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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
a4520375-3907-4dc9-9d6b-9ca8ea1916db | shapley-head-pruning-identifying-and-removing | 2210.05709 | null | https://arxiv.org/abs/2210.05709v1 | https://arxiv.org/pdf/2210.05709v1.pdf | Shapley Head Pruning: Identifying and Removing Interference in Multilingual Transformers | Multilingual transformer-based models demonstrate remarkable zero and few-shot transfer across languages by learning and reusing language-agnostic features. However, as a fixed-size model acquires more languages, its performance across all languages degrades, a phenomenon termed interference. Often attributed to limite... | ['Diyi Yang', 'William Held'] | 2022-10-11 | null | null | null | null | ['sentence-classification'] | ['natural-language-processing'] | [-3.83042581e-02 1.23482376e-01 -1.44835800e-01 5.01072817e-02
-1.12996805e+00 -8.23243260e-01 3.32323730e-01 1.49161428e-01
-7.00031281e-01 8.60191345e-01 4.60304409e-01 -6.80983782e-01
-9.44458395e-02 -4.89630848e-01 -5.64284682e-01 -3.66369605e-01
-1.11586548e-01 5.73476970e-01 -1.31625831e-01 -7.14479089... | [10.696216583251953, 8.460688591003418] |
b479f8b0-7eb7-43c3-ac1b-f0827d779a67 | temporalstereo-efficient-spatial-temporal | 2211.13755 | null | https://arxiv.org/abs/2211.13755v1 | https://arxiv.org/pdf/2211.13755v1.pdf | TemporalStereo: Efficient Spatial-Temporal Stereo Matching Network | We present TemporalStereo, a coarse-to-fine based online stereo matching network which is highly efficient, and able to effectively exploit the past geometry and context information to boost the matching accuracy. Our network leverages sparse cost volume and proves to be effective when a single stereo pair is given, ho... | ['Stefano Mattoccia', 'Matteo Poggi', 'Youmin Zhang'] | 2022-11-24 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [-2.08722159e-01 -2.99072385e-01 -6.50138855e-02 -1.26915202e-01
-6.79466784e-01 -7.61196911e-01 7.59460449e-01 -3.76778305e-01
-3.58861774e-01 4.21553493e-01 2.61691213e-01 -1.28222555e-01
-1.26326993e-01 -8.34649205e-01 -8.87020826e-01 -3.07886899e-01
-4.19129491e-01 3.90295804e-01 5.04471183e-01 -3.39133352... | [8.578851699829102, -2.064683675765991] |
39727383-fec1-4b1e-88fa-0109b6089e56 | belief-revision-based-caption-re-ranker-with | 2209.08163 | null | https://arxiv.org/abs/2209.08163v1 | https://arxiv.org/pdf/2209.08163v1.pdf | Belief Revision based Caption Re-ranker with Visual Semantic Information | In this work, we focus on improving the captions generated by image-caption generation systems. We propose a novel re-ranking approach that leverages visual-semantic measures to identify the ideal caption that maximally captures the visual information in the image. Our re-ranker utilizes the Belief Revision framework (... | ['Lluís Padró', 'Pranava Madhyastha', 'Francesc Moreno-Noguer', 'Ahmed Sabir'] | 2022-09-16 | null | https://aclanthology.org/2022.coling-1.487 | https://aclanthology.org/2022.coling-1.487.pdf | coling-2022-10 | ['visual-reasoning', 'visual-reasoning', 'natural-language-visual-grounding'] | ['computer-vision', 'reasoning', 'reasoning'] | [ 4.57901597e-01 3.93335938e-01 -1.21647611e-01 -2.74559140e-01
-1.09268868e+00 -8.34456861e-01 9.55484807e-01 -2.01656595e-02
-1.23172626e-01 5.93258977e-01 4.85110134e-01 -2.87733823e-01
1.71555310e-01 -2.26275608e-01 -1.10082603e+00 -1.71306819e-01
3.91223341e-01 4.28873807e-01 2.67206252e-01 -1.56706646... | [10.992963790893555, 1.1015948057174683] |
b11ef625-775e-4622-98eb-a5476a6208bf | deep-learning-based-detection-of-the-acute | 2109.12323 | null | https://arxiv.org/abs/2109.12323v1 | https://arxiv.org/pdf/2109.12323v1.pdf | Deep Learning-Based Detection of the Acute Respiratory Distress Syndrome: What Are the Models Learning? | The acute respiratory distress syndrome (ARDS) is a severe form of hypoxemic respiratory failure with in-hospital mortality of 35-46%. High mortality is thought to be related in part to challenges in making a prompt diagnosis, which may in turn delay implementation of evidence-based therapies. A deep neural network (DN... | ['Jason Adams', 'Chen-Nee Chuah', 'Irene Cortes-Puch', 'Chao Wang', 'Gregory B. Rehm'] | 2021-09-25 | null | null | null | null | ['respiratory-failure'] | ['medical'] | [-7.98811391e-02 -3.83366823e-01 -1.14636414e-01 -2.03928709e-01
-4.96997327e-01 -4.48797584e-01 -2.65829772e-01 3.21032852e-01
-3.33489835e-01 7.68696368e-01 2.90057003e-01 -8.47934008e-01
-3.76175195e-01 -7.00250506e-01 -4.37441885e-01 -4.25597250e-01
-4.79688376e-01 4.12052333e-01 -6.11807033e-02 5.37680015... | [8.049768447875977, 6.137688636779785] |
5eaf0873-82b4-4d90-ac4c-1559a1736b50 | a-community-based-algorithm-for-large-scale | 1305.0187 | null | http://arxiv.org/abs/1305.0187v1 | http://arxiv.org/pdf/1305.0187v1.pdf | A Community Based Algorithm for Large Scale Web Service Composition | Web service composition is the process of synthesizing a new composite
service using a set of available Web services in order to satisfy a client
request that cannot be treated by any available Web services. The Web services
space is a dynamic environment characterized by a huge number of elements.
Furthermore, many We... | ['Jean-Francois Santucci', 'Yvan Rivierre', 'Chantal Cherifi'] | 2013-05-01 | null | null | null | null | ['service-composition'] | ['miscellaneous'] | [ 3.10703367e-01 2.57549584e-01 7.73280039e-02 -2.70485610e-01
4.62832861e-02 -7.56210983e-01 6.77447736e-01 -5.93348481e-02
2.78151453e-01 2.92449534e-01 3.96282285e-01 -3.06881875e-01
-4.25050557e-01 -1.12128365e+00 -4.33103135e-03 -8.78875077e-01
-3.60186636e-01 8.33053529e-01 8.77482235e-01 -3.88915241... | [8.599237442016602, 6.969568729400635] |
a633de0e-50c8-48dc-a0cc-9ed4b8276501 | fusing-event-based-camera-and-radar-for-slam | 2210.04236 | null | https://arxiv.org/abs/2210.04236v1 | https://arxiv.org/pdf/2210.04236v1.pdf | Fusing Event-based Camera and Radar for SLAM Using Spiking Neural Networks with Continual STDP Learning | This work proposes a first-of-its-kind SLAM architecture fusing an event-based camera and a Frequency Modulated Continuous Wave (FMCW) radar for drone navigation. Each sensor is processed by a bio-inspired Spiking Neural Network (SNN) with continual Spike-Timing-Dependent Plasticity (STDP) learning, as observed in the ... | ['Georges Gielen', 'Francky Catthoor', 'Hichem Sahli', 'André Bourdoux', 'Ilja Ocket', 'Tim Verbelen', 'Ali Safa'] | 2022-10-09 | null | null | null | null | ['drone-navigation', 'loop-closure-detection'] | ['computer-vision', 'computer-vision'] | [ 6.28558874e-01 -3.74237031e-01 5.26976824e-01 -4.75004017e-01
-2.55695164e-01 -5.62547266e-01 6.22178674e-01 2.69659430e-01
-9.93611336e-01 8.70125055e-01 -5.14371216e-01 1.18073858e-01
-1.45768523e-01 -8.63559663e-01 -1.04982173e+00 -6.33652449e-01
-1.25640899e-01 4.81345356e-01 7.06712961e-01 -4.35954452... | [7.51577615737915, -1.6917216777801514] |
fd0a5583-8b87-4782-a05a-529d18786629 | comprehension-guided-referring-expressions | 1701.03439 | null | http://arxiv.org/abs/1701.03439v1 | http://arxiv.org/pdf/1701.03439v1.pdf | Comprehension-guided referring expressions | We consider generation and comprehension of natural language referring
expression for objects in an image. Unlike generic "image captioning" which
lacks natural standard evaluation criteria, quality of a referring expression
may be measured by the receiver's ability to correctly infer which object is
being described. F... | ['Gregory Shakhnarovich', 'Ruotian Luo'] | 2017-01-12 | comprehension-guided-referring-expressions-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Luo_Comprehension-Guided_Referring_Expressions_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Luo_Comprehension-Guided_Referring_Expressions_CVPR_2017_paper.pdf | cvpr-2017-7 | ['referring-expression-generation'] | ['computer-vision'] | [ 7.02534616e-01 6.87337697e-01 -6.19966388e-02 -7.44856298e-01
-1.28926075e+00 -5.22654772e-01 8.09336722e-01 8.79983157e-02
-5.20699918e-01 7.12736011e-01 6.06469870e-01 -1.35461360e-01
4.84625220e-01 -8.42257857e-01 -9.28652823e-01 -3.44295949e-01
4.58293617e-01 5.31992555e-01 -2.92581558e-01 -2.37292960... | [10.840110778808594, 1.4335837364196777] |
3633e1a3-38a5-42d7-adfa-fbea950f6d46 | codebleu-a-method-for-automatic-evaluation-of | 2009.10297 | null | https://arxiv.org/abs/2009.10297v2 | https://arxiv.org/pdf/2009.10297v2.pdf | CodeBLEU: a Method for Automatic Evaluation of Code Synthesis | Evaluation metrics play a vital role in the growth of an area as it defines the standard of distinguishing between good and bad models. In the area of code synthesis, the commonly used evaluation metric is BLEU or perfect accuracy, but they are not suitable enough to evaluate codes, because BLEU is originally designed ... | ['Ambrosio Blanco', 'Long Zhou', 'Shuai Ma', 'Shuai Lu', 'Ming Zhou', 'Shuo Ren', 'Shujie Liu', 'Duyu Tang', 'Daya Guo', 'Neel Sundaresan'] | 2020-09-22 | null | null | null | null | ['code-translation'] | ['computer-code'] | [-1.35197386e-01 -1.61487490e-01 -2.61733919e-01 -4.77549374e-01
-7.79483378e-01 -6.91336334e-01 3.95464778e-01 5.03034890e-01
-1.49052635e-01 2.98467219e-01 4.13402230e-01 -6.27295613e-01
1.96244210e-01 -7.94355750e-01 -6.99430466e-01 1.25988185e-01
2.97191203e-01 4.23490480e-02 3.17318350e-01 -2.87432313... | [7.69765567779541, 7.897199630737305] |
f4b00924-d467-4854-ba9b-bb4923159b3b | fisher-efficient-inference-of-intractable | 1805.07454 | null | https://arxiv.org/abs/1805.07454v5 | https://arxiv.org/pdf/1805.07454v5.pdf | Fisher Efficient Inference of Intractable Models | Maximum Likelihood Estimators (MLE) has many good properties. For example, the asymptotic variance of MLE solution attains equality of the asymptotic Cram{\'e}r-Rao lower bound (efficiency bound), which is the minimum possible variance for an unbiased estimator. However, obtaining such MLE solution requires calculating... | ['Yu Chen', 'Wittawat Jitkrittum', 'Takafumi Kanamori', 'Song Liu'] | 2018-05-18 | fisher-efficient-inference-of-intractable-1 | http://papers.nips.cc/paper/9083-fisher-efficient-inference-of-intractable-models | http://papers.nips.cc/paper/9083-fisher-efficient-inference-of-intractable-models.pdf | neurips-2019-12 | ['density-ratio-estimation'] | ['methodology'] | [-1.28846571e-01 1.92132011e-01 -1.21204875e-01 -4.20163959e-01
-1.09353018e+00 -2.85373837e-01 1.35462061e-01 -3.38488340e-01
-6.37995243e-01 1.07170343e+00 -3.03481907e-01 -4.51638639e-01
-4.79921490e-01 -6.28364205e-01 -8.60140979e-01 -7.23619998e-01
-1.56608269e-01 3.46608281e-01 -3.90653998e-01 4.49062198... | [7.149463176727295, 4.013396263122559] |
07f73fa8-4519-4d18-a679-faef2638e00e | synthesis-of-a-machine-learning-model-for | null | null | https://www.researchgate.net/publication/347631637_Synthesis_of_a_Machine_Learning_Model_for_Detecting_Computer_Attacks_Based_on_the_CICIDS2017_Dataset | https://ispranproceedings.elpub.ru/jour/article/view/1348/1147 | Synthesis of a Machine Learning Model for Detecting Computer Attacks Based on the CICIDS2017 Dataset | The paper deals with the construction and practical implementation of the model of computer attack detection based on machine learning methods. Among available public datasets one of the most relevant was chosen - CICIDS2017. For this dataset, the procedures of data preprocessing and sampling were developed in detail. ... | ['Andrey Matskevich', 'Maxim Goryunov', 'Dmitry Rybolovlev'] | 2020-01-01 | null | null | null | proceedings-of-the-institute-for-system | ['network-intrusion-detection'] | ['miscellaneous'] | [ 1.06145546e-01 -2.13541687e-01 -1.27062351e-01 -1.22388326e-01
-2.62797461e-03 -3.08522493e-01 4.15896267e-01 6.75539553e-01
-8.28243732e-01 6.44210160e-01 -2.34783232e-01 -7.85689175e-01
-6.21599793e-01 -1.13726473e+00 -1.07422955e-01 -5.55197597e-01
-2.93610841e-01 5.88496804e-01 3.63955438e-01 -5.06560542... | [5.266815185546875, 7.188900470733643] |
4b565488-cf68-48a7-baf7-5b55fec50735 | style-transfer-for-co-speech-gesture | 2007.12553 | null | https://arxiv.org/abs/2007.12553v1 | https://arxiv.org/pdf/2007.12553v1.pdf | Style Transfer for Co-Speech Gesture Animation: A Multi-Speaker Conditional-Mixture Approach | How can we teach robots or virtual assistants to gesture naturally? Can we go further and adapt the gesturing style to follow a specific speaker? Gestures that are naturally timed with corresponding speech during human communication are called co-speech gestures. A key challenge, called gesture style transfer, is to le... | ['Louis-Philippe Morency', 'Chaitanya Ahuja', 'Dong Won Lee', 'Yukiko I. Nakano'] | 2020-07-24 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2962_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123630239.pdf | eccv-2020-8 | ['gesture-generation'] | ['robots'] | [ 0.403063 0.09475458 0.1459082 -0.7149108 -0.56050396 -0.8172905
1.0399344 -0.6478888 -0.5096812 0.20246916 0.5276597 -0.04266408
0.33173016 -0.4553102 -0.7777997 -0.7620715 0.04080038 0.81620216
-0.11482365 -0.4375054 -0.12254949 0.62827486 -1.3278494 0.45924202
0.1486134 0.39348692 0.4... | [5.6192216873168945, -0.11828003823757172] |
4aca96d7-517a-45a4-b248-29940a4844c3 | rankings-dependent-preferences-a-real-goods | 2305.03644 | null | https://arxiv.org/abs/2305.03644v1 | https://arxiv.org/pdf/2305.03644v1.pdf | Rankings-Dependent Preferences: A Real Goods Matching Experiment | We investigate whether preferences for objects received via a matching mechanism are influenced by how highly agents rank them in their reported rank order list. We hypothesize that all else equal, agents receive greater utility for the same object when they rank it higher. The addition of rankings-dependent utility im... | ['Peter Troyan', 'Andrew Kloosterman'] | 2023-05-05 | null | null | null | null | ['experimental-design'] | ['methodology'] | [-2.87566006e-01 2.51919806e-01 -7.58035839e-01 -4.21194375e-01
-4.94362116e-01 -1.18556702e+00 4.27726239e-01 9.04246699e-03
-9.44784939e-01 1.07145858e+00 5.27133942e-01 -4.12936687e-01
-7.47767150e-01 -8.95933509e-01 -4.10876423e-01 -3.92228723e-01
-9.40343738e-02 6.18554413e-01 8.40175897e-02 -3.20521183... | [4.266723155975342, 2.968151569366455] |
4150768d-e67f-4300-a4b9-6a7f86bbd12c | tree-gated-deep-mixture-of-experts-for-pose | 1910.09450 | null | https://arxiv.org/abs/1910.09450v1 | https://arxiv.org/pdf/1910.09450v1.pdf | Tree-gated Deep Mixture-of-Experts For Pose-robust Face Alignment | Face alignment consists of aligning a shape model on a face image. It is an active domain in computer vision as it is a preprocessing for a number of face analysis and synthesis applications. Current state-of-the-art methods already perform well on "easy" datasets, with moderate head pose variations, but may not be rob... | ['Kevin Bailly', 'Estephe Arnaud', 'Arnaud Dapogny'] | 2019-10-21 | null | null | null | null | ['robust-face-alignment'] | ['computer-vision'] | [ 1.14901036e-01 1.46274626e-01 1.15089573e-01 -8.94429326e-01
-5.07771015e-01 -2.08592921e-01 6.11020148e-01 -6.28735125e-02
-4.91783261e-01 4.80632991e-01 1.38873219e-01 9.89146680e-02
4.93725799e-02 -5.24450779e-01 -9.42166805e-01 -8.20012689e-01
1.46359012e-01 7.45478988e-01 2.25617528e-01 -4.14999664... | [13.509075164794922, 0.33108603954315186] |
3468aa77-266f-4b68-865e-dc742af0015c | advances-of-transformer-based-models-for-news | 2007.05044 | null | https://arxiv.org/abs/2007.05044v2 | https://arxiv.org/pdf/2007.05044v2.pdf | Advances of Transformer-Based Models for News Headline Generation | Pretrained language models based on Transformer architecture are the reason for recent breakthroughs in many areas of NLP, including sentiment analysis, question answering, named entity recognition. Headline generation is a special kind of text summarization task. Models need to have strong natural language understandi... | ['Ilya Gusev', 'Alexey Bukhtiyarov'] | 2020-07-09 | null | null | null | null | ['headline-generation'] | ['natural-language-processing'] | [ 2.08673805e-01 2.97211915e-01 -1.74006522e-01 -2.51950711e-01
-1.02659428e+00 -6.63956761e-01 7.17148185e-01 4.74175304e-01
-4.72800583e-01 1.03715897e+00 9.78708684e-01 -2.58196503e-01
-1.24290057e-01 -6.58844829e-01 -6.49842680e-01 -3.25822830e-01
1.39660180e-01 7.80603468e-01 2.70985961e-01 -9.60999668... | [12.452510833740234, 9.4715576171875] |
458c7dd8-4e96-416b-8d86-0ab1122590a9 | a-star-test-time-attention-segregation-and | 2306.14544 | null | https://arxiv.org/abs/2306.14544v1 | https://arxiv.org/pdf/2306.14544v1.pdf | A-STAR: Test-time Attention Segregation and Retention for Text-to-image Synthesis | While recent developments in text-to-image generative models have led to a suite of high-performing methods capable of producing creative imagery from free-form text, there are several limitations. By analyzing the cross-attention representations of these models, we notice two key issues. First, for text prompts that c... | ['Balaji Vasan Srinivasan', 'Koustava Goswami', 'Apoorv Saxena', 'K J Joseph', 'Srikrishna Karanam', 'Aishwarya Agarwal'] | 2023-06-26 | null | null | null | null | ['image-generation'] | ['computer-vision'] | [ 5.98719239e-01 -9.15857032e-02 4.48905021e-01 8.94466564e-02
-6.56616509e-01 -7.40676641e-01 8.23393285e-01 2.37861261e-01
-1.77436650e-01 5.38177371e-01 3.63634855e-01 -1.01378135e-01
-2.09321544e-01 -8.52176368e-01 -6.85174346e-01 -7.48091936e-01
4.00287867e-01 2.68275529e-01 2.66586512e-01 -2.24945292... | [11.35998249053955, -0.1927841305732727] |
125eb2e9-3feb-497d-97c6-72010e327ce3 | hd2reg-hierarchical-descriptors-and-detectors | 2305.03487 | null | https://arxiv.org/abs/2305.03487v1 | https://arxiv.org/pdf/2305.03487v1.pdf | HD2Reg: Hierarchical Descriptors and Detectors for Point Cloud Registration | Feature Descriptors and Detectors are two main components of feature-based point cloud registration. However, little attention has been drawn to the explicit representation of local and global semantics in the learning of descriptors and detectors. In this paper, we present a framework that explicitly extracts dual-lev... | ['Zhiqiang Tian', 'Guofa Wang', 'Shaoyi Du', 'Yiheng Li', 'Canhui Tang'] | 2023-05-05 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [-1.26193330e-01 -3.08921218e-01 -4.88930792e-01 -3.91321361e-01
-9.84804273e-01 -2.39053577e-01 6.89486861e-01 4.21320617e-01
-2.60466754e-01 1.67717546e-01 1.57275379e-01 3.41729879e-01
-4.59012926e-01 -8.63784909e-01 -5.94411731e-01 -4.15887058e-01
-8.06005485e-03 3.35807443e-01 6.61257446e-01 -2.35534459... | [7.822451591491699, -2.181776285171509] |
5e4f1739-2d01-46bf-8d1a-7a7973f82957 | label-name-is-mantra-unifying-point-cloud | 2303.10585 | null | https://arxiv.org/abs/2303.10585v1 | https://arxiv.org/pdf/2303.10585v1.pdf | Label Name is Mantra: Unifying Point Cloud Segmentation across Heterogeneous Datasets | Point cloud segmentation is a fundamental task in 3D vision that serves a wide range of applications. Although great progresses have been made these years, its practical usability is still limited by the availability of training data. Existing approaches cannot make full use of multiple datasets on hand due to the labe... | ['Yingcong Chen', 'Hao Lu', 'Shishi Xiao', 'Hao He', 'Yixun Liang'] | 2023-03-19 | null | null | null | null | ['point-cloud-segmentation'] | ['computer-vision'] | [ 1.18207783e-01 -1.42411605e-01 -5.29340625e-01 -5.02851665e-01
-9.40324366e-01 -6.53694808e-01 5.12202621e-01 -6.61665052e-02
-3.61494511e-01 2.48319685e-01 4.28564698e-02 -2.48999432e-01
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5.64138293e-01 5.29615819e-01 4.74328518e-01 3.06148797... | [8.04272174835205, -3.1417038440704346] |
e5b4682e-e3ee-4ff1-ba50-d25c16292dca | fnr-a-similarity-and-transformer-based-1 | null | null | https://scholar.google.com/citations?view_op=view_citation&hl=en&user=GQTf-pYAAAAJ&citation_for_view=GQTf-pYAAAAJ:u-x6o8ySG0sC | https://link.springer.com/article/10.1007/s13278-023-01065-0 | FNR: a similarity and transformer-based approach to detect multi-modal fake news in social media | Many people today get their news from social media. It is possible to propagate news using textual, visual, or multi-modal information. The popularity of social networks and their wide use by people make them attractive platforms for spreading fake news. Detecting fake news is essential to preventing its spread. Fake n... | ['Hamid R. Rabiee', 'Mohammad Amin Fazli', 'Maryam Ramezani', 'Faeze Ghorbanpour'] | 2023-03-28 | null | null | null | social-network-analysis-and-mining-2023-3 | ['fake-news-detection'] | ['natural-language-processing'] | [-1.06197633e-01 1.62956771e-02 -9.26740244e-02 -9.22279730e-02
-4.46439505e-01 -6.22862399e-01 1.10115314e+00 2.06956759e-01
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3.86261731e-01 3.80686760e-01 8.84401798e-01 -6.64977610... | [8.137957572937012, 10.25755500793457] |
58ef7180-30df-436b-9694-86253fe5e39a | sign-language-production-with-avatar-layering | null | null | https://aclanthology.org/2022.lrec-1.163 | https://aclanthology.org/2022.lrec-1.163.pdf | Sign Language Production With Avatar Layering: A Critical Use Case over Rare Words | Sign language production (SLP) is the process of generating sign language videos from spoken language expressions. Since sign languages are highly under-resourced, existing vision-based SLP approaches suffer from out-of-vocabulary (OOV) and test-time generalization problems and thus generate low-quality translations. T... | ['Jong Park', 'Du Hui Lee', 'Sukmin Cho', 'Eui Jun Hwang', 'Jung-Ho Kim'] | null | null | null | null | lrec-2022-6 | ['sign-language-translation', 'sign-language-production'] | ['computer-vision', 'natural-language-processing'] | [ 8.04691166e-02 1.65651768e-01 -1.37675256e-01 -3.34151089e-01
-1.35858893e+00 -7.21019626e-01 6.72399580e-01 -8.44669640e-01
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3.40022802e-01 5.55072725e-01 2.85057932e-01 -3.13006818... | [9.19657039642334, -6.520587921142578] |
07046d1c-7b01-4bf2-ac63-53ff93c2a543 | solving-learn-to-race-autonomous-racing | 2207.01275 | null | https://arxiv.org/abs/2207.01275v2 | https://arxiv.org/pdf/2207.01275v2.pdf | Solving Learn-to-Race Autonomous Racing Challenge by Planning in Latent Space | Learn-to-Race Autonomous Racing Virtual Challenge hosted on www<dot>aicrowd<dot>com platform consisted of two tracks: Single and Multi Camera. Our UniTeam team was among the final winners in the Single Camera track. The agent is required to pass the previously unknown F1-style track in the minimum time with the least a... | ['Andrew Melnik', 'Helge Ritter', 'Rahul Kala', 'Fabian Heinrich', 'Shivansh Beohar'] | 2022-07-04 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [-2.15801701e-01 1.49648115e-01 -3.49892318e-01 -3.32951695e-01
-7.80848444e-01 -7.99341738e-01 3.53769362e-01 -3.17196906e-01
-7.61280179e-01 8.99170637e-01 -3.96531671e-01 -5.44856548e-01
-3.92406255e-01 -7.21295297e-01 -9.72669661e-01 -4.40247327e-01
2.42967941e-02 6.16189718e-01 5.31226516e-01 -3.11373025... | [5.191470623016357, 1.1756867170333862] |
9a772ada-6575-4e7c-a1f5-ea096f126fee | bros-a-layout-aware-pre-trained-language | 2108.04539 | null | https://arxiv.org/abs/2108.04539v5 | https://arxiv.org/pdf/2108.04539v5.pdf | BROS: A Pre-trained Language Model Focusing on Text and Layout for Better Key Information Extraction from Documents | Key information extraction (KIE) from document images requires understanding the contextual and spatial semantics of texts in two-dimensional (2D) space. Many recent studies try to solve the task by developing pre-trained language models focusing on combining visual features from document images with texts and their la... | ['Sungrae Park', 'Daehyun Nam', 'Wonseok Hwang', 'Mingi Ji', 'Donghyun Kim', 'Teakgyu Hong'] | 2021-08-10 | null | null | null | null | ['key-information-extraction'] | ['natural-language-processing'] | [ 3.17808688e-01 -1.71032324e-01 5.93094379e-02 -3.19709152e-01
-7.94539332e-01 -7.17075109e-01 8.97503138e-01 2.48845890e-01
-3.69243473e-01 2.34274343e-01 3.72627616e-01 -5.53595424e-01
-1.19925193e-01 -4.51764375e-01 -6.72636092e-01 -5.72655976e-01
1.67961746e-01 5.01190960e-01 2.71908075e-01 -1.00901239... | [11.586295127868652, 2.2901511192321777] |
0b25c745-f320-4425-8e67-b531bd0ac8d9 | multi-view-reconstruction-of-bullet-time | 2304.00330 | null | https://arxiv.org/abs/2304.00330v1 | https://arxiv.org/pdf/2304.00330v1.pdf | Multi-view reconstruction of bullet time effect based on improved NSFF model | Bullet time is a type of visual effect commonly used in film, television and games that makes time seem to slow down or stop while still preserving dynamic details in the scene. It usually requires multiple sets of cameras to move slowly with the subject and is synthesized using post-production techniques, which is cos... | ['Wentao Zeng', 'Yangtian Yan', 'Yan Gao', 'Linquan Yu'] | 2023-04-01 | null | null | null | null | ['neural-rendering'] | ['computer-vision'] | [ 1.02939025e-01 -5.05576372e-01 9.70061794e-02 -1.79966763e-01
6.00526392e-01 -2.99822122e-01 3.24060284e-02 -8.76509249e-01
-2.82650739e-01 4.89845455e-01 1.97661206e-01 4.33080122e-02
-2.50740409e-01 -8.31381202e-01 -4.58297879e-01 -7.41450727e-01
8.82557705e-02 -3.25875670e-01 6.36994064e-01 -2.67084032... | [11.00754165649414, -1.8016690015792847] |
bf078e02-fddf-468b-baa8-1306bbbea9fd | abstraction-and-analogy-making-in-artificial | 2102.10717 | null | https://arxiv.org/abs/2102.10717v2 | https://arxiv.org/pdf/2102.10717v2.pdf | Abstraction and Analogy-Making in Artificial Intelligence | Conceptual abstraction and analogy-making are key abilities underlying humans' abilities to learn, reason, and robustly adapt their knowledge to new domains. Despite of a long history of research on constructing AI systems with these abilities, no current AI system is anywhere close to a capability of forming humanlike... | ['Melanie Mitchell'] | 2021-02-22 | null | null | null | null | ['program-induction'] | ['computer-code'] | [-5.48034087e-02 1.39857173e-01 -1.68717191e-01 -6.24070287e-01
-3.96327168e-01 -5.49638033e-01 8.83085608e-01 5.03742993e-01
-1.71896845e-01 8.13864410e-01 1.62415430e-01 -5.72406411e-01
-2.49126852e-01 -9.00819361e-01 -5.18882155e-01 -6.43809140e-02
-1.34022519e-01 9.01854694e-01 1.90657645e-01 -4.06220496... | [9.214394569396973, 7.011345386505127] |
75ad603c-0a7e-4527-b27e-baa732a623db | semi-supervised-disentangled-framework-for | 2012.11805 | null | https://arxiv.org/abs/2012.11805v1 | https://arxiv.org/pdf/2012.11805v1.pdf | Semi-Supervised Disentangled Framework for Transferable Named Entity Recognition | Named entity recognition (NER) for identifying proper nouns in unstructured text is one of the most important and fundamental tasks in natural language processing. However, despite the widespread use of NER models, they still require a large-scale labeled data set, which incurs a heavy burden due to manual annotation. ... | ['Boyan Xu', 'Wen Wen', 'Ruichu Cai', 'Zijian Li', 'Di Lv', 'Zhifeng Hao'] | 2020-12-22 | null | null | null | null | ['cross-lingual-ner'] | ['natural-language-processing'] | [-8.66469890e-02 -1.51186273e-01 -3.43744576e-01 -5.71383297e-01
-7.98897624e-01 -8.25340867e-01 3.71256769e-01 2.54271515e-02
-7.90817857e-01 7.69708097e-01 4.37584490e-01 -2.05899036e-04
-1.28376573e-01 -7.42295504e-01 -3.05394232e-01 -8.20062816e-01
3.87373418e-01 5.45078278e-01 -1.02537973e-02 -2.73894608... | [9.77961540222168, 9.561347007751465] |
a10057b1-a89b-45ce-bea3-3c064219249a | robust-category-level-6d-pose-estimation-with | 2209.05624 | null | https://arxiv.org/abs/2209.05624v1 | https://arxiv.org/pdf/2209.05624v1.pdf | Robust Category-Level 6D Pose Estimation with Coarse-to-Fine Rendering of Neural Features | We consider the problem of category-level 6D pose estimation from a single RGB image. Our approach represents an object category as a cuboid mesh and learns a generative model of the neural feature activations at each mesh vertex to perform pose estimation through differentiable rendering. A common problem of rendering... | ['Adam Kortylewski', 'Alan Yuille', 'Angtian Wang', 'Wufei Ma'] | 2022-09-12 | null | null | null | null | ['6d-pose-estimation-1'] | ['computer-vision'] | [ 2.00258374e-01 2.27845415e-01 -9.48558673e-02 -4.94380146e-01
-9.25577462e-01 -5.31779528e-01 6.62585437e-01 -6.42687827e-02
-1.69851363e-01 3.44608665e-01 1.41556010e-01 2.41864592e-01
1.99355692e-01 -7.52453268e-01 -1.06568038e+00 -3.51545006e-01
4.76234630e-02 8.63527894e-01 1.64868653e-01 -4.41336669... | [7.641812324523926, -2.546248197555542] |
4cf0c87d-77cb-491f-b8dc-11e71ef06064 | event-based-vision-meets-deep-learning-on | 1804.01310 | null | http://arxiv.org/abs/1804.01310v1 | http://arxiv.org/pdf/1804.01310v1.pdf | Event-based Vision meets Deep Learning on Steering Prediction for Self-driving Cars | Event cameras are bio-inspired vision sensors that naturally capture the
dynamics of a scene, filtering out redundant information. This paper presents a
deep neural network approach that unlocks the potential of event cameras on a
challenging motion-estimation task: prediction of a vehicle's steering angle.
To make the... | ['Davide Scaramuzza', 'Guillermo Gallego', 'Antonio Loquercio', 'Ana I. Maqueda', 'Narciso Garcia'] | 2018-04-04 | event-based-vision-meets-deep-learning-on-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Maqueda_Event-Based_Vision_Meets_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Maqueda_Event-Based_Vision_Meets_CVPR_2018_paper.pdf | cvpr-2018-6 | ['event-based-vision'] | ['computer-vision'] | [ 1.96334794e-01 -3.27713698e-01 9.15399939e-02 -4.17327434e-01
-5.25715649e-01 -5.44670045e-01 8.40244055e-01 -2.66961098e-01
-6.39181972e-01 2.86823183e-01 4.83552784e-01 -9.84798595e-02
3.86489183e-02 -5.56369245e-01 -1.15123570e+00 -6.01584256e-01
-1.06238022e-01 -6.28326610e-02 6.31774783e-01 -1.41306579... | [8.491018295288086, -1.0363715887069702] |
d1a0ce02-2cf4-4e6e-a086-083318c9a84d | temporal-information-extraction-from-korean | null | null | https://aclanthology.org/K15-1028 | https://aclanthology.org/K15-1028.pdf | Temporal Information Extraction from Korean Texts | null | ['Ho-Jin Choi', 'Chae-Gyun Lim', 'Hyun-Woo Do', 'Young-Seob Jeong', 'Zae Myung Kim'] | 2015-07-01 | null | null | null | conll-2015-7 | ['temporal-information-extraction'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.157752990722656, 3.9809765815734863] |
77944cc8-e793-41cb-9823-d66dc1707df2 | graph-convolutional-neural-networks-with | 2212.02055 | null | https://arxiv.org/abs/2212.02055v2 | https://arxiv.org/pdf/2212.02055v2.pdf | Graph Convolutional Neural Networks with Diverse Negative Samples via Decomposed Determinant Point Processes | Graph convolutional networks (GCNs) have achieved great success in graph representation learning by extracting high-level features from nodes and their topology. Since GCNs generally follow a message-passing mechanism, each node aggregates information from its first-order neighbour to update its representation. As a re... | ['Jie Lu', 'Maoying Qiao', 'Junyu Xuan', 'Wei Duan'] | 2022-12-05 | null | null | null | null | ['point-processes'] | ['methodology'] | [ 5.65641858e-02 1.08829886e-01 -1.16229601e-01 -3.63902539e-01
-8.45571980e-02 -3.34450454e-01 5.21252811e-01 5.25186002e-01
-3.20890546e-01 6.24167860e-01 2.09034141e-02 -2.42078722e-01
-1.07406318e-01 -1.45681763e+00 -5.37711203e-01 -1.11916924e+00
-5.53411901e-01 2.93896198e-01 1.96597993e-01 -2.56605595... | [7.194202423095703, 6.194403648376465] |
744e19b1-f7e6-4d81-aa11-758622d9b028 | encoding-sentiment-information-into-word | null | null | https://aclanthology.org/C18-1085 | https://aclanthology.org/C18-1085.pdf | Encoding Sentiment Information into Word Vectors for Sentiment Analysis | General-purpose pre-trained word embeddings have become a mainstay of natural language processing, and more recently, methods have been proposed to encode external knowledge into word embeddings to benefit specific downstream tasks. The goal of this paper is to encode sentiment knowledge into pre-trained word vectors t... | ['Timothy Baldwin', 'Fang Li', 'Zhe Ye'] | 2018-08-01 | encoding-sentiment-information-into-word-1 | https://aclanthology.org/C18-1085 | https://aclanthology.org/C18-1085.pdf | coling-2018-8 | ['learning-word-embeddings'] | ['methodology'] | [ 6.88268915e-02 3.04387906e-03 -3.56485963e-01 -7.24157453e-01
-2.83719033e-01 -4.35881764e-01 7.00916886e-01 4.45703804e-01
-9.16492641e-01 3.70312154e-01 6.72927022e-01 -2.20401451e-01
4.57549602e-01 -9.93733466e-01 -1.16615601e-01 -4.22591686e-01
3.29994410e-01 -1.17385224e-01 6.88191205e-02 -6.53546810... | [10.539731979370117, 8.398943901062012] |
0aeaad2d-0fc6-4af4-a225-efa15168a353 | meta-gmvae-mixture-of-gaussian-vae-for | null | null | https://openreview.net/forum?id=wS0UFjsNYjn | https://openreview.net/pdf?id=wS0UFjsNYjn | Meta-GMVAE: Mixture of Gaussian VAE for Unsupervised Meta-Learning | Unsupervised learning aims to learn meaningful representations from unlabeled data which can captures its intrinsic structure, that can be transferred to downstream tasks. Meta-learning, whose objective is to learn to generalize across tasks such that the learned model can rapidly adapt to a novel task, shares the spir... | ['Sung Ju Hwang', 'Seanie Lee', 'Dongchan Min', 'Dong Bok Lee'] | 2021-01-01 | null | null | null | iclr-2021-1 | ['unsupervised-few-shot-learning', 'unsupervised-few-shot-image-classification'] | ['computer-vision', 'computer-vision'] | [ 2.64994860e-01 2.61283338e-01 -6.08136773e-01 -5.37038982e-01
-1.00028551e+00 -2.68115729e-01 8.54096889e-01 -1.26658678e-02
-5.07796943e-01 5.28854787e-01 1.53503448e-01 1.97700679e-01
-2.37083621e-02 -6.98610246e-01 -7.49847174e-01 -7.75351763e-01
3.90471548e-01 6.16903961e-01 -6.98797256e-02 1.36809677... | [9.771493911743164, 2.9120562076568604] |
13c0b6bc-48c4-4db0-8502-324d7a45cee8 | di-nids-domain-invariant-network-intrusion | 2210.08252 | null | https://arxiv.org/abs/2210.08252v1 | https://arxiv.org/pdf/2210.08252v1.pdf | DI-NIDS: Domain Invariant Network Intrusion Detection System | The performance of machine learning based network intrusion detection systems (NIDSs) severely degrades when deployed on a network with significantly different feature distributions from the ones of the training dataset. In various applications, such as computer vision, domain adaptation techniques have been successful... | ['Marius Portmann', 'Mahsa Baktashmotlagh', 'Siamak Layeghy'] | 2022-10-15 | null | null | null | null | ['network-intrusion-detection'] | ['miscellaneous'] | [ 6.21502399e-01 -3.65319341e-01 -2.01852858e-01 -3.77007872e-01
-5.03691435e-02 -9.43216085e-01 6.25948131e-01 5.53666055e-02
-3.92576307e-01 8.06344092e-01 -5.68297327e-01 -5.78362584e-01
-2.62216717e-01 -9.81163025e-01 -4.18378800e-01 -8.08450103e-01
8.11800547e-03 7.31871068e-01 5.98310947e-01 -2.82182246... | [5.37531852722168, 7.337593078613281] |
67b56eb9-28c3-4d68-82b5-2bc118b68785 | r2former-unified-retrieval-and-reranking | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhu_R2Former_Unified_Retrieval_and_Reranking_Transformer_for_Place_Recognition_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhu_R2Former_Unified_Retrieval_and_Reranking_Transformer_for_Place_Recognition_CVPR_2023_paper.pdf | R2Former: Unified Retrieval and Reranking Transformer for Place Recognition | Visual Place Recognition (VPR) estimates the location of query images by matching them with images in a reference database. Conventional methods generally adopt aggregated CNN features for global retrieval and RANSAC-based geometric verification for reranking. However, RANSAC only employs geometric information but ... | ['Heng Wang', 'Xiaohui Shen', 'Mubarak Shah', 'Chen Chen', 'Linjie Yang', 'Sijie Zhu'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['visual-place-recognition'] | ['computer-vision'] | [-4.47105557e-01 -4.54463661e-01 -3.50259513e-01 -3.87491226e-01
-1.19397211e+00 -6.09190106e-01 5.76826513e-01 1.84620366e-01
-5.61133325e-01 2.86605954e-01 1.29098371e-01 -6.43794090e-02
-1.35769919e-01 -7.75650144e-01 -9.39403892e-01 -5.37512004e-01
3.75910550e-02 5.51722050e-01 3.72641265e-01 -1.47096798... | [7.805339336395264, -1.8683099746704102] |
215c4e5f-4e8b-419b-8050-980b8e8e7f16 | scalable-end-to-end-ml-platforms-from-automl | 2302.14139 | null | https://arxiv.org/abs/2302.14139v3 | https://arxiv.org/pdf/2302.14139v3.pdf | Scalable End-to-End ML Platforms: from AutoML to Self-serve | ML platforms help enable intelligent data-driven applications and maintain them with limited engineering effort. Upon sufficiently broad adoption, such platforms reach economies of scale that bring greater component reuse while improving efficiency of system development and maintenance. For an end-to-end ML platform wi... | ['Mia R. Garrard', 'Norm Zhou', 'Ryan Maghsoudian', 'Tanya Qie', 'George Han', 'Cesar Cardoso', 'Anika Li', 'Tanvi Gupta', 'Yin Huang', 'Pavlos A. Apostolopoulos', 'Igor L. Markov'] | 2023-02-27 | null | null | null | null | ['automl'] | ['methodology'] | [-5.33071101e-01 2.67928779e-01 -6.93905056e-01 -3.92880291e-01
-8.78049016e-01 -8.51673961e-01 3.20961416e-01 -1.34306848e-01
2.56031573e-01 2.54570514e-01 1.02413744e-01 -7.07970858e-01
-1.19483672e-01 -3.29081029e-01 -2.72542596e-01 2.56158233e-01
-2.19139025e-01 1.58680350e-01 1.52251422e-01 -9.54547152... | [8.527649879455566, 7.216139793395996] |
943170c7-13ca-4160-b7ef-ceac4417e817 | plan-eliminate-and-track-language-models-are | 2305.02412 | null | https://arxiv.org/abs/2305.02412v2 | https://arxiv.org/pdf/2305.02412v2.pdf | Plan, Eliminate, and Track -- Language Models are Good Teachers for Embodied Agents | Pre-trained large language models (LLMs) capture procedural knowledge about the world. Recent work has leveraged LLM's ability to generate abstract plans to simplify challenging control tasks, either by action scoring, or action modeling (fine-tuning). However, the transformer architecture inherits several constraints ... | ['Shrimai Prabhumoye', 'Tom Mitchell', 'Yuanzhi Li', 'Amos Azaria', 'Ruslan Salakhutdinov', 'Yonatan Bisk', 'So Yeon Min', 'Yue Wu'] | 2023-05-03 | null | null | null | null | ['instruction-following'] | ['natural-language-processing'] | [ 2.90192038e-01 5.41825950e-01 -8.88575912e-02 -3.09100866e-01
-7.77358174e-01 -6.70076072e-01 8.59862268e-01 1.43220618e-01
-4.35209364e-01 5.87228835e-01 6.14445925e-01 -6.16370797e-01
1.22610256e-01 -7.45154440e-01 -8.22694540e-01 -3.65349263e-01
1.69814810e-01 7.50420392e-01 3.87861311e-01 -1.40822113... | [4.323593616485596, 1.002880334854126] |
804e2d29-2f23-46aa-89b2-f876ae1d546f | fully-convolutional-line-parsing | 2104.11207 | null | https://arxiv.org/abs/2104.11207v3 | https://arxiv.org/pdf/2104.11207v3.pdf | Fully Convolutional Line Parsing | We present a one-stage Fully Convolutional Line Parsing network (F-Clip) that detects line segments from images. The proposed network is very simple and flexible with variations that gracefully trade off between speed and accuracy for different applications. F-Clip detects line segments in an end-to-end fashion by pred... | ['Yi Ma', 'Shuai Wu', 'Haigang Gong', 'Xiaojun Yuan', 'Xili Dai'] | 2021-04-22 | null | null | null | null | ['line-segment-detection'] | ['computer-vision'] | [-1.66433468e-01 -2.12148041e-01 -9.08673629e-02 -3.67160797e-01
-6.59227908e-01 -7.41624951e-01 1.27679020e-01 3.02956492e-01
-4.97115612e-01 1.58131585e-01 -4.97620046e-01 -5.23603261e-01
3.78583014e-01 -9.63850796e-01 -1.07548416e+00 -2.22798824e-01
-7.85792544e-02 2.17566252e-01 7.37737298e-01 -1.27667069... | [8.33554744720459, -1.6019787788391113] |
5ceb95a7-105c-46d1-972c-8700996cc1b8 | understanding-tables-with-intermediate-pre | 2010.00571 | null | https://arxiv.org/abs/2010.00571v2 | https://arxiv.org/pdf/2010.00571v2.pdf | Understanding tables with intermediate pre-training | Table entailment, the binary classification task of finding if a sentence is supported or refuted by the content of a table, requires parsing language and table structure as well as numerical and discrete reasoning. While there is extensive work on textual entailment, table entailment is less well studied. We adapt TAP... | ['Syrine Krichene', 'Thomas Müller', 'Julian Martin Eisenschlos'] | 2020-10-01 | null | https://aclanthology.org/2020.findings-emnlp.27 | https://aclanthology.org/2020.findings-emnlp.27.pdf | findings-of-the-association-for-computational | ['table-based-fact-verification'] | ['natural-language-processing'] | [ 0.40808877 0.39329994 -0.32722855 -0.63234746 -1.1184582 -0.90446556
0.59523755 0.856365 -0.1364945 0.93683654 0.26654014 -1.0410935
-0.00970453 -1.2024466 -1.0534503 0.21309094 0.03395933 0.8245433
0.15095785 -0.45883343 0.37050965 0.14983389 -1.5497577 1.261355
0.9707079 1.4014025 -0.4427... | [9.721380233764648, 7.8270158767700195] |
660bf105-76a7-4ea0-8659-8d0ac16fc6f6 | part-aware-context-network-for-human-parsing | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Part-Aware_Context_Network_for_Human_Parsing_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhang_Part-Aware_Context_Network_for_Human_Parsing_CVPR_2020_paper.pdf | Part-Aware Context Network for Human Parsing | Recent works have made significant progress in human parsing by exploiting rich contexts. However, human parsing still faces a challenge of how to generate adaptive contextual features for the various sizes and shapes of human parts. In this work, we propose a Part-aware Context Network (PCNet), a novel and effective a... | [' Ming Tang', ' Jinqiao Wang', ' Bingke Zhu', ' Yingying Chen', 'Xiaomei Zhang'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['human-parsing'] | ['computer-vision'] | [ 2.42506370e-01 2.22639427e-01 -3.56025510e-02 -6.83477640e-01
-5.40077746e-01 -4.14260179e-01 1.86940461e-01 1.75226793e-01
-4.04653490e-01 4.53202367e-01 3.50080699e-01 2.01631799e-01
1.29406974e-01 -1.00733149e+00 -5.44128835e-01 -6.58878982e-01
2.36636266e-01 5.69210649e-01 8.19646239e-01 -2.13295043... | [8.74142074584961, 0.06326030939817429] |
986bc358-c826-4c6c-a48e-ba6613d0097d | synwmd-syntax-aware-word-mover-s-distance-for | 2206.10029 | null | https://arxiv.org/abs/2206.10029v1 | https://arxiv.org/pdf/2206.10029v1.pdf | SynWMD: Syntax-aware Word Mover's Distance for Sentence Similarity Evaluation | Word Mover's Distance (WMD) computes the distance between words and models text similarity with the moving cost between words in two text sequences. Yet, it does not offer good performance in sentence similarity evaluation since it does not incorporate word importance and fails to take inherent contextual and structura... | ['C. -C. Jay Kuo', 'Bin Wang', 'Chengwei Wei'] | 2022-06-20 | null | null | null | null | ['sentence-classification'] | ['natural-language-processing'] | [ 3.44912350e-01 -1.42567858e-01 -1.01489238e-01 -5.76287270e-01
-4.84250993e-01 -2.85057902e-01 4.56643850e-01 9.58019555e-01
-7.44541645e-01 2.39023492e-01 6.91858232e-01 -4.93353575e-01
-3.43005210e-01 -8.30512822e-01 1.27555002e-02 -4.46248740e-01
-2.91745905e-02 1.31403685e-01 5.71526110e-01 -5.22582829... | [11.009225845336914, 8.788773536682129] |
8dd28013-6b10-4909-9da9-6a1373e70933 | star-a-structure-aware-lightweight | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zhang_STAR_A_Structure-Aware_Lightweight_Transformer_for_Real-Time_Image_Enhancement_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zhang_STAR_A_Structure-Aware_Lightweight_Transformer_for_Real-Time_Image_Enhancement_ICCV_2021_paper.pdf | STAR: A Structure-Aware Lightweight Transformer for Real-Time Image Enhancement | Image and video enhancement such as color constancy, low light enhancement, and tone mapping on smartphones is challenging because high-quality images should be achieved efficiently with a limited resource budget. Unlike prior works that either used very deep CNNs or large Transformer models, we propose a \underlin... | ['Jinwei Gu', 'Ping Luo', 'Xiaogang Wang', 'Jun Jiang', 'Yitong Jiang', 'Zhaoyang Zhang'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['photo-retouching', 'color-constancy', 'video-enhancement', 'tone-mapping'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 4.37818915e-01 -3.83948714e-01 1.55517295e-01 -2.84703881e-01
-4.11450177e-01 -1.23868302e-01 7.16764182e-02 -2.89553136e-01
-3.96709710e-01 5.11543751e-01 -9.60999951e-02 -2.80901104e-01
-7.74239898e-02 -6.68041646e-01 -9.61518764e-01 -7.03701794e-01
1.44108951e-01 -6.98971808e-01 2.81176955e-01 -6.10033751... | [10.92416763305664, -2.1779685020446777] |
f82e6df6-6795-4573-88e8-386a56e6b24f | espt-a-self-supervised-episodic-spatial | 2304.13287 | null | https://arxiv.org/abs/2304.13287v1 | https://arxiv.org/pdf/2304.13287v1.pdf | ESPT: A Self-Supervised Episodic Spatial Pretext Task for Improving Few-Shot Learning | Self-supervised learning (SSL) techniques have recently been integrated into the few-shot learning (FSL) framework and have shown promising results in improving the few-shot image classification performance. However, existing SSL approaches used in FSL typically seek the supervision signals from the global embedding of... | ['Shengwu Xiong', 'Yaxiong Chen', 'Zhaoyang Sun', 'Xiongbo Lu', 'Yi Rong'] | 2023-04-26 | null | null | null | null | ['few-shot-image-classification'] | ['computer-vision'] | [ 1.44028276e-01 -1.83504313e-01 -5.67265809e-01 -4.63111281e-01
-6.00640833e-01 -8.94922391e-02 7.03246057e-01 1.21226266e-01
-1.23790279e-01 4.98545885e-01 1.20314904e-01 3.72124553e-01
-3.36987495e-01 -8.79665792e-01 -8.16498160e-01 -8.14439476e-01
6.70852140e-02 1.51470631e-01 2.83201993e-01 -1.09321922... | [9.967394828796387, 2.6007578372955322] |
2dd267a4-5f42-4e51-98c3-9e42e562dae6 | guided-perturbations-self-corrective-behavior | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Sankaranarayanan_Guided_Perturbations_Self-Corrective_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Sankaranarayanan_Guided_Perturbations_Self-Corrective_ICCV_2017_paper.pdf | Guided Perturbations: Self-Corrective Behavior in Convolutional Neural Networks | Convolutional Neural Networks have been a subject of great importance over the past decade and great strides have been made in their utility for producing state of the art performance in many computer vision problems. However, the behavior of deep networks is yet to be fully understood and is still an active area of re... | ['Ser Nam Lim', 'Swami Sankaranarayanan', 'Arpit Jain'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['scene-labeling'] | ['computer-vision'] | [ 7.15428054e-01 3.21056157e-01 -9.59093962e-03 -7.84448326e-01
-1.93520039e-01 -7.25768209e-01 5.48743069e-01 -6.29458949e-02
-6.34077430e-01 6.47401273e-01 -7.02614263e-02 -3.13393444e-01
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1.77966210e-03 2.48361886e-01 6.37092054e-01 -2.99877673... | [9.41407585144043, 1.947060465812683] |
385069ba-f554-4498-97c5-acea0fe22b44 | deep-transfer-tensor-factorization-for-multi | 2302.06133 | null | https://arxiv.org/abs/2302.06133v1 | https://arxiv.org/pdf/2302.06133v1.pdf | Deep Transfer Tensor Factorization for Multi-View Learning | This paper studies the data sparsity problem in multi-view learning. To solve data sparsity problem in multiview ratings, we propose a generic architecture of deep transfer tensor factorization (DTTF) by integrating deep learning and cross-domain tensor factorization, where the side information is embedded to provide e... | ['Chunxi Li', 'Ke Xin', 'Penghao Jiang'] | 2023-02-13 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [-0.73879576 -0.51522434 -0.4562071 -0.6672969 -0.6671911 -0.5450509
0.3658083 -0.6275893 0.3891027 0.16270673 1.1238569 -0.01506319
-0.30008823 -0.5652731 -0.73219836 -0.5203514 -0.01042493 0.42104042
-0.55737287 -0.6208074 -0.10498082 -0.1752168 -1.2617844 1.2978095
0.65796614 1.2090728 -0.19... | [8.672783851623535, 4.633760452270508] |
5a4eb508-e1c2-471b-9d2e-bba0be38fc0c | table-based-fact-verification-with-salience | 2109.04053 | null | https://arxiv.org/abs/2109.04053v1 | https://arxiv.org/pdf/2109.04053v1.pdf | Table-based Fact Verification with Salience-aware Learning | Tables provide valuable knowledge that can be used to verify textual statements. While a number of works have considered table-based fact verification, direct alignments of tabular data with tokens in textual statements are rarely available. Moreover, training a generalized fact verification model requires abundant lab... | ['Muhao Chen', 'Pedro Szekely', 'Jay Pujara', 'Kexuan Sun', 'Fei Wang'] | 2021-09-09 | null | https://aclanthology.org/2021.findings-emnlp.338 | https://aclanthology.org/2021.findings-emnlp.338.pdf | findings-emnlp-2021-11 | ['table-based-fact-verification'] | ['natural-language-processing'] | [ 2.36765280e-01 3.52691919e-01 -9.11111712e-01 -3.22101414e-01
-1.15503788e+00 -4.39463109e-01 7.23122537e-01 7.81500459e-01
-1.88001655e-02 9.36261952e-01 8.45091581e-01 -3.92598927e-01
8.57390165e-02 -8.77556205e-01 -8.52473140e-01 -4.40149069e-01
3.50642018e-02 2.07066476e-01 1.73081666e-01 -2.15400010... | [9.389863014221191, 7.867068290710449] |
c85f7929-7795-4961-8c4a-161e74d73e30 | cityscapes-panoptic-parts-and-pascal-panoptic | 2004.07944 | null | https://arxiv.org/abs/2004.07944v1 | https://arxiv.org/pdf/2004.07944v1.pdf | Cityscapes-Panoptic-Parts and PASCAL-Panoptic-Parts datasets for Scene Understanding | In this technical report, we present two novel datasets for image scene understanding. Both datasets have annotations compatible with panoptic segmentation and additionally they have part-level labels for selected semantic classes. This report describes the format of the two datasets, the annotation protocols, the merg... | ['Gijs Dubbelman', 'Panagiotis Meletis', 'Xiaoxiao Wen', 'Chenyang Lu', 'Daan de Geus'] | 2020-04-16 | null | null | null | null | ['human-part-segmentation', 'part-level-panoptic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 9.89474878e-02 -1.64768815e-01 -2.57525206e-01 -5.73046684e-01
-2.25546539e-01 -1.07233691e+00 5.14060438e-01 2.28921950e-01
1.53925940e-01 4.32988435e-01 -6.68782517e-02 -3.55536550e-01
-3.40284675e-01 -7.82556593e-01 -1.37202427e-01 -6.29244566e-01
-3.73113781e-01 3.94545525e-01 5.94016254e-01 -2.60131452... | [8.974746704101562, -1.4717501401901245] |
4d72e50a-cc25-4dfa-9980-2b061d89613b | hierarchical-graph-generation-with-k-2-trees | 2305.19125 | null | https://arxiv.org/abs/2305.19125v2 | https://arxiv.org/pdf/2305.19125v2.pdf | Hierarchical Graph Generation with $K^2$-trees | Generating graphs from a target distribution is a significant challenge across many domains, including drug discovery and social network analysis. In this work, we introduce a novel graph generation method leveraging $K^2$-tree representation which was originally designed for lossless graph compression. Our motivation ... | ['Sungsoo Ahn', 'Dongwoo Kim', 'Yunhui Jang'] | 2023-05-30 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 8.41672003e-01 5.04721999e-01 -1.72979563e-01 -4.09645736e-02
-5.72878301e-01 -4.59477007e-01 2.09394261e-01 7.05194056e-01
-3.49683538e-02 9.73809183e-01 -8.88415799e-03 -7.13722944e-01
-1.76577494e-01 -1.35132539e+00 -5.47773063e-01 -4.01890010e-01
-6.73748016e-01 2.62799710e-01 2.72316992e-01 -2.29422256... | [7.014904975891113, 6.035726070404053] |
54a24166-cace-4423-b922-b56e851d011d | promoting-the-knowledge-of-source-syntax-in | 1910.11218 | null | https://arxiv.org/abs/1910.11218v1 | https://arxiv.org/pdf/1910.11218v1.pdf | Promoting the Knowledge of Source Syntax in Transformer NMT Is Not Needed | The utility of linguistic annotation in neural machine translation seemed to had been established in past papers. The experiments were however limited to recurrent sequence-to-sequence architectures and relatively small data settings. We focus on the state-of-the-art Transformer model and use comparably larger corpora.... | ['Ondřej Bojar', 'Dominik Macháček', 'Thuong-Hai Pham'] | 2019-10-24 | null | null | null | null | ['small-data'] | ['computer-vision'] | [ 1.78305358e-01 5.06117880e-01 -8.16935077e-02 -3.22857559e-01
-9.55825031e-01 -6.35601282e-01 8.52995336e-01 -3.53683122e-02
-4.07174796e-01 8.71325791e-01 5.93602121e-01 -7.69441783e-01
9.22201276e-02 -4.15184289e-01 -8.85626614e-01 -8.30667019e-01
1.05378740e-01 7.46871293e-01 6.77868798e-02 -6.72453523... | [11.118412971496582, 9.713634490966797] |
5c789a6a-c635-4e97-ae9b-d94708c2e9ad | graph-representation-learning-for-interactive | 2304.02656 | null | https://arxiv.org/abs/2304.02656v1 | https://arxiv.org/pdf/2304.02656v1.pdf | Graph Representation Learning for Interactive Biomolecule Systems | Advances in deep learning models have revolutionized the study of biomolecule systems and their mechanisms. Graph representation learning, in particular, is important for accurately capturing the geometric information of biomolecules at different levels. This paper presents a comprehensive review of the methodologies u... | ['Yu Guang Wang', 'Bingxin Zhou', 'Xinye Xiong'] | 2023-04-05 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 2.86310345e-01 6.90542394e-03 1.16120186e-02 -3.71792875e-02
-8.12277421e-02 -4.92473066e-01 4.13285017e-01 8.96169662e-01
2.16172218e-01 7.74872482e-01 -1.81765631e-01 -6.07330620e-01
5.04886881e-02 -1.07362604e+00 -8.79305601e-01 -9.22714889e-01
-5.32608628e-01 5.02327263e-01 -1.38747454e-01 -2.20160514... | [5.115475654602051, 5.79793643951416] |
f28be5cd-8459-4a8f-ac48-b4af04199de2 | achieving-reliable-human-assessment-of-open | null | null | https://openreview.net/forum?id=7SQh5IvfeLq | https://openreview.net/pdf?id=7SQh5IvfeLq | Achieving Reliable Human Assessment of Open-Domain Dialogue Systems | Evaluation of open-domain dialogue systems is highly challenging and development of better techniques is highlighted time and again as desperately needed. Despite substantial efforts to carry out reliable live evaluation of systems in recent competitions, annotations have been abandoned and reported as too unreliable t... | ['Anonymous'] | 2021-09-17 | null | null | null | acl-arr-september-2021-9 | ['dialogue-evaluation'] | ['natural-language-processing'] | [-7.55537674e-02 2.87946135e-01 3.26565117e-01 -5.61136901e-01
-9.47480142e-01 -6.95885420e-01 1.07429683e+00 3.15702021e-01
-8.14694047e-01 1.12872362e+00 6.61730826e-01 -2.70932585e-01
-1.15990080e-01 -3.45661640e-01 -1.68673739e-01 -3.74213427e-01
1.86774004e-02 6.65667653e-01 1.86704755e-01 -5.95227420... | [12.867280006408691, 8.050719261169434] |
dee688f4-5867-4f80-9c10-b46891999e25 | seizure-prediction-using-bidirectional-lstm | 1912.06385 | null | https://arxiv.org/abs/1912.06385v1 | https://arxiv.org/pdf/1912.06385v1.pdf | Seizure Prediction Using Bidirectional LSTM | Approximately, 50 million people in the world are affected by epilepsy. For patients, the anti-epileptic drugs are not always useful and these drugs may have undesired side effects on a patient's health. If the seizure is predicted the patients will have enough time to take preventive measures. The purpose of this work... | ['Junaid Javed Qureshi', 'Mohammad Farhad Bulbul', 'Hazrat Ali', 'Feroz Karim', 'Adnan Omer Abuassba'] | 2019-12-13 | null | null | null | null | ['seizure-prediction'] | ['medical'] | [-1.99919999e-01 9.32854190e-02 5.70248030e-02 -4.03713256e-01
-4.49013263e-01 -1.53885201e-01 3.34967643e-01 7.59614706e-02
-5.17343938e-01 1.20117879e+00 9.08523425e-02 -3.61926854e-01
1.26339495e-01 -5.13981462e-01 -6.66568816e-01 -6.86930776e-01
-5.77294409e-01 3.05571735e-01 1.84807926e-01 -1.12123333... | [13.225833892822266, 3.535714864730835] |
844cca18-ebb8-4f84-9fc2-2789fbe8e2e1 | ablation-study-of-how-run-time-assurance | 2207.04117 | null | https://arxiv.org/abs/2207.04117v1 | https://arxiv.org/pdf/2207.04117v1.pdf | Ablation Study of How Run Time Assurance Impacts the Training and Performance of Reinforcement Learning Agents | Reinforcement Learning (RL) has become an increasingly important research area as the success of machine learning algorithms and methods grows. To combat the safety concerns surrounding the freedom given to RL agents while training, there has been an increase in work concerning Safe Reinforcement Learning (SRL). Howeve... | ['Kerianne L Hobbs', 'Taylor T Johnson', 'Kyle Dunlap', 'Nathaniel Hamilton'] | 2022-07-08 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 1.35553390e-01 1.32472053e-01 -6.09805346e-01 -1.13952033e-01
-7.68238902e-01 -8.77928317e-01 8.84890199e-01 2.23563790e-01
-8.43849242e-01 1.09039581e+00 1.93670485e-02 -7.77473807e-01
-3.43926549e-01 -5.39623260e-01 -6.89938188e-01 -9.05120969e-01
-5.82153678e-01 1.04016431e-01 5.31255342e-02 -1.79950848... | [4.504345417022705, 2.1707725524902344] |
646bd43e-5322-40ea-b255-932afe0ad638 | learning-to-play-the-chess-variant-crazyhouse | 1908.06660 | null | https://arxiv.org/abs/1908.06660v2 | https://arxiv.org/pdf/1908.06660v2.pdf | Learning to play the Chess Variant Crazyhouse above World Champion Level with Deep Neural Networks and Human Data | Deep neural networks have been successfully applied in learning the board games Go, chess and shogi without prior knowledge by making use of reinforcement learning. Although starting from zero knowledge has been shown to yield impressive results, it is associated with high computationally costs especially for complex g... | ['Johannes Fürnkranz', 'Johannes Czech', 'Moritz Willig', 'Kristian Kersting', 'Alena Beyer'] | 2019-08-19 | null | null | null | null | ['board-games'] | ['playing-games'] | [-3.56451511e-01 -2.34829523e-02 1.37620121e-01 1.27553836e-01
-7.85446584e-01 -5.01371443e-01 1.65280268e-01 -2.10697725e-01
-1.03092396e+00 8.37281346e-01 -3.45475584e-01 -6.42486393e-01
-3.93316805e-01 -1.07823622e+00 -8.73713911e-01 -3.88982892e-01
-1.37039572e-01 7.64487088e-01 3.88679415e-01 -8.49261880... | [3.4454593658447266, 1.4243119955062866] |
67e4f936-4fe8-4044-94a7-ee6910107bf9 | context-aware-self-supervised-learning-of | 2306.04763 | null | https://arxiv.org/abs/2306.04763v1 | https://arxiv.org/pdf/2306.04763v1.pdf | Context-Aware Self-Supervised Learning of Whole Slide Images | Presenting whole slide images (WSIs) as graph will enable a more efficient and accurate learning framework for cancer diagnosis. Due to the fact that a single WSI consists of billions of pixels and there is a lack of vast annotated datasets required for computational pathology, the problem of learning from WSIs using t... | ['Nasim Yahyasoltani', 'Milan Aryal'] | 2023-06-07 | null | null | null | null | ['whole-slide-images'] | ['computer-vision'] | [ 5.59804142e-01 5.49320042e-01 -2.48598814e-01 -2.44923711e-01
-5.68784595e-01 -2.48235032e-01 3.27123761e-01 7.89153397e-01
-2.34500736e-01 7.38415897e-01 -5.17299213e-02 -3.46782804e-01
-1.93044350e-01 -1.08639300e+00 -6.92132056e-01 -9.48351741e-01
-2.21497536e-01 2.36746311e-01 4.32742000e-01 -2.17171639... | [15.05008316040039, -2.912081480026245] |
3425879e-668e-4661-a3b6-ca05ea559c17 | a-lightweight-machine-learning-pipeline-for | 2208.03130 | null | https://arxiv.org/abs/2208.03130v1 | https://arxiv.org/pdf/2208.03130v1.pdf | A Lightweight Machine Learning Pipeline for LiDAR-simulation | Virtual testing is a crucial task to ensure safety in autonomous driving, and sensor simulation is an important task in this domain. Most current LiDAR simulations are very simplistic and are mainly used to perform initial tests, while the majority of insights are gathered on the road. In this paper, we propose a light... | ['Marc Stamminger', 'Bernhard Egger', 'Niklas Knoop', 'Richard Marcus'] | 2022-08-05 | null | null | null | null | ['machine-learning', 'machine-learning'] | ['methodology', 'miscellaneous'] | [ 2.01514602e-01 7.21283183e-02 3.03717196e-01 -7.84444749e-01
-6.24043822e-01 -6.37222528e-01 6.51570559e-01 -3.24456424e-01
-5.64082265e-01 8.80358636e-01 -6.61347806e-01 -7.55681217e-01
1.45434558e-01 -1.17405355e+00 -1.13712347e+00 -4.10820633e-01
2.14619748e-02 8.29116642e-01 6.47533476e-01 -5.10368943... | [8.078468322753906, -2.474738359451294] |
7caa0702-0bd0-45e8-a179-473a176f9894 | orthonormal-product-quantization-network-for | 2107.00327 | null | https://arxiv.org/abs/2107.00327v4 | https://arxiv.org/pdf/2107.00327v4.pdf | Orthonormal Product Quantization Network for Scalable Face Image Retrieval | Existing deep quantization methods provided an efficient solution for large-scale image retrieval. However, the significant intra-class variations like pose, illumination, and expressions in face images, still pose a challenge for face image retrieval. In light of this, face image retrieval requires sufficiently powerf... | ['Hong Yan', 'Xuefei Zhe', 'Ming Zhang'] | 2021-07-01 | null | null | null | null | ['face-image-retrieval'] | ['computer-vision'] | [-5.51557578e-02 -5.71334779e-01 -2.58389920e-01 -6.49174750e-01
-1.05025160e+00 -4.12076741e-01 4.95125353e-01 -8.32192153e-02
-3.02827448e-01 4.39401925e-01 3.66406664e-02 3.30503434e-01
-3.91115725e-01 -6.03435457e-01 -4.32589740e-01 -9.34102058e-01
2.06121653e-02 2.85230875e-01 -4.70285952e-01 -8.30376148... | [11.46330451965332, 0.9127699732780457] |
9582b444-fcb6-4046-bcb1-04c3b2db13fc | context-or-no-context-a-preliminary | null | null | https://aclanthology.org/2021.newsum-1.11 | https://aclanthology.org/2021.newsum-1.11.pdf | Context or No Context? A preliminary exploration of human-in-the-loop approach for Incremental Temporal Summarization in meetings | Incremental meeting temporal summarization, summarizing relevant information of partial multi-party meeting dialogue, is emerging as the next challenge in summarization research. Here we examine the extent to which human abstractive summaries of the preceding increments (context) can be combined with extractive meeting... | ['Ramesh Manuvinakurike', 'Saurav Sahay', 'Shachi H Kumar', 'Nicole Beckage'] | null | null | null | null | emnlp-newsum-2021-11 | ['semantic-role-labeling', 'keyphrase-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.87415135e-01 6.84600949e-01 -5.03170490e-01 -1.73138782e-01
-1.31241071e+00 -8.99077773e-01 1.01904905e+00 8.55338693e-01
-5.07330477e-01 1.14289236e+00 1.75053561e+00 -2.08773632e-02
3.23748678e-01 -1.68685809e-01 -2.89604723e-01 -1.87841896e-02
3.31616938e-01 3.78226697e-01 6.10159412e-02 -5.86203277... | [12.570655822753906, 9.362797737121582] |
64a72cc5-1a7c-41a0-b5fb-1212e07d170f | srcn3d-sparse-r-cnn-3d-surround-view-camera | 2206.14451 | null | https://arxiv.org/abs/2206.14451v3 | https://arxiv.org/pdf/2206.14451v3.pdf | SRCN3D: Sparse R-CNN 3D for Compact Convolutional Multi-View 3D Object Detection and Tracking | Detection and tracking of moving objects is an essential component in environmental perception for autonomous driving. In the flourishing field of multi-view 3D camera-based detectors, different transformer-based pipelines are designed to learn queries in 3D space from 2D feature maps of perspective views, but the domi... | ['Diange Yang', 'Kun Jiang', 'Shiqi Sun', 'Jiaxin Li', 'Yunlong Wang', 'Yifan Sun', 'Jingyan Shen', 'Yining Shi'] | 2022-06-29 | null | null | null | null | ['3d-multi-object-tracking'] | ['computer-vision'] | [-3.78985316e-01 -5.43010354e-01 -2.53277481e-01 -4.13128316e-01
-7.02536464e-01 -4.09858972e-01 4.76666152e-01 -2.76250064e-01
-4.72394466e-01 -9.04127955e-02 7.05163330e-02 4.16624807e-02
1.87646061e-01 -6.66926265e-01 -8.21863294e-01 -3.54917884e-01
3.44339490e-01 4.01904106e-01 1.21560121e+00 -1.68490723... | [7.70259428024292, -2.3428804874420166] |
297d6be7-2105-4030-aca4-2c0dc82022d9 | phmospell-phonological-and-morphological | null | null | https://aclanthology.org/2021.acl-long.464 | https://aclanthology.org/2021.acl-long.464.pdf | PHMOSpell: Phonological and Morphological Knowledge Guided Chinese Spelling Check | Chinese Spelling Check (CSC) is a challenging task due to the complex characteristics of Chinese characters. Statistics reveal that most Chinese spelling errors belong to phonological or visual errors. However, previous methods rarely utilize phonological and morphological knowledge of Chinese characters or heavily rel... | ['Jing Xiao', 'Shaojun Wang', 'Minchuan Chen', 'ZhiYu Zhang', 'Weiwei Jiang', 'Junjie Li', 'Li Huang'] | 2021-08-01 | null | null | null | acl-2021-5 | ['chinese-spell-checking'] | ['natural-language-processing'] | [ 1.77131057e-01 -7.36960292e-01 -9.77868773e-03 -1.33729890e-01
-1.08500338e+00 -6.37379467e-01 5.35481095e-01 1.18755065e-01
-6.37610435e-01 3.80645186e-01 5.35600483e-01 -4.15870041e-01
7.35171497e-01 -4.14356321e-01 -5.54698050e-01 -4.25177574e-01
3.71733785e-01 1.50984265e-02 3.52528840e-01 -1.47801623... | [10.9162015914917, 10.810404777526855] |
0b4255b3-40d5-440a-97de-3d63093edd97 | cloud-removal-in-satellite-images-using | 1912.06838 | null | https://arxiv.org/abs/1912.06838v1 | https://arxiv.org/pdf/1912.06838v1.pdf | Cloud Removal in Satellite Images Using Spatiotemporal Generative Networks | Satellite images hold great promise for continuous environmental monitoring and earth observation. Occlusions cast by clouds, however, can severely limit coverage, making ground information extraction more difficult. Existing pipelines typically perform cloud removal with simple temporal composites and hand-crafted fil... | ['Vishnu Sarukkai', 'Burak Uzkent', 'Anirudh Jain', 'Stefano Ermon'] | 2019-12-14 | null | null | null | null | ['cloud-removal'] | ['computer-vision'] | [ 2.89698362e-01 -5.48607230e-01 1.06351666e-01 -2.60980099e-01
-8.79068613e-01 -9.33237135e-01 7.62341440e-01 -1.99343145e-01
-3.01528186e-01 8.11895847e-01 1.61589421e-02 -4.99135703e-01
3.20861578e-01 -1.10167670e+00 -8.38611186e-01 -8.60719264e-01
-3.57073635e-01 4.02703732e-02 1.27477109e-01 -2.03055978... | [9.725188255310059, -1.673419713973999] |
994a88c9-0c21-4afd-8312-8b8a5f37577d | tido-source-free-task-incremental-learning-in | 2301.12055 | null | https://arxiv.org/abs/2301.12055v1 | https://arxiv.org/pdf/2301.12055v1.pdf | TIDo: Source-free Task Incremental Learning in Non-stationary Environments | This work presents an incremental learning approach for autonomous agents to learn new tasks in a non-stationary environment. Updating a DNN model-based agent to learn new target tasks requires us to store past training data and needs a large labeled target task dataset. Few-shot task incremental learning methods overc... | ['Leong Tze Yun', 'Abhinit Kumar Ambastha'] | 2023-01-28 | null | null | null | null | ['disease-prediction'] | ['medical'] | [ 4.89294142e-01 1.12918161e-01 -3.81635725e-01 -5.69063485e-01
-7.28586972e-01 -3.46867770e-01 8.41817319e-01 -9.15337205e-02
-9.27843153e-01 1.40447831e+00 -1.30635545e-01 2.33505294e-01
7.74458647e-02 -6.05332077e-01 -1.00863147e+00 -6.10853970e-01
-2.10900202e-01 1.04375875e+00 7.61809409e-01 1.09055683... | [9.852714538574219, 3.3735783100128174] |
3b538bf3-7abe-47f3-aed1-07fe22124c85 | basen-time-domain-brain-assisted-speech | 2305.09994 | null | https://arxiv.org/abs/2305.09994v1 | https://arxiv.org/pdf/2305.09994v1.pdf | BASEN: Time-Domain Brain-Assisted Speech Enhancement Network with Convolutional Cross Attention in Multi-talker Conditions | Time-domain single-channel speech enhancement (SE) still remains challenging to extract the target speaker without any prior information on multi-talker conditions. It has been shown via auditory attention decoding that the brain activity of the listener contains the auditory information of the attended speaker. In thi... | ['Zhen-Hua Ling', 'Qiu-Shi Zhu', 'Qing-Tian Xu', 'Jie Zhang'] | 2023-05-17 | null | null | null | null | ['speech-enhancement'] | ['speech'] | [ 1.25541061e-01 -2.11783841e-01 5.60560584e-01 -3.37550253e-01
-1.23058105e+00 -2.00553373e-01 2.90173113e-01 -2.09141284e-01
-3.82257074e-01 5.52931786e-01 4.05040920e-01 7.92124942e-02
-1.13061070e-01 -1.32113338e-01 -6.52617991e-01 -8.16421807e-01
1.48933064e-02 -2.50236392e-01 -1.46062493e-01 -5.16651431... | [15.193021774291992, 5.533141613006592] |
5b28fe7a-cfa5-4586-85fe-940bf29acc04 | unsupervised-summarization-re-ranking | 2212.09593 | null | https://arxiv.org/abs/2212.09593v3 | https://arxiv.org/pdf/2212.09593v3.pdf | Unsupervised Summarization Re-ranking | With the rise of task-specific pre-training objectives, abstractive summarization models like PEGASUS offer appealing zero-shot performance on downstream summarization tasks. However, the performance of such unsupervised models still lags significantly behind their supervised counterparts. Similarly to the supervised s... | ['Nancy Chen', 'Shafiq Joty', 'Mathieu Ravaut'] | 2022-12-19 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 3.75332624e-01 5.63590467e-01 -3.77746880e-01 -2.04832152e-01
-1.62752855e+00 -5.75849593e-01 1.01566482e+00 7.37620711e-01
-5.47021985e-01 1.00847936e+00 1.00356841e+00 3.66795212e-02
-9.93950516e-02 -4.27810282e-01 -6.41101360e-01 -4.11214560e-01
1.15188986e-01 6.27213597e-01 2.41850272e-01 -3.97742748... | [12.371068954467773, 9.394088745117188] |
8c73b27c-7f35-4131-a8dd-f9250c7f7470 | scene-flow-estimation-a-survey | 1612.02590 | null | http://arxiv.org/abs/1612.02590v3 | http://arxiv.org/pdf/1612.02590v3.pdf | Scene Flow Estimation: A Survey | This paper is the first to review the scene flow estimation field, which
analyzes and compares methods, technical challenges, evaluation methodologies
and performance of scene flow estimation. Existing algorithms are categorized
in terms of scene representation, data source, and calculation scheme, and the
pros and con... | ['Xuezhi Xiang', 'Zike Yan'] | 2016-12-08 | null | null | null | null | ['scene-flow-estimation'] | ['computer-vision'] | [ 1.83203951e-01 -6.17966831e-01 -3.04790109e-01 -2.80705184e-01
2.43916765e-01 -6.50418222e-01 5.11522412e-01 1.08448096e-01
-2.99596727e-01 7.36201525e-01 4.15125668e-01 -2.28860602e-01
-2.89442122e-01 -8.10593724e-01 2.20264301e-01 -6.36859477e-01
-3.68134886e-01 -1.96234345e-01 5.45234382e-01 -3.91260535... | [8.707001686096191, -1.815508246421814] |
eca67481-8e79-499c-8311-ab231291db1e | learning-environment-models-with-continuous | 2306.17204 | null | https://arxiv.org/abs/2306.17204v1 | https://arxiv.org/pdf/2306.17204v1.pdf | Learning Environment Models with Continuous Stochastic Dynamics | Solving control tasks in complex environments automatically through learning offers great potential. While contemporary techniques from deep reinforcement learning (DRL) provide effective solutions, their decision-making is not transparent. We aim to provide insights into the decisions faced by the agent by learning an... | ['Bettina Könighofer', 'Bernhard K. Aichernig', 'Edi Muškardin', 'Martin Tappler'] | 2023-06-29 | null | null | null | null | ['dimensionality-reduction', 'benchmarking', 'openai-gym', 'acrobot', 'decision-making', 'benchmarking'] | ['methodology', 'miscellaneous', 'playing-games', 'playing-games', 'reasoning', 'robots'] | [-5.23914993e-02 1.24089740e-01 -1.83850378e-01 1.05487704e-01
-5.54844618e-01 -7.52500951e-01 9.07535553e-01 2.17278242e-01
-5.25258780e-01 9.32600021e-01 1.78983301e-01 -2.71452010e-01
-2.82203138e-01 -9.09943759e-01 -8.88875663e-01 -9.46294010e-01
-4.26634699e-01 9.13054645e-01 3.72437760e-02 -5.34940422... | [4.149211406707764, 1.7296420335769653] |
7d7b1962-7358-47dd-b0f0-48b3ea5b515a | leveraging-commonsense-knowledge-on | 2108.03731 | null | https://arxiv.org/abs/2108.03731v1 | https://arxiv.org/pdf/2108.03731v1.pdf | Leveraging Commonsense Knowledge on Classifying False News and Determining Checkworthiness of Claims | Widespread and rapid dissemination of false news has made fact-checking an indispensable requirement. Given its time-consuming and labor-intensive nature, the task calls for an automated support to meet the demand. In this paper, we propose to leverage commonsense knowledge for the tasks of false news classification an... | ['Zeyd Boukhers', 'Oul Han', 'Selma Tekir', 'Erhan Sezerer', 'Ipek Baris Schlicht'] | 2021-08-08 | null | null | null | null | ['news-classification'] | ['natural-language-processing'] | [ 1.43818960e-01 3.88113782e-02 -3.57332766e-01 -1.32220894e-01
-1.13891375e+00 -3.39023411e-01 8.02679300e-01 5.15940011e-01
-3.97544295e-01 8.25854897e-01 3.37872386e-01 -5.29857457e-01
-5.67921028e-02 -7.94501662e-01 -6.32077992e-01 4.43501547e-02
4.27333504e-01 3.60466003e-01 5.59165239e-01 -6.38897538... | [8.704639434814453, 9.75560188293457] |
1e434176-1fe7-44e3-a27f-df4e371b27fa | what-transfers-in-morphological-inflection | null | null | https://aclanthology.org/2021.sigmorphon-1.18 | https://aclanthology.org/2021.sigmorphon-1.18.pdf | What transfers in morphological inflection? Experiments with analogical models | We investigate how abstract processes like suffixation can be learned from morphological inflection task data using an analogical memory-based framework. In this framework, the inflection target form is specified by providing an example inflection of another word in the language. We show that this model is capable of n... | ['Micha Elsner'] | null | null | null | null | acl-sigmorphon-2021-8 | ['morphological-inflection'] | ['natural-language-processing'] | [ 4.17807013e-01 1.03967801e-01 -2.33119667e-01 -5.29835343e-01
-6.48840606e-01 -1.22168565e+00 9.22996461e-01 2.35152841e-01
-7.70873547e-01 6.62437320e-01 5.57786226e-01 -6.23166859e-01
1.70740962e-01 -8.67871344e-01 -1.15091789e+00 -3.18198800e-01
-1.69198625e-02 8.93976748e-01 5.98433390e-02 -5.33408642... | [10.757308006286621, 9.468131065368652] |
dd9d4393-4097-4795-b0c8-26c4686a4c0a | selformer-molecular-representation-learning | 2304.04662 | null | https://arxiv.org/abs/2304.04662v2 | https://arxiv.org/pdf/2304.04662v2.pdf | SELFormer: Molecular Representation Learning via SELFIES Language Models | Automated computational analysis of the vast chemical space is critical for numerous fields of research such as drug discovery and material science. Representation learning techniques have recently been employed with the primary objective of generating compact and informative numerical expressions of complex data. One ... | ['Tunca Doğan', 'Atabey Ünlü', 'Erva Ulusoy', 'Atakan Yüksel'] | 2023-04-10 | null | null | null | null | ['drug-discovery', 'molecular-property-prediction'] | ['medical', 'miscellaneous'] | [ 4.94296163e-01 -3.57339345e-02 -3.06518734e-01 -1.52147561e-01
-5.18326998e-01 -8.27306688e-01 4.35666561e-01 8.30127001e-01
6.21116944e-02 1.10713208e+00 1.07322305e-01 -8.74306440e-01
-3.80902588e-01 -1.06680727e+00 -7.79477715e-01 -7.40879238e-01
-3.45408201e-01 3.66572112e-01 -3.53440881e-01 -2.49822795... | [5.1146674156188965, 5.85244607925415] |
643cb166-a91a-4991-8dbe-e597d0463847 | tensor-representations-via-kernel | 1604.00239 | null | http://arxiv.org/abs/1604.00239v2 | http://arxiv.org/pdf/1604.00239v2.pdf | Tensor Representations via Kernel Linearization for Action Recognition from 3D Skeletons (Extended Version) | In this paper, we explore tensor representations that can compactly capture
higher-order relationships between skeleton joints for 3D action recognition.
We first define RBF kernels on 3D joint sequences, which are then linearized to
form kernel descriptors. The higher-order outer-products of these kernel
descriptors f... | ['Piotr Koniusz', 'Fatih Porikli', 'Anoop Cherian'] | 2016-04-01 | null | null | null | null | ['3d-human-action-recognition'] | ['computer-vision'] | [ 1.30097230e-03 -6.06941640e-01 -4.03627008e-01 -1.21637933e-01
-1.47409484e-01 -5.16391814e-01 6.76799476e-01 -1.86638579e-01
-2.83489645e-01 3.44745815e-02 5.86693645e-01 -1.10752605e-01
-2.63582468e-01 -2.47255713e-01 -4.40095246e-01 -7.53740847e-01
-6.09063268e-01 7.42495954e-02 5.09798825e-01 -1.23340033... | [7.894011497497559, 0.3995693027973175] |
0b86d38c-0093-4c8d-bc45-eace2df55c7c | semantic-image-translation-for-repairing-the | 2303.17418 | null | https://arxiv.org/abs/2303.17418v2 | https://arxiv.org/pdf/2303.17418v2.pdf | Semantic Image Translation for Repairing the Texture Defects of Building Models | The accurate representation of 3D building models in urban environments is significantly hindered by challenges such as texture occlusion, blurring, and missing details, which are difficult to mitigate through standard photogrammetric texture mapping pipelines. Current image completion methods often struggle to produce... | ['Libin Wang', 'Qing Zhu', 'Bo Xu', 'Haojia Yu', 'Han Hu', 'Qisen Shang'] | 2023-03-30 | null | null | null | null | ['texture-synthesis'] | ['computer-vision'] | [ 6.58980727e-01 8.09307694e-02 3.61794740e-01 -2.47236893e-01
-6.22683942e-01 -6.26916111e-01 6.82918489e-01 -2.44483218e-01
5.55424809e-01 4.79802728e-01 1.43765047e-01 -1.48299724e-01
-2.83784747e-01 -1.17601299e+00 -7.27909148e-01 -5.39786458e-01
2.33442500e-01 5.73805690e-01 2.81075776e-01 -5.96600771... | [9.270792961120605, -3.1941323280334473] |
abc5994d-9c3c-48f1-8319-9777d7c460a2 | a-safe-genetic-algorithm-approach-for-energy | 2306.14237 | null | https://arxiv.org/abs/2306.14237v2 | https://arxiv.org/pdf/2306.14237v2.pdf | A Safe Genetic Algorithm Approach for Energy Efficient Federated Learning in Wireless Communication Networks | Federated Learning (FL) has emerged as a decentralized technique, where contrary to traditional centralized approaches, devices perform a model training in a collaborative manner, while preserving data privacy. Despite the existing efforts made in FL, its environmental impact is still under investigation, since several... | ['Ramin Khalili', 'M. A. Gutierrez-Estevez', 'Nikolaos Petropouleas', 'Theodora Panagea', 'Alexandros-Ioannis Thanopoulos', 'Nikolaos Koursioumpas', 'Lina Magoula'] | 2023-06-25 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [ 4.43572134e-01 1.91942632e-01 -1.69699460e-01 -1.26296088e-01
-2.94892609e-01 -6.81614161e-01 4.98795480e-01 3.47890258e-01
-3.34386528e-01 6.58947885e-01 -3.58284026e-01 -2.51716524e-01
-5.83133757e-01 -1.02400792e+00 -3.54992360e-01 -1.17392921e+00
1.53407156e-02 6.67565241e-02 -3.04987848e-01 5.09772837... | [5.865357398986816, 6.057925224304199] |
45a8bb08-bb09-442b-9a48-5c60f1a6344c | proteinnet-a-standardized-data-set-for | 1902.00249 | null | http://arxiv.org/abs/1902.00249v1 | http://arxiv.org/pdf/1902.00249v1.pdf | ProteinNet: a standardized data set for machine learning of protein structure | Rapid progress in deep learning has spurred its application to bioinformatics
problems including protein structure prediction and design. In classic machine
learning problems like computer vision, progress has been driven by
standardized data sets that facilitate fair assessment of new methods and lower
the barrier to ... | ['Mohammed AlQuraishi'] | 2019-02-01 | null | null | null | null | ['protein-secondary-structure-prediction'] | ['medical'] | [ 5.31000316e-01 -1.67887494e-01 -1.19669646e-01 -7.47607589e-01
-9.08419609e-01 -7.00135767e-01 2.58311719e-01 8.31178427e-01
-7.26115763e-01 1.17683113e+00 -1.06110632e-01 -6.90876901e-01
-6.00243807e-02 -1.98828638e-01 -7.44759023e-01 -7.88331509e-01
-2.56367356e-01 9.11022902e-01 1.45104125e-01 -2.99780011... | [4.736576557159424, 5.5799384117126465] |
e2113d45-0287-4c56-98b7-49db41e40272 | imperceptible-transfer-attack-and-defense-on | 2111.10990 | null | https://arxiv.org/abs/2111.10990v2 | https://arxiv.org/pdf/2111.10990v2.pdf | Imperceptible Transfer Attack and Defense on 3D Point Cloud Classification | Although many efforts have been made into attack and defense on the 2D image domain in recent years, few methods explore the vulnerability of 3D models. Existing 3D attackers generally perform point-wise perturbation over point clouds, resulting in deformed structures or outliers, which is easily perceivable by humans.... | ['Wei Hu', 'Daizong Liu'] | 2021-11-22 | null | null | null | null | ['point-cloud-classification'] | ['computer-vision'] | [ 2.66571462e-01 3.00403386e-02 1.83496207e-01 1.91475004e-01
-5.44820964e-01 -1.08590627e+00 7.30889678e-01 -5.68240732e-02
4.64944504e-02 9.43178963e-03 -2.32182741e-01 -4.26241159e-01
9.86585468e-02 -9.24180329e-01 -9.65385079e-01 -7.35936403e-01
-2.19308063e-01 8.60861391e-02 2.82818437e-01 -3.65055710... | [7.702663898468018, -4.469666481018066] |
2827bb4d-bc21-4cfd-95bd-c2d41e77e9fd | d-terminer-online-demo-for-monolingual-and | null | null | https://aclanthology.org/2022.term-1.7 | https://aclanthology.org/2022.term-1.7.pdf | D-Terminer: Online Demo for Monolingual and Bilingual Automatic Term Extraction | This contribution presents D-Terminer: an open access, online demo for monolingual and multilingual automatic term extraction from parallel corpora. The monolingual term extraction is based on a recurrent neural network, with a supervised methodology that relies on pretrained embeddings. Candidate terms can be tagged i... | ['Els Lefever', 'Veronique Hoste', 'Ayla Rigouts Terryn'] | null | null | null | null | term-lrec-2022-6 | ['term-extraction'] | ['natural-language-processing'] | [ 1.06705293e-01 2.49217704e-01 -5.78563273e-01 -1.09906875e-01
-1.01654971e+00 -8.33730757e-01 7.75794327e-01 6.19103968e-01
-8.65235269e-01 8.64784479e-01 6.27372086e-01 -5.93780220e-01
-1.00224763e-01 -4.04010415e-01 -3.77442032e-01 -2.01090753e-01
-1.12265505e-01 7.15283453e-01 -2.92577833e-01 -7.74861753... | [10.53520679473877, 9.793434143066406] |
85899996-3de8-48ff-99ab-69be70dac3ec | learning-semantic-person-image-generation-by | 2104.06650 | null | https://arxiv.org/abs/2104.06650v1 | https://arxiv.org/pdf/2104.06650v1.pdf | Learning Semantic Person Image Generation by Region-Adaptive Normalization | Human pose transfer has received great attention due to its wide applications, yet is still a challenging task that is not well solved. Recent works have achieved great success to transfer the person image from the source to the target pose. However, most of them cannot well capture the semantic appearance, resulting i... | ['WangMeng Zuo', 'Dongliang He', 'Tianwei Lin', 'Fu Li', 'Xin Li', 'Xiaoming Li', 'Zhengyao Lv'] | 2021-04-14 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Lv_Learning_Semantic_Person_Image_Generation_by_Region-Adaptive_Normalization_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Lv_Learning_Semantic_Person_Image_Generation_by_Region-Adaptive_Normalization_CVPR_2021_paper.pdf | cvpr-2021-1 | ['pose-transfer'] | ['computer-vision'] | [ 2.27186948e-01 -1.76376089e-01 2.47164339e-01 -4.88989294e-01
-4.54308212e-01 -2.46468857e-01 4.34515625e-01 -3.31030428e-01
-2.69853830e-01 6.37765169e-01 2.48338819e-01 4.04614478e-01
2.06038743e-01 -8.30832541e-01 -6.80132866e-01 -6.03523195e-01
7.39361525e-01 5.44490099e-01 5.39220095e-01 -3.30290765... | [11.9827241897583, -0.860615611076355] |
2b81653a-9d88-427e-84f0-92bb73d2e330 | emph-mensa-mix-up-ensemble-average-for | 2304.01554 | null | https://arxiv.org/abs/2304.01554v2 | https://arxiv.org/pdf/2304.01554v2.pdf | MEnsA: Mix-up Ensemble Average for Unsupervised Multi Target Domain Adaptation on 3D Point Clouds | Unsupervised domain adaptation (UDA) addresses the problem of distribution shift between the unlabelled target domain and labelled source domain. While the single target domain adaptation (STDA) is well studied in the literature for both 2D and 3D vision tasks, multi-target domain adaptation (MTDA) is barely explored f... | ['Jonghyun Choi', 'Ashish Sinha'] | 2023-04-04 | null | null | null | null | ['multi-target-domain-adaptation'] | ['computer-vision'] | [ 2.88796335e-01 -3.44618894e-02 -1.55459523e-01 -3.96715581e-01
-8.81809056e-01 -8.69410992e-01 1.14909732e+00 6.49295300e-02
-2.52863348e-01 7.42223084e-01 -2.38170219e-03 -1.47925511e-01
-2.88573533e-01 -5.39848447e-01 -6.47498190e-01 -9.69518840e-01
6.99282512e-02 8.64546359e-01 4.72654372e-01 -1.79065108... | [8.141557693481445, -2.509727716445923] |
089c9683-7d1b-4e31-892d-0fb2f649bda0 | graph-ladling-shockingly-simple-parallel-gnn | 2306.10466 | null | https://arxiv.org/abs/2306.10466v1 | https://arxiv.org/pdf/2306.10466v1.pdf | Graph Ladling: Shockingly Simple Parallel GNN Training without Intermediate Communication | Graphs are omnipresent and GNNs are a powerful family of neural networks for learning over graphs. Despite their popularity, scaling GNNs either by deepening or widening suffers from prevalent issues of unhealthy gradients, over-smoothening, information squashing, which often lead to sub-standard performance. In this w... | ['Zhangyang Wang', 'Ying Ding', 'Tianlong Chen', 'Shiwei Liu', 'Ajay Jaiswal'] | 2023-06-18 | null | null | null | null | ['graph-sampling', 'graph-partitioning'] | ['graphs', 'graphs'] | [ 6.59772754e-02 3.64183456e-01 -3.89618784e-01 1.11656368e-01
-4.05220121e-01 -4.60728884e-01 4.62135017e-01 2.85629392e-01
-1.59919426e-01 6.10523343e-01 5.67638725e-02 -4.93126124e-01
-1.49909616e-01 -1.22559226e+00 -7.43862391e-01 -5.30349493e-01
-3.10937256e-01 4.84659225e-01 2.60735333e-01 -4.69963789... | [6.9795122146606445, 6.137726306915283] |
08ae7e79-8626-4df3-bc1c-94c19048d5f9 | a-survey-on-efficient-processing-of | 2204.07922 | null | https://arxiv.org/abs/2204.07922v1 | https://arxiv.org/pdf/2204.07922v1.pdf | A Survey on Efficient Processing of Similarity Queries over Neural Embeddings | Similarity query is the family of queries based on some similarity metrics. Unlike the traditional database queries which are mostly based on value equality, similarity queries aim to find targets "similar enough to" the given data objects, depending on some similarity metric, e.g., Euclidean distance, cosine similarit... | ['Yifan Wang'] | 2022-04-17 | null | null | null | null | ['entity-resolution'] | ['natural-language-processing'] | [-9.36017632e-02 -4.17496681e-01 -2.66258001e-01 -4.76235896e-01
-3.92999589e-01 -4.61149096e-01 5.43364942e-01 8.82012129e-01
-6.82146788e-01 2.97263619e-02 3.20592642e-01 8.58077332e-02
-5.26463449e-01 -1.31313181e+00 -3.73039305e-01 -4.66332734e-01
1.23487751e-03 4.62513298e-01 4.18624729e-01 -6.23977482... | [10.464266777038574, 8.558061599731445] |
01060343-0266-44f9-996c-d9ff492421e2 | learning-data-teaching-strategies-via | 2111.07083 | null | https://arxiv.org/abs/2111.07083v1 | https://arxiv.org/pdf/2111.07083v1.pdf | Learning Data Teaching Strategies Via Knowledge Tracing | Teaching plays a fundamental role in human learning. Typically, a human teaching strategy would involve assessing a student's knowledge progress for tailoring the teaching materials in a way that enhances the learning progress. A human teacher would achieve this by tracing a student's knowledge over important learning ... | ['Qing Wang', 'Ghodai Abdelrahman'] | 2021-11-13 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [ 3.77382427e-01 1.03013083e-01 -2.76819319e-01 -6.18542194e-01
-2.37408772e-01 -4.70864207e-01 6.24156177e-01 6.01749361e-01
-5.64245582e-01 2.96274960e-01 1.19632157e-02 -2.46054262e-01
-3.61213356e-01 -1.06835866e+00 -7.07860410e-01 -6.75871670e-01
3.90720755e-01 4.46814448e-01 5.98170340e-01 -2.12984905... | [10.131913185119629, 7.023647308349609] |
739f551d-765a-46f6-af28-6c593efe76fe | adaptive-re-calibration-of-channel-wise | 2210.11722 | null | https://arxiv.org/abs/2210.11722v1 | https://arxiv.org/pdf/2210.11722v1.pdf | Adaptive re-calibration of channel-wise features for Adversarial Audio Classification | DeepFake Audio, unlike DeepFake images and videos, has been relatively less explored from detection perspective, and the solutions which exist for the synthetic speech classification either use complex networks or dont generalize to different varieties of synthetic speech obtained using different generative and optimiz... | ['Nikhitha Reddeddy', 'Abhinav Thimma Reddy', 'Vardhan Dongre'] | 2022-10-21 | null | null | null | null | ['synthetic-speech-detection'] | ['audio'] | [ 1.91346914e-01 2.01396957e-01 4.22845662e-01 -1.02061376e-01
-8.08409631e-01 -4.24012870e-01 1.01966012e+00 -5.36401153e-01
-2.15429828e-01 7.20625758e-01 5.41323721e-01 -4.69062738e-02
-9.07931700e-02 -6.67123854e-01 -5.87947786e-01 -6.48196220e-01
-2.16760281e-02 4.66069654e-02 2.46600807e-01 -4.94259298... | [15.160063743591309, 6.243263244628906] |
c2a61d49-bb6d-4c7a-b33f-51ef0ee7e25f | medical-image-denoising-using-convolutional | 1608.04667 | null | http://arxiv.org/abs/1608.04667v2 | http://arxiv.org/pdf/1608.04667v2.pdf | Medical image denoising using convolutional denoising autoencoders | Image denoising is an important pre-processing step in medical image
analysis. Different algorithms have been proposed in past three decades with
varying denoising performances. More recently, having outperformed all
conventional methods, deep learning based models have shown a great promise.
These methods are however ... | ['Lovedeep Gondara'] | 2016-08-16 | null | null | null | null | ['medical-image-denoising'] | ['computer-vision'] | [ 1.60046950e-01 -5.49996532e-02 4.52950001e-01 -3.27715963e-01
-5.99262714e-01 2.33009402e-02 2.32392982e-01 7.45330751e-02
-6.75953865e-01 7.24642158e-01 1.56084567e-01 1.16176486e-01
-2.66683679e-02 -8.43781888e-01 -2.98551053e-01 -1.08759522e+00
-2.07787454e-02 -1.32744551e-01 2.01283336e-01 -3.11145395... | [13.145330429077148, -2.524794578552246] |
b35276b4-a834-437d-96bd-5ca5736c43d7 | a-robust-learning-approach-to-domain-adaptive | 1904.02361 | null | https://arxiv.org/abs/1904.02361v3 | https://arxiv.org/pdf/1904.02361v3.pdf | A Robust Learning Approach to Domain Adaptive Object Detection | Domain shift is unavoidable in real-world applications of object detection. For example, in self-driving cars, the target domain consists of unconstrained road environments which cannot all possibly be observed in training data. Similarly, in surveillance applications sufficiently representative training data may be la... | ['Mani Ranjbar', 'Mehran Khodabandeh', 'Arash Vahdat', 'William G. Macready'] | 2019-04-04 | a-robust-learning-approach-to-domain-adaptive-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Khodabandeh_A_Robust_Learning_Approach_to_Domain_Adaptive_Object_Detection_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Khodabandeh_A_Robust_Learning_Approach_to_Domain_Adaptive_Object_Detection_ICCV_2019_paper.pdf | iccv-2019-10 | ['robust-object-detection'] | ['computer-vision'] | [ 4.02220070e-01 1.35985196e-01 -7.38685951e-02 -5.55665910e-01
-8.66347373e-01 -7.39580214e-01 5.63101530e-01 5.19641265e-02
-5.40124238e-01 7.62664616e-01 -4.53263015e-01 -9.44911465e-02
1.12335540e-01 -5.78355789e-01 -8.74278188e-01 -6.64652288e-01
1.07350163e-01 8.25642586e-01 8.57992649e-01 -6.62156716... | [9.542756080627441, 1.7562556266784668] |
3a98fe54-8e06-494f-abfd-60dfd16f138e | the-p-destre-a-fully-annotated-dataset-for | 2004.02782 | null | https://arxiv.org/abs/2004.02782v1 | https://arxiv.org/pdf/2004.02782v1.pdf | The P-DESTRE: A Fully Annotated Dataset for Pedestrian Detection, Tracking, Re-Identification and Search from Aerial Devices | Over the last decades, the world has been witnessing growing threats to the security in urban spaces, which has augmented the relevance given to visual surveillance solutions able to detect, track and identify persons of interest in crowds. In particular, unmanned aerial vehicles (UAVs) are a potential tool for this ki... | ['Hugo Proença', 'S. V. Aruna Kumar', 'Abhijit Das', 'Ehsan Yaghoubi', 'B. S. Harish'] | 2020-04-06 | null | null | null | null | ['person-search'] | ['computer-vision'] | [-2.40951404e-01 -5.42965770e-01 7.09191263e-02 3.24360691e-02
-1.88318133e-01 -7.95242965e-01 9.07277882e-01 4.33250040e-01
-8.28401387e-01 8.05535316e-01 -3.71983111e-01 -5.02896011e-02
-1.03802122e-01 -8.57431591e-01 -3.83719206e-01 -8.27689409e-01
-1.51146770e-01 4.30898488e-01 5.22451639e-01 -3.60658109... | [14.393697738647461, 1.0420860052108765] |
ca755666-a2eb-44ca-b144-fd378887f1ae | astrometric-calibration-and-source | 2211.09939 | null | https://arxiv.org/abs/2211.09939v1 | https://arxiv.org/pdf/2211.09939v1.pdf | Astrometric Calibration and Source Characterisation of the Latest Generation Neuromorphic Event-based Cameras for Space Imaging | As an emerging approach to space situational awareness and space imaging, the practical use of an event-based camera in space imaging for precise source analysis is still in its infancy. The nature of event-based space imaging and data collection needs to be further explored to develop more effective event-based space ... | ['Gregory Cohen', 'André van Schaik', 'Nicholas Tothill', 'Saeed Afshar', 'Alexandre Marcireau', 'Nicholas Owen Ralph'] | 2022-11-17 | null | null | null | null | ['camera-calibration', 'astronomy'] | ['computer-vision', 'miscellaneous'] | [ 5.75353026e-01 -7.62354672e-01 2.44890720e-01 -4.59014952e-01
-5.79129159e-01 -7.24227071e-01 1.07430053e+00 2.26017218e-02
-4.48781937e-01 5.92552423e-01 -2.41864711e-01 -4.28194135e-01
-6.01626992e-01 -7.00168073e-01 -3.04237753e-01 -8.54074478e-01
1.27964526e-01 2.99366504e-01 8.62203240e-01 -5.93898930... | [10.925222396850586, -2.5027825832366943] |
b05d9680-27d5-4d2e-a9a8-71af3026aa43 | federated-variational-inference-methods-for | 2302.03314 | null | https://arxiv.org/abs/2302.03314v2 | https://arxiv.org/pdf/2302.03314v2.pdf | Federated Variational Inference Methods for Structured Latent Variable Models | Federated learning methods enable model training across distributed data sources without data leaving their original locations and have gained increasing interest in various fields. However, existing approaches are limited, excluding many structured probabilistic models. We present a general and elegant solution based ... | ['Kerrie Mengersen', 'Robert Salomone', 'Conor Hassan'] | 2023-02-07 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [-2.69485235e-01 2.16799095e-01 -4.23624545e-01 -5.22369623e-01
-1.12812567e+00 -2.85837352e-01 7.20639110e-01 4.02250923e-02
-1.80161372e-01 9.93203044e-01 1.03492305e-01 -1.94810748e-01
-7.20123827e-01 -8.70591760e-01 -7.35943556e-01 -9.73126292e-01
-2.42076844e-01 9.06325281e-01 2.13321492e-01 5.56394577... | [5.850080490112305, 6.3189592361450195] |
3443002b-d960-49cb-8a44-33783f921f5c | long-range-language-modeling-with-self | 2306.13421 | null | https://arxiv.org/abs/2306.13421v1 | https://arxiv.org/pdf/2306.13421v1.pdf | Long-range Language Modeling with Self-retrieval | Retrieval-augmented language models (LMs) have received much attention recently. However, typically the retriever is not trained jointly as a native component of the LM, but added to an already-pretrained LM, which limits the ability of the LM and the retriever to adapt to one another. In this work, we propose the Retr... | ['Jonathan Berant', 'Ohad Rubin'] | 2023-06-23 | null | null | null | null | ['retrieval'] | ['methodology'] | [ 1.82174519e-01 1.42561167e-01 -2.63877064e-01 -1.39604911e-01
-1.37597179e+00 -3.86004984e-01 7.61654496e-01 8.98692980e-02
-5.45655370e-01 4.82876629e-01 5.57027280e-01 -2.73033194e-02
8.61361921e-02 -5.48980594e-01 -1.10743260e+00 -4.51079279e-01
2.66915798e-01 1.02297533e+00 2.46417597e-01 -2.86127895... | [11.462100982666016, 7.797205924987793] |
fec4c822-69e3-4dfb-8b3e-aa6b195305f2 | when-the-majority-is-wrong-leveraging | 2305.06626 | null | https://arxiv.org/abs/2305.06626v3 | https://arxiv.org/pdf/2305.06626v3.pdf | When the Majority is Wrong: Modeling Annotator Disagreement for Subjective Tasks | Though majority vote among annotators is typically used for ground truth labels in natural language processing, annotator disagreement in tasks such as hate speech detection may reflect differences in opinion across groups, not noise. Thus, a crucial problem in hate speech detection is determining whether a statement i... | ['Dan Klein', 'Rediet Abebe', 'Eve Fleisig'] | 2023-05-11 | null | null | null | null | ['hate-speech-detection'] | ['natural-language-processing'] | [-1.71016902e-01 5.59117079e-01 -3.92040163e-01 -6.32993698e-01
-8.23140919e-01 -1.24087870e+00 3.13265413e-01 6.13070726e-01
-4.93642598e-01 4.46682811e-01 7.78504074e-01 -1.13291092e-01
5.43840766e-01 -2.15996146e-01 -2.13661883e-02 -3.31332147e-01
4.02853131e-01 2.29911506e-01 -2.20827311e-01 -9.94832441... | [8.76060962677002, 10.468401908874512] |
c5f87b86-a795-4ad0-9fd8-f3ce9205485b | robust-video-stabilization-by-optimization-in | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Yu_Robust_Video_Stabilization_by_Optimization_in_CNN_Weight_Space_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Yu_Robust_Video_Stabilization_by_Optimization_in_CNN_Weight_Space_CVPR_2019_paper.pdf | Robust Video Stabilization by Optimization in CNN Weight Space | We propose a novel robust video stabilization method. Unlike traditional video stabilization techniques that involve complex motion models, we directly model the appearance change of the frames as the dense optical flow field of consecutive frames. We introduce a new formulation of the video stabilization task based on... | [' Ravi Ramamoorthi', 'Jiyang Yu'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['video-stabilization'] | ['computer-vision'] | [-1.35913137e-02 -1.39107570e-01 -2.29941040e-01 -2.54229996e-02
-4.13701385e-01 -4.84916091e-01 3.11044991e-01 -2.98640490e-01
-5.81942439e-01 7.04631507e-01 2.27975801e-01 -2.37003326e-01
4.28021193e-01 -2.60655731e-01 -9.69064593e-01 -7.46222019e-01
1.46924600e-01 -1.69834778e-01 2.76566356e-01 -3.72330874... | [10.568838119506836, -1.3896687030792236] |
e08b7abf-2feb-4b1c-a506-a9c9b0de6b90 | a-simple-and-effective-approach-to-the-story | 1803.05547 | null | http://arxiv.org/abs/1803.05547v1 | http://arxiv.org/pdf/1803.05547v1.pdf | A Simple and Effective Approach to the Story Cloze Test | In the Story Cloze Test, a system is presented with a 4-sentence prompt to a
story, and must determine which one of two potential endings is the 'right'
ending to the story. Previous work has shown that ignoring the training set and
training a model on the validation set can achieve high accuracy on this task
due to st... | ['Siddarth Srinivasan', 'Mark Riedl', 'Richa Arora'] | 2018-03-15 | a-simple-and-effective-approach-to-the-story-1 | https://aclanthology.org/N18-2015 | https://aclanthology.org/N18-2015.pdf | naacl-2018-6 | ['cloze-test'] | ['natural-language-processing'] | [-4.13474962e-02 1.53761178e-01 7.88966194e-02 -5.44854581e-01
-7.96246588e-01 -6.49336994e-01 7.32570052e-01 2.64140695e-01
-4.95938629e-01 3.52287114e-01 6.69976652e-01 -3.71726900e-01
1.54175863e-01 -6.39074326e-01 -5.53713381e-01 -1.94984362e-01
2.03938186e-01 4.54994678e-01 1.62675753e-01 -4.06409711... | [11.307680130004883, 8.829998016357422] |
e314dc09-b8a0-4776-80da-f296eea9d975 | hierarchical-region-learning-for-nested-named | null | null | https://aclanthology.org/2020.findings-emnlp.430 | https://aclanthology.org/2020.findings-emnlp.430.pdf | Hierarchical Region Learning for Nested Named Entity Recognition | Named Entity Recognition (NER) is deeply explored and widely used in various tasks. Usually, some entity mentions are nested in other entities, which leads to the nested NER problem. Leading region based models face both the efficiency and effectiveness challenge due to the high subsequence enumeration complexity. To t... | ['Yucheng Li', 'Shuzi Niu', 'Xinwei Long'] | 2020-11-01 | null | null | null | findings-of-the-association-for-computational | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [-3.08092743e-01 -8.42160285e-02 -5.75577021e-01 -4.95930314e-01
-1.16229796e+00 -9.29262161e-01 2.82805294e-01 4.80067223e-01
-7.51584113e-01 9.60498095e-01 6.24536693e-01 -3.01171213e-01
2.34991893e-01 -8.05026948e-01 -7.03722060e-01 -2.20971227e-01
-1.22491486e-01 2.00297445e-01 4.81155634e-01 -9.79469940... | [9.54858684539795, 9.505696296691895] |
573df1db-0386-43b5-953b-1c183ad79442 | a-closer-look-at-temporal-ordering-in-the | 2209.15501 | null | https://arxiv.org/abs/2209.15501v2 | https://arxiv.org/pdf/2209.15501v2.pdf | A Closer Look at Temporal Ordering in the Segmentation of Instructional Videos | Understanding the steps required to perform a task is an important skill for AI systems. Learning these steps from instructional videos involves two subproblems: (i) identifying the temporal boundary of sequentially occurring segments and (ii) summarizing these steps in natural language. We refer to this task as Proced... | ['Shreyank N Gowda', 'Frank Keller', 'Laura Sevilla-Lara', 'Anil Batra'] | 2022-09-30 | null | null | null | null | ['dense-video-captioning'] | ['computer-vision'] | [ 5.78052342e-01 2.03063134e-02 -4.20724809e-01 -4.29913849e-01
-9.48396623e-01 -8.33767056e-01 5.01614571e-01 5.04620850e-01
-4.23042774e-01 4.37581688e-01 1.83028176e-01 -1.77626297e-01
-1.72176078e-01 -6.47162974e-01 -1.05435920e+00 -3.99216592e-01
1.58207253e-01 4.19475943e-01 5.76089025e-01 5.67871854... | [8.785028457641602, 0.5623630285263062] |
ec80d79b-eaa5-4f28-892a-c066b683382a | nisq-ready-community-detection-based-on | 2212.14717 | null | https://arxiv.org/abs/2212.14717v2 | https://arxiv.org/pdf/2212.14717v2.pdf | NISQ-ready community detection based on separation-node identification | The analysis of network structure is essential to many scientific areas, ranging from biology to sociology. As the computational task of clustering these networks into partitions, i.e., solving the community detection problem, is generally NP-hard, heuristic solutions are indispensable. The exploration of expedient heu... | ['Mirco Schoenfeld', 'David Bucher', 'Jonas Nüßlein', 'Sebastian Feld', 'Dominik Ott', 'Jonas Stein'] | 2022-12-30 | null | null | null | null | ['community-detection'] | ['graphs'] | [ 2.85905659e-01 2.93750405e-01 1.51031390e-01 2.42216632e-01
-3.59938622e-01 -7.14655161e-01 2.95896202e-01 5.26334405e-01
-2.34034166e-01 6.43187344e-01 -3.24843913e-01 -5.10998666e-01
-5.00631034e-01 -1.21525037e+00 -2.70762175e-01 -1.06862152e+00
-4.70448762e-01 8.97888839e-01 2.64862090e-01 -3.97545844... | [6.933660984039307, 5.1836256980896] |
89a86eca-ebcc-405e-80db-dcdfed5122a4 | an-efficient-convex-hull-based-vehicle-pose | 2302.01034 | null | https://arxiv.org/abs/2302.01034v2 | https://arxiv.org/pdf/2302.01034v2.pdf | An Efficient Convex Hull-based Vehicle Pose Estimation Method for 3D LiDAR | Vehicle pose estimation with LiDAR is essential in the perception technology of autonomous driving. However, due to incomplete observation measurements and sparsity of the LiDAR point cloud, it is challenging to achieve satisfactory pose extraction based on 3D LiDAR by using the existing pose estimation methods. In add... | ['Ningning Ding'] | 2023-02-02 | null | null | null | null | ['vehicle-pose-estimation'] | ['computer-vision'] | [ 1.49228182e-02 -2.15399146e-01 2.21743062e-02 -3.76812994e-01
-5.17254114e-01 -2.03000978e-01 2.08541781e-01 1.53291270e-01
-5.78985572e-01 5.41288495e-01 -6.15573168e-01 -3.50469977e-01
-3.53557378e-01 -8.19021225e-01 -4.94364411e-01 -5.38282275e-01
2.45941281e-01 6.37642860e-01 4.31391597e-01 -1.45095244... | [7.7153754234313965, -2.2947449684143066] |
6e59c014-b2fc-4eb5-b49e-1a9766bd82ef | meta-learned-models-of-cognition | 2304.06729 | null | https://arxiv.org/abs/2304.06729v1 | https://arxiv.org/pdf/2304.06729v1.pdf | Meta-Learned Models of Cognition | Meta-learning is a framework for learning learning algorithms through repeated interactions with an environment as opposed to designing them by hand. In recent years, this framework has established itself as a promising tool for building models of human cognition. Yet, a coherent research program around meta-learned mo... | ['Eric Schulz', 'Jane X. Wang', 'Matthew Botvinick', 'Akshay Jagadish', 'Ishita Dasgupta', 'Marcel Binz'] | 2023-04-12 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [ 2.02640463e-02 3.45283121e-01 -2.92912513e-01 -4.57924277e-01
-4.06540245e-01 -3.91459852e-01 7.04887211e-01 3.87901425e-01
-6.42117023e-01 5.10832369e-01 1.10957950e-01 -5.85117579e-01
-8.05497885e-01 -7.81230032e-01 -8.13445508e-01 -3.83726150e-01
1.52134567e-01 3.51396620e-01 -3.27524878e-02 5.15584871... | [9.462590217590332, 7.113786220550537] |
71d4a7f4-0916-4254-bc44-c2f48b42e484 | location-aware-web-service-qos-prediction-via | null | null | https://scholar.lanfanshu.cn/scholar?q=Location-Aware+Web+Service+QoS+Prediction+via+Deep+Collaborative+Filtering | https://scholar.lanfanshu.cn/scholar?q=Location-Aware+Web+Service+QoS+Prediction+via+Deep+Collaborative+Filtering | Location-Aware Web Service QoS Prediction via Deep Collaborative Filtering | Abstract—Nowadays, there is a large number of web ser-vices with similar functions, from which users choose the best
according to the quality of service (QoS). Hence, QoS prediction is a primary challenge in service recommendation. Most existing approaches model the user-service interaction relationship.However, the l... | ['Zhaohong Jia，Li Jin，Yiwen Zhang，Chuang Liu，Kai Li，Yun Yang'] | 2022-11-04 | null | null | null | journal-2022-11 | ['collaborative-filtering'] | ['miscellaneous'] | [-4.25731272e-01 -7.70942748e-01 -6.04055703e-01 -7.15828180e-01
-1.61686108e-01 -5.03074601e-02 2.54268587e-01 1.33727426e-02
-3.10964942e-01 3.76909763e-01 3.37800056e-01 -8.24400038e-02
-6.78987920e-01 -8.84294331e-01 -1.07324757e-01 -8.14347982e-01
-2.25414902e-01 5.32961965e-01 5.26605360e-02 -3.36513996... | [10.049534797668457, 5.67059850692749] |
6a2010e4-0556-4d21-ac86-870ce710c9c2 | cross-directional-feature-fusion-network-for | 2010.14014 | null | https://arxiv.org/abs/2010.14014v2 | https://arxiv.org/pdf/2010.14014v2.pdf | Cross-directional Feature Fusion Network for Building Damage Assessment from Satellite Imagery | Fast and effective responses are required when a natural disaster (e.g., earthquake, hurricane, etc.) strikes. Building damage assessment from satellite imagery is critical before an effective response is conducted. High-resolution satellite images provide rich information with pre- and post-disaster scenes for analysi... | ['Chen Chen', 'Taojiannan Yang', 'Sijie Zhu', 'Yu Shen'] | 2020-10-27 | null | null | null | null | ['2d-semantic-segmentation'] | ['computer-vision'] | [ 4.23424006e-01 -6.55269325e-01 1.40732929e-01 -2.71643579e-01
-1.25714958e+00 -3.59736755e-02 5.10982394e-01 5.70667088e-01
-6.26840472e-01 5.84552109e-01 5.54910898e-01 3.30762081e-02
-3.24684978e-01 -1.34634483e+00 -2.09994048e-01 -1.10889828e+00
-8.08091536e-02 2.95559615e-01 -1.51999444e-02 -6.13221765... | [9.555876731872559, -1.3296061754226685] |
20e957e2-1b88-4d4b-af52-526b13f131da | rhythmnet-end-to-end-heart-rate-estimation | 1910.11515 | null | https://arxiv.org/abs/1910.11515v2 | https://arxiv.org/pdf/1910.11515v2.pdf | RhythmNet: End-to-end Heart Rate Estimation from Face via Spatial-temporal Representation | Heart rate (HR) is an important physiological signal that reflects the physical and emotional status of a person. Traditional HR measurements usually rely on contact monitors, which may cause inconvenience and discomfort. Recently, some methods have been proposed for remote HR estimation from face videos; however, most... | ['Shiguang Shan', 'Xilin Chen', 'Xuesong Niu', 'Hu Han'] | 2019-10-25 | null | null | null | null | ['heart-rate-estimation'] | ['medical'] | [-6.27975762e-02 -3.95773619e-01 -6.13371693e-02 -4.82877612e-01
-6.54699862e-01 1.55640754e-03 -1.48556352e-01 -5.10483205e-01
-8.59516561e-02 7.47714281e-01 3.63011271e-01 2.21378312e-01
2.20183551e-01 -4.08900440e-01 -2.24125251e-01 -8.60667706e-01
-2.67912876e-02 -5.68622112e-01 -5.40258586e-01 -1.68015659... | [13.887093544006348, 2.7075819969177246] |
b140d949-e637-4937-b4a6-18f8537c57bd | space-time-correspondence-as-a-contrastive | 2006.14613 | null | https://arxiv.org/abs/2006.14613v2 | https://arxiv.org/pdf/2006.14613v2.pdf | Space-Time Correspondence as a Contrastive Random Walk | This paper proposes a simple self-supervised approach for learning a representation for visual correspondence from raw video. We cast correspondence as prediction of links in a space-time graph constructed from video. In this graph, the nodes are patches sampled from each frame, and nodes adjacent in time can share a d... | ['Alexei A. Efros', 'Allan Jabri', 'Andrew Owens'] | 2020-06-25 | null | http://proceedings.neurips.cc/paper/2020/hash/e2ef524fbf3d9fe611d5a8e90fefdc9c-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/e2ef524fbf3d9fe611d5a8e90fefdc9c-Paper.pdf | neurips-2020-12 | ['dense-pixel-correspondence-estimation'] | ['computer-vision'] | [ 3.61796081e-01 3.88125181e-01 -5.31480193e-01 -5.73894799e-01
-6.73443913e-01 -5.09056628e-01 6.40435219e-01 2.70454496e-01
-3.64475280e-01 3.76492113e-01 3.46557647e-01 1.94814369e-01
3.80313359e-02 -8.73808920e-01 -1.40996242e+00 -4.62299138e-01
-5.03143847e-01 7.57765591e-01 5.38425803e-01 1.18928313... | [8.88036823272705, -0.3429740071296692] |
63c11fb3-4f65-414f-9626-accb2d1469c5 | data-interpretation-over-plots | 1909.00997 | null | https://arxiv.org/abs/1909.00997v3 | https://arxiv.org/pdf/1909.00997v3.pdf | PlotQA: Reasoning over Scientific Plots | Existing synthetic datasets (FigureQA, DVQA) for reasoning over plots do not contain variability in data labels, real-valued data, or complex reasoning questions. Consequently, proposed models for these datasets do not fully address the challenge of reasoning over plots. In particular, they assume that the answer comes... | ['Mitesh M. Khapra', 'Nitesh Methani', 'Pratyush Kumar', 'Pritha Ganguly'] | 2019-09-03 | null | null | null | null | ['chart-question-answering', 'chart-question-answering'] | ['computer-code', 'computer-vision'] | [ 6.16002269e-02 4.59841222e-01 1.50197059e-01 -4.02731717e-01
-1.07004952e+00 -1.23845792e+00 4.58644450e-01 1.45241827e-01
1.38641953e-01 5.11230767e-01 1.34891585e-01 -6.08097970e-01
-1.06874302e-01 -1.25661552e+00 -8.89998555e-01 -1.89693049e-02
6.01064861e-01 6.01408184e-01 5.63042521e-01 -4.10137594... | [11.10448169708252, 1.9622236490249634] |
ae76cd7a-9cab-484a-88cf-76e627102018 | neural-architecture-transfer-2-a-paradigm-for | 2307.00960 | null | https://arxiv.org/abs/2307.00960v1 | https://arxiv.org/pdf/2307.00960v1.pdf | Neural Architecture Transfer 2: A Paradigm for Improving Efficiency in Multi-Objective Neural Architecture Search | Deep learning is increasingly impacting various aspects of contemporary society. Artificial neural networks have emerged as the dominant models for solving an expanding range of tasks. The introduction of Neural Architecture Search (NAS) techniques, which enable the automatic design of task-optimal networks, has led to... | ['Matteo Matteucci', 'Eugenio Lomurno', 'Simone Sarti'] | 2023-07-03 | null | null | null | null | ['architecture-search'] | ['methodology'] | [ 3.12684417e-01 4.36722562e-02 -1.27173528e-01 -3.99961144e-01
-4.19375271e-01 -5.80168605e-01 5.11222184e-01 -1.23037338e-01
-8.43927622e-01 7.46826887e-01 -8.48094150e-02 -3.51745963e-01
-7.71410227e-01 -5.71944892e-01 -6.24799252e-01 -5.03730714e-01
-9.14444923e-02 7.71891952e-01 2.46754259e-01 -1.97864518... | [8.447500228881836, 3.2792813777923584] |
d9c000ca-6f14-4c6d-b3c5-613750c834e1 | visual-aware-text-to-speech | 2306.12020 | null | https://arxiv.org/abs/2306.12020v1 | https://arxiv.org/pdf/2306.12020v1.pdf | Visual-Aware Text-to-Speech | Dynamically synthesizing talking speech that actively responds to a listening head is critical during the face-to-face interaction. For example, the speaker could take advantage of the listener's facial expression to adjust the tones, stressed syllables, or pauses. In this work, we present a new visual-aware text-to-sp... | ['Tao Mei', 'Tiejun Zhao', 'Ting Yao', 'Wei zhang', 'Yalong Bai', 'Mohan Zhou'] | 2023-06-21 | null | null | null | null | ['speech-synthesis'] | ['speech'] | [ 2.92803973e-01 3.02093595e-01 7.52013028e-02 -6.34805441e-01
-8.53766799e-01 -6.05819881e-01 7.15419412e-01 -4.51422542e-01
2.06756309e-01 5.05580664e-01 7.39799976e-01 -1.18396088e-01
6.97379529e-01 -2.89631635e-01 -3.44481349e-01 -7.33753741e-01
5.18858433e-01 9.24623162e-02 -1.54576108e-01 -3.56657147... | [13.276756286621094, -0.4130769371986389] |
7e51a2b7-05d0-4db9-8f5a-9401c1661c7c | characterizing-the-efficiency-of-graph-neural | 2211.03021 | null | https://arxiv.org/abs/2211.03021v1 | https://arxiv.org/pdf/2211.03021v1.pdf | Characterizing the Efficiency of Graph Neural Network Frameworks with a Magnifying Glass | Graph neural networks (GNNs) have received great attention due to their success in various graph-related learning tasks. Several GNN frameworks have then been developed for fast and easy implementation of GNN models. Despite their popularity, they are not well documented, and their implementations and system performanc... | ['Chul-Ho Lee', 'Bradley Rees', 'Jongryool Kim', 'Xin Huang'] | 2022-11-06 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [-4.49667126e-02 5.72880879e-02 -3.71070445e-01 -3.06565195e-01
1.63344759e-02 -3.07522416e-01 3.66844177e-01 1.81633487e-01
-2.46481746e-01 5.21379769e-01 -4.23311681e-01 -5.62800646e-01
-1.92416117e-01 -1.21615911e+00 -6.73464358e-01 -6.90018535e-01
-3.99572879e-01 4.14046794e-01 2.67804384e-01 -1.82397515... | [7.010208606719971, 5.908237934112549] |
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