paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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ec818eb5-d895-4413-9fb7-61de0d2f4984 | automatic-diagnosis-of-the-short-duration-12 | 1904.01949 | null | https://arxiv.org/abs/1904.01949v2 | https://arxiv.org/pdf/1904.01949v2.pdf | Automatic diagnosis of the 12-lead ECG using a deep neural network | The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. Deep Neural Networks (DNNs) are models composed of stacked transformations that learn tasks by examples. This technology has recently achieved striking success in a variety of task and there are gr... | ['Thomas B. Schön', 'Wagner Meira Jr.', 'Peter W. Macfarlane', 'Jéssica A. Canazart', 'Gabriela M. M. Paixão', 'Antônio H. Ribeiro', 'Milton P. S. Ferreira', 'Derick M. Oliveira', 'Carl R. Andersson', 'Paulo R. Gomes', 'Manoel Horta Ribeiro', 'Antonio Luiz P. Ribeiro'] | 2019-04-02 | null | null | null | null | ['ecg-classification', 'electrocardiography-ecg'] | ['medical', 'methodology'] | [ 4.41669196e-01 3.21573883e-01 1.33230194e-01 -5.96228004e-01
-7.69972861e-01 -5.84107399e-01 -3.33753526e-02 2.16311499e-01
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-4.06371623e-01 9.12117362e-01 -6.52033985e-01 8.98789391... | [14.36816692352295, 3.339888334274292] |
0b0f3a9d-5853-42e7-a8c6-cfc325945137 | uncertainty-sensitive-learning-and-planning-1 | 1912.09996 | null | https://arxiv.org/abs/1912.09996v3 | https://arxiv.org/pdf/1912.09996v3.pdf | Uncertainty-sensitive Learning and Planning with Ensembles | We propose a reinforcement learning framework for discrete environments in which an agent makes both strategic and tactical decisions. The former manifests itself through the use of value function, while the latter is powered by a tree search planner. These tools complement each other. The planning module performs a lo... | ['Łukasz Kuciński', 'Piotr Miłoś', 'Konrad Czechowski', 'Maciek Klimek', 'Piotr Kozakowski'] | 2019-12-19 | null | null | null | null | ['montezumas-revenge'] | ['playing-games'] | [-1.90717384e-01 5.30584872e-01 -9.82921794e-02 -2.49421801e-02
-5.64203262e-01 -7.26209998e-01 8.89309108e-01 2.16257587e-01
-7.36400008e-01 1.15774775e+00 3.12172174e-01 -4.13898855e-01
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-4.75116968e-01 6.34438097e-01 9.20046344e-02 -3.76175910... | [4.136070251464844, 2.2246451377868652] |
efee7040-fc46-4e2e-908e-80418d53aed3 | deformernet-a-deep-learning-approach-to-3d | 2107.08067 | null | https://arxiv.org/abs/2107.08067v1 | https://arxiv.org/pdf/2107.08067v1.pdf | DeformerNet: A Deep Learning Approach to 3D Deformable Object Manipulation | In this paper, we propose a novel approach to 3D deformable object manipulation leveraging a deep neural network called DeformerNet. Controlling the shape of a 3D object requires an effective state representation that can capture the full 3D geometry of the object. Current methods work around this problem by defining a... | ['Tucker Hermans', 'Alan Kuntz', 'Bao Thach'] | 2021-07-16 | null | null | null | null | ['deformable-object-manipulation'] | ['robots'] | [-1.08699828e-01 1.33167610e-01 -1.93454772e-01 -2.10495576e-01
-2.35162482e-01 -8.79611969e-01 3.32617819e-01 -1.97088137e-01
-2.53879666e-01 1.21081583e-01 -3.58815998e-01 2.65620160e-03
-2.93137968e-01 -6.69498920e-01 -1.05575716e+00 -4.57672685e-01
6.10790886e-02 1.21975076e+00 2.56003112e-01 -3.73611808... | [5.011669158935547, 0.1887478083372116] |
2d0d0713-7bfe-4b9e-818d-a669a3b4f13d | usr-an-unsupervised-and-reference-free | 2005.00456 | null | https://arxiv.org/abs/2005.00456v1 | https://arxiv.org/pdf/2005.00456v1.pdf | USR: An Unsupervised and Reference Free Evaluation Metric for Dialog Generation | The lack of meaningful automatic evaluation metrics for dialog has impeded open-domain dialog research. Standard language generation metrics have been shown to be ineffective for evaluating dialog models. To this end, this paper presents USR, an UnSupervised and Reference-free evaluation metric for dialog. USR is a ref... | ['Shikib Mehri', 'Maxine Eskenazi'] | 2020-05-01 | usr-an-unsupervised-and-reference-free-1 | https://aclanthology.org/2020.acl-main.64 | https://aclanthology.org/2020.acl-main.64.pdf | acl-2020-6 | ['dialogue-evaluation', 'open-domain-dialog'] | ['natural-language-processing', 'natural-language-processing'] | [-3.42289448e-01 6.67451143e-01 2.06977166e-02 -8.57708216e-01
-8.69059622e-01 -1.11216259e+00 1.22352469e+00 3.58244777e-01
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-7.18674436e-02 -4.06470418e-01 4.50171679e-01 -7.64272064e-02
1.00955687e-01 9.09608126e-01 8.55448768e-02 -9.45032656... | [12.911748886108398, 8.076598167419434] |
01199ff2-bfb0-4eca-b6ab-360462faa128 | synonymous-generalization-in-sequence-to | 2003.06658 | null | https://arxiv.org/abs/2003.06658v5 | https://arxiv.org/pdf/2003.06658v5.pdf | Revisit Systematic Generalization via Meaningful Learning | Humans can systematically generalize to novel compositions of existing concepts. Recent studies argue that neural networks appear inherently ineffective in such cognitive capacity, leading to a pessimistic view and a lack of attention to optimistic results. We revisit this controversial topic from the perspective of me... | ['Zhouhan Lin', 'Xiangyu Liu', 'Wei Wang', 'Boxin Wang', 'Ning Shi'] | 2020-03-14 | from-scan-to-real-data-systematic | https://openreview.net/forum?id=9qKAGxS1Tq2 | https://openreview.net/pdf?id=9qKAGxS1Tq2 | null | ['systematic-generalization', 'novel-concepts'] | ['reasoning', 'reasoning'] | [ 7.20053494e-01 5.99225700e-01 -2.87164897e-01 -6.11113369e-01
-4.61429954e-01 -8.69367242e-01 7.07850456e-01 2.71304250e-01
-3.97866070e-01 1.07629478e+00 3.21921259e-01 -7.32512772e-01
-6.66468292e-02 -8.24520171e-01 -1.14074945e+00 -2.41832972e-01
-2.49046504e-01 6.34732008e-01 1.55674696e-01 -5.05564451... | [9.685881614685059, 7.335309982299805] |
07992888-b598-445d-9901-a4af39f2d76c | a-comparison-of-latent-semantic-analysis-and | 2108.06197 | null | https://arxiv.org/abs/2108.06197v4 | https://arxiv.org/pdf/2108.06197v4.pdf | A comparison of latent semantic analysis and correspondence analysis of document-term matrices | Latent semantic analysis (LSA) and correspondence analysis (CA) are two techniques that use a singular value decomposition (SVD) for dimensionality reduction. LSA has been extensively used to obtain low-dimensional representations that capture relationships among documents and terms. In this article, we present a theor... | ['Peter G. M. van der Heijden', 'Tejaswini Deoskar', 'David J. Hessen', 'Qianqian Qi'] | 2021-07-25 | null | null | null | null | ['text-categorization'] | ['natural-language-processing'] | [ 1.68964192e-01 -1.94290921e-01 -2.89793223e-01 -2.76966870e-01
-3.31707865e-01 -8.13557088e-01 1.03983104e+00 3.65892738e-01
-3.78302217e-01 1.84260949e-01 9.37083066e-01 -3.89409631e-01
-8.20125163e-01 -4.95086670e-01 4.04458195e-02 -5.97480536e-01
-3.99329662e-02 4.68381673e-01 -2.28443205e-01 -3.80914122... | [10.193150520324707, 7.4711012840271] |
319f00bd-0970-4d11-86a2-75b7abd6453e | variational-psom-deep-probabilistic | 1910.01590 | null | https://arxiv.org/abs/1910.01590v3 | https://arxiv.org/pdf/1910.01590v3.pdf | DPSOM: Deep Probabilistic Clustering with Self-Organizing Maps | Generating interpretable visualizations from complex data is a common problem in many applications. Two key ingredients for tackling this issue are clustering and representation learning. However, current methods do not yet successfully combine the strengths of these two approaches. Existing representation learning mod... | ['Gunnar Rätsch', 'Matthias Hüser', 'Laura Manduchi', 'Julia Vogt', 'Vincent Fortuin'] | 2019-10-03 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [-2.70342380e-01 2.83086121e-01 3.17284048e-01 -3.31622303e-01
-5.23080885e-01 -4.64364767e-01 8.59590828e-01 4.47289437e-01
1.69885978e-01 4.17464495e-01 7.72399843e-01 -5.70476651e-01
-7.60886192e-01 -5.47856331e-01 -3.32098961e-01 -9.26249683e-01
-7.99413741e-01 9.37960625e-01 -2.13983059e-01 2.14066878... | [8.108152389526367, 3.945328950881958] |
b174b573-6235-4d8a-8d6e-4a94a4b4835f | variational-learning-for-the-inverted-beta | 2112.14375 | null | https://arxiv.org/abs/2112.14375v1 | https://arxiv.org/pdf/2112.14375v1.pdf | Variational Learning for the Inverted Beta-Liouville Mixture Model and Its Application to Text Categorization | The finite invert Beta-Liouville mixture model (IBLMM) has recently gained some attention due to its positive data modeling capability. Under the conventional variational inference (VI) framework, the analytically tractable solution to the optimization of the variational posterior distribution cannot be obtained, since... | ['Yuping Lai', 'Heping Song', 'Qiang Ruan', 'Wenbo Guan', 'Yongfa Ling'] | 2021-12-29 | null | null | null | null | ['text-categorization'] | ['natural-language-processing'] | [ 4.51163985e-02 9.38084163e-03 -1.64751440e-01 -2.71517247e-01
-9.79743719e-01 -1.80594534e-01 8.85232985e-01 -9.73198935e-02
-5.19419491e-01 8.25789869e-01 -4.64683175e-01 -5.18595278e-01
-3.86622161e-01 -3.90333265e-01 -4.46521729e-01 -1.10844064e+00
5.14035881e-01 6.27902269e-01 -1.15909286e-01 2.48299330... | [7.072032928466797, 3.9644968509674072] |
d8c048b7-732b-49c3-81e5-e928506e40ae | bridging-the-gap-using-deep-acoustic | 2112.13758 | null | https://arxiv.org/abs/2112.13758v1 | https://arxiv.org/pdf/2112.13758v1.pdf | Bridging the Gap: Using Deep Acoustic Representations to Learn Grounded Language from Percepts and Raw Speech | Learning to understand grounded language, which connects natural language to percepts, is a critical research area. Prior work in grounded language acquisition has focused primarily on textual inputs. In this work we demonstrate the feasibility of performing grounded language acquisition on paired visual percepts and r... | ['Cynthia Matuszek', 'Francis Ferraro', 'Edward Raff', 'Luke E. Richards', 'Gaoussou Youssouf Kebe'] | 2021-12-27 | null | null | null | null | ['language-acquisition'] | ['natural-language-processing'] | [ 3.41781735e-01 6.23695254e-01 -2.69833177e-01 -8.19042683e-01
-5.23067772e-01 -6.56609237e-01 7.99953282e-01 7.60100424e-01
-5.26469231e-01 4.92729366e-01 9.12873864e-01 -7.17464149e-01
1.59522831e-01 -7.61526167e-01 -8.12930644e-01 4.62877713e-02
-1.68875247e-01 4.33504552e-01 -1.80716410e-01 -4.60063905... | [9.850224494934082, 7.599193096160889] |
c46248c7-1e48-4270-8c0f-e25bf38f2b9f | robust-node-classification-on-graphs-jointly | 2208.09779 | null | https://arxiv.org/abs/2208.09779v1 | https://arxiv.org/pdf/2208.09779v1.pdf | Robust Node Classification on Graphs: Jointly from Bayesian Label Transition and Topology-based Label Propagation | Node classification using Graph Neural Networks (GNNs) has been widely applied in various real-world scenarios. However, in recent years, compelling evidence emerges that the performance of GNN-based node classification may deteriorate substantially by topological perturbation, such as random connections or adversarial... | ['Mohammad Al Hasan', 'Jun Zhuang'] | 2022-08-21 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [-4.33611013e-02 1.02197260e-01 -2.08763540e-01 -3.19222063e-01
-1.10900251e-03 -3.77768040e-01 6.11866832e-01 3.89429748e-01
1.31326178e-02 7.22701907e-01 -2.50021726e-01 -4.11571622e-01
-3.31192374e-01 -1.33482730e+00 -5.43173134e-01 -9.45527375e-01
-1.35384902e-01 3.86345655e-01 6.56152129e-01 -2.57000417... | [7.142207622528076, 6.065285682678223] |
185805da-cba8-4ce9-b32e-e549195dcf4f | rf-clust-for-leave-one-problem-out | 2301.09524 | null | https://arxiv.org/abs/2301.09524v2 | https://arxiv.org/pdf/2301.09524v2.pdf | RF+clust for Leave-One-Problem-Out Performance Prediction | Per-instance automated algorithm configuration and selection are gaining significant moments in evolutionary computation in recent years. Two crucial, sometimes implicit, ingredients for these automated machine learning (AutoML) methods are 1) feature-based representations of the problem instances and 2) performance pr... | ['Tome Eftimov', 'Carola Doerr', 'Ana Nikolikj'] | 2023-01-23 | null | null | null | null | ['automl'] | ['methodology'] | [ 4.87321049e-01 -2.86384195e-01 -2.76721567e-01 -3.70815575e-01
-6.83943748e-01 -4.66516793e-01 4.79146272e-01 6.71241403e-01
-3.27984184e-01 7.70289063e-01 -2.75106996e-01 1.41828638e-02
-8.92405212e-01 -9.83602107e-01 -3.54966223e-01 -9.12664473e-01
1.35908753e-03 8.27044904e-01 1.22190908e-01 -6.66506439... | [8.309303283691406, 4.342864990234375] |
c5d16d16-3a2a-49cb-9f7e-cebcfdbebadf | a-reproducible-analysis-of-rssi | 1908.06851 | null | https://arxiv.org/abs/1908.06851v1 | https://arxiv.org/pdf/1908.06851v1.pdf | A Reproducible Analysis of RSSI Fingerprinting for Outdoor Localization Using Sigfox: Preprocessing and Hyperparameter Tuning | Fingerprinting techniques, which are a common method for indoor localization, have been recently applied with success into outdoor settings. Particularly, the communication signals of Low Power Wide Area Networks (LPWAN) such as Sigfox, have been used for localization. In this rather recent field of study, not many pub... | ['Alexandros Kalousis', 'Grigorios G. Anagnostopoulos'] | 2019-08-14 | null | null | null | null | ['outdoor-localization'] | ['robots'] | [ 5.73236234e-02 -3.24250251e-01 1.08483806e-01 -3.95301014e-01
-5.49598515e-01 -6.69845462e-01 4.41784382e-01 6.05102837e-01
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-1.13630593e+00 -9.12682176e-01 -3.52140814e-01 -8.41171622e-01
-4.11357939e-01 2.87001580e-01 2.99212247e-01 3.54871862... | [6.422292709350586, 1.0323995351791382] |
8e469661-433f-46f3-84b2-65b0da1c5dbf | simplifying-subgraph-representation-learning | 2301.12562 | null | https://arxiv.org/abs/2301.12562v2 | https://arxiv.org/pdf/2301.12562v2.pdf | Simplifying Subgraph Representation Learning for Scalable Link Prediction | Link prediction on graphs is a fundamental problem. Subgraph representation learning approaches (SGRLs), by transforming link prediction to graph classification on the subgraphs around the links, have achieved state-of-the-art performance in link prediction. However, SGRLs are computationally expensive, and not scalabl... | ['Amirali Salehi-Abari', 'Shweta Ann Jacob', 'Paul Louis'] | 2023-01-29 | null | null | null | null | ['graph-classification'] | ['graphs'] | [ 5.93052842e-02 3.98986429e-01 -7.06082463e-01 -9.11390930e-02
-6.77798510e-01 -4.65860009e-01 4.65708643e-01 4.46567088e-01
3.43796790e-01 7.63188004e-01 -1.24656983e-01 -8.17754865e-01
-3.76755387e-01 -1.46280038e+00 -7.21544206e-01 -3.67304325e-01
-6.49958372e-01 4.97178018e-01 6.53184533e-01 -1.08636089... | [7.021145820617676, 6.052331447601318] |
8db5ca0f-c7bb-49e4-923f-b876bad0b47b | otter-knowledge-benchmarks-of-multimodal | 2306.12802 | null | https://arxiv.org/abs/2306.12802v2 | https://arxiv.org/pdf/2306.12802v2.pdf | Otter-Knowledge: benchmarks of multimodal knowledge graph representation learning from different sources for drug discovery | Recent research in representation learning utilizes large databases of proteins or molecules to acquire knowledge of drug and protein structures through unsupervised learning techniques. These pre-trained representations have proven to significantly enhance the accuracy of subsequent tasks, such as predicting the affin... | ['Marcos Martínez Galindo', 'Vanessa López', 'Cesar Berrospi Ramis', 'Gabriele Picco', 'Víctor Valls', 'Raúl Fernández-Díaz', 'Mykhaylo Zayats', 'Marco Luca Sbodio', 'Hoang Thanh Lam'] | 2023-06-22 | null | null | null | null | ['knowledge-graphs', 'drug-discovery', 'graph-representation-learning'] | ['knowledge-base', 'medical', 'methodology'] | [ 7.78370202e-01 7.54803121e-02 -9.61134493e-01 -5.39153576e-01
-1.09313786e+00 -5.41141391e-01 5.00406265e-01 8.29531610e-01
-1.13320366e-01 1.33798349e+00 5.75902581e-01 -4.64760423e-01
-4.20394570e-01 -5.82066655e-01 -1.05463421e+00 -6.97118461e-01
-2.14354977e-01 6.33350790e-01 -2.96083778e-01 -8.91633481... | [5.153172016143799, 5.887620449066162] |
4e89c922-41ac-4ec6-a856-db662f0e045d | deep-multimodal-transfer-learned-regression | 2006.09310 | null | https://arxiv.org/abs/2006.09310v1 | https://arxiv.org/pdf/2006.09310v1.pdf | Deep Multimodal Transfer-Learned Regression in Data-Poor Domains | In many real-world applications of deep learning, estimation of a target may rely on various types of input data modes, such as audio-video, image-text, etc. This task can be further complicated by a lack of sufficient data. Here we propose a Deep Multimodal Transfer-Learned Regressor (DMTL-R) for multimodal learning o... | ['Ulisses Braga-Neto', 'Brian Sadler', 'Vahid Attari', 'Raymundo Arroyave', 'Mulugeta Haile', 'Levi McClenny'] | 2020-06-16 | null | null | null | null | ['multi-target-regression'] | ['miscellaneous'] | [ 3.72965097e-01 -2.26596445e-01 -6.51005539e-04 -5.62033355e-01
-1.05916798e+00 -1.07114144e-01 6.91335976e-01 1.67169169e-01
-6.82744324e-01 8.01612556e-01 -3.49357903e-01 3.95274758e-02
-4.64670748e-01 -6.50088847e-01 -8.08463991e-01 -1.08414447e+00
4.40949574e-02 6.82909787e-01 1.95258722e-01 -3.64315212... | [10.119160652160645, -0.04922923445701599] |
e6ee064a-b3d2-4ae9-b40b-7f7935fdeb6b | learning-steerable-filters-for-rotation | 1711.07289 | null | http://arxiv.org/abs/1711.07289v3 | http://arxiv.org/pdf/1711.07289v3.pdf | Learning Steerable Filters for Rotation Equivariant CNNs | In many machine learning tasks it is desirable that a model's prediction
transforms in an equivariant way under transformations of its input.
Convolutional neural networks (CNNs) implement translational equivariance by
construction; for other transformations, however, they are compelled to learn
the proper mapping. In ... | ['Maurice Weiler', 'Martin Storath', 'Fred A. Hamprecht'] | 2017-11-20 | learning-steerable-filters-for-rotation-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Weiler_Learning_Steerable_Filters_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Weiler_Learning_Steerable_Filters_CVPR_2018_paper.pdf | cvpr-2018-6 | ['rotated-mnist', 'colorectal-gland-segmentation', 'breast-tumour-classification', 'multi-tissue-nucleus-segmentation'] | ['computer-vision', 'medical', 'medical', 'medical'] | [ 1.70129582e-01 1.94796398e-01 1.64951235e-01 -6.92937553e-01
-5.42817898e-02 -4.79957700e-01 6.88522279e-01 -6.54668808e-01
-7.16618955e-01 4.43630874e-01 1.76923588e-01 -2.13072866e-01
-3.22991498e-02 -8.65726173e-01 -1.27871513e+00 -7.46540964e-01
2.63135463e-01 3.33930999e-01 3.19088280e-01 -4.57608104... | [8.993579864501953, 2.3266761302948] |
67305692-d490-4700-b4d1-e04e472b90d1 | destruction-of-image-steganography-using | 1912.10070 | null | https://arxiv.org/abs/1912.10070v1 | https://arxiv.org/pdf/1912.10070v1.pdf | Destruction of Image Steganography using Generative Adversarial Networks | Digital image steganalysis, or the detection of image steganography, has been studied in depth for years and is driven by Advanced Persistent Threat (APT) groups', such as APT37 Reaper, utilization of steganographic techniques to transmit additional malware to perform further post-exploitation activity on a compromised... | ['Justin Hoffman', 'Jonathan Lwowski', 'Isaac Corley'] | 2019-12-20 | null | null | null | null | ['steganalysis', 'image-steganography'] | ['computer-vision', 'computer-vision'] | [ 1.06338155e+00 4.98106420e-01 3.81619424e-01 4.66348886e-01
-1.58337414e-01 -6.00553513e-01 7.51849711e-01 -3.92177075e-01
-2.28892311e-01 5.75045288e-01 -5.04826963e-01 -8.54543269e-01
9.74468514e-02 -1.11574078e+00 -9.20135379e-01 -1.13335621e+00
-7.12640226e-01 1.93955153e-01 4.22503144e-01 -4.21159297... | [4.301888465881348, 8.061725616455078] |
d08d8a21-9ab9-499c-8866-f49cd171a2a3 | robot-navigation-in-risky-crowded | 2303.08284 | null | https://arxiv.org/abs/2303.08284v1 | https://arxiv.org/pdf/2303.08284v1.pdf | Robot Navigation in Risky, Crowded Environments: Understanding Human Preferences | Risky and crowded environments (RCE) contain abstract sources of risk and uncertainty, which are perceived differently by humans, leading to a variety of behaviors. Thus, robots deployed in RCEs, need to exhibit diverse perception and planning capabilities in order to interpret other human agents' behavior and act acco... | ['Sonia Martinez', 'Laurel D. Riek', 'Angelique Taylor', 'Aamodh Suresh'] | 2023-03-15 | null | null | null | null | ['robot-navigation'] | ['robots'] | [-4.79179561e-01 6.71664715e-01 -6.14132658e-02 -3.87249112e-01
-9.93381441e-02 -5.80580354e-01 2.78556198e-01 1.95271865e-01
-6.63158357e-01 5.32033145e-01 5.94184995e-01 -5.59454083e-01
-5.23028672e-01 -6.74183488e-01 -3.11288714e-01 -2.85489429e-02
-4.92355198e-01 4.84651208e-01 -3.48245740e-01 -7.03649640... | [4.882781505584717, 1.0189814567565918] |
6d9c3b6f-2241-4fae-986d-e4829c4a9caa | compression-aware-video-super-resolution | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_Compression-Aware_Video_Super-Resolution_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_Compression-Aware_Video_Super-Resolution_CVPR_2023_paper.pdf | Compression-Aware Video Super-Resolution | Videos stored on mobile devices or delivered on the Internet are usually in compressed format and are of various unknown compression parameters, but most video super-resolution (VSR) methods often assume ideal inputs resulting in large performance gap between experimental settings and real-world applications. In sp... | ['Yu-Wing Tai', 'Huchuan Lu', 'Takashi Isobe', 'Xin Tao', 'Xu Jia', 'Yingwei Wang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['video-super-resolution', 'video-enhancement', 'model-compression'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 8.75316739e-01 -2.66525298e-01 -4.83401984e-01 -3.14211369e-01
-6.61206484e-01 -1.53322995e-01 3.26438159e-01 -8.20933878e-02
-1.43844649e-01 5.61687171e-01 5.50578415e-01 5.28726913e-02
-2.56578416e-01 -7.99598277e-01 -6.60843611e-01 -5.08444488e-01
-9.16869100e-03 -1.24918461e-01 2.86827058e-01 -2.54295766... | [11.118399620056152, -1.848184585571289] |
0cadf673-3a0f-40db-a148-6df7b6ab2676 | a-systematic-evaluation-of-methods-for-cell | 2110.00681 | null | https://arxiv.org/abs/2110.00681v1 | https://arxiv.org/pdf/2110.00681v1.pdf | A systematic evaluation of methods for cell phenotype classification using single-cell RNA sequencing data | Background: Single-cell RNA sequencing (scRNA-seq) yields valuable insights about gene expression and gives critical information about complex tissue cellular composition. In the analysis of single-cell RNA sequencing, the annotations of cell subtypes are often done manually, which is time-consuming and irreproducible.... | ['Xuekui Zhang', 'Junhua Gu', 'Hua He', 'Elham Majd', 'Li Xing', 'Xiaowen Cao'] | 2021-10-01 | null | null | null | null | ['phenotype-classification'] | ['medical'] | [ 1.12365156e-01 -4.12582308e-01 -2.99948812e-01 -1.06974840e-01
-5.55405140e-01 -5.70861280e-01 1.25301167e-01 3.50983799e-01
-4.00952309e-01 1.34746385e+00 1.97959840e-02 -2.93855052e-02
-4.23033893e-01 -8.59040499e-01 -1.03526480e-01 -1.22973180e+00
-6.76404461e-02 7.51581788e-01 1.79263994e-01 -1.10878140... | [5.94075870513916, 5.68444299697876] |
581cc176-2720-41b2-90f7-5bcc55a9b740 | beyond-auroc-co-for-evaluating-out-of | 2306.14658 | null | https://arxiv.org/abs/2306.14658v1 | https://arxiv.org/pdf/2306.14658v1.pdf | Beyond AUROC & co. for evaluating out-of-distribution detection performance | While there has been a growing research interest in developing out-of-distribution (OOD) detection methods, there has been comparably little discussion around how these methods should be evaluated. Given their relevance for safe(r) AI, it is important to examine whether the basis for comparing OOD detection methods is ... | ['Thomas B. Moeslund', 'Sergio Escalera', 'Galadrielle Humblot-Renaux'] | 2023-06-26 | null | null | null | null | ['out-of-distribution-detection', 'ood-detection'] | ['computer-vision', 'computer-vision'] | [ 2.08259434e-01 -8.89901165e-03 -3.10324430e-01 -5.51907182e-01
-9.50804293e-01 -8.15606117e-01 9.12137687e-01 5.76542497e-01
-1.38908073e-01 2.76315510e-01 2.16649532e-01 -8.28751862e-01
-1.39588311e-01 -5.11915326e-01 -1.03737898e-01 -3.93227994e-01
-4.47648130e-02 3.33571821e-01 3.61654103e-01 3.55508566... | [9.14396858215332, 3.3568639755249023] |
7110d9fa-e96a-4fc1-a139-77764a698acc | learning-joint-wasserstein-auto-encoders-for | null | null | https://openreview.net/forum?id=HJe3TsR5K7 | https://openreview.net/pdf?id=HJe3TsR5K7 | Learning Joint Wasserstein Auto-Encoders for Joint Distribution Matching | We study the joint distribution matching problem which aims at learning bidirectional mappings to match the joint distribution of two domains. This problem occurs in unsupervised image-to-image translation and video-to-video synthesis tasks, which, however, has two critical challenges: (i) it is difficult to exploit su... | ['Mingkui Tan', 'Junzhou Huang', 'Peilin Zhao', 'Langyuan Mo', 'Yong Guo', 'JieZhang Cao'] | 2018-09-27 | null | null | null | null | ['video-to-video-synthesis'] | ['computer-vision'] | [ 3.76036376e-01 -7.60092214e-02 -4.59166393e-02 -1.36327088e-01
-9.50659394e-01 -4.68438119e-01 6.17145717e-01 -3.75093460e-01
-1.28353417e-01 8.91555071e-01 -1.91176739e-02 -7.32319653e-02
-2.96593785e-01 -5.85004091e-01 -9.40413594e-01 -7.93711662e-01
3.13608684e-02 1.38261303e-01 4.51387949e-02 1.05827618... | [11.292860984802246, -0.6712010502815247] |
17d0f451-5d2f-4f58-8b79-68874fa1e1dc | answering-questions-by-meta-reasoning-over | 2304.13007 | null | https://arxiv.org/abs/2304.13007v2 | https://arxiv.org/pdf/2304.13007v2.pdf | Answering Questions by Meta-Reasoning over Multiple Chains of Thought | Modern systems for multi-hop question answering (QA) typically break questions into a sequence of reasoning steps, termed chain-of-thought (CoT), before arriving at a final answer. Often, multiple chains are sampled and aggregated through a voting mechanism over the final answers, but the intermediate steps themselves ... | ['Jonathan Berant', 'Daniel Deutch', 'Uri Katz', 'Ben Bogin', 'Tomer Wolfson', 'Ori Yoran'] | 2023-04-25 | null | null | null | null | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 2.77694672e-01 7.88535058e-01 -3.51977289e-01 -5.77693820e-01
-1.74119806e+00 -8.93494308e-01 8.49738240e-01 6.93491578e-01
2.53132209e-02 7.40177393e-01 7.95256734e-01 -8.47510517e-01
-1.85990751e-01 -8.77184629e-01 -7.54671574e-01 5.10859266e-02
4.86052245e-01 1.09842753e+00 7.27058589e-01 -4.39875066... | [11.024347305297852, 7.8939080238342285] |
55628c30-7560-42a0-b316-cf78ce340d9b | aspect-category-detection-via-topic-attention | 1901.01183 | null | https://arxiv.org/abs/1901.01183v2 | https://arxiv.org/pdf/1901.01183v2.pdf | Aspect Category Detection via Topic-Attention Network | The e-commerce has started a new trend in natural language processing through sentiment analysis of user-generated reviews. Different consumers have different concerns about various aspects of a specific product or service. Aspect category detection, as a subtask of aspect-based sentiment analysis, tackles the problem ... | ['Sajad Movahedi', 'Azadeh Shakery', 'Erfan Ghadery', 'Heshaam Faili'] | 2019-01-04 | null | null | null | null | ['aspect-category-detection'] | ['natural-language-processing'] | [ 1.84592027e-02 5.91016039e-02 -2.24114969e-01 -7.92210460e-01
-5.37844777e-01 -4.81779248e-01 5.91136992e-01 6.89229190e-01
-3.11580777e-01 7.78477490e-02 5.86371243e-01 -3.26603025e-01
2.37376243e-01 -9.53837395e-01 -2.91616976e-01 -3.74967098e-01
4.00210261e-01 3.95914018e-01 -1.10042937e-01 -5.21957695... | [11.380671501159668, 6.675124168395996] |
aa7e88bb-066b-4988-b132-c887ac08dc95 | data-driven-computing-with-noisy-material | null | null | https://www.sciencedirect.com/science/article/pii/S0045782517304012 | https://www.sciencedirect.com/science/article/pii/S0045782517304012 | Data Driven Computing with Noisy Material Data Sets | We formulate a Data Driven Computing paradigm, termed max-ent Data Driven Computing, that generalizes distance-minimizing Data Driven Computing and is robust with respect to outliers. Robustness is achieved by means of clustering analysis. Specifically, we assign data points a variable relevance depending on distance t... | ['T.Kirchdoerfer', 'M.Ortiz'] | 2017-11-01 | null | null | null | computer-methods-in-applied-mechanics-and | ['stress-strain-relation'] | ['miscellaneous'] | [ 8.63101110e-02 -1.07821606e-01 2.21602187e-01 -4.68443245e-01
-8.43457341e-01 -4.91673082e-01 7.05878079e-01 5.16765237e-01
-3.86873007e-01 5.92604816e-01 -1.04822978e-01 1.69406906e-01
-1.00867939e+00 -6.60125256e-01 -5.18551767e-01 -1.13478887e+00
-2.34225988e-01 9.26223636e-01 -1.71194136e-01 -3.42389613... | [7.408164024353027, 4.241969585418701] |
67139983-ed88-4493-b040-e0248b5763b4 | extend-extractive-entity-disambiguation | null | null | https://aclanthology.org/2022.acl-long.177 | https://aclanthology.org/2022.acl-long.177.pdf | ExtEnD: Extractive Entity Disambiguation | Local models for Entity Disambiguation (ED) have today become extremely powerful, in most part thanks to the advent of large pre-trained language models. However, despite their significant performance achievements, most of these approaches frame ED through classification formulations that have intrinsic limitations, bo... | ['Roberto Navigli', 'Luigi Procopio', 'Edoardo Barba'] | null | null | null | null | acl-2022-5 | ['entity-disambiguation'] | ['natural-language-processing'] | [-4.05659936e-02 -1.67305991e-02 -3.66543770e-01 -2.64101505e-01
-1.08899915e+00 -8.76594067e-01 1.16766393e+00 5.22960603e-01
-8.17866504e-01 7.76828527e-01 2.21596256e-01 -3.50853890e-01
-2.85291195e-01 -5.96922934e-01 -4.73301560e-01 -3.26282859e-01
3.88916060e-02 8.01867068e-01 2.07498685e-01 -3.45573395... | [9.7316312789917, 8.800028800964355] |
fd02dcc9-5a7e-41c1-92fa-d8956efd5589 | semi-supervised-graph-imbalanced-regression | 2305.12087 | null | https://arxiv.org/abs/2305.12087v1 | https://arxiv.org/pdf/2305.12087v1.pdf | Semi-Supervised Graph Imbalanced Regression | Data imbalance is easily found in annotated data when the observations of certain continuous label values are difficult to collect for regression tasks. When they come to molecule and polymer property predictions, the annotated graph datasets are often small because labeling them requires expensive equipment and effort... | ['Meng Jiang', 'Tengfei Luo', 'Eric Inae', 'Tong Zhao', 'Gang Liu'] | 2023-05-20 | null | null | null | null | ['graph-regression'] | ['graphs'] | [ 6.37638390e-01 5.64158022e-01 -7.43049443e-01 -6.15099728e-01
-7.82249987e-01 -4.03407395e-01 6.02798797e-02 7.61007786e-01
-5.68085443e-03 1.15372705e+00 -7.23141208e-02 -1.55266851e-01
-9.97389555e-02 -8.55330884e-01 -8.63664627e-01 -9.61928725e-01
1.40172213e-01 9.56037700e-01 -1.06812073e-02 2.43108168... | [9.375925064086914, 4.08336877822876] |
aebeac5d-61b1-44be-885c-93d173c062a8 | don-t-retrain-just-rewrite-countering | 2305.16444 | null | https://arxiv.org/abs/2305.16444v1 | https://arxiv.org/pdf/2305.16444v1.pdf | Don't Retrain, Just Rewrite: Countering Adversarial Perturbations by Rewriting Text | Can language models transform inputs to protect text classifiers against adversarial attacks? In this work, we present ATINTER, a model that intercepts and learns to rewrite adversarial inputs to make them non-adversarial for a downstream text classifier. Our experiments on four datasets and five attack mechanisms reve... | ['Vivek Srikumar', 'Alakananda Vempala', 'Shalin Shah', 'Yingjie Fei', 'Temma Choji', 'Carter Wood Blum', 'Ashim Gupta'] | 2023-05-25 | null | null | null | null | ['adversarial-robustness', 'sentiment-analysis', 'news-classification'] | ['adversarial', 'natural-language-processing', 'natural-language-processing'] | [ 4.16778028e-01 3.95167828e-01 5.54048270e-02 -2.92328894e-01
-1.01041067e+00 -1.51447797e+00 7.98295975e-01 1.08348794e-01
-4.11198288e-01 4.53024089e-01 3.39359134e-01 -8.27177942e-01
4.31109816e-01 -7.17297792e-01 -9.29633617e-01 -5.83698988e-01
2.31344774e-01 8.88583958e-02 1.75197706e-01 -8.10998261... | [6.018228054046631, 8.109905242919922] |
e67a3585-e940-4a9d-aef5-7e4cb563ee39 | natural-language-processing-via-lda-topic | 1909.09551 | null | https://arxiv.org/abs/1909.09551v1 | https://arxiv.org/pdf/1909.09551v1.pdf | Natural Language Processing via LDA Topic Model in Recommendation Systems | Today, Internet is one of the widest available media worldwide. Recommendation systems are increasingly being used in various applications such as movie recommendation, mobile recommendation, article recommendation and etc. Collaborative Filtering (CF) and Content-Based (CB) are Well-known techniques for building recom... | ['Mahdi Rabbani', 'Yongli Wang', 'Hamed Jelodar', 'SeyedValyAllah Ayobi'] | 2019-09-20 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [-4.08401787e-01 -5.90464592e-01 -4.21029389e-01 -1.83133990e-01
-4.09289032e-01 -3.22940379e-01 7.75029778e-01 2.62707800e-01
-1.01337023e-02 7.16691852e-01 6.15019977e-01 -3.32909763e-01
-3.26726884e-01 -1.29228175e+00 1.02197111e-01 -5.31726539e-01
1.24497429e-01 1.94223836e-01 6.94826186e-01 -1.26050457... | [10.164551734924316, 6.021831512451172] |
912dc482-497b-4804-8413-d0fb67f9736a | estimating-blink-probability-for-highlight | 2007.01089 | null | https://arxiv.org/abs/2007.01089v1 | https://arxiv.org/pdf/2007.01089v1.pdf | Estimating Blink Probability for Highlight Detection in Figure Skating Videos | Highlight detection in sports videos has a broad viewership and huge commercial potential. It is thus imperative to detect highlight scenes more suitably for human interest with high temporal accuracy. Since people instinctively suppress blinks during attention-grabbing events and synchronously generate blinks at atten... | ['Tamami Nakano', 'Akihiro Kishimoto', 'Atsuya Sakata'] | 2020-07-02 | null | null | null | null | ['highlight-detection'] | ['computer-vision'] | [ 7.48173967e-02 -7.01721311e-01 -3.06780517e-01 6.38810918e-02
-4.85665023e-01 -3.58350307e-01 2.09060729e-01 3.43189865e-01
-4.77947176e-01 5.36026001e-01 2.05947116e-01 3.50837052e-01
2.51844198e-01 -4.40669000e-01 -6.12258911e-01 -6.96119487e-01
-2.16024458e-01 -6.44925237e-01 4.17959183e-01 -1.56327039... | [10.096881866455078, 0.37418416142463684] |
70d1c349-8fa9-4c6a-a98f-d8696b3d5859 | mit-lab-at-semeval-2017-task-4-an-integrated | null | null | https://aclanthology.org/S17-2114 | https://aclanthology.org/S17-2114.pdf | MI\&T Lab at SemEval-2017 task 4: An Integrated Training Method of Word Vector for Sentiment Classification | A CNN method for sentiment classification task in Task 4A of SemEval 2017 is presented. To solve the problem of word2vec training word vector slowly, a method of training word vector by integrating word2vec and Convolutional Neural Network (CNN) is proposed. This training method not only improves the training speed of ... | ['Jingjing Zhao', 'Yan Yang', 'Bing Xu'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-3.96930635e-01 -7.54962489e-02 -1.09038293e-01 -4.85519111e-01
5.26382811e-02 -2.30204269e-01 3.86321455e-01 1.25640035e-01
-9.33428526e-01 3.76745075e-01 5.11023521e-01 -5.31018853e-01
6.60877764e-01 -1.06882596e+00 -1.78452045e-01 -5.35480261e-01
5.58827698e-01 -1.46773234e-01 7.57238688e-03 -7.60571778... | [10.506964683532715, 8.445016860961914] |
fdb71aa3-eca6-4147-a524-d176bb5bd0ee | identifying-and-measuring-annotator-bias | null | null | https://aclanthology.org/2020.alw-1.21 | https://aclanthology.org/2020.alw-1.21.pdf | Identifying and Measuring Annotator Bias Based on Annotators’ Demographic Characteristics | Machine learning is recently used to detect hate speech and other forms of abusive language in online platforms. However, a notable weakness of machine learning models is their vulnerability to bias, which can impair their performance and fairness. One type is annotator bias caused by the subjective perception of the a... | ['Georg Groh', 'Maximilian Wich', 'Hala Al Kuwatly'] | null | null | null | null | emnlp-alw-2020-11 | ['abusive-language'] | ['natural-language-processing'] | [-3.91286045e-01 -1.05022669e-01 -6.04887843e-01 -4.65839148e-01
-1.59021497e-01 -8.53976071e-01 5.48340619e-01 5.27334392e-01
-5.37368655e-01 6.17575586e-01 3.71421158e-01 -2.35777840e-01
2.72777706e-01 -3.92795473e-01 1.77878980e-02 -5.08273721e-01
3.08407784e-01 -4.68161553e-02 -1.20636843e-01 -1.00646585... | [8.730613708496094, 10.480405807495117] |
68a81576-0119-4cc3-8fab-dc8141abbc7a | detecting-table-region-in-pdf-documents-using | 1506.08891 | null | http://arxiv.org/abs/1506.08891v6 | http://arxiv.org/pdf/1506.08891v6.pdf | Detecting Table Region in PDF Documents Using Distant Supervision | Superior to state-of-the-art approaches which compete in table recognition
with 67 annotated government reports in PDF format released by {\it ICDAR 2013
Table Competition}, this paper contributes a novel paradigm leveraging
large-scale unlabeled PDF documents to open-domain table detection. We
integrate the paradigm i... | ['Miao Fan', 'Doo Soon Kim'] | 2015-06-29 | null | null | null | null | ['table-recognition', 'table-detection'] | ['computer-vision', 'miscellaneous'] | [-2.02957407e-01 3.40976179e-01 -4.60653603e-01 -2.30095550e-01
-1.60083890e+00 -1.46155775e+00 8.38611424e-01 6.68403268e-01
-2.36888617e-01 8.67711544e-01 4.42476332e-01 -6.52393699e-01
-1.72401667e-01 -9.08621967e-01 -1.04625750e+00 -1.51917011e-01
2.43446678e-01 9.44813728e-01 3.06759775e-01 -1.42282113... | [9.639230728149414, 8.003806114196777] |
39a3dc4b-cc69-4e93-9157-1fca5ce6ba7d | weakly-and-semi-supervised-object-detection | 1702.08740 | null | http://arxiv.org/abs/1702.08740v1 | http://arxiv.org/pdf/1702.08740v1.pdf | Weakly- and Semi-Supervised Object Detection with Expectation-Maximization Algorithm | Object detection when provided image-level labels instead of instance-level
labels (i.e., bounding boxes) during training is an important problem in
computer vision, since large scale image datasets with instance-level labels
are extremely costly to obtain. In this paper, we address this challenging
problem by developi... | ['Chang-Shui Zhang', 'Weishen Pan', 'Ziang Yan', 'Jin Li', 'Jian Liang'] | 2017-02-28 | null | null | null | null | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 2.89758801e-01 1.10158874e-02 -1.79547578e-01 -4.09875512e-01
-8.42715383e-01 -6.91567719e-01 4.95773196e-01 -9.85922385e-03
-8.90532196e-01 3.66095632e-01 -4.95623827e-01 -4.49449837e-01
4.18022305e-01 -5.99003553e-01 -1.10108447e+00 -6.47037029e-01
5.26807047e-02 2.85307020e-01 5.38358212e-01 3.00216794... | [9.23666763305664, 1.0668950080871582] |
583bbb7e-96c9-4c1d-a019-191710bb5ded | interpretable-visual-understanding-with | 2108.02924 | null | https://arxiv.org/abs/2108.02924v2 | https://arxiv.org/pdf/2108.02924v2.pdf | Interpretable Visual Understanding with Cognitive Attention Network | While image understanding on recognition-level has achieved remarkable advancements, reliable visual scene understanding requires comprehensive image understanding on recognition-level but also cognition-level, which calls for exploiting the multi-source information as well as learning different levels of understanding... | ['Eirini Ntoutsi', 'Mengyu Wang', 'Tyler Derr', 'Kea Turner', 'Yi Yu', 'Wenbin Zhang', 'Xuejiao Tang'] | 2021-08-06 | null | null | null | null | ['visual-commonsense-reasoning'] | ['reasoning'] | [ 4.47915643e-01 6.40200749e-02 -1.14324994e-01 -4.93189514e-01
-4.83034700e-01 -4.08674955e-01 6.95806503e-01 1.08469330e-01
-1.15459487e-01 4.37117577e-01 4.76412684e-01 -4.60373014e-01
5.48456907e-02 -7.18697429e-01 -8.54654729e-01 -1.38142869e-01
7.59605944e-01 2.94331294e-02 4.41328138e-02 -2.06990525... | [10.719162940979004, 1.767513394355774] |
a189a219-3f76-472f-a160-0ebf23eb215e | rgb-event-fusion-for-moving-object-detection | 2209.08323 | null | https://arxiv.org/abs/2209.08323v2 | https://arxiv.org/pdf/2209.08323v2.pdf | RGB-Event Fusion for Moving Object Detection in Autonomous Driving | Moving Object Detection (MOD) is a critical vision task for successfully achieving safe autonomous driving. Despite plausible results of deep learning methods, most existing approaches are only frame-based and may fail to reach reasonable performance when dealing with dynamic traffic participants. Recent advances in se... | ['Dominique Ginhac', 'Cédric Demonceaux', 'Fan Yang', 'Rémi Boutteau', 'Zongwei Wu', 'Zhuyun Zhou'] | 2022-09-17 | null | null | null | null | ['moving-object-detection'] | ['computer-vision'] | [ 1.81551263e-01 -6.35650873e-01 -1.44550204e-01 -4.47617382e-01
-8.76214743e-01 -3.43410313e-01 7.69135356e-01 1.96838174e-02
-5.70802271e-01 5.10157704e-01 -3.75803001e-02 -5.69883548e-02
-6.63560331e-02 -8.58667135e-01 -7.04590619e-01 -8.34618151e-01
1.46637321e-01 -1.32041693e-01 6.81326151e-01 -1.68416649... | [8.43293571472168, -1.0261341333389282] |
c92a2554-bc3c-440f-b858-d8aae28f0117 | explanation-guided-training-for-cross-domain | 2007.08790 | null | https://arxiv.org/abs/2007.08790v2 | https://arxiv.org/pdf/2007.08790v2.pdf | Explanation-Guided Training for Cross-Domain Few-Shot Classification | Cross-domain few-shot classification task (CD-FSC) combines few-shot classification with the requirement to generalize across domains represented by datasets. This setup faces challenges originating from the limited labeled data in each class and, additionally, from the domain shift between training and test sets. In t... | ['Ngai-Man Cheung', 'Wojciech Samek', 'Alexander Binder', 'Jiamei Sun', 'Yunqing Zhao', 'Sebastian Lapuschkin'] | 2020-07-17 | null | null | null | null | ['cross-domain-few-shot'] | ['computer-vision'] | [ 5.56026578e-01 4.76364404e-01 -3.22113276e-01 -4.06607866e-01
-4.64445412e-01 -2.48889357e-01 8.93871367e-01 3.92222524e-01
1.74929246e-01 5.82845807e-01 2.19177768e-01 -2.36392230e-01
-6.12595320e-01 -7.40733385e-01 -6.12567604e-01 -2.85340518e-01
2.72087473e-03 6.88966870e-01 5.29462099e-01 -5.58553517... | [9.992653846740723, 2.8441431522369385] |
274a405e-2cc1-49c4-a837-9cf6514f141c | bootstrap-your-own-latent-a-new-approach-to | 2006.07733 | null | https://arxiv.org/abs/2006.07733v3 | https://arxiv.org/pdf/2006.07733v3.pdf | Bootstrap your own latent: A new approach to self-supervised Learning | We introduce Bootstrap Your Own Latent (BYOL), a new approach to self-supervised image representation learning. BYOL relies on two neural networks, referred to as online and target networks, that interact and learn from each other. From an augmented view of an image, we train the online network to predict the target ne... | ['Rémi Munos', 'Bilal Piot', 'Carl Doersch', 'Florent Altché', 'Jean-bastien Grill', 'Koray Kavukcuoglu', 'Bernardo Avila Pires', 'Zhaohan Daniel Guo', 'Michal Valko', 'Florian Strub', 'Pierre H. Richemond', 'Mohammad Gheshlaghi Azar', 'Elena Buchatskaya', 'Corentin Tallec'] | 2020-06-13 | null | null | null | null | ['self-supervised-image-classification', 'self-supervised-person-re-identification'] | ['computer-vision', 'computer-vision'] | [ 1.53585106e-01 2.80028939e-01 -5.17390311e-01 -5.92184365e-01
-7.07937479e-01 -4.21837330e-01 5.33123136e-01 -1.74540117e-01
-6.21586800e-01 4.75842357e-01 4.67099575e-03 5.48598655e-02
3.20964128e-01 -5.88010490e-01 -1.08537436e+00 -3.70570570e-01
-1.34413391e-01 6.78001583e-01 1.88202903e-01 -4.37015817... | [9.522059440612793, 2.579665422439575] |
9c485be4-60da-4cb6-b444-f181e1f19a8a | low-resource-machine-translation-for-low | 2103.13272 | null | https://arxiv.org/abs/2103.13272v2 | https://arxiv.org/pdf/2103.13272v2.pdf | Low-Resource Machine Translation Training Curriculum Fit for Low-Resource Languages | We conduct an empirical study of neural machine translation (NMT) for truly low-resource languages, and propose a training curriculum fit for cases when both parallel training data and compute resource are lacking, reflecting the reality of most of the world's languages and the researchers working on these languages. P... | ['Derry Wijaya', 'Alexander Gregory Jones', 'Siyang Li', 'Isidora Chara Tourni', 'Afra Feyza Akyürek', 'Garry Kuwanto'] | 2021-03-24 | null | null | null | null | ['low-resource-neural-machine-translation', 'cross-lingual-bitext-mining'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.34212101e-01 -2.04094782e-01 -5.04589081e-01 -1.91100091e-01
-1.13296342e+00 -7.44654536e-01 7.11080909e-01 -1.77205548e-01
-8.31985533e-01 7.42258132e-01 2.71567404e-01 -1.00840342e+00
3.92165363e-01 -5.36342144e-01 -1.01712179e+00 -4.36318278e-01
1.67244300e-01 8.71653140e-01 -2.98743844e-01 -4.96163934... | [11.546218872070312, 10.31428050994873] |
2eee19f9-01e3-427f-b149-de915edb2418 | physics-informed-neural-network-for-seismic | 2305.05150 | null | https://arxiv.org/abs/2305.05150v1 | https://arxiv.org/pdf/2305.05150v1.pdf | Physics-informed neural network for seismic wave inversion in layered semi-infinite domain | Estimating the material distribution of Earth's subsurface is a challenging task in seismology and earthquake engineering. The recent development of physics-informed neural network (PINN) has shed new light on seismic inversion. In this paper, we present a PINN framework for seismic wave inversion in layered (1D) semi-... | ['Yang Liu', 'Hao Sun', 'Chengping Rao', 'Pu Ren'] | 2023-05-09 | null | null | null | null | ['seismic-inversion'] | ['miscellaneous'] | [ 9.33333039e-02 2.43394580e-02 4.47939962e-01 -1.23136491e-01
-5.73558509e-01 1.45438135e-01 7.04684388e-03 -5.64526081e-01
-2.39687935e-01 8.91632617e-01 2.78571606e-01 -2.29775876e-01
-3.43972743e-01 -9.03145254e-01 -8.62545669e-01 -9.69042242e-01
-3.43358636e-01 3.93997967e-01 6.17168806e-02 -2.41992667... | [6.861574649810791, 2.4897589683532715] |
7994ad1a-c8e8-4019-a911-6e665cf7e533 | factually-consistent-summarization-via | 2306.00186 | null | https://arxiv.org/abs/2306.00186v1 | https://arxiv.org/pdf/2306.00186v1.pdf | Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback | Despite the seeming success of contemporary grounded text generation systems, they often tend to generate factually inconsistent text with respect to their input. This phenomenon is emphasized in tasks like summarization, in which the generated summaries should be corroborated by their source article. In this work, we ... | ['Idan Szpektor', 'Olivier Pietquin', 'Avinatan Hassidim', 'Gal Elidan', 'Olivier Bachem', 'Nino Vieillard', 'Piotr Stanczyk', 'Sabela Ramos', 'Nikola Momchev', 'Orgad Keller', 'Léonard Hussenot', 'Sertan Girgin', 'Matthieu Geist', 'Robert Dadashi', 'Geoffrey Cideron', 'Roee Aharoni', 'Lior Shani', 'Johan Ferret', 'Pau... | 2023-05-31 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 3.77168208e-01 6.86597228e-01 -3.00257534e-01 -3.40805143e-01
-1.16189122e+00 -6.18488312e-01 1.19830918e+00 7.83202112e-01
-2.58928031e-01 1.35554278e+00 1.12770426e+00 -2.10382402e-01
-1.32175293e-02 -6.77286923e-01 -6.40350163e-01 -1.80652574e-01
2.08259881e-01 4.33343053e-01 -1.93886340e-01 -3.08904648... | [12.270413398742676, 9.322680473327637] |
63622030-e526-4739-8859-d4af6560c04f | discourse-coherence-concurrent-explicit-and | null | null | https://aclanthology.org/P18-1210 | https://aclanthology.org/P18-1210.pdf | Discourse Coherence: Concurrent Explicit and Implicit Relations | Theories of discourse coherence posit relations between discourse segments as a key feature of coherent text. Our prior work suggests that multiple discourse relations can be simultaneously operative between two segments for reasons not predicted by the literature. Here we test how this joint presence can lead particip... | ['Nathan Schneider', 'er', 'Bonnie Webber', 'Alex Johnson', 'Hannah Rohde'] | 2018-07-01 | null | null | null | acl-2018-7 | ['implicit-relations'] | ['natural-language-processing'] | [ 5.45263767e-01 7.76195168e-01 -2.67960370e-01 -6.63169086e-01
-7.78114140e-01 -8.36769342e-01 1.27823317e+00 8.26626658e-01
-2.45193064e-01 7.71075070e-01 1.04011786e+00 -7.99589813e-01
3.35048884e-02 -6.39997840e-01 -4.30299312e-01 -1.94672570e-01
-9.98084396e-02 2.14025989e-01 3.98574859e-01 -4.84198183... | [10.66592025756836, 9.319313049316406] |
f77b4007-3e8d-4cd3-a58d-8b8291045edb | idas-intent-discovery-with-abstractive | 2305.19783 | null | https://arxiv.org/abs/2305.19783v1 | https://arxiv.org/pdf/2305.19783v1.pdf | IDAS: Intent Discovery with Abstractive Summarization | Intent discovery is the task of inferring latent intents from a set of unlabeled utterances, and is a useful step towards the efficient creation of new conversational agents. We show that recent competitive methods in intent discovery can be outperformed by clustering utterances based on abstractive summaries, i.e., "l... | ['Chris Develder', 'Thomas Demeester', 'Fréderic Godin', 'Maarten De Raedt'] | 2023-05-31 | null | null | null | null | ['abstractive-text-summarization', 'intent-discovery'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.17752808e-01 5.07260859e-01 -1.57779798e-01 -8.91714156e-01
-1.11677861e+00 -6.19398773e-01 9.57423270e-01 1.84127629e-01
-3.06984514e-01 5.40864527e-01 9.13766444e-01 -1.48021400e-01
2.38036543e-01 -7.61694089e-02 -4.85256463e-01 -8.55660439e-01
-2.31929794e-01 1.14061916e+00 -2.40688026e-01 -1.05847716... | [12.491523742675781, 7.546449184417725] |
51b48a8a-f721-4ecf-85dd-a5e98eb6368d | self-supervised-driven-consistency-training | 2102.03897 | null | https://arxiv.org/abs/2102.03897v3 | https://arxiv.org/pdf/2102.03897v3.pdf | Self-supervised driven consistency training for annotation efficient histopathology image analysis | Training a neural network with a large labeled dataset is still a dominant paradigm in computational histopathology. However, obtaining such exhaustive manual annotations is often expensive, laborious, and prone to inter and Intra-observer variability. While recent self-supervised and semi-supervised methods can allevi... | ['Anne L. Martel', 'Fu-Der Chen', 'Seung Wook Kim', 'Chetan L. Srinidhi'] | 2021-02-07 | null | null | null | null | ['histopathological-image-classification'] | ['medical'] | [ 5.14969409e-01 1.37628660e-01 -4.96880203e-01 -6.79605007e-01
-1.31062198e+00 -5.02770066e-01 4.91739899e-01 5.19107103e-01
-4.10511017e-01 8.70285630e-01 1.02201261e-01 -2.65245497e-01
-9.05228853e-02 -5.11605740e-01 -6.30778551e-01 -1.20264184e+00
6.78319409e-02 5.19524038e-01 1.28204182e-01 6.69731200... | [14.984136581420898, -2.595343589782715] |
11354630-66cc-4523-ad30-22f1d88efff7 | age-group-and-gender-estimation-in-the-wild | 1710.02985 | null | http://arxiv.org/abs/1710.02985v1 | http://arxiv.org/pdf/1710.02985v1.pdf | Age Group and Gender Estimation in the Wild with Deep RoR Architecture | Automatically predicting age group and gender from face images acquired in
unconstrained conditions is an important and challenging task in many
real-world applications. Nevertheless, the conventional methods with
manually-designed features on in-the-wild benchmarks are unsatisfactory because
of incompetency to tackle ... | ['Xingfang Yuan', 'Liru Guo', 'Zhenbing Zhao', 'Tony X. Han', 'Baogang Li', 'Ke Zhang', 'Ce Gao', 'Miao Sun'] | 2017-10-09 | null | null | null | null | ['age-and-gender-estimation', 'age-and-gender-classification'] | ['computer-vision', 'computer-vision'] | [-4.03289348e-01 1.32919371e-01 -9.02468339e-02 -7.99151897e-01
-1.24641798e-01 2.41083100e-01 4.13519889e-01 -4.08604205e-01
-6.04080558e-01 5.73305964e-01 2.40063835e-02 1.99613288e-01
-1.60292745e-01 -8.28554094e-01 -2.58145332e-01 -9.15594995e-01
-1.78007394e-01 4.83670175e-01 -5.88369191e-01 -1.83423877... | [13.53469181060791, 0.8985046148300171] |
566cff8b-f8f6-4c16-a3d2-33c2c7e014a6 | video4mri-an-empirical-study-on-brain | 2302.12688 | null | https://arxiv.org/abs/2302.12688v1 | https://arxiv.org/pdf/2302.12688v1.pdf | Video4MRI: An Empirical Study on Brain Magnetic Resonance Image Analytics with CNN-based Video Classification Frameworks | To address the problem of medical image recognition, computer vision techniques like convolutional neural networks (CNN) are frequently used. Recently, 3D CNN-based models dominate the field of magnetic resonance image (MRI) analytics. Due to the high similarity between MRI data and videos, we conduct extensive empiric... | ['Haoyi Xiong', 'Dejing Dou', 'Yanwu Xu', 'Yi Liu', 'Jiang Bian', 'Qingzhong Wang', 'Yuxuan Zhang'] | 2023-02-24 | null | null | null | null | ['video-classification', 'video-recognition', 'video-understanding'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.15417820e-01 -4.57569286e-02 -5.06960332e-01 -3.30745429e-01
-3.64602804e-01 -3.40564139e-02 2.44373471e-01 -1.00882828e-01
-6.82163894e-01 3.73896658e-01 5.08915305e-01 -4.03819412e-01
3.04592978e-02 -3.73370439e-01 -6.48431778e-01 -6.07633591e-01
-6.04372561e-01 2.24736691e-01 -2.33442448e-02 -6.76435158... | [8.48510456085205, 0.540360152721405] |
d5259b44-3c42-439f-becf-9ede6c051664 | image-based-table-recognition-data-model-and | 1911.10683 | null | https://arxiv.org/abs/1911.10683v5 | https://arxiv.org/pdf/1911.10683v5.pdf | Image-based table recognition: data, model, and evaluation | Important information that relates to a specific topic in a document is often organized in tabular format to assist readers with information retrieval and comparison, which may be difficult to provide in natural language. However, tabular data in unstructured digital documents, e.g., Portable Document Format (PDF) and ... | ['Xu Zhong', 'Antonio Jimeno Yepes', 'Elaheh ShafieiBavani'] | 2019-11-25 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3802_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123660562.pdf | eccv-2020-8 | ['table-recognition'] | ['computer-vision'] | [ 3.96293849e-01 -3.07094634e-01 -2.37515613e-01 -2.81785578e-01
-1.21899569e+00 -8.34679186e-01 1.58007801e-01 7.34017670e-01
-3.07504594e-01 8.38567793e-01 2.88946599e-01 -3.01584393e-01
7.95751810e-02 -7.92453229e-01 -1.16723788e+00 -4.44278389e-01
4.07890528e-01 7.19093263e-01 -2.40444928e-01 1.66925982... | [11.700796127319336, 2.9828097820281982] |
0e64babe-5679-42da-8a19-10e7ea83775b | agronav-autonomous-navigation-framework-for | 2304.04333 | null | https://arxiv.org/abs/2304.04333v1 | https://arxiv.org/pdf/2304.04333v1.pdf | Agronav: Autonomous Navigation Framework for Agricultural Robots and Vehicles using Semantic Segmentation and Semantic Line Detection | The successful implementation of vision-based navigation in agricultural fields hinges upon two critical components: 1) the accurate identification of key components within the scene, and 2) the identification of lanes through the detection of boundary lines that separate the crops from the traversable ground. We propo... | ['M. Khalid Jawed', 'Yongkyu Lee', 'Shivam K Panda'] | 2023-04-10 | null | null | null | null | ['autonomous-navigation', 'line-detection'] | ['computer-vision', 'computer-vision'] | [ 1.07329212e-01 -1.15476221e-01 4.75232117e-02 -3.89684647e-01
-2.82560885e-01 -1.41126883e+00 2.74913907e-01 4.16996390e-01
9.05141458e-02 1.06777959e-01 -3.87833774e-01 -8.86603475e-01
-2.42780060e-01 -1.01015317e+00 -7.46504486e-01 -1.84146270e-01
-2.46650025e-01 2.55530119e-01 5.65503120e-01 -5.65975010... | [9.031684875488281, -1.5762301683425903] |
1d30392b-7cb9-422c-a892-a48536b1235f | a-regional-news-corpora-for-contextualized | null | null | https://aclanthology.org/L16-1531 | https://aclanthology.org/L16-1531.pdf | A Regional News Corpora for Contextualized Entity Discovery and Linking | This paper presents a German corpus for Named Entity Linking (NEL) and Knowledge Base Population (KBP) tasks. We describe the annotation guideline, the annotation process, NIL clustering techniques and conversion to popular NEL formats such as NIF and TAC that have been used to construct this corpus based on news trans... | ['Adrian Bra{\\c{s}}oveanu', 'Lyndon J.B. Nixon', 'Albert Weichselbraun', 'Arno Scharl'] | 2016-05-01 | a-regional-news-corpora-for-contextualized-1 | https://aclanthology.org/L16-1531 | https://aclanthology.org/L16-1531.pdf | lrec-2016-5 | ['knowledge-base-population'] | ['natural-language-processing'] | [-3.94123048e-01 8.39745164e-01 -1.80927023e-01 -6.28591180e-01
-1.03507113e+00 -7.29987144e-01 6.19273424e-01 6.93113863e-01
-9.85859811e-01 1.36922610e+00 9.78451252e-01 -5.25178611e-02
-4.76965040e-01 -6.34771407e-01 -3.28681797e-01 1.27304150e-02
-6.36452511e-02 1.15908360e+00 5.79829335e-01 -3.59989792... | [9.5209379196167, 9.134490966796875] |
458fade9-7474-4797-87ba-247d3dd52221 | reset-revisiting-trajectory-sets-for | 2304.05856 | null | https://arxiv.org/abs/2304.05856v1 | https://arxiv.org/pdf/2304.05856v1.pdf | RESET: Revisiting Trajectory Sets for Conditional Behavior Prediction | It is desirable to predict the behavior of traffic participants conditioned on different planned trajectories of the autonomous vehicle. This allows the downstream planner to estimate the impact of its decisions. Recent approaches for conditional behavior prediction rely on a regression decoder, meaning that coordinate... | ['Klaus Dietmayer', 'Vasileios Belagiannis', 'Julian Jordan', 'Julian Wiederer', 'Pascal Huissel', 'Julian Schmidt'] | 2023-04-12 | null | null | null | null | ['trajectory-prediction'] | ['computer-vision'] | [ 7.02843964e-02 3.84598166e-01 -3.39931726e-01 -4.58666921e-01
-7.22194135e-01 -4.49149370e-01 7.90560067e-01 4.58313316e-01
-2.26497412e-01 6.63961053e-01 3.44483286e-01 -7.20634818e-01
-1.81534454e-01 -1.05247843e+00 -8.72847438e-01 -4.38616246e-01
-2.10713193e-01 7.09752560e-01 7.09385872e-01 -3.37688506... | [5.834871768951416, 1.0121999979019165] |
90a34982-f735-482a-bcd2-9ec900cdd4d8 | rogue-signs-deceiving-traffic-sign | 1801.02780 | null | http://arxiv.org/abs/1801.02780v3 | http://arxiv.org/pdf/1801.02780v3.pdf | Rogue Signs: Deceiving Traffic Sign Recognition with Malicious Ads and Logos | We propose a new real-world attack against the computer vision based systems
of autonomous vehicles (AVs). Our novel Sign Embedding attack exploits the
concept of adversarial examples to modify innocuous signs and advertisements in
the environment such that they are classified as the adversary's desired
traffic sign wi... | ['Chawin Sitawarin', 'Mung Chiang', 'Arsalan Mosenia', 'Arjun Nitin Bhagoji', 'Prateek Mittal'] | 2018-01-09 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 3.93027186e-01 2.39459518e-02 3.11777115e-01 -1.85626820e-01
-4.58621442e-01 -1.22581899e+00 1.18716884e+00 -7.57008970e-01
-3.63429159e-01 5.31387031e-01 -4.34318662e-01 -6.49967134e-01
3.89771134e-01 -8.32832277e-01 -1.11901581e+00 -6.27063096e-01
-1.06449448e-01 2.37989053e-01 5.18924713e-01 -4.31192458... | [5.437412261962891, 7.852640151977539] |
320d8ad9-52f2-4b38-95ba-4af6fe59a9ac | vlg-general-video-recognition-with-web | 2212.01638 | null | https://arxiv.org/abs/2212.01638v1 | https://arxiv.org/pdf/2212.01638v1.pdf | VLG: General Video Recognition with Web Textual Knowledge | Video recognition in an open and dynamic world is quite challenging, as we need to handle different settings such as close-set, long-tail, few-shot and open-set. By leveraging semantic knowledge from noisy text descriptions crawled from the Internet, we focus on the general video recognition (GVR) problem of solving di... | ['LiMin Wang', 'Wayne Wu', 'Wenhai Wang', 'Zhaoyang Liu', 'Jintao Lin'] | 2022-12-03 | null | null | null | null | ['video-recognition'] | ['computer-vision'] | [-6.60175532e-02 -7.41734982e-01 -3.97976696e-01 -3.17221671e-01
-9.28381920e-01 -3.49864423e-01 6.14041805e-01 -7.70354629e-01
-3.63712162e-01 3.49343419e-01 3.61662030e-01 -1.28858715e-01
1.76867828e-01 -4.74042296e-01 -7.44745493e-01 -5.67246616e-01
7.42360875e-02 7.72315636e-02 1.38847575e-01 -1.40403226... | [10.186241149902344, 0.9179513454437256] |
2f7e6e5a-497e-4807-a572-b3f8e6cc46ee | wifi-motion-detection-a-study-into-efficacy | 1908.08476 | null | http://arxiv.org/abs/1908.08476v1 | http://arxiv.org/pdf/1908.08476v1.pdf | WiFi Motion Detection: A Study into Efficacy and Classification | WiFi and security pose both an issue and act as a growing presence in
everyday life. Today's motions detection implementations are severely lacking
in the areas of secrecy, scope, and cost. To combat this problem, we aim to
develop a motion detection system that utilizes WiFi Channel State Information
(CSI), which desc... | [] | 2019-08-20 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 3.57079916e-02 -4.60546672e-01 -2.31796205e-01 -3.47923040e-02
-4.85287338e-01 -6.29987955e-01 1.35967672e-01 -3.06084901e-01
-2.81284541e-01 7.41362989e-01 -1.24373501e-02 -6.96096361e-01
-1.12370774e-01 -8.88160288e-01 -5.10934532e-01 -4.95326281e-01
-7.77347803e-01 -3.05790931e-01 6.48190677e-01 8.25860351... | [6.696852684020996, 0.7183588147163391] |
15c05a8f-9478-4c82-99fc-d81a7033dc38 | multi-view-3d-reconstruction-with | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Wang_Multi-View_3D_Reconstruction_With_Transformers_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Wang_Multi-View_3D_Reconstruction_With_Transformers_ICCV_2021_paper.pdf | Multi-View 3D Reconstruction With Transformers | Deep CNN-based methods have so far achieved the state of the art results in multi-view 3D object reconstruction. Despite the considerable progress, the two core modules of these methods - view feature extraction and multi-view fusion, are usually investigated separately, and the relations among multiple input views... | ['Rabab Ward', 'Z. Jane Wang', 'Septimiu Salcudean', 'Tianyang Shi', 'Zhengxia Zou', 'Xun Chen', 'Xinrui Cui', 'Dan Wang'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['3d-object-reconstruction', 'object-reconstruction'] | ['computer-vision', 'computer-vision'] | [-9.37164798e-02 -2.40943328e-01 -3.38290036e-02 -4.49498296e-01
-9.10131693e-01 -5.47272921e-01 7.25717306e-01 -4.15420532e-01
1.94409057e-01 2.41936803e-01 6.96249843e-01 -1.72269315e-01
2.82870643e-02 -7.69958377e-01 -8.77990186e-01 -4.36061025e-01
2.63507664e-01 5.37861764e-01 2.65240312e-01 -3.18999141... | [8.261924743652344, -3.5866246223449707] |
009b8f99-4138-4589-b85a-003120c024ed | text-reading-order-in-uncontrolled-conditions | 2305.02577 | null | https://arxiv.org/abs/2305.02577v1 | https://arxiv.org/pdf/2305.02577v1.pdf | Text Reading Order in Uncontrolled Conditions by Sparse Graph Segmentation | Text reading order is a crucial aspect in the output of an OCR engine, with a large impact on downstream tasks. Its difficulty lies in the large variation of domain specific layout structures, and is further exacerbated by real-world image degradations such as perspective distortions. We propose a lightweight, scalable... | ['Alessandro Bissacco', 'Yasuhisa Fujii', 'Renshen Wang'] | 2023-05-04 | null | null | null | null | ['optical-character-recognition'] | ['computer-vision'] | [ 4.92097199e-01 -4.72404122e-01 6.46945760e-02 -2.40077645e-01
-6.45832598e-01 -1.08066595e+00 4.53663915e-01 3.56571048e-01
-1.64958090e-02 -3.29988562e-02 3.54715616e-01 -6.90454245e-01
-2.47425511e-01 -4.89124566e-01 -7.87808657e-01 -2.86380917e-01
5.13524413e-02 4.54672813e-01 2.61107832e-01 -3.59295875... | [11.658778190612793, 2.3460381031036377] |
abfde15f-a8f1-4381-bdeb-51c4eeed3683 | anchor-retouching-via-model-interaction-for | 2112.06701 | null | https://arxiv.org/abs/2112.06701v1 | https://arxiv.org/pdf/2112.06701v1.pdf | Anchor Retouching via Model Interaction for Robust Object Detection in Aerial Images | Object detection has made tremendous strides in computer vision. Small object detection with appearance degradation is a prominent challenge, especially for aerial observations. To collect sufficient positive/negative samples for heuristic training, most object detectors preset region anchors in order to calculate Inte... | ['Huiyu Zhou', 'Mingqiang Wei', 'Ekaterina L. Kim', 'Dmitry A. Vorontsov', 'Zongqi Wei', 'Qixiang Geng', 'Dong Liang'] | 2021-12-13 | null | null | null | null | ['robust-object-detection', 'object-detection-in-aerial-images', 'small-object-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.47965419e-01 1.05472133e-01 -2.25373387e-01 -1.96553990e-01
-9.63624716e-01 -4.43585247e-01 3.51757079e-01 -3.10285371e-02
-7.00537324e-01 5.38522184e-01 -5.52130997e-01 -3.52293700e-01
1.67058215e-01 -8.05233777e-01 -9.35599923e-01 -8.38496506e-01
-6.39720121e-03 2.38236383e-01 9.56492782e-01 -4.15458530... | [8.711813926696777, -0.5509023070335388] |
3015ef69-b5bb-4a75-9c66-1929b5137e3b | making-vision-transformers-efficient-from-a | 2303.08685 | null | https://arxiv.org/abs/2303.08685v2 | https://arxiv.org/pdf/2303.08685v2.pdf | Making Vision Transformers Efficient from A Token Sparsification View | The quadratic computational complexity to the number of tokens limits the practical applications of Vision Transformers (ViTs). Several works propose to prune redundant tokens to achieve efficient ViTs. However, these methods generally suffer from (i) dramatic accuracy drops, (ii) application difficulty in the local vi... | ['Mike Zheng Shou', 'Rong Jin', 'David Junhao Zhang', 'Fan Wang', 'Ming Lin', 'Pichao Wang', 'Shuning Chang'] | 2023-03-15 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chang_Making_Vision_Transformers_Efficient_From_a_Token_Sparsification_View_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chang_Making_Vision_Transformers_Efficient_From_a_Token_Sparsification_View_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-recognition'] | ['computer-vision'] | [ 1.52260857e-02 -3.95799894e-03 -1.78700000e-01 -1.65541485e-01
-7.44999528e-01 -4.19886619e-01 2.37188086e-01 -2.94519603e-01
-6.03240311e-01 4.08150613e-01 -1.12596028e-01 -3.82354528e-01
1.29088014e-01 -7.19860315e-01 -7.43248940e-01 -8.83317888e-01
4.17515904e-01 2.02188477e-01 8.82948816e-01 6.49840059... | [9.616374015808105, 0.22986207902431488] |
170c7c27-6c39-4681-b6da-31dc23b6be25 | mokb6-a-multilingual-open-knowledge-base | 2211.06959 | null | https://arxiv.org/abs/2211.06959v2 | https://arxiv.org/pdf/2211.06959v2.pdf | mOKB6: A Multilingual Open Knowledge Base Completion Benchmark | Automated completion of open knowledge bases (Open KBs), which are constructed from triples of the form (subject phrase, relation phrase, object phrase), obtained via open information extraction (Open IE) system, are useful for discovering novel facts that may not be directly present in the text. However, research in O... | ['Mausam', 'Soumen Chakrabarti', 'Keshav Kolluru', 'Shubham Mittal'] | 2022-11-13 | null | null | null | null | ['knowledge-base-completion', 'knowledge-base-completion', 'open-information-extraction', 'coreference-resolution'] | ['graphs', 'knowledge-base', 'natural-language-processing', 'natural-language-processing'] | [-7.69590259e-01 7.65595496e-01 -4.43319440e-01 1.66181456e-02
-1.16523540e+00 -1.04312956e+00 6.66266739e-01 5.81516147e-01
-5.24556637e-01 1.55336988e+00 9.33365643e-01 -3.91130358e-01
-2.36147583e-01 -6.69000566e-01 -1.04896021e+00 1.21355921e-01
-1.26392037e-01 1.02914691e+00 3.01488250e-01 -7.19369709... | [9.488286972045898, 8.775092124938965] |
0a4ba9e7-7c40-4bf4-ba03-15ba77b556c1 | deflow-self-supervised-3d-motion-estimation | 2304.02569 | null | https://arxiv.org/abs/2304.02569v1 | https://arxiv.org/pdf/2304.02569v1.pdf | DEFLOW: Self-supervised 3D Motion Estimation of Debris Flow | Existing work on scene flow estimation focuses on autonomous driving and mobile robotics, while automated solutions are lacking for motion in nature, such as that exhibited by debris flows. We propose DEFLOW, a model for 3D motion estimation of debris flows, together with a newly captured dataset. We adopt a novel mult... | ['Jordan Aaron', 'Konrad Schindler', 'Andreas Wieser', 'Nicholas Meyer', 'Shengyu Huang', 'Yuru Jia', 'Liyuan Zhu'] | 2023-04-05 | null | null | null | null | ['scene-flow-estimation', 'motion-estimation'] | ['computer-vision', 'computer-vision'] | [-2.74637222e-01 -3.33447844e-01 -1.62411675e-01 -2.63333827e-01
-4.59051840e-02 -4.62320954e-01 6.67233467e-01 -1.99707225e-01
-3.25425953e-01 6.31157160e-01 5.40508628e-01 -1.62899271e-01
8.18556845e-02 -8.63462031e-01 -4.68096733e-01 -3.13046575e-01
-2.99648553e-01 2.78712749e-01 7.06710935e-01 -1.41160071... | [8.548986434936523, -1.941285252571106] |
1e00efef-144f-4d80-91f8-5c6c1f7351a6 | the-structurally-complex-with-additive-parent | 2304.14109 | null | https://arxiv.org/abs/2304.14109v1 | https://arxiv.org/pdf/2304.14109v1.pdf | The Structurally Complex with Additive Parent Causality (SCARY) Dataset | Causal datasets play a critical role in advancing the field of causality. However, existing datasets often lack the complexity of real-world issues such as selection bias, unfaithful data, and confounding. To address this gap, we propose a new synthetic causal dataset, the Structurally Complex with Additive paRent caus... | ['Haytham M. Fayek', 'Jarry Chen'] | 2023-04-27 | null | null | null | null | ['causal-discovery', 'additive-models', 'selection-bias'] | ['knowledge-base', 'methodology', 'natural-language-processing'] | [ 1.87412754e-01 2.61525154e-01 -7.47229517e-01 -5.03154933e-01
-3.80242169e-01 -6.53308570e-01 7.79993594e-01 1.99351817e-01
1.61359206e-01 1.18236458e+00 6.61128819e-01 -6.76819980e-01
-4.40575957e-01 -1.19684207e+00 -8.56490076e-01 -3.45244795e-01
-5.30091524e-01 2.38150492e-01 -1.34789348e-01 1.06796101... | [7.912816047668457, 5.347589015960693] |
f4cb03d7-ac0c-4e95-8513-8f3626361816 | minimum-n-rank-approximation-via-iterative | 1311.4291 | null | http://arxiv.org/abs/1311.4291v2 | http://arxiv.org/pdf/1311.4291v2.pdf | Minimum $n$-Rank Approximation via Iterative Hard Thresholding | The problem of recovering a low $n$-rank tensor is an extension of sparse
recovery problem from the low dimensional space (matrix space) to the high
dimensional space (tensor space) and has many applications in computer vision
and graphics such as image inpainting and video inpainting. In this paper, we
consider a new ... | ['Zheng-Hai Huang', 'Lei Yang', 'Min Zhang'] | 2013-11-18 | null | null | null | null | ['video-inpainting'] | ['computer-vision'] | [ 2.51354188e-01 -2.10490733e-01 8.53015259e-02 1.75461508e-02
-8.36635232e-01 -2.19347075e-01 -1.98262244e-01 -3.35150987e-01
-4.61437285e-01 5.91593206e-01 1.41237691e-01 -1.44921914e-01
-5.40658116e-01 -4.60669547e-01 -6.38457716e-01 -9.11950529e-01
-3.87490660e-01 2.38784209e-01 -2.04914093e-01 -1.27059236... | [7.369986057281494, 4.46036434173584] |
32906bf0-23c5-4757-b473-b00d04df88c8 | massively-multilingual-corpus-of-sentiment | 2306.07902 | null | https://arxiv.org/abs/2306.07902v1 | https://arxiv.org/pdf/2306.07902v1.pdf | Massively Multilingual Corpus of Sentiment Datasets and Multi-faceted Sentiment Classification Benchmark | Despite impressive advancements in multilingual corpora collection and model training, developing large-scale deployments of multilingual models still presents a significant challenge. This is particularly true for language tasks that are culture-dependent. One such example is the area of multilingual sentiment analysi... | ['Tomasz Kajdanowicz', 'Mikołaj Morzy', 'Krzysztof Rajda', 'Piotr Gramacki', 'Marcin Gruza', 'Szymon Woźniak', 'Łukasz Augustyniak'] | 2023-06-13 | null | null | null | null | ['sentiment-analysis'] | ['natural-language-processing'] | [-2.32790008e-01 -3.21242660e-01 -7.01419473e-01 -6.21269882e-01
-1.02919507e+00 -9.53056812e-01 5.28506041e-01 5.66558301e-01
-6.04243994e-01 1.01452637e+00 4.65252161e-01 6.92236936e-03
2.61942267e-01 -3.85501206e-01 -5.34618139e-01 -2.56800920e-01
1.74192503e-01 5.63281357e-01 -4.82881576e-01 -8.56355727... | [11.184361457824707, 7.085400104522705] |
d3737dd5-723b-414f-a0fd-5c07ccebd056 | pfml-based-semantic-bci-agent-for-game-of-go | 1901.02999 | null | http://arxiv.org/abs/1901.02999v1 | http://arxiv.org/pdf/1901.02999v1.pdf | PFML-based Semantic BCI Agent for Game of Go Learning and Prediction | This paper presents a semantic brain computer interface (BCI) agent with
particle swarm optimization (PSO) based on a Fuzzy Markup Language (FML) for Go
learning and prediction applications. Additionally, we also establish an Open
Go Darkforest (OGD) cloud platform with Facebook AI research (FAIR) open source
Darkfores... | ['Yi-Hsiu Lee', 'Lu-An Lin', 'Sheng-Chi Yang', 'Yi-Lin Tsai', 'Bo-Yu Tsai', 'Li-Wei Ko', 'Mei-Hui Wang', 'Chang-Shing Lee', 'Nan Shuo', 'Hirofumi Ohashi', 'Naoyuki Kubota'] | 2019-01-10 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [-4.96561199e-01 -1.18906803e-01 -4.60366998e-03 3.31430836e-03
4.01959926e-01 -2.40716096e-02 2.16356337e-01 -5.42889178e-01
-5.95200121e-01 9.61160064e-01 -4.48558122e-01 -3.94370370e-02
-5.84676862e-01 -1.00522614e+00 -4.88546729e-01 -6.16145253e-01
6.26750141e-02 7.85277128e-01 3.81063372e-01 -8.89061749... | [3.8231375217437744, 1.4930357933044434] |
7b5a96b1-56ad-4fa9-b0dc-7596a7c45660 | improving-speech-to-speech-translation | 2210.14514 | null | https://arxiv.org/abs/2210.14514v1 | https://arxiv.org/pdf/2210.14514v1.pdf | Improving Speech-to-Speech Translation Through Unlabeled Text | Direct speech-to-speech translation (S2ST) is among the most challenging problems in the translation paradigm due to the significant scarcity of S2ST data. While effort has been made to increase the data size from unlabeled speech by cascading pretrained speech recognition (ASR), machine translation (MT) and text-to-sp... | ['Hongyu Gong', 'Ilia Kulikov', 'Yun Tang', 'Changhan Wang', 'Sravya Popuri', 'Xuan-Phi Nguyen'] | 2022-10-26 | null | null | null | null | ['speech-to-speech-translation'] | ['speech'] | [ 4.80351865e-01 8.18197355e-02 -1.55411050e-01 -3.82421941e-01
-1.64420199e+00 -6.52136266e-01 9.25725520e-01 -5.57667971e-01
-2.50592232e-01 9.75174367e-01 3.94419372e-01 -7.28045821e-01
7.26050854e-01 -1.19895935e-01 -6.57880366e-01 -4.86349314e-01
6.88953042e-01 7.87665009e-01 3.89071405e-02 -5.28668940... | [14.49428653717041, 7.1816558837890625] |
0b378734-a892-4755-a8f8-a071e5d60382 | building-extractive-question-answering-system | 2305.19707 | null | https://arxiv.org/abs/2305.19707v1 | https://arxiv.org/pdf/2305.19707v1.pdf | Building Extractive Question Answering System to Support Human-AI Health Coaching Model for Sleep Domain | Non-communicable diseases (NCDs) are a leading cause of global deaths, necessitating a focus on primary prevention and lifestyle behavior change. Health coaching, coupled with Question Answering (QA) systems, has the potential to transform preventive healthcare. This paper presents a human-Artificial Intelligence (AI) ... | ['Josip Car', 'Shafiq Joty', 'Qi Chwen Ong', 'Iva Bojic'] | 2023-05-31 | null | null | null | null | ['passage-retrieval'] | ['natural-language-processing'] | [ 4.05388474e-01 5.45226514e-01 -2.85538644e-01 -4.38719064e-01
-1.54100847e+00 -3.19911540e-01 3.97712976e-01 5.50385416e-01
-6.23815358e-01 7.57133067e-01 9.36160624e-01 -5.52563906e-01
-2.92869896e-01 -6.07658386e-01 -2.17054829e-01 -2.16538116e-01
4.74020422e-01 1.02168667e+00 1.33278549e-01 -3.04610789... | [8.785486221313477, 8.583466529846191] |
13009b10-4fec-4100-a1f4-d34a3e2dd8d8 | srpcn-structure-retrieval-based-point | 2202.02669 | null | https://arxiv.org/abs/2202.02669v3 | https://arxiv.org/pdf/2202.02669v3.pdf | SRPCN: Structure Retrieval based Point Completion Network | Given partial objects and some complete ones as references, point cloud completion aims to recover authentic shapes. However, existing methods pay little attention to general shapes, which leads to the poor authenticity of completion results. Besides, the missing patterns are diverse in reality, but existing methods ca... | ['Cheng Jin', 'Yuan Wu', 'Ximing Yang', 'Kaiyi Zhang'] | 2022-02-06 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [-1.79875925e-01 -3.94635588e-01 -5.41159585e-02 -1.64233923e-01
-6.60108387e-01 -6.99394166e-01 3.09989095e-01 -5.88560812e-02
-4.14220558e-04 3.88844967e-01 5.68503095e-03 1.33509010e-01
-3.72987449e-01 -9.73516047e-01 -6.87682569e-01 -9.13630545e-01
4.24573988e-01 8.39961767e-01 3.49679381e-01 -1.75784677... | [8.384265899658203, -3.5744705200195312] |
3135d6a0-9892-4389-8cbc-aa3856acc51e | community-recovery-in-the-geometric-block | 2206.11303 | null | https://arxiv.org/abs/2206.11303v1 | https://arxiv.org/pdf/2206.11303v1.pdf | Community Recovery in the Geometric Block Model | To capture inherent geometric features of many community detection problems, we propose to use a new random graph model of communities that we call a \emph{Geometric Block Model}. The geometric block model builds on the \emph{random geometric graphs} (Gilbert, 1961), one of the basic models of random graphs for spatial... | ['Barna Saha', 'Soumyabrata Pal', 'Arya Mazumdar', 'Sainyam Galhotra'] | 2022-06-22 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 2.34441876e-01 3.48106682e-01 9.31397751e-02 4.49668765e-01
-1.32437706e-01 -8.00567627e-01 3.09462756e-01 5.05087614e-01
-1.26858965e-01 5.49128771e-01 -3.86643708e-01 -6.06218457e-01
-5.09862542e-01 -1.36133969e+00 -5.34864724e-01 -8.18957567e-01
-8.81086171e-01 7.52242386e-01 6.52496099e-01 -2.26755187... | [6.868375301361084, 5.136017799377441] |
c9997058-333b-4765-b680-adc68ceafc26 | intra-speaker-topic-modeling-for-improved | null | null | https://aclanthology.org/N12-1041 | https://aclanthology.org/N12-1041.pdf | Intra-Speaker Topic Modeling for Improved Multi-Party Meeting Summarization with Integrated Random Walk | null | ['Yun-Nung Chen', 'Florian Metze'] | 2012-06-01 | null | null | null | naacl-2012-6 | ['meeting-summarization'] | ['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... | [-7.448444843292236, 3.6738669872283936] |
0afee54e-f610-45c3-8be9-e1e5b3f82bbe | unsupervised-3d-keypoint-estimation-with | 2211.12829 | null | https://arxiv.org/abs/2211.12829v1 | https://arxiv.org/pdf/2211.12829v1.pdf | Unsupervised 3D Keypoint Estimation with Multi-View Geometry | Given enough annotated training data, 3D human pose estimation models can achieve high accuracy. However, annotations are not always available, especially for people performing unusual activities. In this paper, we propose an algorithm that learns to detect 3D keypoints on human bodies from multiple-views without any s... | ['Pascal Fua', 'Sina Honari'] | 2022-11-23 | null | null | null | null | ['3d-human-pose-estimation', 'unsupervised-3d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-3.71501148e-01 3.75766546e-01 -1.11693271e-01 -3.85146230e-01
-6.04282975e-01 -3.87462795e-01 2.71333903e-01 -1.46108940e-01
-5.46479881e-01 5.55606127e-01 2.65085250e-01 5.13547659e-01
3.55937809e-01 -2.44154423e-01 -8.35841715e-01 -3.64320159e-01
-6.29803818e-03 1.29629886e+00 4.86784041e-01 -3.29673178... | [7.006881237030029, -1.000365972518921] |
bbcc402e-47a6-4253-a962-d0097138dffa | the-theory-behind-overfitting-cross | 1905.12787 | null | https://arxiv.org/abs/1905.12787v2 | https://arxiv.org/pdf/1905.12787v2.pdf | The Theory Behind Overfitting, Cross Validation, Regularization, Bagging, and Boosting: Tutorial | In this tutorial paper, we first define mean squared error, variance, covariance, and bias of both random variables and classification/predictor models. Then, we formulate the true and generalization errors of the model for both training and validation/test instances where we make use of the Stein's Unbiased Risk Estim... | ['Mark Crowley', 'Benyamin Ghojogh'] | 2019-05-28 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [ 1.66065991e-02 -8.45157914e-03 -4.22408581e-01 -5.93815684e-01
-9.63835955e-01 -2.27091089e-01 2.24608481e-01 1.00432359e-01
-4.85743314e-01 1.24387991e+00 -3.00007701e-01 -4.69689459e-01
-2.26845175e-01 -8.83434892e-01 -6.30049109e-01 -9.53339458e-01
-1.12467363e-01 -3.21489088e-02 4.27345671e-02 -1.65621087... | [8.265610694885254, 4.288925647735596] |
44bf6547-0c0d-489b-8fc8-834afc3c12cd | npc-neuron-path-coverage-via-characterizing | 2203.12915 | null | https://arxiv.org/abs/2203.12915v2 | https://arxiv.org/pdf/2203.12915v2.pdf | NPC: Neuron Path Coverage via Characterizing Decision Logic of Deep Neural Networks | Deep learning has recently been widely applied to many applications across different domains, e.g., image classification and audio recognition. However, the quality of Deep Neural Networks (DNNs) still raises concerns in the practical operational environment, which calls for systematic testing, especially in safety-cri... | ['Yang Liu', 'Felix Juefei-Xu', 'Qing Guo', 'Lei Ma', 'Jian Wang', 'Tianlin Li', 'Xiaofei Xie'] | 2022-03-24 | null | null | null | null | ['dnn-testing', 'defect-detection'] | ['adversarial', 'computer-vision'] | [ 2.54754335e-01 2.41359279e-01 -2.58119315e-01 -3.64364892e-01
2.17979327e-01 -6.88646197e-01 9.27653462e-02 -3.61372344e-02
1.10881580e-02 5.93463838e-01 -3.03788692e-01 -8.23476791e-01
-4.07988787e-01 -1.12301600e+00 -1.01457512e+00 -5.15150964e-01
2.73628160e-02 -1.70965001e-01 4.01095897e-01 2.11471003... | [6.537837505340576, 7.650041103363037] |
3734f9f3-51e3-47a9-b901-c39eb45dca85 | videofactory-swap-attention-in-spatiotemporal | 2305.10874 | null | https://arxiv.org/abs/2305.10874v2 | https://arxiv.org/pdf/2305.10874v2.pdf | VideoFactory: Swap Attention in Spatiotemporal Diffusions for Text-to-Video Generation | We present VideoFactory, an innovative framework for generating high-quality open-domain videos. VideoFactory excels in producing high-definition (1376x768), widescreen (16:9) videos without watermarks, creating an engaging user experience. Generating videos guided by text instructions poses significant challenges, suc... | ['Jiaying Liu', 'Jianlong Fu', 'Junchen Zhu', 'Huiguo He', 'Zixi Tuo', 'Huan Yang', 'Wenjing Wang'] | 2023-05-18 | null | null | null | null | ['video-generation', 'video-alignment', 'text-to-video-generation'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 2.62611032e-01 -4.76600200e-01 -1.68680668e-01 7.66080543e-02
-5.85688174e-01 -7.50024676e-01 5.76250255e-01 -3.13351750e-01
-2.02590421e-01 4.59117532e-01 3.98874342e-01 -4.48495209e-01
2.67406777e-02 -5.96229136e-01 -9.26608622e-01 -3.44892919e-01
-1.57311723e-01 -6.56454206e-01 2.56262451e-01 1.40677327... | [10.858745574951172, -0.5856627225875854] |
1cf9a888-83a4-4762-9849-5804db40b1e3 | an-image-dehazing-approach-based-on-the | 1805.02142 | null | http://arxiv.org/abs/1805.02142v1 | http://arxiv.org/pdf/1805.02142v1.pdf | An Image dehazing approach based on the airlight field estimation | This paper proposes a scheme for single image haze removal based on the
airlight field (ALF) estimation. Conventional image dehazing methods which are
based on a physical model generally take the global atmospheric light as a
constant. However, the constant-airlight assumption may be unsuitable for
images with large sk... | ['Yu-Jin Zhang', 'Yongbin Gao', 'Lijun Zhang'] | 2018-05-06 | null | null | null | null | ['single-image-haze-removal'] | ['computer-vision'] | [ 3.02934200e-01 -5.15124738e-01 5.81380844e-01 -2.28831589e-01
-1.91608012e-01 -9.01302472e-02 4.40444559e-01 -4.99810904e-01
-2.46129215e-01 7.52393603e-01 -1.51932850e-01 -3.65216017e-01
-3.24717648e-02 -9.11714792e-01 -4.76684988e-01 -1.40858972e+00
1.18725553e-01 -2.27163389e-01 5.18915415e-01 -3.17497402... | [10.85962200164795, -3.184847116470337] |
834d9bc4-2d1e-45eb-be76-a05ee24c2594 | doubly-stochastic-graph-based-non | 2306.06119 | null | https://arxiv.org/abs/2306.06119v1 | https://arxiv.org/pdf/2306.06119v1.pdf | Doubly Stochastic Graph-based Non-autoregressive Reaction Prediction | Organic reaction prediction is a critical task in drug discovery. Recently, researchers have achieved non-autoregressive reaction prediction by modeling the redistribution of electrons, resulting in state-of-the-art top-1 accuracy, and enabling parallel sampling. However, the current non-autoregressive decoder does not... | ['Irwin King', 'Yang Yu', 'Peilin Zhao', 'Ziqiao Meng'] | 2023-06-05 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 5.64036131e-01 1.40900716e-01 -4.02409345e-01 -2.54316151e-01
-9.26876783e-01 -2.55063742e-01 6.44431889e-01 1.70126766e-01
-2.53379315e-01 1.05336964e+00 2.39275515e-01 -6.23800933e-01
1.26973614e-01 -6.09871507e-01 -9.76066411e-01 -8.76421690e-01
5.04387736e-01 7.25410521e-01 1.55782580e-01 -5.58841154... | [4.633363246917725, 5.986285209655762] |
316c09cc-3820-4430-9992-e61d59b544f4 | cross-speaker-emotion-transfer-based-on | 2110.04153 | null | https://arxiv.org/abs/2110.04153v2 | https://arxiv.org/pdf/2110.04153v2.pdf | Cross-speaker Emotion Transfer Based on Speaker Condition Layer Normalization and Semi-Supervised Training in Text-To-Speech | In expressive speech synthesis, there are high requirements for emotion interpretation. However, it is time-consuming to acquire emotional audio corpus for arbitrary speakers due to their deduction ability. In response to this problem, this paper proposes a cross-speaker emotion transfer method that can realize the tra... | ['Zejun Ma', 'Xiang Yin', 'Lin Wu', 'Junhui Zhang', 'Chenchang Xu', 'Junjie Pan', 'Pengfei Wu'] | 2021-10-08 | null | null | null | null | ['expressive-speech-synthesis'] | ['speech'] | [ 2.29055613e-01 -1.05737343e-01 2.48081848e-01 -7.49034882e-01
-9.43792939e-01 -3.14521432e-01 2.70276159e-01 -2.18432918e-01
-1.38456523e-01 6.29541218e-01 4.32369500e-01 3.32289100e-01
1.29679605e-01 -4.89254057e-01 -3.44409376e-01 -7.26556957e-01
1.46363884e-01 -1.92364126e-01 -3.49071950e-01 -2.83096522... | [14.326436042785645, 6.251211643218994] |
28f5524c-b514-464c-9e47-8098218a33dc | overlapping-word-removal-is-all-you-need | 2204.05488 | null | https://arxiv.org/abs/2204.05488v1 | https://arxiv.org/pdf/2204.05488v1.pdf | Overlapping Word Removal is All You Need: Revisiting Data Imbalance in Hope Speech Detection | Hope Speech Detection, a task of recognizing positive expressions, has made significant strides recently. However, much of the current works focus on model development without considering the issue of inherent imbalance in the data. Our work revisits this issue in hope-speech detection by introducing focal loss, data a... | ['Bharathi Raja Chakravarthi', 'Anand Kumar Madasamy', 'Adithya Madhusoodanan', 'Navyasree Balamuralidhar', 'Gayathri Nisha', 'Manikandan Ravikiran', 'Hariharan RamakrishnaIyer LekshmiAmmal'] | 2022-04-12 | null | null | null | null | ['hope-speech-detection'] | ['natural-language-processing'] | [ 2.18300745e-01 2.02246785e-01 -5.35329342e-01 -5.52599251e-01
-1.47570169e+00 -3.52653861e-01 4.75026190e-01 4.91220206e-01
-5.86190701e-01 5.55244684e-01 5.72657108e-01 -4.97684240e-01
2.68114716e-01 -3.87101114e-01 -4.94588792e-01 -4.48019773e-01
1.22019820e-01 1.96057320e-01 -1.86698698e-02 -5.79276979... | [11.007536888122559, 8.78824520111084] |
11bc3523-aaa5-46ad-b7eb-91ed6b886947 | exploring-the-in-context-learning-ability-of | 2307.01137 | null | https://arxiv.org/abs/2307.01137v1 | https://arxiv.org/pdf/2307.01137v1.pdf | Exploring the In-context Learning Ability of Large Language Model for Biomedical Concept Linking | The biomedical field relies heavily on concept linking in various areas such as literature mining, graph alignment, information retrieval, question-answering, data, and knowledge integration. Although large language models (LLMs) have made significant strides in many natural language processing tasks, their effectivene... | ['Rong Xu', 'Zhenxiang Gao', 'Qinyong Wang'] | 2023-07-03 | null | null | null | null | ['retrieval', 'question-answering', 'information-retrieval', 'literature-mining'] | ['methodology', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 5.51591218e-01 3.20258677e-01 -5.93377173e-01 -1.43487215e-01
-8.88413727e-01 -1.37984723e-01 5.40969670e-01 1.26370394e+00
-6.63376331e-01 8.23637605e-01 4.36883003e-01 -1.79377899e-01
-4.70721751e-01 -6.50278330e-01 -1.28102109e-01 -5.66869199e-01
6.88431486e-02 4.55406606e-01 3.62155885e-02 -9.15743411... | [8.4998779296875, 8.652421951293945] |
ce6b6df2-0699-4ef5-a5c6-834c8a6c6755 | time-frequency-distributions-of-heart-sound | 2208.03128 | null | https://arxiv.org/abs/2208.03128v1 | https://arxiv.org/pdf/2208.03128v1.pdf | Time-Frequency Distributions of Heart Sound Signals: A Comparative Study using Convolutional Neural Networks | Time-Frequency Distributions (TFDs) support the heart sound characterisation and classification in early cardiac screening. However, despite the frequent use of TFDs in signal analysis, no study comprehensively compared their performances on deep learning for automatic diagnosis. Furthermore, the combination of signal ... | ['Ernest N. Kamavuako', 'Lilia Sidhom', 'Ines Chihi', 'Mohamed Trabelsi', 'Hak-Keung Lam', 'Yujia Xu', 'Xinqi Bao'] | 2022-08-05 | null | null | null | null | ['sound-classification'] | ['audio'] | [ 4.37813476e-02 -2.22445410e-02 1.35157093e-01 -1.08766168e-01
-2.04687398e-02 -1.75461262e-01 2.59294122e-01 1.48547143e-01
-6.32088363e-01 4.84979600e-01 -1.11096412e-01 -6.72970951e-01
-4.01322246e-01 -7.48167336e-01 -2.98937261e-01 -8.43000174e-01
-3.05680901e-01 -6.76857531e-02 3.04441571e-01 -2.33018547... | [14.30984115600586, 3.2981154918670654] |
3dc63042-1b5d-4fe2-b2cf-05fbe170a869 | stimuli-sensitive-hawkes-processes-for | 2102.00089 | null | https://arxiv.org/abs/2102.00089v1 | https://arxiv.org/pdf/2102.00089v1.pdf | Stimuli-Sensitive Hawkes Processes for Personalized Student Procrastination Modeling | Student procrastination and cramming for deadlines are major challenges in online learning environments, with negative educational and well-being side effects. Modeling student activities in continuous time and predicting their next study time are important problems that can help in creating personalized timely interve... | ['Reza Feyzi Behnagh', 'Shaghayegh Sahebi', 'Siqian Zhao', 'Mengfan Yao'] | 2021-01-29 | null | null | null | null | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [-8.07720721e-02 -3.20286632e-01 -6.03752792e-01 -1.93482727e-01
1.17334291e-01 -6.22919858e-01 2.68810868e-01 6.66622996e-01
-2.08856151e-01 6.01145148e-01 2.42408410e-01 -4.17086691e-01
-7.70405352e-01 -9.25531745e-01 -5.26637614e-01 -7.02165425e-01
1.14348069e-01 3.37151527e-01 3.56871873e-01 -1.74775332... | [10.118080139160156, 7.132706165313721] |
9fbae3ae-82fa-489c-b341-e9c9216da27d | forecasting-social-navigation-in-crowded | 1601.00998 | null | http://arxiv.org/abs/1601.00998v1 | http://arxiv.org/pdf/1601.00998v1.pdf | Forecasting Social Navigation in Crowded Complex Scenes | When humans navigate a crowed space such as a university campus or the
sidewalks of a busy street, they follow common sense rules based on social
etiquette. In this paper, we argue that in order to enable the design of new
algorithms that can take fully advantage of these rules to better solve tasks
such as target trac... | ['Silvio Savarese', 'Eli Wu', 'John Doherty', 'Amir Sadeghian', 'Alexandre Alahi', 'Bryan Anenberg', 'Alexandre Robicquet'] | 2016-01-05 | null | null | null | null | ['social-navigation'] | ['robots'] | [-1.17641211e-01 -2.08070353e-01 -3.00875485e-01 -2.57470608e-01
-1.34007573e-01 -6.13415062e-01 8.80856812e-01 3.80152911e-01
-3.35377365e-01 8.18295121e-01 2.47984365e-01 -4.71871883e-01
-4.48021501e-01 -1.09092534e+00 -6.31516099e-01 -5.91189086e-01
-1.89196512e-01 3.01986694e-01 8.56325090e-01 -7.02756763... | [5.932242393493652, 0.9466549158096313] |
40ee985b-6812-4316-b55a-407d1a253cfc | patchnet-unsupervised-object-discovery-based | 2106.08599 | null | https://arxiv.org/abs/2106.08599v1 | https://arxiv.org/pdf/2106.08599v1.pdf | PatchNet: Unsupervised Object Discovery based on Patch Embedding | We demonstrate that frequently appearing objects can be discovered by training randomly sampled patches from a small number of images (100 to 200) by self-supervision. Key to this approach is the pattern space, a latent space of patterns that represents all possible sub-images of the given image data. The distance stru... | ['Patrick Bangert', 'Jae Oh Woo', 'Sima Didari', 'Heng Hao', 'Hankyu Moon'] | 2021-06-16 | null | null | null | null | ['multi-object-discovery'] | ['computer-vision'] | [ 6.30064845e-01 1.25663176e-01 -3.77455652e-01 -3.63356769e-01
-1.89863220e-01 -2.81807601e-01 5.18240213e-01 -2.41773188e-01
-1.09737009e-01 4.62337583e-01 -8.11124071e-02 5.60349762e-01
-2.26762280e-01 -6.76978588e-01 -1.12954891e+00 -1.12820888e+00
-4.02442813e-01 3.72139007e-01 1.83707565e-01 2.85830498... | [9.776880264282227, 2.0262584686279297] |
74ded33f-ee45-4d2c-a370-71d18d30ffb6 | fusepose-imu-vision-sensor-fusion-in | 2208.11960 | null | https://arxiv.org/abs/2208.11960v1 | https://arxiv.org/pdf/2208.11960v1.pdf | FusePose: IMU-Vision Sensor Fusion in Kinematic Space for Parametric Human Pose Estimation | There exist challenging problems in 3D human pose estimation mission, such as poor performance caused by occlusion and self-occlusion. Recently, IMU-vision sensor fusion is regarded as valuable for solving these problems. However, previous researches on the fusion of IMU and vision data, which is heterogeneous, fail to... | ['Dahong Qian', 'Xu Zhao', 'Yiming Bao'] | 2022-08-25 | null | null | null | null | ['3d-human-pose-estimation'] | ['computer-vision'] | [-2.67368674e-01 -3.91701549e-01 1.16613522e-01 -1.58731416e-01
-6.80930674e-01 -4.03962225e-01 4.65479761e-01 -2.56746262e-01
-9.20317948e-01 6.57126367e-01 -7.09814355e-02 1.75983444e-01
5.00626266e-02 -4.64659274e-01 -9.02594566e-01 -3.64421308e-01
3.03522199e-01 5.75971723e-01 1.98487654e-01 -4.52907771... | [7.040083885192871, -0.9038002490997314] |
52519610-df0b-4250-910c-a614586c4a76 | tcn-table-convolutional-network-for-web-table | 2102.09460 | null | https://arxiv.org/abs/2102.09460v1 | https://arxiv.org/pdf/2102.09460v1.pdf | TCN: Table Convolutional Network for Web Table Interpretation | Information extraction from semi-structured webpages provides valuable long-tailed facts for augmenting knowledge graph. Relational Web tables are a critical component containing additional entities and attributes of rich and diverse knowledge. However, extracting knowledge from relational tables is challenging because... | ['Meng Jiang', 'Xin Luna Dong', 'Binxuan Huang', 'Colin Lockard', 'Prashant Shiralkar', 'Daheng Wang'] | 2021-02-17 | null | null | null | null | ['type-prediction', 'table-annotation', 'table-annotation'] | ['computer-code', 'knowledge-base', 'natural-language-processing'] | [-1.87469184e-01 4.25552011e-01 -7.93631434e-01 -2.93509126e-01
-9.24521863e-01 -6.77275836e-01 2.56727368e-01 9.32924271e-01
-6.69617206e-02 1.06453121e+00 4.85788554e-01 -2.37245128e-01
-2.18823344e-01 -1.33605313e+00 -1.38182259e+00 -3.60541403e-01
-1.74943078e-02 7.33549416e-01 2.58118540e-01 -3.15105438... | [9.54647159576416, 7.850513935089111] |
73276270-2b27-4bae-8ba1-41e16fae97db | gri-general-reinforced-imitation-and-its | 2111.08575 | null | https://arxiv.org/abs/2111.08575v2 | https://arxiv.org/pdf/2111.08575v2.pdf | GRI: General Reinforced Imitation and its Application to Vision-Based Autonomous Driving | Deep reinforcement learning (DRL) has been demonstrated to be effective for several complex decision-making applications such as autonomous driving and robotics. However, DRL is notoriously limited by its high sample complexity and its lack of stability. Prior knowledge, e.g. as expert demonstrations, is often availabl... | ['Fabien Moutarde', 'Sascha Hornauer', 'Marin Toromanoff', 'Raphael Chekroun'] | 2021-11-16 | null | null | null | null | ['carla-map-leaderboard'] | ['robots'] | [-2.88873762e-01 2.89991111e-01 -2.76305199e-01 -9.94236693e-02
-7.03848243e-01 -6.33565187e-01 8.88514102e-01 -3.57727408e-02
-9.38508987e-01 1.09012055e+00 -1.69436678e-01 -4.90104109e-01
4.52063419e-02 -5.03273845e-01 -1.03620243e+00 -6.74317300e-01
-4.50351417e-01 6.86825693e-01 2.77200639e-01 -6.10260844... | [4.700588703155518, 1.228374719619751] |
b9e81ce0-abcc-452e-909a-485373966b24 | efficient-block-contrastive-learning-via | 2209.14067 | null | https://arxiv.org/abs/2209.14067v1 | https://arxiv.org/pdf/2209.14067v1.pdf | Efficient block contrastive learning via parameter-free meta-node approximation | Contrastive learning has recently achieved remarkable success in many domains including graphs. However contrastive loss, especially for graphs, requires a large number of negative samples which is unscalable and computationally prohibitive with a quadratic time complexity. Sub-sampling is not optimal and incorrect neg... | ['Shekhar S. Chandra', 'Marius Portmann', 'Gayan K. Kulatilleke'] | 2022-09-28 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [ 1.87133893e-01 5.02876081e-02 -3.61130714e-01 -1.66307420e-01
-9.16104674e-01 -5.88015854e-01 3.70677054e-01 6.17987692e-01
-4.01540846e-01 6.74048364e-01 -4.17052925e-01 -4.09225345e-01
-7.14860633e-02 -1.06066346e+00 -9.20903504e-01 -7.69540370e-01
-6.66562378e-01 6.67700529e-01 2.75920719e-01 1.68915242... | [7.0912766456604, 6.004348278045654] |
dbce96ca-89df-4cea-b0f4-d3b9aaa6343b | improving-inductive-link-prediction-using | 2107.04894 | null | https://arxiv.org/abs/2107.04894v1 | https://arxiv.org/pdf/2107.04894v1.pdf | Improving Inductive Link Prediction Using Hyper-Relational Facts | For many years, link prediction on knowledge graphs (KGs) has been a purely transductive task, not allowing for reasoning on unseen entities. Recently, increasing efforts are put into exploring semi- and fully inductive scenarios, enabling inference over unseen and emerging entities. Still, all these approaches only co... | ['Jens Lehmann', 'Volker Tresp', 'Tengfei Ma', 'Veronika Thost', 'Mikhail Galkin', 'Max Berrendorf', 'Mehdi Ali'] | 2021-07-10 | null | null | null | null | ['inductive-link-prediction'] | ['graphs'] | [-9.25732702e-02 7.77357638e-01 -6.99100792e-01 -2.26793572e-01
-6.24736965e-01 -6.01213515e-01 6.78361833e-01 5.35253882e-01
-6.08513691e-02 9.73597169e-01 1.73829928e-01 -6.08706772e-01
-3.34178746e-01 -1.27469492e+00 -1.00772786e+00 -1.37890488e-01
-5.43942869e-01 7.88906574e-01 4.31608558e-01 -3.27539861... | [8.893924713134766, 8.01285171508789] |
246c134c-bd6a-4693-a83c-297a0d70b635 | tencent-mvse-a-large-scale-benchmark-dataset | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zeng_Tencent-MVSE_A_Large-Scale_Benchmark_Dataset_for_Multi-Modal_Video_Similarity_Evaluation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zeng_Tencent-MVSE_A_Large-Scale_Benchmark_Dataset_for_Multi-Modal_Video_Similarity_Evaluation_CVPR_2022_paper.pdf | Tencent-MVSE: A Large-Scale Benchmark Dataset for Multi-Modal Video Similarity Evaluation | Multi-modal video similarity evaluation is important for video recommendation systems such as video de-duplication, relevance matching, ranking, and diversity control. However, there still lacks a benchmark dataset that can support supervised training and accurate evaluation. In this paper, we propose the Tencent-M... | ['Zhen Wen', 'Weidong Guo', 'Dian Li', 'Fengyun Rao', 'Zhenhua Liu', 'Yongsheng Luo', 'Zhaoyang Zeng'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['video-similarity'] | ['computer-vision'] | [ 5.00977598e-02 -8.27193022e-01 -4.28743362e-01 -4.84513372e-01
-1.28294098e+00 -6.08373284e-01 6.72703981e-01 -1.18743092e-01
-3.86319667e-01 2.54335850e-01 7.08412647e-01 1.41118944e-01
1.36674121e-01 -3.44411552e-01 -5.90864778e-01 -4.15502936e-01
9.03657749e-02 2.81293154e-01 5.45495212e-01 -1.92799002... | [10.275556564331055, 0.8553334474563599] |
32d73fe2-5272-48ae-9a4b-d32f3ca16c40 | llavar-enhanced-visual-instruction-tuning-for | 2306.17107 | null | https://arxiv.org/abs/2306.17107v1 | https://arxiv.org/pdf/2306.17107v1.pdf | LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding | Instruction tuning unlocks the superior capability of Large Language Models (LLM) to interact with humans. Furthermore, recent instruction-following datasets include images as visual inputs, collecting responses for image-based instructions. However, visual instruction-tuned models cannot comprehend textual details wit... | ['Tong Sun', 'Diyi Yang', 'Nedim Lipka', 'Yufan Zhou', 'Jiuxiang Gu', 'Ruiyi Zhang', 'Yanzhe Zhang'] | 2023-06-29 | null | null | null | null | ['optical-character-recognition', 'image-captioning', 'instruction-following'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 1.56016633e-01 5.78262769e-02 -9.85671356e-02 -3.86358023e-01
-8.92197073e-01 -8.03550541e-01 7.67943621e-01 7.83317462e-02
-4.54492003e-01 1.31906092e-01 4.14368123e-01 -8.68709683e-01
2.92815268e-01 -6.98885858e-01 -1.08767247e+00 -2.01694146e-01
5.52882314e-01 5.75475574e-01 9.08765495e-02 -4.73175496... | [10.959626197814941, 1.5917023420333862] |
3f83bb1f-062d-41c3-aa6e-433f72d57d9e | training-entire-space-models-for-target | 2204.07337 | null | https://arxiv.org/abs/2204.07337v1 | https://arxiv.org/pdf/2204.07337v1.pdf | Training Entire-Space Models for Target-oriented Opinion Words Extraction | Target-oriented opinion words extraction (TOWE) is a subtask of aspect-based sentiment analysis (ABSA). Given a sentence and an aspect term occurring in the sentence, TOWE extracts the corresponding opinion words for the aspect term. TOWE has two types of instance. In the first type, aspect terms are associated with at... | ['Sheng-hua Zhong', 'Fang Wang', 'Yuncong Li'] | 2022-04-15 | null | null | null | null | ['aspect-based-sentiment-analysis', 'target-oriented-opinion-words-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.48250470e-01 -4.62080789e-04 -1.07642718e-01 -4.73848999e-01
-3.97078514e-01 -4.88954693e-01 4.27711517e-01 4.03855085e-01
-4.31585252e-01 5.78991771e-01 -1.32972360e-01 -3.74328941e-01
7.56157795e-03 -1.15232599e+00 -5.05569220e-01 -7.31797218e-01
1.73064217e-01 1.99069843e-01 2.01493427e-01 -4.98924285... | [11.464884757995605, 6.64493989944458] |
06a569c3-06e2-4594-bf4e-7e32d947f4ff | vision-datasets-a-benchmark-for-vision-based | 2306.07890 | null | https://arxiv.org/abs/2306.07890v2 | https://arxiv.org/pdf/2306.07890v2.pdf | VISION Datasets: A Benchmark for Vision-based InduStrial InspectiON | Despite progress in vision-based inspection algorithms, real-world industrial challenges -- specifically in data availability, quality, and complex production requirements -- often remain under-addressed. We introduce the VISION Datasets, a diverse collection of 14 industrial inspection datasets, uniquely poised to mee... | ['Meng Cao', 'Jianjun Shi', 'Jiulong Shan', 'Ping Huang', 'Oncel Tuzel', 'Ramazan Gokberk Cinbis', 'Tatiana Likhomanenko', 'Shancong Mou', 'Haoping Bai'] | 2023-06-13 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 4.38408047e-01 1.37103964e-02 3.06986243e-01 -3.02851826e-01
-7.19081998e-01 -6.75154209e-01 1.36502534e-01 3.27628851e-01
3.88075024e-01 9.85855460e-02 -3.80827904e-01 -2.07607150e-01
-2.92666912e-01 -6.19593263e-01 -4.61972713e-01 -2.49465793e-01
-1.49160445e-01 4.43189144e-01 2.25789368e-01 -3.55476677... | [7.463834762573242, 1.9305537939071655] |
a7e32290-b769-41b9-ba08-b8408274534b | semi-supervised-domain-adaptive-medical-image | 2307.02798 | null | https://arxiv.org/abs/2307.02798v1 | https://arxiv.org/pdf/2307.02798v1.pdf | Semi-supervised Domain Adaptive Medical Image Segmentation through Consistency Regularized Disentangled Contrastive Learning | Although unsupervised domain adaptation (UDA) is a promising direction to alleviate domain shift, they fall short of their supervised counterparts. In this work, we investigate relatively less explored semi-supervised domain adaptation (SSDA) for medical image segmentation, where access to a few labeled target samples ... | ['Zhaozheng Yin', 'Hritam Basak'] | 2023-07-06 | null | null | null | null | ['contrastive-learning', 'medical-image-segmentation', 'contrastive-learning', 'unsupervised-domain-adaptation', 'self-learning'] | ['computer-vision', 'medical', 'methodology', 'methodology', 'natural-language-processing'] | [ 6.41940057e-01 2.74348587e-01 -5.80534339e-01 -5.78597784e-01
-1.07957458e+00 -5.76374471e-01 5.47379673e-01 3.49574909e-02
-5.40985942e-01 7.07161903e-01 1.02956317e-01 -8.51157680e-02
-1.41759872e-01 -5.33644974e-01 -7.25740671e-01 -1.13869476e+00
3.09316456e-01 4.65941012e-01 1.44879967e-01 2.35250350... | [14.559592247009277, -1.9997211694717407] |
59a95777-e086-480b-8473-6a3884d60db4 | the-mgb-2-challenge-arabic-multi-dialect | 1609.05625 | null | https://arxiv.org/abs/1609.05625v3 | https://arxiv.org/pdf/1609.05625v3.pdf | The MGB-2 Challenge: Arabic Multi-Dialect Broadcast Media Recognition | This paper describes the Arabic Multi-Genre Broadcast (MGB-2) Challenge for SLT-2016. Unlike last year's English MGB Challenge, which focused on recognition of diverse TV genres, this year, the challenge has an emphasis on handling the diversity in dialect in Arabic speech. Audio data comes from 19 distinct programmes ... | ['Yifan Zhang', 'Steve Renals', 'James Glass', 'Yacine Messaoui', 'Peter Bell', 'Hamdy Mubarak', 'Ahmed Ali'] | 2016-09-19 | null | null | null | null | ['acoustic-modelling'] | ['speech'] | [ 9.98721495e-02 -7.31884912e-02 1.77298874e-01 -6.05568051e-01
-1.78111982e+00 -6.32913470e-01 5.95731676e-01 2.91848868e-01
-4.09408778e-01 6.25944436e-01 5.52550018e-01 -1.55086219e-01
-8.71362630e-03 -3.52231979e-01 -5.23888648e-01 -7.05769300e-01
-2.21725971e-01 9.16817307e-01 3.10947094e-02 -9.60836709... | [14.393726348876953, 6.792703151702881] |
370a2c44-caf2-4033-8423-08f5a8cdc47f | task-preferences-across-languages-on | 2212.09045 | null | https://arxiv.org/abs/2212.09045v1 | https://arxiv.org/pdf/2212.09045v1.pdf | Task Preferences across Languages on Community Question Answering Platforms | With the steady emergence of community question answering (CQA) platforms like Quora, StackExchange, and WikiHow, users now have an unprecedented access to information on various kind of queries and tasks. Moreover, the rapid proliferation and localization of these platforms spanning geographic and linguistic boundarie... | ['Rishabh Mehrotra', 'Prasanta Bhattacharya', 'Sebastin Santy'] | 2022-12-18 | null | null | null | null | ['community-question-answering', 'community-question-answering'] | ['miscellaneous', 'natural-language-processing'] | [-9.25411224e-01 -2.40424857e-01 -2.64105320e-01 -7.52911121e-02
-8.32484901e-01 -7.34496951e-01 5.70457757e-01 7.29201555e-01
-5.95816314e-01 9.50348675e-02 9.11690116e-01 -4.13590223e-01
-2.78074533e-01 -5.66689670e-01 -7.99496099e-03 -7.67443031e-02
-5.71058020e-02 1.40707538e-01 2.54140049e-01 -4.56097960... | [11.390453338623047, 7.923440456390381] |
807001c5-5606-4d07-9ece-1d6148aea6f7 | aligning-coordinated-text-streams-through | 1609.08237 | null | http://arxiv.org/abs/1609.08237v1 | http://arxiv.org/pdf/1609.08237v1.pdf | Aligning Coordinated Text Streams through Burst Information Network Construction and Decipherment | Aligning coordinated text streams from multiple sources and multiple
languages has opened many new research venues on cross-lingual knowledge
discovery. In this paper we aim to advance state-of-the-art by: (1). extending
coarse-grained topic-level knowledge mining to fine-grained information units
such as entities and ... | ['Zhifang Sui', 'Qing Dou', 'Heng Ji', 'Tao Ge', 'Ming Zhou', 'Lei Cui', 'Baobao Chang', 'Xiaoman Pan'] | 2016-09-27 | null | null | null | null | ['decipherment'] | ['natural-language-processing'] | [-2.13456675e-01 -1.52764887e-01 -5.46408296e-01 -1.40494585e-01
-7.07117498e-01 -6.28659129e-01 6.46055639e-01 8.37128580e-01
-3.59569401e-01 7.31400192e-01 4.97325540e-01 -3.74813199e-01
-6.36267245e-01 -9.71020579e-01 -5.24881840e-01 -2.75358528e-01
-7.34025419e-01 8.97106230e-01 7.30215073e-01 -3.72068286... | [9.987642288208008, 9.006909370422363] |
da80cb9f-1421-408f-844f-0a7d79b59701 | dual-attention-networks-for-few-shot-fine | null | null | https://ojs.aaai.org/index.php/AAAI/article/view/20196 | https://ojs.aaai.org/index.php/AAAI/article/view/20196/19955 | Dual Attention Networks for Few-Shot Fine-Grained Recognition | The task of few-shot fine-grained recognition is to classify images belonging to subordinate categories merely depending on few examples. Due to the fine-grained nature, it is desirable to capture subtle but discriminative part-level patterns from limited training data, which makes it a challenging problem. In this pap... | ['Jianhua Wang', 'Xiu-Shen Wei', 'Faen Zhang', 'Shu-Lin Xu'] | 2022-06-28 | null | null | null | proceedings-of-the-aaai-conference-on-4 | ['hard-attention'] | ['methodology'] | [ 2.58632302e-01 -5.89244999e-02 -2.23751187e-01 -6.09728098e-01
-7.51144767e-01 -1.99776009e-01 7.41559744e-01 6.56975582e-02
-1.35553911e-01 6.37802839e-01 2.52375752e-01 3.81527156e-01
-3.44984502e-01 -1.10958433e+00 -7.59378254e-01 -9.26214814e-01
3.92164081e-01 1.73325449e-01 2.21382737e-01 -2.22296938... | [9.720487594604492, 2.1973583698272705] |
1b88914a-95ec-4bd1-9ca6-9e9edbecec4c | generalizing-monocular-3d-human-pose | 1904.05512 | null | http://arxiv.org/abs/1904.05512v1 | http://arxiv.org/pdf/1904.05512v1.pdf | Generalizing Monocular 3D Human Pose Estimation in the Wild | The availability of the large-scale labeled 3D poses in the Human3.6M dataset
plays an important role in advancing the algorithms for 3D human pose
estimation from a still image. We observe that recent innovation in this area
mainly focuses on new techniques that explicitly address the generalization
issue when using t... | ['Jimmy S. Ren', 'Zhenhua Guo', 'Hongsheng Li', 'Yan Chen', 'Mude Lin', 'Luyang Wang', 'Keyuan Qian'] | 2019-04-11 | null | null | null | null | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [ 1.41280577e-01 6.57867566e-02 1.40528426e-01 -3.41645122e-01
-8.14854145e-01 -2.93904096e-01 5.03672183e-01 -3.50806206e-01
-6.84102118e-01 6.06372595e-01 1.37174964e-01 2.26298094e-01
3.34792100e-02 -6.23623133e-01 -9.25126076e-01 -4.49938565e-01
-3.95624600e-02 9.88896132e-01 2.05086112e-01 -4.70568895... | [7.03851318359375, -0.9320592284202576] |
5947f096-c567-4a59-b57c-34a1abc4c54a | a-data-driven-approach-to-improve-3d-head | null | null | https://link.springer.com/chapter/10.1007/978-3-030-90439-5_43 | https://link.springer.com/content/pdf/10.1007%2F978-3-030-90439-5.pdf | A Data-Driven Approach to Improve 3D Head-Pose Estimation | Head-pose estimation from images is an important research topic in computer vision. Its many applications include detecting focus of attention, tracking driver behavior, and human-computer interaction. Recent research on head-pose estimation has focused on developing models based on deep convolutional neural networks (... | ['Eraldo Ribeiro', 'Nima Aghli'] | 2022-01-01 | null | null | null | isvc-2022-1 | ['head-pose-estimation', 'image-augmentation'] | ['computer-vision', 'computer-vision'] | [-1.82068899e-01 1.08223990e-01 -1.36614606e-01 -8.90737772e-01
-5.77762842e-01 1.51842713e-01 3.16271901e-01 -3.76756698e-01
-6.12521768e-01 4.01412904e-01 1.92451045e-01 -6.50200620e-02
2.60942131e-01 -2.89751083e-01 -7.71025419e-01 -5.81907988e-01
2.26121023e-01 1.64976642e-01 2.82974154e-01 -6.59283111... | [13.698381423950195, 0.2798674404621124] |
f9209366-60d4-4799-b116-2ae82fc26198 | unsupervised-super-resolution-of-satellite | 2105.07322 | null | https://arxiv.org/abs/2105.07322v1 | https://arxiv.org/pdf/2105.07322v1.pdf | Unsupervised Super-Resolution of Satellite Imagery for High Fidelity Material Label Transfer | Urban material recognition in remote sensing imagery is a highly relevant, yet extremely challenging problem due to the difficulty of obtaining human annotations, especially on low resolution satellite images. To this end, we propose an unsupervised domain adaptation based approach using adversarial learning. We aim to... | ['Rama Chellappa', 'Larry Davis', 'Max Ehrlich', 'Arthita Ghosh'] | 2021-05-16 | null | null | null | null | ['material-recognition'] | ['computer-vision'] | [ 8.93806875e-01 8.15968364e-02 -9.52455401e-02 -1.96399450e-01
-1.32281470e+00 -7.44183064e-01 8.03453565e-01 -3.57036293e-02
-3.72738898e-01 1.36704504e+00 1.24465816e-01 -4.52088602e-02
-1.34514961e-02 -1.25491357e+00 -6.83940113e-01 -8.78499329e-01
1.04102775e-01 5.37670195e-01 2.55364984e-01 -3.15426797... | [9.70543098449707, -1.3454324007034302] |
ada1cba7-4151-40d4-92af-757416d63e27 | gfm-building-geospatial-foundation-models-via | 2302.04476 | null | https://arxiv.org/abs/2302.04476v2 | https://arxiv.org/pdf/2302.04476v2.pdf | GFM: Building Geospatial Foundation Models via Continual Pretraining | Geospatial technologies are becoming increasingly essential in our world for a wide range of applications, including agriculture, urban planning, and disaster response. To help improve the applicability and performance of deep learning models on these geospatial tasks, various works have begun investigating foundation ... | ['Chen Chen', 'Yi Zhu', 'Xingjian Shi', 'Boran Han', 'Matias Mendieta'] | 2023-02-09 | null | null | null | null | ['change-detection', 'continual-pretraining'] | ['computer-vision', 'methodology'] | [ 4.06662673e-01 -4.19843346e-01 -2.78112680e-01 -4.19288605e-01
-5.63647687e-01 -2.79346049e-01 7.41536319e-01 3.68087262e-01
-5.03320813e-01 7.42108226e-01 2.10128382e-01 -4.92062181e-01
-3.87747526e-01 -1.36334956e+00 -9.50682700e-01 -6.40009522e-01
-1.61484256e-01 9.35489982e-02 4.54384595e-01 -3.40971410... | [9.543540954589844, -1.2922884225845337] |
0889c017-abda-4dc4-b156-3066b08753f0 | from-unsupervised-to-semi-supervised-anomaly | 2106.11168 | null | https://arxiv.org/abs/2106.11168v1 | https://arxiv.org/pdf/2106.11168v1.pdf | From Unsupervised to Semi-supervised Anomaly Detection Methods for HRRP Targets | Responding to the challenge of detecting unusual radar targets in a well identified environment, innovative anomaly and novelty detection methods keep emerging in the literature. This work aims at presenting a benchmark gathering common and recently introduced unsupervised anomaly detection (AD) methods, the results be... | ['Olivier Airiau', 'Claude Adnet', 'Jesus Angulo', 'Santiago Velasco-Forero', 'Martin Bauw'] | 2021-06-10 | null | null | null | null | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [ 3.58163863e-01 -1.06256805e-01 5.95057487e-01 -4.50262815e-01
-2.31209472e-01 -3.55001092e-01 1.24700129e+00 2.56809473e-01
-5.23599148e-01 6.65883839e-01 3.92391026e-01 -1.77647918e-01
-5.51159203e-01 -7.89319456e-01 -4.87034678e-01 -8.77918541e-01
-7.11870074e-01 4.57509130e-01 2.66261250e-01 -5.90740561... | [7.53929328918457, 2.3299713134765625] |
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