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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ba3e6497-a832-4a94-a573-c98f7639697d | end-to-end-emotion-cause-pair-extraction | null | null | https://aclanthology.org/2020.emnlp-main.290 | https://aclanthology.org/2020.emnlp-main.290.pdf | End-to-End Emotion-Cause Pair Extraction based on Sliding Window Multi-Label Learning | Emotion-cause pair extraction (ECPE) is a new task that aims to extract the potential pairs of emotions and their corresponding causes in a document. The existing methods first perform emotion extraction and cause extraction independently, and then perform emotion-cause pairing and filtering. However, the above methods... | ['Jianfei Yu', 'Rui Xia', 'Zixiang Ding'] | null | null | null | null | emnlp-2020-11 | ['emotion-cause-pair-extraction'] | ['natural-language-processing'] | [ 1.11145951e-01 -1.95852946e-02 -1.98217720e-01 -5.33718169e-01
-1.03905821e+00 -6.92144394e-01 4.19463605e-01 4.01126772e-01
-1.90883949e-01 5.99195659e-01 2.74246544e-01 1.47316888e-01
-8.67415071e-02 -3.44151765e-01 -2.14944690e-01 -8.71689796e-01
-3.27672921e-02 2.90676266e-01 -9.88738611e-02 1.70393407... | [12.641186714172363, 6.215455055236816] |
84b4cc44-7b7e-4ebf-aad6-b65bdb207bca | synthesizing-artistic-cinemagraphs-from-text | 2307.03190 | null | https://arxiv.org/abs/2307.03190v1 | https://arxiv.org/pdf/2307.03190v1.pdf | Synthesizing Artistic Cinemagraphs from Text | We introduce Artistic Cinemagraph, a fully automated method for creating cinemagraphs from text descriptions - an especially challenging task when prompts feature imaginary elements and artistic styles, given the complexity of interpreting the semantics and motions of these images. Existing single-image animation metho... | ['Jun-Yan Zhu', 'Sergey Tulyakov', 'Hsin-Ying Lee', 'Aliaksandr Siarohin', 'Aniruddha Mahapatra'] | 2023-07-06 | null | null | null | null | ['image-animation'] | ['computer-vision'] | [ 7.21119702e-01 -1.85503252e-02 1.71457231e-01 -2.91799545e-01
-3.03217173e-01 -1.07872450e+00 9.20337379e-01 -2.96827346e-01
2.84471631e-01 4.60997641e-01 3.65978330e-01 -6.25380427e-02
2.57002503e-01 -6.60486162e-01 -6.09212577e-01 -2.70752817e-01
8.72901604e-02 4.21452790e-01 4.32814747e-01 -5.22234499... | [11.170757293701172, 0.04060830920934677] |
04d84b37-eb8f-4824-8a9c-23ff50de9b05 | h4d-human-4d-modeling-by-learning-neural | 2203.01247 | null | https://arxiv.org/abs/2203.01247v2 | https://arxiv.org/pdf/2203.01247v2.pdf | H4D: Human 4D Modeling by Learning Neural Compositional Representation | Despite the impressive results achieved by deep learning based 3D reconstruction, the techniques of directly learning to model 4D human captures with detailed geometry have been less studied. This work presents a novel framework that can effectively learn a compact and compositional representation for dynamic human by ... | ['Yanwei Fu', 'xiangyang xue', 'Xingkui Wei', 'yinda zhang', 'Boyan Jiang'] | 2022-03-02 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Jiang_H4D_Human_4D_Modeling_by_Learning_Neural_Compositional_Representation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Jiang_H4D_Human_4D_Modeling_by_Learning_Neural_Compositional_Representation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['motion-retargeting'] | ['computer-vision'] | [-1.14768051e-01 7.64443725e-02 -2.87834585e-01 -5.04423864e-02
-5.40374935e-01 -2.26855546e-01 3.36226583e-01 -5.08286595e-01
-1.90376982e-01 3.29728842e-01 6.75620139e-01 3.52966815e-01
2.07359090e-01 -3.54050249e-01 -7.05397904e-01 -5.32491565e-01
-1.98879704e-01 6.56347096e-01 4.33058143e-02 -2.67990291... | [7.189009666442871, -0.653018057346344] |
0a6a81d0-6035-4dd7-9c7e-329926243d1d | understanding-interlocking-dynamics-of | 2110.13880 | null | https://arxiv.org/abs/2110.13880v1 | https://arxiv.org/pdf/2110.13880v1.pdf | Understanding Interlocking Dynamics of Cooperative Rationalization | Selective rationalization explains the prediction of complex neural networks by finding a small subset of the input that is sufficient to predict the neural model output. The selection mechanism is commonly integrated into the model itself by specifying a two-component cascaded system consisting of a rationale generato... | ['Tommi S. Jaakkola', 'Shiyu Chang', 'Yang Zhang', 'Mo Yu'] | 2021-10-26 | null | http://proceedings.neurips.cc/paper/2021/hash/6a711a119a8a7a9f877b5f379bfe9ea2-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/6a711a119a8a7a9f877b5f379bfe9ea2-Paper.pdf | neurips-2021-12 | ['hard-attention'] | ['methodology'] | [ 2.02499747e-01 6.96715057e-01 -4.41572905e-01 -4.99926060e-01
-5.79100430e-01 -3.78911257e-01 2.92529792e-01 -1.43444389e-01
-1.18452922e-01 5.94813585e-01 2.34504566e-01 -1.77738011e-01
2.74571814e-02 -6.12763941e-01 -9.08147037e-01 -7.22388327e-01
3.22090030e-01 5.19037127e-01 -1.99989579e-03 -2.46913150... | [9.001008987426758, 5.772383689880371] |
4e630bec-f707-4d47-9dc5-6ef4e9a58c7e | graph-and-graphon-neural-network-stability | 2010.12529 | null | https://arxiv.org/abs/2010.12529v4 | https://arxiv.org/pdf/2010.12529v4.pdf | Graph and graphon neural network stability | Graph neural networks (GNNs) are learning architectures that rely on knowledge of the graph structure to generate meaningful representations of large-scale network data. GNN stability is thus important as in real-world scenarios there are typically uncertainties associated with the graph. We analyze GNN stability using... | ['Alejandro Ribeiro', 'Zhiyang Wang', 'Luana Ruiz'] | 2020-10-23 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [ 1.32422596e-01 6.42631650e-01 2.85707563e-01 6.76591229e-03
8.14279318e-02 -7.97677517e-01 3.82403970e-01 1.82892784e-01
1.44627899e-01 3.78593624e-01 7.65883625e-02 -5.22000253e-01
-4.77307111e-01 -1.05293965e+00 -1.21908140e+00 -5.47222316e-01
-8.06444883e-01 3.45051140e-01 1.46338508e-01 -4.01344270... | [6.827754020690918, 6.060400485992432] |
c6ab8906-26b4-4eb4-b33d-dd38116b3488 | optimal-timing-for-power-plant-maintenance-in | 2302.00185 | null | https://arxiv.org/abs/2302.00185v3 | https://arxiv.org/pdf/2302.00185v3.pdf | Optimal timing for power plant maintenance in the Electricity Reliability Council of Texas in a changing climate | We analyzed data for the Electricity Reliability Council of Texas (ERCOT) to assess shoulder seasons -- that is, the 45 days of lowest total energy use and peak demand in the spring and fall -- and whether their occurrence has changed over time. Over the period 1996--2022, the shoulder seasons never started earlier tha... | ['Aidan Pyrcz', 'Michael E. Webber', 'Joshua D. Rhodes', 'Hugh Daigle'] | 2023-02-01 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [-3.34428340e-01 -8.59766603e-02 1.38341472e-01 1.00111449e-02
-2.81537592e-01 -8.44268143e-01 7.18351245e-01 2.74745554e-01
-2.72100180e-01 1.17754209e+00 1.88748036e-02 -7.81547487e-01
-3.42190713e-01 -8.71170640e-01 5.70029989e-02 -5.39770544e-01
-5.09905636e-01 1.98914617e-01 -2.22953081e-01 -5.68795085... | [6.001705646514893, 2.718507766723633] |
7377659c-4283-4aa5-99a7-cb3e32136fa9 | auxiliary-functions-as-koopman-observables | 2303.01483 | null | https://arxiv.org/abs/2303.01483v3 | https://arxiv.org/pdf/2303.01483v3.pdf | Auxiliary Functions as Koopman Observables: Data-Driven Analysis of Dynamical Systems via Polynomial Optimization | We present a flexible data-driven method for dynamical system analysis that does not require explicit model discovery. The method is rooted in well-established techniques for approximating the Koopman operator from data and is implemented as a semidefinite program that can be solved numerically. Furthermore, the method... | ['Giovanni Fantuzzi', 'Jason J. Bramburger'] | 2023-03-02 | null | null | null | null | ['model-discovery'] | ['miscellaneous'] | [-1.67330988e-02 -1.24954529e-01 -3.90349627e-01 -2.50688884e-02
-5.64762712e-01 -9.06478584e-01 7.32756317e-01 -1.68046832e-01
-2.76056975e-01 9.30955529e-01 1.56071171e-01 -3.47301751e-01
-7.95496821e-01 -3.70330900e-01 -4.15564358e-01 -8.40806901e-01
-5.61485946e-01 6.20113015e-01 -2.01537594e-01 -4.46021676... | [6.618040561676025, 3.542778730392456] |
28c89c2a-1558-4bbe-981f-475eeda062b3 | weatherbench-probability-a-benchmark-dataset | 2205.00865 | null | https://arxiv.org/abs/2205.00865v1 | https://arxiv.org/pdf/2205.00865v1.pdf | WeatherBench Probability: A benchmark dataset for probabilistic medium-range weather forecasting along with deep learning baseline models | WeatherBench is a benchmark dataset for medium-range weather forecasting of geopotential, temperature and precipitation, consisting of preprocessed data, predefined evaluation metrics and a number of baseline models. WeatherBench Probability extends this to probabilistic forecasting by adding a set of established proba... | ['Nils Thuerey', 'Stephan Rasp', 'Sagar Garg'] | 2022-05-02 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [-3.43855470e-01 -1.39111429e-01 -8.25857446e-02 -7.88451552e-01
-1.14933729e+00 -9.32901800e-01 1.12607110e+00 3.04406643e-01
-8.17654282e-02 1.08857405e+00 5.26191533e-01 -8.24607491e-01
-3.67529273e-01 -8.44318926e-01 -2.64600456e-01 -8.67696643e-01
-4.48835641e-01 7.89133012e-01 2.57308573e-01 -2.14340732... | [6.599457263946533, 3.1427485942840576] |
bd4bc3b9-7bde-436a-8eed-fabe73a470e0 | a-survey-on-knowledge-graphs-for-healthcare | 2306.04802 | null | https://arxiv.org/abs/2306.04802v1 | https://arxiv.org/pdf/2306.04802v1.pdf | A Survey on Knowledge Graphs for Healthcare: Resources, Applications, and Promises | Healthcare knowledge graphs (HKGs) have emerged as a promising tool for organizing medical knowledge in a structured and interpretable way, which provides a comprehensive view of medical concepts and their relationships. However, challenges such as data heterogeneity and limited coverage remain, emphasizing the need fo... | ['Carl Yang', 'Fei Wang', 'Joyce Ho', 'Chen Ling', 'Xuan Kan', 'Yue Yu', 'Shaojun Yu', 'Wenjing Ma', 'ran Xu', 'Shiyu Wang', 'Jiaying Lu', 'Hejie Cui'] | 2023-06-07 | null | null | null | null | ['knowledge-graphs'] | ['knowledge-base'] | [-1.06357664e-01 4.37215388e-01 -7.73747802e-01 -3.25256884e-01
-7.42434561e-01 -4.12599355e-01 1.33638298e-02 8.00351083e-01
-1.04405209e-02 6.76612079e-01 6.29829347e-01 -5.88607907e-01
-5.02278268e-01 -8.72276187e-01 -3.29297751e-01 -4.81354952e-01
-1.73635229e-01 6.33054733e-01 -2.05314398e-01 -1.19147055... | [8.449039459228516, 8.590336799621582] |
e867c811-4f31-4778-b1e9-1e865cc997f4 | dual-task-mutual-learning-for-semi-supervised | 2103.04708 | null | https://arxiv.org/abs/2103.04708v2 | https://arxiv.org/pdf/2103.04708v2.pdf | Dual-Task Mutual Learning for Semi-Supervised Medical Image Segmentation | The success of deep learning methods in medical image segmentation tasks usually requires a large amount of labeled data. However, obtaining reliable annotations is expensive and time-consuming. Semi-supervised learning has attracted much attention in medical image segmentation by taking the advantage of unlabeled data... | ['Jicong Zhang', 'Yichi Zhang'] | 2021-03-08 | null | null | null | null | ['semi-supervised-medical-image-segmentation'] | ['computer-vision'] | [ 3.75025809e-01 5.92838109e-01 -3.07609946e-01 -7.55746722e-01
-1.22837627e+00 -3.59117568e-01 3.06739844e-02 3.02841008e-01
-7.08481610e-01 4.88570005e-01 -1.83666557e-01 -2.30628252e-01
7.72806257e-02 -6.96878731e-01 -7.61218011e-01 -8.67446303e-01
3.10922891e-01 6.88328207e-01 4.14154261e-01 1.32990554... | [14.620734214782715, -2.11541485786438] |
ba6dd1f3-ad56-4d0a-aa4a-d70b5145a152 | deep-gaussian-process-for-crop-yield | null | null | https://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14435 | https://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/viewFile/14435/14067 | Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data | Agricultural monitoring, especially in developing countries,
can help prevent famine and support humanitarian efforts.
A central challenge is yield estimation, i.e., predicting crop
yields before harvest.
We introduce a scalable, accurate, and inexpensive method
to predict crop yields using publicly available remo... | ['Xiaocheng Li', 'Melvin Low', 'Jiaxuan You', 'Stefano Ermon', 'David Lobell'] | 2017-02-12 | null | null | null | aaai-2017-2017-2 | ['crop-yield-prediction', 'crop-yield-prediction'] | ['computer-vision', 'miscellaneous'] | [ 7.97939748e-02 -1.90333202e-01 -3.19482893e-01 -3.19601029e-01
-5.44678748e-01 -6.90877974e-01 6.23217523e-01 3.67632568e-01
-2.13988423e-01 7.95168161e-01 3.66292834e-01 -6.98157609e-01
-1.01577625e-01 -1.44203365e+00 -6.66761041e-01 -7.75781691e-01
-2.34714597e-01 4.59770858e-02 -4.39855963e-01 -4.23178434... | [9.365687370300293, -1.589890480041504] |
c5e1a419-b2df-4849-8861-1ffd0ebe63dd | reducing-information-loss-for-spiking-neural | 2307.04356 | null | https://arxiv.org/abs/2307.04356v1 | https://arxiv.org/pdf/2307.04356v1.pdf | Reducing Information Loss for Spiking Neural Networks | The Spiking Neural Network (SNN) has attracted more and more attention recently. It adopts binary spike signals to transmit information. Benefitting from the information passing paradigm of SNNs, the multiplications of activations and weights can be replaced by additions, which are more energy-efficient. However, its `... | ['Zhe Ma', 'Xuhui Huang', 'Yuanyuan Ou', 'Xinyi Tong', 'Xiaode Liu', 'Liwen Zhang', 'Yuanpei Chen', 'Yufei Guo'] | 2023-07-10 | null | null | null | null | ['quantization'] | ['methodology'] | [ 5.75985253e-01 -2.83677518e-01 6.17215037e-02 -1.80629492e-01
2.75032246e-04 -3.04094374e-01 3.07491839e-01 3.69877517e-01
-7.02802718e-01 8.98142457e-01 -1.27188653e-01 4.40606549e-02
2.21983328e-01 -1.07874346e+00 -7.65052736e-01 -1.28356063e+00
1.57361969e-01 -2.74542212e-01 6.79883897e-01 -1.50628313... | [8.2382230758667, 2.5096311569213867] |
b923ecdb-7cf7-4cac-8b65-5801ae79f542 | automatic-hemisphere-segmentation-in-rodent | 2108.01941 | null | https://arxiv.org/abs/2108.01941v3 | https://arxiv.org/pdf/2108.01941v3.pdf | Automatic cerebral hemisphere segmentation in rat MRI with lesions via attention-based convolutional neural networks | We present MedicDeepLabv3+, a convolutional neural network that is the first completely automatic method to segment cerebral hemispheres in magnetic resonance (MR) volumes of rats with lesions. MedicDeepLabv3+ improves the state-of-the-art DeepLabv3+ with an advanced decoder, incorporating spatial attention layers and ... | ['Jussi Tohka', 'Riccardo de Feo', 'Artem Shatillo', 'Juan Miguel Valverde'] | 2021-08-04 | null | null | null | null | ['skull-stripping'] | ['medical'] | [-3.74905735e-01 -6.28601685e-02 3.04806143e-01 -3.32868576e-01
-6.52185798e-01 -3.50547612e-01 2.78861433e-01 2.31698573e-01
-9.62569773e-01 7.74305582e-01 4.04836610e-03 -4.67591226e-01
9.60254893e-02 -5.91633618e-01 -8.42773497e-01 -4.33946103e-01
-6.09836817e-01 6.67424262e-01 5.47538042e-01 -1.91017613... | [14.150733947753906, -2.388071298599243] |
511ba1d8-d552-4714-b6cb-ef5bd56fcb26 | bongard-logo-a-new-benchmark-for-human-level | 2010.00763 | null | https://arxiv.org/abs/2010.00763v4 | https://arxiv.org/pdf/2010.00763v4.pdf | Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning | Humans have an inherent ability to learn novel concepts from only a few samples and generalize these concepts to different situations. Even though today's machine learning models excel with a plethora of training data on standard recognition tasks, a considerable gap exists between machine-level pattern recognition and... | ['Animashree Anandkumar', 'Yuke Zhu', 'Ankit B. Patel', 'Lei Mao', 'Zhiding Yu', 'Weili Nie'] | 2020-10-02 | null | http://proceedings.neurips.cc/paper/2020/hash/bf15e9bbff22c7719020f9df4badc20a-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/bf15e9bbff22c7719020f9df4badc20a-Paper.pdf | neurips-2020-12 | ['novel-concepts'] | ['reasoning'] | [ 4.25785750e-01 1.52697548e-01 9.51601043e-02 -4.21115279e-01
-2.49064267e-01 -6.39317513e-01 8.83786321e-01 3.63317817e-01
-1.69020548e-01 3.77490312e-01 3.17536108e-02 -6.38504088e-01
-1.55344307e-01 -9.04527903e-01 -7.17926502e-01 -2.69597441e-01
-9.99735370e-02 4.97705638e-01 2.23282248e-01 -5.17149508... | [10.552327156066895, 2.21075701713562] |
d55eaa3c-7e44-43fa-aa92-22b3055e7513 | estimating-the-direction-and-radius-of-pipe | 2201.10184 | null | https://arxiv.org/abs/2201.10184v1 | https://arxiv.org/pdf/2201.10184v1.pdf | Estimating the Direction and Radius of Pipe from GPR Image by Ellipse Inversion Model | Ground Penetrating Radar (GPR) is widely used as a non-destructive approach to estimate buried utilities. When the GPR's detecting direction is perpendicular to a pipeline, a hyperbolic characteristic would be formed on the GPR B-scan image. However, in real-world applications, the direction of pipelines on the existin... | ['Huanhuan Chen', 'Shengfei Lyu', 'Qiuju Chen', 'Xiren Zhou'] | 2022-01-25 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 1.23947039e-01 1.21389374e-01 7.31486261e-01 -3.86827737e-01
-4.47416872e-01 -2.85044104e-01 -1.61911264e-01 -1.24199532e-01
-7.52938613e-02 2.20587835e-01 -8.87379572e-02 -5.49834907e-01
-4.53312933e-01 -1.12968445e+00 -2.27780417e-01 -6.87744260e-01
-1.19900495e-01 4.79615301e-01 4.99847621e-01 -3.07343960... | [6.833602428436279, 1.4079837799072266] |
5c7ff065-8564-484f-b01f-35d76bde7311 | mepnet-a-model-driven-equivariant-proximal | 2306.14274 | null | https://arxiv.org/abs/2306.14274v1 | https://arxiv.org/pdf/2306.14274v1.pdf | MEPNet: A Model-Driven Equivariant Proximal Network for Joint Sparse-View Reconstruction and Metal Artifact Reduction in CT Images | Sparse-view computed tomography (CT) has been adopted as an important technique for speeding up data acquisition and decreasing radiation dose. However, due to the lack of sufficient projection data, the reconstructed CT images often present severe artifacts, which will be further amplified when patients carry metallic... | ['Yefeng Zheng', 'Yuexiang Li', 'Dong Wei', 'Minghao Zhou', 'Hong Wang'] | 2023-06-25 | null | null | null | null | ['metal-artifact-reduction', 'computed-tomography-ct'] | ['medical', 'methodology'] | [ 2.22529806e-02 2.63235569e-02 -1.93269268e-01 -2.02551261e-01
-4.56126690e-01 -1.03910707e-01 3.05861384e-01 -3.32579106e-01
-1.63661331e-01 2.74373412e-01 4.30731922e-01 -3.04262936e-01
-3.47146332e-01 -8.22431922e-01 -7.34746277e-01 -7.06288099e-01
3.14105153e-01 2.48854846e-01 1.20333634e-01 -2.52808601... | [13.528203964233398, -2.5448341369628906] |
94d964be-93d4-4f8b-9f31-926f67b402fc | blpnet-a-new-dnn-model-and-bengali-ocr-engine | 2202.12250 | null | https://arxiv.org/abs/2202.12250v1 | https://arxiv.org/pdf/2202.12250v1.pdf | BLPnet: A new DNN model and Bengali OCR engine for Automatic License Plate Recognition | The development of the Automatic License Plate Recognition (ALPR) system has received much attention for the English license plate. However, despite being the sixth largest population around the world, no significant progress can be tracked in the Bengali language countries or states for the ALPR system addressing thei... | ['Tareque Bashar Ovi', 'Md. Akiful Hoque Akif', 'Abtahi Ishmam', 'Mahmudul Hasan', 'Koushik Roy', 'Hussain Nyeem', 'Md. Saif Hassan Onim'] | 2022-02-18 | null | null | null | null | ['license-plate-recognition'] | ['computer-vision'] | [-6.73773736e-02 -5.29875934e-01 -8.80196597e-03 -5.28156497e-02
-8.53075862e-01 -6.84224725e-01 4.54485834e-01 -6.12836778e-01
-6.76216006e-01 4.25576001e-01 -5.66598356e-01 -4.86817986e-01
3.38185698e-01 -8.18487883e-01 -6.96779668e-01 -6.30629539e-01
5.24258018e-01 3.51664573e-01 7.87319362e-01 -3.20757240... | [9.836042404174805, -4.941368579864502] |
2922def7-983c-4522-ba43-64c0f1b27889 | learning-a-fast-3d-spectral-approach-to | 2212.08058 | null | https://arxiv.org/abs/2212.08058v1 | https://arxiv.org/pdf/2212.08058v1.pdf | Learning a Fast 3D Spectral Approach to Object Segmentation and Tracking over Space and Time | We pose video object segmentation as spectral graph clustering in space and time, with one graph node for each pixel and edges forming local space-time neighborhoods. We claim that the strongest cluster in this video graph represents the salient object. We start by introducing a novel and efficient method based on 3D f... | ['Marius Leordeanu', 'Elena Burceanu'] | 2022-12-15 | null | null | null | null | ['video-object-segmentation', 'graph-clustering', 'spectral-graph-clustering'] | ['computer-vision', 'graphs', 'graphs'] | [ 2.26735815e-01 -4.24098484e-02 6.67490959e-02 3.37344073e-02
-7.28659689e-01 -9.02261078e-01 2.24804938e-01 1.28155231e-01
-2.29964808e-01 -9.30727497e-02 -6.16741776e-02 -5.58075458e-02
-1.96097568e-01 -5.13953030e-01 -8.64100218e-01 -7.66862094e-01
-3.75720024e-01 4.42613840e-01 6.37019992e-01 2.52594888... | [9.09134578704834, -0.25005388259887695] |
8f152fbd-d083-47ee-9b7c-6535dde7d3a8 | scale-invariant-fully-convolutional-network | 1906.04634 | null | https://arxiv.org/abs/1906.04634v1 | https://arxiv.org/pdf/1906.04634v1.pdf | Scale Invariant Fully Convolutional Network: Detecting Hands Efficiently | Existing hand detection methods usually follow the pipeline of multiple stages with high computation cost, i.e., feature extraction, region proposal, bounding box regression, and additional layers for rotated region detection. In this paper, we propose a new Scale Invariant Fully Convolutional Network (SIFCN) trained i... | ['Tiejian Luo', 'Siwei Lyu', 'Dan Liu', 'Yanjun Wu', 'Libo Zhang', 'Dawei Du', 'Feiyue Huang'] | 2019-06-11 | null | null | null | null | ['hand-detection'] | ['computer-vision'] | [-1.36415064e-01 -5.35591364e-01 9.99490395e-02 -2.68793166e-01
-4.52780873e-01 -4.60873306e-01 1.44499123e-01 -2.69390315e-01
-1.01493716e+00 3.70771885e-01 -1.34316767e-02 -6.22199886e-02
3.40977371e-01 -5.68721890e-01 -5.07546186e-01 -6.44704580e-01
1.19166441e-01 9.99537259e-02 7.76436269e-01 -6.49863929... | [6.567039966583252, -0.6388445496559143] |
cdd50d02-1f4e-4dcd-85d9-c280f0945a8b | document-shadow-removal-with-foreground | null | null | https://ieeexplore.ieee.org/document/9897217 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9897217 | Document Shadow Removal with Foreground Detection Learning From Fully Synthetic Images | Shadow removal for document images is a major task for digitized document applications. Recent shadow removal models have been trained on pairs of shadow images and shadow-free images. However, obtaining a large-scale and diverse dataset is laborious and remains a great challenge. Thus, only small real datasets are ava... | ['Yoshimitsu Aoki', 'Naofumi Akimoto', 'Yuhi Matsuo'] | 2022-10-18 | null | null | null | 2022-2022-10 | ['shadow-removal'] | ['computer-vision'] | [ 4.68726516e-01 -2.58163601e-01 2.46320903e-01 -3.79994839e-01
-6.73948526e-01 -5.37538111e-01 5.65280974e-01 -6.04619563e-01
-1.29758179e-01 8.45625103e-01 2.27066111e-02 -3.52831423e-01
3.27191502e-01 -6.25556827e-01 -7.59784520e-01 -8.55178475e-01
3.42877448e-01 2.58724838e-01 7.68877029e-01 5.67522310... | [10.852837562561035, -4.083192825317383] |
b7758c11-7927-4b61-aa53-15cea27a327a | facial-information-analysis-technology-for | 2111.09303 | null | https://arxiv.org/abs/2111.09303v1 | https://arxiv.org/pdf/2111.09303v1.pdf | Facial Information Analysis Technology for Gender and Age Estimation | This is a study on facial information analysis technology for estimating gender and age, and poses are estimated using a transformation relationship matrix between the camera coordinate system and the world coordinate system for estimating the pose of a face image. Gender classification was relatively simple compared t... | ['Sua Jung', 'Gilheum Park'] | 2021-11-17 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [-6.95648134e-01 1.83205456e-01 -5.47199324e-02 -7.96488523e-01
2.18817100e-01 8.51498246e-02 3.96320492e-01 -1.48983940e-01
-6.11740351e-01 4.79758561e-01 -1.01278303e-02 5.04370272e-01
2.39942759e-01 -9.08117354e-01 -2.03610197e-01 -7.96795428e-01
-2.31153414e-01 3.60418707e-01 -5.13264835e-01 3.55615839... | [13.550028800964355, 0.9527871012687683] |
2afca225-324e-4471-83ae-1ffbf6af15be | invariant-collaborative-filtering-to | 2302.05328 | null | https://arxiv.org/abs/2302.05328v3 | https://arxiv.org/pdf/2302.05328v3.pdf | Invariant Collaborative Filtering to Popularity Distribution Shift | Collaborative Filtering (CF) models, despite their great success, suffer from severe performance drops due to popularity distribution shifts, where these changes are ubiquitous and inevitable in real-world scenarios. Unfortunately, most leading popularity debiasing strategies, rather than tackling the vulnerability of ... | ['Tat-Seng Chua', 'Yancheng Yuan', 'Xiang Wang', 'Jingnan Zheng', 'An Zhang'] | 2023-02-10 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [-1.92291856e-01 -6.72444105e-01 -5.68532884e-01 -1.93316624e-01
-5.09189904e-01 -7.27679908e-01 4.66137320e-01 -2.92585697e-03
2.91199377e-03 7.12664843e-01 5.34331381e-01 -2.00041518e-01
-3.32535356e-01 -8.35163057e-01 -7.51637757e-01 -9.08772230e-01
-1.44838676e-01 5.40171742e-01 -5.93156591e-02 -2.03951687... | [9.89201831817627, 5.501735687255859] |
711eda0d-349f-474c-8a8b-124c6546a496 | rarely-a-problem-language-models-exhibit | 2212.08700 | null | https://arxiv.org/abs/2212.08700v2 | https://arxiv.org/pdf/2212.08700v2.pdf | Rarely a problem? Language models exhibit inverse scaling in their predictions following few-type quantifiers | How well do language models deal with quantification? In this study, we focus on 'few'-type quantifiers, as in 'few children like toys', which might pose a particular challenge for language models because the sentence components with out the quantifier are likely to co-occur, and 'few'-type quantifiers are rare. We pre... | ['Benjamin K. Bergen', 'James A. Michaelov'] | 2022-12-16 | null | null | null | null | ['type'] | ['speech'] | [-1.98843837e-01 2.70057350e-01 1.95307970e-01 -2.97053248e-01
-6.35058403e-01 -5.99649608e-01 3.88380289e-01 4.72005516e-01
-7.37691164e-01 4.85816509e-01 5.63009977e-01 -6.88998044e-01
-1.90249421e-02 -9.27944958e-01 -4.82664227e-01 -1.57911509e-01
2.84194686e-02 6.40634358e-01 2.75309265e-01 -6.97374225... | [10.52440357208252, 9.08178424835205] |
7e273516-a248-452b-83a3-3ef055a78225 | paddleseg-a-high-efficient-development | 2101.06175 | null | https://arxiv.org/abs/2101.06175v1 | https://arxiv.org/pdf/2101.06175v1.pdf | PaddleSeg: A High-Efficient Development Toolkit for Image Segmentation | Image Segmentation plays an essential role in computer vision and image processing with various applications from medical diagnosis to autonomous car driving. A lot of segmentation algorithms have been proposed for addressing specific problems. In recent years, the success of deep learning techniques has tremendously i... | ['Yuying Hao', 'Baohua Lai', 'Zeyu Chen', 'Zewu Wu', 'Guowei Chen', 'Lutao Chu', 'Yi Liu'] | 2021-01-15 | null | null | null | null | ['human-part-segmentation'] | ['computer-vision'] | [-2.02843934e-01 -2.08201885e-01 -3.00521165e-01 -4.52399701e-01
-5.92787981e-01 -2.63898373e-01 -5.62904775e-02 -1.23597980e-01
-3.23720694e-01 8.06083679e-02 -5.49099386e-01 -5.54742694e-01
3.11721057e-01 -8.11983705e-01 -4.38339025e-01 -6.05031848e-01
7.29706809e-02 4.96078849e-01 6.52606845e-01 1.17335897... | [9.523313522338867, 0.0024700062349438667] |
399e0261-f8ce-4217-b8fd-3a3f89d5881e | predicting-team-performance-with-spatial | 2206.10720 | null | https://arxiv.org/abs/2206.10720v1 | https://arxiv.org/pdf/2206.10720v1.pdf | Predicting Team Performance with Spatial Temporal Graph Convolutional Networks | This paper presents a new approach for predicting team performance from the behavioral traces of a set of agents. This spatiotemporal forecasting problem is very relevant to sports analytics challenges such as coaching and opponent modeling. We demonstrate that our proposed model, Spatial Temporal Graph Convolutional N... | ['Gita Sukthankar', 'Shengnan Hu'] | 2022-06-21 | null | null | null | null | ['sports-analytics'] | ['computer-vision'] | [-3.09594512e-01 -3.43246162e-01 -3.00704181e-01 -2.92594619e-02
-2.12549657e-01 -5.38254857e-01 4.23182040e-01 3.68430950e-02
-5.13713479e-01 3.29169005e-01 4.65781957e-01 -2.20202729e-01
-3.97949040e-01 -1.07816172e+00 -4.41411614e-01 -2.64778048e-01
-8.76406908e-01 2.97363043e-01 6.72161996e-01 -9.64656293... | [6.759116172790527, 0.3353029787540436] |
9361ed17-eaf4-46c8-99a8-87e836d8f7bf | on-automatic-data-augmentation-for-3d-point | 2112.06029 | null | https://arxiv.org/abs/2112.06029v1 | https://arxiv.org/pdf/2112.06029v1.pdf | On Automatic Data Augmentation for 3D Point Cloud Classification | Data augmentation is an important technique to reduce overfitting and improve learning performance, but existing works on data augmentation for 3D point cloud data are based on heuristics. In this work, we instead propose to automatically learn a data augmentation strategy using bilevel optimization. An augmentor is de... | ['Chuan-Sheng Foo', 'Fayao Liu', 'Xun Xu', 'Wanyue Zhang'] | 2021-12-11 | null | null | null | null | ['3d-object-classification', '3d-object-recognition', 'point-cloud-classification'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.67553777e-01 4.12535280e-01 -3.42573404e-01 -6.18457913e-01
-8.55581403e-01 -5.04029274e-01 5.40890217e-01 2.85325468e-01
-4.64325815e-01 5.15905142e-01 -2.43865684e-01 -5.41390240e-01
2.68484533e-01 -6.41584039e-01 -9.69177246e-01 -7.03915238e-01
-1.19493626e-01 9.22003567e-01 -1.38837993e-01 1.56669673... | [8.020560264587402, -3.315605401992798] |
f8e5c28f-0f76-4d67-a144-92f967694374 | yolo-and-k-means-based-3d-object-detection | 2004.11465 | null | https://arxiv.org/abs/2004.11465v1 | https://arxiv.org/pdf/2004.11465v1.pdf | YOLO and K-Means Based 3D Object Detection Method on Image and Point Cloud | Lidar based 3D object detection and classification tasks are essential for automated driving(AD). A Lidar sensor can provide the 3D point coud data reconstruction of the surrounding environment. But the detection in 3D point cloud still needs a strong algorithmic challenge. This paper consists of three parts.(1)Lidar-c... | ['Yoko SASAKI', 'Weimin WANG', 'Kentaro SHIMIZU', 'Xuanyu YIN'] | 2020-04-21 | null | null | null | null | ['3d-object-recognition'] | ['computer-vision'] | [-1.87198147e-01 -6.92239821e-01 5.97909987e-02 -3.39442402e-01
-3.69767666e-01 -4.56715643e-01 2.04956189e-01 2.59153754e-01
-4.71621275e-01 -4.99440357e-02 -7.40082026e-01 -5.99712014e-01
1.67517275e-01 -1.06917787e+00 -5.60348034e-01 -4.13470954e-01
8.65168422e-02 1.11639535e+00 9.32698905e-01 -1.95530728... | [7.735830307006836, -2.6410229206085205] |
38f61d9c-8bc3-423a-a190-5df9a1d9dbb2 | efficient-multi-scale-3d-cnn-with-fully | 1603.05959 | null | http://arxiv.org/abs/1603.05959v3 | http://arxiv.org/pdf/1603.05959v3.pdf | Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation | We propose a dual pathway, 11-layers deep, three-dimensional Convolutional
Neural Network for the challenging task of brain lesion segmentation. The
devised architecture is the result of an in-depth analysis of the limitations
of current networks proposed for similar applications. To overcome the
computational burden o... | ['Ben Glocker', 'Daniel Rueckert', 'David K. Menon', 'Andrew D. Kane', 'Joanna P. Simpson', 'Virginia F. J. Newcombe', 'Christian Ledig', 'Konstantinos Kamnitsas'] | 2016-03-18 | null | null | null | null | ['3d-medical-imaging-segmentation', 'brain-lesion-segmentation-from-mri'] | ['medical', 'medical'] | [ 5.45964062e-01 2.28609100e-01 1.63504221e-02 -4.37213361e-01
-9.56124485e-01 -2.36831307e-01 3.55405122e-01 2.14719251e-01
-8.13453972e-01 2.87484705e-01 1.74492136e-01 -5.38365185e-01
-1.74019188e-01 -4.60997909e-01 -5.91535330e-01 -6.91147923e-01
-3.45348835e-01 6.10983908e-01 6.85795724e-01 1.16500564... | [14.327427864074707, -2.2741119861602783] |
3dcad040-7a24-45d3-9a8c-5701c7695fe7 | multi-task-knowledge-enhancement-for-zero | 2306.06302 | null | https://arxiv.org/abs/2306.06302v1 | https://arxiv.org/pdf/2306.06302v1.pdf | Multi-Task Knowledge Enhancement for Zero-Shot and Multi-Domain Recommendation in an AI Assistant Application | Recommender systems have found significant commercial success but still struggle with integrating new users. Since users often interact with content in different domains, it is possible to leverage a user's interactions in previous domains to improve that user's recommendations in a new one (multi-domain recommendation... | ['Aram Galstyan', 'Greg Ver Steeg', 'Tony Chen', 'Xing Fan', 'Fan Yang', 'Ziyan Jiang', 'Elan Markowitz'] | 2023-06-09 | null | null | null | null | ['knowledge-graphs'] | ['knowledge-base'] | [ 1.20914117e-01 2.58797556e-01 -5.17913878e-01 -2.97757775e-01
-1.88466281e-01 -8.40420723e-01 5.05906522e-01 2.94316262e-01
2.61310134e-02 6.76252186e-01 6.94126070e-01 -2.21228540e-01
-6.00612462e-01 -8.71604919e-01 -3.57107311e-01 8.86393636e-02
1.58894271e-01 8.50358605e-01 7.00048983e-01 -8.08470190... | [10.02224063873291, 5.759999752044678] |
2455bac0-3d78-4c8e-b2b7-1264645fc8c2 | personalized-federated-learning-via-gradient | 2304.11524 | null | https://arxiv.org/abs/2304.11524v1 | https://arxiv.org/pdf/2304.11524v1.pdf | Personalized Federated Learning via Gradient Modulation for Heterogeneous Text Summarization | Text summarization is essential for information aggregation and demands large amounts of training data. However, concerns about data privacy and security limit data collection and model training. To eliminate this concern, we propose a federated learning text summarization scheme, which allows users to share the global... | ['Jing Xiao', 'Zhangcheng Huang', 'Lingwei Kong', 'Jianzong Wang', 'Rongfeng Pan'] | 2023-04-23 | null | null | null | null | ['personalized-federated-learning', 'text-summarization'] | ['methodology', 'natural-language-processing'] | [ 2.11373270e-02 8.96882564e-02 -5.70267379e-01 -6.04961872e-01
-1.17599678e+00 -5.54864168e-01 2.03052431e-01 3.28338236e-01
-4.15702403e-01 9.26545203e-01 7.55956531e-01 7.69903213e-02
-8.38637501e-02 -5.88154376e-01 -5.35851955e-01 -6.38202369e-01
5.83909154e-02 2.91089028e-01 -1.06284223e-01 8.66794661... | [5.836994647979736, 6.292841911315918] |
6b63f936-25fc-4460-89e7-d3fe7b366857 | occuseg-occupancy-aware-3d-instance | 2003.06537 | null | https://arxiv.org/abs/2003.06537v3 | https://arxiv.org/pdf/2003.06537v3.pdf | OccuSeg: Occupancy-aware 3D Instance Segmentation | 3D instance segmentation, with a variety of applications in robotics and augmented reality, is in large demands these days. Unlike 2D images that are projective observations of the environment, 3D models provide metric reconstruction of the scenes without occlusion or scale ambiguity. In this paper, we define "3D occup... | ['Lan Xu', 'Lu Fang', 'Tian Zheng', 'Lei Han'] | 2020-03-14 | occuseg-occupancy-aware-3d-instance-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Han_OccuSeg_Occupancy-Aware_3D_Instance_Segmentation_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Han_OccuSeg_Occupancy-Aware_3D_Instance_Segmentation_CVPR_2020_paper.pdf | cvpr-2020-6 | ['3d-instance-segmentation-1'] | ['computer-vision'] | [ 1.18200086e-01 3.69783938e-01 7.48975854e-03 -4.35508609e-01
-6.03391647e-01 -2.54765391e-01 5.00314653e-01 1.63861796e-01
-4.72890794e-01 4.60623175e-01 -1.75555665e-02 2.49661077e-02
-2.86283195e-01 -7.15989709e-01 -7.19387770e-01 -8.36561680e-01
-1.80685282e-01 8.56496096e-01 4.31881011e-01 2.71597832... | [8.075126647949219, -2.6271541118621826] |
31dd3c45-6607-4ee1-abbe-1da93e26d661 | detection-of-3d-bounding-boxes-of-vehicles | 2003.13137 | null | https://arxiv.org/abs/2003.13137v2 | https://arxiv.org/pdf/2003.13137v2.pdf | Detection of 3D Bounding Boxes of Vehicles Using Perspective Transformation for Accurate Speed Measurement | Detection and tracking of vehicles captured by traffic surveillance cameras is a key component of intelligent transportation systems. We present an improved version of our algorithm for detection of 3D bounding boxes of vehicles, their tracking and subsequent speed estimation. Our algorithm utilizes the known geometry ... | ['Milan Ftáčnik', 'Viktor Kocur'] | 2020-03-29 | null | null | null | null | ['vehicle-speed-estimation'] | ['computer-vision'] | [-1.19431578e-01 -2.30644166e-01 7.97663406e-02 -1.13289267e-01
-5.04103422e-01 -8.51870298e-01 8.33917975e-01 1.06445104e-01
-9.08081770e-01 3.66263509e-01 -4.55417365e-01 -6.20032191e-01
1.45163685e-01 -8.43270361e-01 -7.09962070e-01 -6.57079697e-01
-1.22031219e-01 7.51463175e-01 1.14484990e+00 -1.36170998... | [7.975043296813965, -1.1930713653564453] |
e39bf389-3f24-4e9e-b3b7-3111ffe03d10 | direct-cortical-thickness-estimation-using | null | null | https://doi.org/10.1002/hbm.25159 | https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.25159 | Direct cortical thickness estimation using deep learning‐based anatomy segmentation and cortex parcellation | Accurate and reliable measures of cortical thickness from magnetic resonance imaging are an important biomarker to study neurodegenerative and neurological disorders. Diffeomorphic registration‐based cortical thickness (DiReCT) is a known technique to derive such measures from non‐surface‐based volumetric tissue maps. ... | ['Richard McKinley', 'Roland Wiest', 'Mauricio Reyes', 'Christian Rummel', 'Michael Rebsamen'] | 2020-11-05 | null | null | null | null | ['3d-medical-imaging-segmentation', 'diffeomorphic-medical-image-registration', 'brain-morphometry'] | ['medical', 'medical', 'medical'] | [-3.09085339e-01 7.28597343e-02 3.33011180e-01 -4.94271278e-01
-7.77502298e-01 -1.19761400e-01 4.57379580e-01 -1.49771973e-01
-9.27437246e-01 1.08585191e+00 -4.13656011e-02 1.23712316e-01
-1.83201283e-01 -1.06808913e+00 -4.50388908e-01 -6.64464951e-01
-5.16058326e-01 1.12571073e+00 6.56552851e-01 -9.23765451... | [14.054495811462402, -2.119762420654297] |
44136e45-7430-474a-9cdd-bf60ebac0a9c | temporal-feature-warping-for-video-shadow | 2107.14287 | null | https://arxiv.org/abs/2107.14287v1 | https://arxiv.org/pdf/2107.14287v1.pdf | Temporal Feature Warping for Video Shadow Detection | While single image shadow detection has been improving rapidly in recent years, video shadow detection remains a challenging task due to data scarcity and the difficulty in modelling temporal consistency. The current video shadow detection method achieves this goal via co-attention, which mostly exploits information th... | ['Dimitris Samaras', 'Hieu Le', 'Shilin Hu'] | 2021-07-29 | null | null | null | null | ['shadow-detection'] | ['computer-vision'] | [ 4.78141069e-01 -5.23577988e-01 -8.61267820e-02 -2.90796548e-01
-4.14896131e-01 -3.36221427e-01 5.37024021e-01 -4.63743925e-01
-4.04191285e-01 9.21509147e-01 4.40636456e-01 -2.28573009e-01
3.21270525e-01 -3.51062834e-01 -5.50593972e-01 -8.40272188e-01
-2.17383489e-01 -1.20254323e-01 1.06172431e+00 -2.37235101... | [10.835538864135742, -4.1108622550964355] |
9ea56af6-3b27-4302-8134-e3725c837441 | adding-guardrails-to-advanced-chatbots | 2306.07500 | null | https://arxiv.org/abs/2306.07500v1 | https://arxiv.org/pdf/2306.07500v1.pdf | Adding guardrails to advanced chatbots | Generative AI models continue to become more powerful. The launch of ChatGPT in November 2022 has ushered in a new era of AI. ChatGPT and other similar chatbots have a range of capabilities, from answering student homework questions to creating music and art. There are already concerns that humans may be replaced by ch... | ['Lisa Singh', 'Yanchen Wang'] | 2023-06-13 | null | null | null | null | ['code-generation', 'chatbot', 'chatbot'] | ['computer-code', 'methodology', 'natural-language-processing'] | [-2.90413618e-01 4.33961719e-01 -3.79722901e-02 -4.25691217e-01
-6.91642582e-01 -6.60297513e-01 5.98473728e-01 3.23029272e-02
-3.60807568e-01 9.57356453e-01 6.89140856e-01 -6.60017073e-01
-1.64336562e-01 -7.51588225e-01 -1.84104726e-01 -3.73747766e-01
6.86758995e-01 7.86403477e-01 2.59822682e-02 -5.80022216... | [10.36596965789795, 7.534711837768555] |
94ee2b1b-722c-4f24-92c8-c16407074d3a | modetr-moving-object-detection-with | 2106.11422 | null | https://arxiv.org/abs/2106.11422v1 | https://arxiv.org/pdf/2106.11422v1.pdf | MODETR: Moving Object Detection with Transformers | Moving Object Detection (MOD) is a crucial task for the Autonomous Driving pipeline. MOD is usually handled via 2-stream convolutional architectures that incorporates both appearance and motion cues, without considering the inter-relations between the spatial or motion features. In this paper, we tackle this problem th... | ['Ahmad El-Sallab', 'Eslam Mohamed'] | 2021-06-21 | null | null | null | null | ['moving-object-detection'] | ['computer-vision'] | [ 2.33909905e-01 -2.77571887e-01 -4.56571579e-02 -4.05956060e-01
-7.09813118e-01 -3.44486952e-01 9.28485692e-01 -2.92880923e-01
-8.57238770e-01 2.58662164e-01 9.35656875e-02 -2.82592714e-01
8.81625339e-02 -6.90725207e-01 -9.54357684e-01 -7.42073417e-01
-1.14571638e-01 2.22813860e-01 9.05054867e-01 -3.04977983... | [8.234055519104004, -1.2922035455703735] |
0506c779-1fe8-4efd-bd15-c10298636744 | semi-automatic-sign-language-corpora | null | null | https://aclanthology.org/L12-1433 | https://aclanthology.org/L12-1433.pdf | Semi-Automatic Sign Language Corpora Annotation using Lexical Representations of Signs | Nowadays many researches focus on the automatic recognition of sign language. High recognition rates are achieved using lot of training data. This data is, generally, collected by manual annotating SL video corpus. However this is time consuming and the results depend on the annotators knowledge. In this work we intend... | ['Michael Filhol', 'Christophe Collet', 'Matilde Gonzalez'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['hand-segmentation'] | ['computer-vision'] | [ 2.82720506e-01 -2.33696029e-01 -1.27737358e-01 -5.52719831e-01
-3.94699544e-01 -5.25185764e-01 6.35566950e-01 -3.35986130e-02
-9.37262118e-01 7.35552907e-01 2.09744707e-01 4.19386327e-02
-4.60173339e-01 -3.51429969e-01 -1.99943736e-01 -6.86533332e-01
1.23834744e-01 7.16134489e-01 5.96129298e-01 -2.88451046... | [9.103132247924805, -6.345076084136963] |
94a54897-a035-4410-858b-914684476cd9 | deep-learning-based-instance-segmentation-in | 1806.11137 | null | http://arxiv.org/abs/1806.11137v1 | http://arxiv.org/pdf/1806.11137v1.pdf | Deep Learning Based Instance Segmentation in 3D Biomedical Images Using Weak Annotation | Instance segmentation in 3D images is a fundamental task in biomedical image
analysis. While deep learning models often work well for 2D instance
segmentation, 3D instance segmentation still faces critical challenges, such as
insufficient training data due to various annotation difficulties in 3D
biomedical images. Com... | ['Si-Yuan Zhang', 'Zhuo Zhao', 'Danny Z. Chen', 'Lin Yang', 'Ian H. Guldner', 'Hao Zheng'] | 2018-06-28 | null | null | null | null | ['3d-instance-segmentation-1'] | ['computer-vision'] | [ 2.15594411e-01 5.99726737e-01 -3.12423587e-01 -5.63251555e-01
-9.71989095e-01 -3.40187162e-01 2.77374871e-02 3.74837577e-01
-6.44267142e-01 5.84822834e-01 -4.83023494e-01 -4.01084810e-01
2.72825837e-01 -5.43281734e-01 -8.72102797e-01 -5.54781973e-01
1.14202768e-01 8.92392278e-01 7.94613898e-01 2.93366879... | [14.668722152709961, -2.2716152667999268] |
10cac1a4-4d13-403f-bc57-ff5e1b00d044 | pseudo-convolutional-policy-gradient-for | 2003.03983 | null | https://arxiv.org/abs/2003.03983v1 | https://arxiv.org/pdf/2003.03983v1.pdf | Pseudo-Convolutional Policy Gradient for Sequence-to-Sequence Lip-Reading | Lip-reading aims to infer the speech content from the lip movement sequence and can be seen as a typical sequence-to-sequence (seq2seq) problem which translates the input image sequence of lip movements to the text sequence of the speech content. However, the traditional learning process of seq2seq models always suffer... | ['Mingshuang Luo', 'Xilin Chen', 'Shuang Yang', 'Shiguang Shan'] | 2020-03-09 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 3.86200756e-01 -1.53789014e-01 -2.70137459e-01 -3.09165597e-01
-1.23591673e+00 -1.35075301e-01 5.34180641e-01 -1.78496063e-01
-6.91640675e-01 7.29695201e-01 5.00964522e-01 -1.88481390e-01
2.11750537e-01 -2.18383566e-01 -8.59328568e-01 -1.00875890e+00
2.00202838e-01 -2.36805640e-02 1.15460210e-01 9.04466435... | [14.334310531616211, 5.007132053375244] |
c1377006-8359-4106-af44-15a8ccf8e787 | dual-memory-units-with-uncertainty-regulation | 2302.05160 | null | https://arxiv.org/abs/2302.05160v1 | https://arxiv.org/pdf/2302.05160v1.pdf | Dual Memory Units with Uncertainty Regulation for Weakly Supervised Video Anomaly Detection | Learning discriminative features for effectively separating abnormal events from normality is crucial for weakly supervised video anomaly detection (WS-VAD) tasks. Existing approaches, both video and segment-level label oriented, mainly focus on extracting representations for anomaly data while neglecting the implicati... | ['Wei Yang', 'Junqing Yu', 'Hang Zhou'] | 2023-02-10 | null | null | null | null | ['video-anomaly-detection'] | ['computer-vision'] | [ 6.45658225e-02 -5.81237264e-02 -2.86014408e-01 -4.24536943e-01
-4.48061764e-01 -1.81524470e-01 5.54956079e-01 2.41757810e-01
-2.05781981e-01 2.06370771e-01 2.67312467e-01 -2.35376433e-01
3.91732603e-02 -6.81207657e-01 -8.52579832e-01 -7.30958581e-01
-3.48651588e-01 1.98795706e-01 1.80579320e-01 2.01666772... | [7.843392372131348, 1.6237285137176514] |
1b122463-182a-44a5-81d3-2d20676657b8 | semantics-ontology-and-explanation | 2304.11124 | null | https://arxiv.org/abs/2304.11124v1 | https://arxiv.org/pdf/2304.11124v1.pdf | Semantics, Ontology and Explanation | The terms 'semantics' and 'ontology' are increasingly appearing together with 'explanation', not only in the scientific literature, but also in organizational communication. However, all of these terms are also being significantly overloaded. In this paper, we discuss their strong relation under particular interpretati... | ['Nicola Guarino', 'Giancarlo Guizzardi'] | 2023-04-21 | null | null | null | null | ['philosophy'] | ['miscellaneous'] | [ 3.23732883e-01 1.20101190e+00 -1.78980842e-01 -4.44386214e-01
3.12315017e-01 -4.73622024e-01 1.00514030e+00 4.09849465e-01
3.22859645e-01 4.37371284e-01 5.02762020e-01 -6.48449838e-01
-9.42108691e-01 -9.02515411e-01 -3.38022202e-01 -1.48486167e-01
1.33064076e-01 6.07987225e-01 1.98495328e-01 -6.13275766... | [8.860614776611328, 6.820672035217285] |
992f0c06-7739-4467-9879-5c12e37f0156 | expunations-augmenting-puns-with-keywords-and | 2210.13513 | null | https://arxiv.org/abs/2210.13513v1 | https://arxiv.org/pdf/2210.13513v1.pdf | ExPUNations: Augmenting Puns with Keywords and Explanations | The tasks of humor understanding and generation are challenging and subjective even for humans, requiring commonsense and real-world knowledge to master. Puns, in particular, add the challenge of fusing that knowledge with the ability to interpret lexical-semantic ambiguity. In this paper, we present the ExPUNations (E... | ['Nanyun Peng', 'Yang Liu', 'Jing Huang', 'Tagyoung Chung', 'Alessandra Cervone', 'Shereen Oraby', 'Anjali Narayan-Chen', 'Jiao Sun'] | 2022-10-24 | null | null | null | null | ['explanation-generation'] | ['natural-language-processing'] | [ 4.65767048e-02 2.14445144e-01 6.54549003e-02 -8.94789770e-02
-4.66798961e-01 -7.54394352e-01 8.08702230e-01 2.09180161e-01
-2.58200858e-02 9.11881268e-01 9.99449968e-01 -1.01415262e-01
3.69633406e-01 -6.04050875e-01 -3.65262687e-01 -1.47106936e-02
4.18144494e-01 6.80363297e-01 -2.66723245e-01 -8.05539668... | [8.925348281860352, 10.949594497680664] |
1119d2bb-bf92-46fd-b89f-f63bd66d7c7b | principled-paraphrase-generation-with-1 | 2205.12213 | null | https://arxiv.org/abs/2205.12213v3 | https://arxiv.org/pdf/2205.12213v3.pdf | Principled Paraphrase Generation with Parallel Corpora | Round-trip Machine Translation (MT) is a popular choice for paraphrase generation, which leverages readily available parallel corpora for supervision. In this paper, we formalize the implicit similarity function induced by this approach, and show that it is susceptible to non-paraphrase pairs sharing a single ambiguous... | ['Gorka Labaka', 'Aitor Soroa', 'Eneko Agirre', 'Mikel Artetxe', 'Aitor Ormazabal'] | 2022-05-24 | principled-paraphrase-generation-with | https://aclanthology.org/2022.acl-long.114 | https://aclanthology.org/2022.acl-long.114.pdf | acl-2022-5 | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 4.80050892e-01 4.21721816e-01 -4.78867561e-01 -3.40549260e-01
-1.20342529e+00 -1.01033926e+00 8.95292103e-01 -1.48640852e-02
-3.11485261e-01 9.90489185e-01 4.61973459e-01 -5.99015236e-01
1.77245006e-01 -7.22999811e-01 -1.08676267e+00 -5.24331391e-01
4.43031937e-01 5.76234400e-01 -1.49695069e-01 -4.96239543... | [11.686931610107422, 9.847975730895996] |
31dede66-97c5-476f-acdc-61f0d714c975 | attentive-history-selection-for | 1908.09456 | null | https://arxiv.org/abs/1908.09456v1 | https://arxiv.org/pdf/1908.09456v1.pdf | Attentive History Selection for Conversational Question Answering | Conversational question answering (ConvQA) is a simplified but concrete setting of conversational search. One of its major challenges is to leverage the conversation history to understand and answer the current question. In this work, we propose a novel solution for ConvQA that involves three aspects. First, we propose... | ['W. Bruce Croft', 'Cen Chen', 'Yongfeng Zhang', 'Liu Yang', 'Mohit Iyyer', 'Minghui Qiu', 'Chen Qu'] | 2019-08-26 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 9.32688266e-03 1.39766410e-01 4.00627851e-02 -6.67923093e-01
-7.49763191e-01 -6.20416343e-01 8.43819678e-01 -1.15590781e-01
-3.18485081e-01 4.79829550e-01 1.11346877e+00 -4.73725736e-01
-3.18014674e-04 -7.19920218e-01 -2.64061451e-01 -4.09250677e-01
3.65229920e-02 7.32437074e-01 2.82917351e-01 -7.80508339... | [12.221796035766602, 7.874264717102051] |
f344f3d7-ef56-4e42-9e31-c0aad8f6aa49 | balf-simple-and-efficient-blur-aware-local | 2211.14731 | null | https://arxiv.org/abs/2211.14731v2 | https://arxiv.org/pdf/2211.14731v2.pdf | BALF: Simple and Efficient Blur Aware Local Feature Detector | Local feature detection is a key ingredient of many image processing and computer vision applications, such as visual odometry and localization. Most existing algorithms focus on feature detection from a sharp image. They would thus have degraded performance once the image is blurred, which could happen easily under lo... | ['Peidong Liu', 'Ben M. Chen', 'Yu Zhai', 'Zhenjun Zhao'] | 2022-11-27 | null | null | null | null | ['keypoint-detection'] | ['computer-vision'] | [ 6.54740110e-02 -3.67655456e-01 7.13013932e-02 -5.43001816e-02
-2.86081702e-01 -2.82844692e-01 5.69256961e-01 1.10856451e-01
-7.44831085e-01 3.63034666e-01 -2.39361241e-01 -1.05069116e-01
-1.16389342e-01 -4.50726509e-01 -7.17392385e-01 -5.05502403e-01
-5.13870716e-02 -8.54808912e-02 7.26304591e-01 2.75969077... | [8.363675117492676, -1.4841102361679077] |
d9ab7284-1260-49a9-b740-a2e5c64fef0a | self-supervised-learning-of-3d-objects-from | 1911.08850 | null | https://arxiv.org/abs/1911.08850v1 | https://arxiv.org/pdf/1911.08850v1.pdf | Self-supervised Learning of 3D Objects from Natural Images | We present a method to learn single-view reconstruction of the 3D shape, pose, and texture of objects from categorized natural images in a self-supervised manner. Since this is a severely ill-posed problem, carefully designing a training method and introducing constraints are essential. To avoid the difficulty of train... | ['Tatsuya Harada', 'Hiroharu Kato'] | 2019-11-20 | null | null | null | null | ['3d-object-reconstruction'] | ['computer-vision'] | [ 2.68109679e-01 -1.61665305e-02 4.45194066e-01 -6.10210240e-01
-3.89988661e-01 -7.01850355e-01 4.73750114e-01 -4.34001118e-01
-4.75118756e-02 4.81255174e-01 -2.77343154e-01 3.53545733e-02
8.10533538e-02 -6.60419643e-01 -9.58234608e-01 -7.08510995e-01
2.65350431e-01 8.55574906e-01 3.61009777e-01 1.08437181... | [8.536688804626465, -3.0149855613708496] |
6b3586fa-c707-4e54-812e-3f4126f217b9 | an-end-to-end-neural-network-framework-for | 1903.09424 | null | http://arxiv.org/abs/1903.09424v1 | http://arxiv.org/pdf/1903.09424v1.pdf | An end-to-end Neural Network Framework for Text Clustering | The unsupervised text clustering is one of the major tasks in natural
language processing (NLP) and remains a difficult and complex problem.
Conventional \mbox{methods} generally treat this task using separated steps,
including text representation learning and clustering the representations. As
an improvement, neural m... | ['Jinchao Zhang', 'Xingyi Cheng', 'Jie Zhou'] | 2019-03-22 | null | null | null | null | ['text-clustering'] | ['natural-language-processing'] | [-1.56193338e-02 1.80712715e-02 -2.35746101e-01 -8.02063167e-01
-8.77599180e-01 -3.15991431e-01 5.55384040e-01 4.09214377e-01
-7.73113191e-01 2.96343565e-01 3.53894383e-01 -9.59556699e-02
-9.57154259e-02 -5.36238194e-01 -5.06998301e-01 -8.46526146e-01
3.14341813e-01 6.46260262e-01 -1.32627770e-01 -4.80354168... | [10.428473472595215, 6.730825901031494] |
9a15436d-31bc-4481-a08d-db63bbd968f2 | nuclear-segmentation-and-classification-on | 2301.03418 | null | https://arxiv.org/abs/2301.03418v1 | https://arxiv.org/pdf/2301.03418v1.pdf | Nuclear Segmentation and Classification: On Color & Compression Generalization | Since the introduction of digital and computational pathology as a field, one of the major problems in the clinical application of algorithms has been the struggle to generalize well to examples outside the distribution of the training data. Existing work to address this in both pathology and natural images has focused... | ['Nasir Rajpoot', 'Abhir Bhalerao', 'Fayyaz Minhas', 'Shan E Ahmed Raza', 'Mostafa Jahanifar', 'Simon Graham', 'Robert Jewsbury', 'Quoc Dang Vu'] | 2023-01-09 | null | null | null | null | ['nuclear-segmentation'] | ['medical'] | [ 7.84815669e-01 -1.37636170e-01 1.53240770e-01 -2.47997761e-01
-1.21918142e+00 -5.80569804e-01 3.97915244e-01 1.01174161e-01
-6.76812887e-01 7.86669493e-01 -3.65942940e-02 -1.81549221e-01
-5.94780296e-02 -1.77978486e-01 -5.23636818e-01 -9.49441254e-01
1.46983922e-01 5.57900369e-01 4.54656631e-01 -1.10400081... | [15.06659984588623, -2.8944811820983887] |
0fcba34b-13b1-467d-a6ff-cf9e9dbfa82e | game-of-tones-faculty-detection-of-gpt-4 | 2305.18081 | null | https://arxiv.org/abs/2305.18081v1 | https://arxiv.org/pdf/2305.18081v1.pdf | Game of Tones: Faculty detection of GPT-4 generated content in university assessments | This study explores the robustness of university assessments against the use of Open AI's Generative Pre-Trained Transformer 4 (GPT-4) generated content and evaluates the ability of academic staff to detect its use when supported by the Turnitin Artificial Intelligence (AI) detection tool. The research involved twenty-... | ['Don Hickerson', 'James McGaughran', 'Darius Postma', 'Jasper Roe', 'Mike Perkins'] | 2023-05-29 | null | null | null | null | ['prompt-engineering'] | ['natural-language-processing'] | [-3.54088061e-02 4.45810795e-01 1.93190172e-01 2.24457443e-01
-7.96008229e-01 -1.14649880e+00 4.19099748e-01 3.25490564e-01
-8.98483098e-02 2.88582623e-01 2.18893252e-02 -8.78982008e-01
-1.79092482e-01 -9.56135035e-01 -8.76868010e-01 -3.22008312e-01
5.02040207e-01 4.12746221e-01 -1.31214634e-01 -3.40568185... | [10.019489288330078, 7.294789791107178] |
bfa1e721-9c21-4342-bd38-7604917c67dd | it-s-raw-audio-generation-with-state-space | 2202.09729 | null | https://arxiv.org/abs/2202.09729v1 | https://arxiv.org/pdf/2202.09729v1.pdf | It's Raw! Audio Generation with State-Space Models | Developing architectures suitable for modeling raw audio is a challenging problem due to the high sampling rates of audio waveforms. Standard sequence modeling approaches like RNNs and CNNs have previously been tailored to fit the demands of audio, but the resultant architectures make undesirable computational tradeoff... | ['Christopher Ré', 'Chris Donahue', 'Albert Gu', 'Karan Goel'] | 2022-02-20 | null | null | null | null | ['audio-generation', 'music-generation', 'music-generation'] | ['audio', 'audio', 'music'] | [-2.90505420e-02 3.53932083e-02 1.20442830e-01 8.80333111e-02
-1.17881119e+00 -7.60410190e-01 5.59245527e-01 -5.27242184e-01
1.97147429e-01 5.24049520e-01 5.98325372e-01 -3.88055295e-01
-1.72879845e-02 -4.47123885e-01 -7.34909952e-01 -5.84760904e-01
-3.07266593e-01 4.54455912e-01 -3.01439762e-01 -2.09032595... | [15.569672584533691, 5.882933139801025] |
d548c746-0e46-4f1c-b033-e3702ca52330 | directional-diffusion-models-for-graph | 2306.13210 | null | https://arxiv.org/abs/2306.13210v1 | https://arxiv.org/pdf/2306.13210v1.pdf | Directional diffusion models for graph representation learning | In recent years, diffusion models have achieved remarkable success in various domains of artificial intelligence, such as image synthesis, super-resolution, and 3D molecule generation. However, the application of diffusion models in graph learning has received relatively little attention. In this paper, we address this... | ['Qiang Sun', 'Fan Zhou', 'Yuling Yang', 'Run Yang'] | 2023-06-22 | null | null | null | null | ['super-resolution', 'graph-learning', '3d-molecule-generation', 'graph-representation-learning'] | ['computer-vision', 'graphs', 'medical', 'methodology'] | [ 4.34431255e-01 8.35595652e-02 -1.11358322e-01 -3.68423201e-02
-3.56245041e-01 -4.59984273e-01 7.26065159e-01 1.56209975e-01
-1.99971527e-01 3.02428693e-01 5.35334587e-01 -2.47107074e-01
-1.25018567e-01 -9.90778327e-01 -4.02832121e-01 -9.16403890e-01
2.38556657e-02 3.19463253e-01 2.63630152e-01 -2.08568409... | [6.983514785766602, 6.094307899475098] |
5cfc52a0-09c9-49f0-8243-1ab1a1d201df | self-supervised-learning-by-view-synthesis | 2304.11330 | null | https://arxiv.org/abs/2304.11330v1 | https://arxiv.org/pdf/2304.11330v1.pdf | Self-supervised Learning by View Synthesis | We present view-synthesis autoencoders (VSA) in this paper, which is a self-supervised learning framework designed for vision transformers. Different from traditional 2D pretraining methods, VSA can be pre-trained with multi-view data. In each iteration, the input to VSA is one view (or multiple views) of a 3D object a... | ['Jiaya Jia', 'Tao Hu', 'Xiangyu Zhang', 'Shaoteng Liu'] | 2023-04-22 | null | null | null | null | ['3d-classification'] | ['computer-vision'] | [-1.55115426e-01 2.45447263e-01 -4.53772809e-04 -4.71466959e-01
-7.47650921e-01 -6.63593292e-01 6.41358614e-01 -9.01338458e-01
2.90597863e-02 1.91107348e-01 3.15191984e-01 -4.33651656e-01
3.96901011e-01 -8.88196349e-01 -1.14444721e+00 -6.87639952e-01
5.53915858e-01 8.90749395e-01 2.81817436e-01 7.43965432... | [8.229355812072754, -3.3811769485473633] |
4a4369a4-5634-4214-89b8-a458b2cca0ab | graph-neural-networks-for-cross-camera-data | 2201.06311 | null | https://arxiv.org/abs/2201.06311v1 | https://arxiv.org/pdf/2201.06311v1.pdf | Graph Neural Networks for Cross-Camera Data Association | Cross-camera image data association is essential for many multi-camera computer vision tasks, such as multi-camera pedestrian detection, multi-camera multi-target tracking, 3D pose estimation, etc. This association task is typically stated as a bipartite graph matching problem and often solved by applying minimum-cost ... | ['Pablo Carballeira', 'José M. Martínez', 'Juan C. SanMiguel', 'Elena Luna'] | 2022-01-17 | null | null | null | null | ['3d-pose-estimation'] | ['computer-vision'] | [-8.34757984e-02 -3.81858915e-01 1.97423995e-01 -1.08421758e-01
-6.80793762e-01 -7.05716074e-01 5.70037723e-01 5.28948843e-01
-8.07830751e-01 6.19157553e-01 -2.32127219e-01 -1.50281608e-01
-1.63730800e-01 -6.68337107e-01 -8.30713987e-01 -6.42739534e-01
-1.13694198e-01 5.48959851e-01 2.38356471e-01 -1.16966786... | [8.231216430664062, -1.7834471464157104] |
270c6f43-08dd-458a-98e1-2771e62e9f60 | ptse-a-multi-model-ensemble-method-for | 2305.11304 | null | https://arxiv.org/abs/2305.11304v1 | https://arxiv.org/pdf/2305.11304v1.pdf | pTSE: A Multi-model Ensemble Method for Probabilistic Time Series Forecasting | Various probabilistic time series forecasting models have sprung up and shown remarkably good performance. However, the choice of model highly relies on the characteristics of the input time series and the fixed distribution that the model is based on. Due to the fact that the probability distributions cannot be averag... | ['Sheng Li', 'Yuchen Huang', 'Ge Jin', 'Yijia Ruan', 'Zhixuan Chu', 'Yunyi Zhou'] | 2023-05-16 | null | null | null | null | ['probabilistic-time-series-forecasting'] | ['time-series'] | [-8.82963017e-02 -5.47289252e-01 1.76894560e-01 -4.48026717e-01
-8.74102592e-01 -7.33230770e-01 4.54347283e-01 -2.71205425e-01
1.67526618e-01 5.82469404e-01 -1.24185331e-01 -5.78335285e-01
-3.19497079e-01 -6.74613535e-01 -6.47926927e-01 -1.29014754e+00
-1.36053249e-01 6.05839729e-01 2.71849521e-02 -4.31648418... | [6.9586029052734375, 3.2743217945098877] |
ed0c2692-2a27-4dec-9796-e4bef1c8f8c6 | restricted-generative-projection-for-one | 2307.04097 | null | https://arxiv.org/abs/2307.04097v1 | https://arxiv.org/pdf/2307.04097v1.pdf | Restricted Generative Projection for One-Class Classification and Anomaly Detection | We present a simple framework for one-class classification and anomaly detection. The core idea is to learn a mapping to transform the unknown distribution of training (normal) data to a known target distribution. Crucially, the target distribution should be sufficiently simple, compact, and informative. The simplicity... | ['Jicong Fan', 'Ruoyu Sun', 'Feng Xiao'] | 2023-07-09 | null | null | null | null | ['anomaly-detection', 'one-class-classification'] | ['methodology', 'miscellaneous'] | [-9.60999131e-02 1.51019931e-01 5.06322794e-02 -4.50009763e-01
-1.28645211e-01 -2.78216332e-01 3.26520950e-01 5.96933179e-02
-1.30827069e-01 7.67748833e-01 -9.92904603e-02 -3.08536272e-02
-2.71530509e-01 -9.27111208e-01 -5.18964648e-01 -1.06469667e+00
3.66923288e-02 7.26590931e-01 3.19485277e-01 9.53137651... | [7.588520526885986, 2.4209837913513184] |
c6fd0f54-fa3b-422a-8c91-e067a32e0f40 | sketch-me-that-shoe | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Yu_Sketch_Me_That_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Yu_Sketch_Me_That_CVPR_2016_paper.pdf | Sketch Me That Shoe | We investigate the problem of fine-grained sketch-based image retrieval (SBIR), where free-hand human sketches are used as queries to perform instance-level retrieval of images. This is an extremely challenging task because (i) visual comparisons not only need to be fine-grained but also executed cross-domain, (ii) fre... | ['Chen-Change Loy', 'Yi-Zhe Song', 'Qian Yu', 'Timothy M. Hospedales', 'Tao Xiang', 'Feng Liu'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 2.86235362e-01 -7.92054474e-01 -3.13531280e-01 -4.14122671e-01
-1.26595914e+00 -9.63590205e-01 9.09951329e-01 -4.22747098e-02
-3.00093681e-01 5.19912064e-01 -3.94559912e-02 -6.07829317e-02
-4.66648579e-01 -6.23809099e-01 -6.92206204e-01 -3.49622220e-01
9.47883353e-02 7.94047356e-01 1.48460045e-01 -3.87552053... | [11.606618881225586, 0.5889549851417542] |
9b892554-c427-4f08-98aa-e7944b293375 | impact-of-experiencing-misrecognition-by | 2306.07302 | null | https://arxiv.org/abs/2306.07302v1 | https://arxiv.org/pdf/2306.07302v1.pdf | Impact of Experiencing Misrecognition by Teachable Agents on Learning and Rapport | While speech-enabled teachable agents have some advantages over typing-based ones, they are vulnerable to errors stemming from misrecognition by automatic speech recognition (ASR). These errors may propagate, resulting in unexpected changes in the flow of conversation. We analyzed how such changes are linked with learn... | ['Erin Walker', 'Adriana Kovashka', 'Timothy Nokes-Malach', 'Nikki Lobczowski', 'Mingzhi Yu', 'Diane Litman', 'Yuya Asano'] | 2023-06-11 | null | null | null | null | ['automatic-speech-recognition'] | ['speech'] | [-9.03465450e-02 4.54700232e-01 5.02313673e-02 -4.63204116e-01
-5.53751349e-01 -8.04632485e-01 6.89613163e-01 3.89342844e-01
-5.87991059e-01 9.56594288e-01 4.22526151e-01 -6.93017364e-01
-2.72010893e-01 -7.46684134e-01 -7.23703384e-01 -1.87605441e-01
1.27185866e-01 4.40880120e-01 3.52059364e-01 -6.97184265... | [12.359941482543945, 7.957873821258545] |
2f9a01a7-8bf2-47f7-89ff-99f8f3cd4ca7 | docee-a-large-scale-dataset-for-document | null | null | https://openreview.net/forum?id=t5zJTjgwngX | https://openreview.net/pdf?id=t5zJTjgwngX | DocEE: A Large-Scale Dataset for Document-level Event Extraction | Event extraction (EE) is the task of identifying events and their types, along with the involved arguments. Despite the great success in sentence-level event extraction, events are more naturally presented in the form of document, with event arguments scattering in multiple sentences. However, a major barrier to promot... | ['Anonymous'] | 2021-09-17 | null | null | null | acl-arr-september-2021-9 | ['document-level-event-extraction'] | ['natural-language-processing'] | [ 3.08532357e-01 2.31341153e-01 -1.41604379e-01 -2.84196705e-01
-1.38543987e+00 -9.90037978e-01 9.79055405e-01 7.82733083e-01
-7.22634852e-01 1.03088355e+00 8.00996482e-01 -2.76579142e-01
-1.51266098e-01 -6.66998625e-01 -7.83570945e-01 -1.35390118e-01
-3.56267780e-01 4.52557772e-01 4.47411805e-01 4.99589294... | [9.04712963104248, 9.244243621826172] |
c36d6702-749f-43c2-a39a-778d69ea3d03 | incremental-and-iterative-learning-of-answer | 1802.07966 | null | http://arxiv.org/abs/1802.07966v2 | http://arxiv.org/pdf/1802.07966v2.pdf | Incremental and Iterative Learning of Answer Set Programs from Mutually Distinct Examples | Over the years the Artificial Intelligence (AI) community has produced
several datasets which have given the machine learning algorithms the
opportunity to learn various skills across various domains. However, a subclass
of these machine learning algorithms that aimed at learning logic programs,
namely the Inductive Lo... | ['Arindam Mitra', 'Chitta Baral'] | 2018-02-22 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 1.56772926e-01 2.85439879e-01 -8.31605718e-02 -5.47812223e-01
-4.11453247e-01 -8.16040576e-01 5.27036428e-01 2.97484338e-01
-1.67676225e-01 7.97268212e-01 -3.45013380e-01 -8.19388628e-01
-4.31827039e-01 -1.16244578e+00 -7.59131610e-01 -2.05156147e-01
5.58988824e-02 7.84193695e-01 4.99717236e-01 -1.90502673... | [8.935501098632812, 7.10919713973999] |
d8a8df04-b8fc-44d1-a518-4622d72c0f6c | robust-small-object-detection-on-the-water | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Cheng_Robust_Small_Object_Detection_on_the_Water_Surface_Through_Fusion_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Cheng_Robust_Small_Object_Detection_on_the_Water_Surface_Through_Fusion_ICCV_2021_paper.pdf | Robust Small Object Detection on the Water Surface Through Fusion of Camera and Millimeter Wave Radar | In recent years, unmanned surface vehicles (USVs) have been experiencing growth in various applications. With the expansion of USVs' application scenes from the typical marine areas to inland waters, new challenges arise for the object detection task, which is an essential part of the perception system of USVs. In ... | ['Yimin Liu', 'Hu Xu', 'Yuwei Cheng'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['small-object-detection'] | ['computer-vision'] | [ 1.94966137e-01 -4.10488665e-01 5.87796092e-01 -2.37795830e-01
-5.02955675e-01 -5.40063858e-01 4.16458398e-01 -1.16634354e-01
-6.06413007e-01 2.85203487e-01 -2.11045474e-01 -1.67639837e-01
-4.33695093e-02 -1.07294190e+00 -5.48029900e-01 -8.61694217e-01
-1.43385828e-01 -1.04685500e-02 7.25186586e-01 -5.48182011... | [7.973013877868652, -1.6344987154006958] |
342f1a1a-8a1a-47aa-a63d-e7278e3812e7 | cnn-lstm-models-for-multi-speaker-source | 1912.09254 | null | https://arxiv.org/abs/1912.09254v1 | https://arxiv.org/pdf/1912.09254v1.pdf | CNN-LSTM models for Multi-Speaker Source Separation using Bayesian Hyper Parameter Optimization | In recent years there have been many deep learning approaches towards the multi-speaker source separation problem. Most use Long Short-Term Memory - Recurrent Neural Networks (LSTM-RNN) or Convolutional Neural Networks (CNN) to model the sequential behavior of speech. In this paper we propose a novel network for source... | ['Hugo Van hamme', 'Jeroen Zegers'] | 2019-12-19 | null | null | null | null | ['multi-speaker-source-separation'] | ['speech'] | [-6.84330473e-04 5.34250624e-02 -1.76740527e-01 -6.85374737e-02
-1.35829687e+00 -3.00238490e-01 3.06071639e-01 -1.15480654e-01
-4.74992841e-01 7.92484045e-01 3.53488922e-01 -1.80594027e-01
-1.85197651e-01 -5.19056439e-01 -5.95839739e-01 -1.09965956e+00
-1.12548940e-01 6.62793636e-01 3.07453603e-01 -2.96402648... | [14.680815696716309, 6.139276504516602] |
1007e2f0-2d4d-496b-847d-56e6c3b05d39 | improving-prosody-for-unseen-texts-in-speech | 2111.07549 | null | https://arxiv.org/abs/2111.07549v1 | https://arxiv.org/pdf/2111.07549v1.pdf | Improving Prosody for Unseen Texts in Speech Synthesis by Utilizing Linguistic Information and Noisy Data | Recent advancements in end-to-end speech synthesis have made it possible to generate highly natural speech. However, training these models typically requires a large amount of high-fidelity speech data, and for unseen texts, the prosody of synthesized speech is relatively unnatural. To address these issues, we propose ... | ['Caixia Gong', 'Ruixiong Zhang', 'Mengnan He', 'Ming Yan', 'Mengxi Nie', 'Yuqing Zhang', 'Zhu Li'] | 2021-11-15 | null | null | null | null | ['polyphone-disambiguation', 'chinese-word-segmentation'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.47875458e-01 9.50012431e-02 -1.86127990e-01 -4.15318578e-01
-1.47571588e+00 -4.58125085e-01 6.72946796e-02 -2.26094931e-01
-3.17631304e-01 5.66852450e-01 6.24039352e-01 -4.07641023e-01
7.30000734e-01 -5.34972608e-01 -6.08436525e-01 -4.81396973e-01
4.54227686e-01 4.08666611e-01 4.53290284e-01 -2.77090788... | [14.68235969543457, 6.7828049659729] |
497067c4-d8e9-4c91-bd00-f9e7f9be7d11 | linguistic-cues-to-deception-assessed-by | null | null | https://aclanthology.org/W12-0401 | https://aclanthology.org/W12-0401.pdf | Linguistic Cues to Deception Assessed by Computer Programs: A Meta-Analysis | null | ["Iris {\\'o}n-Gitlin", 'Bl', 'Siegfried Ludwig Sporer', 'Valerie Hauch', 'Jaume Masip'] | 2012-04-01 | null | null | null | ws-2012-4 | ['deception-detection'] | ['miscellaneous'] | [-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... | [-7.283079624176025, 3.8269400596618652] |
e201e8f8-3dbc-438e-b03c-a2c5d875ee35 | exploring-effectiveness-of-gpt-3-in | 2305.18156 | null | https://arxiv.org/abs/2305.18156v1 | https://arxiv.org/pdf/2305.18156v1.pdf | Exploring Effectiveness of GPT-3 in Grammatical Error Correction: A Study on Performance and Controllability in Prompt-Based Methods | Large-scale pre-trained language models such as GPT-3 have shown remarkable performance across various natural language processing tasks. However, applying prompt-based methods with GPT-3 for Grammatical Error Correction (GEC) tasks and their controllability remains underexplored. Controllability in GEC is crucial for ... | ['Naoaki Okazaki', 'Sho Takase', 'Masahiro Kaneko', 'Mengsay Loem'] | 2023-05-29 | null | null | null | null | ['grammatical-error-correction'] | ['natural-language-processing'] | [ 2.29696244e-01 1.66901767e-01 1.69262171e-01 -6.42941117e-01
-6.16788149e-01 -3.51979375e-01 4.14257735e-01 7.86801934e-01
-7.18882740e-01 4.32818413e-01 2.58744121e-01 -4.29750204e-01
-1.70416191e-01 -6.29090905e-01 -7.74376631e-01 -1.92430854e-01
-7.07723647e-02 4.92598981e-01 2.54286435e-02 -5.60431540... | [11.236106872558594, 9.212557792663574] |
ad197b89-9eca-4b6a-955c-1d7cf21c8dde | composable-text-control-operations-in-latent | 2208.00638 | null | https://arxiv.org/abs/2208.00638v2 | https://arxiv.org/pdf/2208.00638v2.pdf | Composable Text Controls in Latent Space with ODEs | Real-world text applications often involve composing a wide range of text control operations, such as editing the text w.r.t. an attribute, manipulating keywords and structure, and generating new text of desired properties. Prior work typically learns/finetunes a language model (LM) to perform individual or specific su... | ['Zhiting Hu', 'Zhen Li', 'Shuguang Cui', 'Xiaodong He', 'Junwei Bao', 'Xiaodan Liang', 'Zichao Yang', 'Yuan Gao', 'Zeyu Feng', 'Guangyi Liu'] | 2022-08-01 | null | null | null | null | ['text-style-transfoer'] | ['natural-language-processing'] | [ 5.57657182e-01 -1.36689439e-01 -5.62195219e-02 -1.99159399e-01
-6.48990452e-01 -1.03653479e+00 1.03909564e+00 2.67809242e-01
-4.77511585e-01 6.36217058e-01 7.40076974e-02 -2.96164781e-01
-1.49065843e-02 -8.24883282e-01 -7.42067099e-01 -6.58196390e-01
2.94321150e-01 9.85441566e-01 -9.47268754e-02 -3.99716526... | [11.797492027282715, 9.168097496032715] |
79b8e1e6-2a66-437d-bfc6-d26a849bc01e | improving-facial-attribute-prediction-using | 1704.08740 | null | http://arxiv.org/abs/1704.08740v1 | http://arxiv.org/pdf/1704.08740v1.pdf | Improving Facial Attribute Prediction using Semantic Segmentation | Attributes are semantically meaningful characteristics whose applicability
widely crosses category boundaries. They are particularly important in
describing and recognizing concepts where no explicit training example is
given, \textit{e.g., zero-shot learning}. Additionally, since attributes are
human describable, they... | ['Mahdi M. Kalayeh', 'Boqing Gong', 'Mubarak Shah'] | 2017-04-27 | improving-facial-attribute-prediction-using-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Kalayeh_Improving_Facial_Attribute_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Kalayeh_Improving_Facial_Attribute_CVPR_2017_paper.pdf | cvpr-2017-7 | ['face-parsing'] | ['computer-vision'] | [ 3.16623867e-01 4.16619956e-01 -3.06674480e-01 -8.41119289e-01
-4.11622792e-01 -4.85701472e-01 6.64947987e-01 1.45106837e-01
-3.60188127e-01 5.47693253e-01 -5.00546992e-02 1.50211066e-01
-1.34514287e-01 -9.38404024e-01 -7.98981786e-01 -7.54013002e-01
2.14115262e-01 7.54034817e-01 -1.08897030e-01 -4.75191846... | [9.949938774108887, 1.9494751691818237] |
6d344b9c-7399-4dba-9260-ffc4b1c03f2d | generalized-local-optimality-for-video | 2112.11729 | null | https://arxiv.org/abs/2112.11729v1 | https://arxiv.org/pdf/2112.11729v1.pdf | Generalized Local Optimality for Video Steganalysis in Motion Vector Domain | The local optimality of motion vectors (MVs) is an intrinsic property in video coding, and any modifications to the MVs will inevitably destroy this optimality, making it a sensitive indicator of steganography in the MV domain. Thus the local optimality is commonly used to design steganalytic features, and the estimati... | ['Yang Liu', 'Yanzhen Ren', 'Lina Wang', 'Liming Zhai'] | 2021-12-22 | null | null | null | null | ['steganalysis', 'video-prediction'] | ['computer-vision', 'computer-vision'] | [ 0.25582975 -0.58184713 -0.32169345 0.10262836 -0.18599166 -0.3707903
0.44545087 -0.3322485 0.02472542 0.27636814 0.21733116 -0.23882647
0.13714644 -0.77912945 -0.63554716 -1.1978058 -0.22086197 -0.51945263
0.48178896 -0.38229838 0.5668113 0.27028176 -1.3176374 0.124423
0.73989016 1.1249127 0.479... | [4.297179698944092, 8.056249618530273] |
1ae9029c-d079-4ef4-9fb8-f96b518512b2 | neural-object-descriptors-for-multi-view | 2004.04485 | null | https://arxiv.org/abs/2004.04485v2 | https://arxiv.org/pdf/2004.04485v2.pdf | NodeSLAM: Neural Object Descriptors for Multi-View Shape Reconstruction | The choice of scene representation is crucial in both the shape inference algorithms it requires and the smart applications it enables. We present efficient and optimisable multi-class learned object descriptors together with a novel probabilistic and differential rendering engine, for principled full object shape infe... | ['Kentaro Wada', 'Edgar Sucar', 'Andrew Davison'] | 2020-04-09 | null | null | null | null | ['3d-object-reconstruction'] | ['computer-vision'] | [ 1.98216468e-01 -1.40023381e-01 1.55569851e-01 -3.51785213e-01
-7.00359643e-01 -9.28847313e-01 7.74649501e-01 1.69897050e-01
-2.14450821e-01 2.20658287e-01 -4.68850106e-01 -1.56016961e-01
-5.31540275e-01 -6.95187211e-01 -8.21742654e-01 -5.92354774e-01
1.54590011e-01 1.37336946e+00 3.79998505e-01 1.35802314... | [7.407236576080322, -2.528921604156494] |
48136f20-12cd-4406-8efc-2c1cfcb489bb | bifsmn-binary-neural-network-for-keyword | 2202.06483 | null | https://arxiv.org/abs/2202.06483v5 | https://arxiv.org/pdf/2202.06483v5.pdf | BiFSMN: Binary Neural Network for Keyword Spotting | The deep neural networks, such as the Deep-FSMN, have been widely studied for keyword spotting (KWS) applications. However, computational resources for these networks are significantly constrained since they usually run on-call on edge devices. In this paper, we present BiFSMN, an accurate and extreme-efficient binary ... | ['Xianglong Liu', 'Jie Luo', 'Zejun Ma', 'Yao Tian', 'Yang Zhang', 'Xiaoyang Li', 'Yifu Ding', 'Xudong Ma', 'Haotong Qin'] | 2022-02-14 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [-8.33949074e-02 -3.46896142e-01 -5.90459108e-01 -3.79049957e-01
-4.58656549e-01 -2.08294153e-01 6.63991868e-02 -7.53371269e-02
-6.13428950e-01 4.03819621e-01 -1.05545372e-01 -1.08529222e+00
-9.41923708e-02 -9.27378356e-01 -9.88920808e-01 -6.86543643e-01
1.00252740e-01 -7.39889964e-02 2.19489634e-01 -2.69831061... | [8.514071464538574, 2.985804319381714] |
96a76384-aa40-4b86-8eb3-0b272528862a | learning-spatial-temporal-graphs-for-active | 2112.01479 | null | https://arxiv.org/abs/2112.01479v2 | https://arxiv.org/pdf/2112.01479v2.pdf | Learning Spatial-Temporal Graphs for Active Speaker Detection | We address the problem of active speaker detection through a new framework, called SPELL, that learns long-range multimodal graphs to encode the inter-modal relationship between audio and visual data. We cast active speaker detection as a node classification task that is aware of longer-term dependencies. We first cons... | ['Somdeb Majumdar', 'Tanaya Guha', 'Subarna Tripathi', 'Kyle Min', 'Sourya Roy'] | 2021-12-02 | null | null | null | null | ['audio-visual-active-speaker-detection'] | ['computer-vision'] | [ 2.27537081e-01 1.81054130e-01 -2.84661382e-01 -4.69239414e-01
-9.69459653e-01 -8.05644572e-01 6.82822883e-01 2.72273511e-01
-2.47598529e-01 7.35314516e-03 6.94016039e-01 1.61806583e-01
4.01718579e-02 -3.68392259e-01 -5.42833209e-01 -6.18295848e-01
-8.18872988e-01 3.39532793e-01 2.75712013e-01 6.00967519... | [14.464519500732422, 5.102067470550537] |
f3af10aa-a9ab-478c-8419-89164a864667 | self-supervised-predictive-coding-models | 2305.12464 | null | https://arxiv.org/abs/2305.12464v2 | https://arxiv.org/pdf/2305.12464v2.pdf | Self-supervised Predictive Coding Models Encode Speaker and Phonetic Information in Orthogonal Subspaces | Self-supervised speech representations are known to encode both speaker and phonetic information, but how they are distributed in the high-dimensional space remains largely unexplored. We hypothesize that they are encoded in orthogonal subspaces, a property that lends itself to simple disentanglement. Applying principa... | ['Sharon Goldwater', 'Hao Tang', 'Oli Liu'] | 2023-05-21 | null | null | null | null | ['disentanglement'] | ['methodology'] | [ 2.62307167e-01 6.50694370e-02 -4.00349647e-01 -4.79990870e-01
-7.54444778e-01 -9.79224384e-01 6.10308170e-01 -2.55996615e-01
-1.45024676e-02 3.41094345e-01 9.40093815e-01 -3.45070332e-01
2.61161197e-03 -2.16267884e-01 -3.53193998e-01 -8.49021316e-01
2.34049022e-01 4.77804691e-01 -2.25993410e-01 -1.74815785... | [14.498603820800781, 6.366350173950195] |
dc7357e0-ea0c-4624-bd22-08c5ca109b5d | semantic-guided-zero-shot-learning-for-low | 2110.00970 | null | https://arxiv.org/abs/2110.00970v4 | https://arxiv.org/pdf/2110.00970v4.pdf | Semantic-Guided Zero-Shot Learning for Low-Light Image/Video Enhancement | Low-light images challenge both human perceptions and computer vision algorithms. It is crucial to make algorithms robust to enlighten low-light images for computational photography and computer vision applications such as real-time detection and segmentation. This paper proposes a semantic-guided zero-shot low-light e... | ['Gaurav Gupta', 'Shen Zheng'] | 2021-10-03 | null | null | null | null | ['unsupervised-semantic-segmentation', 'video-enhancement'] | ['computer-vision', 'computer-vision'] | [ 7.94441283e-01 -2.29929611e-01 8.85223001e-02 -6.56866729e-01
-7.23144948e-01 -3.05422336e-01 2.08552852e-01 -7.83329830e-02
-6.00206733e-01 4.87736672e-01 -1.94985837e-01 -2.63160855e-01
2.17287660e-01 -9.15716052e-01 -8.06008875e-01 -8.30836594e-01
5.18441856e-01 -3.85581911e-01 5.62332153e-01 -2.43970007... | [10.668170928955078, -2.567002296447754] |
38432f57-7dec-473c-8966-ae54ca63e36e | rethinking-cnn-models-for-audio | 2007.11154 | null | https://arxiv.org/abs/2007.11154v2 | https://arxiv.org/pdf/2007.11154v2.pdf | Rethinking CNN Models for Audio Classification | In this paper, we show that ImageNet-Pretrained standard deep CNN models can be used as strong baseline networks for audio classification. Even though there is a significant difference between audio Spectrogram and standard ImageNet image samples, transfer learning assumptions still hold firmly. To understand what enab... | ['Dipika Singhania', 'Angela Yao', 'Kamalesh Palanisamy'] | 2020-07-22 | null | null | null | null | ['environmental-sound-classification'] | ['audio'] | [ 4.17179130e-02 -1.59650221e-01 1.03692360e-01 -4.02991444e-01
-7.48656511e-01 -4.56017166e-01 4.12255973e-01 -2.76896656e-01
-5.83324492e-01 4.56322849e-01 4.46606666e-01 4.97510750e-03
-3.37298401e-02 -7.89185345e-01 -8.14651966e-01 -6.78279638e-01
-5.59734643e-01 -2.83147907e-03 2.03390613e-01 -3.80427390... | [15.383883476257324, 5.267778396606445] |
e61baf12-0c9c-465c-b361-3017ded76590 | analysis-of-cardiovascular-changes-caused-by | 1912.05083 | null | http://arxiv.org/abs/1912.05083v1 | http://arxiv.org/pdf/1912.05083v1.pdf | Analysis of Cardiovascular Changes Caused by Epileptic Seizures in Human Photoplethysmogram Signal | Objectives: This study examines human Photoplethysmogram (PPG) along with
Electrocardiogram (ECG) signals to study cardiac autonomic imbalance in
epileptic seizures. The significance and the prevalence of changes in PPG
morphological parameters have been investigated to find common patterns among
subjects. Alterations ... | [] | 2019-12-11 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [-1.72716647e-01 -5.24814904e-01 4.02622670e-01 -2.77357191e-01
1.53289974e-01 -2.28942782e-01 -5.16128652e-02 5.68134040e-02
-3.43630522e-01 9.54205155e-01 -8.88250694e-02 5.15400246e-02
-1.86545849e-01 -3.44701022e-01 -5.23893908e-02 -8.42939734e-01
-8.55923057e-01 -2.00767830e-01 -6.61332667e-01 -1.20314568... | [13.533238410949707, 3.2873823642730713] |
5c512fee-7bc2-4101-844f-c9637a758515 | improving-generalization-in-meta-learning-via | 2306.08460 | null | https://arxiv.org/abs/2306.08460v1 | https://arxiv.org/pdf/2306.08460v1.pdf | Improving Generalization in Meta-Learning via Meta-Gradient Augmentation | Meta-learning methods typically follow a two-loop framework, where each loop potentially suffers from notorious overfitting, hindering rapid adaptation and generalization to new tasks. Existing schemes solve it by enhancing the mutual-exclusivity or diversity of training samples, but these data manipulation strategies ... | ['Yilong Yin', 'Yuling Ma', 'Xiushan Nie', 'Qi Wei', 'Haoliang Sun', 'Ren Wang'] | 2023-06-14 | null | null | null | null | ['meta-learning', 'network-pruning', 'memorization'] | ['methodology', 'methodology', 'natural-language-processing'] | [ 9.86662805e-02 -6.30174438e-03 -4.80838865e-01 -3.44142258e-01
-6.77783012e-01 -2.41857156e-01 3.54710311e-01 1.96610734e-01
-8.18996251e-01 8.45435441e-01 1.15260236e-01 -3.10089231e-01
-2.71335185e-01 -9.26522017e-01 -9.36391175e-01 -8.54751289e-01
1.97447717e-01 4.70964648e-02 3.57504576e-01 -3.21619719... | [9.498403549194336, 3.429732084274292] |
d2123c54-9fb1-4cae-81a2-8cc165953770 | proof-artifact-co-training-for-theorem | 2102.06203 | null | https://arxiv.org/abs/2102.06203v2 | https://arxiv.org/pdf/2102.06203v2.pdf | Proof Artifact Co-training for Theorem Proving with Language Models | Labeled data for imitation learning of theorem proving in large libraries of formalized mathematics is scarce as such libraries require years of concentrated effort by human specialists to be built. This is particularly challenging when applying large Transformer language models to tactic prediction, because the scalin... | ['Stanislas Polu', 'Edward W. Ayers', 'Yuhuai Wu', 'Jason Rute', 'Jesse Michael Han'] | 2021-02-11 | proof-artifact-co-training-for-theorem-1 | https://openreview.net/forum?id=rpxJc9j04U | https://openreview.net/pdf?id=rpxJc9j04U | iclr-2022-4 | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 3.03425133e-01 3.48259419e-01 -3.63467067e-01 -1.32773951e-01
-9.60289061e-01 -9.73393679e-01 5.59186041e-01 1.37942001e-01
-3.99481952e-02 8.55191350e-01 -4.18160856e-01 -1.25124395e+00
-5.00790358e-01 -6.29218161e-01 -1.26206291e+00 -2.04863355e-01
-4.21138287e-01 7.78266966e-01 -5.60263358e-02 -3.39334607... | [8.937151908874512, 7.057676792144775] |
9889adb6-31d6-4370-b44c-bf037d5b38a6 | one-shot-fine-grained-instance-retrieval | 1707.00811 | null | http://arxiv.org/abs/1707.00811v1 | http://arxiv.org/pdf/1707.00811v1.pdf | One-Shot Fine-Grained Instance Retrieval | Fine-Grained Visual Categorization (FGVC) has achieved significant progress
recently. However, the number of fine-grained species could be huge and
dynamically increasing in real scenarios, making it difficult to recognize
unseen objects under the current FGVC framework. This raises an open issue to
perform large-scale... | ['Hantao Yao', 'Qi Tian', 'Yongdong Zhang', 'Shiliang Zhang', 'Jintao Li'] | 2017-07-04 | null | null | null | null | ['fine-grained-visual-categorization'] | ['computer-vision'] | [ 7.27137104e-02 -8.06767344e-01 -6.64016157e-02 -3.98836166e-01
-1.03527117e+00 -9.43654895e-01 7.45794594e-01 1.36160642e-01
-5.66005468e-01 6.14830077e-01 -8.73601902e-03 2.44822994e-01
-3.73121560e-01 -7.86170900e-01 -4.75686640e-01 -7.06838608e-01
2.53282070e-01 5.08600175e-01 3.06711465e-01 7.87799954... | [9.714996337890625, 1.927155613899231] |
ae5f620c-c47b-4959-9af3-4ccfbe0ded90 | semantic-space-grounded-weighted-decoding-for | 2305.02820 | null | https://arxiv.org/abs/2305.02820v1 | https://arxiv.org/pdf/2305.02820v1.pdf | Semantic Space Grounded Weighted Decoding for Multi-Attribute Controllable Dialogue Generation | Controlling chatbot utterance generation with multiple attributes such as personalities, emotions and dialogue acts is a practically useful but under-studied problem. We propose a novel controllable generation framework called DASC that possesses strong controllability with weighted decoding paradigm, while improving g... | ['Kenny Q. Zhu', 'Mengyue Wu', 'Zhiling Zhang'] | 2023-05-04 | null | null | null | null | ['dialogue-generation', 'dialogue-generation'] | ['natural-language-processing', 'speech'] | [ 1.84833437e-01 6.79937720e-01 -1.04930729e-01 -6.82266831e-01
-7.04078496e-01 -4.71966207e-01 8.82709205e-01 3.15390006e-02
3.82155813e-05 9.68070030e-01 7.11654246e-01 3.49629074e-01
-1.83993295e-01 -9.93733346e-01 -2.53223568e-01 -7.43688464e-01
1.32646963e-01 7.89555609e-01 -4.28912610e-01 -1.02212608... | [12.780756950378418, 8.12799072265625] |
add22b24-7941-48cd-82a8-77057963d61c | toward-unlimited-self-learning-monte-carlo | 2211.14024 | null | https://arxiv.org/abs/2211.14024v1 | https://arxiv.org/pdf/2211.14024v1.pdf | Toward Unlimited Self-Learning Monte Carlo with Annealing Process Using VAE's Implicit Isometricity | Self-learning Monte Carlo (SLMC) methods are recently proposed to accelerate Markov chain Monte Carlo (MCMC) methods by using a machine learning model.With generative models having latent variables, SLMC methods realize efficient Monte Carlo updates with less autocorrelation. However, SLMC methods are difficult to dire... | ['Yuhei Umeda', 'Hiromoto Masayuki', 'Akira Nakagawa', 'Yuma Ichikawa'] | 2022-11-25 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [ 4.84861135e-02 -2.11271048e-01 -8.76258388e-02 -2.68871814e-01
-9.18761313e-01 -2.35138580e-01 9.67071652e-01 -4.31548715e-01
-4.73024487e-01 1.20692205e+00 -1.42352963e-02 -3.16592097e-01
2.16071263e-01 -9.49222565e-01 -8.57080400e-01 -1.25434959e+00
4.47055519e-01 1.18942034e+00 3.04291159e-01 1.30954534... | [6.936182022094727, 3.895627498626709] |
49a0e396-e17d-4817-ba59-2d2688a40c9d | open-long-tailed-recognition-in-a-dynamic | 2208.08349 | null | https://arxiv.org/abs/2208.08349v1 | https://arxiv.org/pdf/2208.08349v1.pdf | Open Long-Tailed Recognition in a Dynamic World | Real world data often exhibits a long-tailed and open-ended (with unseen classes) distribution. A practical recognition system must balance between majority (head) and minority (tail) classes, generalize across the distribution, and acknowledge novelty upon the instances of unseen classes (open classes). We define Open... | ['Stella X. Yu', 'Boqing Gong', 'Jiayun Wang', 'Xiaohang Zhan', 'Zhongqi Miao', 'Ziwei Liu'] | 2022-08-17 | null | null | null | null | ['imbalanced-classification', 'open-set-learning'] | ['miscellaneous', 'miscellaneous'] | [ 2.08159029e-01 1.27517313e-01 -6.02561057e-01 -4.40262109e-01
-9.62387145e-01 -6.02761865e-01 5.56268275e-01 2.43619859e-01
-3.85891885e-01 4.87954885e-01 1.53258711e-01 -4.77555096e-02
-3.66934419e-01 -8.93418014e-01 -7.28536963e-01 -7.50753462e-01
-2.99013734e-01 8.08870554e-01 1.51918111e-02 1.04606412... | [9.636489868164062, 2.8284690380096436] |
9f360d60-06da-4e38-9a5a-9c3e2f60f016 | multi-relational-classification-via-bayesian | null | null | https://dl.acm.org/doi/pdf/10.1145/3292500.3330863 | https://dl.acm.org/doi/pdf/10.1145/3292500.3330863 | Multi-Relational Classification via Bayesian Ranked Non-Linear Embeddings | The task of classifying multi-relational data spans a wide range of domains such as document classification in citation networks, classification of emails, and protein labeling in proteins interaction graphs. Current state-of-the-art classification models rely on learning per-entity latent representations by mining the... | ['Ahmed Rashed; Josif Grabocka; Lars Schmidt-Thieme'] | 2019-08-06 | null | null | null | the-25th-acm-sigkdd-conference-on-knowledge | ['heterogeneous-node-classification'] | ['graphs'] | [ 1.91254154e-01 3.35765004e-01 -7.50514746e-01 -3.84516835e-01
-6.12377942e-01 -1.68408424e-01 4.62807298e-01 6.21301293e-01
-1.15673564e-01 7.53431201e-01 3.10407221e-01 -1.67342484e-01
-8.42005730e-01 -9.49640393e-01 -6.33050799e-01 -5.01487374e-01
-1.87494084e-01 6.64349854e-01 1.26968414e-01 -1.53934479... | [8.651556015014648, 7.782830715179443] |
4f59c38c-c857-45f3-86d1-2d2f18ebe085 | computable-stability-for-persistence-rank | 2307.02904 | null | https://arxiv.org/abs/2307.02904v1 | https://arxiv.org/pdf/2307.02904v1.pdf | Computable Stability for Persistence Rank Function Machine Learning | Persistent homology barcodes and diagrams are a cornerstone of topological data analysis. Widely used in many real data settings, they relate variation in topological information (as measured by cellular homology) with variation in data, however, they are challenging to use in statistical settings due to their complex ... | ['Gregory Henselman-Petrusek', 'Anthea Monod', 'Pierre Faugère', 'Inés García-Redondo', 'Qiquan Wang'] | 2023-07-06 | null | null | null | null | ['topological-data-analysis'] | ['graphs'] | [ 2.53187772e-02 -1.83630437e-01 -1.98949486e-01 6.83856979e-02
-5.17566442e-01 -8.03273797e-01 5.52173734e-01 5.48630416e-01
-2.61317223e-01 1.00133789e+00 1.05114445e-01 -2.84297734e-01
-7.41055250e-01 -8.70882273e-01 -7.97199130e-01 -1.00796568e+00
-8.09261322e-01 2.77365476e-01 4.69286501e-01 -4.92368370... | [7.481867790222168, 4.214142322540283] |
65870482-89fa-4e6a-9384-0bed89535895 | native-language-identification-using-phonetic | null | null | https://aclanthology.org/W17-5046 | https://aclanthology.org/W17-5046.pdf | Native Language Identification using Phonetic Algorithms | In this paper, we discuss the results of the IUCL system in the NLI Shared Task 2017. For our system, we explore a variety of phonetic algorithms to generate features for Native Language Identification. These features are contrasted with one of the most successful type of features in NLI, character n-grams. We find tha... | ['ra', 'S K{\\"u}bler', 'Charese Smiley'] | 2017-09-01 | null | null | null | ws-2017-9 | ['native-language-identification'] | ['natural-language-processing'] | [ 5.43591864e-02 -4.84130085e-01 -5.41745186e-01 -4.49831307e-01
-1.02190936e+00 -1.00096452e+00 9.52526510e-01 2.68712342e-02
-7.90096700e-01 6.37570083e-01 6.82943821e-01 -4.40804452e-01
-9.97095183e-02 -1.62165046e-01 -1.69228286e-01 -4.23428297e-01
3.11097682e-01 6.38368130e-01 -1.90639347e-01 -1.03578471... | [10.489344596862793, 10.518136024475098] |
e4f40fb1-1235-4975-ad3b-4ccdfdd0e27c | a-weakly-supervised-learning-framework-for | 2209.02957 | null | https://arxiv.org/abs/2209.02957v1 | https://arxiv.org/pdf/2209.02957v1.pdf | A Weakly Supervised Learning Framework for Salient Object Detection via Hybrid Labels | Fully-supervised salient object detection (SOD) methods have made great progress, but such methods often rely on a large number of pixel-level annotations, which are time-consuming and labour-intensive. In this paper, we focus on a new weakly-supervised SOD task under hybrid labels, where the supervision labels include... | ['Sam Kwong', 'Yao Zhao', 'Shiqi Wang', 'Qiuping Jiang', 'Chen Zhang', 'Qi Qin', 'Runmin Cong'] | 2022-09-07 | null | null | null | null | ['saliency-detection', 'salient-object-detection'] | ['computer-vision', 'computer-vision'] | [ 4.25171614e-01 3.19231391e-01 -1.71855703e-01 -3.53189796e-01
-8.16458285e-01 -1.08811080e-01 4.04599398e-01 4.06162404e-02
-4.93497252e-01 6.18949890e-01 1.38916727e-02 2.29167752e-02
4.68808353e-01 -6.05782926e-01 -6.02314055e-01 -1.05827653e+00
4.21128452e-01 1.96877360e-01 7.80509710e-01 8.24186429... | [9.286413192749023, 1.243064284324646] |
ab940292-7ef7-4f83-94b5-4a901dcebe3c | empowering-llm-based-machine-translation-with | 2305.14328 | null | https://arxiv.org/abs/2305.14328v1 | https://arxiv.org/pdf/2305.14328v1.pdf | Empowering LLM-based Machine Translation with Cultural Awareness | Traditional neural machine translation (NMT) systems often fail to translate sentences that contain culturally specific information. Most previous NMT methods have incorporated external cultural knowledge during training, which requires fine-tuning on low-frequency items specific to the culture. Recent in-context learn... | ['Junjie Hu', 'Diyi Yang', 'Ming Jiang', 'Binwei Yao'] | 2023-05-23 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 2.07882077e-01 -5.73653243e-02 -5.17067552e-01 -4.97169405e-01
-1.15408504e+00 -8.69419098e-01 7.27825165e-01 -6.21505268e-02
-6.30847633e-01 1.06774104e+00 6.72944427e-01 -6.05757594e-01
5.23744285e-01 -5.39502382e-01 -1.03015339e+00 -5.99467345e-02
5.71640790e-01 8.01875234e-01 -4.96956497e-01 -6.62107766... | [11.616621971130371, 10.325987815856934] |
c140108f-4773-4fd8-a301-615b1f407dcb | novel-chapter-abstractive-summarization-using-1 | 2211.04903 | null | https://arxiv.org/abs/2211.04903v1 | https://arxiv.org/pdf/2211.04903v1.pdf | Novel Chapter Abstractive Summarization using Spinal Tree Aware Sub-Sentential Content Selection | Summarizing novel chapters is a difficult task due to the input length and the fact that sentences that appear in the desired summaries draw content from multiple places throughout the chapter. We present a pipelined extractive-abstractive approach where the extractive step filters the content that is passed to the abs... | ['Kathleen McKeown', 'Vittorio Castelli', 'Muhammad Khalifa', 'Faisal Ladhak', 'Miguel Ballesteros', 'Hardy Hardy'] | 2022-11-09 | null | null | null | null | ['abstractive-text-summarization', 'extractive-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 6.98675752e-01 6.22154832e-01 -2.57448405e-01 -2.65974134e-01
-1.33236980e+00 -1.02737367e+00 6.26757264e-01 8.01628888e-01
-5.25171876e-01 9.90915775e-01 9.22053277e-01 -3.17276299e-01
6.21746890e-02 -5.42741597e-01 -4.90397424e-01 -1.81059763e-01
1.87162310e-01 3.80239308e-01 2.59179235e-01 -1.06360681... | [12.511263847351074, 9.501860618591309] |
f26b6286-aaab-4cf3-a9a4-267700580ecc | online-decision-based-visual-tracking-via | null | null | http://proceedings.neurips.cc/paper/2020/hash/885b2c7a6deb4fea10f319c4ce993e02-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/885b2c7a6deb4fea10f319c4ce993e02-Paper.pdf | Online Decision Based Visual Tracking via Reinforcement Learning | A deep visual tracker is typically based on either object detection or template matching while each of them is only suitable for a particular group of scenes. It is straightforward to consider fusing them together to pursue more reliable tracking. However, this is not wise as they follow different tracking principles. ... | ['Yibin Li', 'Ran Song', 'Wei zhang', 'Ke Song'] | 2020-12-01 | null | null | null | neurips-2020-12 | ['template-matching'] | ['computer-vision'] | [-3.85617405e-01 -4.63914186e-01 -1.41007498e-01 -1.51800811e-02
-4.07058239e-01 -6.01987839e-01 5.43793023e-01 -2.55005509e-01
-5.19702256e-01 6.44302189e-01 -2.21764639e-01 -7.33895674e-02
1.35384306e-01 -5.70024848e-01 -6.08431816e-01 -8.22816670e-01
1.79121181e-01 2.99085259e-01 8.31711590e-01 1.09833121... | [6.392798900604248, -2.0803847312927246] |
4cadd82d-3145-40fd-9a97-3d35c8828551 | diagnosing-model-performance-under | 2303.02011 | null | https://arxiv.org/abs/2303.02011v4 | https://arxiv.org/pdf/2303.02011v4.pdf | Diagnosing Model Performance Under Distribution Shift | Prediction models can perform poorly when deployed to target distributions different from the training distribution. To understand these operational failure modes, we develop a method, called DIstribution Shift DEcomposition (DISDE), to attribute a drop in performance to different types of distribution shifts. Our appr... | ['Tiffany Tianhui Cai', 'Steve Yadlowsky', 'Hongseok Namkoong'] | 2023-03-03 | null | null | null | null | ['satellite-image-classification'] | ['computer-vision'] | [ 2.48691380e-01 -7.67888352e-02 -4.06400442e-01 -6.18805647e-01
-7.59653330e-01 -4.43918735e-01 5.75129807e-01 3.91090751e-01
-6.11770511e-01 8.84074152e-01 3.07657897e-01 -6.42153740e-01
-5.94223976e-01 -1.12706304e+00 -7.40340233e-01 -5.99474311e-01
-2.08712935e-01 5.97717822e-01 -4.31781299e-02 -8.71056989... | [8.61192798614502, 4.6730875968933105] |
6ea10ba5-fcc9-4b16-9a0f-e0a4b05642b1 | ac-band-a-combinatorial-bandit-based-approach | 2212.00333 | null | https://arxiv.org/abs/2212.00333v1 | https://arxiv.org/pdf/2212.00333v1.pdf | AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration | We study the algorithm configuration (AC) problem, in which one seeks to find an optimal parameter configuration of a given target algorithm in an automated way. Recently, there has been significant progress in designing AC approaches that satisfy strong theoretical guarantees. However, a significant gap still remains ... | ['Kevin Tierney', 'Eyke Hüllermeier', 'Björn Haddenhorst', 'Viktor Bengs', 'Elias Schede', 'Jasmin Brandt'] | 2022-12-01 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [-8.03978592e-02 -1.97660252e-01 -9.84190762e-01 -6.85165869e-03
-1.34662783e+00 -1.15214217e+00 2.04835325e-01 -5.06814010e-02
-1.55276284e-01 1.19575453e+00 -1.42514125e-01 -9.33311760e-01
-7.40278363e-01 -6.60753489e-01 -7.14811981e-01 -9.16887224e-01
-1.40670806e-01 9.77391720e-01 3.38491388e-02 2.51526479... | [4.537922382354736, 3.294719696044922] |
c4e5691f-94d3-4d0e-9b1a-fa40e3116c15 | state-representation-and-polyomino-placement | 2001.04233 | null | https://arxiv.org/abs/2001.04233v1 | https://arxiv.org/pdf/2001.04233v1.pdf | State Representation and Polyomino Placement for the Game Patchwork | Modern board games are a rich source of entertainment for many people, but also contain interesting and challenging structures for game playing research and implementing game playing agents. This paper studies the game Patchwork, a two player strategy game using polyomino tile drafting and placement. The core polyomino... | ['Mikael Zayenz Lagerkvist'] | 2020-01-13 | null | null | null | null | ['board-games'] | ['playing-games'] | [ 2.25402210e-02 2.08092913e-01 -2.58258969e-01 2.29445174e-01
-3.22175533e-01 -6.95091903e-01 -9.41348001e-02 4.17759150e-01
-4.90169585e-01 1.05348146e+00 -1.37139931e-01 -4.12480652e-01
-6.93912387e-01 -9.52172399e-01 -5.78899205e-01 -8.05983961e-01
-5.74897110e-01 1.10277784e+00 6.99447155e-01 -5.09304762... | [3.446017026901245, 1.5071183443069458] |
0f910413-3c3e-46ce-9523-a36e57d0e623 | echo-aware-adaptation-of-sound-event | 2202.09121 | null | https://arxiv.org/abs/2202.09121v1 | https://arxiv.org/pdf/2202.09121v1.pdf | Echo-aware Adaptation of Sound Event Localization and Detection in Unknown Environments | Our goal is to develop a sound event localization and detection (SELD) system that works robustly in unknown environments. A SELD system trained on known environment data is degraded in an unknown environment due to environmental effects such as reverberation and noise not contained in the training data. Previous studi... | ['Shoichiro Saito', 'Yasunori Ohishi', 'Masahiro Yasuda'] | 2022-02-18 | null | null | null | null | ['sound-event-localization-and-detection'] | ['audio'] | [ 1.64899990e-01 -8.14316988e-01 8.65996838e-01 -4.84824002e-01
-8.72358143e-01 -5.97219467e-01 -2.07267143e-02 1.41016707e-01
-5.92224717e-01 5.24508178e-01 3.26064020e-01 2.71823734e-01
-2.09998056e-01 -4.71668184e-01 -5.50465167e-01 -6.65992558e-01
-2.45747924e-01 -2.00492442e-01 5.01654208e-01 -2.10375994... | [15.171523094177246, 5.328629016876221] |
4f1d404d-6360-4e5d-93b0-fa4024f4713c | a-peer-to-peer-federated-continual-learning | 2306.02037 | null | https://arxiv.org/abs/2306.02037v1 | https://arxiv.org/pdf/2306.02037v1.pdf | A Peer-to-peer Federated Continual Learning Network for Improving CT Imaging from Multiple Institutions | Deep learning techniques have been widely used in computed tomography (CT) but require large data sets to train networks. Moreover, data sharing among multiple institutions is limited due to data privacy constraints, which hinders the development of high-performance DL-based CT imaging models from multi-institutional c... | ['Jianhua Ma', 'Dong Zeng', 'Zhaoying Bian', 'XiaoYu Zhang', 'Ruihong He', 'Hao Wang'] | 2023-06-03 | null | null | null | null | ['computed-tomography-ct'] | ['methodology'] | [-5.87760329e-01 -1.42460465e-01 -2.86494941e-01 -5.09512305e-01
-1.15947926e+00 -3.06751788e-01 1.79526612e-01 2.39508316e-01
-6.56369507e-01 9.11580563e-01 -1.37281045e-02 -3.28360677e-01
-5.89714706e-01 -7.03551471e-01 -7.19230056e-01 -1.13430274e+00
-1.13032117e-01 6.85857534e-01 1.87679857e-01 4.42594141... | [6.04835319519043, 6.465025424957275] |
62032082-d6cf-4661-90fe-e7286859412f | epilnet-a-novel-approach-to-iot-based | 2111.03265 | null | https://arxiv.org/abs/2111.03265v1 | https://arxiv.org/pdf/2111.03265v1.pdf | EpilNet: A Novel Approach to IoT based Epileptic Seizure Prediction and Diagnosis System using Artificial Intelligence | Epilepsy is one of the most occurring neurological diseases. The main characteristic of this disease is a frequent seizure, which is an electrical imbalance in the brain. It is generally accompanied by shaking of body parts and even leads (fainting). In the past few years, many treatments have come up. These mainly inv... | ['Priyansh Agrawal', 'Virender Ranga', 'Shivam Gupta'] | 2021-11-05 | null | null | null | null | ['seizure-prediction'] | ['medical'] | [-3.27293932e-01 -2.53768057e-01 2.79741913e-01 -1.51640132e-01
1.79187506e-01 -2.03910708e-01 4.73595336e-02 1.34685516e-01
-3.14652473e-01 9.11155760e-01 2.95483544e-02 -2.21283715e-02
-2.95543015e-01 -7.46482432e-01 -6.17900118e-02 -9.72682297e-01
-1.99464306e-01 2.88845360e-01 2.12495685e-01 -1.42671615... | [13.270492553710938, 3.469743013381958] |
3c34b03d-2597-4c70-a133-3381640c7830 | deep-unsupervised-key-frame-extraction-for | 2211.06742 | null | https://arxiv.org/abs/2211.06742v1 | https://arxiv.org/pdf/2211.06742v1.pdf | Deep Unsupervised Key Frame Extraction for Efficient Video Classification | Video processing and analysis have become an urgent task since a huge amount of videos (e.g., Youtube, Hulu) are uploaded online every day. The extraction of representative key frames from videos is very important in video processing and analysis since it greatly reduces computing resources and time. Although great pro... | ['Paolo Rota', 'Nicu Sebe', 'Bin Ren', 'Songsong Wu', 'Lei Ding', 'Hao Tang'] | 2022-11-12 | null | null | null | null | ['video-classification'] | ['computer-vision'] | [-1.51171163e-01 -8.23975861e-01 -3.24556828e-01 -5.63645884e-02
-5.21953106e-01 3.73628251e-02 2.36110255e-01 4.93515991e-02
-8.09447050e-01 4.91060227e-01 -2.48535462e-02 2.34024059e-02
-6.93214387e-02 -7.69868553e-01 -5.17604411e-01 -8.93670678e-01
-7.31296884e-03 -2.31744230e-01 7.55039990e-01 1.47707656... | [9.121851921081543, -0.07246740907430649] |
08be73b6-10b0-438d-bf24-9ac3874ddae1 | towards-speaker-age-estimation-with-label | 2202.11424 | null | https://arxiv.org/abs/2202.11424v1 | https://arxiv.org/pdf/2202.11424v1.pdf | Towards Speaker Age Estimation with Label Distribution Learning | Existing methods for speaker age estimation usually treat it as a multi-class classification or a regression problem. However, precise age identification remains a challenge due to label ambiguity, \emph{i.e.}, utterances from adjacent age of the same person are often indistinguishable. To address this, we utilize the ... | ['Jing Xiao', 'Junqing Peng', 'Jianzong Wang', 'Shijing Si'] | 2022-02-23 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [ 1.32582873e-01 -9.81695368e-04 -1.53832406e-01 -9.03366983e-01
-1.35958755e+00 -5.84122598e-01 3.78890693e-01 2.70482987e-01
-3.67531836e-01 6.45404816e-01 4.25005972e-01 5.43660834e-04
2.06936911e-01 -2.78332472e-01 -3.51705104e-01 -8.14039767e-01
1.54871359e-01 3.89045417e-01 -2.58255720e-01 5.54530501... | [14.13963508605957, 6.019794940948486] |
d733fb85-55c6-4617-8249-c6fb6b5eefcf | mixste-seq2seq-mixed-spatio-temporal-encoder | 2203.00859 | null | https://arxiv.org/abs/2203.00859v4 | https://arxiv.org/pdf/2203.00859v4.pdf | MixSTE: Seq2seq Mixed Spatio-Temporal Encoder for 3D Human Pose Estimation in Video | Recent transformer-based solutions have been introduced to estimate 3D human pose from 2D keypoint sequence by considering body joints among all frames globally to learn spatio-temporal correlation. We observe that the motions of different joints differ significantly. However, the previous methods cannot efficiently mo... | ['Junsong Yuan', 'Yujin Chen', 'Jianyu Yang', 'Zhigang Tu', 'Jinlu Zhang'] | 2022-03-02 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhang_MixSTE_Seq2seq_Mixed_Spatio-Temporal_Encoder_for_3D_Human_Pose_Estimation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhang_MixSTE_Seq2seq_Mixed_Spatio-Temporal_Encoder_for_3D_Human_Pose_Estimation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [-4.25658315e-01 -3.14700603e-01 -1.17681675e-01 -2.02777497e-02
-4.22510862e-01 -1.15168750e-01 3.28051150e-01 -4.64734972e-01
-4.81575221e-01 4.02433157e-01 2.32642740e-01 2.95138657e-01
1.23275667e-01 -4.03676629e-01 -7.10573256e-01 -5.33999145e-01
-3.10128540e-01 2.75577903e-01 6.36227608e-01 -1.33237109... | [7.199881076812744, -0.632171630859375] |
20547f04-f4de-47cc-a5d4-4fed2dfaa2f6 | netflick-adversarial-flickering-attacks-on | 2304.01441 | null | https://arxiv.org/abs/2304.01441v1 | https://arxiv.org/pdf/2304.01441v1.pdf | NetFlick: Adversarial Flickering Attacks on Deep Learning Based Video Compression | Video compression plays a significant role in IoT devices for the efficient transport of visual data while satisfying all underlying bandwidth constraints. Deep learning-based video compression methods are rapidly replacing traditional algorithms and providing state-of-the-art results on edge devices. However, recently... | ['Farinaz Koushanfar', 'Seira Hidano', 'Mojan Javaheripi', 'Shehzeen Samarah Hussain', 'Nojan Sheybani', 'Jung-Woo Chang'] | 2023-04-04 | null | null | null | null | ['video-classification'] | ['computer-vision'] | [ 4.75165129e-01 -6.09075315e-02 -3.91418636e-01 1.07618392e-01
-5.42344034e-01 -7.04016805e-01 3.09580475e-01 -3.96256834e-01
-1.09117053e-01 5.97313106e-01 1.16360351e-01 -7.80368984e-01
-4.16029058e-02 -6.48103297e-01 -1.23388410e+00 -7.47933090e-01
-6.85928285e-01 -3.69800150e-01 1.39167368e-01 7.52195567... | [5.389522552490234, 7.9357757568359375] |
1c31579f-8073-4d12-80a0-af84604eb5fb | li-net-large-pose-identity-preserving-face | 2104.02850 | null | https://arxiv.org/abs/2104.02850v1 | https://arxiv.org/pdf/2104.02850v1.pdf | LI-Net: Large-Pose Identity-Preserving Face Reenactment Network | Face reenactment is a challenging task, as it is difficult to maintain accurate expression, pose and identity simultaneously. Most existing methods directly apply driving facial landmarks to reenact source faces and ignore the intrinsic gap between two identities, resulting in the identity mismatch issue. Besides, they... | ['Jizhong Han', 'Jiao Dai', 'Shuqiao Zou', 'Cai Yu', 'Zhaoxing Li', 'Tao Liang', 'Peng Chen', 'Jin Liu'] | 2021-04-07 | null | null | null | null | ['face-reenactment'] | ['computer-vision'] | [ 9.36804935e-02 4.18647602e-02 -3.37992385e-02 -7.91199207e-01
-3.58526260e-01 -7.10017741e-01 4.86994326e-01 -7.22461522e-01
-4.05577570e-02 4.34354961e-01 2.44761363e-01 4.84207869e-01
4.33079712e-02 -6.66404784e-01 -5.09627283e-01 -7.60848343e-01
4.64029580e-01 -3.47991823e-03 -4.42991972e-01 -3.40917200... | [12.699849128723145, -0.04083140566945076] |
f8610014-7ae8-40d2-8e85-afd68fc3409e | ic3d-image-conditioned-3d-diffusion-for-shape | 2211.10865 | null | https://arxiv.org/abs/2211.10865v2 | https://arxiv.org/pdf/2211.10865v2.pdf | IC3D: Image-Conditioned 3D Diffusion for Shape Generation | In the last years, Denoising Diffusion Probabilistic Models (DDPMs) obtained state-of-the-art results in many generative tasks, outperforming GANs and other classes of generative models. In particular, they reached impressive results in various image generation sub-tasks, among which conditional generation tasks such a... | ['Matteo Matteucci', 'Matteo Frosi', 'Paolo Cudrano', 'Cristian Sbrolli'] | 2022-11-20 | null | null | null | null | ['single-view-3d-reconstruction', '3d-shape-generation'] | ['computer-vision', 'computer-vision'] | [ 4.11354095e-01 4.36161667e-01 1.48070827e-01 -6.71814457e-02
-9.34144974e-01 -6.69350445e-01 1.20699823e+00 -5.50268173e-01
5.95506802e-02 6.02308214e-01 4.24143940e-01 -1.68183729e-01
2.41216403e-02 -9.79125082e-01 -8.92249584e-01 -9.08916354e-01
2.41791159e-01 7.81713426e-01 1.60540566e-02 -8.85120705... | [11.416590690612793, -0.48460322618484497] |
5952802c-6d1a-4ee2-91ff-493da36c3d4f | shape-of-you-precise-3d-shape-estimations-for | 2304.07389 | null | https://arxiv.org/abs/2304.07389v1 | https://arxiv.org/pdf/2304.07389v1.pdf | Shape of You: Precise 3D shape estimations for diverse body types | This paper presents Shape of You (SoY), an approach to improve the accuracy of 3D body shape estimation for vision-based clothing recommendation systems. While existing methods have successfully estimated 3D poses, there remains a lack of work in precise shape estimation, particularly for diverse human bodies. To addre... | ['Benjamin Biggs', 'Gerard Medioni', 'Achal Dave', 'Rohan Sarkar'] | 2023-04-14 | null | null | null | null | ['3d-human-reconstruction'] | ['computer-vision'] | [-7.10472018e-02 -1.28112286e-01 -4.23687920e-02 -4.18963701e-01
-6.79580748e-01 -7.55173385e-01 -2.76140925e-02 -1.62719548e-01
2.58560386e-02 3.22120517e-01 3.23126048e-01 -8.52895086e-04
2.65686989e-01 -3.71822715e-01 -7.93650270e-01 -1.18367195e-01
2.28128135e-01 9.21078324e-01 2.27287397e-01 -3.06558371... | [7.013289928436279, -1.1194239854812622] |
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