paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
40a9d33b-4ecd-40a8-90dc-43c0cac780b5 | cylks-unsupervised-cycle-lucas-kanade-network | 1811.11325 | null | https://arxiv.org/abs/1811.11325v4 | https://arxiv.org/pdf/1811.11325v4.pdf | CyLKs: Unsupervised Cycle Lucas-Kanade Network for Landmark Tracking | Across a majority of modern learning-based tracking systems, expensive annotations are needed to achieve state-of-the-art performance. In contrast, the Lucas-Kanade (LK) algorithm works well without any annotation. However, LK has a strong assumption of photometric (brightness) consistency on image intensity and is eas... | ['Wentao Han', 'Xinshuo Weng'] | 2018-11-28 | null | null | null | null | ['landmark-tracking'] | ['computer-vision'] | [-2.96636999e-01 -2.18071327e-01 -3.96919966e-01 -4.88987267e-01
-6.78021252e-01 -5.74497461e-01 5.86357951e-01 -3.67147356e-01
-6.82991803e-01 4.84231710e-01 -1.51887476e-01 -2.04299659e-01
1.79396167e-01 -5.31977773e-01 -9.44038987e-01 -8.48803639e-01
1.57725647e-01 4.08891231e-01 6.45007908e-01 1.27157480... | [6.5254998207092285, -2.060889959335327] |
6cf50415-3996-42e6-85d7-1a618cabdd6e | semantic-dense-reconstruction-with-consistent | 2109.14821 | null | https://arxiv.org/abs/2109.14821v1 | https://arxiv.org/pdf/2109.14821v1.pdf | Semantic Dense Reconstruction with Consistent Scene Segments | In this paper, a method for dense semantic 3D scene reconstruction from an RGB-D sequence is proposed to solve high-level scene understanding tasks. First, each RGB-D pair is consistently segmented into 2D semantic maps based on a camera tracking backbone that propagates objects' labels with high probabilities from ful... | ['Federico Tombari', 'Lijin Fang', 'Cheng Guo', 'Yingxuan You', 'Yanyan Li', 'Yingcai Wan'] | 2021-09-30 | null | null | null | null | ['3d-scene-reconstruction'] | ['computer-vision'] | [ 3.54050100e-01 4.10326630e-01 -1.79393008e-01 -8.24950814e-01
-6.49719656e-01 -3.60776335e-01 2.58071184e-01 -2.28710085e-01
1.34299174e-01 1.44033954e-01 -1.65249363e-01 3.54152694e-02
2.20740214e-01 -1.17794919e+00 -1.16844285e+00 -2.42183492e-01
4.25905794e-01 1.10068429e+00 8.47686589e-01 2.42722139... | [8.505988121032715, -2.928380012512207] |
ed14bcbb-3458-4c73-a0f8-b556a5361a9a | spectral-unmixing-of-hyperspectral-images | 2204.04638 | null | https://arxiv.org/abs/2204.04638v2 | https://arxiv.org/pdf/2204.04638v2.pdf | Spectral Unmixing of Hyperspectral Images Based on Block Sparse Structure | Spectral unmixing (SU) of hyperspectral images (HSIs) is one of the important areas in remote sensing (RS) that needs to be carefully addressed in different RS applications. Despite the high spectral resolution of the hyperspectral data, the relatively low spatial resolution of the sensors may lead to mixture of differ... | ['Amin Zehtabian', 'Hadi Zayyani', 'Roozbeh Rajabi', 'Seyed Hossein Mosavi Azarang'] | 2022-04-10 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 9.48190808e-01 -6.72912180e-01 6.50981292e-02 1.59364194e-01
-5.32748103e-01 -5.16069353e-01 3.44562620e-01 -1.39733762e-01
9.08015370e-02 8.99961352e-01 -2.93762004e-03 1.30584452e-03
-5.77206075e-01 -7.43543148e-01 -4.00266975e-01 -1.46885061e+00
2.13241100e-01 2.56469786e-01 -1.43378034e-01 -7.10865930... | [10.075997352600098, -2.062713384628296] |
d8c59517-297d-4efe-89f0-6f5247a72b37 | learning-and-exploiting-multiple-subgoals-for | 1905.05180 | null | https://arxiv.org/abs/1905.05180v1 | https://arxiv.org/pdf/1905.05180v1.pdf | Learning and Exploiting Multiple Subgoals for Fast Exploration in Hierarchical Reinforcement Learning | Hierarchical Reinforcement Learning (HRL) exploits temporally extended actions, or options, to make decisions from a higher-dimensional perspective to alleviate the sparse reward problem, one of the most challenging problems in reinforcement learning. The majority of existing HRL algorithms require either significant m... | ['Libo Xing'] | 2019-05-13 | null | null | null | null | ['montezumas-revenge'] | ['playing-games'] | [-8.55481774e-02 1.09496005e-01 -4.33463275e-01 6.80109635e-02
-8.09529245e-01 -5.17896235e-01 3.54102343e-01 1.53656527e-01
-6.63926005e-01 1.17778385e+00 2.80586362e-01 -3.15093011e-01
-1.91741884e-01 -6.31223142e-01 -4.93897647e-01 -7.76834905e-01
-5.21049798e-01 6.79187775e-01 2.04543054e-01 -4.35475707... | [4.100903034210205, 1.703028917312622] |
9665cf4d-e35f-42a7-a52e-aeb767a2ba19 | adaptive-face-recognition-using-adversarial | 2305.13605 | null | https://arxiv.org/abs/2305.13605v1 | https://arxiv.org/pdf/2305.13605v1.pdf | Adaptive Face Recognition Using Adversarial Information Network | In many real-world applications, face recognition models often degenerate when training data (referred to as source domain) are different from testing data (referred to as target domain). To alleviate this mismatch caused by some factors like pose and skin tone, the utilization of pseudo-labels generated by clustering ... | ['Weihong Deng', 'Mei Wang'] | 2023-05-23 | null | null | null | null | ['face-recognition', 'unsupervised-domain-adaptation'] | ['computer-vision', 'methodology'] | [ 4.85631734e-01 1.11714229e-01 -2.77584642e-01 -6.72716558e-01
-3.36805373e-01 -6.00108862e-01 3.67218167e-01 -4.19928610e-01
-8.46584514e-02 7.31800854e-01 -2.23331332e-01 3.39266397e-02
-1.86544418e-01 -8.31980526e-01 -5.61980009e-01 -9.56805170e-01
1.02453902e-01 3.50872755e-01 -4.40839976e-02 2.85338126... | [13.092738151550293, 1.0093599557876587] |
328cdc21-fd60-4f41-8fe8-8408090a9b88 | generating-diverse-and-consistent-qa-pairs | 2005.13837 | null | https://arxiv.org/abs/2005.13837v5 | https://arxiv.org/pdf/2005.13837v5.pdf | Generating Diverse and Consistent QA pairs from Contexts with Information-Maximizing Hierarchical Conditional VAEs | One of the most crucial challenges in question answering (QA) is the scarcity of labeled data, since it is costly to obtain question-answer (QA) pairs for a target text domain with human annotation. An alternative approach to tackle the problem is to use automatically generated QA pairs from either the problem context ... | ['Seanie Lee', 'Donghwan Kim', 'Sung Ju Hwang', 'Dong Bok Lee', 'Woo Tae Jeong'] | 2020-05-28 | generating-diverse-and-consistent-qa-pairs-1 | https://aclanthology.org/2020.acl-main.20 | https://aclanthology.org/2020.acl-main.20.pdf | acl-2020-6 | ['question-answer-generation'] | ['natural-language-processing'] | [ 3.50636393e-02 4.93627578e-01 5.20923257e-01 -4.03177559e-01
-1.73612905e+00 -7.65755057e-01 6.75266981e-01 1.84929520e-02
-4.72478509e-01 9.54445302e-01 2.91848391e-01 -2.70422459e-01
2.73455471e-01 -8.08196008e-01 -7.29931712e-01 -4.02173281e-01
7.51734734e-01 1.34027600e+00 2.97514528e-01 -5.00428557... | [11.370841026306152, 8.206477165222168] |
cfe9ce4d-f6c3-4320-936a-af6dfff1b84b | learning-srgb-to-raw-rgb-de-rendering-with-1 | 2206.01813 | null | https://arxiv.org/abs/2206.01813v1 | https://arxiv.org/pdf/2206.01813v1.pdf | Learning sRGB-to-Raw-RGB De-rendering with Content-Aware Metadata | Most camera images are rendered and saved in the standard RGB (sRGB) format by the camera's hardware. Due to the in-camera photo-finishing routines, nonlinear sRGB images are undesirable for computer vision tasks that assume a direct relationship between pixel values and scene radiance. For such applications, linear ra... | ['Michael S. Brown', 'Marcus A. Brubaker', 'Abhijith Punnappurath', 'Seonghyeon Nam'] | 2022-06-03 | learning-srgb-to-raw-rgb-de-rendering-with | http://openaccess.thecvf.com//content/CVPR2022/html/Nam_Learning_sRGB-to-Raw-RGB_De-Rendering_With_Content-Aware_Metadata_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Nam_Learning_sRGB-to-Raw-RGB_De-Rendering_With_Content-Aware_Metadata_CVPR_2022_paper.pdf | cvpr-2022-1 | ['raw-reconstruction'] | ['computer-vision'] | [ 6.72561824e-01 -3.00460815e-01 -2.33667549e-02 -7.14603126e-01
-6.22578502e-01 -1.54436857e-01 1.95373476e-01 -6.50568306e-01
-5.24427593e-01 5.17962933e-01 -9.17655975e-02 -2.81733185e-01
2.59119153e-01 -9.61650729e-01 -9.15131032e-01 -8.58619809e-01
4.71340507e-01 8.17924645e-03 3.44176531e-01 4.60890755... | [10.24644947052002, -2.531107187271118] |
b46fed99-ed98-4b6a-8f44-ea6008f95def | leveraging-structured-biological-knowledge | 2101.05136 | null | https://arxiv.org/abs/2101.05136v1 | https://arxiv.org/pdf/2101.05136v1.pdf | Leveraging Structured Biological Knowledge for Counterfactual Inference: a Case Study of Viral Pathogenesis | Counterfactual inference is a useful tool for comparing outcomes of interventions on complex systems. It requires us to represent the system in form of a structural causal model, complete with a causal diagram, probabilistic assumptions on exogenous variables, and functional assignments. Specifying such models can be e... | ['Olga Vitek', 'Robert Ness', 'Kristie Oxford', 'Charles Tapley Hoyt', 'Jeremy Teuton', 'Craig Bakker', 'Pallavi Kolambkar', 'Somya Bhargava', 'Sara Mohammad-Taheri', 'Kaushal Paneri', 'Jeremy Zucker'] | 2021-01-13 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 5.48224151e-01 1.82782754e-01 -4.14695770e-01 -4.06164825e-02
-3.16799492e-01 -7.83568323e-01 6.16403937e-01 5.14089048e-01
8.50958154e-02 1.29226649e+00 1.89428404e-01 -1.05399156e+00
-6.35054588e-01 -8.54589522e-01 -1.06379771e+00 -5.57190597e-01
-4.68935102e-01 5.59278071e-01 -7.31780976e-02 6.88872486... | [7.938242435455322, 5.393410682678223] |
e3ad2fd3-c942-49e1-ad9d-3879c5226c8b | a-compositional-feature-embedding-and | 2109.12380 | null | https://arxiv.org/abs/2109.12380v3 | https://arxiv.org/pdf/2109.12380v3.pdf | A Compositional Feature Embedding and Similarity Metric for Ultra-Fine-Grained Visual Categorization | Fine-grained visual categorization (FGVC), which aims at classifying objects with small inter-class variances, has been significantly advanced in recent years. However, ultra-fine-grained visual categorization (ultra-FGVC), which targets at identifying subclasses with extremely similar patterns, has not received much a... | ['Yongsheng Gao', 'Yi Liao', 'Xiaohan Yu', 'Miaohua Zhang', 'Yajie Sun'] | 2021-09-25 | null | null | null | null | ['fine-grained-visual-categorization'] | ['computer-vision'] | [ 1.22049026e-01 -4.39560741e-01 -9.99166965e-02 -4.27839369e-01
-3.16630483e-01 -4.10183817e-01 5.22463977e-01 1.71959028e-01
-2.40330920e-01 4.65656757e-01 8.65052342e-02 2.09677145e-01
-2.20526829e-01 -8.21334243e-01 -1.88557699e-01 -9.43753719e-01
3.35880518e-01 -1.64041027e-01 5.68619668e-01 4.39327061... | [9.715106964111328, 2.045559883117676] |
210305bc-f537-49d1-839a-f1961091b46e | multi-contrast-computed-tomography-atlas-of | 2306.01853 | null | https://arxiv.org/abs/2306.01853v1 | https://arxiv.org/pdf/2306.01853v1.pdf | Multi-Contrast Computed Tomography Atlas of Healthy Pancreas | With the substantial diversity in population demographics, such as differences in age and body composition, the volumetric morphology of pancreas varies greatly, resulting in distinctive variations in shape and appearance. Such variations increase the difficulty at generalizing population-wide pancreas features. A volu... | ['Bennett A. Landman', 'Yuankai Huo', 'Jeffrey M. Spraggins', 'Shunxing Bao', 'Qi Yang', 'Xin Yu', 'Yucheng Tang', 'Ho Hin Lee', 'Yinchi Zhou'] | 2023-06-02 | null | null | null | null | ['computed-tomography-ct', 'anatomy'] | ['methodology', 'miscellaneous'] | [-1.21403530e-01 1.19812243e-01 -1.69137537e-01 -5.17519534e-01
-9.15358961e-01 -9.06353772e-01 4.89543796e-01 4.96660531e-01
-2.00101197e-01 1.99409887e-01 5.98378062e-01 4.29058485e-02
-1.48043320e-01 -6.12000525e-01 -5.89271069e-01 -8.86118472e-01
-5.18152416e-01 7.05914974e-01 6.37874752e-02 2.80135542... | [14.480670928955078, -2.668203115463257] |
e8f744ea-0ad3-40d2-aaae-a97a167b37f8 | deep-mr-fingerprinting-with-total-variation | 1902.10205 | null | http://arxiv.org/abs/1902.10205v1 | http://arxiv.org/pdf/1902.10205v1.pdf | Deep MR Fingerprinting with total-variation and low-rank subspace priors | Deep learning (DL) has recently emerged to address the heavy storage and
computation requirements of the baseline dictionary-matching (DM) for Magnetic
Resonance Fingerprinting (MRF) reconstruction. Fed with non-iterated
back-projected images, the network is unable to fully resolve
spatially-correlated corruptions caus... | ['Pedro A. Gómez', 'Marion I. Menzel', 'Carolin M. Pirkl', 'Guido Buonincontri', 'Mohammad Golbabaee'] | 2019-02-26 | null | null | null | null | ['magnetic-resonance-fingerprinting'] | ['medical'] | [ 5.06667733e-01 -2.12181564e-02 1.79170251e-01 -3.93035620e-01
-7.50379086e-01 -1.54431537e-01 3.05870622e-01 8.25498477e-02
-6.47278547e-01 6.26487792e-01 2.84201503e-01 -2.14922979e-01
-2.73903579e-01 -4.25255775e-01 -7.82398164e-01 -7.27190733e-01
-3.72054905e-01 2.66338378e-01 -8.67297407e-03 2.20863596... | [13.428775787353516, -2.4409239292144775] |
01ef5639-5f79-481a-bde8-0da82516a4da | meta-learning-for-low-resource-neural-machine-1 | null | null | https://openreview.net/forum?id=S1g5ylbm1Q | https://openreview.net/pdf?id=S1g5ylbm1Q | Meta-Learning for Low-Resource Neural Machine Translation | In this paper, we propose to extend the recently introduced model-agnostic meta-learning algorithm (MAML, Finn et al., 2017) for low resource neural machine translation (NMT). We frame low-resource translation as a meta-learning problem, and we learn to adapt to low-resource languages based on multilingual high-resourc... | ['Anonymous'] | 2018-05-23 | null | null | null | null | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 3.08444537e-02 -1.91575050e-01 -5.11181474e-01 7.85730779e-02
-1.38857615e+00 -7.23096132e-01 1.11882770e+00 -9.69930068e-02
-8.75879705e-01 1.38875651e+00 -1.03367325e-02 -9.22835350e-01
2.60146320e-01 -4.43021387e-01 -9.78759289e-01 -1.49213418e-01
4.22622442e-01 7.76682258e-01 -2.32899606e-01 -5.89276731... | [11.609410285949707, 10.316668510437012] |
b2418421-8ad3-428c-a0c7-060a68d0584a | conformal-quantitative-predictive-monitoring | 2211.02375 | null | https://arxiv.org/abs/2211.02375v2 | https://arxiv.org/pdf/2211.02375v2.pdf | Conformal Quantitative Predictive Monitoring of STL Requirements for Stochastic Processes | We consider the problem of predictive monitoring (PM), i.e., predicting at runtime the satisfaction of a desired property from the current system's state. Due to its relevance for runtime safety assurance and online control, PM methods need to be efficient to enable timely interventions against predicted violations, wh... | ['Luca Bortolussi', 'Nicola Paoletti', 'Francesca Cairoli'] | 2022-11-04 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [ 4.73941743e-01 2.79103428e-01 -3.58987957e-01 -1.80552989e-01
-1.22704017e+00 -6.20596528e-01 6.58349335e-01 4.81777966e-01
1.93237692e-01 6.95112109e-01 -2.77656019e-01 -7.89608300e-01
-6.17052734e-01 -8.68917465e-01 -8.82055342e-01 -4.72470790e-01
-7.02694356e-01 4.03584778e-01 5.17592430e-01 2.41144672... | [4.753530979156494, 2.2891292572021484] |
cb713d67-1de3-4a15-a43f-6201fcb7a8d0 | bggan-bokeh-glass-generative-adversarial | 2011.02242 | null | https://arxiv.org/abs/2011.02242v1 | https://arxiv.org/pdf/2011.02242v1.pdf | BGGAN: Bokeh-Glass Generative Adversarial Network for Rendering Realistic Bokeh | A photo captured with bokeh effect often means objects in focus are sharp while the out-of-focus areas are all blurred. DSLR can easily render this kind of effect naturally. However, due to the limitation of sensors, smartphones cannot capture images with depth-of-field effects directly. In this paper, we propose a nov... | ['Jian Cheng', 'Cong Leng', 'Chenghua Li', 'Zhenyu Guo', 'Jiamin Lin', 'Congyu Qiao', 'Ming Qian'] | 2020-11-04 | null | null | null | null | ['bokeh-effect-rendering'] | ['computer-vision'] | [ 4.97317344e-01 2.64681317e-02 5.64374804e-01 -2.28352889e-01
-5.20243883e-01 -3.69043231e-01 5.17793536e-01 -4.69168603e-01
-1.56531841e-01 4.78532284e-01 -3.22315134e-02 -2.61062622e-01
2.39118740e-01 -9.66072381e-01 -8.50660920e-01 -5.66935718e-01
2.98868895e-01 -2.04802066e-01 3.66024584e-01 -2.17723206... | [10.640488624572754, -2.286220073699951] |
1c62f8ef-3318-4d86-a206-7c47dbdf94d8 | hierarchical-multi-building-and-multi-floor | 2112.12478 | null | https://arxiv.org/abs/2112.12478v1 | https://arxiv.org/pdf/2112.12478v1.pdf | Hierarchical Multi-Building And Multi-Floor Indoor Localization Based On Recurrent Neural Networks | There has been an increasing tendency to move from outdoor to indoor lifestyle in modern cities. The emergence of big shopping malls, indoor sports complexes, factories, and warehouses is accelerating this tendency. In such an environment, indoor localization becomes one of the essential services, and the indoor locali... | ['Kyeong Soo Kim', 'Abdalla Elmokhtar Ahmed Elesawi'] | 2021-12-23 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [-8.20150673e-02 -5.03148973e-01 -5.96055463e-02 -4.22371387e-01
-5.55213749e-01 -4.45647061e-01 9.41483155e-02 -2.87585020e-01
-4.39867437e-01 7.74696112e-01 3.00860815e-02 -7.17478156e-01
-4.92270470e-01 -1.15948462e+00 -5.09475172e-01 -7.50940561e-01
-3.90311480e-02 2.74057053e-02 1.71984941e-01 -7.04226121... | [6.402823448181152, 0.9138978123664856] |
bb85a5b4-3464-4fe2-b5d5-6f61aba56af1 | learning-from-the-best-contrastive | 2210.01459 | null | https://arxiv.org/abs/2210.01459v1 | https://arxiv.org/pdf/2210.01459v1.pdf | Learning from the Best: Contrastive Representations Learning Across Sensor Locations for Wearable Activity Recognition | We address the well-known wearable activity recognition problem of having to work with sensors that are non-optimal in terms of information they provide but have to be used due to wearability/usability concerns (e.g. the need to work with wrist-worn IMUs because they are embedded in most smart watches). To mitigate thi... | ['Paul Lukowicz', 'Sungho Suh', 'Vitor Fortes Rey'] | 2022-10-04 | null | null | null | null | ['wearable-activity-recognition'] | ['time-series'] | [ 7.28832006e-01 2.70678222e-01 -2.82623619e-01 -2.82061011e-01
-8.89125884e-01 -3.69921207e-01 4.40835744e-01 3.04626584e-01
-7.23569274e-01 8.59592259e-01 2.73085117e-01 1.71032742e-01
-4.93978620e-01 -5.64522386e-01 -8.27149093e-01 -6.01660073e-01
-2.10954890e-01 1.46840960e-01 1.54147640e-01 1.24517538... | [7.485788822174072, 0.942331850528717] |
c41345cf-2dfc-4365-be46-353fb6e8ed81 | learning-entity-and-relation-embeddings-for | null | null | https://dl.acm.org/doi/10.5555/2886521.2886624 | https://dl.acm.org/doi/10.5555/2886521.2886624 | Learning Entity and Relation Embeddings for Knowledge Graph Completion | Knowledge graph completion aims to perform link prediction between entities. In this paper, we consider the approach of knowledge graph embeddings. Recently, models such as TransE and TransH build entity and relation embeddings by regarding a relation as translation from head entity to tail entity. We note that these m... | ['Xuan Zhu', 'Yang Liu', 'Maosong Sun', 'Zhiyuan Liu', 'Yankai Lin'] | 2015-01-25 | null | null | null | aaai-2015-2015-1 | ['triple-classification', 'knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'graphs', 'methodology'] | [-3.88081312e-01 4.87687886e-01 -5.80374897e-01 -3.34644556e-01
-3.10984522e-01 -4.87209797e-01 5.53959250e-01 5.34817398e-01
-2.40314111e-01 6.95170522e-01 4.94270146e-01 -2.40175515e-01
-2.45578736e-01 -1.24863648e+00 -7.40841866e-01 -1.30198002e-01
-1.75393566e-01 7.31537342e-01 2.27568641e-01 -2.42315382... | [8.84736156463623, 7.994575500488281] |
33752631-1dcb-45ff-8fd2-1c4a775c590a | conditional-generation-of-medical-images-via | 2012.04764 | null | https://arxiv.org/abs/2012.04764v3 | https://arxiv.org/pdf/2012.04764v3.pdf | Conditional Generation of Medical Images via Disentangled Adversarial Inference | Synthetic medical image generation has a huge potential for improving healthcare through many applications, from data augmentation for training machine learning systems to preserving patient privacy. Conditional Adversarial Generative Networks (cGANs) use a conditioning factor to generate images and have shown great su... | ['Qicheng Lao', 'Yiping Wang', 'Ximeng Mao', 'Mohammad Havaei'] | 2020-12-08 | null | null | null | null | ['medical-image-generation'] | ['medical'] | [ 7.44096100e-01 4.53056991e-01 -2.38526106e-01 -3.53089601e-01
-5.21439075e-01 -4.95745748e-01 7.23932385e-01 -1.34881616e-01
-3.47930819e-01 6.57431424e-01 3.47515523e-01 -1.10769063e-01
1.77936092e-01 -9.89127100e-01 -8.01666141e-01 -1.08444166e+00
3.21632087e-01 1.66966334e-01 -3.94766212e-01 -6.08975403... | [11.650036811828613, -0.30870458483695984] |
ca7e8edd-f95f-49f4-a0ee-62b43d95ce69 | jet-images-deep-learning-edition | 1511.05190 | null | http://arxiv.org/abs/1511.05190v3 | http://arxiv.org/pdf/1511.05190v3.pdf | Jet-Images -- Deep Learning Edition | Building on the notion of a particle physics detector as a camera and the
collimated streams of high energy particles, or jets, it measures as an image,
we investigate the potential of machine learning techniques based on deep
learning architectures to identify highly boosted W bosons. Modern deep
learning algorithms t... | ['Lester Mackey', 'Benjamin Nachman', 'Luke de Oliveira', 'Michael Kagan', 'Ariel Schwartzman'] | 2015-11-16 | null | null | null | null | ['jet-tagging'] | ['graphs'] | [-2.38154858e-01 -2.67500967e-01 -4.61913794e-01 -4.17921156e-01
-2.85562575e-01 -6.60848081e-01 1.07149863e+00 1.85512722e-01
-4.57140595e-01 3.28108400e-01 3.15054297e-01 -3.98411393e-01
-2.42949650e-01 -1.07350719e+00 -5.97703218e-01 -7.23329127e-01
-1.32152244e-01 8.55176806e-01 5.24459124e-01 -1.48563579... | [15.700263977050781, 2.9188120365142822] |
91a353b5-579f-480f-a90c-93bfa6524d8c | vibe-video-inference-for-human-body-pose-and | 1912.05656 | null | https://arxiv.org/abs/1912.05656v3 | https://arxiv.org/pdf/1912.05656v3.pdf | VIBE: Video Inference for Human Body Pose and Shape Estimation | Human motion is fundamental to understanding behavior. Despite progress on single-image 3D pose and shape estimation, existing video-based state-of-the-art methods fail to produce accurate and natural motion sequences due to a lack of ground-truth 3D motion data for training. To address this problem, we propose Video I... | ['Nikos Athanasiou', 'Michael J. Black', 'Muhammed Kocabas'] | 2019-12-11 | vibe-video-inference-for-human-body-pose-and-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Kocabas_VIBE_Video_Inference_for_Human_Body_Pose_and_Shape_Estimation_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Kocabas_VIBE_Video_Inference_for_Human_Body_Pose_and_Shape_Estimation_CVPR_2020_paper.pdf | cvpr-2020-6 | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [ 3.05625647e-02 -2.92330384e-02 -3.30722153e-01 -1.41607821e-01
-8.91512692e-01 -7.57174373e-01 5.17212033e-01 -6.21471941e-01
-4.35776621e-01 4.51993287e-01 3.57381076e-01 1.60005726e-02
3.27879846e-01 -3.58420312e-01 -1.11136472e+00 -3.50942314e-01
-2.24231914e-01 5.47964036e-01 2.96965182e-01 -1.29759401... | [7.187196731567383, -0.5875267386436462] |
ad6e9de2-81a2-4ed1-830e-561c137b797a | a-deep-learning-approach-to-clustering-visual | 2106.06234 | null | https://arxiv.org/abs/2106.06234v2 | https://arxiv.org/pdf/2106.06234v2.pdf | A deep learning approach to clustering visual arts | Clustering artworks is difficult for several reasons. On the one hand, recognizing meaningful patterns based on domain knowledge and visual perception is extremely hard. On the other hand, applying traditional clustering and feature reduction techniques to the highly dimensional pixel space can be ineffective. To addre... | ['Gennaro Vessio', 'Giovanna Castellano'] | 2021-06-11 | null | null | null | null | ['art-analysis'] | ['computer-vision'] | [ 4.12565619e-02 -4.96658474e-01 -9.88947451e-02 -2.73308605e-01
-3.64832014e-01 -5.33404112e-01 5.59298575e-01 1.39856443e-01
-1.25947520e-01 1.92006975e-01 1.33315861e-01 1.37737751e-01
-5.76882601e-01 -8.67849827e-01 -4.87443209e-01 -8.58425438e-01
1.50167346e-01 5.41492581e-01 -4.41162810e-02 3.71847242... | [9.138351440429688, 3.2024831771850586] |
8bfc5675-a5e9-438e-aa67-29bb3b06c4f2 | road-traffic-reservoir-computing | 1912.00554 | null | https://arxiv.org/abs/1912.00554v1 | https://arxiv.org/pdf/1912.00554v1.pdf | Road traffic reservoir computing | Reservoir computing derived from recurrent neural networks is more applicable to real world systems than deep learning because of its low computational cost and potential for physical implementation. Specifically, physical reservoir computing, which replaces the dynamics of reservoir units with physical phenomena, has ... | ['Hiroyasu Ando', 'Hanten Chang'] | 2019-12-02 | null | null | null | null | ['3d-car-instance-understanding'] | ['computer-vision'] | [-1.26627341e-01 -4.43759143e-01 -1.88417256e-01 3.94984305e-01
1.07414208e-01 6.32206798e-02 9.48864222e-01 -4.96643819e-02
-3.93113166e-01 1.12541902e+00 -8.72291401e-02 -3.99402589e-01
1.30635887e-01 -1.06747377e+00 -6.34209216e-01 -8.20246279e-01
-4.48558897e-01 7.56021887e-02 1.20680161e-01 -4.06929433... | [6.65191650390625, 3.3306615352630615] |
1fe74197-cc9a-4b0b-87f4-eb79048cb6de | stock-movement-prediction-based-on-bi-typed | null | null | https://openreview.net/forum?id=nXyXgrVOk8k | https://openreview.net/pdf?id=nXyXgrVOk8k | Stock Movement Prediction Based on Bi-typed and Hybrid-relational Market Knowledge Graph via Dual Attention Networks | Stock Movement Prediction (SMP) aims at predicting listed company's stock future price trend, which is a challenging task due to the volatile nature of financial markets. Recent financial studies show that the momentum spillover effect plays a significant role in stock fluctuation. However, previous studies typically o... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['stock-prediction'] | ['time-series'] | [-8.72432411e-01 7.33166263e-02 -6.46118045e-01 -1.31392479e-01
-1.45695940e-01 -6.49104655e-01 6.58678830e-01 -4.19468135e-01
3.42832282e-02 8.43617022e-01 5.18590152e-01 -6.23144686e-01
-1.95901498e-01 -1.26269495e+00 -7.49907196e-01 -2.86230534e-01
-1.86312765e-01 5.44387341e-01 3.25360298e-01 -5.83983302... | [4.336575508117676, 4.32593297958374] |
a4d27dca-6617-44b6-957a-8aeecc387f52 | m2ts-multi-scale-multi-modal-approach-based | 2203.09707 | null | https://arxiv.org/abs/2203.09707v2 | https://arxiv.org/pdf/2203.09707v2.pdf | M2TS: Multi-Scale Multi-Modal Approach Based on Transformer for Source Code Summarization | Source code summarization aims to generate natural language descriptions of code snippets. Many existing studies learn the syntactic and semantic knowledge of code snippets from their token sequences and Abstract Syntax Trees (ASTs). They use the learned code representations as input to code summarization models, which... | ['Chen Lyu', 'Yuexiu Gao'] | 2022-03-18 | null | null | null | null | ['code-summarization'] | ['computer-code'] | [ 2.04208717e-01 5.50827496e-02 -3.75529677e-01 -3.11508536e-01
-1.10912800e+00 -5.99451661e-01 1.58081681e-01 5.56591928e-01
2.87979722e-01 1.12755872e-01 7.51193225e-01 -9.12364051e-02
1.75405845e-01 -6.32650137e-01 -6.40928209e-01 -2.97756821e-01
1.01430506e-01 -2.08406195e-01 4.55715984e-01 -1.10548370... | [7.602161884307861, 7.997286796569824] |
a2a4df8b-64c5-4b95-bc7f-8191c4670b33 | automated-story-generation-as-question | 2112.03808 | null | https://arxiv.org/abs/2112.03808v1 | https://arxiv.org/pdf/2112.03808v1.pdf | Automated Story Generation as Question-Answering | Neural language model-based approaches to automated story generation suffer from two important limitations. First, language model-based story generators generally do not work toward a given goal or ending. Second, they often lose coherence as the story gets longer. We propose a novel approach to automated story generat... | ['Mark Riedl', 'Nitya Tarakad', 'Jonathan Balloch', 'Spencer Frazier', 'Louis Castricato'] | 2021-12-07 | null | null | null | null | ['generative-question-answering'] | ['natural-language-processing'] | [ 4.74640161e-01 4.96097475e-01 7.27569684e-02 -2.05324635e-01
-9.80471551e-01 -5.69531500e-01 9.48690355e-01 6.03120148e-01
4.75990213e-02 1.07088423e+00 9.01403904e-01 -1.59774214e-01
2.49275371e-01 -1.22515547e+00 -4.40440893e-01 -3.33846420e-01
4.40197974e-01 7.00685084e-01 2.45133430e-01 -4.25807923... | [11.672494888305664, 8.846604347229004] |
99de7658-5f3a-4664-9615-dc36d415dfc0 | safer-situation-aware-facial-emotion | 2306.09372 | null | https://arxiv.org/abs/2306.09372v1 | https://arxiv.org/pdf/2306.09372v1.pdf | SAFER: Situation Aware Facial Emotion Recognition | In this paper, we present SAFER, a novel system for emotion recognition from facial expressions. It employs state-of-the-art deep learning techniques to extract various features from facial images and incorporates contextual information, such as background and location type, to enhance its performance. The system has b... | ['Bharat Bhargava', 'Mijanur Palash'] | 2023-06-14 | null | null | null | null | ['facial-emotion-recognition'] | ['computer-vision'] | [ 3.33822295e-02 -3.13532948e-01 -1.40780434e-01 -8.01343679e-01
-1.30558804e-01 -2.52386987e-01 3.31159085e-01 -4.07671839e-01
-4.95555788e-01 4.12807375e-01 -2.64476147e-02 1.44855112e-01
1.80260211e-01 -4.18348521e-01 -2.67549008e-01 -8.98987889e-01
-4.40154344e-01 -1.71869367e-01 -2.72085696e-01 -3.85333210... | [13.484009742736816, 1.854284644126892] |
7893c7f5-3b65-4fc7-a3cc-294ae45fe6df | glipv2-unifying-localization-and-vision | 2206.05836 | null | https://arxiv.org/abs/2206.05836v2 | https://arxiv.org/pdf/2206.05836v2.pdf | GLIPv2: Unifying Localization and Vision-Language Understanding | We present GLIPv2, a grounded VL understanding model, that serves both localization tasks (e.g., object detection, instance segmentation) and Vision-Language (VL) understanding tasks (e.g., VQA, image captioning). GLIPv2 elegantly unifies localization pre-training and Vision-Language Pre-training (VLP) with three pre-t... | ['Jianfeng Gao', 'Jenq-Neng Hwang', 'Lu Yuan', 'Lijuan Wang', 'Xiyang Dai', 'Liunian Harold Li', 'Yen-Chun Chen', 'Xiaowei Hu', 'Pengchuan Zhang', 'Haotian Zhang'] | 2022-06-12 | null | null | null | null | ['open-vocabulary-object-detection', 'referring-expression-segmentation', 'phrase-grounding'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 1.48331240e-01 2.73072660e-01 -3.88517171e-01 -4.30640429e-01
-1.30671728e+00 -5.96038401e-01 6.92854404e-01 -8.75426084e-03
-5.55276573e-01 3.29711586e-01 1.56744450e-01 -5.50962389e-01
5.48556507e-01 -4.04944718e-01 -8.87032032e-01 -3.66480440e-01
2.83592224e-01 4.52008545e-01 4.23088312e-01 -5.21224700... | [10.505387306213379, 1.6264230012893677] |
3e7ea0d5-8064-4f38-b739-f840c33079ac | the-repere-corpus-a-multimodal-corpus-for | null | null | https://aclanthology.org/L12-1410 | https://aclanthology.org/L12-1410.pdf | The REPERE Corpus : a multimodal corpus for person recognition | The REPERE Challenge aims to support research on people recognition in multimodal conditions. To assess the technology progression, annual evaluation campaigns will be organized from 2012 to 2014. In this context, the REPERE corpus, a French videos corpus with multimodal annotation, has been developed. This paper prese... | ["Val{\\'e}rie Mapelli", "Matthieu Carr{\\'e}", 'Ludovic Quintard', 'Juliette Kahn', 'Olivier Galibert', 'Aude Giraudel'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['person-recognition'] | ['computer-vision'] | [ 6.52734488e-02 2.14603227e-02 -4.88733426e-02 -5.63588381e-01
-3.97004157e-01 -5.66332579e-01 9.90470767e-01 1.33581445e-01
-6.31630123e-01 8.24488521e-01 7.73497701e-01 4.84945059e-01
1.32476792e-01 -1.85031816e-01 -1.15119338e-01 -1.74870178e-01
-2.70959675e-01 2.83173472e-01 -2.35158741e-01 -2.18386605... | [10.64124584197998, 0.7847983837127686] |
792db795-7023-498d-8adf-bef9cefdfa40 | nonsmooth-analysis-and-subgradient-methods | 1701.06393 | null | http://arxiv.org/abs/1701.06393v1 | http://arxiv.org/pdf/1701.06393v1.pdf | Nonsmooth Analysis and Subgradient Methods for Averaging in Dynamic Time Warping Spaces | Time series averaging in dynamic time warping (DTW) spaces has been
successfully applied to improve pattern recognition systems. This article
proposes and analyzes subgradient methods for the problem of finding a sample
mean in DTW spaces. The class of subgradient methods generalizes existing
sample mean algorithms suc... | ['Brijnesh Jain', 'David Schultz'] | 2017-01-23 | null | null | null | null | ['time-series-averaging'] | ['time-series'] | [ 2.97244132e-01 -3.48791331e-01 -2.27170229e-01 -3.89900297e-01
-1.20998573e+00 -4.98047054e-01 2.24253476e-01 -1.69037774e-01
-4.01668608e-01 9.44342732e-01 1.64683759e-01 -2.32929468e-01
-7.64091432e-01 -5.02396107e-01 -6.58319771e-01 -1.22662139e+00
-7.22492576e-01 3.05102766e-01 -1.15552060e-01 4.70326021... | [6.959786891937256, 4.345320701599121] |
4766353d-9e51-4bb0-9da6-fe7a93164526 | wsfe-wasserstein-sub-graph-feature-encoder | 2305.04410 | null | https://arxiv.org/abs/2305.04410v1 | https://arxiv.org/pdf/2305.04410v1.pdf | WSFE: Wasserstein Sub-graph Feature Encoder for Effective User Segmentation in Collaborative Filtering | Maximizing the user-item engagement based on vectorized embeddings is a standard procedure of recent recommender models. Despite the superior performance for item recommendations, these methods however implicitly deprioritize the modeling of user-wise similarity in the embedding space; consequently, identifying similar... | ['Irwin King', 'Chen Ma', 'Zixing Song', 'Menglin Yang', 'Yifei Zhang', 'Yankai Chen'] | 2023-05-08 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [ 3.20238657e-02 -2.53351867e-01 -5.75404823e-01 -5.23651302e-01
-4.36350733e-01 -8.06120336e-01 5.85133851e-01 -1.90994099e-01
-2.47369528e-01 1.95999190e-01 4.35542643e-01 -5.54388821e-01
-6.83280349e-01 -7.32911289e-01 -4.24256116e-01 -5.46209395e-01
-1.58519089e-01 3.84451598e-01 -2.13414043e-01 -2.87661016... | [10.084259033203125, 5.622222423553467] |
84db4700-bcb7-4065-b0ba-84a475118988 | transformer-based-entity-typing-in-knowledge | 2210.11151 | null | https://arxiv.org/abs/2210.11151v1 | https://arxiv.org/pdf/2210.11151v1.pdf | Transformer-based Entity Typing in Knowledge Graphs | We investigate the knowledge graph entity typing task which aims at inferring plausible entity types. In this paper, we propose a novel Transformer-based Entity Typing (TET) approach, effectively encoding the content of neighbors of an entity. More precisely, TET is composed of three different mechanisms: a local trans... | ['Jeff Z. Pan', 'Ru Li', 'Zhiliang Xiang', 'Víctor Gutiérrez-Basulto', 'Zhiwei Hu'] | 2022-10-20 | null | null | null | null | ['entity-typing'] | ['natural-language-processing'] | [ 2.54778732e-02 4.87502337e-01 -2.62441963e-01 -4.23899055e-01
-3.92254740e-01 -7.59170711e-01 5.07206678e-01 8.90951931e-01
-3.37892711e-01 9.55881596e-01 2.17798784e-01 -3.17685217e-01
-9.42756236e-02 -1.61647797e+00 -1.11619437e+00 -4.53099668e-01
-8.96399096e-02 5.11217356e-01 5.92222631e-01 -2.10592240... | [9.4928617477417, 8.623994827270508] |
cb4dcf90-af61-408b-8df7-c54ce69af01b | few-shot-3d-multi-modal-medical-image | 1810.12241 | null | http://arxiv.org/abs/1810.12241v1 | http://arxiv.org/pdf/1810.12241v1.pdf | Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning | We address the problem of segmenting 3D multi-modal medical images in
scenarios where very few labeled examples are available for training.
Leveraging the recent success of adversarial learning for semi-supervised
segmentation, we propose a novel method based on Generative Adversarial
Networks (GANs) to train a segment... | ['Christian Desrosiers', 'Arnab Kumar Mondal', 'Jose Dolz'] | 2018-10-29 | null | null | null | null | ['3d-medical-imaging-segmentation', 'brain-image-segmentation'] | ['medical', 'medical'] | [ 6.46286666e-01 7.20876276e-01 -6.18702266e-03 -5.18070638e-01
-1.35422480e+00 -6.43554032e-01 4.54901278e-01 -3.91869098e-01
-3.51892769e-01 7.08770573e-01 -9.03345719e-02 -3.36945057e-01
4.87350851e-01 -6.83756232e-01 -9.60390627e-01 -6.79603338e-01
1.13113865e-01 8.61093283e-01 -2.30348334e-02 1.28966440... | [14.455228805541992, -2.0779223442077637] |
4d0e3946-df3c-4f76-a3fb-8d44d18723e4 | openmask3d-open-vocabulary-3d-instance | 2306.13631 | null | https://arxiv.org/abs/2306.13631v1 | https://arxiv.org/pdf/2306.13631v1.pdf | OpenMask3D: Open-Vocabulary 3D Instance Segmentation | We introduce the task of open-vocabulary 3D instance segmentation. Traditional approaches for 3D instance segmentation largely rely on existing 3D annotated datasets, which are restricted to a closed-set of object categories. This is an important limitation for real-life applications where one might need to perform tas... | ['Francis Engelmann', 'Federico Tombari', 'Marc Pollefeys', 'Robert W. Sumner', 'Elisabetta Fedele', 'Ayça Takmaz'] | 2023-06-23 | null | null | null | null | ['3d-instance-segmentation-1', 'instance-segmentation', 'scene-understanding'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.16263518e-01 2.53278583e-01 -2.28820547e-01 -4.61636305e-01
-1.09458387e+00 -9.49891269e-01 6.29223168e-01 1.95758104e-01
-9.04430822e-02 -1.96270202e-03 -6.42054901e-02 -1.41008928e-01
-2.18024954e-01 -7.02046633e-01 -8.83551717e-01 -4.15188760e-01
-3.56321521e-02 1.05471694e+00 5.94843388e-01 -1.89043313... | [8.00770378112793, -3.144414186477661] |
a35ebfdf-3f7d-463e-8189-f088ab8a7617 | dfpenet-geology-a-deep-learning-framework-for | 1908.10907 | null | https://arxiv.org/abs/1908.10907v2 | https://arxiv.org/pdf/1908.10907v2.pdf | DFPENet-geology: A Deep Learning Framework for High Precision Recognition and Segmentation of Co-seismic Landslides | The following lists two main reasons for withdrawal for the public. 1. There are some problems in the method and results, and there is a lot of room for improvement. In terms of method, "Pre-trained Datasets (PD)" represents selecting a small amount from the online test set, which easily causes the model to overfit the... | ['Tianhai Jiang', 'Duoxiang Cheng', 'Chaojun Ouyang', 'Qingsong Xu', 'Xuanmei Fan'] | 2019-08-28 | null | null | null | null | ['scene-recognition'] | ['computer-vision'] | [-1.32912129e-01 9.82686877e-02 2.54239738e-01 -6.73182726e-01
-8.83877337e-01 -4.27603543e-01 -1.05980970e-01 2.17033383e-02
-3.82778466e-01 7.88511813e-01 8.87822583e-02 -6.58187687e-01
-3.87948066e-01 -1.00621665e+00 -8.08410645e-01 -7.77797401e-01
-1.85926765e-01 1.09636739e-01 4.32048589e-01 -4.11430597... | [7.114596843719482, 2.3044590950012207] |
f9b0fa5b-b4cf-42be-85da-00d6e1c057f1 | bartpho-pre-trained-sequence-to-sequence | 2109.09701 | null | https://arxiv.org/abs/2109.09701v3 | https://arxiv.org/pdf/2109.09701v3.pdf | BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese | We present BARTpho with two versions, BARTpho-syllable and BARTpho-word, which are the first public large-scale monolingual sequence-to-sequence models pre-trained for Vietnamese. BARTpho uses the "large" architecture and the pre-training scheme of the sequence-to-sequence denoising autoencoder BART, thus it is especia... | ['Dat Quoc Nguyen', 'Duong Minh Le', 'Nguyen Luong Tran'] | 2021-09-20 | null | null | null | null | ['punctuation-restoration'] | ['natural-language-processing'] | [-6.14140853e-02 2.26376146e-01 -1.00846432e-01 -2.51814336e-01
-1.26822615e+00 -5.68431973e-01 6.23155653e-01 -4.70293909e-02
-6.42240524e-01 1.11594415e+00 1.05332708e+00 -2.09183246e-01
5.68718016e-01 -5.58247924e-01 -8.82339001e-01 -6.37845576e-01
2.53283054e-01 8.22331071e-01 -1.23863980e-01 -5.12318313... | [12.149945259094238, 9.518951416015625] |
94cba812-a8ca-4168-9a44-cafcfdcb2920 | malibo-meta-learning-for-likelihood-free | 2307.03565 | null | https://arxiv.org/abs/2307.03565v1 | https://arxiv.org/pdf/2307.03565v1.pdf | MALIBO: Meta-learning for Likelihood-free Bayesian Optimization | Bayesian optimization (BO) is a popular method to optimize costly black-box functions. While traditional BO optimizes each new target task from scratch, meta-learning has emerged as a way to leverage knowledge from related tasks to optimize new tasks faster. However, existing meta-learning BO methods rely on surrogate ... | ['Joaquin Vanschoren', 'Felix Berkenkamp', 'Stefan Falkner', 'Jiarong Pan'] | 2023-07-07 | null | null | null | null | ['meta-learning', 'bayesian-optimization'] | ['methodology', 'methodology'] | [ 4.77774478e-02 -4.19505000e-01 -2.81817555e-01 -5.66914797e-01
-1.24743688e+00 -3.98602486e-01 5.04556000e-01 1.78641260e-01
-7.02893615e-01 8.39246154e-01 8.56622607e-02 1.41564474e-01
-3.58601809e-01 -2.92692631e-01 -8.23784590e-01 -5.33305943e-01
1.55986130e-01 7.03230023e-01 3.01289320e-01 -3.58387292... | [9.207237243652344, 3.6400439739227295] |
f210671f-6fed-42ac-ad54-519cc6b74534 | improved-twitter-sentiment-analysis-using | 1711.11081 | null | http://arxiv.org/abs/1711.11081v1 | http://arxiv.org/pdf/1711.11081v1.pdf | Improved Twitter Sentiment Analysis Using Naive Bayes and Custom Language Model | In the last couple decades, social network services like Twitter have
generated large volumes of data about users and their interests, providing
meaningful business intelligence so organizations can better understand and
engage their customers. All businesses want to know who is promoting their
products, who is complai... | ['Angela Lin'] | 2017-11-10 | null | null | null | null | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-2.12377742e-01 1.60036355e-01 -7.76659250e-01 -2.50882030e-01
-3.78589451e-01 -4.18118924e-01 5.47799706e-01 9.29031670e-01
-4.51384395e-01 1.31501824e-01 6.97856426e-01 -3.71059179e-01
1.80005319e-02 -1.11267543e+00 -1.38825029e-01 -3.81606966e-01
2.68229961e-01 2.62467742e-01 8.27561915e-02 -7.62712836... | [10.688695907592773, 6.854066848754883] |
4b828218-d545-4fe3-bc25-51a5ee4027aa | neural-entropic-estimation-a-faster-path-to | 1905.12957 | null | https://arxiv.org/abs/1905.12957v2 | https://arxiv.org/pdf/1905.12957v2.pdf | Neural Entropic Estimation: A faster path to mutual information estimation | We point out a limitation of the mutual information neural estimation (MINE) where the network fails to learn at the initial training phase, leading to slow convergence in the number of training iterations. To solve this problem, we propose a faster method called the mutual information neural entropic estimation (MI-NE... | ['Ali Al-Bashabsheh', 'Da Sun Handason Tam', 'Hing Pang Huang', 'Chung Chan', 'Chao Zhao', 'Michael Lim'] | 2019-05-30 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [-3.50308069e-03 3.60201508e-01 -7.08789751e-02 -2.69516140e-01
-5.72673619e-01 -3.26668292e-01 4.19632405e-01 -3.55750918e-02
-8.78705859e-01 1.18235278e+00 1.06935248e-01 -2.41281196e-01
-1.67391717e-01 -7.73040414e-01 -6.11829937e-01 -8.20226669e-01
-6.37813658e-02 3.83017987e-01 7.37562999e-02 2.84186900... | [7.797804832458496, 3.6735384464263916] |
541f5843-9dfa-474b-9699-fb1f227d61ab | intelligent-detect-for-substation-insulator | 2208.14598 | null | https://arxiv.org/abs/2208.14598v1 | https://arxiv.org/pdf/2208.14598v1.pdf | Intelligent detect for substation insulator defects based on CenterMask | With the development of intelligent operation and maintenance of substations, the daily inspection of substations needs to process massive video and image data. This puts forward higher requirements on the processing speed and accuracy of defect detection. Based on the end-to-end learning paradigm, this paper proposes ... | ['Lihua Wang', 'Huiting Yang', 'Peipei Yan', 'Mingxuan Li', 'Feng Li', 'Bo Ye'] | 2022-08-31 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 1.93507239e-01 -2.88368583e-01 4.52123582e-01 -2.37553075e-01
-3.44099522e-01 -8.58727768e-02 -1.82818770e-01 -3.44656259e-02
-8.10711011e-02 4.98870552e-01 -1.54567972e-01 -2.42422700e-01
-4.04632777e-01 -8.47656965e-01 -9.77926776e-02 -1.11123061e+00
-6.85602650e-02 -9.74805504e-02 1.75382808e-01 -4.37235236... | [7.468461513519287, 1.6704504489898682] |
afa37973-a521-480f-895f-4246a332b35f | when-bots-take-over-the-stock-market-evasion | 2010.09246 | null | https://arxiv.org/abs/2010.09246v2 | https://arxiv.org/pdf/2010.09246v2.pdf | Taking Over the Stock Market: Adversarial Perturbations Against Algorithmic Traders | In recent years, machine learning has become prevalent in numerous tasks, including algorithmic trading. Stock market traders utilize machine learning models to predict the market's behavior and execute an investment strategy accordingly. However, machine learning models have been shown to be susceptible to input manip... | ['Yuval Elovici', 'Asaf Shabtai', 'Yael Mathov', 'Elior Nehemya'] | 2020-10-19 | null | null | null | null | ['real-world-adversarial-attack', 'algorithmic-trading'] | ['adversarial', 'time-series'] | [ 2.99881995e-01 -5.78767434e-03 4.05125283e-02 1.00075997e-01
-4.88811731e-01 -1.53839648e+00 9.27126288e-01 9.89644378e-02
-3.66146088e-01 3.93723249e-01 -3.99515718e-01 -8.14881504e-01
1.64297491e-01 -1.10314655e+00 -9.68104005e-01 -4.77499634e-01
-5.16146362e-01 2.88679898e-01 9.19123292e-02 -1.84088498... | [5.712240695953369, 7.668399810791016] |
5279a5ba-d5ad-4416-ad87-eca5d397e132 | training-like-a-medical-resident-universal | 2306.02416 | null | https://arxiv.org/abs/2306.02416v2 | https://arxiv.org/pdf/2306.02416v2.pdf | Training Like a Medical Resident: Universal Medical Image Segmentation via Context Prior Learning | A major enduring focus of clinical workflows is disease analytics and diagnosis, leading to medical imaging datasets where the modalities and annotations are strongly tied to specific clinical objectives. To date, building task-specific segmentation models is intuitive yet a restrictive approach, lacking insights gaine... | ['Dimitris N. Metaxas', 'Shaoting Zhang', 'Mu Zhou', 'Di Liu', 'Zhuowei Li', 'Yunhe Gao'] | 2023-06-04 | null | null | null | null | ['incremental-learning'] | ['methodology'] | [ 3.94687444e-01 1.37203366e-01 -5.31948626e-01 -4.29367483e-01
-1.26125860e+00 -5.39774776e-01 3.17478150e-01 1.65481135e-01
-2.60758251e-01 5.45953929e-01 3.46024036e-01 -5.35488069e-01
-1.62439570e-01 -1.96689785e-01 -5.70530713e-01 -6.68553889e-01
-5.10599464e-02 4.82176542e-01 1.97211444e-01 6.43986091... | [14.798856735229492, -2.2038090229034424] |
48c0166b-03b3-4886-a875-4e209fcba607 | v3det-vast-vocabulary-visual-detection | 2304.03752 | null | https://arxiv.org/abs/2304.03752v1 | https://arxiv.org/pdf/2304.03752v1.pdf | V3Det: Vast Vocabulary Visual Detection Dataset | Recent advances in detecting arbitrary objects in the real world are trained and evaluated on object detection datasets with a relatively restricted vocabulary. To facilitate the development of more general visual object detection, we propose V3Det, a vast vocabulary visual detection dataset with precisely annotated bo... | ['Dahua Lin', 'Conghui He', 'Bin Wang', 'Tong Wu', 'Yujie Zhou', 'Yuhang Cao', 'Tao Chu', 'Pan Zhang', 'Jiaqi Wang'] | 2023-04-07 | null | null | null | null | ['open-vocabulary-object-detection'] | ['computer-vision'] | [-3.24141562e-01 -7.82103986e-02 -2.46858731e-01 -1.69881880e-01
-2.74569064e-01 -8.95966828e-01 7.55634725e-01 5.91332838e-02
-4.43663955e-01 2.34677359e-01 1.50179133e-01 -1.32204518e-01
2.80175239e-01 -4.86212581e-01 -6.99154317e-01 -4.62672889e-01
-2.25048568e-02 4.15695935e-01 8.27460110e-01 -3.91145319... | [9.697671890258789, 1.5315479040145874] |
3c29a36a-def2-4314-8e5d-cb2f834116e2 | cola-weakly-supervised-temporal-action | 2103.16392 | null | https://arxiv.org/abs/2103.16392v2 | https://arxiv.org/pdf/2103.16392v2.pdf | CoLA: Weakly-Supervised Temporal Action Localization with Snippet Contrastive Learning | Weakly-supervised temporal action localization (WS-TAL) aims to localize actions in untrimmed videos with only video-level labels. Most existing models follow the "localization by classification" procedure: locate temporal regions contributing most to the video-level classification. Generally, they process each snippet... | ['Yuexian Zou', 'Jie Chen', 'Dongming Yang', 'Meng Cao', 'Can Zhang'] | 2021-03-30 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Zhang_CoLA_Weakly-Supervised_Temporal_Action_Localization_With_Snippet_Contrastive_Learning_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Zhang_CoLA_Weakly-Supervised_Temporal_Action_Localization_With_Snippet_Contrastive_Learning_CVPR_2021_paper.pdf | cvpr-2021-1 | ['weakly-supervised-action-localization', 'weakly-supervised-temporal-action'] | ['computer-vision', 'computer-vision'] | [ 2.50750035e-01 -6.99633285e-02 -9.90575135e-01 -1.51287794e-01
-7.76805401e-01 -4.05989081e-01 3.76477510e-01 -7.45147094e-02
-1.72231793e-01 6.20204151e-01 2.37871185e-01 -2.03596354e-02
-4.35763299e-01 -1.37501478e-01 -5.89002252e-01 -8.00993383e-01
-5.35826504e-01 -1.68298021e-01 4.38660920e-01 2.24967748... | [8.466031074523926, 0.6451312303543091] |
a54dacd1-00ed-4589-b558-fa8911ab50ad | quantifying-and-learning-static-vs-dynamic | 2211.01783 | null | https://arxiv.org/abs/2211.01783v1 | https://arxiv.org/pdf/2211.01783v1.pdf | Quantifying and Learning Static vs. Dynamic Information in Deep Spatiotemporal Networks | There is limited understanding of the information captured by deep spatiotemporal models in their intermediate representations. For example, while evidence suggests that action recognition algorithms are heavily influenced by visual appearance in single frames, no quantitative methodology exists for evaluating such sta... | ['Konstantinos G. Derpanis', 'Richard P. Wildes', 'Neil D. B. Bruce', 'Md Amirul Islam', 'Mennatullah Siam', 'Matthew Kowal'] | 2022-11-03 | null | null | null | null | ['video-instance-segmentation', 'video-object-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.31810832e-01 -1.14056356e-01 -4.24464196e-01 -4.50265557e-01
-2.30788901e-01 -7.11232483e-01 9.43118989e-01 -2.69064277e-01
-4.77193147e-01 5.13997138e-01 4.59546924e-01 -2.83862744e-02
1.39509186e-01 -3.97847086e-01 -9.80861127e-01 -8.99924040e-01
5.93152456e-02 3.97931695e-01 5.88130832e-01 -8.14606547... | [8.804965019226074, 0.4334627091884613] |
98b6bc83-608f-4536-8f36-5ca3d94f3bfc | bnn-dp-robustness-certification-of-bayesian | 2306.10742 | null | https://arxiv.org/abs/2306.10742v1 | https://arxiv.org/pdf/2306.10742v1.pdf | BNN-DP: Robustness Certification of Bayesian Neural Networks via Dynamic Programming | In this paper, we introduce BNN-DP, an efficient algorithmic framework for analysis of adversarial robustness of Bayesian Neural Networks (BNNs). Given a compact set of input points $T\subset \mathbb{R}^n$, BNN-DP computes lower and upper bounds on the BNN's predictions for all the points in $T$. The framework is based... | ['Luca Laurenti', 'Morteza Lahijanian', 'Andrea Patane', 'Steven Adams'] | 2023-06-19 | null | null | null | null | ['adversarial-robustness'] | ['adversarial'] | [ 1.71370924e-01 3.62115562e-01 4.91971821e-02 -3.36708754e-01
-7.48682737e-01 -7.02602267e-01 2.97491550e-01 -2.47169822e-01
-4.93452132e-01 7.97518015e-01 -4.32413965e-01 -5.93032479e-01
-5.76308072e-01 -6.93734169e-01 -1.08102548e+00 -1.00315833e+00
-3.99331242e-01 3.90582651e-01 4.92524654e-01 -2.05107898... | [5.706859588623047, 7.688052177429199] |
6173496e-c9ee-40ef-965c-fb0f9a7eb4a5 | personalized-federated-learning-for-multi | 2211.09406 | null | https://arxiv.org/abs/2211.09406v1 | https://arxiv.org/pdf/2211.09406v1.pdf | Personalized Federated Learning for Multi-task Fault Diagnosis of Rotating Machinery | Intelligent fault diagnosis is essential to safe operation of machinery. However, due to scarce fault samples and data heterogeneity in field machinery, deep learning based diagnosis methods are prone to over-fitting with poor generalization ability. To solve the problem, this paper proposes a personalized federated le... | ['Cheng Hao Jin', 'Shubao Zhao', 'Hui Liu', 'Zengxiang Li', 'Sheng Guo'] | 2022-11-17 | null | null | null | null | ['personalized-federated-learning'] | ['methodology'] | [-1.90959856e-01 -1.57179430e-01 1.36565462e-01 -1.54696926e-01
-5.76200128e-01 -3.51898342e-01 -2.79259324e-01 -2.12515146e-01
4.57683712e-01 3.91935259e-01 -1.51328042e-01 -1.83408577e-02
-7.43121922e-01 -7.91472673e-01 -6.58426940e-01 -1.11680794e+00
8.66033584e-02 3.70359302e-01 -4.76320058e-01 2.36883044... | [7.028772354125977, 2.400217056274414] |
cf72c7bf-fb1e-4de0-b0e5-cb3c12411b3d | ju_cse-a-crf-based-approach-to-annotation-of | null | null | https://aclanthology.org/S13-2011 | https://aclanthology.org/S13-2011.pdf | JU\_CSE: A CRF Based Approach to Annotation of Temporal Expression, Event and Temporal Relations | null | ['B', 'Asif Ekbal', 'Rajdeep Gupta', 'Anup Kumar Kolya', 'Sivaji yopadhyay', 'Amitava Kundu'] | 2013-06-01 | null | null | null | semeval-2013-6 | ['temporal-information-extraction'] | ['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... | [-7.2375712394714355, 3.7010931968688965] |
2111c1d3-6202-4839-91e6-39e0e8023c0a | unicon-combating-label-noise-through-uniform | 2203.14542 | null | https://arxiv.org/abs/2203.14542v4 | https://arxiv.org/pdf/2203.14542v4.pdf | UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning | Supervised deep learning methods require a large repository of annotated data; hence, label noise is inevitable. Training with such noisy data negatively impacts the generalization performance of deep neural networks. To combat label noise, recent state-of-the-art methods employ some sort of sample selection mechanism ... | ['Mubarak Shah', 'Ajmal Mian', 'Nazanin Rahnavard', 'Mamshad Nayeem Rizve', 'Nazmul Karim'] | 2022-03-28 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Karim_UniCon_Combating_Label_Noise_Through_Uniform_Selection_and_Contrastive_Learning_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Karim_UniCon_Combating_Label_Noise_Through_Uniform_Selection_and_Contrastive_Learning_CVPR_2022_paper.pdf | cvpr-2022-1 | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 2.36779734e-01 -1.11243874e-01 -2.50239402e-01 -7.67523229e-01
-1.03132522e+00 -4.07979190e-01 2.95594513e-01 3.75398189e-01
-9.07047927e-01 9.87231791e-01 -2.84409732e-01 -6.04461506e-03
-1.12501411e-02 -8.05997074e-01 -5.88479757e-01 -1.11567032e+00
2.97015727e-01 4.90647376e-01 1.96265221e-01 1.68281779... | [9.340409278869629, 3.857887029647827] |
2d9a2062-3d35-4a17-b9ac-a741e45128e6 | global-wheat-head-dataset-2021-an-update-to | 2105.07660 | null | https://arxiv.org/abs/2105.07660v2 | https://arxiv.org/pdf/2105.07660v2.pdf | Global Wheat Head Dataset 2021: more diversity to improve the benchmarking of wheat head localization methods | The Global Wheat Head Detection (GWHD) dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4,700 RGB images acquired from various acquisition platforms and 7 countries/institutions. With an associated competition hosted in Kaggle, GWHD has successfully attracted attention from both the compu... | ['Wei Guo', 'Ian Stavness', 'Frédéric Baret', 'Benoit de Solan', 'Scott Chapman', 'Jesse Poland', 'Morten Lilimo', 'David Shaner LeBauer', 'Curtis Pozniak', 'Minhajul A. Badhon', 'Masanori Ishii', 'Haozhou Wang', 'Ken Kuroki', 'Benoit Mercatoris', 'Alexis Carlier', 'Sébastien Dandrifosse', 'Goro Ishikawa', 'Koichi Naga... | 2021-05-17 | null | null | null | null | ['head-detection'] | ['computer-vision'] | [-7.63516268e-03 6.82018921e-02 2.46663485e-02 -3.92170161e-01
-6.30839646e-01 -1.04621053e+00 3.46028030e-01 3.44135404e-01
-1.50035322e-01 7.30451345e-01 7.23331198e-02 -2.62483150e-01
6.54770136e-02 -9.81934786e-01 -4.91607666e-01 -7.55842626e-01
-1.62424281e-01 2.08722159e-01 1.32925838e-01 -3.06510597... | [9.229683876037598, -1.5168362855911255] |
2d173faa-98c6-4d08-a10c-e1daef2efd98 | video-object-tracking-based-on-yolov7-and | 2207.12202 | null | https://arxiv.org/abs/2207.12202v1 | https://arxiv.org/pdf/2207.12202v1.pdf | Video object tracking based on YOLOv7 and DeepSORT | Multiple object tracking (MOT) is an important technology in the field of computer vision, which is widely used in automatic driving, intelligent monitoring, behavior recognition and other directions. Among the current popular MOT methods based on deep learning, Detection Based Tracking (DBT) is the most widely used in... | ['Bo Liu', 'Xingle Zhang', 'Feng Yang'] | 2022-07-25 | null | null | null | null | ['video-object-tracking'] | ['computer-vision'] | [-4.07508969e-01 -6.51102245e-01 -2.31010333e-01 4.17108804e-01
9.73664597e-02 1.74485609e-01 3.64031225e-01 1.11509059e-02
-6.83660388e-01 4.91449356e-01 -4.55544740e-01 -1.35990858e-01
-1.12441465e-01 -8.15891862e-01 -6.43195570e-01 -8.55909288e-01
1.36955917e-01 3.43917936e-01 1.23696661e+00 -4.66285080... | [6.46063756942749, -2.103574514389038] |
9f782cd5-dfc6-4f35-a520-0e48649c51ff | learning-spatial-knowledge-for-text-to-3d | null | null | https://aclanthology.org/D14-1217 | https://aclanthology.org/D14-1217.pdf | Learning Spatial Knowledge for Text to 3D Scene Generation | null | ['Angel Chang', 'Manolis Savva', 'Christopher D. Manning'] | 2014-10-01 | null | null | null | emnlp-2014-10 | ['scene-generation', 'text-to-3d'] | ['computer-vision', 'computer-vision'] | [-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... | [-7.271348476409912, 3.734673261642456] |
407e8331-ef3e-4787-ad80-5b50bbabe8cd | learning-spatial-semantic-context-with-fully | 1610.02616 | null | http://arxiv.org/abs/1610.02616v2 | http://arxiv.org/pdf/1610.02616v2.pdf | Learning Spatial-Semantic Context with Fully Convolutional Recurrent Network for Online Handwritten Chinese Text Recognition | Online handwritten Chinese text recognition (OHCTR) is a challenging problem
as it involves a large-scale character set, ambiguous segmentation, and
variable-length input sequences. In this paper, we exploit the outstanding
capability of path signature to translate online pen-tip trajectories into
informative signature... | ['Zenghui Sun', 'Lianwen Jin', 'Hao Ni', 'Terry Lyons', 'Zecheng Xie'] | 2016-10-09 | null | null | null | null | ['handwritten-chinese-text-recognition', 'handwritten-chinese-text-recognition'] | ['computer-vision', 'natural-language-processing'] | [ 6.04497075e-01 -7.14704931e-01 -1.51114687e-01 -4.09155846e-01
-6.72808766e-01 -9.04010653e-01 3.48859489e-01 -3.35718453e-01
-3.89001161e-01 5.08351207e-01 -4.41617556e-02 -4.11930472e-01
-1.40734985e-01 -5.87351322e-01 -5.69127381e-01 -7.63982058e-01
3.26601475e-01 2.88208961e-01 2.75434107e-01 -1.32870883... | [11.962557792663574, 2.331563711166382] |
4e79ad53-3094-4916-9475-35668f1e8d93 | transvos-video-object-segmentation-with | 2106.00588 | null | https://arxiv.org/abs/2106.00588v2 | https://arxiv.org/pdf/2106.00588v2.pdf | TransVOS: Video Object Segmentation with Transformers | Recently, Space-Time Memory Network (STM) based methods have achieved state-of-the-art performance in semi-supervised video object segmentation (VOS). A crucial problem in this task is how to model the dependency both among different frames and inside every frame. However, most of these methods neglect the spatial rela... | ['Yong liu', 'Yi Yuan', 'Yeneng Lin', 'Mengmeng Wang', 'Jianbiao Mei'] | 2021-06-01 | null | null | null | null | ['one-shot-visual-object-segmentation'] | ['computer-vision'] | [ 1.32890597e-01 -4.89942044e-01 -3.13537270e-01 -3.80424470e-01
-3.54311138e-01 -3.61412972e-01 4.80920643e-01 -1.09102100e-01
-6.62810504e-01 3.10257733e-01 -2.13307157e-01 -1.44152015e-01
2.35814437e-01 -6.75419271e-01 -7.44096816e-01 -5.02265811e-01
3.08160514e-01 1.31624127e-02 1.10742664e+00 8.83064643... | [9.228320121765137, -0.04471762478351593] |
f50955a6-eb9c-4fe6-b213-68fd1f106c8d | proposal-based-multiple-instance-learning-for-1 | 2305.17861 | null | https://arxiv.org/abs/2305.17861v1 | https://arxiv.org/pdf/2305.17861v1.pdf | Proposal-Based Multiple Instance Learning for Weakly-Supervised Temporal Action Localization | Weakly-supervised temporal action localization aims to localize and recognize actions in untrimmed videos with only video-level category labels during training. Without instance-level annotations, most existing methods follow the Segment-based Multiple Instance Learning (S-MIL) framework, where the predictions of segme... | ['Yongdong Zhang', 'Tianzhu Zhang', 'Wenfei Yang', 'Huan Ren'] | 2023-05-29 | proposal-based-multiple-instance-learning-for | http://openaccess.thecvf.com//content/CVPR2023/html/Ren_Proposal-Based_Multiple_Instance_Learning_for_Weakly-Supervised_Temporal_Action_Localization_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ren_Proposal-Based_Multiple_Instance_Learning_for_Weakly-Supervised_Temporal_Action_Localization_CVPR_2023_paper.pdf | cvpr-2023-1 | ['weakly-supervised-action-localization', 'weakly-supervised-temporal-action', 'action-localization', 'action-recognition', 'multiple-instance-learning'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'methodology'] | [ 3.98021579e-01 -1.33379713e-01 -6.46460116e-01 -3.52434188e-01
-9.18660045e-01 -2.35207438e-01 4.08569366e-01 -2.29417786e-01
-3.92592937e-01 4.89346206e-01 2.96554416e-01 2.10874811e-01
3.90266329e-02 -3.82256478e-01 -7.66117811e-01 -7.97063291e-01
-5.13683539e-03 1.13935567e-01 8.92202079e-01 1.80859491... | [8.442315101623535, 0.6059369444847107] |
23098084-261a-4872-8ef3-76efbb62532b | 2305-14872 | 2305.14872 | null | https://arxiv.org/abs/2305.14872v2 | https://arxiv.org/pdf/2305.14872v2.pdf | Timeseries-aware Uncertainty Wrappers for Uncertainty Quantification of Information-Fusion-Enhanced AI Models based on Machine Learning | As the use of Artificial Intelligence (AI) components in cyber-physical systems is becoming more common, the need for reliable system architectures arises. While data-driven models excel at perception tasks, model outcomes are usually not dependable enough for safety-critical applications. In this work,we present a tim... | ['Pascal Gerber', 'Lisa Jöckel', 'Michael Kläs', 'Janek Groß'] | 2023-05-24 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 1.53411493e-01 8.72246772e-02 1.33808389e-01 -7.41880655e-01
-7.67118037e-01 -3.57617110e-01 8.12399983e-01 4.45021749e-01
-2.33185261e-01 8.28002751e-01 -1.80576846e-01 -4.03304785e-01
-7.95802295e-01 -7.67020106e-01 -4.91865188e-01 -4.52809215e-01
-3.40109110e-01 5.31152606e-01 3.95779014e-01 -9.86005738... | [5.818093776702881, 2.0488767623901367] |
c5ae2f6a-a5ea-4943-9d1f-b555f3e56cd9 | label-correction-model-for-aspect-based | null | null | https://aclanthology.org/2020.coling-main.71 | https://aclanthology.org/2020.coling-main.71.pdf | Label Correction Model for Aspect-based Sentiment Analysis | Aspect-based sentiment analysis includes opinion aspect extraction and aspect sentiment classification. Researchers have attempted to discover the relationship between these two sub-tasks and have proposed the joint model for solving aspect-based sentiment analysis. However, they ignore a phenomenon: aspect boundary la... | ['Jiangtao Ren', 'Qianlong Wang'] | 2020-12-01 | null | null | null | coling-2020-8 | ['aspect-extraction'] | ['natural-language-processing'] | [ 3.60703796e-01 2.71562412e-02 -3.58742207e-01 -7.91782737e-01
-6.85527146e-01 -6.37388408e-01 3.66940886e-01 9.11855325e-02
-2.40199640e-01 4.40972000e-01 4.74264234e-01 -2.43113995e-01
6.06679380e-01 -8.64945829e-01 -4.32057470e-01 -6.69755638e-01
6.52413964e-01 3.96714240e-01 -5.11293150e-02 -1.81574121... | [11.44726276397705, 6.645628929138184] |
16287424-550d-44f7-8391-98744c87b821 | learn-interpretable-word-embeddings | null | null | https://openreview.net/forum?id=Bke02gHYwB | https://openreview.net/pdf?id=Bke02gHYwB | Learn Interpretable Word Embeddings Efficiently with von Mises-Fisher Distribution | Word embedding plays a key role in various tasks of natural language processing. However, the dominant word embedding models don't explain what information is carried with the resulting embeddings. To generate interpretable word embeddings we intend to replace the word vector with a probability density distribution. Th... | ['Shafei Wang', 'Jian Yang', 'Houqiang Li', 'Liansheng Zhuang', 'Minghong Yao'] | 2019-09-25 | null | null | null | null | ['word-similarity'] | ['natural-language-processing'] | [-1.53844267e-01 1.49147660e-01 -2.29922831e-01 -2.01371908e-01
3.19304015e-03 -5.48977077e-01 8.63282144e-01 3.79392833e-01
-7.54097641e-01 3.59759390e-01 7.00346351e-01 -5.85551918e-01
-3.07312589e-02 -8.98406148e-01 -1.36394650e-01 -5.75439870e-01
3.07833627e-02 5.37184715e-01 6.57742098e-03 -5.30375540... | [10.473849296569824, 8.770050048828125] |
9b4b077f-6bdf-4987-98dc-d1886d260ce1 | a-perspective-on-objects-and-systematic | 1906.01035 | null | https://arxiv.org/abs/1906.01035v1 | https://arxiv.org/pdf/1906.01035v1.pdf | A Perspective on Objects and Systematic Generalization in Model-Based RL | In order to meet the diverse challenges in solving many real-world problems, an intelligent agent has to be able to dynamically construct a model of its environment. Objects facilitate the modular reuse of prior knowledge and the combinatorial construction of such models. In this work, we argue that dynamically bound f... | ['Jürgen Schmidhuber', 'Sjoerd van Steenkiste', 'Klaus Greff'] | 2019-06-03 | null | null | null | null | ['systematic-generalization'] | ['reasoning'] | [ 2.65750587e-01 4.12437469e-01 -1.33977324e-01 -4.16932672e-01
1.43561319e-01 -6.52032375e-01 1.08241737e+00 2.34186620e-01
-5.85342646e-01 7.81681657e-01 9.67324376e-02 -6.81091845e-02
-7.64812529e-01 -8.61942351e-01 -5.66113949e-01 -4.66262400e-01
-2.52737850e-01 7.87494302e-01 3.97033691e-01 -5.38991392... | [9.281251907348633, 6.710438251495361] |
f4effb82-9923-4b0c-9ed4-58a647c1b4b4 | collaborative-ranking-with-17-parameters | null | null | http://papers.nips.cc/paper/4829-collaborative-ranking-with-17-parameters | http://papers.nips.cc/paper/4829-collaborative-ranking-with-17-parameters.pdf | Collaborative Ranking With 17 Parameters | The primary application of collaborate filtering (CF) is to recommend a small set of items to a user, which entails ranking. Most approaches, however, formulate the CF problem as rating prediction, overlooking the ranking perspective. In this work we present a method for collaborative ranking that leverages the strengt... | ['Richard S. Zemel', 'Maksims Volkovs'] | 2012-12-01 | null | null | null | neurips-2012-12 | ['collaborative-ranking'] | ['graphs'] | [-9.95578840e-02 -2.96297491e-01 -5.66091716e-01 -6.41884089e-01
-9.05346870e-01 -7.98873603e-01 5.42992592e-01 1.82782620e-01
-1.53100222e-01 7.23268986e-01 5.49094081e-01 -3.09733331e-01
-7.51602411e-01 -8.60640883e-01 -2.50720918e-01 -3.14812422e-01
-5.51198684e-02 7.72424519e-01 4.92808104e-01 -4.83088583... | [10.006855010986328, 5.694890022277832] |
ea9ff778-4c55-44bd-a00e-aea561818afb | natural-language-processing-for-ehr-based | 1806.04820 | null | http://arxiv.org/abs/1806.04820v2 | http://arxiv.org/pdf/1806.04820v2.pdf | Natural Language Processing for EHR-Based Computational Phenotyping | This article reviews recent advances in applying natural language processing
(NLP) to Electronic Health Records (EHRs) for computational phenotyping.
NLP-based computational phenotyping has numerous applications including
diagnosis categorization, novel phenotype discovery, clinical trial screening,
pharmacogenomics, d... | ['Tristan Naumann', 'Yuan Luo', 'Zexian Zeng', 'Yu Deng', 'Xiaoyu Li'] | 2018-06-13 | null | null | null | null | ['computational-phenotyping'] | ['medical'] | [ 2.65866667e-01 -2.78313130e-01 -5.98808229e-01 -2.96584457e-01
-8.91401649e-01 -4.78424549e-01 2.52791137e-01 1.12018991e+00
-2.08922401e-01 9.97702479e-01 1.73689380e-01 -3.24067533e-01
-5.93055487e-01 -7.23570228e-01 -2.31966659e-01 -7.59073257e-01
-2.97215164e-01 9.16801929e-01 -5.09943128e-01 4.70748186... | [6.236780643463135, 5.832752227783203] |
fbbd9eda-2de0-4084-a4bc-861601cdc66f | flexible-modal-deception-detection-with-audio | 2302.05727 | null | https://arxiv.org/abs/2302.05727v1 | https://arxiv.org/pdf/2302.05727v1.pdf | Flexible-modal Deception Detection with Audio-Visual Adapter | Detecting deception by human behaviors is vital in many fields such as custom security and multimedia anti-fraud. Recently, audio-visual deception detection attracts more attention due to its better performance than using only a single modality. However, in real-world multi-modal settings, the integrity of data can be ... | ['Alex Kot', 'Adams Wai-Kin Kong', 'Bingquan Shen', 'Xiaobao Guo', 'Nithish Muthuchamy Selvaraj', 'Zitong Yu', 'Zhaoxu Li'] | 2023-02-11 | null | null | null | null | ['deception-detection'] | ['miscellaneous'] | [ 2.18587145e-01 -7.13537693e-01 -1.42376825e-01 -2.52301186e-01
-1.06031477e+00 -4.93815631e-01 4.84439880e-01 -1.07304111e-01
-1.28098503e-01 5.91833651e-01 2.88419396e-01 9.32629278e-04
-2.45620571e-02 -3.69586736e-01 -4.29395646e-01 -8.49815547e-01
4.79348242e-01 -2.26982743e-01 3.45010459e-01 1.01433702... | [13.18422794342041, 1.5161834955215454] |
503f24a9-d42d-45af-907b-9eeb8bb08f6b | cobit-a-contrastive-bi-directional-image-text | 2303.13455 | null | https://arxiv.org/abs/2303.13455v1 | https://arxiv.org/pdf/2303.13455v1.pdf | CoBIT: A Contrastive Bi-directional Image-Text Generation Model | The field of vision and language has witnessed a proliferation of pre-trained foundation models. Most existing methods are independently pre-trained with contrastive objective like CLIP, image-to-text generative objective like PaLI, or text-to-image generative objective like Parti. However, the three objectives can be ... | ['Jiahui Yu', 'Jason Baldridge', 'Kai-Wei Chang', 'Zhecan Wang', 'Mandy Guo', 'Haoxuan You'] | 2023-03-23 | null | null | null | null | ['zero-shot-text-to-image-generation'] | ['natural-language-processing'] | [ 6.50148690e-01 3.41732979e-01 6.39352500e-02 -3.79792154e-01
-9.63384628e-01 -3.74557137e-01 1.14826107e+00 -3.91345620e-01
-2.68085122e-01 7.04147577e-01 3.59257817e-01 -8.31952468e-02
1.30438209e-01 -7.68041015e-01 -9.68740463e-01 -6.18745863e-01
5.87283373e-01 7.64896631e-01 1.73521936e-02 -3.47182691... | [11.037555694580078, 1.0820796489715576] |
5638c3da-322a-4f0f-8ffa-e9893451dc89 | continuous-copy-paste-for-one-stage-multi | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Xu_Continuous_Copy-Paste_for_One-Stage_Multi-Object_Tracking_and_Segmentation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Xu_Continuous_Copy-Paste_for_One-Stage_Multi-Object_Tracking_and_Segmentation_ICCV_2021_paper.pdf | Continuous Copy-Paste for One-Stage Multi-Object Tracking and Segmentation | Current one-step multi-object tracking and segmentation (MOTS) methods lag behind recent two-step methods. By separating the instance segmentation stage from the tracking stage, two-step methods can exploit non-video datasets as extra data for training instance segmentation. Moreover, instances belonging to differe... | ['Liusheng Huang', 'Zhi Chen', 'Wei Yang', 'Zhenbo Shi', 'Ajin Meng', 'Zhenbo Xu'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['multi-object-tracking-and-segmentation'] | ['computer-vision'] | [ 3.49510223e-01 -4.26810933e-03 -2.70565063e-01 -2.21829981e-01
-8.67669165e-01 -5.28106451e-01 5.52270651e-01 -1.70060679e-01
-6.25908911e-01 7.56151438e-01 -2.50512958e-01 -8.95624980e-02
3.07114482e-01 -4.55112606e-01 -9.96811152e-01 -5.69112360e-01
7.87706003e-02 5.90624094e-01 1.04833066e+00 2.79181208... | [6.546874046325684, -1.9836654663085938] |
bcf0a889-d8f1-422d-9e8c-2795103bde11 | digital-twin-based-3d-map-management-for-edge | 2305.16571 | null | https://arxiv.org/abs/2305.16571v1 | https://arxiv.org/pdf/2305.16571v1.pdf | Digital Twin-Based 3D Map Management for Edge-Assisted Mobile Augmented Reality | In this paper, we design a 3D map management scheme for edge-assisted mobile augmented reality (MAR) to support the pose estimation of individual MAR device, which uploads camera frames to an edge server. Our objective is to minimize the pose estimation uncertainty of the MAR device by periodically selecting a proper s... | ['Weihua Zhuang', 'Xuemin Shen', 'Nan Cheng', 'Mushu Li', 'Jie Gao', 'Conghao Zhou'] | 2023-05-26 | null | null | null | null | ['pose-estimation', 'model-based-reinforcement-learning'] | ['computer-vision', 'reasoning'] | [-2.76301175e-01 -5.17028496e-02 -2.05583468e-01 -1.96464770e-02
-8.83323193e-01 -4.02669191e-01 -3.12393606e-02 -3.10472101e-01
-2.18113020e-01 6.43014014e-01 1.22389689e-01 -3.73246878e-01
-4.90104184e-02 -7.84083366e-01 -1.00684893e+00 -5.83722889e-01
-2.66476721e-01 5.18986046e-01 4.63974476e-01 1.13922618... | [7.201964855194092, -1.6411545276641846] |
b5770049-4f9f-4e9f-a70d-69c050097ab2 | multi-level-wavelet-cnn-for-image-restoration | 1805.07071 | null | http://arxiv.org/abs/1805.07071v2 | http://arxiv.org/pdf/1805.07071v2.pdf | Multi-level Wavelet-CNN for Image Restoration | The tradeoff between receptive field size and efficiency is a crucial issue
in low level vision. Plain convolutional networks (CNNs) generally enlarge the
receptive field at the expense of computational cost. Recently, dilated
filtering has been adopted to address this issue. But it suffers from gridding
effect, and th... | ['WangMeng Zuo', 'Pengju Liu', 'Liang Lin', 'Kai Zhang', 'Hongzhi Zhang'] | 2018-05-18 | null | null | null | null | ['jpeg-artifact-correction'] | ['computer-vision'] | [ 4.17535841e-01 -2.02358723e-01 2.40877017e-01 -1.29451752e-01
-6.20968230e-02 -1.12059796e-02 1.27661929e-01 -3.83398294e-01
-5.61469793e-01 5.23097992e-01 3.47866476e-01 1.24310814e-01
-5.27711697e-02 -1.15307128e+00 -6.04997098e-01 -1.00111938e+00
2.60053873e-01 -7.56878912e-01 6.57275558e-01 -3.96024168... | [11.232606887817383, -2.1629555225372314] |
eaafaa96-4d5a-475b-b6ae-b673bdd66665 | lorentz-equivariant-model-for-knowledge | 2302.04545 | null | https://arxiv.org/abs/2302.04545v2 | https://arxiv.org/pdf/2302.04545v2.pdf | Lorentz Equivariant Model for Knowledge-Enhanced Hyperbolic Collaborative Filtering | Introducing prior auxiliary information from the knowledge graph (KG) to assist the user-item graph can improve the comprehensive performance of the recommender system. Many recent studies show that the ensemble properties of hyperbolic spaces fit the scale-free and hierarchical characteristics exhibited in the above t... | ['Jin Huang', 'Jing Xiao', 'Ruzhong Xie', 'Weihao Yu', 'Bosong Huang'] | 2023-02-09 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [-5.70929706e-01 6.77820593e-02 -4.82707731e-02 -4.14127797e-01
-1.02680244e-01 -7.02078938e-01 3.99670601e-01 -1.05163135e-01
-4.58696000e-02 2.26623744e-01 5.71366668e-01 1.43620549e-02
-9.26000655e-01 -9.06631708e-01 -5.50740957e-01 -1.04739368e+00
-7.87357092e-02 2.80034751e-01 1.60269737e-01 -5.28335392... | [10.239176750183105, 5.655519962310791] |
508da9a9-e9c9-4f4b-a871-ff2c5f5cfe78 | named-entities-troubling-your-neural-methods | 1804.09540 | null | https://arxiv.org/abs/1804.09540v2 | https://arxiv.org/pdf/1804.09540v2.pdf | NE-Table: A Neural key-value table for Named Entities | Many Natural Language Processing (NLP) tasks depend on using Named Entities (NEs) that are contained in texts and in external knowledge sources. While this is easy for humans, the present neural methods that rely on learned word embeddings may not perform well for these NLP tasks, especially in the presence of Out-Of-V... | ['Lazaros Polymenakos', 'Satinder Singh', 'Xiaoxiao Guo', 'Mo Yu', 'Jatin Ganhotra', 'Janarthanan Rajendran'] | 2018-04-22 | ne-table-a-neural-key-value-table-for-named | https://aclanthology.org/R19-1114 | https://aclanthology.org/R19-1114.pdf | ranlp-2019-9 | ['goal-oriented-dialog'] | ['natural-language-processing'] | [-2.60225922e-01 2.42748469e-01 7.20996633e-02 -4.58743185e-01
-5.22174895e-01 -6.90110147e-01 7.67657101e-01 6.69473648e-01
-9.81352687e-01 9.88208294e-01 5.13840199e-01 -4.34444994e-01
-6.29695430e-02 -7.55143583e-01 -2.55818188e-01 -1.24131411e-01
9.85489339e-02 9.58593071e-01 5.45373023e-01 -7.15899885... | [12.571678161621094, 7.991552352905273] |
d0b67964-5d49-4c27-8142-2d9152980ed7 | two-stage-single-image-reflection-removal | 2012.00945 | null | https://arxiv.org/abs/2012.00945v2 | https://arxiv.org/pdf/2012.00945v2.pdf | Two-Stage Single Image Reflection Removal with Reflection-Aware Guidance | Removing undesired reflection from an image captured through a glass surface is a very challenging problem with many practical application scenarios. For improving reflection removal, cascaded deep models have been usually adopted to estimate the transmission in a progressive manner. However, most existing methods are ... | ['WangMeng Zuo', 'Dongwei Ren', 'Qince Li', 'Yaling Yi', 'Ming Liu', 'Yu Li'] | 2020-12-02 | null | null | null | null | ['reflection-removal'] | ['computer-vision'] | [ 6.91595554e-01 1.34994864e-01 3.82969379e-01 -2.58068144e-01
-7.15162992e-01 3.34731132e-01 4.34584141e-01 -3.32056880e-01
-6.82311878e-02 4.06263351e-01 4.55297828e-01 -7.09319264e-02
2.66561992e-02 -7.98388302e-01 -7.39612818e-01 -1.00390661e+00
3.00191075e-01 -3.12776774e-01 1.51965335e-01 -2.69014210... | [10.683148384094238, -2.8670310974121094] |
d5bd91c6-6a24-49c4-a60f-e0d61c627db2 | blur-invariants-for-image-recognition | 2301.07581 | null | https://arxiv.org/abs/2301.07581v1 | https://arxiv.org/pdf/2301.07581v1.pdf | Blur Invariants for Image Recognition | Blur is an image degradation that is difficult to remove. Invariants with respect to blur offer an alternative way of a~description and recognition of blurred images without any deblurring. In this paper, we present an original unified theory of blur invariants. Unlike all previous attempts, the new theory does not req... | ['Jitka Kostkova', 'Filip Sroubek', 'Matteo Pedone', 'Matej Lebl', 'Jan Flusser'] | 2023-01-18 | null | null | null | null | ['deblurring'] | ['computer-vision'] | [ 1.57848131e-02 -5.56881666e-01 2.24975735e-01 -2.52325654e-01
1.85566620e-04 -4.93648082e-01 5.55712163e-01 -6.01817548e-01
-2.11606219e-01 9.35638309e-01 3.22015524e-01 -8.36613253e-02
-5.11298418e-01 -7.00745434e-02 -7.48864859e-02 -7.22823799e-01
-1.77342385e-01 -1.50993809e-01 3.10712427e-01 -8.72560143... | [11.632221221923828, -2.7661683559417725] |
a913ac37-e1d4-45d6-8774-9173074a8357 | effective-features-of-remote-sensing-image | 1401.7743 | null | http://arxiv.org/abs/1401.7743v1 | http://arxiv.org/pdf/1401.7743v1.pdf | Effective Features of Remote Sensing Image Classification Using Interactive Adaptive Thresholding Method | Remote sensing image classification can be performed in many different ways
to extract meaningful features. One common approach is to perform edge
detection. A second approach is to try and detect whole shapes, given the fact
that these shapes usually tend to have distinctive properties such as object
foreground or bac... | ['T. Balaji', 'Dr. M. Sumathi'] | 2014-01-30 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 7.44256973e-01 -6.13076210e-01 1.71733052e-01 -4.36954707e-01
-2.03730866e-01 -5.11727929e-01 1.78934664e-01 1.35688201e-01
-4.93848562e-01 4.98676360e-01 -3.96099240e-01 -4.43198472e-01
-4.38083768e-01 -1.31007802e+00 -1.02027744e-01 -1.10631895e+00
-5.09430543e-02 1.53797075e-01 3.52811962e-01 -1.26912102... | [9.668492317199707, -1.7505896091461182] |
8483c22a-0795-4f26-9984-b7f8e4fef366 | learning-to-predict-3d-objects-with-an | 1908.01210 | null | https://arxiv.org/abs/1908.01210v2 | https://arxiv.org/pdf/1908.01210v2.pdf | Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer | Many machine learning models operate on images, but ignore the fact that images are 2D projections formed by 3D geometry interacting with light, in a process called rendering. Enabling ML models to understand image formation might be key for generalization. However, due to an essential rasterization step involving disc... | ['Sanja Fidler', 'Jaakko Lehtinen', 'Alec Jacobson', 'Jun Gao', 'Edward J. Smith', 'Wenzheng Chen', 'Huan Ling'] | 2019-08-03 | learning-to-predict-3d-objects-with-an-1 | http://papers.nips.cc/paper/9156-learning-to-predict-3d-objects-with-an-interpolation-based-differentiable-renderer | http://papers.nips.cc/paper/9156-learning-to-predict-3d-objects-with-an-interpolation-based-differentiable-renderer.pdf | neurips-2019-12 | ['single-view-3d-reconstruction'] | ['computer-vision'] | [ 4.58674192e-01 2.28859987e-02 1.33880526e-01 -3.83897007e-01
-4.46485788e-01 -6.73836589e-01 9.60565329e-01 -8.64829421e-02
-2.78271791e-02 4.38285053e-01 -2.90909290e-01 -5.73266566e-01
5.20675898e-01 -9.52453613e-01 -1.07272005e+00 -6.71542466e-01
1.39821935e-02 5.24331033e-01 2.79732734e-01 6.30270764... | [9.28954029083252, -3.146739959716797] |
d6eb185d-b761-4d86-8a00-b6381bf94ecc | 190409409 | 1904.09409 | null | http://arxiv.org/abs/1904.09409v1 | http://arxiv.org/pdf/1904.09409v1.pdf | Funnel Transform for Straight Line Detection | Most of the classical approaches to straight line detection only deal with a
binary edge image and need to use 2D interpolation operation. This paper
proposes a new transform method figuratively named as funnel transform which
can efficiently and rapidly detect straight lines. The funnel transform
consists of three 1D ... | ['Da-Zheng Feng', 'Weixing Zheng', 'QianRu Wei'] | 2019-04-20 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [-5.06316386e-02 -6.55991912e-01 -4.31784302e-01 3.78070399e-02
-2.72183776e-01 -4.56265271e-01 1.08419329e-01 -2.01274175e-02
-2.95084089e-01 4.57030982e-01 -2.25266948e-01 -5.42202890e-01
-6.28923848e-02 -8.71010244e-01 -3.80381495e-01 -6.53347909e-01
-1.52531281e-01 -1.32297873e-01 6.89511716e-01 -4.67765443... | [8.511765480041504, -1.5828051567077637] |
a5973e15-8400-412c-8965-36cba2bbf355 | lstm-pose-machines | 1712.06316 | null | http://arxiv.org/abs/1712.06316v4 | http://arxiv.org/pdf/1712.06316v4.pdf | LSTM Pose Machines | We observed that recent state-of-the-art results on single image human pose
estimation were achieved by multi-stage Convolution Neural Networks (CNN).
Notwithstanding the superior performance on static images, the application of
these models on videos is not only computationally intensive, it also suffers
from performa... | ['Zhouxia Wang', 'Liang Lin', 'Jimmy Ren', 'Jianbo Liu', 'Jiahao Pang', 'Yue Luo', 'Wenxiu Sun', 'Jinshan Pan'] | 2017-12-18 | lstm-pose-machines-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Luo_LSTM_Pose_Machines_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Luo_LSTM_Pose_Machines_CVPR_2018_paper.pdf | cvpr-2018-6 | ['2d-human-pose-estimation'] | ['computer-vision'] | [ 1.78394347e-01 -6.06994294e-02 2.98761087e-03 -2.86146533e-02
-4.09320503e-01 -2.19578326e-01 3.32252532e-01 -6.23533487e-01
-6.59306765e-01 5.43620110e-01 2.15785712e-01 -7.80163929e-02
9.30703804e-02 -4.32329357e-01 -1.29256964e+00 -6.53150022e-01
-2.21047938e-01 1.71412408e-01 3.54691982e-01 -1.23067118... | [7.530975818634033, -0.5758183598518372] |
62ad1cc7-41b7-4aba-a177-fe9bf6f83a46 | automating-cluster-analysis-to-generate | 2006.07197 | null | https://arxiv.org/abs/2006.07197v4 | https://arxiv.org/pdf/2006.07197v4.pdf | Clustering Residential Electricity Consumption Data to Create Archetypes that Capture Household Behaviour in South Africa | Clustering is frequently used in the energy domain to identify dominant electricity consumption patterns of households, which can be used to construct customer archetypes for long term energy planning. Selecting a useful set of clusters however requires extensive experimentation and domain knowledge. While internal clu... | ['Wiebke Toussaint', 'Deshendran Moodley'] | 2020-06-11 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [-3.45720619e-01 5.25561161e-02 9.18584242e-02 -6.00192666e-01
-6.06498182e-01 -1.06541002e+00 5.22547483e-01 6.92781329e-01
-2.16524854e-01 4.45701301e-01 1.69536069e-01 -4.07528013e-01
-6.08075261e-01 -1.18063354e+00 6.74114451e-02 -8.10580254e-01
-2.08788604e-01 1.11841345e+00 -1.61949337e-01 1.73692890... | [7.567553997039795, 4.493282318115234] |
706db914-362f-496d-b46e-d6f6547b6e29 | hybrid-score-and-rank-level-fusion-for-person | 2008.03353 | null | https://arxiv.org/abs/2008.03353v1 | https://arxiv.org/pdf/2008.03353v1.pdf | Hybrid Score- and Rank-level Fusion for Person Identification using Face and ECG Data | Uni-modal identification systems are vulnerable to errors in sensor data collection and are therefore more likely to misidentify subjects. For instance, relying on data solely from an RGB face camera can cause problems in poorly lit environments or if subjects do not face the camera. Other identification methods such a... | ['Thomas Truong', 'Jonathan Graf', 'Svetlana Yanushkevich'] | 2020-08-07 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [ 3.85588348e-01 -1.25287890e-01 2.34069705e-01 -4.88668382e-01
-6.73041463e-01 -5.37709296e-01 -7.57738948e-02 6.96328357e-02
-4.25984919e-01 7.18279660e-01 -2.73121834e-01 3.02682132e-01
-1.91984728e-01 -4.33190286e-01 -6.48699999e-02 -8.01598728e-01
1.99238151e-01 2.47081280e-01 -5.55800080e-01 4.17758614... | [13.389857292175293, 1.1667373180389404] |
4079e5ce-686f-44b9-afcc-b89fd19be46a | vegfru-a-domain-specific-dataset-for-fine | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Hou_VegFru_A_Domain-Specific_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Hou_VegFru_A_Domain-Specific_ICCV_2017_paper.pdf | VegFru: A Domain-Specific Dataset for Fine-Grained Visual Categorization | VegFru: A Domain-Specific Dataset for Fine-grained Visual Categorization In this paper, we propose a novel domain-specific dataset named VegFru for fine-grained visual categorization (FGVC). While the existing datasets for FGVC are mainly focused on animal breeds or man-made objects with limited labelled data, VegFru i... | ['Yushan Feng', 'Saihui Hou', 'Zilei Wang'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['fine-grained-visual-categorization'] | ['computer-vision'] | [-4.04687107e-01 -4.29559708e-01 -4.35106099e-01 -3.47607136e-01
-2.10209474e-01 -6.32049143e-01 4.25134420e-01 3.72982681e-01
-1.17855616e-01 2.85451680e-01 2.03800157e-01 -7.54776821e-02
2.05936283e-01 -1.19082916e+00 -4.66535151e-01 -7.31487811e-01
6.02328628e-02 -9.84877571e-02 2.50209391e-01 -1.72847658... | [9.679930686950684, 2.072456121444702] |
f5426ea2-3536-4eb7-82b8-596e395d70d5 | a-photometrically-calibrated-benchmark-for | 1607.02555 | null | http://arxiv.org/abs/1607.02555v2 | http://arxiv.org/pdf/1607.02555v2.pdf | A Photometrically Calibrated Benchmark For Monocular Visual Odometry | We present a dataset for evaluating the tracking accuracy of monocular visual
odometry and SLAM methods. It contains 50 real-world sequences comprising more
than 100 minutes of video, recorded across dozens of different environments --
ranging from narrow indoor corridors to wide outdoor scenes. All sequences
contain m... | ['Jakob Engel', 'Daniel Cremers', 'Vladyslav Usenko'] | 2016-07-09 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-2.77681518e-02 -5.19667149e-01 1.03642315e-01 -4.12136495e-01
-4.42495197e-01 -9.96612787e-01 7.23758042e-01 -2.95663267e-01
-5.32908797e-01 8.50578487e-01 -4.62908074e-02 7.73516148e-02
1.38131440e-01 -3.11589152e-01 -9.27690148e-01 -6.25023603e-01
-1.65399626e-01 4.60370541e-01 4.65073913e-01 -1.90940220... | [7.638036727905273, -2.16703724861145] |
8d611aba-f886-4858-a2c5-21ca0deb18c7 | assessing-project-level-fine-tuning-of-ml4se | 2206.03333 | null | https://arxiv.org/abs/2206.03333v1 | https://arxiv.org/pdf/2206.03333v1.pdf | Assessing Project-Level Fine-Tuning of ML4SE Models | Machine Learning for Software Engineering (ML4SE) is an actively growing research area that focuses on methods that help programmers in their work. In order to apply the developed methods in practice, they need to achieve reasonable quality in order to help rather than distract developers. While the development of new ... | ['Timofey Bryksin', 'Egor Spirin', 'Sergey Zhuravlev', 'Egor Bogomolov'] | 2022-06-07 | null | null | null | null | ['method-name-prediction'] | ['natural-language-processing'] | [-2.17663735e-01 -1.32665187e-01 -1.62456125e-01 -5.19776940e-01
-5.32230437e-01 -4.59030211e-01 2.42403284e-01 3.16612482e-01
-2.43148670e-01 1.17108025e-01 1.88766532e-02 -3.99774551e-01
-1.35262042e-01 -7.54694402e-01 -7.07026899e-01 -6.29667416e-02
4.02317405e-01 2.44063109e-01 3.55386734e-01 -2.62438446... | [7.733841419219971, 7.7625813484191895] |
ca746887-1857-4030-82ca-681d31129617 | recurrent-convolutional-fusion-for-rgb-d | 1806.01673 | null | http://arxiv.org/abs/1806.01673v3 | http://arxiv.org/pdf/1806.01673v3.pdf | Recurrent Convolutional Fusion for RGB-D Object Recognition | Providing machines with the ability to recognize objects like humans has
always been one of the primary goals of machine vision. The introduction of
RGB-D cameras has paved the way for a significant leap forward in this
direction thanks to the rich information provided by these sensors. However,
the machine vision comm... | ['Mirco Planamente', 'Mohammad Reza Loghmani', 'Barbara Caputo', 'Markus Vincze'] | 2018-06-05 | null | null | null | null | ['object-categorization'] | ['computer-vision'] | [ 3.45618487e-03 -3.96361619e-01 -2.20101364e-02 -8.70678663e-01
-8.21134090e-01 -4.24436867e-01 7.04565048e-01 1.55876219e-01
-4.09743965e-01 1.19688205e-01 1.23937130e-01 1.39781490e-01
-9.90891829e-02 -6.96155012e-01 -3.57911050e-01 -7.65282750e-01
3.57296109e-01 2.15796322e-01 2.38087609e-01 -7.27111250... | [9.548813819885254, -0.9084527492523193] |
7a982356-0fb4-492f-98e7-432e4155da87 | deepfilternet-perceptually-motivated-real | 2305.08227 | null | https://arxiv.org/abs/2305.08227v1 | https://arxiv.org/pdf/2305.08227v1.pdf | DeepFilterNet: Perceptually Motivated Real-Time Speech Enhancement | Multi-frame algorithms for single-channel speech enhancement are able to take advantage from short-time correlations within the speech signal. Deep Filtering (DF) was proposed to directly estimate a complex filter in frequency domain to take advantage of these correlations. In this work, we present a real-time speech e... | ['Andreas Maier', 'Alberto N. Escalante-B.', 'Tobias Rosenkranz', 'Hendrik Schröter'] | 2023-05-14 | null | null | null | null | ['speech-enhancement'] | ['speech'] | [ 2.00025678e-01 -9.82857272e-02 4.72134769e-01 -2.62906790e-01
-9.70516205e-01 -2.51059115e-01 5.48173785e-01 2.29696501e-02
-8.14684391e-01 3.94438833e-01 5.09589911e-01 -2.80728012e-01
-6.94474280e-02 -4.71147388e-01 -4.60100502e-01 -5.40404201e-01
-3.16196322e-01 -2.66565174e-01 2.80008674e-01 -5.10308027... | [15.181109428405762, 5.876350402832031] |
1bf59636-3adf-47f7-8bbf-29cc3553e2f8 | artificial-intelligence-security-competition | 2212.03412 | null | https://arxiv.org/abs/2212.03412v1 | https://arxiv.org/pdf/2212.03412v1.pdf | Artificial Intelligence Security Competition (AISC) | The security of artificial intelligence (AI) is an important research area towards safe, reliable, and trustworthy AI systems. To accelerate the research on AI security, the Artificial Intelligence Security Competition (AISC) was organized by the Zhongguancun Laboratory, China Industrial Control Systems Cyber Emergency... | ['Mengxin Zhang', 'Kun Hu', 'Huafeng Shi', 'Guoshuai Liu', 'Zhichao Cui', 'Chenhao Lin', 'Chao Shen', 'Yijia Li', 'Junhao Zheng', 'Chen Ma', 'Jitao Sang', 'Haonan Wang', 'Shuang Li', 'YiFei Gao', 'Zhiyu Lin', 'Haoran Lyu', 'Shangbo Wu', 'Yuhang Zhao', 'Yajie Wang', 'Huipeng Zhou', 'Chengqi Duan', 'Yuanzhe Pang', 'Zeyu ... | 2022-12-07 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [-3.82160932e-01 -7.88313523e-02 -1.27622560e-01 1.21874608e-01
-3.34709696e-02 -6.86497152e-01 6.95561469e-01 -2.99972743e-01
-3.43364000e-01 3.98730576e-01 -4.47839707e-01 -6.70301676e-01
-6.64983988e-02 -8.72578502e-01 -4.71374154e-01 -6.10060632e-01
1.49620235e-01 3.72013390e-01 -8.85461122e-02 -4.77594316... | [5.515892028808594, 7.406710624694824] |
cc447856-ef0c-49bb-9294-05dc74fb1ad8 | learning-scene-dynamics-from-point-cloud | 2111.08755 | null | https://arxiv.org/abs/2111.08755v1 | https://arxiv.org/pdf/2111.08755v1.pdf | Learning Scene Dynamics from Point Cloud Sequences | Understanding 3D scenes is a critical prerequisite for autonomous agents. Recently, LiDAR and other sensors have made large amounts of data available in the form of temporal sequences of point cloud frames. In this work, we propose a novel problem -- sequential scene flow estimation (SSFE) -- that aims to predict 3D sc... | ['Anand Rangarajan', 'Sanjay Ranka', 'Patrick Emami', 'Pan He'] | 2021-11-16 | null | null | null | null | ['scene-flow-estimation'] | ['computer-vision'] | [ 2.55892128e-01 -5.32267094e-01 -1.82561984e-03 -1.83329895e-01
-4.01323706e-01 -5.63010573e-01 8.60344946e-01 4.16666158e-02
-4.29720819e-01 6.06716573e-01 -1.18447185e-01 -3.36218417e-01
1.03375174e-01 -8.05399060e-01 -6.54853463e-01 -5.08477390e-01
-4.78874922e-01 3.59082550e-01 8.17463756e-01 -1.24067143... | [8.505866050720215, -1.9682425260543823] |
7f9bb06a-14f6-49b3-bd85-9cb0e30b341a | learning-to-execute | 1410.4615 | null | http://arxiv.org/abs/1410.4615v3 | http://arxiv.org/pdf/1410.4615v3.pdf | Learning to Execute | Recurrent Neural Networks (RNNs) with Long Short-Term Memory units (LSTM) are
widely used because they are expressive and are easy to train. Our interest
lies in empirically evaluating the expressiveness and the learnability of LSTMs
in the sequence-to-sequence regime by training them to evaluate short computer
program... | ['Ilya Sutskever', 'Wojciech Zaremba'] | 2014-10-17 | null | null | null | null | ['learning-to-execute'] | ['computer-code'] | [ 4.77658540e-01 -1.22217610e-01 -8.57981592e-02 -3.06431562e-01
-6.65885091e-01 -7.63139427e-01 4.90209162e-01 1.54726237e-01
-7.78512955e-01 7.65476227e-01 -1.30868614e-01 -9.73593116e-01
1.69667035e-01 -1.03542626e+00 -1.12091970e+00 -4.23863024e-01
-3.11234325e-01 2.01986015e-01 3.46316606e-01 -4.59543228... | [8.67476749420166, 7.216052055358887] |
ea813b8e-edaa-455b-97b9-8feebdeaecd1 | lifelong-learning-of-spatiotemporal | 1805.10966 | null | http://arxiv.org/abs/1805.10966v4 | http://arxiv.org/pdf/1805.10966v4.pdf | Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-Organization | Artificial autonomous agents and robots interacting in complex environments
are required to continually acquire and fine-tune knowledge over sustained
periods of time. The ability to learn from continuous streams of information is
referred to as lifelong learning and represents a long-standing challenge for
neural netw... | ['Cornelius Weber', 'Stefan Wermter', 'Jun Tani', 'German I. Parisi'] | 2018-05-28 | null | null | null | null | ['continuous-object-recognition'] | ['computer-vision'] | [ 4.51004744e-01 -1.28617771e-02 -1.43793285e-01 -1.55062079e-01
7.23107904e-02 -4.12372887e-01 9.15924847e-01 3.00280839e-01
-6.64570153e-01 1.17318368e+00 9.60578844e-02 3.09450567e-01
-5.12233496e-01 -9.69033241e-01 -1.10503078e+00 -9.97197390e-01
-5.14839351e-01 5.72597384e-01 5.72459519e-01 -5.98242953... | [9.822473526000977, 3.390072822570801] |
9b2e0570-8c65-4ba7-8603-541907611231 | on-the-performance-of-differential-evolution | 1904.06960 | null | http://arxiv.org/abs/1904.06960v1 | http://arxiv.org/pdf/1904.06960v1.pdf | On the Performance of Differential Evolution for Hyperparameter Tuning | Automated hyperparameter tuning aspires to facilitate the application of
machine learning for non-experts. In the literature, different optimization
approaches are applied for that purpose. This paper investigates the
performance of Differential Evolution for tuning hyperparameters of supervised
learning algorithms for... | ['Anett Schülke', 'Mischa Schmidt', 'Shahd Safarani', 'Tobias Jacobs', 'Sebastien Nicolas', 'Julia Gastinger'] | 2019-04-15 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [ 1.02721982e-01 -7.57882893e-02 -3.58845234e-01 -2.40520492e-01
-8.43392074e-01 -5.06376624e-01 6.83502972e-01 4.54700410e-01
-8.69231343e-01 8.59731972e-01 -1.54644504e-01 -2.74698317e-01
-8.46614897e-01 -3.99791002e-01 -3.26582789e-01 -1.08171093e+00
-4.78737522e-03 9.86690044e-01 1.99133202e-01 1.18916221... | [6.694573402404785, 3.9825186729431152] |
cc2ce379-f68f-40a6-b7b2-e37531ede16d | segmentation-of-multiple-myeloma-plasma-cells | 2111.05125 | null | https://arxiv.org/abs/2111.05125v1 | https://arxiv.org/pdf/2111.05125v1.pdf | Segmentation of Multiple Myeloma Plasma Cells in Microscopy Images with Noisy Labels | A key component towards an improved and fast cancer diagnosis is the development of computer-assisted tools. In this article, we present the solution that won the SegPC-2021 competition for the segmentation of multiple myeloma plasma cells in microscopy images. The labels used in the competition dataset were generated ... | ['Danijel Skočaj', 'Tomaž Martinčič', 'Dejan Štepec', 'Álvaro García Faura'] | 2021-11-08 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 3.75123620e-01 4.02925521e-01 4.03396845e-01 -4.59914982e-01
-1.01217699e+00 -3.02108735e-01 6.70532703e-01 5.89105308e-01
-8.47363174e-01 8.70496631e-01 -5.69037735e-01 -8.88340510e-05
-1.20474249e-01 -4.52498823e-01 -3.86243343e-01 -9.22974646e-01
2.80098081e-01 1.34045756e+00 4.28677469e-01 -3.50760780... | [15.033942222595215, -3.0063188076019287] |
1a6d9091-05c0-442e-bc04-ace6955be07f | disentangled-representation-for-age-invariant | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Hou_Disentangled_Representation_for_Age-Invariant_Face_Recognition_A_Mutual_Information_Minimization_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Hou_Disentangled_Representation_for_Age-Invariant_Face_Recognition_A_Mutual_Information_Minimization_ICCV_2021_paper.pdf | Disentangled Representation for Age-Invariant Face Recognition: A Mutual Information Minimization Perspective | General face recognition has seen remarkable progress in recent years. However, large age gap still remains a big challenge due to significant alterations in facial appearance and bone structure. Disentanglement plays a key role in partitioning face representations into identity-dependent and age-dependent componen... | ['Shengjin Wang', 'YaLi Li', 'Xuege Hou'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['age-invariant-face-recognition'] | ['computer-vision'] | [ 2.03126654e-01 -8.72362852e-02 -2.36960754e-01 -6.17839992e-01
-5.76814711e-01 -9.25877914e-02 4.40658659e-01 -2.92451590e-01
-3.58952850e-01 7.51014054e-01 1.57930925e-01 4.02358115e-01
-2.18128368e-01 -5.19134998e-01 -2.99108565e-01 -9.30663049e-01
-2.26490915e-01 5.32481849e-01 -5.40856302e-01 -6.44683540... | [13.35424518585205, 0.7041165828704834] |
ac559824-9877-48fc-9ff5-97fc27bf408b | detecting-mitoses-with-a-convolutional-neural | 2208.12437 | null | https://arxiv.org/abs/2208.12437v2 | https://arxiv.org/pdf/2208.12437v2.pdf | Detecting Mitoses with a Convolutional Neural Network for MIDOG 2022 Challenge | This work presents a mitosis detection method with only one vanilla Convolutional Neural Network (CNN). Our method consists of two steps: given an image, we first apply a CNN using a sliding window technique to extract patches that have mitoses; we then calculate each extracted patch's class activation map to obtain th... | ["Xiang 'Anthony' Chen", 'Shino Magaki', 'Neda Zarrin-Khameh', 'Christopher Kazu Williams', 'Shuo Ni', 'Mohammad Haeri', 'Hongyan Gu'] | 2022-08-26 | null | null | null | null | ['mitosis-detection'] | ['medical'] | [ 5.76396525e-01 4.62457299e-01 -2.66983479e-01 -1.07433490e-01
-1.33263886e+00 -4.01884764e-01 5.15877426e-01 5.33554256e-01
-1.00138807e+00 8.18981588e-01 -2.09244788e-01 -3.56522143e-01
2.99007773e-01 -6.95430577e-01 -6.16609395e-01 -1.06335294e+00
-5.95106557e-02 4.56353217e-01 6.59176946e-01 6.39217123... | [15.096780776977539, -3.0804970264434814] |
773780c4-dbba-419e-866a-b9d00c427bcf | exploiting-visual-semantic-reasoning-for | 2006.08889 | null | https://arxiv.org/abs/2006.08889v1 | https://arxiv.org/pdf/2006.08889v1.pdf | Exploiting Visual Semantic Reasoning for Video-Text Retrieval | Video retrieval is a challenging research topic bridging the vision and language areas and has attracted broad attention in recent years. Previous works have been devoted to representing videos by directly encoding from frame-level features. In fact, videos consist of various and abundant semantic relations to which ex... | ['Caili Guo', 'Zheng Li', 'Zhimin Zeng', 'Zerun Feng'] | 2020-06-16 | null | null | null | null | ['video-text-retrieval'] | ['computer-vision'] | [ 2.20183939e-01 -1.64092943e-01 -3.79017949e-01 -3.76504213e-01
-2.53907025e-01 -2.78151900e-01 7.22142339e-01 2.56619960e-01
-7.96853155e-02 4.08539414e-01 4.35866177e-01 2.43011233e-03
-2.20222771e-01 -1.00276554e+00 -6.64463341e-01 -4.51025009e-01
6.10049106e-02 -2.77700782e-01 5.16705871e-01 -3.32453698... | [10.1245756149292, 0.9215598106384277] |
e4eb5396-7b43-4e6d-b6d6-67129f328552 | contextual-guided-segmentation-framework-for | 2106.03330 | null | https://arxiv.org/abs/2106.03330v2 | https://arxiv.org/pdf/2106.03330v2.pdf | Contextual Guided Segmentation Framework for Semi-supervised Video Instance Segmentation | In this paper, we propose Contextual Guided Segmentation (CGS) framework for video instance segmentation in three passes. In the first pass, i.e., preview segmentation, we propose Instance Re-Identification Flow to estimate main properties of each instance (i.e., human/non-human, rigid/deformable, known/unknown categor... | ['Minh-Triet Tran', 'Tam V. Nguyen', 'Trung-Nghia Le'] | 2021-06-07 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 5.14078021e-01 9.39134955e-02 -4.03703935e-02 -2.72267938e-01
-6.94450855e-01 -5.62015831e-01 2.64282763e-01 -1.60550550e-01
-3.23178947e-01 7.09164262e-01 -4.82562967e-02 1.16373911e-01
4.93982658e-02 -5.44897437e-01 -8.71992052e-01 -7.15749800e-01
3.41251135e-01 5.38298190e-01 9.01188910e-01 2.01699585... | [9.201553344726562, -0.20248228311538696] |
825aaff8-07c9-4176-ad2f-8f54618789d1 | selectively-hard-negative-mining-for | 2303.00181 | null | https://arxiv.org/abs/2303.00181v1 | https://arxiv.org/pdf/2303.00181v1.pdf | Selectively Hard Negative Mining for Alleviating Gradient Vanishing in Image-Text Matching | Recently, a series of Image-Text Matching (ITM) methods achieve impressive performance. However, we observe that most existing ITM models suffer from gradients vanishing at the beginning of training, which makes these models prone to falling into local minima. Most ITM models adopt triplet loss with Hard Negative minin... | ['Zhongtian Du', 'Zerun Feng', 'Xin Wang', 'Caili Guo', 'Zheng Li'] | 2023-03-01 | null | null | null | null | ['text-matching'] | ['natural-language-processing'] | [ 2.38130823e-01 -3.77782690e-03 -4.61007357e-01 -5.61791539e-01
-4.29159850e-01 -4.62317979e-03 5.02542317e-01 -1.23669868e-02
-4.08252001e-01 1.92764342e-01 -1.38043523e-01 -2.96876818e-01
6.83345348e-02 -7.57632852e-01 -8.18589687e-01 -6.75413072e-01
1.90813377e-01 1.58204675e-01 2.85397828e-01 -8.79876167... | [9.446127891540527, 3.1464428901672363] |
a2e6861e-ba70-45d8-a45d-8ef878447d1d | hearing-lips-improving-lip-reading-by | 1911.11502 | null | https://arxiv.org/abs/1911.11502v1 | https://arxiv.org/pdf/1911.11502v1.pdf | Hearing Lips: Improving Lip Reading by Distilling Speech Recognizers | Lip reading has witnessed unparalleled development in recent years thanks to deep learning and the availability of large-scale datasets. Despite the encouraging results achieved, the performance of lip reading, unfortunately, remains inferior to the one of its counterpart speech recognition, due to the ambiguous nature... | ['Rui Xu', 'Mingli Song', 'Haihong Tang', 'Ya Zhao', 'Xinchao Wang', 'Peng Hou'] | 2019-11-26 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 4.12402153e-01 6.17864057e-02 -5.81824481e-01 -4.25234810e-02
-1.23728144e+00 -3.81569415e-01 4.12343085e-01 -2.77713835e-01
-3.02646637e-01 5.97544074e-01 5.53002656e-01 -2.00728133e-01
-7.24080205e-02 -9.33060199e-02 -5.00045121e-01 -9.11079943e-01
5.38311124e-01 -1.05249183e-02 1.59155071e-01 8.09929371... | [14.326064109802246, 5.000001907348633] |
92fea935-7193-4df6-b758-53a239915cb4 | on-the-curious-case-of-ell-2-norm-of-sense | 2210.14815 | null | https://arxiv.org/abs/2210.14815v1 | https://arxiv.org/pdf/2210.14815v1.pdf | On the Curious Case of $\ell_2$ norm of Sense Embeddings | We show that the $\ell_2$ norm of a static sense embedding encodes information related to the frequency of that sense in the training corpus used to learn the sense embeddings. This finding can be seen as an extension of a previously known relationship for word embeddings to sense embeddings. Our experimental results s... | ['Danushka Bollegala', 'Yi Zhou'] | 2022-10-26 | null | null | null | null | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 1.74398586e-01 -6.00205697e-02 -2.07692593e-01 -3.77095133e-01
-4.68073845e-01 -7.95475304e-01 6.88529313e-01 8.73943746e-01
-1.04017866e+00 6.33844316e-01 3.86099041e-01 -5.07554531e-01
-1.60572648e-01 -9.65341330e-01 -1.02827482e-01 -7.45558202e-01
-1.35011554e-01 1.71798930e-01 2.90265799e-01 -7.64951229... | [10.34162712097168, 8.893814086914062] |
19bace99-89d7-451d-b770-aff4397fef88 | improving-word-embeddings-through-iterative | null | null | https://aclanthology.org/2020.coling-main.104 | https://aclanthology.org/2020.coling-main.104.pdf | Improving Word Embeddings through Iterative Refinement of Word- and Character-level Models | Embedding of rare and out-of-vocabulary (OOV) words is an important open NLP problem. A popular solution is to train a character-level neural network to reproduce the embeddings from a standard word embedding model. The trained network is then used to assign vectors to any input string, including OOV and rare words. We... | ['Slobodan Vucetic', 'Nemanja Djuric', 'Shanshan Zhang', 'Phong Ha'] | 2020-12-01 | null | null | null | coling-2020-8 | ['word-similarity'] | ['natural-language-processing'] | [ 5.50830364e-01 6.58842735e-03 -7.26241112e-01 -3.37707072e-01
-5.66428602e-01 -5.34362555e-01 5.46802282e-01 6.12206936e-01
-8.96417320e-01 4.64247495e-01 4.54462677e-01 -4.77240682e-01
-2.53479451e-01 -8.67332935e-01 -2.72231162e-01 -2.86834747e-01
4.48556930e-01 7.61405885e-01 1.50183961e-02 -4.02526170... | [10.465869903564453, 8.758708953857422] |
8c928ec5-7df5-4421-9b49-32cba4d89e5e | macech-at-semeval-2021-task-5-toxic-spans | null | null | https://aclanthology.org/2021.semeval-1.137 | https://aclanthology.org/2021.semeval-1.137.pdf | macech at SemEval-2021 Task 5: Toxic Spans Detection | Toxic language is often present in online forums, especially when politics and other polarizing topics arise, and can lead to people becoming discouraged from joining or continuing conversations. In this paper, we use data consisting of comments with the indices of toxic text labelled to train an RNN to deter-mine whic... | ['Maggie Cech'] | 2021-08-01 | null | null | null | semeval-2021 | ['toxic-spans-detection'] | ['natural-language-processing'] | [-8.58958364e-02 3.71246934e-01 -3.43917668e-01 -1.56033531e-01
-6.52462482e-01 -7.65965521e-01 7.57309854e-01 2.18429968e-01
-3.48570347e-01 9.31103587e-01 1.27144814e+00 -7.89185286e-01
3.49238634e-01 -6.34270668e-01 -1.62403882e-01 -5.66222429e-01
2.89089587e-02 1.96126357e-01 -5.98554134e-01 -4.99218911... | [8.761497497558594, 10.379647254943848] |
562f02f7-bb02-483c-a1c6-650b2f4fa929 | outcome-oriented-predictive-process | 1707.06766 | null | http://arxiv.org/abs/1707.06766v4 | http://arxiv.org/pdf/1707.06766v4.pdf | Outcome-Oriented Predictive Process Monitoring: Review and Benchmark | Predictive business process monitoring refers to the act of making
predictions about the future state of ongoing cases of a business process,
based on their incomplete execution traces and logs of historical (completed)
traces. Motivated by the increasingly pervasive availability of fine-grained
event data about busine... | ['Marlon Dumas', 'Marcello La Rosa', 'Irene Teinemaa', 'Fabrizio Maria Maggi'] | 2017-07-21 | null | null | null | null | ['predictive-process-monitoring'] | ['time-series'] | [ 6.50066018e-01 1.48241147e-01 -5.55146821e-02 -4.38432962e-01
-3.39547306e-01 -3.79736513e-01 1.19067502e+00 9.67658699e-01
-8.57170373e-02 5.01929104e-01 3.04744065e-01 -3.12819481e-01
-7.25077152e-01 -8.13123405e-01 -3.59544642e-02 -3.51061136e-01
-3.47955048e-01 8.91393006e-01 3.88417184e-01 4.67153668... | [8.59414005279541, 5.9924468994140625] |
11604daf-67a1-4e78-815e-0248dafbe076 | a-graph-based-framework-for-complex-system | 2302.06473 | null | https://arxiv.org/abs/2302.06473v1 | https://arxiv.org/pdf/2302.06473v1.pdf | A Graph-based Framework for Complex System Simulating and Diagnosis with Automatic Reconfiguration | Fault detection has a long tradition: the necessity to provide the most accurate diagnosis possible for a process plant criticality is somehow intrinsic in its functioning. Continuous monitoring is a possible way for early detection. However, it is somehow fundamental to be able to actually simulate failures. Reproduci... | ['Gianluigi Rozza', 'Nicola Demo', 'Martina Teruzzi'] | 2023-02-10 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [ 2.05063775e-01 4.99175638e-01 5.19120634e-01 7.19709098e-02
4.63362843e-01 -4.30498004e-01 6.46010339e-01 6.52017176e-01
-2.10187361e-02 7.28007734e-01 -7.23776639e-01 -2.80021220e-01
-7.57856131e-01 -1.21336544e+00 -4.61496443e-01 -7.63839364e-01
-4.93467510e-01 8.50204110e-01 3.45151931e-01 -4.68032777... | [6.446704387664795, 2.3410909175872803] |
85c6daeb-7d7b-4076-8856-9dc14640333f | crime-prediction-through-urban-metrics-and | 1712.03834 | null | http://arxiv.org/abs/1712.03834v2 | http://arxiv.org/pdf/1712.03834v2.pdf | Crime prediction through urban metrics and statistical learning | Understanding the causes of crime is a longstanding issue in researcher's
agenda. While it is a hard task to extract causality from data, several linear
models have been proposed to predict crime through the existing correlations
between crime and urban metrics. However, because of non-Gaussian distributions
and multic... | ['Haroldo V. Ribeiro', 'Luiz G. A. Alves', 'Francisco A. Rodrigues'] | 2017-12-08 | null | null | null | null | ['crime-prediction'] | ['miscellaneous'] | [-1.89762354e-01 -3.08162093e-01 -5.67080975e-01 -3.12539607e-01
-4.92866844e-01 -2.19419718e-01 5.95317125e-01 6.40516400e-01
-7.02630222e-01 8.36369455e-01 8.44853878e-01 -9.19098079e-01
-4.81377959e-01 -1.27012610e+00 -3.12091768e-01 -5.88525176e-01
2.15724781e-01 1.56068563e-01 -2.57791191e-01 -9.69046056... | [6.725536823272705, 1.983161211013794] |
a6ff8d33-dfb4-4cfb-bed7-58c926a18b92 | disentangling-aesthetic-and-technical-effects | 2211.04894 | null | https://arxiv.org/abs/2211.04894v3 | https://arxiv.org/pdf/2211.04894v3.pdf | Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives | The rapid increase in user-generated-content (UGC) videos calls for the development of effective video quality assessment (VQA) algorithms. However, the objective of the UGC-VQA problem is still ambiguous and can be viewed from two perspectives: the technical perspective, measuring the perception of distortions; and th... | ['Weisi Lin', 'Jingwen Hou', 'Chaofeng Chen', 'Liang Liao', 'Erli Zhang', 'Qiong Yan', 'Wenxiu Sun', 'Annan Wang', 'HaoNing Wu'] | 2022-11-09 | null | null | null | null | ['video-generation'] | ['computer-vision'] | [-2.38825917e-01 -3.87785345e-01 -9.93864238e-02 -2.32310265e-01
-9.04745996e-01 -8.40458214e-01 1.37813196e-01 -1.47262439e-01
-2.12468356e-02 3.06898445e-01 4.93840337e-01 -2.16821972e-02
-1.82487458e-01 -5.57509363e-01 -5.11992216e-01 -6.75354838e-01
-7.78381675e-02 -2.45067313e-01 -3.94308344e-02 -2.82354534... | [11.756007194519043, -1.8192503452301025] |
548483db-bb36-4d7d-a5a3-47ec9692731a | energy-based-detection-of-adverse-weather | 2305.16129 | null | https://arxiv.org/abs/2305.16129v3 | https://arxiv.org/pdf/2305.16129v3.pdf | Energy-based Detection of Adverse Weather Effects in LiDAR Data | Autonomous vehicles rely on LiDAR sensors to perceive the environment. Adverse weather conditions like rain, snow, and fog negatively affect these sensors, reducing their reliability by introducing unwanted noise in the measurements. In this work, we tackle this problem by proposing a novel approach for detecting adver... | ['Klaus Dietmayer', 'Daniel Meissner', 'Marc Walessa', 'Johannes Kopp', 'Vinzenz Dallabetta', 'Aldi Piroli'] | 2023-05-25 | null | null | null | null | ['outlier-detection'] | ['methodology'] | [ 1.89618208e-02 -2.64645278e-01 -4.46098372e-02 -6.71868443e-01
-5.60665429e-01 -5.20461559e-01 2.76094973e-01 4.80679244e-01
-4.28776652e-01 5.31911254e-01 -2.51449376e-01 -7.70394951e-02
3.32460463e-01 -1.09067762e+00 -1.07015300e+00 -5.49439967e-01
-1.01830430e-01 3.43343258e-01 6.37391627e-01 -7.41803125... | [7.839077949523926, -2.3771867752075195] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.