | effective-distant-supervision-for-temporal | 2010.12755 | null | https://arxiv.org/abs/2010.12755v2 | https://arxiv.org/pdf/2010.12755v2.pdf | Effective Distant Supervision for Temporal Relation Extraction | A principal barrier to training temporal relation extraction models in new domains is the lack of varied, high quality examples and the challenge of collecting more. We present a method of automatically collecting distantly-supervised examples of temporal relations. We scrape and automatically label event pairs where t... | ['Greg Durrett', 'Shih-ting Lin', 'Xinyu Zhao'] | 2020-10-24 | null | https://aclanthology.org/2021.adaptnlp-1.20 | https://aclanthology.org/2021.adaptnlp-1.20.pdf | eacl-adaptnlp-2021-4 | ['temporal-relation-extraction'] | ['natural-language-processing'] | [ 2.82614172e-01 3.02453697e-01 -4.96278793e-01 -7.07494497e-01
-9.54566956e-01 -7.47142196e-01 8.91232133e-01 2.44588435e-01
-6.14659905e-01 9.29167211e-01 5.05064309e-01 -9.53614488e-02
-1.56065404e-01 -5.22183776e-01 -5.56093693e-01 -3.35883468e-01
-7.12251186e-01 7.78923035e-01 6.00886583e-01 -3.46243739... | [9.18089771270752, 9.128061294555664] |
d161c846-cb92-4417-a290-c4ab18b7ab01 | robust-graph-structure-learning-with-the | 2307.02126 | null | https://arxiv.org/abs/2307.02126v1 | https://arxiv.org/pdf/2307.02126v1.pdf | Robust Graph Structure Learning with the Alignment of Features and Adjacency Matrix | To improve the robustness of graph neural networks (GNN), graph structure learning (GSL) has attracted great interest due to the pervasiveness of noise in graph data. Many approaches have been proposed for GSL to jointly learn a clean graph structure and corresponding representations. To extend the previous work, this ... | ['Ming Li', 'Linsen Wei', 'Shiyu Liu', 'Gang Wen', 'Shaogao Lv'] | 2023-07-05 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [ 2.45762527e-01 3.39519948e-01 -6.59661144e-02 -1.10778771e-01
-5.51760674e-01 -4.98625189e-01 5.44299245e-01 3.24061900e-01
-9.69848111e-02 5.01859426e-01 2.03760847e-01 -1.45827264e-01
-5.01224101e-01 -9.76296782e-01 -8.27830851e-01 -7.22249329e-01
-4.83153015e-01 -9.14495364e-02 -3.62022384e-03 -3.24237078... | [7.077305793762207, 6.205443382263184] |
61b6546a-59cb-4bbb-b18b-4bb9ac96455d | exploiting-low-rank-tensor-train-deep-neural | 2203.06031 | null | https://arxiv.org/abs/2203.06031v1 | https://arxiv.org/pdf/2203.06031v1.pdf | Exploiting Low-Rank Tensor-Train Deep Neural Networks Based on Riemannian Gradient Descent With Illustrations of Speech Processing | This work focuses on designing low complexity hybrid tensor networks by considering trade-offs between the model complexity and practical performance. Firstly, we exploit a low-rank tensor-train deep neural network (TT-DNN) to build an end-to-end deep learning pipeline, namely LR-TT-DNN. Secondly, a hybrid model combin... | ['Javier Tejedor', 'Pin-Yu Chen', 'Chao-Han Huck Yang', 'Jun Qi'] | 2022-03-11 | null | null | null | null | ['tensor-networks', 'spoken-command-recognition'] | ['methodology', 'speech'] | [-3.11710894e-01 2.71285884e-02 2.62346774e-01 -5.27181566e-01
-7.37214625e-01 -3.06398094e-01 3.46619666e-01 -6.90454185e-01
-4.43718135e-01 -1.36025427e-02 4.13768649e-01 -6.78606391e-01
6.76929653e-02 -2.84997404e-01 -5.88609815e-01 -5.86001039e-01
-4.18067515e-01 1.86651275e-01 -1.54800832e-01 -3.09626937... | [14.244793891906738, 6.225135326385498] |
0d6a9d1c-0b04-427d-a9c5-9f6a017af783 | multivariate-prediction-intervals-for-random | 2205.02260 | null | https://arxiv.org/abs/2205.02260v2 | https://arxiv.org/pdf/2205.02260v2.pdf | Multivariate Prediction Intervals for Random Forests | Accurate uncertainty estimates can significantly improve the performance of iterative design of experiments, as in Sequential and Reinforcement learning. For many such problems in engineering and the physical sciences, the design task depends on multiple correlated model outputs as objectives and/or constraints. To bet... | ['Maxwell Hutchinson', 'Brendan Folie'] | 2022-05-04 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [ 2.66972333e-01 -1.32251695e-01 -3.01420689e-01 -2.97690302e-01
-9.84389842e-01 -5.13842523e-01 4.02296424e-01 7.87692145e-02
-3.64802122e-01 1.55157089e+00 -4.15663421e-01 -6.74513280e-01
-6.98495626e-01 -5.22945940e-01 -9.11298990e-01 -8.29535723e-01
1.98595785e-02 6.46552503e-01 2.56310049e-02 2.61786789... | [5.032345771789551, 2.718290328979492] |
ffbec38f-342b-455f-9c86-5c229fc79360 | evaluation-strategy-of-time-series-anomaly | 2305.09691 | null | https://arxiv.org/abs/2305.09691v1 | https://arxiv.org/pdf/2305.09691v1.pdf | Evaluation Strategy of Time-series Anomaly Detection with Decay Function | Recent algorithms of time-series anomaly detection have been evaluated by applying a Point Adjustment (PA) protocol. However, the PA protocol has a problem of overestimating the performance of the detection algorithms because it only depends on the number of detected abnormal segments and their size. We propose a novel... | ['Kyushik Min', 'Yongwan Gim'] | 2023-05-15 | null | null | null | null | ['time-series-anomaly-detection'] | ['time-series'] | [ 5.50110340e-02 -3.29798609e-01 3.69703770e-01 -1.18954450e-01
-6.05408192e-01 -6.05002284e-01 4.10598278e-01 7.59672284e-01
-3.44357759e-01 2.66484082e-01 -5.23243964e-01 -4.38053966e-01
-4.92854536e-01 -7.72376418e-01 -3.76042247e-01 -5.99039614e-01
-9.25152063e-01 5.14458418e-01 9.70751464e-01 4.67119440... | [7.366203308105469, 2.7445459365844727] |
e2c795ee-10a7-4ba9-97c0-ec662fbee10c | structurallm-structural-pre-training-for-form | 2105.11210 | null | https://arxiv.org/abs/2105.11210v1 | https://arxiv.org/pdf/2105.11210v1.pdf | StructuralLM: Structural Pre-training for Form Understanding | Large pre-trained language models achieve state-of-the-art results when fine-tuned on downstream NLP tasks. However, they almost exclusively focus on text-only representation, while neglecting cell-level layout information that is important for form image understanding. In this paper, we propose a new pre-training appr... | ['Luo Si', 'Fei Huang', 'Songfang Huang', 'Wei Wang', 'Ming Yan', 'Bin Bi', 'Chenliang Li'] | 2021-05-24 | null | https://aclanthology.org/2021.acl-long.493 | https://aclanthology.org/2021.acl-long.493.pdf | acl-2021-5 | ['document-image-classification'] | ['computer-vision'] | [ 2.85912126e-01 -1.40607739e-02 -1.50501072e-01 -3.12778533e-01
-1.00513649e+00 -8.14767897e-01 7.05074549e-01 3.78797561e-01
-2.24311933e-01 4.50326145e-01 2.73787975e-01 -6.18099511e-01
2.58367002e-01 -9.46465671e-01 -1.05173540e+00 -5.83700120e-01
3.20798278e-01 4.87176448e-01 4.49863411e-02 2.46964954... | [11.504968643188477, 2.2591936588287354] |
f5407a76-f7d4-42ce-9fe1-cb05c72fa077 | an-end-to-end-network-for-emotion-cause-pair | 2103.01544 | null | https://arxiv.org/abs/2103.01544v2 | https://arxiv.org/pdf/2103.01544v2.pdf | An End-to-End Network for Emotion-Cause Pair Extraction | The task of Emotion-Cause Pair Extraction (ECPE) aims to extract all potential clause-pairs of emotions and their corresponding causes in a document. Unlike the more well-studied task of Emotion Cause Extraction (ECE), ECPE does not require the emotion clauses to be provided as annotations. Previous works on ECPE have ... | ['Ashutosh Modi', 'Saim Wani', 'Shreeshail Hingane', 'Aaditya Singh'] | 2021-03-02 | an-end-to-end-network-for-emotion-cause-pair-1 | https://arxiv.org/abs/2103.01544 | https://arxiv.org/pdf/2103.01544.pdf | null | ['emotion-cause-pair-extraction', 'emotion-cause-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.96847782e-01 2.96035439e-01 6.27293959e-02 -5.70000589e-01
-1.23310077e+00 -7.22731054e-01 6.47678196e-01 1.84464201e-01
-6.30046129e-01 7.78181255e-01 3.61999512e-01 -1.61781475e-01
3.59398663e-01 -2.07967758e-01 -4.54714566e-01 -3.04155260e-01
-7.36038461e-02 2.36621976e-01 7.93230534e-03 -2.54488587... | [12.646896362304688, 6.212346076965332] |
3144e125-573c-44cb-9990-2d658dd9e3ae | gauss-guided-encoder-decoder-architecture-for | 2204.07713 | null | https://arxiv.org/abs/2204.07713v2 | https://arxiv.org/pdf/2204.07713v2.pdf | GAUSS: Guided Encoder-Decoder Architecture for Hyperspectral Unmixing with Spatial Smoothness | In recent hyperspectral unmixing (HU) literature, the application of deep learning (DL) has become more prominent, especially with the autoencoder (AE) architecture. We propose a split architecture and use a pseudo-ground truth for abundances to guide the `unmixing network' (UN) optimization. Preceding the UN, an `appr... | ['Dulantha Wickramasinghe', 'Neranjan Senarath', 'Lakshitha Ramanayake', 'Dhananjaya Jayasundara', 'Parakrama Ekanayake', 'Vijitha Herath', 'Roshan Godaliyadda', 'Kavinga Weerasooriya', 'Yasiru Ranasinghe'] | 2022-04-16 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 5.85053921e-01 -1.87009364e-01 2.19287798e-01 -1.77339211e-01
-5.31376898e-01 -1.20735668e-01 6.79019511e-01 -1.33954674e-01
-4.61945593e-01 7.94103086e-01 1.61711141e-01 6.45504817e-02
-1.89835653e-01 -9.05729711e-01 -8.61156881e-01 -1.44260442e+00
7.77673796e-02 2.25656405e-01 -3.19961131e-01 -9.19999108... | [10.078027725219727, -2.0335850715637207] |
1b2b0aeb-1729-43b1-8e39-6996f8e94535 | feature-less-stitching-of-cylindrical-tunnel | 1806.10278 | null | http://arxiv.org/abs/1806.10278v1 | http://arxiv.org/pdf/1806.10278v1.pdf | Feature-less Stitching of Cylindrical Tunnel | Traditional image stitching algorithms use transforms such as homography to
combine different views of a scene. They usually work well when the scene is
planar or when the camera is only rotated, keeping its position static. This
severely limits their use in real world scenarios where an unmanned aerial
vehicle (UAV) p... | ['Shaohui Foong', 'Karianto Leman', 'Ramanpreet Singh Pahwa', 'Wei Kiat Leong', 'Minh N. Do'] | 2018-06-27 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [ 8.21137726e-01 -4.05450165e-01 2.64924675e-01 2.53750116e-01
2.88586766e-01 -1.07921767e+00 4.96222585e-01 -4.38266605e-01
-2.40416273e-01 2.69227445e-01 -2.04346672e-01 -4.21892822e-01
2.54264683e-01 -7.67716229e-01 -5.46386361e-01 -4.74784851e-01
2.48090431e-01 4.33526561e-02 2.04714596e-01 -1.29774764... | [9.17900562286377, -2.4804835319519043] |
7d5413fe-531e-44a0-a707-798e661e4eed | text2human-text-driven-controllable-human | 2205.15996 | null | https://arxiv.org/abs/2205.15996v1 | https://arxiv.org/pdf/2205.15996v1.pdf | Text2Human: Text-Driven Controllable Human Image Generation | Generating high-quality and diverse human images is an important yet challenging task in vision and graphics. However, existing generative models often fall short under the high diversity of clothing shapes and textures. Furthermore, the generation process is even desired to be intuitively controllable for layman users... | ['Ziwei Liu', 'Chen Change Loy', 'Wayne Wu', 'Haonan Qiu', 'Shuai Yang', 'Yuming Jiang'] | 2022-05-31 | null | null | null | null | ['human-parsing'] | ['computer-vision'] | [ 4.27253664e-01 1.38309106e-01 1.73844665e-01 -2.37047315e-01
-3.48841369e-01 -3.33988905e-01 4.50820386e-01 -3.61839950e-01
2.28605852e-01 5.56958914e-01 3.00995439e-01 4.58396226e-01
3.01723868e-01 -1.18830752e+00 -9.20201659e-01 -8.31854403e-01
3.90677094e-01 7.76421547e-01 2.23102584e-01 -4.19634074... | [11.935050010681152, -0.7985972166061401] |
023d14eb-0b1f-4e60-8e12-4363dac01b35 | data-efficient-image-recognition-with | 1905.09272 | null | https://arxiv.org/abs/1905.09272v3 | https://arxiv.org/pdf/1905.09272v3.pdf | Data-Efficient Image Recognition with Contrastive Predictive Coding | Human observers can learn to recognize new categories of images from a handful of examples, yet doing so with artificial ones remains an open challenge. We hypothesize that data-efficient recognition is enabled by representations which make the variability in natural signals more predictable. We therefore revisit and i... | ['Aaron van den Oord', 'Olivier J. Hénaff', 'S. M. Ali Eslami', 'Jeffrey De Fauw', 'Carl Doersch', 'Ali Razavi', 'Aravind Srinivas'] | 2019-05-22 | null | https://proceedings.icml.cc/static/paper_files/icml/2020/3694-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/3694-Paper.pdf | icml-2020-1 | ['self-supervised-image-classification'] | ['computer-vision'] | [ 7.91264355e-01 2.37634584e-01 -2.38275409e-01 -8.28006029e-01
-4.52186495e-01 -6.56038284e-01 9.57908332e-01 -7.37951770e-02
-6.20906115e-01 6.67525947e-01 -1.60894826e-01 -3.04965258e-01
3.90678048e-02 -6.35615945e-01 -9.40439224e-01 -6.13310874e-01
-1.62658274e-01 3.28876257e-01 2.35306129e-01 1.00083232... | [9.593189239501953, 2.460481643676758] |
b81e3576-3a7f-49df-bc74-73ed62149bc4 | a-maximum-entropy-approach-to-massive-graph | 1912.09068 | null | https://arxiv.org/abs/1912.09068v1 | https://arxiv.org/pdf/1912.09068v1.pdf | A Maximum Entropy approach to Massive Graph Spectra | Graph spectral techniques for measuring graph similarity, or for learning the cluster number, require kernel smoothing. The choice of kernel function and bandwidth are typically chosen in an ad-hoc manner and heavily affect the resulting output. We prove that kernel smoothing biases the moments of the spectral density.... | ['Michael Osborne', 'Xiaowen Dong', 'Stefan Zohren', 'Stephen Roberts', 'Diego Granziol', 'Robin Ru'] | 2019-12-19 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [ 1.79951444e-01 1.62949115e-01 -3.08918923e-01 -1.59547225e-01
-6.79794848e-01 -7.01111734e-01 4.17987794e-01 6.17850840e-01
-3.15890431e-01 3.51013988e-01 -1.14950530e-01 -6.16920412e-01
-2.41448402e-01 -7.55465686e-01 -5.07278979e-01 -5.72654665e-01
-7.69242227e-01 6.16299987e-01 5.54044545e-01 -5.27990200... | [6.97224760055542, 5.396354675292969] |
fa417104-dba7-4900-8e65-a05963ec0d69 | navya3dseg-navya-3d-semantic-segmentation | 2302.08292 | null | https://arxiv.org/abs/2302.08292v2 | https://arxiv.org/pdf/2302.08292v2.pdf | Navya3DSeg -- Navya 3D Semantic Segmentation Dataset & split generation for autonomous vehicles | Autonomous driving (AD) perception today relies heavily on deep learning based architectures requiring large scale annotated datasets with their associated costs for curation and annotation. The 3D semantic data are useful for core perception tasks such as obstacle detection and ego-vehicle localization. We propose a n... | ['B Ravi Kiran', 'Anh Duong', 'Léo Lemarié', 'Alexandre Almin'] | 2023-02-16 | null | null | null | null | ['semi-supervised-semantic-segmentation'] | ['computer-vision'] | [ 3.27896208e-01 3.57121587e-01 -3.84304464e-01 -7.71997869e-01
-1.23735976e+00 -7.08362699e-01 6.04576707e-01 9.75216776e-02
-5.63660383e-01 5.74187636e-01 -2.62334764e-01 -2.66561776e-01
7.98765279e-04 -7.94199467e-01 -8.76084268e-01 -5.74732482e-01
5.73623627e-02 1.13949966e+00 5.37412643e-01 -2.79646307... | [8.146706581115723, -2.5464861392974854] |
855b43ab-b630-4603-9710-7eeec2be305f | zero-shot-cross-lingual-transfer-language | 2301.13720 | null | https://arxiv.org/abs/2301.13720v1 | https://arxiv.org/pdf/2301.13720v1.pdf | Zero-shot cross-lingual transfer language selection using linguistic similarity | We study the selection of transfer languages for different Natural Language Processing tasks, specifically sentiment analysis, named entity recognition and dependency parsing. In order to select an optimal transfer language, we propose to utilize different linguistic similarity metrics to measure the distance between l... | ['Fumito Masui', 'Michal Ptaszynski', 'Juuso Eronen'] | 2023-01-31 | null | null | null | null | ['zero-shot-cross-lingual-transfer', 'dependency-parsing', 'cross-lingual-transfer'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-2.28800118e-01 -2.63719976e-01 -4.50251520e-01 -6.38878107e-01
-6.97671115e-01 -8.77458870e-01 3.81634563e-01 5.18931210e-01
-7.69438386e-01 6.32769644e-01 2.09094644e-01 -4.44324195e-01
5.92382140e-02 -7.08671629e-01 -5.18487573e-01 -3.74832571e-01
2.46310055e-01 3.33735496e-01 1.96245328e-01 -3.34824175... | [10.819241523742676, 9.9459867477417] |
33ee490d-b13e-4d97-b233-c2dd27b42dd5 | a-simple-and-effective-method-of-cross | 2304.01352 | null | https://arxiv.org/abs/2304.01352v2 | https://arxiv.org/pdf/2304.01352v2.pdf | A Simple and Effective Method of Cross-Lingual Plagiarism Detection | We present a simple cross-lingual plagiarism detection method applicable to a large number of languages. The presented approach leverages open multilingual thesauri for candidate retrieval task and pre-trained multilingual BERT-based language models for detailed analysis. The method does not rely on machine translation... | ['Arutyun Avetisyan', 'Tsolak Ghukasyan', 'Arthur Malajyan', 'Karen Avetisyan'] | 2023-04-03 | null | null | null | null | ['word-sense-disambiguation'] | ['natural-language-processing'] | [-3.19593251e-01 -3.60044152e-01 -4.53583926e-01 3.40363652e-01
-1.59044814e+00 -1.01533234e+00 1.09340870e+00 7.41441548e-01
-8.07613134e-01 7.27221251e-01 2.22579002e-01 -6.41901553e-01
3.43013257e-02 -6.86262906e-01 -4.73920017e-01 -8.57871547e-02
1.19365461e-01 6.55090988e-01 2.75089055e-01 -7.50375092... | [11.15715503692627, 10.014444351196289] |
109196b1-51bd-46c3-a557-e7b40ca08fc1 | a-dataset-for-audio-video-based-vehicle-speed | 2212.01651 | null | https://arxiv.org/abs/2212.01651v1 | https://arxiv.org/pdf/2212.01651v1.pdf | A dataset for audio-video based vehicle speed estimation | Accurate speed estimation of road vehicles is important for several reasons. One is speed limit enforcement, which represents a crucial tool in decreasing traffic accidents and fatalities. Compared with other research areas and domains, the number of available datasets for vehicle speed estimation is still very limited... | ['Ivana Čavor', 'Nikola Bulatović', 'Slobodan Djukanović'] | 2022-12-03 | null | null | null | null | ['vehicle-speed-estimation'] | ['computer-vision'] | [-8.03706869e-02 -3.77875537e-01 -6.67504370e-01 -4.62830603e-01
-7.73122311e-01 -4.89647508e-01 4.83119071e-01 1.66948348e-01
-5.61614692e-01 5.90587974e-01 -2.78512716e-01 -6.00563347e-01
-1.98020250e-01 -8.08757961e-01 -7.98296809e-01 -5.00893891e-01
-1.75598204e-01 2.09333450e-01 6.26035333e-01 -1.53608933... | [7.853906631469727, -0.9286969304084778] |
d1b3eafd-adc7-4eeb-b056-7d0cb21123ec | video-matting-via-sparse-and-low-rank | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Zou_Video_Matting_via_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Zou_Video_Matting_via_ICCV_2015_paper.pdf | Video Matting via Sparse and Low-Rank Representation | We introduce a novel method of video matting via sparse and low-rank representation. Previous matting methods [10, 9] introduced a nonlocal prior to estimate the alpha matte and have achieved impressive results on some data. However, on one hand, searching inadequate or excessive samples may miss good samples or introd... | ['Xiaogang Wang', 'Xiaowu Chen', 'Guangying Cao', 'Dongqing Zou'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['video-matting'] | ['computer-vision'] | [ 1.08415216e-01 -6.68460727e-01 -7.66180605e-02 -6.92071468e-02
-6.29146039e-01 -2.90502489e-01 2.41659507e-01 -3.22350800e-01
7.50704482e-02 8.79753053e-01 4.10500318e-01 2.98539490e-01
-8.39697719e-02 -4.27347332e-01 -7.44094610e-01 -8.47698092e-01
8.83971974e-02 1.13336250e-01 2.71658003e-01 9.78380889... | [10.856633186340332, -1.717824935913086] |
0b1deb24-bf95-45e8-8db2-25d466e9841c | hybrid-transducer-and-attention-based-encoder | 2305.03101 | null | https://arxiv.org/abs/2305.03101v1 | https://arxiv.org/pdf/2305.03101v1.pdf | Hybrid Transducer and Attention based Encoder-Decoder Modeling for Speech-to-Text Tasks | Transducer and Attention based Encoder-Decoder (AED) are two widely used frameworks for speech-to-text tasks. They are designed for different purposes and each has its own benefits and drawbacks for speech-to-text tasks. In order to leverage strengths of both modeling methods, we propose a solution by combining Transdu... | ['Juan Pino', 'Paden D. Tomasello', 'Xutai Ma', 'Ning Dong', 'Xinyue Chen', 'Hirofumi Inaguma', 'Anna Y. Sun', 'Yun Tang'] | 2023-05-04 | null | null | null | null | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 7.49038339e-01 1.15601383e-01 5.79101071e-02 -2.86537081e-01
-1.10384762e+00 -2.95219302e-01 6.29774392e-01 -2.11073846e-01
-3.02879751e-01 2.99058437e-01 5.41174591e-01 -6.99925184e-01
2.38176063e-01 -2.12363571e-01 -7.24898577e-01 -6.39265120e-01
2.31447101e-01 3.96545291e-01 2.22537532e-01 -3.85298580... | [14.47297477722168, 6.9319281578063965] |
34580d53-2e51-4fbc-9a79-59ec0ab6b3de | dual-domain-image-synthesis-using | 2204.09015 | null | https://arxiv.org/abs/2204.09015v1 | https://arxiv.org/pdf/2204.09015v1.pdf | Dual-Domain Image Synthesis using Segmentation-Guided GAN | We introduce a segmentation-guided approach to synthesise images that integrate features from two distinct domains. Images synthesised by our dual-domain model belong to one domain within the semantic mask, and to another in the rest of the image - smoothly integrated. We build on the successes of few-shot StyleGAN and... | ['Dima Damen', 'Andrew Calway', 'Dena Bazazian'] | 2022-04-19 | null | null | null | null | ['caricature'] | ['computer-vision'] | [ 6.84453428e-01 5.25883675e-01 7.77963921e-02 -3.77830803e-01
-8.02837789e-01 -7.85640955e-01 1.03814638e+00 -6.67712510e-01
7.10758194e-02 6.96523845e-01 1.26515225e-01 1.85929477e-01
2.16061428e-01 -8.70045602e-01 -7.63976097e-01 -7.48008430e-01
4.36258405e-01 3.59219283e-01 2.35277370e-01 -4.43633378... | [11.654657363891602, -0.43046432733535767] |
d92c46b3-b198-47ce-a6fc-06dd3568a007 | a-temporal-learning-approach-to-inpainting | 2203.17013 | null | https://arxiv.org/abs/2203.17013v1 | https://arxiv.org/pdf/2203.17013v1.pdf | A Temporal Learning Approach to Inpainting Endoscopic Specularities and Its effect on Image Correspondence | Video streams are utilised to guide minimally-invasive surgery and diagnostic procedures in a wide range of procedures, and many computer assisted techniques have been developed to automatically analyse them. These approaches can provide additional information to the surgeon such as lesion detection, instrument navigat... | ['Danail Stoyanov', 'Francisco Vasconcelos', 'Rema Daher'] | 2022-03-31 | null | null | null | null | ['3d-shape-modeling'] | ['computer-vision'] | [ 3.72499466e-01 1.93044424e-01 1.97836161e-01 2.13830441e-01
-6.76825881e-01 -7.96634674e-01 4.08864230e-01 -1.55379340e-01
-3.73488188e-01 4.41011369e-01 2.26870507e-01 -2.05526814e-01
-3.14783156e-02 -3.66094232e-01 -7.51266003e-01 -9.60991204e-01
-4.04389948e-01 5.92545234e-02 1.55882537e-01 -1.36771485... | [13.871456146240234, -3.110417127609253] |
2d69106e-f73b-4428-833c-bebaf04e6edb | feedback-gradient-descent-efficient-and | 2205.08385 | null | https://arxiv.org/abs/2205.08385v1 | https://arxiv.org/pdf/2205.08385v1.pdf | Feedback Gradient Descent: Efficient and Stable Optimization with Orthogonality for DNNs | The optimization with orthogonality has been shown useful in training deep neural networks (DNNs). To impose orthogonality on DNNs, both computational efficiency and stability are important. However, existing methods utilizing Riemannian optimization or hard constraints can only ensure stability while those using soft ... | ['Dong Eui Chang', 'Fanchen Bu'] | 2022-05-12 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [-4.39035624e-01 -7.10916072e-02 1.48222610e-01 -1.92460790e-01
1.81126699e-01 -3.80107105e-01 5.22441447e-01 -2.55481064e-01
-6.73054576e-01 7.60746598e-01 -2.24675417e-01 -4.46699589e-01
-2.19407976e-01 -4.48049068e-01 -5.99648535e-01 -8.33509922e-01
-1.49951931e-02 -1.08728848e-01 1.24419607e-01 -3.83592308... | [7.528221607208252, 3.9472169876098633] |
aa2351be-3fad-4e79-98b3-aee9b10c098c | propandl-a-modular-architecture-for | 2304.08645 | null | https://arxiv.org/abs/2304.08645v1 | https://arxiv.org/pdf/2304.08645v1.pdf | ProPanDL: A Modular Architecture for Uncertainty-Aware Panoptic Segmentation | We introduce ProPanDL, a family of networks capable of uncertainty-aware panoptic segmentation. Unlike existing segmentation methods, ProPanDL is capable of estimating full probability distributions for both the semantic and spatial aspects of panoptic segmentation. We implement and evaluate ProPanDL variants capable o... | ['Steven Waslander', 'Chang Won Lee', 'Jacob Deery'] | 2023-04-17 | null | null | null | null | ['panoptic-segmentation'] | ['computer-vision'] | [-2.49093905e-01 -8.52243602e-02 -2.11722583e-01 -6.59587085e-01
-1.08220553e+00 -1.04767716e+00 9.11088943e-01 2.78285772e-01
-2.97712415e-01 9.37879920e-01 2.38380283e-01 -5.37401855e-01
-4.95251298e-01 -9.47073698e-01 -5.21746576e-01 -5.92667878e-01
-5.06155729e-01 6.78567648e-01 3.38674486e-01 3.01485419... | [7.272225856781006, 3.4526915550231934] |
b0167dbf-02bf-4dc0-a3e0-3a4386d0727d | spin-road-mapper-extracting-roads-from-aerial | 2109.07701 | null | https://arxiv.org/abs/2109.07701v1 | https://arxiv.org/pdf/2109.07701v1.pdf | SPIN Road Mapper: Extracting Roads from Aerial Images via Spatial and Interaction Space Graph Reasoning for Autonomous Driving | Road extraction is an essential step in building autonomous navigation systems. Detecting road segments is challenging as they are of varying widths, bifurcated throughout the image, and are often occluded by terrain, cloud, or other weather conditions. Using just convolution neural networks (ConvNets) for this problem... | ['Vishal M. Patel', 'Jeya Maria Jose Valanarasu', 'Wele Gedara Chaminda Bandara'] | 2021-09-16 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [ 6.23653680e-02 -4.39346172e-02 7.87710994e-02 -3.15673053e-01
-1.26559749e-01 -7.45739996e-01 4.47112501e-01 -9.07181799e-02
-3.16718161e-01 5.29615343e-01 -5.30610196e-02 -6.32325768e-01
-3.38712752e-01 -1.62302172e+00 -7.60369360e-01 -3.31873596e-01
-1.35784462e-01 1.92708910e-01 8.63779843e-01 -5.02349079... | [8.917546272277832, -1.5981851816177368] |
3e1a9443-4a13-4bd6-ab4f-fa79b249f946 | through-the-looking-glass-neural-3d | 2004.10904 | null | https://arxiv.org/abs/2004.10904v2 | https://arxiv.org/pdf/2004.10904v2.pdf | Through the Looking Glass: Neural 3D Reconstruction of Transparent Shapes | Recovering the 3D shape of transparent objects using a small number of unconstrained natural images is an ill-posed problem. Complex light paths induced by refraction and reflection have prevented both traditional and deep multiview stereo from solving this challenge. We propose a physically-based network to recover 3D... | ['Yu-Ying Yeh', 'Zhengqin Li', 'Manmohan Chandraker'] | 2020-04-22 | through-the-looking-glass-neural-3d-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Li_Through_the_Looking_Glass_Neural_3D_Reconstruction_of_Transparent_Shapes_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Li_Through_the_Looking_Glass_Neural_3D_Reconstruction_of_Transparent_Shapes_CVPR_2020_paper.pdf | cvpr-2020-6 | ['3d-point-cloud-reconstruction', 'transparent-objects', 'point-cloud-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.97052974e-01 -2.69653834e-02 7.51402259e-01 -5.52089095e-01
-3.47937077e-01 -4.95978504e-01 5.35561681e-01 -5.39984584e-01
-4.18865122e-03 3.21089864e-01 -2.37756688e-02 -2.71231439e-02
1.22276269e-01 -9.08915877e-01 -8.97380829e-01 -4.09616053e-01
-1.23318121e-01 1.06758583e+00 3.47227961e-01 -1.30554497... | [9.564826965332031, -3.079742431640625] |
fd1b6e9e-ab85-49e3-89b6-06761bf4668e | the-devil-is-in-the-detail-simple-tricks | 2108.12284 | null | https://arxiv.org/abs/2108.12284v4 | https://arxiv.org/pdf/2108.12284v4.pdf | The Devil is in the Detail: Simple Tricks Improve Systematic Generalization of Transformers | Recently, many datasets have been proposed to test the systematic generalization ability of neural networks. The companion baseline Transformers, typically trained with default hyper-parameters from standard tasks, are shown to fail dramatically. Here we demonstrate that by revisiting model configurations as basic as s... | ['Jürgen Schmidhuber', 'Kazuki Irie', 'Róbert Csordás'] | 2021-08-26 | null | https://aclanthology.org/2021.emnlp-main.49 | https://aclanthology.org/2021.emnlp-main.49.pdf | emnlp-2021-11 | ['systematic-generalization'] | ['reasoning'] | [-4.17299904e-02 7.85463974e-02 -2.08973184e-01 -4.28615421e-01
-4.68179017e-01 -9.10630584e-01 7.12381184e-01 -3.10262050e-02
-6.62895620e-01 5.78795493e-01 1.28986955e-01 -7.87494183e-01
-4.55102861e-01 -7.79296577e-01 -6.77658021e-01 -4.44645852e-01
-1.81440935e-01 3.60627085e-01 3.92244488e-01 -3.40088814... | [9.169183731079102, 3.36297607421875] |
4d8a6ade-7d8d-4e92-ada5-57e3fc0a221d | learning-structure-guided-diffusion-model-for | 2306.17074 | null | https://arxiv.org/abs/2306.17074v1 | https://arxiv.org/pdf/2306.17074v1.pdf | Learning Structure-Guided Diffusion Model for 2D Human Pose Estimation | One of the mainstream schemes for 2D human pose estimation (HPE) is learning keypoints heatmaps by a neural network. Existing methods typically improve the quality of heatmaps by customized architectures, such as high-resolution representation and vision Transformers. In this paper, we propose \textbf{DiffusionPose}, a... | ['Jingdong Wang', 'Errui Ding', 'Junyu Han', 'Kun Yao', 'Dongmei Fu', 'Chang Xu', 'Xiyu Wang', 'Jian Wang', 'Qiansheng Yang', 'Zhongwei Qiu'] | 2023-06-29 | null | null | null | null | ['pose-estimation', '2d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-1.24410085e-01 1.92618221e-01 3.26311350e-01 -3.89166802e-01
-8.28073800e-01 -3.94818753e-01 4.98122483e-01 -2.74489552e-01
-3.89228851e-01 5.51062346e-01 4.27201003e-01 5.24859130e-01
1.78620383e-01 -8.08428168e-01 -1.13919806e+00 -6.66526794e-01
2.61103243e-01 8.83850396e-01 1.13968499e-01 -4.35202152... | [7.046763896942139, -0.9290841817855835] |
ab443413-ac1c-4e0a-9ea6-6253ab53c846 | beyond-the-meta-leveraging-game-design | 2305.18477 | null | https://arxiv.org/abs/2305.18477v2 | https://arxiv.org/pdf/2305.18477v2.pdf | Beyond the Meta: Leveraging Game Design Parameters for Patch-Agnostic Esport Analytics | Esport games comprise a sizeable fraction of the global games market, and is the fastest growing segment in games. This has given rise to the domain of esports analytics, which uses telemetry data from games to inform players, coaches, broadcasters and other stakeholders. Compared to traditional sports, esport titles c... | ['Anders Drachen', 'James Walker', 'Florian Block', 'Alan Pedrassoli Chitayat'] | 2023-05-29 | null | null | null | null | ['dota-2'] | ['playing-games'] | [ 5.50580285e-02 7.58691356e-02 -1.02483273e-01 5.74600883e-02
-4.74567890e-01 -7.16667771e-01 5.27297318e-01 4.42639977e-01
-7.74252772e-01 3.78957927e-01 1.34969711e-01 -2.87399024e-01
-4.10808384e-01 -1.11713982e+00 -5.70452392e-01 -2.93573886e-01
5.60070612e-02 6.56093895e-01 7.24337935e-01 -6.06796205... | [6.550948143005371, 0.37324920296669006] |
3ee1965a-8060-4b2b-aa71-0c9343452de7 | scalable-educational-question-generation-with | 2305.07871 | null | https://arxiv.org/abs/2305.07871v1 | https://arxiv.org/pdf/2305.07871v1.pdf | Scalable Educational Question Generation with Pre-trained Language Models | The automatic generation of educational questions will play a key role in scaling online education, enabling self-assessment at scale when a global population is manoeuvring their personalised learning journeys. We develop \textit{EduQG}, a novel educational question generation model built by adapting a large language ... | ['Emine Yilmaz', 'Hamze Muse', 'Sahan Bulathwela'] | 2023-05-13 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [ 1.02472469e-01 3.27236593e-01 1.50951326e-01 -3.07941645e-01
-1.06295609e+00 -8.22331071e-01 9.44266498e-01 6.27828240e-01
-3.80042315e-01 7.34139502e-01 6.52402282e-01 -1.15804195e+00
-4.56629664e-01 -1.37735081e+00 -6.77930236e-01 1.49637923e-01
1.68873936e-01 4.69101489e-01 3.46533418e-01 -5.78982770... | [10.906654357910156, 7.786113262176514] |
9ea0757f-4c80-44a1-8f62-68c18e6bcc89 | language-independent-sentence-level | null | null | https://aclanthology.org/Y12-1018 | https://aclanthology.org/Y12-1018.pdf | Language Independent Sentence-Level Subjectivity Analysis with Feature Selection | null | ['Vasudeva Varma', 'Aditya Mogadala'] | 2012-11-01 | language-independent-sentence-level-1 | https://aclanthology.org/Y12-1018 | https://aclanthology.org/Y12-1018.pdf | paclic-2012-11 | ['subjectivity-analysis'] | ['natural-language-processing'] | [-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... | [-7.322773456573486, 3.6696999073028564] |
f41b6c13-6f30-4207-8766-d38570855322 | training-end-to-end-dialogue-systems-with-the | null | null | https://core.ac.uk/download/pdf/230775856.pdf | https://core.ac.uk/download/pdf/230775856.pdf | Training End-to-End Dialogue Systems with the Ubuntu Dialogue Corpus | In this paper, we analyze neural network-based dialogue systems trained in an end-to-end manner using an updated version of the recent Ubuntu Dialogue Corpus, a dataset containing almost 1 million multi-turn dialogues, with a total of over 7 million utterances and 100 million words. This dataset is interesting because ... | ['Joelle Pineau', 'Chia-Wei Liu', 'Laurent Charlin', 'Iulian Vlad Serban', 'Nissan Pow', 'Ryan Lowe'] | 2017-01-01 | null | null | null | null | ['conversation-disentanglement'] | ['natural-language-processing'] | [-4.70208675e-02 4.41474557e-01 -1.14513887e-02 -7.94664800e-01
-1.09502828e+00 -7.07909822e-01 7.99557507e-01 1.03337012e-01
-5.59667587e-01 8.38194788e-01 7.81564593e-01 -3.69721591e-01
3.28754038e-01 -5.23990333e-01 -1.78955734e-01 -2.06064790e-01
5.99192008e-02 8.57189834e-01 -5.48661985e-02 -6.98562920... | [12.778046607971191, 8.02440357208252] |
664fef10-e365-4428-9d89-043ef999a12a | dimba-discretely-masked-black-box-attack-in | 2207.08044 | null | https://arxiv.org/abs/2207.08044v1 | https://arxiv.org/pdf/2207.08044v1.pdf | DIMBA: Discretely Masked Black-Box Attack in Single Object Tracking | The adversarial attack can force a CNN-based model to produce an incorrect output by craftily manipulating human-imperceptible input. Exploring such perturbations can help us gain a deeper understanding of the vulnerability of neural networks, and provide robustness to deep learning against miscellaneous adversaries. D... | ['Jonathan Fieldsend', 'Wenjie Ruan', 'Xiangyu Yin'] | 2022-07-17 | null | null | null | null | ['visual-object-tracking', 'miscellaneous'] | ['computer-vision', 'miscellaneous'] | [ 1.08704753e-01 -3.15652430e-01 4.35855687e-02 1.45361900e-01
-6.72610819e-01 -1.13557398e+00 4.02149916e-01 -5.87910354e-01
-3.82754862e-01 5.70935786e-01 -2.08575591e-01 -4.47929621e-01
2.96438903e-01 -4.82541889e-01 -1.20720744e+00 -8.68537784e-01
-3.12401563e-01 -2.15431288e-01 4.50289786e-01 -1.68741763... | [5.408635139465332, 7.956803798675537] |
d04c8453-4c50-4e9d-81f7-7de843c220bb | uboco-unsupervised-boundary-contrastive-1 | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Kang_UBoCo_Unsupervised_Boundary_Contrastive_Learning_for_Generic_Event_Boundary_Detection_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Kang_UBoCo_Unsupervised_Boundary_Contrastive_Learning_for_Generic_Event_Boundary_Detection_CVPR_2022_paper.pdf | UBoCo: Unsupervised Boundary Contrastive Learning for Generic Event Boundary Detection | Generic Event Boundary Detection (GEBD) is a newly suggested video understanding task that aims to find one level deeper semantic boundaries of events. Bridging the gap between natural human perception and video understanding, it has various potential applications, including interpretable and semantically valid vid... | ['Seon Joo Kim', 'Taehyun Kim', 'Jinwoo Kim', 'Hyolim Kang'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['boundary-detection'] | ['computer-vision'] | [ 5.48423886e-01 2.14984313e-01 -4.35906559e-01 -4.27519649e-01
-5.80299258e-01 -4.50367033e-01 5.21571040e-01 -3.52640972e-02
-9.22860652e-02 3.01305801e-01 5.85752904e-01 -1.81777835e-01
-1.81224570e-02 -4.88490015e-01 -1.04289234e+00 -5.72981060e-01
-4.58546132e-02 2.66944766e-01 5.19572616e-01 -5.51532730... | [9.414580345153809, 0.6215787529945374] |
5d89812b-c276-488c-989f-0007a5c9d509 | digital-image-forensics-using-deep-learning | 2210.09052 | null | https://arxiv.org/abs/2210.09052v1 | https://arxiv.org/pdf/2210.09052v1.pdf | Digital Image Forensics using Deep Learning | During the investigation of criminal activity when evidence is available, the issue at hand is determining the credibility of the video and ascertaining that the video is real. Today, one way to authenticate the footage is to identify the camera that was used to capture the image or video in question. While a very comm... | ['Bishesh Sinha', 'Yash Mathur', 'Vivek Kapoor', 'Mukund Sood', 'Akash Nagaraj'] | 2022-10-14 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 4.90310282e-01 -7.19576031e-02 1.16778493e-01 -2.44902790e-01
-6.20877922e-01 -8.73401225e-01 7.12753654e-01 2.46844321e-01
-4.80273008e-01 6.88109517e-01 7.79265761e-02 -5.69823146e-01
1.06497519e-01 -7.12472737e-01 -6.61717653e-01 -5.34188688e-01
3.85454267e-01 2.19735757e-01 3.61095577e-01 1.41915813... | [12.487969398498535, 1.084869623184204] |
a3d70467-c161-4369-8487-86012a1abc12 | detecting-curve-text-in-the-wild-new-dataset | 1712.02170 | null | http://arxiv.org/abs/1712.02170v1 | http://arxiv.org/pdf/1712.02170v1.pdf | Detecting Curve Text in the Wild: New Dataset and New Solution | Scene text detection has been made great progress in recent years. The
detection manners are evolving from axis-aligned rectangle to rotated rectangle
and further to quadrangle. However, current datasets contain very little curve
text, which can be widely observed in scene images such as signboard, product
name and so ... | ['Zhang Shuaitao', 'Jin Lianwen', 'Zhang Sheng', 'Liu Yuliang'] | 2017-12-06 | null | null | null | null | ['curved-text-detection'] | ['computer-vision'] | [ 2.73786604e-01 -2.88336188e-01 -7.23024160e-02 -2.04116609e-02
-7.69187212e-01 -6.38041496e-01 4.76023823e-01 2.29917299e-02
-2.11436689e-01 -9.22428966e-02 -1.87202886e-01 -3.36109430e-01
1.79965928e-01 -5.76613665e-01 -6.37096882e-01 -6.96295202e-01
4.80681002e-01 3.80372524e-01 8.42370152e-01 -3.00987929... | [12.099713325500488, 2.245655059814453] |
d026bd49-fe16-4b8b-a965-e3dd4c5d053a | corporate-culture-and-organizational | 2301.08907 | null | https://arxiv.org/abs/2301.08907v1 | https://arxiv.org/pdf/2301.08907v1.pdf | Corporate Culture and Organizational Fragility | Complex organizations accomplish tasks through many steps of collaboration among workers. Corporate culture supports collaborations by establishing norms and reducing misunderstandings. Because a strong corporate culture relies on costly, voluntary investments by many workers, we model it as an organizational public go... | ['Matthieu V. Leduc', 'Benjamin Golub', 'Matthew Elliott'] | 2023-01-21 | null | null | null | null | ['culture'] | ['speech'] | [-5.52598417e-01 6.00574195e-01 -2.25576043e-01 2.86588848e-01
1.23547986e-01 -6.04765594e-01 4.15577620e-01 1.45244915e-02
-6.20603263e-01 1.14937091e+00 5.48804820e-01 -9.11376029e-02
-1.92806304e-01 -5.03901005e-01 -2.29666248e-01 -5.43329418e-01
4.69248801e-01 1.29423589e-01 -3.05024713e-01 -2.71339983... | [8.997416496276855, 6.15219259262085] |
51a2aab2-bdb1-4d91-80ec-8b47b63d0b2f | target-speaker-voice-activity-detection-with-1 | 2208.13085 | null | https://arxiv.org/abs/2208.13085v3 | https://arxiv.org/pdf/2208.13085v3.pdf | Target Speaker Voice Activity Detection with Transformers and Its Integration with End-to-End Neural Diarization | This paper describes a speaker diarization model based on target speaker voice activity detection (TS-VAD) using transformers. To overcome the original TS-VAD model's drawback of being unable to handle an arbitrary number of speakers, we investigate model architectures that use input tensors with variable-length time a... | ['Jian Wu', 'Takuya Yoshioka', 'Naoyuki Kanda', 'Xiong Xiao', 'Dongmei Wang'] | 2022-08-27 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [-2.28236988e-01 1.70811370e-01 4.05181795e-01 -4.52686638e-01
-9.42838252e-01 -5.33719182e-01 4.42913055e-01 -4.70831931e-01
-3.46108042e-02 -1.79033011e-01 3.87861311e-01 -4.28366989e-01
1.26541898e-01 -1.82409748e-01 -4.42525506e-01 -6.37581885e-01
-2.67689824e-01 3.83737892e-01 -2.41794083e-02 -1.65499493... | [14.47159194946289, 6.153413772583008] |
a12f9aa7-cc53-4c1b-929d-33adce26b125 | biomedgpt-a-unified-and-generalist-biomedical | 2305.17100 | null | https://arxiv.org/abs/2305.17100v1 | https://arxiv.org/pdf/2305.17100v1.pdf | BiomedGPT: A Unified and Generalist Biomedical Generative Pre-trained Transformer for Vision, Language, and Multimodal Tasks | In this paper, we introduce a unified and generalist Biomedical Generative Pre-trained Transformer (BiomedGPT) model, which leverages self-supervision on large and diverse datasets to accept multi-modal inputs and perform a range of downstream tasks. Our experiments demonstrate that BiomedGPT delivers expansive and inc... | ['Lichao Sun', 'Hongfang Liu', 'Yong Chen', 'Quanzheng Li', 'Brian D. Davison', 'Lifang He', 'Xiang Li', 'Yuyin Zhou', 'Chen Chen', 'Xun Chen', 'Sunyang Fu', 'Eashan Adhikarla', 'Yixin Liu', 'Zhiling Yan', 'Jun Yu', 'Kai Zhang'] | 2023-05-26 | null | null | null | null | ['image-captioning', 'text-summarization'] | ['computer-vision', 'natural-language-processing'] | [ 6.49213791e-01 4.55019236e-01 -8.69021788e-02 -6.03749156e-01
-1.45588040e+00 -4.08135504e-01 6.22109592e-01 6.65364116e-02
-2.87062019e-01 1.21428406e+00 4.41336572e-01 -5.18628180e-01
4.34267595e-02 -4.82902378e-01 -8.80495191e-01 -7.25848496e-01
1.96600333e-01 7.96103418e-01 -4.40653831e-01 -1.14984430... | [8.563766479492188, 8.709541320800781] |
8ad30aff-5faf-47b0-a998-217bc507bab9 | progressive-multi-stage-interactive-training | 2112.04223 | null | https://arxiv.org/abs/2112.04223v1 | https://arxiv.org/pdf/2112.04223v1.pdf | Progressive Multi-stage Interactive Training in Mobile Network for Fine-grained Recognition | Fine-grained Visual Classification (FGVC) aims to identify objects from subcategories. It is a very challenging task because of the subtle inter-class differences. Existing research applies large-scale convolutional neural networks or visual transformers as the feature extractor, which is extremely computationally expe... | ['Yang Yu', 'Chengkai Zhu', 'Yinqi Zhang', 'Yifeng Liu', 'Qingliang Chen', 'Zhenxin Wu'] | 2021-12-08 | null | null | null | null | ['fine-grained-image-classification'] | ['computer-vision'] | [ 9.14748609e-02 -5.44487119e-01 3.04597765e-02 -2.58091152e-01
-3.01702946e-01 -4.97298390e-01 6.00309014e-01 -3.27836186e-01
-3.12950253e-01 5.36496580e-01 -6.13771789e-02 -1.81477979e-01
-1.28783494e-01 -1.12240398e+00 -6.84289694e-01 -7.80424237e-01
2.35740930e-01 6.89355433e-02 5.94852984e-01 -1.53951317... | [9.644554138183594, 1.9609624147415161] |
076c1ce9-6369-4232-ab67-270087904985 | a-multi-stage-deep-architecture-for-summary | 2205.00694 | null | https://arxiv.org/abs/2205.00694v1 | https://arxiv.org/pdf/2205.00694v1.pdf | A Multi-stage deep architecture for summary generation of soccer videos | Video content is present in an ever-increasing number of fields, both scientific and commercial. Sports, particularly soccer, is one of the industries that has invested the most in the field of video analytics, due to the massive popularity of the game and the emergence of new markets. Previous state-of-the-art methods... | ['Thomas Menguy', 'Pierre-Alexandre Mattei', 'Frédéric Precioso', 'Melissa Sanabria'] | 2022-05-02 | null | null | null | null | ['sports-analytics'] | ['computer-vision'] | [ 5.46186976e-02 -1.56630114e-01 -4.30229723e-01 -6.65312856e-02
-7.90044487e-01 -4.32958812e-01 4.94774312e-01 5.38714588e-01
-4.52488929e-01 7.13683784e-01 5.75388372e-01 3.64569813e-01
-4.24337059e-01 -7.78356194e-01 -7.71017730e-01 -5.39768457e-01
-5.92632480e-02 4.39041227e-01 7.20017552e-01 -5.09756446... | [8.14765739440918, 0.10782019048929214] |
6e84057b-9c21-402c-9d86-725d514ab43b | learning-language-specific-layers-for | 2305.02665 | null | https://arxiv.org/abs/2305.02665v1 | https://arxiv.org/pdf/2305.02665v1.pdf | Learning Language-Specific Layers for Multilingual Machine Translation | Multilingual Machine Translation promises to improve translation quality between non-English languages. This is advantageous for several reasons, namely lower latency (no need to translate twice), and reduced error cascades (e.g., avoiding losing gender and formality information when translating through English). On th... | ['Stephan Peitz', 'Yi-Hsiu Liao', 'Robin M. Schmidt', 'Telmo Pessoa Pires'] | 2023-05-04 | null | null | null | null | ['architecture-search'] | ['methodology'] | [-6.65254099e-03 2.71564186e-01 -2.12289557e-01 -2.45274305e-01
-7.84901798e-01 -6.00347340e-01 6.23281240e-01 2.20217794e-01
-7.02730179e-01 8.62178504e-01 8.49918574e-02 -5.95695972e-01
3.23793501e-01 -8.73277485e-01 -9.65855718e-01 -5.23241103e-01
3.99162203e-01 5.74583232e-01 1.23576978e-02 -3.22593540... | [11.552321434020996, 10.279289245605469] |
e0d02b38-147a-4efd-b4dc-71ed8f8032af | illumination-aware-multi-task-gans-for | null | null | https://ieeexplore.ieee.org/document/8606933 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8606933 | Illumination-Aware Multi-Task GANs for Foreground Segmentation | Foreground-background segmentation has been an active research area over the years. However, conventional models fail to produce accurate results when challenged with the videos of challenging illumination conditions. In this paper, we present a robust model that allows accurately extracting the foreground even in exce... | ['Hubert P. H.', 'Edmond S. L. Ho', 'Dimitrios Sakkos'] | 2019-01-10 | null | null | null | ieee-access-2019-1 | ['video-background-subtraction', 'foreground-segmentation'] | ['computer-vision', 'computer-vision'] | [ 6.79330587e-01 -2.29253292e-01 2.67479390e-01 -2.72314638e-01
-8.47926974e-01 -6.41933680e-01 4.47598755e-01 -8.32394302e-01
-2.89444566e-01 8.43454957e-01 -3.98800790e-01 -1.99928150e-01
3.97624254e-01 -5.43898940e-01 -1.05599117e+00 -1.01034153e+00
2.68355370e-01 2.65400797e-01 4.12308306e-01 1.05278850... | [10.50694751739502, -0.8590356111526489] |
34c5ea8a-ba35-49e2-ad01-0ef0ce4bad18 | sound-source-detection-localization-and | 1908.00766 | null | https://arxiv.org/abs/1908.00766v2 | https://arxiv.org/pdf/1908.00766v2.pdf | Sound source detection, localization and classification using consecutive ensemble of CRNN models | In this paper, we describe our method for DCASE2019 task3: Sound Event Localization and Detection (SELD). We use four CRNN SELDnet-like single output models which run in a consecutive manner to recover all possible information of occurring events. We decompose the SELD task into estimating number of active sources, est... | ['Sławomir Kapka', 'Mateusz Lewandowski'] | 2019-08-02 | null | null | null | null | ['sound-event-localization-and-detection'] | ['audio'] | [ 3.63599285e-02 -4.36753780e-01 4.14050430e-01 -2.65145004e-01
-1.16004705e+00 -6.33970320e-01 5.41641653e-01 4.78707463e-01
-4.99544263e-01 6.10720217e-01 4.19979960e-01 -2.76494175e-01
-1.31112067e-02 -4.91673797e-01 -5.82738161e-01 -5.36670685e-01
-1.99764669e-01 2.07158491e-01 4.94986385e-01 1.73403680... | [15.144868850708008, 5.206925868988037] |
29575f57-52c4-477c-9e5f-819bebde8950 | collaborative-deep-reinforcement-learning-for-1 | 1702.05573 | null | http://arxiv.org/abs/1702.05573v1 | http://arxiv.org/pdf/1702.05573v1.pdf | Collaborative Deep Reinforcement Learning for Joint Object Search | We examine the problem of joint top-down active search of multiple objects
under interaction, e.g., person riding a bicycle, cups held by the table, etc..
Such objects under interaction often can provide contextual cues to each other
to facilitate more efficient search. By treating each detector as an agent, we
present... | ['Bo Xin', 'Xiangyu Kong', 'Yizhou Wang', 'Gang Hua'] | 2017-02-18 | collaborative-deep-reinforcement-learning-for-2 | http://openaccess.thecvf.com/content_cvpr_2017/html/Kong_Collaborative_Deep_Reinforcement_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Kong_Collaborative_Deep_Reinforcement_CVPR_2017_paper.pdf | cvpr-2017-7 | ['active-object-localization'] | ['computer-vision'] | [-2.81833783e-02 2.54780412e-01 -5.37068665e-01 -1.50912991e-02
-1.07904959e+00 -4.90977049e-01 5.85562944e-01 3.36644828e-01
-6.60079062e-01 6.87329829e-01 5.88706881e-02 1.50189772e-01
-4.99542743e-01 -5.92610419e-01 -9.23946917e-01 -8.18904817e-01
-5.08714557e-01 8.65009785e-01 4.50229704e-01 -8.77272040... | [3.819063425064087, 1.9176979064941406] |
ddadd12c-5d0c-4d91-9aca-351d102095be | decoupled-gradient-harmonized-detector-for | 2004.04455 | null | https://arxiv.org/abs/2004.04455v1 | https://arxiv.org/pdf/2004.04455v1.pdf | Decoupled Gradient Harmonized Detector for Partial Annotation: Application to Signet Ring Cell Detection | Early diagnosis of signet ring cell carcinoma dramatically improves the survival rate of patients. Due to lack of public dataset and expert-level annotations, automatic detection on signet ring cell (SRC) has not been thoroughly investigated. In MICCAI DigestPath2019 challenge, apart from foreground (SRC region)-backgr... | ['Yuanfan Guo', 'Jiancheng Yang', 'Yi Xu', 'Tiancheng Lin', 'Canqian Yang'] | 2020-04-09 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 3.52234751e-01 3.83346558e-01 -5.36766350e-01 -3.35752279e-01
-1.30916893e+00 -4.44911569e-01 3.27064514e-01 1.12864032e-01
-4.62465078e-01 9.26489830e-01 1.38991296e-01 -2.58061618e-01
2.21091807e-01 -1.85290188e-01 -4.19290751e-01 -1.23537767e+00
1.18058175e-01 6.44629002e-02 4.36598480e-01 1.96064606... | [15.004589080810547, -2.9182851314544678] |
c5132add-6e8f-4367-93b2-6bfd2b057805 | permuted-adain-enhancing-the-representation | 2010.05785 | null | https://arxiv.org/abs/2010.05785v3 | https://arxiv.org/pdf/2010.05785v3.pdf | Permuted AdaIN: Reducing the Bias Towards Global Statistics in Image Classification | Recent work has shown that convolutional neural network classifiers overly rely on texture at the expense of shape cues. We make a similar but different distinction between shape and local image cues, on the one hand, and global image statistics, on the other. Our method, called Permuted Adaptive Instance Normalization... | ['Lior Wolf', 'Sagie Benaim', 'Oren Nuriel'] | 2020-10-09 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Nuriel_Permuted_AdaIN_Reducing_the_Bias_Towards_Global_Statistics_in_Image_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Nuriel_Permuted_AdaIN_Reducing_the_Bias_Towards_Global_Statistics_in_Image_CVPR_2021_paper.pdf | cvpr-2021-1 | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 2.60150909e-01 -1.90941706e-01 -2.08714217e-01 -5.34813166e-01
-5.62669098e-01 -5.93067646e-01 6.34885848e-01 2.94734109e-02
-9.12806332e-01 5.84878862e-01 -1.30604699e-01 -2.83659786e-01
-1.11096509e-01 -8.74517918e-01 -1.01583850e+00 -1.13575625e+00
-1.03954978e-01 3.79402816e-01 2.56578624e-01 -6.01558201... | [9.297712326049805, 2.2386579513549805] |
b9240d3e-776f-4967-ae1a-0c2ffddf5024 | a-neuromorphic-architecture-for-reinforcement | 2307.02947 | null | https://arxiv.org/abs/2307.02947v1 | https://arxiv.org/pdf/2307.02947v1.pdf | A Neuromorphic Architecture for Reinforcement Learning from Real-Valued Observations | Reinforcement Learning (RL) provides a powerful framework for decision-making in complex environments. However, implementing RL in hardware-efficient and bio-inspired ways remains a challenge. This paper presents a novel Spiking Neural Network (SNN) architecture for solving RL problems with real-valued observations. Th... | ['Saeed Afshar', 'Teresa B. Ludermir', 'Yeshwanth Bethi', 'Sergio F. Chevtchenko'] | 2023-07-06 | null | null | null | null | ['reinforcement-learning-1', 'acrobot', 'decision-making'] | ['methodology', 'playing-games', 'reasoning'] | [ 6.99657276e-02 -3.51887256e-01 -9.50271171e-03 -3.07915919e-02
-5.48764527e-01 -2.24210188e-01 5.44659793e-01 1.80979267e-01
-1.10079670e+00 1.14071405e+00 -5.00615060e-01 2.58956198e-02
-2.12432668e-01 -4.73868638e-01 -9.19557214e-01 -1.22068179e+00
-3.36080730e-01 2.60126919e-01 5.90119779e-01 -3.85061532... | [8.104851722717285, 2.462430000305176] |
95f61d8a-67d2-4027-82ed-68f55ca0346b | time-series-clustering-via-community | 1508.04757 | null | http://arxiv.org/abs/1508.04757v1 | http://arxiv.org/pdf/1508.04757v1.pdf | Time Series Clustering via Community Detection in Networks | In this paper, we propose a technique for time series clustering using
community detection in complex networks. Firstly, we present a method to
transform a set of time series into a network using different distance
functions, where each time series is represented by a vertex and the most
similar ones are connected. The... | ['Liang Zhao', 'Leonardo N. Ferreira'] | 2015-08-19 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [-8.78183693e-02 -4.14528221e-01 2.10485488e-01 1.07618891e-01
-7.22009502e-03 -1.00979471e+00 6.47511959e-01 7.07259297e-01
-9.44537669e-02 3.54629278e-01 -1.30921885e-01 -2.54703254e-01
-8.27555716e-01 -1.01808679e+00 -1.04679391e-01 -8.27901185e-01
-7.36465991e-01 4.10355628e-01 3.59951079e-01 -1.66500002... | [7.253254413604736, 3.4229860305786133] |
977a2de3-38a9-4529-9a01-5e16101e32b9 | referee-reference-free-sentence-summarization | 2210.13800 | null | https://arxiv.org/abs/2210.13800v1 | https://arxiv.org/pdf/2210.13800v1.pdf | Referee: Reference-Free Sentence Summarization with Sharper Controllability through Symbolic Knowledge Distillation | We present Referee, a novel framework for sentence summarization that can be trained reference-free (i.e., requiring no gold summaries for supervision), while allowing direct control for compression ratio. Our work is the first to demonstrate that reference-free, controlled sentence summarization is feasible via the co... | ['Yejin Choi', 'Yulia Tsvetkov', 'Sachin Kumar', 'Peter West', 'Melanie Sclar'] | 2022-10-25 | null | null | null | null | ['abstractive-sentence-summarization'] | ['natural-language-processing'] | [ 5.57764709e-01 7.58573771e-01 -3.81803066e-01 -1.48082748e-01
-1.24550474e+00 -7.46420443e-01 5.90236247e-01 4.82153177e-01
-1.58668667e-01 1.12038171e+00 8.39949369e-01 -3.77571523e-01
-2.53933609e-01 -7.81876326e-01 -8.56818557e-01 -2.76406050e-01
2.11326238e-02 5.34912288e-01 7.65826553e-02 -2.61420369... | [12.367201805114746, 9.379453659057617] |
6c7d6069-8cff-4614-ab7d-e0393d9f8b30 | sample-factory-egocentric-3d-control-from | 2006.11751 | null | https://arxiv.org/abs/2006.11751v2 | https://arxiv.org/pdf/2006.11751v2.pdf | Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning | Increasing the scale of reinforcement learning experiments has allowed researchers to achieve unprecedented results in both training sophisticated agents for video games, and in sim-to-real transfer for robotics. Typically such experiments rely on large distributed systems and require expensive hardware setups, limitin... | ['Gaurav Sukhatme', 'Vladlen Koltun', 'Tushar Kumar', 'Zhehui Huang', 'Aleksei Petrenko'] | 2020-06-21 | null | https://proceedings.icml.cc/static/paper_files/icml/2020/3314-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/3314-Paper.pdf | icml-2020-1 | ['fps-games'] | ['playing-games'] | [-1.48530960e-01 -3.06883454e-01 8.86133537e-02 -3.25214081e-02
-8.22431087e-01 -5.45309067e-01 4.81077433e-01 1.65885404e-01
-1.08641231e+00 1.05014408e+00 -5.43666363e-01 -4.28571314e-01
1.74119294e-01 -7.92520642e-01 -8.66854846e-01 -7.13077009e-01
-5.24800241e-01 9.54315126e-01 4.29633826e-01 -2.86760062... | [3.9664306640625, 1.4691931009292603] |
ff79313b-49eb-4280-bda5-2a0c509f9d7f | edge-based-tensor-prediction-via-graph-neural | 2201.05770 | null | https://arxiv.org/abs/2201.05770v1 | https://arxiv.org/pdf/2201.05770v1.pdf | Edge-based Tensor prediction via graph neural networks | Message-passing neural networks (MPNN) have shown extremely high efficiency and accuracy in predicting the physical properties of molecules and crystals, and are expected to become the next-generation material simulation tool after the density functional theory (DFT). However, there is currently a lack of a general MPN... | ['Hongjun Xiang', 'Xingao Gong', 'Hongyu Yu', 'Yang Zhong'] | 2022-01-15 | null | null | null | null | ['formation-energy'] | ['miscellaneous'] | [-1.44946352e-02 -4.74953577e-02 -1.04496121e-01 -1.17864385e-01
3.11191082e-01 7.18222633e-02 4.60445464e-01 8.33668858e-02
9.17738944e-04 8.10364306e-01 -5.14861867e-02 -3.82109582e-01
-4.35097814e-01 -1.20813382e+00 -7.98083842e-01 -1.22108269e+00
-5.63308716e-01 3.11974466e-01 1.28644958e-01 -5.16327381... | [5.543237686157227, 5.36561918258667] |
7f914d35-5864-4712-be53-c78d04b11b55 | 190409380 | 1904.09380 | null | http://arxiv.org/abs/1904.09380v1 | http://arxiv.org/pdf/1904.09380v1.pdf | Repurposing Entailment for Multi-Hop Question Answering Tasks | Question Answering (QA) naturally reduces to an entailment problem, namely,
verifying whether some text entails the answer to a question. However, for
multi-hop QA tasks, which require reasoning with multiple sentences, it remains
unclear how best to utilize entailment models pre-trained on large scale
datasets such as... | ['Ashish Sabharwal', 'Tushar Khot', 'Niranjan Balasubramanian', 'Heeyoung Kwon', 'Harsh Trivedi'] | 2019-04-20 | repurposing-entailment-for-multi-hop-question | https://aclanthology.org/N19-1302 | https://aclanthology.org/N19-1302.pdf | naacl-2019-6 | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 1.78660303e-01 3.24840158e-01 3.55233282e-01 -6.27632916e-01
-1.81853652e+00 -7.58880615e-01 4.95239168e-01 2.54295826e-01
-4.55575377e-01 7.63153195e-01 6.32902741e-01 -7.69469500e-01
-1.51369274e-01 -8.66022646e-01 -7.80273557e-01 -4.35960256e-02
2.35843569e-01 8.64475310e-01 3.46627504e-01 -5.92038989... | [11.136187553405762, 7.943655967712402] |
c909bec6-ec1d-4ad9-b45c-fdb9ff3786d0 | classification-based-financial-markets | 1603.08604 | null | http://arxiv.org/abs/1603.08604v2 | http://arxiv.org/pdf/1603.08604v2.pdf | Classification-based Financial Markets Prediction using Deep Neural Networks | Deep neural networks (DNNs) are powerful types of artificial neural networks
(ANNs) that use several hidden layers. They have recently gained considerable
attention in the speech transcription and image recognition community
(Krizhevsky et al., 2012) for their superior predictive properties including
robustness to over... | ['Matthew Dixon', 'Diego Klabjan', 'Jin Hoon Bang'] | 2016-03-29 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-2.12515414e-01 -3.92312199e-01 -1.55149335e-02 -4.71415609e-01
-7.94838667e-02 -8.43859971e-01 6.36715770e-01 -3.15656155e-01
-5.83412588e-01 6.05606616e-01 -1.97639048e-01 -1.06561100e+00
-7.81899393e-02 -8.50439012e-01 -5.76644182e-01 -4.44010079e-01
-3.66087973e-01 6.86577260e-01 8.74553770e-02 -2.77687162... | [4.451401710510254, 4.189846038818359] |
354e8f07-1114-46e6-9c1f-f42ec701a2e1 | the-mucow-word-sense-disambiguation-test | null | null | https://aclanthology.org/2020.wmt-1.40 | https://aclanthology.org/2020.wmt-1.40.pdf | The MUCOW word sense disambiguation test suite at WMT 2020 | This paper reports on our participation with the MUCOW test suite at the WMT 2020 news translation task. We introduced MUCOW at WMT 2019 to measure the ability of MT systems to perform word sense disambiguation (WSD), i.e., to translate an ambiguous word with its correct sense. MUCOW is created automatically using exis... | ['Jörg Tiedemann', 'Alessandro Raganato', 'Yves Scherrer'] | null | null | null | null | wmt-emnlp-2020-11 | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 2.33156934e-01 2.48477757e-01 -2.65192270e-01 -1.28242880e-01
-1.07315445e+00 -9.79151964e-01 1.13001263e+00 3.66415977e-01
-8.16175640e-01 1.32342875e+00 5.69531739e-01 -7.16508687e-01
-1.57209337e-02 -4.44860399e-01 -3.83706391e-01 4.17184122e-02
3.39491457e-01 1.04064202e+00 2.34783754e-01 -7.73661852... | [11.44973087310791, 10.256850242614746] |
3213f0ab-4ad6-4bdc-956a-56cdf3aa283c | mobilevos-real-time-video-object-segmentation | 2303.07815 | null | https://arxiv.org/abs/2303.07815v1 | https://arxiv.org/pdf/2303.07815v1.pdf | MobileVOS: Real-Time Video Object Segmentation Contrastive Learning meets Knowledge Distillation | This paper tackles the problem of semi-supervised video object segmentation on resource-constrained devices, such as mobile phones. We formulate this problem as a distillation task, whereby we demonstrate that small space-time-memory networks with finite memory can achieve competitive results with state of the art, but... | ['Albert Saa-Garriga', 'Bruno Manganelli', 'Mehmet Kerim Yucel', 'Roy Miles'] | 2023-03-14 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Miles_MobileVOS_Real-Time_Video_Object_Segmentation_Contrastive_Learning_Meets_Knowledge_Distillation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Miles_MobileVOS_Real-Time_Video_Object_Segmentation_Contrastive_Learning_Meets_Knowledge_Distillation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['semi-supervised-video-object-segmentation', 'video-object-segmentation', 'video-semantic-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 5.36654830e-01 4.00881190e-03 -6.80949092e-01 -1.36045188e-01
-9.58427429e-01 -6.46020830e-01 4.97984499e-01 -2.86477238e-01
-9.71891165e-01 6.41920924e-01 -2.64136016e-01 -7.50392497e-01
2.55219340e-01 -4.10201043e-01 -1.18095064e+00 -5.12422502e-01
-3.09376828e-02 4.17534262e-01 3.97764295e-01 4.25003499... | [9.210428237915039, 0.023442238569259644] |
3cd88e20-33b3-449d-84da-57f0c0775b3f | a-two-sub-problem-decomposition-for-the | 2101.01022 | null | https://arxiv.org/abs/2101.01022v1 | https://arxiv.org/pdf/2101.01022v1.pdf | A Two Sub-problem Decomposition for the Optimal Design of Filterless Optical Networks | Filterless optical transport networks relies on passive optical interconnections between nodes, i.e., on splitters/couplers and amplifiers. While different studies have investigated their design, none of them offer a solution for an optimal design. We propose a one step solution scheme, which combines network provision... | ['Yan Wang', 'Brigitte Jaumard'] | 2021-01-04 | null | null | null | null | ['problem-decomposition'] | ['miscellaneous'] | [ 2.42121041e-01 1.95005342e-01 3.43415700e-02 -1.26478493e-01
2.51988828e-01 -5.77216685e-01 4.97833975e-02 -2.46876761e-01
-3.44005138e-01 1.18345904e+00 -3.44345152e-01 -3.52189749e-01
-6.50970519e-01 -1.05670440e+00 -2.00381190e-01 -8.96625221e-01
2.09679961e-01 3.45202893e-01 3.01587492e-01 -1.11986853... | [5.961945056915283, 1.6837393045425415] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.