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77b078ed-75d7-4965-9457-9ff8e905d9af | streaming-algorithms-for-diversity | 2208.00194 | null | https://arxiv.org/abs/2208.00194v1 | https://arxiv.org/pdf/2208.00194v1.pdf | Streaming Algorithms for Diversity Maximization with Fairness Constraints | Diversity maximization is a fundamental problem with wide applications in data summarization, web search, and recommender systems. Given a set $X$ of $n$ elements, it asks to select a subset $S$ of $k \ll n$ elements with maximum \emph{diversity}, as quantified by the dissimilarities among the elements in $S$. In this ... | ['Michael Mathioudakis', 'Francesco Fabbri', 'Yanhao Wang'] | 2022-07-30 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [ 1.58539847e-01 -2.54679602e-02 -5.06817758e-01 -4.43630546e-01
-7.49524772e-01 -5.74048042e-01 -3.93514842e-01 6.26826584e-01
-6.59468293e-01 8.18947256e-01 -7.11022168e-02 -2.43655130e-01
-8.08826685e-01 -1.15122032e+00 -4.52319652e-01 -8.07458520e-01
-8.13042462e-01 6.41727328e-01 -1.29587740e-01 -3.34200650... | [6.553548336029053, 4.917885780334473] |
ab62e1a9-4e57-4a92-a801-7d559c612d23 | attribute-guided-encryption-with-facial | 2305.13548 | null | https://arxiv.org/abs/2305.13548v1 | https://arxiv.org/pdf/2305.13548v1.pdf | Attribute-Guided Encryption with Facial Texture Masking | The increasingly pervasive facial recognition (FR) systems raise serious concerns about personal privacy, especially for billions of users who have publicly shared their photos on social media. Several attempts have been made to protect individuals from unauthorized FR systems utilizing adversarial attacks to generate ... | ['Rama Chellappa', 'Jiang Liu', 'Chun Pong Lau'] | 2023-05-22 | null | null | null | null | ['adversarial-attack'] | ['adversarial'] | [ 4.04400736e-01 9.01111066e-02 4.79385823e-01 -6.11884356e-01
-8.51519227e-01 -9.04442549e-01 6.17710471e-01 -5.44297934e-01
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-1.32145450e-01 -4.14430439e-01 -4.32880044e-01 -2.20813572... | [12.87615966796875, 0.8965027928352356] |
4ce5595c-c379-4782-95dd-e547f54d480b | correcting-prepositional-phrase-attachments | null | null | https://aclanthology.org/W17-6311 | https://aclanthology.org/W17-6311.pdf | Correcting prepositional phrase attachments using multimodal corpora | PP-attachments are an important source of errors in parsing natural language. We propose in this article to use data coming from a multimodal corpus, combining textual, visual and conceptual information, as well as a correction strategy, to propose alternative attachments in the output of a parser. | ['Sebastien Delecraz', 'Frederic Bechet', 'Alexis Nasr', 'Benoit Favre'] | 2017-09-01 | null | null | null | ws-2017-9 | ['prepositional-phrase-attachment'] | ['natural-language-processing'] | [ 1.42020341e-02 4.26780105e-01 3.89513552e-01 -4.68919814e-01
-5.24231553e-01 -7.60977507e-01 4.97165799e-01 9.49338794e-01
-5.19992769e-01 6.51547015e-01 5.68164825e-01 -3.14504653e-01
1.35747656e-01 -5.99837840e-01 -3.23088259e-01 1.40671343e-01
3.41984451e-01 5.80869853e-01 6.98724926e-01 -4.72312510... | [10.480956077575684, 9.87310791015625] |
3e770424-d043-4f99-b470-54a9e8d8f9f3 | best-buddies-registration-for-point-clouds | 2010.01912 | null | https://arxiv.org/abs/2010.01912v1 | https://arxiv.org/pdf/2010.01912v1.pdf | Best Buddies Registration for Point Clouds | We propose new, and robust, loss functions for the point cloud registration problem. Our loss functions are inspired by the Best Buddies Similarity (BBS) measure that counts the number of mutual nearest neighbors between two point sets. This measure has been shown to be robust to outliers and missing data in the case o... | ['Raja Giryes', 'Shai Avidan', 'Tal Shomer', 'Amnon Drory'] | 2020-10-05 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [-2.46247903e-01 -3.88075203e-01 1.31298229e-01 -5.71046889e-01
-1.37224960e+00 -4.09149379e-01 7.10756063e-01 3.43641311e-01
-5.71082711e-01 4.35856998e-01 -2.71786332e-01 6.71986840e-04
-4.79539812e-01 -5.64300418e-01 -9.37189460e-01 -6.15600646e-01
-3.64590198e-01 9.90647197e-01 3.61848086e-01 -6.04066372... | [7.738664627075195, -2.832556962966919] |
8844f494-db7b-45b7-ae86-b3ebcd724fa0 | a-little-bit-attention-is-all-you-need-for | 2302.14574 | null | https://arxiv.org/abs/2302.14574v1 | https://arxiv.org/pdf/2302.14574v1.pdf | A Little Bit Attention Is All You Need for Person Re-Identification | Person re-identification plays a key role in applications where a mobile robot needs to track its users over a long period of time, even if they are partially unobserved for some time, in order to follow them or be available on demand. In this context, deep-learning based real-time feature extraction on a mobile robot ... | ['Horst-Michael Gross', 'Dustin Aganian', 'Jannik Lübberstedt', 'Markus Eisenbach'] | 2023-02-28 | null | null | null | null | ['person-re-identification'] | ['computer-vision'] | [-6.85472637e-02 1.00805545e-02 7.90687725e-02 -4.77763832e-01
-2.14809462e-01 -3.03299487e-01 5.88155210e-01 3.55256081e-01
-9.55910206e-01 5.76395154e-01 -3.58920068e-01 -1.25917196e-01
-3.17760706e-01 -6.47665322e-01 -7.18661547e-01 -4.73484427e-01
1.11897886e-01 9.01922703e-01 2.69315958e-01 -8.95229280... | [14.378056526184082, 0.7874850630760193] |
9368890d-5608-4824-89ae-b67125feb25f | emns-imz-corpus-an-emotive-single-speaker | 2305.13137 | null | https://arxiv.org/abs/2305.13137v2 | https://arxiv.org/pdf/2305.13137v2.pdf | EMNS /Imz/ Corpus: An emotive single-speaker dataset for narrative storytelling in games, television and graphic novels | The increasing adoption of text-to-speech technologies has led to a growing demand for natural and emotive voices that adapt to a conversation's context and emotional tone. The Emotive Narrative Storytelling (EMNS) corpus is a unique speech dataset created to enhance conversations' expressiveness and emotive quality in... | ['Jian Jun Zhang', 'Xiaosong Yang', 'Kari Ali Noriy'] | 2023-05-22 | null | null | null | null | ['expressive-speech-synthesis', 'speech-synthesis'] | ['speech', 'speech'] | [-1.45154810e-02 2.67179042e-01 1.78479448e-01 -6.13008976e-01
-6.73082054e-01 -6.70154452e-01 9.79254067e-01 -2.13373937e-02
7.82186091e-02 6.62073135e-01 9.82630551e-01 3.60406607e-01
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-1.79016665e-01 1.27842724e-01 -1.99257597e-01 -6.83436751... | [13.090340614318848, 6.142116546630859] |
28714e73-9bb3-4155-9930-be228f51587a | a-multi-cell-experimental-design-to-recover | 2302.13857 | null | https://arxiv.org/abs/2302.13857v2 | https://arxiv.org/pdf/2302.13857v2.pdf | A multi-cell experimental design to recover policy relevant treatment effects, with an application to online advertising | Experiments are an important tool to measure the impacts of interventions. However, in experimental settings with one-sided noncompliance, extant empirical approaches may not produce the estimands a decision-maker needs to solve their problem. For example, these experimental designs are common in digital advertising se... | ['Brett R. Gordon', 'Caio Waisman'] | 2023-02-27 | null | null | null | null | ['experimental-design'] | ['methodology'] | [ 3.66988033e-01 1.17733411e-01 -1.17583680e+00 -5.68522155e-01
-9.45494294e-01 -6.60368085e-01 3.54264379e-01 1.89300030e-01
-7.19920754e-01 9.73983824e-01 3.90253663e-01 -9.88605857e-01
-1.54404685e-01 -7.36181200e-01 -9.73786175e-01 -2.77928114e-01
9.26544219e-02 4.86581624e-01 -2.49983624e-01 2.73009986... | [8.095216751098633, 5.3411126136779785] |
abb0456c-b1a1-42ee-ad2d-44103134203b | sonnet-a-self-guided-ordinal-regression | null | null | https://ieeexplore.ieee.org/document/9709151 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9709151 | SONNET: A Self-Guided Ordinal Regression Neural Network for Segmentation and Classification of Nuclei in Large-Scale Multi-Tissue Histology Images | Automated nuclei segmentation and classification are the keys to analyze and understand the cellular characteristics and functionality, supporting computer-aided digital pathology in disease diagnosis. However, the task still remains challenging due to the intrinsic variations in size, intensity, and morphology of diff... | ['Jin T. Kwak', 'Kyungeun Kim', 'Trinh T. L. Vuong', 'Boram Song', 'Tan N. N. Doan'] | 2022-02-09 | null | null | null | ieee-journal-of-biomedical-and-health-2 | ['multi-tissue-nucleus-segmentation', 'nuclear-segmentation', 'nuclei-classification'] | ['medical', 'medical', 'medical'] | [ 3.49066645e-01 1.02208316e-01 -2.10253984e-01 -3.43794852e-01
-7.78407574e-01 -4.65277791e-01 1.14116341e-01 7.62464404e-01
-8.80880833e-01 7.37381160e-01 -2.16093838e-01 -3.05568557e-02
-1.55905321e-01 -7.84423292e-01 -3.02589446e-01 -1.31945276e+00
-1.85690001e-02 7.75845289e-01 3.30947012e-01 1.85155541... | [14.892904281616211, -3.1278457641601562] |
6bdb1bd8-edba-45f6-9851-383a2c05d868 | definition-modeling-to-model-definitions | 2306.08433 | null | https://arxiv.org/abs/2306.08433v1 | https://arxiv.org/pdf/2306.08433v1.pdf | "Definition Modeling: To model definitions." Generating Definitions With Little to No Semantics | Definition Modeling, the task of generating definitions, was first proposed as a means to evaluate the semantic quality of word embeddings-a coherent lexical semantic representations of a word in context should contain all the information necessary to generate its definition. The relative novelty of this task entails t... | ['Timothee Mickus', 'Vincent Segonne'] | 2023-06-14 | null | null | null | null | ['word-embeddings'] | ['methodology'] | [ 2.21869245e-01 2.61105746e-01 -8.90942588e-02 -5.17122030e-01
-2.79471010e-01 -7.08658993e-01 1.10467267e+00 6.85374439e-01
-8.35072279e-01 6.40229285e-01 6.49302244e-01 -6.93829417e-01
-3.06642890e-01 -8.94141912e-01 -2.47391716e-01 -4.33237165e-01
3.23813736e-01 3.14897329e-01 2.31307238e-01 -4.49513227... | [10.327476501464844, 8.957219123840332] |
6169d4f3-83c1-499b-8fd8-8cb68ce031d8 | m-cner-a-corpus-for-chinese-named-entity | null | null | https://aclanthology.org/L18-1706 | https://aclanthology.org/L18-1706.pdf | M-CNER: A Corpus for Chinese Named Entity Recognition in Multi-Domains | null | ['Qi Lu', 'Zhenghua Li', 'Wenliang Chen', 'Min Zhang', 'YaoSheng Yang'] | 2018-05-01 | m-cner-a-corpus-for-chinese-named-entity-1 | https://aclanthology.org/L18-1706 | https://aclanthology.org/L18-1706.pdf | lrec-2018-5 | ['chinese-named-entity-recognition'] | ['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... | [-7.2136759757995605, 3.7917046546936035] |
59858a93-2693-416c-831c-dd919a244027 | contextual-dependencies-in-time-continuous | null | null | https://aclanthology.org/L18-1196 | https://aclanthology.org/L18-1196.pdf | Contextual Dependencies in Time-Continuous Multidimensional Affect Recognition | null | ['Maxim Sidorov', 'Wolfgang Minker', 'Dmitrii Fedotov', 'Denis Ivanko'] | 2018-05-01 | contextual-dependencies-in-time-continuous-1 | https://aclanthology.org/L18-1196 | https://aclanthology.org/L18-1196.pdf | lrec-2018-5 | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [-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... | [-7.2173261642456055, 3.5678350925445557] |
fa7d3208-04dc-462e-9918-9b1e737cbe58 | quantum-machine-learning-for-material | 2208.08273 | null | https://arxiv.org/abs/2208.08273v1 | https://arxiv.org/pdf/2208.08273v1.pdf | Quantum Machine Learning for Material Synthesis and Hardware Security | Using quantum computing, this paper addresses two scientifically pressing and day-to-day relevant problems, namely, chemical retrosynthesis which is an important step in drug/material discovery and security of the semiconductor supply chain. We show that Quantum Long Short-Term Memory (QLSTM) is a viable tool for retro... | ['Swaroop Ghosh', 'Rasit Onur Topaloglu', 'Satwik Kundu', 'Collin Beaudoin'] | 2022-08-16 | null | null | null | null | ['retrosynthesis'] | ['medical'] | [ 6.45711839e-01 -2.71683007e-01 -5.56416273e-01 4.10021514e-01
-1.05667841e+00 -6.05267942e-01 3.93207788e-01 4.38547939e-01
-5.99495471e-01 8.19410920e-01 -7.43141532e-01 -1.15280354e+00
3.53628516e-01 -8.78666937e-01 -8.91293406e-01 -9.36559558e-01
1.37833536e-01 -1.83332101e-01 5.18907234e-02 -5.19653440... | [5.528236389160156, 5.107929229736328] |
4b4b3f9d-5051-4401-905c-ecb190eb0962 | a-multiscale-graph-convolutional-network | 2006.12542 | null | https://arxiv.org/abs/2006.12542v1 | https://arxiv.org/pdf/2006.12542v1.pdf | A Multiscale Graph Convolutional Network Using Hierarchical Clustering | The information contained in hierarchical topology, intrinsic to many networks, is currently underutilised. A novel architecture is explored which exploits this information through a multiscale decomposition. A dendrogram is produced by a Girvan-Newman hierarchical clustering algorithm. It is segmented and fed through ... | ['Pietro Liò', 'Alex Lipov'] | 2020-06-22 | null | null | null | null | ['protein-interface-prediction'] | ['miscellaneous'] | [ 1.29920870e-01 5.05016148e-01 -1.26366958e-01 -1.07403837e-01
1.08962491e-01 -5.94526947e-01 6.22784793e-01 3.39158773e-01
4.80856113e-02 6.80941284e-01 1.72583759e-01 -3.63184601e-01
-5.73739052e-01 -9.43715930e-01 -2.17590287e-01 -8.10949862e-01
-8.69744301e-01 7.81788707e-01 2.56411910e-01 -1.90788180... | [6.900984764099121, 5.797737121582031] |
1e755a76-5003-4864-97c2-dce49d2045b4 | programs-as-black-box-explanations | 1611.07579 | null | http://arxiv.org/abs/1611.07579v1 | http://arxiv.org/pdf/1611.07579v1.pdf | Programs as Black-Box Explanations | Recent work in model-agnostic explanations of black-box machine learning has
demonstrated that interpretability of complex models does not have to come at
the cost of accuracy or model flexibility. However, it is not clear what kind
of explanations, such as linear models, decision trees, and rule lists, are the
appropr... | ['Sameer Singh', 'Carlos Guestrin', 'Marco Tulio Ribeiro'] | 2016-11-22 | null | null | null | null | ['program-induction'] | ['computer-code'] | [ 3.22879761e-01 9.02184725e-01 -5.68962276e-01 -8.13013494e-01
-2.95834839e-01 -4.97063726e-01 5.90093732e-01 2.65069216e-01
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-6.34478033e-01 -5.73987901e-01 -8.55189562e-01 -4.22139853e-01
2.86114067e-02 8.11156571e-01 -3.14342119e-02 -5.77261858... | [8.782017707824707, 5.808671951293945] |
32d58f15-2e2b-4979-a6e3-0a0a4355aa7c | unsupervised-multi-document-opinion | 1911.02247 | null | https://arxiv.org/abs/1911.02247v2 | https://arxiv.org/pdf/1911.02247v2.pdf | Unsupervised Opinion Summarization as Copycat-Review Generation | Opinion summarization is the task of automatically creating summaries that reflect subjective information expressed in multiple documents, such as product reviews. While the majority of previous work has focused on the extractive setting, i.e., selecting fragments from input reviews to produce a summary, we let the mod... | ['Arthur Bražinskas', 'Ivan Titov', 'Mirella Lapata'] | 2019-11-06 | unsupervised-opinion-summarization-as-copycat | https://aclanthology.org/2020.acl-main.461 | https://aclanthology.org/2020.acl-main.461.pdf | acl-2020-6 | ['review-generation', 'unsupervised-opinion-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.65536356e-01 4.72286403e-01 -2.80393660e-01 -3.99247527e-01
-7.95380235e-01 -7.99633265e-01 6.95355356e-01 3.63748819e-01
-4.47865017e-03 6.79693997e-01 6.24517679e-01 -1.70626909e-01
4.40546215e-01 -8.28594685e-01 -7.35266685e-01 -6.69014812e-01
5.56924164e-01 5.70000172e-01 -3.12788844e-01 -1.06207877... | [12.404030799865723, 9.283031463623047] |
658e8732-c23f-46b3-8d9c-b111c9286c2c | extreme-classification-for-answer-type | 2304.12395 | null | https://arxiv.org/abs/2304.12395v2 | https://arxiv.org/pdf/2304.12395v2.pdf | Extreme Classification for Answer Type Prediction in Question Answering | Semantic answer type prediction (SMART) is known to be a useful step towards effective question answering (QA) systems. The SMART task involves predicting the top-$k$ knowledge graph (KG) types for a given natural language question. This is challenging due to the large number of types in KGs. In this paper, we propose ... | ['Vinay Setty'] | 2023-04-24 | null | null | null | null | ['type-prediction', 'extreme-multi-label-classification', 'type'] | ['computer-code', 'methodology', 'speech'] | [-5.40922768e-03 3.08884442e-01 -1.94632590e-01 -4.70973462e-01
-1.05461931e+00 -7.07640171e-01 2.83867508e-01 5.04014909e-01
-1.60072207e-01 3.13727438e-01 2.59680748e-01 -5.89516103e-01
-4.17440295e-01 -9.84015822e-01 -7.07053900e-01 -1.40594989e-01
3.76145810e-01 8.20026398e-01 6.95551753e-01 -4.67879564... | [10.507360458374023, 7.906509876251221] |
88290d66-1881-47c1-a62d-16caecc208a2 | diffsurv-differentiable-sorting-for-censored | 2304.13594 | null | https://arxiv.org/abs/2304.13594v1 | https://arxiv.org/pdf/2304.13594v1.pdf | Diffsurv: Differentiable sorting for censored time-to-event data | Survival analysis is a crucial semi-supervised task in machine learning with numerous real-world applications, particularly in healthcare. Currently, the most common approach to survival analysis is based on Cox's partial likelihood, which can be interpreted as a ranking model optimized on a lower bound of the concorda... | ['Spiros Denaxas', 'Roland Eils', 'Aylin Cakiroglu', 'Benjamin Wild', 'Andre Vauvelle'] | 2023-04-26 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [ 3.53903264e-01 7.04695135e-02 -6.87224150e-01 -9.58749831e-01
-1.14792764e+00 -3.45611393e-01 4.15352285e-01 5.70699871e-01
-4.50205833e-01 1.13183701e+00 3.91173869e-01 -4.65147465e-01
-7.24479139e-01 -6.47601008e-01 -5.56164980e-01 -6.59622729e-01
-4.79854614e-01 7.12710917e-01 -5.26461937e-02 4.92380746... | [7.773879051208496, 5.463418483734131] |
8b581b38-1a58-466f-a438-73b18fc8157f | a-close-look-at-deep-learning-with-small-data | 2003.12843 | null | https://arxiv.org/abs/2003.12843v3 | https://arxiv.org/pdf/2003.12843v3.pdf | A Close Look at Deep Learning with Small Data | In this work, we perform a wide variety of experiments with different deep learning architectures on datasets of limited size. According to our study, we show that model complexity is a critical factor when only a few samples per class are available. Differently from the literature, we show that in some configurations,... | ['L. Iocchi', 'L. Brigato'] | 2020-03-28 | null | null | null | null | ['small-data'] | ['computer-vision'] | [ 1.32106438e-01 -8.17630067e-02 -4.75620598e-01 -4.23916042e-01
-6.44881129e-01 -2.98609078e-01 6.00092232e-01 1.98979110e-01
-9.49267387e-01 9.46542799e-01 -6.45185113e-02 -1.94315180e-01
-5.68253733e-02 -5.79847038e-01 -1.05119145e+00 -5.93136907e-01
-5.51817343e-02 4.76837248e-01 -2.50296351e-02 -2.77611911... | [8.978752136230469, 3.2660391330718994] |
0594c29e-c25a-4459-a047-96051d1a4ab3 | gaitgs-temporal-feature-learning-in | 2305.19700 | null | https://arxiv.org/abs/2305.19700v2 | https://arxiv.org/pdf/2305.19700v2.pdf | GaitGS: Temporal Feature Learning in Granularity and Span Dimension for Gait Recognition | Gait recognition is an emerging biological recognition technology that identifies and verifies individuals based on their walking patterns. However, many current methods are limited in their use of temporal information. In order to fully harness the potential of gait recognition, it is crucial to consider temporal feat... | ['Bin Feng', 'Wenyu Liu', 'Xinggang Wang', 'Xiaohu Huang', 'Yunze Deng', 'Haijun Xiong'] | 2023-05-31 | null | null | null | null | ['gait-recognition'] | ['computer-vision'] | [ 4.88202535e-02 -7.47191489e-01 -2.79712915e-01 -1.44768670e-01
-6.26036108e-01 -1.85589522e-01 4.65261310e-01 2.82718763e-02
-1.77656591e-01 9.65381920e-01 2.16289982e-01 3.96359652e-01
-2.02831700e-01 -7.87318707e-01 -7.60862157e-02 -8.01786005e-01
-5.56832016e-01 -1.84317634e-01 4.58266884e-01 -6.91047609... | [14.272857666015625, 1.4439592361450195] |
82034608-0c07-48d1-85db-e100655b8056 | correcting-underrepresentation-and | 2306.11112 | null | https://arxiv.org/abs/2306.11112v1 | https://arxiv.org/pdf/2306.11112v1.pdf | Correcting Underrepresentation and Intersectional Bias for Fair Classification | We consider the problem of learning from data corrupted by underrepresentation bias, where positive examples are filtered from the data at different, unknown rates for a fixed number of sensitive groups. We show that with a small amount of unbiased data, we can efficiently estimate the group-wise drop-out parameters, e... | ['Emily Diana', 'Alexander Williams Tolbert'] | 2023-06-19 | null | null | null | null | ['classification-1'] | ['methodology'] | [ 2.10157841e-01 6.29527092e-01 -5.73394060e-01 -5.12758732e-01
-1.51980329e+00 -6.45332575e-01 3.29248130e-01 5.04874349e-01
-8.54202509e-01 1.08927739e+00 6.91357702e-02 -2.38133103e-01
-2.68322885e-01 -9.43453491e-01 -1.33472395e+00 -8.78434598e-01
-4.40016627e-01 8.09485495e-01 9.35419127e-02 5.77743709... | [8.315094947814941, 4.8316874504089355] |
809b35ed-6bfd-43b7-9865-5fec75b0535c | improved-decipherment-of-homophonic-ciphers | null | null | https://aclanthology.org/D14-1184 | https://aclanthology.org/D14-1184.pdf | Improved Decipherment of Homophonic Ciphers | null | ['Hermann Ney', 'Julian Schamper', 'Malte Nuhn'] | 2014-10-01 | null | null | null | emnlp-2014-10 | ['decipherment'] | ['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... | [-7.42834997177124, 3.658884048461914] |
eec70365-d2a9-4763-87b3-561d1eec40c8 | em-driven-unsupervised-learning-for-efficient | 2201.02074 | null | https://arxiv.org/abs/2201.02074v3 | https://arxiv.org/pdf/2201.02074v3.pdf | EM-driven unsupervised learning for efficient motion segmentation | In this paper, we present a CNN-based fully unsupervised method for motion segmentation from optical flow. We assume that the input optical flow can be represented as a piecewise set of parametric motion models, typically, affine or quadratic motion models. The core idea of our work is to leverage the Expectation-Maxim... | ['Patrick Bouthemy', 'Anaïs Badoual', 'Etienne Meunier'] | 2022-01-06 | null | null | null | null | ['unsupervised-object-segmentation', 'motion-segmentation'] | ['computer-vision', 'computer-vision'] | [-1.13876648e-02 1.87139641e-02 -1.70561820e-01 -1.40697315e-01
-1.73828974e-01 -6.41653836e-01 4.89602447e-01 -1.93270326e-01
-7.81501293e-01 6.65163994e-01 -1.27128139e-01 -3.31569493e-01
1.14331640e-01 -6.55004501e-01 -9.16379809e-01 -6.23776555e-01
2.14541078e-01 5.47420919e-01 4.65828419e-01 9.30471644... | [9.084859848022461, -0.4034520387649536] |
779e0422-22b4-44b3-930e-83b89e221ca9 | ordinal-time-series-analysis-with-the-r | 2304.12251 | null | https://arxiv.org/abs/2304.12251v1 | https://arxiv.org/pdf/2304.12251v1.pdf | Ordinal time series analysis with the R package otsfeatures | The 21st century has witnessed a growing interest in the analysis of time series data. Whereas most of the literature on the topic deals with real-valued time series, ordinal time series have typically received much less attention. However, the development of specific analytical tools for the latter objects has substan... | ['José Antonio Vilar Fernández', 'Ángel López Oriona'] | 2023-04-24 | null | null | null | null | ['outlier-detection'] | ['methodology'] | [-2.05223784e-01 -3.92484307e-01 4.79034707e-02 -5.89934409e-01
-4.53683227e-01 -6.80860996e-01 6.91759527e-01 7.78011620e-01
-3.22616756e-01 5.08991957e-01 -2.53503054e-01 -4.97530162e-01
-5.73946595e-01 -6.65846109e-01 -6.30326718e-02 -7.22829640e-01
-7.62163281e-01 4.25891906e-01 5.58863319e-02 -1.27370775... | [7.2028350830078125, 3.3841192722320557] |
db9b0f86-5eef-4820-a735-66d7fb54fc5e | multiview-detection-with-feature-perspective | 2007.07247 | null | https://arxiv.org/abs/2007.07247v2 | https://arxiv.org/pdf/2007.07247v2.pdf | Multiview Detection with Feature Perspective Transformation | Incorporating multiple camera views for detection alleviates the impact of occlusions in crowded scenes. In a multiview system, we need to answer two important questions when dealing with ambiguities that arise from occlusions. First, how should we aggregate cues from the multiple views? Second, how should we aggregate... | ['Yunzhong Hou', 'Stephen Gould', 'Liang Zheng'] | 2020-07-14 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/116_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123520001.pdf | eccv-2020-8 | ['multiview-detection'] | ['computer-vision'] | [-1.04559429e-01 -1.77181408e-01 2.83766508e-01 -4.41433400e-01
-1.07830441e+00 -6.86239779e-01 3.26897353e-01 4.06041406e-02
-4.30406362e-01 3.51502925e-01 1.69675469e-01 8.79959986e-02
3.48742962e-01 -5.00443101e-01 -9.05320883e-01 -3.51649612e-01
1.03420012e-01 1.51239857e-01 7.17703819e-01 3.33106215... | [7.901167392730713, -2.054199695587158] |
a1bcb897-7965-4bb5-8311-5bfa7bc29563 | self-training-improves-pre-training-for-few | 2108.12589 | null | https://arxiv.org/abs/2108.12589v1 | https://arxiv.org/pdf/2108.12589v1.pdf | Self-training Improves Pre-training for Few-shot Learning in Task-oriented Dialog Systems | As the labeling cost for different modules in task-oriented dialog (ToD) systems is expensive, a major challenge is to train different modules with the least amount of labeled data. Recently, large-scale pre-trained language models, have shown promising results for few-shot learning in ToD. In this paper, we devise a s... | ['Boi Faltings', 'Minlie Huang', 'Lingjing Kong', 'Fengyu Cai', 'Wanhao Zhou', 'Fei Mi'] | 2021-08-28 | null | https://aclanthology.org/2021.emnlp-main.142 | https://aclanthology.org/2021.emnlp-main.142.pdf | emnlp-2021-11 | ['text-augmentation'] | ['natural-language-processing'] | [ 5.56370616e-02 4.44361448e-01 -4.29101974e-01 -9.05749798e-01
-7.33693063e-01 -3.69173348e-01 6.20083988e-01 1.01990834e-01
-5.83488584e-01 8.22130322e-01 4.19051111e-01 -4.75867093e-01
5.24419725e-01 -3.67974699e-01 6.69915900e-02 -3.23952347e-01
4.35851574e-01 8.40913355e-01 5.74006617e-01 -6.44183576... | [12.803311347961426, 7.876428127288818] |
dca67423-84f0-4e4e-9183-9b6151ca8703 | melnet-a-generative-model-for-audio-in-the | 1906.01083 | null | https://arxiv.org/abs/1906.01083v1 | https://arxiv.org/pdf/1906.01083v1.pdf | MelNet: A Generative Model for Audio in the Frequency Domain | Capturing high-level structure in audio waveforms is challenging because a single second of audio spans tens of thousands of timesteps. While long-range dependencies are difficult to model directly in the time domain, we show that they can be more tractably modelled in two-dimensional time-frequency representations suc... | ['Mike Lewis', 'Sean Vasquez'] | 2019-06-04 | null | https://openreview.net/forum?id=r1gIa0NtDH | https://openreview.net/pdf?id=r1gIa0NtDH | null | ['audio-generation'] | ['audio'] | [ 1.65836170e-01 3.65402699e-02 9.52738523e-02 -3.37756544e-01
-1.54596698e+00 -7.52550125e-01 7.84450531e-01 2.71117333e-02
2.12852120e-01 8.97023857e-01 8.59494328e-01 -1.23763554e-01
-1.89710394e-01 -5.98378062e-01 -6.42257273e-01 -3.70388150e-01
-4.34631437e-01 4.20487702e-01 1.05368398e-01 -4.19319496... | [15.625884056091309, 5.8140645027160645] |
f129bfdd-a366-41ef-b002-94be9327ea9f | imlovenet-misaligned-image-supported | 2207.00826 | null | https://arxiv.org/abs/2207.00826v1 | https://arxiv.org/pdf/2207.00826v1.pdf | ImLoveNet: Misaligned Image-supported Registration Network for Low-overlap Point Cloud Pairs | Low-overlap regions between paired point clouds make the captured features very low-confidence, leading cutting edge models to point cloud registration with poor quality. Beyond the traditional wisdom, we raise an intriguing question: Is it possible to exploit an intermediate yet misaligned image between two low-overla... | ['Jun Wang', 'Mingqiang Wei', 'Yabin Xu', 'Zeyong Wei', 'Honghua Chen'] | 2022-07-02 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [-1.07188247e-01 1.02182291e-01 -2.19330147e-01 -3.46344739e-01
-8.38508666e-01 -4.93578345e-01 6.74794734e-01 -6.14894368e-02
-1.49246931e-01 1.03649355e-01 -1.68133348e-01 7.45092854e-02
-1.82310551e-01 -5.41741252e-01 -9.69457865e-01 -4.91845965e-01
2.39116445e-01 6.60157621e-01 2.71510065e-01 -1.94716841... | [7.694187641143799, -3.0463266372680664] |
b8cc8b6c-c5ea-4911-a3f0-a65c0c95ab23 | neuro-symbolic-program-synthesis | 1611.01855 | null | http://arxiv.org/abs/1611.01855v1 | http://arxiv.org/pdf/1611.01855v1.pdf | Neuro-Symbolic Program Synthesis | Recent years have seen the proposal of a number of neural architectures for
the problem of Program Induction. Given a set of input-output examples, these
architectures are able to learn mappings that generalize to new test inputs.
While achieving impressive results, these approaches have a number of important
limitatio... | ['Dengyong Zhou', 'Abdel-rahman Mohamed', 'Emilio Parisotto', 'Rishabh Singh', 'Pushmeet Kohli', 'Lihong Li'] | 2016-11-06 | null | null | null | null | ['program-induction'] | ['computer-code'] | [ 6.62014127e-01 2.44917586e-01 -3.36819291e-01 -4.32501525e-01
-2.41564184e-01 -5.53383827e-01 4.41637248e-01 1.63575530e-01
-8.23706165e-02 6.97157323e-01 -4.86404181e-01 -9.22449648e-01
-7.70157203e-02 -1.30912733e+00 -1.23171926e+00 -1.81355327e-01
-1.25950247e-01 4.39946294e-01 4.21450675e-01 -2.71513849... | [8.232442855834961, 7.387650012969971] |
82967c0d-ec36-4626-9ad2-2c050fb9636d | multi-stream-extension-of-variational | 2305.13580 | null | https://arxiv.org/abs/2305.13580v1 | https://arxiv.org/pdf/2305.13580v1.pdf | Multi-Stream Extension of Variational Bayesian HMM Clustering (MS-VBx) for Combined End-to-End and Vector Clustering-based Diarization | Combining end-to-end neural speaker diarization (EEND) with vector clustering (VC), known as EEND-VC, has gained interest for leveraging the strengths of both methods. EEND-VC estimates activities and speaker embeddings for all speakers within an audio chunk and uses VC to associate these activities with speaker identi... | ['Shoko Araki', 'Lukas Burget', 'Tomohiro Nakatani', 'Atsunori Ogawa', 'Anna Silnova', 'Federico Landini', 'Mireia Diez', 'Naohiro Tawara', 'Marc Delcroix'] | 2023-05-23 | null | null | null | null | ['speaker-diarization'] | ['speech'] | [-1.41921476e-01 7.36838160e-03 4.65714969e-02 -5.70921659e-01
-1.00784576e+00 -4.65155900e-01 7.14008331e-01 2.36033261e-01
-3.56829733e-01 -4.29657251e-02 6.26204491e-01 2.48813815e-02
-1.17160104e-01 -3.13969165e-01 -3.20672601e-01 -6.94689512e-01
-4.16276842e-01 8.44767511e-01 2.23341689e-01 5.39515078... | [14.503596305847168, 6.190752983093262] |
adc58927-b8e5-41d7-89b4-8d34f3159288 | a-normalized-bottleneck-distance-on | 2306.06727 | null | https://arxiv.org/abs/2306.06727v1 | https://arxiv.org/pdf/2306.06727v1.pdf | A Normalized Bottleneck Distance on Persistence Diagrams and Homology Preservation under Dimension Reduction | Persistence diagrams are used as signatures of point cloud data assumed to be sampled from manifolds, and represent their topology in a compact fashion. Further, two given clouds of points can be compared by directly comparing their persistence diagrams using the bottleneck distance, d_B. But one potential drawback of ... | ['Nathan H. May', 'Bala Krishnamoorthy'] | 2023-06-11 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [-1.53238997e-01 -1.84864048e-02 1.09713301e-02 1.07571512e-01
-2.32965529e-01 -1.08647406e+00 6.69636607e-01 1.96238562e-01
-1.36405617e-01 4.00707006e-01 1.13088012e-01 -2.35106170e-01
-4.87068206e-01 -1.00950980e+00 -8.94377530e-01 -7.65931308e-01
-4.99858409e-01 4.71370518e-01 2.66956836e-01 -3.10884535... | [7.410229206085205, 4.303731441497803] |
69a0441d-dbc0-4f78-8229-da2ae9d5452a | action-matching-a-variational-method-for | 2210.06662 | null | https://arxiv.org/abs/2210.06662v3 | https://arxiv.org/pdf/2210.06662v3.pdf | Action Matching: Learning Stochastic Dynamics from Samples | Learning the continuous dynamics of a system from snapshots of its temporal marginals is a problem which appears throughout natural sciences and machine learning, including in quantum systems, single-cell biological data, and generative modeling. In these settings, we assume access to cross-sectional samples that are u... | ['Daniel Severo', 'Rob Brekelmans', 'Alireza Makhzani', 'Kirill Neklyudov'] | 2022-10-13 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 2.33474940e-01 -2.67995745e-02 1.30641431e-01 8.04588646e-02
-4.53865319e-01 -7.11283922e-01 8.98202956e-01 9.01700333e-02
-3.56826991e-01 1.07692361e+00 -1.19556129e-01 -2.66888171e-01
-1.95988685e-01 -8.15649390e-01 -9.54783201e-01 -1.20738316e+00
-1.57976776e-01 7.89464772e-01 5.16235270e-02 -7.54635558... | [6.668600082397461, 3.9641172885894775] |
b8186b87-f9d7-4971-961c-98bf128c665a | ensemble-of-jointly-trained-deep-neural | 1608.04983 | null | http://arxiv.org/abs/1608.04983v1 | http://arxiv.org/pdf/1608.04983v1.pdf | Ensemble of Jointly Trained Deep Neural Network-Based Acoustic Models for Reverberant Speech Recognition | Distant speech recognition is a challenge, particularly due to the corruption
of speech signals by reverberation caused by large distances between the
speaker and microphone. In order to cope with a wide range of reverberations in
real-world situations, we present novel approaches for acoustic modeling
including an ens... | ['Joon-Hyuk Chang', 'Jeehye Lee', 'Myungin Lee'] | 2016-08-17 | null | null | null | null | ['distant-speech-recognition'] | ['speech'] | [ 1.33966342e-01 -4.24979150e-01 5.94437063e-01 -4.10970718e-01
-1.20587206e+00 -4.78543639e-01 4.22265410e-01 -2.90540367e-01
-4.74680662e-01 5.65979064e-01 5.18711507e-01 -3.82445157e-01
1.06309410e-02 -3.83013815e-01 -6.51747704e-01 -1.12334573e+00
1.80570036e-01 3.52376163e-01 4.95225638e-02 1.49843454... | [15.01656436920166, 5.894041061401367] |
f85b0cb6-4cce-4d5b-adce-bdc917805573 | joint-3d-instance-segmentation-and-object | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_Joint_3D_Instance_Segmentation_and_Object_Detection_for_Autonomous_Driving_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhou_Joint_3D_Instance_Segmentation_and_Object_Detection_for_Autonomous_Driving_CVPR_2020_paper.pdf | Joint 3D Instance Segmentation and Object Detection for Autonomous Driving | Currently, in Autonomous Driving (AD), most of the 3D object detection frameworks (either anchor- or anchor-free-based) consider the detection as a Bounding Box (BBox) regression problem. However, this compact representation is not sufficient to explore all the information of the objects. To tackle this problem, we pro... | [' Ruigang Yang', ' Hongdong Li', ' Yuchao Dai', ' Junbo Yin', ' Liu Liu', ' Xibin Song', ' Jin Fang', 'Dingfu Zhou'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['3d-instance-segmentation-1'] | ['computer-vision'] | [-6.43049553e-02 2.16774479e-01 -2.22970769e-01 -4.18397516e-01
-4.83051509e-01 -1.86222985e-01 5.52738547e-01 7.99049139e-02
-4.91508693e-01 2.68884271e-01 -4.03905958e-01 -2.88683504e-01
6.54555932e-02 -6.92704320e-01 -7.11948931e-01 -8.89861107e-01
2.73439586e-01 3.68773937e-01 1.08395183e+00 3.05117927... | [7.910488128662109, -2.4557979106903076] |
b0db6f1e-5a62-46bf-a0ca-bacb685d522e | when-combinatorial-thompson-sampling-meets | 2302.11182 | null | https://arxiv.org/abs/2302.11182v1 | https://arxiv.org/pdf/2302.11182v1.pdf | When Combinatorial Thompson Sampling meets Approximation Regret | We study the Combinatorial Thompson Sampling policy (CTS) for combinatorial multi-armed bandit problems (CMAB), within an approximation regret setting. Although CTS has attracted a lot of interest, it has a drawback that other usual CMAB policies do not have when considering non-exact oracles: for some oracles, CTS has... | ['Pierre Perrault'] | 2023-02-22 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 2.44943663e-01 5.06731927e-01 -6.43749416e-01 -1.72210932e-01
-1.25430918e+00 -1.08373797e+00 -5.44817783e-02 1.82513446e-01
-4.74418461e-01 1.02542853e+00 -1.87428206e-01 -7.91569173e-01
-8.92428577e-01 -8.51540685e-01 -1.18721640e+00 -1.04780400e+00
-9.51760933e-02 6.73128307e-01 -1.66767556e-02 -2.75209621... | [4.563113689422607, 3.351210355758667] |
d74f3fb4-2ec6-48ce-a9b3-f08670ec50fc | quantitative-stock-investment-by-routing | 2207.07578 | null | https://arxiv.org/abs/2207.07578v1 | https://arxiv.org/pdf/2207.07578v1.pdf | Quantitative Stock Investment by Routing Uncertainty-Aware Trading Experts: A Multi-Task Learning Approach | Quantitative investment is a fundamental financial task that highly relies on accurate stock prediction and profitable investment decision making. Despite recent advances in deep learning (DL) have shown stellar performance on capturing trading opportunities in the stochastic stock market, we observe that the performan... | ['Bo An', 'Rundong Wang', 'Shuo Sun'] | 2022-06-07 | null | null | null | null | ['stock-prediction'] | ['time-series'] | [-4.65822965e-01 -1.11052714e-01 -5.01642644e-01 -2.78672725e-01
-4.98217195e-01 -6.39784455e-01 5.09176373e-01 -4.27388906e-01
-3.30104649e-01 6.81401253e-01 -8.79060179e-02 -6.57604694e-01
-4.38462555e-01 -9.70368326e-01 -6.87871099e-01 -4.02666569e-01
-1.35081619e-01 1.11101699e+00 1.45607114e-01 -1.93651915... | [4.523787975311279, 4.09350061416626] |
682bfd2f-3cc4-4af9-9952-4475187122d5 | constructing-natural-language-explanations | 2210.07222 | null | https://arxiv.org/abs/2210.07222v3 | https://arxiv.org/pdf/2210.07222v3.pdf | Saliency Map Verbalization: Comparing Feature Importance Representations from Model-free and Instruction-based Methods | Saliency maps can explain a neural model's predictions by identifying important input features. They are difficult to interpret for laypeople, especially for instances with many features. In order to make them more accessible, we formalize the underexplored task of translating saliency maps into natural language and co... | ['Sebastian Möller', 'Robert Schwarzenberg', 'Christopher Ebert', 'Maximilian Dustin Nasert', 'Leonhard Hennig', 'Nils Feldhus'] | 2022-10-13 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 4.30158645e-01 8.88521910e-01 -2.22083077e-01 -5.23467600e-01
-4.18182731e-01 -4.57485884e-01 7.84669638e-01 7.45945334e-01
-2.99332470e-01 7.06957459e-01 7.29987085e-01 -5.38574636e-01
-3.33773941e-01 -4.12611842e-01 -5.58936775e-01 -2.08171755e-02
6.13879800e-01 5.14009058e-01 -1.02764390e-01 -3.82498533... | [9.576805114746094, 6.74660587310791] |
2b25e920-b784-43ba-b9ac-e5882b66beae | order-planning-neural-text-generation-from | 1709.00155 | null | http://arxiv.org/abs/1709.00155v1 | http://arxiv.org/pdf/1709.00155v1.pdf | Order-Planning Neural Text Generation From Structured Data | Generating texts from structured data (e.g., a table) is important for
various natural language processing tasks such as question answering and dialog
systems. In recent studies, researchers use neural language models and
encoder-decoder frameworks for table-to-text generation. However, these neural
network-based appro... | ['Zhifang Sui', 'Sujian Li', 'Lili Mou', 'Pascal Poupart', 'Lei Sha', 'Baobao Chang', 'Tianyu Liu'] | 2017-09-01 | null | null | null | null | ['table-to-text-generation'] | ['natural-language-processing'] | [ 5.20956874e-01 4.07572865e-01 -1.84952766e-01 -5.93917191e-01
-5.70093453e-01 -4.39032823e-01 9.97725606e-01 5.70543528e-01
-3.38668495e-01 1.04417932e+00 9.22066927e-01 -4.18467999e-01
2.15965837e-01 -1.19561327e+00 -7.18589067e-01 1.57639548e-01
7.45791078e-01 5.85448146e-01 -2.20424667e-01 -4.91207212... | [11.815862655639648, 8.88070011138916] |
d621dc8e-c9ab-4add-b52b-3e61784ea0b7 | short-and-long-range-relation-based-spatio | 2112.05851 | null | https://arxiv.org/abs/2112.05851v4 | https://arxiv.org/pdf/2112.05851v4.pdf | Short and Long Range Relation Based Spatio-Temporal Transformer for Micro-Expression Recognition | Being spontaneous, micro-expressions are useful in the inference of a person's true emotions even if an attempt is made to conceal them. Due to their short duration and low intensity, the recognition of micro-expressions is a difficult task in affective computing. The early work based on handcrafted spatio-temporal fea... | ['Guoying Zhao', 'Ognjen Arandjelovic', 'Xiaopeng Hong', 'Liangfei Zhang'] | 2021-12-10 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [ 8.74582753e-02 -2.16169104e-01 -2.00913008e-02 -4.88529563e-01
-6.24616146e-01 -4.60081100e-01 8.87575686e-01 1.37799203e-01
-4.09883082e-01 8.54030132e-01 7.25256186e-03 1.16697058e-01
-3.20016116e-01 -5.13543069e-01 -3.71676385e-01 -1.01965630e+00
-4.01139677e-01 2.05619261e-01 -2.12789804e-01 -4.35306817... | [13.632964134216309, 1.9518814086914062] |
a205e753-44f2-4655-8a23-410fe6cde940 | npvforensics-jointing-non-critical-phonemes | 2306.06885 | null | https://arxiv.org/abs/2306.06885v1 | https://arxiv.org/pdf/2306.06885v1.pdf | NPVForensics: Jointing Non-critical Phonemes and Visemes for Deepfake Detection | Deepfake technologies empowered by deep learning are rapidly evolving, creating new security concerns for society. Existing multimodal detection methods usually capture audio-visual inconsistencies to expose Deepfake videos. More seriously, the advanced Deepfake technology realizes the audio-visual calibration of the c... | ['Haoliang Li', 'Yao Zhao', 'Rongrong Ni', 'Yang Yu', 'Yu Chen'] | 2023-06-12 | null | null | null | null | ['deepfake-detection', 'face-swapping'] | ['computer-vision', 'computer-vision'] | [-1.34933427e-01 -3.89777333e-01 -2.31866807e-01 -2.97508478e-01
-8.16982388e-01 -4.77022856e-01 5.52504420e-01 -5.30579567e-01
-7.63385184e-03 1.87244847e-01 4.11455214e-01 2.64235198e-01
-2.97648013e-01 -4.91137415e-01 -6.93417609e-01 -1.04674566e+00
5.50356470e-02 -1.76096529e-01 -1.59899563e-01 -3.13568056... | [13.105860710144043, 1.1765352487564087] |
209b594a-5c26-40ae-a433-f048feeb199f | sequential-item-recommendation-in-the-moba | 2201.08724 | null | https://arxiv.org/abs/2201.08724v1 | https://arxiv.org/pdf/2201.08724v1.pdf | Sequential Item Recommendation in the MOBA Game Dota 2 | Multiplayer Online Battle Arena (MOBA) games such as Dota 2 attract hundreds of thousands of players every year. Despite the large player base, it is still important to attract new players to prevent the community of a game from becoming inactive. Entering MOBA games is, however, often demanding, requiring the player t... | ['Andreas Hotho', 'Daniel Zoller', 'Johannes Kohlmann', 'Alexander Dallmann'] | 2022-01-17 | null | null | null | null | ['movie-recommendation', 'dota-2'] | ['miscellaneous', 'playing-games'] | [-5.61027586e-01 -4.38729167e-01 -2.14717016e-01 -6.60619140e-02
-3.93107951e-01 -7.22373068e-01 -6.51163310e-02 1.40344352e-01
-7.78490901e-01 3.61107558e-01 2.90876001e-01 -4.22554195e-01
-6.42662942e-01 -1.20455158e+00 -5.01509428e-01 -2.82361001e-01
-1.57506675e-01 8.28173578e-01 2.21278727e-01 -1.12408483... | [6.580568313598633, 0.40051648020744324] |
33ccb270-dbb6-4d44-9c6e-a6771aa45571 | exploiting-the-generative-adversarial-network | 2301.11871 | null | https://arxiv.org/abs/2301.11871v1 | https://arxiv.org/pdf/2301.11871v1.pdf | Exploiting the Generative Adversarial Network Approach to Create a Synthetic Topography Corneal Image | Corneal diseases are the most common eye disorders. Deep learning techniques are used to per-form automated diagnoses of cornea. Deep learning networks require large-scale annotated datasets, which is conceded as a weakness of deep learning. In this work, a method for synthesizing medical images using conditional gener... | ['P. S. JosephNg', 'Sinan Q. Salih', 'Tarik A. Rashid', 'Jafar Majidpour', 'Nebras H. Ghaeb', 'Sezgin Aydin', 'Samer Kais Jameel'] | 2022-12-25 | null | null | null | null | ['medical-diagnosis'] | ['medical'] | [ 7.50877336e-02 2.69225329e-01 1.92869529e-01 -2.45104179e-01
-3.32329750e-01 -1.89279452e-01 7.50751123e-02 -7.76707530e-02
-1.98660970e-01 8.85729730e-01 -2.87088770e-02 -2.25723192e-01
1.66965239e-02 -8.33270788e-01 -5.46004832e-01 -9.42090094e-01
2.33422413e-01 5.22785246e-01 -3.80419195e-01 1.38440579... | [15.60062313079834, -3.317481517791748] |
3309ba12-d4c6-4d31-9dd3-17700d67434d | learning-adaptive-discriminative-correlation | 1807.11348 | null | https://arxiv.org/abs/1807.11348v3 | https://arxiv.org/pdf/1807.11348v3.pdf | Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Tracking | With efficient appearance learning models, Discriminative Correlation Filter (DCF) has been proven to be very successful in recent video object tracking benchmarks and competitions. However, the existing DCF paradigm suffers from two major issues, i.e., spatial boundary effect and temporal filter degradation. To mitiga... | ['Josef Kittler', 'Xiao-Jun Wu', 'Zhen-Hua Feng', 'Tianyang Xu'] | 2018-07-30 | null | null | null | null | ['video-object-tracking'] | ['computer-vision'] | [-1.81606203e-01 -6.87737167e-01 -2.71243185e-01 5.99745922e-02
-4.04058784e-01 -3.47767800e-01 6.19959950e-01 -3.57723206e-01
-4.87766474e-01 7.17713594e-01 4.31782231e-02 3.76887560e-01
-4.39956725e-01 -9.04640704e-02 -6.72318101e-01 -8.94217670e-01
-2.34033659e-01 -1.74406752e-01 4.04281855e-01 1.88424170... | [6.331315994262695, -2.182359457015991] |
5d560886-424a-4384-829f-ef4312c2968f | revisiting-rubik-s-cube-self-supervised | 2007.08826 | null | https://arxiv.org/abs/2007.08826v1 | https://arxiv.org/pdf/2007.08826v1.pdf | Revisiting Rubik's Cube: Self-supervised Learning with Volume-wise Transformation for 3D Medical Image Segmentation | Deep learning highly relies on the quantity of annotated data. However, the annotations for 3D volumetric medical data require experienced physicians to spend hours or even days for investigation. Self-supervised learning is a potential solution to get rid of the strong requirement of training data by deeply exploiting... | ['Xing Tao', 'Yefeng Zheng', 'Wenhui Zhou', 'Yuexiang Li', 'Kai Ma'] | 2020-07-17 | null | null | null | null | ['rubik-s-cube', 'pancreas-segmentation'] | ['graphs', 'medical'] | [ 2.41280809e-01 4.60505664e-01 -8.64741728e-02 -7.13805437e-01
-5.24901688e-01 -1.70611128e-01 2.24250436e-01 3.42592895e-01
-5.39491832e-01 5.73504627e-01 1.75180286e-01 -3.69931579e-01
5.68033867e-02 -7.55092919e-01 -7.29046643e-01 -8.52704406e-01
-8.94748345e-02 3.70771825e-01 3.03960383e-01 4.84785773... | [14.741047859191895, -2.2317774295806885] |
5e69468d-20b0-480c-8c75-61c97d9f4331 | qpgesture-quantization-based-and-phase-guided-1 | 2305.11094 | null | https://arxiv.org/abs/2305.11094v1 | https://arxiv.org/pdf/2305.11094v1.pdf | QPGesture: Quantization-Based and Phase-Guided Motion Matching for Natural Speech-Driven Gesture Generation | Speech-driven gesture generation is highly challenging due to the random jitters of human motion. In addition, there is an inherent asynchronous relationship between human speech and gestures. To tackle these challenges, we introduce a novel quantization-based and phase-guided motion-matching framework. Specifically, w... | ['Haolin Zhuang', 'Weihong Bao', 'Lei Hao', 'Zhensong Zhang', 'Minglei Li', 'Zhiyong Wu', 'Sicheng Yang'] | 2023-05-18 | qpgesture-quantization-based-and-phase-guided | http://openaccess.thecvf.com//content/CVPR2023/html/Yang_QPGesture_Quantization-Based_and_Phase-Guided_Motion_Matching_for_Natural_Speech-Driven_Gesture_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yang_QPGesture_Quantization-Based_and_Phase-Guided_Motion_Matching_for_Natural_Speech-Driven_Gesture_CVPR_2023_paper.pdf | cvpr-2023-1 | ['gesture-generation'] | ['robots'] | [ 1.02983743e-01 -5.17758548e-01 -2.25069299e-01 -4.38644618e-01
-1.05061483e+00 -6.03806794e-01 6.62153125e-01 -3.37592274e-01
-3.13629657e-01 1.17518224e-01 8.50825489e-01 1.83508590e-01
-2.81256866e-02 -3.72475713e-01 -3.01085562e-01 -7.82630205e-01
-9.00267586e-02 3.30508322e-01 4.24079806e-01 -2.30873495... | [5.69223690032959, -0.1756144016981125] |
2c452979-512d-4a70-9d76-a2b3d616f511 | plenoptic-monte-carlo-object-localization-for | 1806.09769 | null | http://arxiv.org/abs/1806.09769v4 | http://arxiv.org/pdf/1806.09769v4.pdf | Plenoptic Monte Carlo Object Localization for Robot Grasping under Layered Translucency | In order to fully function in human environments, robot perception will need
to account for the uncertainty caused by translucent materials. Translucency
poses several open challenges in the form of transparent objects (e.g.,
drinking glasses), refractive media (e.g., water), and diffuse partial
occlusions (e.g., objec... | ['Zheming Zhou', 'Zhiqiang Sui', 'Odest Chadwicke Jenkins'] | 2018-06-26 | null | null | null | null | ['transparent-objects'] | ['computer-vision'] | [ 1.97980955e-01 -1.01455323e-01 4.66776162e-01 -1.07771590e-01
-4.52882379e-01 -6.81410611e-01 3.32553118e-01 -2.72068948e-01
-4.05698001e-01 7.99921691e-01 -1.16755612e-01 7.57620782e-02
9.67497155e-02 -6.39047623e-01 -1.07935858e+00 -6.36910737e-01
1.40952185e-01 1.13070953e+00 5.06925166e-01 2.88666129... | [5.98126220703125, -1.0792629718780518] |
17792b84-2ee5-457a-8dcb-12f14289e6d1 | incipient-fault-detection-in-power | 2302.09332 | null | https://arxiv.org/abs/2302.09332v1 | https://arxiv.org/pdf/2302.09332v1.pdf | Incipient Fault Detection in Power Distribution System: A Time-Frequency Embedded Deep Learning Based Approach | Incipient fault detection in power distribution systems is crucial to improve the reliability of the grid. However, the non-stationary nature and the inadequacy of the training dataset due to the self-recovery of the incipient fault signal, make the incipient fault detection in power distribution systems a great challe... | ['Zhi Liu', 'Weitao Li', 'Wei Sun', 'Yuxing Deng', 'Hong Cheng', 'Huan Luo', 'Qiyue Li'] | 2023-02-18 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [-2.41386935e-01 -7.10956335e-01 -2.15591080e-02 4.97362874e-02
-5.86058140e-01 -3.88544351e-01 1.19628415e-01 -4.75473017e-01
3.72981310e-01 5.70166111e-01 4.47050333e-02 -4.63531911e-01
-3.48504692e-01 -7.07677841e-01 -2.55265683e-01 -9.59265232e-01
-5.05482674e-01 7.99145997e-02 -9.60178748e-02 -1.39811978... | [6.495669364929199, 2.7309763431549072] |
6ef4dcc0-6908-402f-9608-f399fb79b742 | jojogan-one-shot-face-stylization-1 | 2112.11641 | null | https://arxiv.org/abs/2112.11641v4 | https://arxiv.org/pdf/2112.11641v4.pdf | JoJoGAN: One Shot Face Stylization | A style mapper applies some fixed style to its input images (so, for example, taking faces to cartoons). This paper describes a simple procedure -- JoJoGAN -- to learn a style mapper from a single example of the style. JoJoGAN uses a GAN inversion procedure and StyleGAN's style-mixing property to produce a substantial ... | ['David Forsyth', 'Min Jin Chong'] | 2021-12-22 | jojogan-one-shot-face-stylization | https://arxiv.org/abs/2112.11641 | https://arxiv.org/pdf/2112.11641.pdf | arxiv-2021-12 | ['image-stylization', 'one-shot-face-stylization'] | ['computer-vision', 'computer-vision'] | [ 5.17857850e-01 2.63937384e-01 2.30749875e-01 -5.01760662e-01
-6.60095215e-01 -6.97327256e-01 7.16840327e-01 -8.52851391e-01
-3.45466398e-02 8.75030339e-01 5.05380407e-02 -7.64674693e-03
3.87691200e-01 -1.01672614e+00 -7.64327943e-01 -6.41122997e-01
6.04930580e-01 6.42024398e-01 -1.23148136e-01 -5.22588074... | [11.712017059326172, -0.44369709491729736] |
3a3a6c5c-87cf-4b91-b721-afbd72847daa | spatio-temporal-modeling-for-large-scale | 2103.07636 | null | https://arxiv.org/abs/2103.07636v1 | https://arxiv.org/pdf/2103.07636v1.pdf | Spatio-temporal Modeling for Large-scale Vehicular Networks Using Graph Convolutional Networks | The effective deployment of connected vehicular networks is contingent upon maintaining a desired performance across spatial and temporal domains. In this paper, a graph-based framework, called SMART, is proposed to model and keep track of the spatial and temporal statistics of vehicle-to-infrastructure (V2I) communica... | ['H. Vincent Poor', 'Walid Saad', 'Guangming Shiyz', 'Yingyu Li', 'Yong Xiao', 'Juntong Liu'] | 2021-03-13 | null | null | null | null | ['graph-reconstruction'] | ['graphs'] | [-8.33747864e-01 -1.77926004e-01 -2.53036022e-01 -2.51725852e-01
-3.13094109e-01 -3.98794234e-01 6.49862289e-01 2.08007529e-01
1.26065820e-01 5.51696956e-01 -1.61432281e-01 -8.16620231e-01
-5.79453111e-01 -1.04018509e+00 -8.27977896e-01 -3.22928578e-01
-1.30708444e+00 4.25586402e-01 3.57250929e-01 -1.77191570... | [6.347537517547607, 1.8739615678787231] |
7f1ff4b4-05cb-4f4d-9f86-30714785fb78 | dut-learning-video-stabilization-by-simply | 2011.14574 | null | https://arxiv.org/abs/2011.14574v3 | https://arxiv.org/pdf/2011.14574v3.pdf | DUT: Learning Video Stabilization by Simply Watching Unstable Videos | Previous deep learning-based video stabilizers require a large scale of paired unstable and stable videos for training, which are difficult to collect. Traditional trajectory-based stabilizers, on the other hand, divide the task into several sub-tasks and tackle them subsequently, which are fragile in textureless and o... | ['Stephen J. Maybank', 'DaCheng Tao', 'Jing Zhang', 'Yufei Xu'] | 2020-11-30 | null | null | null | null | ['video-stabilization', 'homography-estimation'] | ['computer-vision', 'computer-vision'] | [-4.03640896e-01 -4.79347169e-01 -2.69044250e-01 9.21551287e-02
-5.37787557e-01 -4.67669010e-01 5.21593213e-01 -2.26189375e-01
-1.79022461e-01 6.06027603e-01 2.65327781e-01 -1.55278057e-01
3.05286236e-02 -5.88258088e-01 -8.48786414e-01 -1.15461159e+00
2.50248536e-02 2.86319964e-02 3.88234198e-01 -3.20271403... | [10.608251571655273, -1.186396598815918] |
ea9c7e5a-4ba8-4ec9-84e0-ab1669cb9af6 | robust-optimization-over-multiple-domains | 1805.07588 | null | http://arxiv.org/abs/1805.07588v2 | http://arxiv.org/pdf/1805.07588v2.pdf | Robust Optimization over Multiple Domains | In this work, we study the problem of learning a single model for multiple
domains. Unlike the conventional machine learning scenario where each domain
can have the corresponding model, multiple domains (i.e., applications/users)
may share the same machine learning model due to maintenance loads in cloud
computing serv... | ['Shenghuo Zhu', 'Qi Qian', 'Baigui Sun', 'Rong Jin', 'Jiasheng Tang', 'Hao Li'] | 2018-05-19 | null | null | null | null | ['fine-grained-visual-categorization'] | ['computer-vision'] | [ 1.88269038e-02 -3.55481684e-01 3.37277394e-04 -2.80221313e-01
-9.58368361e-01 -7.27215886e-01 1.29782215e-01 -1.13460265e-01
-3.35649312e-01 6.42127514e-01 -4.55458194e-01 -2.19540879e-01
-2.57015586e-01 -6.72107458e-01 -9.53545749e-01 -8.92926633e-01
8.22650194e-02 5.44487119e-01 -2.53712445e-01 2.06118926... | [6.884095668792725, 4.432368755340576] |
d57e94f9-ce26-451f-aec8-22b00f009b5b | posscore-a-simple-yet-effective-evaluation-of | 2109.03039 | null | https://arxiv.org/abs/2109.03039v1 | https://arxiv.org/pdf/2109.03039v1.pdf | POSSCORE: A Simple Yet Effective Evaluation of Conversational Search with Part of Speech Labelling | Conversational search systems, such as Google Assistant and Microsoft Cortana, provide a new search paradigm where users are allowed, via natural language dialogues, to communicate with search systems. Evaluating such systems is very challenging since search results are presented in the format of natural language sente... | ['Max L. Wilson', 'Jiaxin Mao', 'Ke Zhou', 'Zeyang Liu'] | 2021-09-07 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 3.50574367e-02 1.91365574e-02 -3.64056110e-01 -4.23597783e-01
-8.99997294e-01 -8.76314402e-01 1.04685795e+00 2.39440337e-01
-8.14563751e-01 5.94651401e-01 6.14953816e-01 -6.21730983e-01
-5.22292964e-02 -4.82690364e-01 -1.77955199e-02 -1.03379436e-01
1.96072802e-01 6.12750709e-01 2.55951554e-01 -6.18919671... | [12.132999420166016, 7.804937839508057] |
00fdf6d8-c64f-400e-a4a2-48b55e6de8ff | unlocking-masked-autoencoders-as-loss | 2303.16411 | null | https://arxiv.org/abs/2303.16411v1 | https://arxiv.org/pdf/2303.16411v1.pdf | Unlocking Masked Autoencoders as Loss Function for Image and Video Restoration | Image and video restoration has achieved a remarkable leap with the advent of deep learning. The success of deep learning paradigm lies in three key components: data, model, and loss. Currently, many efforts have been devoted to the first two while seldom study focuses on loss function. With the question ``are the de f... | ['Chongyi Li', 'Chunle Guo', 'Jie Huang', 'Naishan Zheng', 'Man Zhou'] | 2023-03-29 | null | null | null | null | ['image-super-resolution', 'video-denoising', 'image-enhancement', 'video-enhancement', 'video-restoration'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 3.89578819e-01 -5.10615371e-02 4.22252566e-02 -3.03071320e-01
-5.89599073e-01 7.66732672e-04 2.70916581e-01 -4.53520566e-01
-4.33021843e-01 5.64097404e-01 2.31156006e-01 -7.09463507e-02
-2.70552933e-01 -6.85128748e-01 -9.60372031e-01 -9.22385037e-01
2.24636998e-02 -3.49138260e-01 4.84665185e-02 -3.76577228... | [11.308062553405762, -2.1008849143981934] |
83558431-3a9b-4e8e-9c51-3d46225432a9 | quran-qa-2022-overview-of-the-first-shared | null | null | https://aclanthology.org/2022.osact-1.9 | https://aclanthology.org/2022.osact-1.9.pdf | Qur’an QA 2022: Overview of The First Shared Task on Question Answering over the Holy Qur’an | Motivated by the resurgence of the machine reading comprehension (MRC) research, we have organized the first Qur’an Question Answering shared task, “Qur’an QA 2022”. The task in its first year aims to promote state-of-the-art research on Arabic QA in general and MRC in particular on the Holy Qur’an, which constitutes a... | ['Tamer Elsayed', 'Watheq Mansour', 'Rana Malhas'] | null | null | null | null | osact-lrec-2022-6 | ['machine-reading-comprehension'] | ['natural-language-processing'] | [-1.67029858e-01 6.83516562e-01 2.28637770e-01 -2.95448214e-01
-1.34036851e+00 -8.14132512e-01 6.09117448e-01 4.34491485e-01
-3.78764957e-01 9.83282208e-01 5.22069275e-01 -5.37617028e-01
-1.50334105e-01 -7.98468113e-01 -5.14562130e-01 -1.89113721e-01
2.29046270e-01 9.08752441e-01 1.29931167e-01 -1.26475501... | [11.328455924987793, 8.184185028076172] |
9409ae80-5529-45c2-932a-266714261972 | zero-day-backdoor-attack-against-text-to | 2305.10701 | null | https://arxiv.org/abs/2305.10701v1 | https://arxiv.org/pdf/2305.10701v1.pdf | Zero-Day Backdoor Attack against Text-to-Image Diffusion Models via Personalization | Although recent personalization methods have democratized high-resolution image synthesis by enabling swift concept acquisition with minimal examples and lightweight computation, they also present an exploitable avenue for high accessible backdoor attacks. This paper investigates a critical and unexplored aspect of tex... | ['Felix Juefei-Xu', 'Qing Guo', 'Yihao Huang'] | 2023-05-18 | null | null | null | null | ['backdoor-attack'] | ['adversarial'] | [ 4.04601306e-01 -8.86925310e-02 -6.27914816e-02 3.03770989e-01
-6.15482569e-01 -9.97472465e-01 1.30185974e+00 4.53295298e-02
-6.98440313e-01 3.66509706e-01 1.19080372e-01 -4.21131492e-01
-4.52304870e-01 -8.51670027e-01 -5.42593002e-01 -6.41400874e-01
-4.60562676e-01 2.41316572e-01 3.37483525e-01 -4.52567458... | [5.746319770812988, 7.804510593414307] |
b641f053-ce2d-424d-8509-dffd2bd3e3fe | ukara-10-challenge-track-1-automatic-short | 2002.12540 | null | https://arxiv.org/abs/2002.12540v1 | https://arxiv.org/pdf/2002.12540v1.pdf | UKARA 1.0 Challenge Track 1: Automatic Short-Answer Scoring in Bahasa Indonesia | We describe our third-place solution to the UKARA 1.0 challenge on automated essay scoring. The task consists of a binary classification problem on two datasets | answers from two different questions. We ended up using two different models for the two datasets. For task A, we applied a random forest algorithm on featur... | ['Yosef Ardhito Winatmoko', 'Ali Akbar Septiandri'] | 2020-02-28 | null | null | null | null | ['automated-essay-scoring'] | ['natural-language-processing'] | [ 6.39100596e-02 3.70332710e-02 -3.98500651e-01 -4.95695651e-01
-1.24421930e+00 -7.71407127e-01 5.72467506e-01 4.69465584e-01
-6.60218060e-01 9.97500122e-01 4.90785897e-01 -3.41750890e-01
-3.26660067e-01 -4.82365102e-01 -4.29755375e-02 -2.55259693e-01
5.38563550e-01 5.01058519e-01 1.50068685e-01 -6.92737997... | [11.182600021362305, 9.40710735321045] |
3b7b8a97-8856-40e2-adbc-ec51418a01c8 | understanding-and-improving-zero-shot-multi | 2210.04234 | null | https://arxiv.org/abs/2210.04234v1 | https://arxiv.org/pdf/2210.04234v1.pdf | Understanding and Improving Zero-shot Multi-hop Reasoning in Generative Question Answering | Generative question answering (QA) models generate answers to questions either solely based on the parameters of the model (the closed-book setting) or additionally retrieving relevant evidence (the open-book setting). Generative QA models can answer some relatively complex questions, but the mechanism through which th... | ['Graham Neubig', 'Haibo Ding', 'Jun Araki', 'Zhengbao Jiang'] | 2022-10-09 | null | https://aclanthology.org/2022.coling-1.152 | https://aclanthology.org/2022.coling-1.152.pdf | coling-2022-10 | ['generative-question-answering'] | ['natural-language-processing'] | [ 1.81025546e-03 9.13308263e-01 2.38648146e-01 -3.35632950e-01
-1.63751280e+00 -9.78372335e-01 7.74916828e-01 1.05249591e-01
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-3.77858698e-01 -1.44378996e+00 -8.78648639e-01 4.21641432e-02
5.60733795e-01 1.32323968e+00 5.68900645e-01 -7.08573341... | [11.05844497680664, 7.96763277053833] |
a10a0f76-301c-47d1-8bf1-b6d58045316f | a-report-on-the-first-native-language | null | null | https://aclanthology.org/W13-1706 | https://aclanthology.org/W13-1706.pdf | A Report on the First Native Language Identification Shared Task | null | ['Joel Tetreault', 'Daniel Blanchard', 'Aoife Cahill'] | 2013-06-01 | null | null | null | ws-2013-6 | ['native-language-identification'] | ['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... | [-7.330080032348633, 3.788383722305298] |
871c50f4-dc0e-4256-bce0-a789e4f41df8 | uif-an-objective-quality-assessment-for | 2205.09392 | null | https://arxiv.org/abs/2205.09392v1 | https://arxiv.org/pdf/2205.09392v1.pdf | UIF: An Objective Quality Assessment for Underwater Image Enhancement | Due to complex and volatile lighting environment, underwater imaging can be readily impaired by light scattering, warping, and noises. To improve the visual quality, Underwater Image Enhancement (UIE) techniques have been widely studied. Recent efforts have also been contributed to evaluate and compare the UIE performa... | ['Tiesong Zhao', 'Rongfu Lin', 'Weiling Chen', 'Yannan Zheng'] | 2022-05-19 | null | null | null | null | ['uie'] | ['computer-vision'] | [ 2.78835207e-01 -6.81140304e-01 8.47029686e-01 -3.20032597e-01
-5.87064803e-01 -8.36433470e-02 1.57925278e-01 1.71081275e-01
-9.43957448e-01 7.04066992e-01 3.86970758e-01 2.95828640e-01
-3.53786975e-01 -8.52248132e-01 -4.51893598e-01 -1.01078224e+00
-3.44383836e-01 -7.26957083e-01 2.83312172e-01 -5.51700234... | [10.697052001953125, -3.517683267593384] |
d138f78f-2840-48a7-93a3-6680e615e9c1 | keyphrase-generation-for-scientific-articles | 1909.12229 | null | https://arxiv.org/abs/1909.12229v1 | https://arxiv.org/pdf/1909.12229v1.pdf | Keyphrase Generation for Scientific Articles using GANs | In this paper, we present a keyphrase generation approach using conditional Generative Adversarial Networks (GAN). In our GAN model, the generator outputs a sequence of keyphrases based on the title and abstract of a scientific article. The discriminator learns to distinguish between machine-generated and human-curated... | ['Rajiv Ratn Shah', 'Haimin Zhang', 'Avinash Swaminathan', 'Rakesh Gosangi', 'Raj Kuwar Gupta', 'Debanjan Mahata'] | 2019-09-24 | null | null | null | null | ['keyphrase-generation'] | ['natural-language-processing'] | [ 4.22385752e-01 1.24292344e-01 -2.31332809e-01 3.49303126e-01
-1.39828300e+00 -1.33001649e+00 1.19649184e+00 2.14608535e-01
-4.77229804e-01 1.20112789e+00 2.98082858e-01 -5.90524316e-01
3.30422014e-01 -1.20064330e+00 -9.99979079e-01 -5.73911071e-01
3.75394225e-02 3.15089107e-01 -6.96437806e-02 -2.99301654... | [12.277429580688477, 8.89411735534668] |
362d473e-ac41-4e1b-9051-f6cc92230fa4 | probabilistic-assumptions-matter-improved | 2005.01898 | null | https://arxiv.org/abs/2005.01898v1 | https://arxiv.org/pdf/2005.01898v1.pdf | Probabilistic Assumptions Matter: Improved Models for Distantly-Supervised Document-Level Question Answering | We address the problem of extractive question answering using document-level distant super-vision, pairing questions and relevant documents with answer strings. We compare previously used probability space and distant super-vision assumptions (assumptions on the correspondence between the weak answer string labels and ... | ['Kristina Toutanova', 'Ming-Wei Chang', 'Kenton Lee', 'Hao Cheng'] | 2020-05-05 | probabilistic-assumptions-matter-improved-1 | https://aclanthology.org/2020.acl-main.501 | https://aclanthology.org/2020.acl-main.501.pdf | acl-2020-6 | ['triviaqa'] | ['miscellaneous'] | [-3.69432718e-02 2.11692989e-01 3.03669386e-02 -2.89457142e-01
-1.76337111e+00 -1.29889262e+00 7.29802728e-01 3.42091173e-01
-5.08324802e-01 5.87327778e-01 6.06373668e-01 -3.00157368e-01
-6.16762877e-01 -4.81663495e-01 -7.01004326e-01 -9.84645486e-02
3.99150163e-01 1.16689563e+00 6.05663896e-01 -4.90044951... | [11.254068374633789, 8.001913070678711] |
f2f8a0c7-3709-4931-bf6b-1f66b146b29c | towards-provably-secure-encrypted-control | 2210.08849 | null | https://arxiv.org/abs/2210.08849v1 | https://arxiv.org/pdf/2210.08849v1.pdf | Towards Provably Secure Encrypted Control Using Homomorphic Encryption | Encrypted control is a promising method for the secure outsourcing of controller computation to a public cloud. However, a feasible method for security proofs of control has not yet been developed in the field of encrypted control systems. Additionally, cryptography does not consider certain types of attacks on encrypt... | ['Kiminao Kogiso', 'Kaoru Teranishi'] | 2022-10-17 | null | null | null | null | ['mathematical-proofs'] | ['miscellaneous'] | [ 3.54645044e-01 -1.92126706e-01 -3.64469923e-02 -1.03125505e-01
2.72078902e-01 -1.20044887e+00 8.55627775e-01 1.14279516e-01
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-3.70014399e-01 -1.28237891e+00 -4.86707389e-01 -8.87466967e-01
-2.51284122e-01 -3.51539493e-01 1.14826620e-01 -4.82321650... | [5.718376636505127, 4.861716270446777] |
01285115-c8aa-4b11-96a4-60111e03ca76 | petsgan-rethinking-priors-for-single-image | 2203.01488 | null | https://arxiv.org/abs/2203.01488v1 | https://arxiv.org/pdf/2203.01488v1.pdf | PetsGAN: Rethinking Priors for Single Image Generation | Single image generation (SIG), described as generating diverse samples that have similar visual content with the given single image, is first introduced by SinGAN which builds a pyramid of GANs to progressively learn the internal patch distribution of the single image. It also shows great potentials in a wide range of ... | ['BoWen Zhou', 'Tiande Guo', 'Hailin Shi', 'Congying Han', 'Yinglu Liu', 'ZiCheng Zhang'] | 2022-03-03 | null | null | null | null | ['single-image-generation'] | ['computer-vision'] | [ 4.12369817e-01 9.02436078e-02 1.04115695e-01 7.45441169e-02
-6.32733762e-01 -3.36181432e-01 5.32260835e-01 -7.50513494e-01
4.92506847e-02 8.63928735e-01 -6.49545416e-02 1.58137366e-01
-8.44952986e-02 -8.94884050e-01 -7.56603837e-01 -9.87836540e-01
5.07863045e-01 1.64314523e-01 1.37279466e-01 -2.79628545... | [11.470954895019531, -0.7894147038459778] |
fa33044f-9616-4ca9-9ef5-87a77a421a04 | picie-unsupervised-semantic-segmentation | 2103.17070 | null | https://arxiv.org/abs/2103.17070v1 | https://arxiv.org/pdf/2103.17070v1.pdf | PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering | We present a new framework for semantic segmentation without annotations via clustering. Off-the-shelf clustering methods are limited to curated, single-label, and object-centric images yet real-world data are dominantly uncurated, multi-label, and scene-centric. We extend clustering from images to pixels and assign se... | ['Bharath Hariharan', 'Kavita Bala', 'Utkarsh Mall', 'Jang Hyun Cho'] | 2021-03-30 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Cho_PiCIE_Unsupervised_Semantic_Segmentation_Using_Invariance_and_Equivariance_in_Clustering_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Cho_PiCIE_Unsupervised_Semantic_Segmentation_Using_Invariance_and_Equivariance_in_Clustering_CVPR_2021_paper.pdf | cvpr-2021-1 | ['unsupervised-semantic-segmentation'] | ['computer-vision'] | [ 1.11756451e-01 -2.38772005e-01 -2.10089758e-01 -6.95458710e-01
-9.79772568e-01 -9.74696755e-01 4.75676954e-01 2.36217920e-02
-3.58617157e-01 1.32762238e-01 -2.45366663e-01 1.40996715e-02
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1.60192952e-01 6.12956345e-01 3.73616934e-01 2.56936938... | [9.567609786987305, 0.672742486000061] |
a359cb10-8e70-474d-97fc-a2e5ff2b780a | learning-identity-invariant-motion | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Huang_Learning_Identity-Invariant_Motion_Representations_for_Cross-ID_Face_Reenactment_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Huang_Learning_Identity-Invariant_Motion_Representations_for_Cross-ID_Face_Reenactment_CVPR_2020_paper.pdf | Learning Identity-Invariant Motion Representations for Cross-ID Face Reenactment | Human face reenactment aims at transferring motion patterns from one face (from a source-domain video) to an-other (in the target domain with the identity of interest).While recent works report impressive results, they are notable to handle multiple identities in a unified model. In this paper, we propose a unique netw... | [' Yu-Chiang Frank Wang', ' Fu-En Yang', 'Po-Hsiang Huang'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['face-reenactment'] | ['computer-vision'] | [ 3.54473412e-01 2.83603460e-01 -4.42265213e-01 -3.68342876e-01
-7.14862645e-01 -7.70658731e-01 6.51146591e-01 -1.19533038e+00
-7.21077397e-02 5.74168384e-01 5.26276767e-01 4.52383399e-01
1.54549390e-01 -4.00867850e-01 -7.74471343e-01 -7.13344157e-01
1.28445804e-01 6.32161796e-01 -3.76566380e-01 -1.09075233... | [12.703385353088379, -0.1822313666343689] |
3c994d06-ce33-4893-bcca-3209417fc5c2 | improved-disentangled-speech-representations | 2211.08191 | null | https://arxiv.org/abs/2211.08191v2 | https://arxiv.org/pdf/2211.08191v2.pdf | Improved disentangled speech representations using contrastive learning in factorized hierarchical variational autoencoder | Leveraging the fact that speaker identity and content vary on different time scales, \acrlong{fhvae} (\acrshort{fhvae}) uses different latent variables to symbolize these two attributes. Disentanglement of these attributes is carried out by different prior settings of the corresponding latent variables. For the prior o... | ['Zheng-Hua Tan', 'Thomas Arildsen', 'Yuying Xie'] | 2022-11-15 | null | null | null | null | ['voice-conversion', 'voice-conversion', 'speaker-verification'] | ['audio', 'speech', 'speech'] | [ 5.09452522e-02 3.02114874e-01 -1.92639932e-01 -4.99942392e-01
-7.47009873e-01 -8.09316218e-01 8.19706619e-01 -1.62290409e-01
-3.02403331e-01 6.16844416e-01 1.18269525e-01 -2.42367342e-01
4.53798361e-02 -5.52311897e-01 -4.07765806e-01 -1.05402255e+00
1.40116826e-01 4.24074918e-01 -6.76688999e-02 -1.04603566... | [14.682943344116211, 6.256300926208496] |
989057dc-2b2b-41e5-be02-450740205103 | accelerated-and-quantitative-3d-semisolid-mt | 2207.11297 | null | https://arxiv.org/abs/2207.11297v1 | https://arxiv.org/pdf/2207.11297v1.pdf | Accelerated and Quantitative 3D Semisolid MT/CEST Imaging using a Generative Adversarial Network (GAN-CEST) | Purpose: To substantially shorten the acquisition time required for quantitative 3D chemical exchange saturation transfer (CEST) and semisolid magnetization transfer (MT) imaging and allow for rapid chemical exchange parameter map reconstruction. Methods: Three-dimensional CEST and MT magnetic resonance fingerprinting ... | ['Or Perlman', 'Christian T. Farrar', 'Moritz Zaiss', 'Christopher Nguyen', 'Elizabeth R. Gerstner', 'Anna N. Foster', 'Jaume Coll-Font', 'Kai Herz', 'Maria Sedykh', 'Jonah Weigand-Whittier'] | 2022-07-22 | null | null | null | null | ['magnetic-resonance-fingerprinting'] | ['medical'] | [ 5.21519065e-01 2.61834979e-01 1.70983493e-01 -1.92157492e-01
-1.01588476e+00 -3.20026606e-01 1.80087209e-01 -8.82100612e-02
-6.01711988e-01 9.57020164e-01 -5.93058802e-02 -2.54039139e-01
1.07411832e-01 -2.69440025e-01 -6.62044287e-01 -9.95530128e-01
-3.63815010e-01 7.52803445e-01 2.75483638e-01 1.66559145... | [13.5971040725708, -2.418259859085083] |
883d082b-a86e-4f4f-8415-96bacb0bb865 | fine-grained-classification-with-noisy-labels | 2303.02404 | null | https://arxiv.org/abs/2303.02404v1 | https://arxiv.org/pdf/2303.02404v1.pdf | Fine-Grained Classification with Noisy Labels | Learning with noisy labels (LNL) aims to ensure model generalization given a label-corrupted training set. In this work, we investigate a rarely studied scenario of LNL on fine-grained datasets (LNL-FG), which is more practical and challenging as large inter-class ambiguities among fine-grained classes cause more noisy... | ['Yilong Yin', 'Chenhui Guo', 'Ren Wang', 'Haoliang Sun', 'Lei Feng', 'Qi Wei'] | 2023-03-04 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wei_Fine-Grained_Classification_With_Noisy_Labels_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wei_Fine-Grained_Classification_With_Noisy_Labels_CVPR_2023_paper.pdf | cvpr-2023-1 | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 2.22347021e-01 -6.84663951e-02 -1.42719001e-01 -5.95747471e-01
-1.11825669e+00 -5.44754624e-01 4.22221661e-01 1.40531734e-01
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-4.33016479e-01 -7.04656065e-01 -5.38255990e-01 -8.13134789e-01
3.41798246e-01 3.28790277e-01 6.45350367e-02 4.49312441... | [9.358393669128418, 3.8900411128997803] |
0e596832-b5d4-4f5a-afa5-bccba903559e | revisiting-evaluation-of-knowledge-base | null | null | https://openreview.net/forum?id=1uufzxsxfL | https://openreview.net/pdf?id=1uufzxsxfL | Revisiting Evaluation of Knowledge Base Completion Models | Representing knowledge graphs (KGs) by learning embeddings for entities and relations has led to accurate models for existing KG completion benchmarks. However, due to the open-world assumption of existing KGs, evaluation of KG completion uses ranking metrics and triple classification with negative samples, and is thus... | ['Sameer Singh', 'Yifan Tian', 'Pouya Pezeshkpour'] | 2020-02-14 | null | null | null | akbc-2020-6 | ['knowledge-base-completion', 'triple-classification', 'knowledge-base-completion'] | ['graphs', 'graphs', 'knowledge-base'] | [-3.56252342e-01 2.72402912e-01 -6.23027802e-01 -1.47528291e-01
-7.39499688e-01 -6.31028771e-01 5.79539239e-01 4.30953681e-01
-6.93008602e-02 8.10684979e-01 2.82346904e-01 -4.21140164e-01
-3.59729558e-01 -1.05745494e+00 -7.82009840e-01 -2.43642539e-01
-3.28272998e-01 5.61161101e-01 6.08259626e-02 -5.25650904... | [8.809741973876953, 7.868429660797119] |
1130df8f-6737-4340-a732-45d0ce611d04 | wide-deep-learning-for-spatial-intensity | 2305.18708 | null | https://arxiv.org/abs/2305.18708v1 | https://arxiv.org/pdf/2305.18708v1.pdf | Wide & deep learning for spatial & intensity adaptive image restoration | Most existing deep learning-based image restoration methods usually aim to remove degradation with uniform spatial distribution and constant intensity, making insufficient use of degradation prior knowledge. Here we bootstrap the deep neural networks to suppress complex image degradation whose intensity is spatially va... | ['Xiangzhi Bai', 'Yadong Wang'] | 2023-05-30 | null | null | null | null | ['image-restoration'] | ['computer-vision'] | [ 2.59545654e-01 -6.69704020e-01 3.48681569e-01 -2.29051113e-02
-2.88621426e-01 -2.94544965e-01 1.12158969e-01 -5.66687107e-01
-2.63118237e-01 8.29976022e-01 4.13794369e-01 -1.00377344e-01
-1.81553841e-01 -6.48210883e-01 -6.84909284e-01 -1.34572232e+00
3.05378642e-02 -6.57215953e-01 -9.74716805e-03 -4.11640346... | [11.089553833007812, -2.443153142929077] |
c1fb6a1b-9a9c-45f9-a890-aed475912d84 | complex-valued-time-frequency-self-attention | 2211.12632 | null | https://arxiv.org/abs/2211.12632v1 | https://arxiv.org/pdf/2211.12632v1.pdf | Complex-Valued Time-Frequency Self-Attention for Speech Dereverberation | Several speech processing systems have demonstrated considerable performance improvements when deep complex neural networks (DCNN) are coupled with self-attention (SA) networks. However, the majority of DCNN-based studies on speech dereverberation that employ self-attention do not explicitly account for the inter-depen... | ['John H. L. Hansen', 'Vinay Kothapally'] | 2022-11-22 | null | null | null | null | ['speech-dereverberation'] | ['speech'] | [ 1.06008612e-01 1.38808368e-02 3.49314421e-01 -3.39717031e-01
-6.92308128e-01 -2.70108640e-01 5.85371912e-01 -9.91984531e-02
-2.73255736e-01 1.54847950e-01 7.96589911e-01 -7.82471061e-01
-2.33443361e-02 -2.79128879e-01 -5.09434938e-01 -4.67595011e-01
-1.71489254e-01 -1.74363077e-01 -1.11703485e-01 -5.11654198... | [14.71248722076416, 6.028441429138184] |
ef21ca56-c762-4dc2-b9ed-f8a3f37d6858 | backproptools-a-fast-portable-deep | 2306.03530 | null | https://arxiv.org/abs/2306.03530v1 | https://arxiv.org/pdf/2306.03530v1.pdf | BackpropTools: A Fast, Portable Deep Reinforcement Learning Library for Continuous Control | Deep Reinforcement Learning (RL) has been demonstrated to yield capable agents and control policies in several domains but is commonly plagued by prohibitively long training times. Additionally, in the case of continuous control problems, the applicability of learned policies on real-world embedded devices is limited d... | ['Giuseppe Loianno', 'Dario Albani', 'Jonas Eschmann'] | 2023-06-06 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [-5.06213605e-01 -1.48329875e-02 -4.77424532e-01 -1.07654139e-01
-3.90437216e-01 -6.46626174e-01 4.93010759e-01 -2.09855720e-01
-6.31516278e-01 9.11744595e-01 -4.28431153e-01 -8.70227993e-01
9.97192562e-02 -8.90182436e-01 -8.46195936e-01 -6.37332976e-01
-3.49202126e-01 4.36364919e-01 1.32492617e-01 -4.75121439... | [4.02696418762207, 1.4802101850509644] |
67e562bb-266c-4945-8287-30645814dbaa | infinite-physical-monkey-do-deep-learning | 2304.10494 | null | https://arxiv.org/abs/2304.10494v1 | https://arxiv.org/pdf/2304.10494v1.pdf | Infinite Physical Monkey: Do Deep Learning Methods Really Perform Better in Conformation Generation? | Conformation Generation is a fundamental problem in drug discovery and cheminformatics. And organic molecule conformation generation, particularly in vacuum and protein pocket environments, is most relevant to drug design. Recently, with the development of geometric neural networks, the data-driven schemes have been su... | ['Yafeng Deng', 'Dejun Jiang', 'Huifeng Zhao', 'Jintu Zhang', 'Haotian Zhang'] | 2023-03-08 | null | null | null | null | ['pose-prediction', 'drug-discovery', 'molecular-docking'] | ['computer-vision', 'medical', 'medical'] | [-7.46553624e-03 1.90950781e-01 -9.25545543e-02 1.66899641e-03
-5.75064242e-01 -6.52268469e-01 4.40319449e-01 1.29306629e-01
-2.08062306e-01 1.54099381e+00 -2.71011174e-01 -6.94892585e-01
-8.29583779e-02 -9.76612747e-01 -1.13020778e+00 -1.15497446e+00
-2.29454786e-01 6.33661926e-01 -2.56675947e-02 -4.90381271... | [4.962181568145752, 5.563250541687012] |
21074099-1239-43c1-96e0-d8ed4d6bd5a9 | 3d-object-reconstruction-from-hand-object | 1704.00529 | null | http://arxiv.org/abs/1704.00529v1 | http://arxiv.org/pdf/1704.00529v1.pdf | 3D Object Reconstruction from Hand-Object Interactions | Recent advances have enabled 3d object reconstruction approaches using a
single off-the-shelf RGB-D camera. Although these approaches are successful for
a wide range of object classes, they rely on stable and distinctive geometric
or texture features. Many objects like mechanical parts, toys, household or
decorative ar... | ['Dimitrios Tzionas', 'Juergen Gall'] | 2017-04-03 | 3d-object-reconstruction-from-hand-object-1 | http://openaccess.thecvf.com/content_iccv_2015/html/Tzionas_3D_Object_Reconstruction_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Tzionas_3D_Object_Reconstruction_ICCV_2015_paper.pdf | iccv-2015-12 | ['3d-object-reconstruction'] | ['computer-vision'] | [ 1.49483770e-01 -3.33749771e-01 -1.29841641e-01 -3.14723492e-01
-2.92853504e-01 -7.52048433e-01 3.98720324e-01 -3.26896280e-01
3.46839428e-02 9.05740559e-02 -8.43009651e-02 -1.77876040e-01
-1.81488663e-01 -5.56789577e-01 -3.20785493e-01 -2.38626227e-01
2.92359233e-01 1.00882709e+00 8.34271789e-01 -4.07210588... | [6.692664623260498, -1.0341027975082397] |
7f4464c4-407b-452d-b4ab-34461f546a2f | multi-branch-neural-networks-for-video | null | null | https://biblio.ugent.be/publication/8735199 | https://openaccess.thecvf.com/content/WACV2022/papers/Leroux_Multi-Branch_Neural_Networks_for_Video_Anomaly_Detection_in_Adverse_Lighting_WACV_2022_paper.pdf | Multi-branch Neural Networks for Video Anomaly Detection in Adverse Lighting and Weather Conditions | Automated anomaly detection in surveillance videos has attracted much interest as it provides a scalable alternative to manual monitoring. Most existing approaches achieve good performance on clean benchmark datasets recorded in well-controlled environments. However, detecting anomalies is much more challenging in the ... | ['Pieter Simoens', 'Bo Li', 'Sam Leroux'] | 2021-10-10 | null | null | null | wacv-2021-10 | ['anomaly-detection-in-surveillance-videos', 'anomaly-detection-in-surveillance-videos'] | ['computer-vision', 'methodology'] | [ 3.91770482e-01 -6.30007386e-01 2.31269717e-01 -4.49821889e-01
-7.40194470e-02 -6.22495830e-01 5.29016972e-01 2.99000204e-01
-3.73912781e-01 3.13801527e-01 -1.42331421e-01 -2.61019677e-01
2.36219227e-01 -6.74245000e-01 -7.39612818e-01 -7.79683888e-01
-3.45376670e-01 5.33743761e-02 8.21652174e-01 -1.23724848... | [7.858386516571045, 1.5775470733642578] |
ccaa68de-acb7-4fec-afaf-9c717a393b54 | defining-data-science-a-new-field-of-inquiry | 2306.16177 | null | https://arxiv.org/abs/2306.16177v2 | https://arxiv.org/pdf/2306.16177v2.pdf | Defining data science: a new field of inquiry | Data science is not a science. It is a research paradigm. Its power, scope, and scale will surpass science, our most powerful research paradigm, to enable knowledge discovery and change our world. We have yet to understand and define it, vital to realizing its potential and managing its risks. Modern data science is in... | ['Michael L Brodie'] | 2023-06-28 | null | null | null | null | ['philosophy'] | ['miscellaneous'] | [-2.00306132e-01 -2.47031480e-01 -6.51865125e-01 -1.51777595e-01
1.33818984e-01 -7.83988297e-01 8.00166547e-01 4.10408318e-01
-4.11779374e-01 2.56650954e-01 3.98331940e-01 -5.99683046e-01
-9.41355705e-01 -8.01656008e-01 -3.96559209e-01 -1.78600475e-01
3.26727033e-01 4.69915986e-01 1.09474482e-02 -2.82916307... | [9.043131828308105, 7.143357753753662] |
f4736690-25c2-41ab-af86-2e0dec332ef7 | reference-based-image-and-video-super | 2212.09581 | null | https://arxiv.org/abs/2212.09581v2 | https://arxiv.org/pdf/2212.09581v2.pdf | Reference-based Image and Video Super-Resolution via C2-Matching | Reference-based Super-Resolution (Ref-SR) has recently emerged as a promising paradigm to enhance a low-resolution (LR) input image or video by introducing an additional high-resolution (HR) reference image. Existing Ref-SR methods mostly rely on implicit correspondence matching to borrow HR textures from reference ima... | ['Ziwei Liu', 'Chen Change Loy', 'Xintao Wang', 'Kelvin C. K. Chan', 'Yuming Jiang'] | 2022-12-19 | null | null | null | null | ['video-super-resolution', 'reference-based-video-super-resolution', 'reference-based-super-resolution'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 6.29277170e-01 -2.44544744e-01 -8.89724940e-02 -2.29026139e-01
-1.31308472e+00 -2.14136004e-01 5.62184691e-01 -5.75950325e-01
-2.33869165e-01 6.46539927e-01 4.05137688e-01 1.34249598e-01
-3.08271758e-02 -7.90638685e-01 -9.21332598e-01 -6.80790246e-01
3.48560303e-01 -1.13890387e-01 4.82303888e-01 -4.06880498... | [10.903375625610352, -2.0870606899261475] |
80021808-715c-485e-98b8-50699de71fe2 | detecting-troll-tweets-in-a-bilingual-corpus | null | null | https://aclanthology.org/2020.lrec-1.766 | https://aclanthology.org/2020.lrec-1.766.pdf | Detecting Troll Tweets in a Bilingual Corpus | During the past several years, a large amount of troll accounts has emerged with efforts to manipulate public opinion on social network sites. They are often involved in spreading misinformation, fake news, and propaganda with the intent of distracting and sowing discord. This paper aims to detect troll tweets in both ... | ['Marina Litvak', 'Mark Last', 'Lin Miao'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['authorship-verification'] | ['natural-language-processing'] | [-2.38227285e-03 1.05890259e-02 -3.31858307e-01 -1.13728782e-02
-4.65060353e-01 -7.35855341e-01 1.04380846e+00 4.80725020e-01
-6.56507313e-01 9.03345406e-01 1.48950979e-01 -6.11025691e-01
3.85801733e-01 -8.47486913e-01 -4.90879714e-01 -4.28845078e-01
2.67733306e-01 4.11688626e-01 -3.25172186e-01 -5.90676486... | [8.262774467468262, 10.289885520935059] |
d3bb71af-edb6-459b-8a00-ac44c6b103aa | openie6-iterative-grid-labeling-and | 2010.03147 | null | https://arxiv.org/abs/2010.03147v1 | https://arxiv.org/pdf/2010.03147v1.pdf | OpenIE6: Iterative Grid Labeling and Coordination Analysis for Open Information Extraction | A recent state-of-the-art neural open information extraction (OpenIE) system generates extractions iteratively, requiring repeated encoding of partial outputs. This comes at a significant computational cost. On the other hand, sequence labeling approaches for OpenIE are much faster, but worse in extraction quality. In ... | ['Soumen Chakrabarti', 'Mausam', 'Samarth Aggarwal', 'Vaibhav Adlakha', 'Keshav Kolluru'] | 2020-10-07 | null | https://aclanthology.org/2020.emnlp-main.306 | https://aclanthology.org/2020.emnlp-main.306.pdf | emnlp-2020-11 | ['open-information-extraction'] | ['natural-language-processing'] | [-1.45204976e-01 6.56420708e-01 -2.52059102e-01 -2.88886391e-02
-1.13325179e+00 -8.74028027e-01 3.34534526e-01 3.74926776e-01
-3.64981323e-01 8.71128023e-01 1.05913341e-01 -5.07885635e-01
7.41258785e-02 -8.61694515e-01 -6.97611988e-01 -2.78603673e-01
-2.28873625e-01 8.39562774e-01 2.48168945e-01 -2.88233936... | [9.64733600616455, 8.703424453735352] |
fa484b2f-d184-4d7a-8b86-a68dcf5bd29c | biogpt-generative-pre-trained-transformer-for | 2210.10341 | null | https://arxiv.org/abs/2210.10341v3 | https://arxiv.org/pdf/2210.10341v3.pdf | BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining | Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain. Among the two main branches of pre-trained language models in the general language domain, i.e., BERT (and its variants) and GPT (and its variants), the first... | ['Tie-Yan Liu', 'Hoifung Poon', 'Sheng Zhang', 'Tao Qin', 'Yingce Xia', 'Liai Sun', 'Renqian Luo'] | 2022-10-19 | null | null | null | null | ['document-classification'] | ['natural-language-processing'] | [ 1.70292467e-01 4.99413967e-01 -3.07434052e-01 -2.76244640e-01
-1.10514069e+00 -3.57828081e-01 5.89548707e-01 2.18386248e-01
-3.13555121e-01 1.33231258e+00 3.04050684e-01 -4.50660706e-01
-7.90047497e-02 -8.20769906e-01 -5.88384211e-01 -5.09316206e-01
2.32687026e-01 9.27380204e-01 -1.10574707e-01 -2.55985975... | [8.58874797821045, 8.726927757263184] |
16eba130-2504-4c61-abf5-cedbd5d1d953 | unsupervised-monocular-depth-and-ego-motion | 1906.05717 | null | https://arxiv.org/abs/1906.05717v1 | https://arxiv.org/pdf/1906.05717v1.pdf | Unsupervised Monocular Depth and Ego-motion Learning with Structure and Semantics | We present an approach which takes advantage of both structure and semantics for unsupervised monocular learning of depth and ego-motion. More specifically, we model the motion of individual objects and learn their 3D motion vector jointly with depth and ego-motion. We obtain more accurate results, especially for chall... | ['Anelia Angelova', 'Reza Mahjourian', 'Vincent Casser', 'Soeren Pirk'] | 2019-06-12 | null | null | null | null | ['depth-and-camera-motion'] | ['computer-vision'] | [-5.00810444e-01 -6.97566345e-02 -3.81744117e-01 -6.69613004e-01
-3.01643372e-01 -7.29494631e-01 6.90119803e-01 -2.68923491e-01
-2.31782123e-01 6.95323765e-01 5.40272057e-01 8.18661228e-02
3.42872471e-01 -5.37997365e-01 -6.57463968e-01 -3.79879028e-01
-1.16659924e-01 4.49611217e-01 5.35823762e-01 1.59212202... | [8.456494331359863, -2.014842987060547] |
ba19eb0e-fa2a-4ee3-8e47-d76ac06ac744 | blockchain-platform-for-covid-19-vaccine | 2101.00983 | null | https://arxiv.org/abs/2101.00983v1 | https://arxiv.org/pdf/2101.00983v1.pdf | Blockchain platform for COVID-19 vaccine supply management | In the context of the COVID-19 pandemic, the rapid roll-out of a vaccine and the implementation of a worldwide immunization campaign is critical, but its success will depend on the availability of an operational and transparent distribution chain that can be audited by all relevant stakeholders. In this paper, we discu... | ['Ionut Anghel', 'Marcel Antal', 'Tudor Cioara', 'Claudia Daniela Antal'] | 2021-01-04 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [ 1.03669629e-01 1.26704305e-01 -3.84257823e-01 -2.65041031e-02
1.46782428e-01 -1.40658534e+00 7.42615163e-01 7.27756023e-01
-4.25437957e-01 1.00529921e+00 -5.25947241e-03 -7.82976031e-01
-1.54652640e-01 -9.27703261e-01 -6.95794582e-01 -6.03929698e-01
-3.21479738e-01 7.88172483e-01 2.33019099e-01 -1.58543870... | [5.980743408203125, 6.434967041015625] |
3bab3e9a-8175-49b3-ad3f-46b240db93c1 | adversarial-text-generation-without | 1810.06640 | null | http://arxiv.org/abs/1810.06640v2 | http://arxiv.org/pdf/1810.06640v2.pdf | Adversarial Text Generation Without Reinforcement Learning | Generative Adversarial Networks (GANs) have experienced a recent surge in
popularity, performing competitively in a variety of tasks, especially in
computer vision. However, GAN training has shown limited success in natural
language processing. This is largely because sequences of text are discrete,
and thus gradients ... | ['Anna Rumshisky', 'David Donahue'] | 2018-10-11 | null | null | null | null | ['adversarial-text'] | ['adversarial'] | [ 2.89299130e-01 2.93726593e-01 5.56437187e-02 -3.98355335e-01
-9.92239356e-01 -8.06605458e-01 1.03042638e+00 -3.95590156e-01
-1.91778511e-01 9.16679323e-01 7.54850030e-01 -1.46168545e-01
7.63414323e-01 -8.19734335e-01 -8.28955531e-01 -6.11482263e-01
3.04335684e-01 6.51207328e-01 -4.65072095e-01 -2.42119193... | [11.86758804321289, 9.350616455078125] |
e663a1c0-7e7a-4ab1-b510-6a321b646f01 | semi-tensor-product-based-tensordecomposition | 2109.15200 | null | https://arxiv.org/abs/2109.15200v1 | https://arxiv.org/pdf/2109.15200v1.pdf | Semi-tensor Product-based TensorDecomposition for Neural Network Compression | The existing tensor networks adopt conventional matrix product for connection. The classical matrix product requires strict dimensionality consistency between factors, which can result in redundancy in data representation. In this paper, the semi-tensor product is used to generalize classical matrix product-based mode ... | ['Ce Zhu', 'Xiaolin Huang', 'Yipeng Liu', 'Hengling Zhao'] | 2021-09-30 | null | null | null | null | ['low-rank-compression', 'neural-network-compression', 'tensor-networks', 'neural-network-compression'] | ['computer-code', 'methodology', 'methodology', 'miscellaneous'] | [-2.49436304e-01 -3.53105754e-01 -8.97275582e-02 3.69836316e-02
9.33149233e-02 -2.71295071e-01 4.05451894e-01 -4.62133557e-01
-4.68309104e-01 4.03094500e-01 4.37699497e-01 -5.44043303e-01
-7.64656067e-01 -4.63341027e-01 -5.48871934e-01 -7.84086466e-01
-3.35176557e-01 2.62961686e-01 2.77909756e-01 -3.26294899... | [8.178206443786621, 3.428417205810547] |
d14c93db-fb27-4ee3-9721-e30bd24fb5f4 | human-in-the-loop-optimization-for-deep | 2306.13104 | null | https://arxiv.org/abs/2306.13104v1 | https://arxiv.org/pdf/2306.13104v1.pdf | Human-in-the-Loop Optimization for Deep Stimulus Encoding in Visual Prostheses | Neuroprostheses show potential in restoring lost sensory function and enhancing human capabilities, but the sensations produced by current devices often seem unnatural or distorted. Exact placement of implants and differences in individual perception lead to significant variations in stimulus response, making personali... | ['Michael Beyeler', 'Matthew Chalk', 'Tristan Fauvel', 'Jacob Granley'] | 2023-06-16 | null | null | null | null | ['bayesian-optimization'] | ['methodology'] | [ 4.84316856e-01 3.38623598e-02 -1.22812539e-01 -2.51835585e-01
-8.42302144e-01 -6.21720552e-01 6.64867461e-02 -2.15975851e-01
-5.46590507e-01 9.18792844e-01 5.57678461e-01 -3.67630161e-02
-4.78691280e-01 -2.33854726e-01 -7.50652850e-01 -6.84136331e-01
1.36930436e-01 5.10475934e-01 1.78722497e-02 1.44700542... | [6.880618572235107, 0.17196016013622284] |
005ed018-f5d2-4751-8c7e-5443a1240e17 | corrmatch-label-propagation-via-correlation | 2306.04300 | null | https://arxiv.org/abs/2306.04300v2 | https://arxiv.org/pdf/2306.04300v2.pdf | CorrMatch: Label Propagation via Correlation Matching for Semi-Supervised Semantic Segmentation | In this paper, we present a simple but performant semi-supervised semantic segmentation approach, termed CorrMatch. Our goal is to mine more high-quality regions from the unlabeled images to leverage the unlabeled data more efficiently via consistency regularization. The key contributions of our CorrMatch are two novel... | ['Weifeng Yuan', 'Qibin Hou', 'Ming-Ming Cheng', 'Le Zhang', 'YuQi Yang', 'Boyuan Sun'] | 2023-06-07 | null | null | null | null | ['semi-supervised-semantic-segmentation'] | ['computer-vision'] | [ 4.35402125e-01 4.67481166e-01 -4.08590794e-01 -8.41899157e-01
-1.21730840e+00 -6.17309570e-01 2.33082265e-01 -2.16277137e-01
-5.93556404e-01 6.02035403e-01 -5.18930629e-02 -6.11329963e-03
3.11916620e-01 -4.20785457e-01 -9.29687738e-01 -5.06642461e-01
3.69458109e-01 6.27528846e-01 7.31944561e-01 1.37203962... | [9.558653831481934, 0.5828022360801697] |
87d5bd42-a862-4a99-9fc9-9f484546e24b | fast-fixed-backbone-protein-sequence-and | null | null | https://openreview.net/forum?id=jLClifZ6YER | https://openreview.net/pdf?id=jLClifZ6YER | Fast fixed-backbone protein sequence and rotamer design | Protein fixed-backbone sequence design is an important task in computational protein design, and being able to quickly and accurately modify or redesign side-chains is a useful subroutine in the context of functional design problems such as ligand binding site, enzyme, and binder design. We present a fast and accurate ... | ['Tudor Achim', 'Namrata Anand'] | 2021-09-29 | null | null | null | null | ['protein-design'] | ['medical'] | [ 4.65557218e-01 4.67843354e-01 -4.25127417e-01 -4.32111382e-01
-5.10016978e-01 -8.61561835e-01 2.19762530e-02 8.32232535e-02
-3.02180678e-01 1.47854078e+00 2.58012682e-01 -8.38344514e-01
4.06880751e-02 -2.92507976e-01 -1.34020770e+00 -8.70822072e-01
-1.62457898e-01 8.06040943e-01 -9.88462847e-03 -2.71044970... | [4.735812664031982, 5.602683067321777] |
9c9b8c6d-c08a-4a00-957c-469b2f6d02b6 | conservative-bayesian-model-based-value | 2210.03802 | null | https://arxiv.org/abs/2210.03802v2 | https://arxiv.org/pdf/2210.03802v2.pdf | Conservative Bayesian Model-Based Value Expansion for Offline Policy Optimization | Offline reinforcement learning (RL) addresses the problem of learning a performant policy from a fixed batch of data collected by following some behavior policy. Model-based approaches are particularly appealing in the offline setting since they can extract more learning signals from the logged dataset by learning a mo... | ['Scott Sanner', 'Baher Abdulhai', 'Hyunwoo Kim', 'Michael Gimelfarb', 'Xiaoyu Wang', 'Jihwan Jeong'] | 2022-10-07 | null | null | null | null | ['d4rl'] | ['robots'] | [-2.08601594e-01 3.14902425e-01 -6.75874829e-01 -1.69545725e-01
-1.22005427e+00 -6.69736564e-01 4.26343650e-01 2.27769807e-01
-8.39463174e-01 1.17767513e+00 -8.11651349e-02 -5.77302158e-01
-4.07995671e-01 -5.62849224e-01 -1.05960274e+00 -7.45235980e-01
-4.27993983e-01 5.61851144e-01 -9.55553949e-02 -5.66454642... | [4.204972267150879, 2.454413890838623] |
e6c6c8de-0ca6-4a8f-8c4b-6c5a514b8abb | on-uncertainty-calibration-and-selective | 2304.08653 | null | https://arxiv.org/abs/2304.08653v1 | https://arxiv.org/pdf/2304.08653v1.pdf | On Uncertainty Calibration and Selective Generation in Probabilistic Neural Summarization: A Benchmark Study | Modern deep models for summarization attains impressive benchmark performance, but they are prone to generating miscalibrated predictive uncertainty. This means that they assign high confidence to low-quality predictions, leading to compromised reliability and trustworthiness in real-world applications. Probabilistic d... | ['Jeremiah Liu', 'Jie Ren', 'Shashi Narayan', 'Joshua Maynez', 'Du Phan', 'Polina Zablotskaia'] | 2023-04-17 | null | null | null | null | ['probabilistic-deep-learning'] | ['computer-vision'] | [ 8.40299502e-02 3.13257575e-01 -2.27221876e-01 -3.98828536e-01
-1.47514081e+00 -4.44964886e-01 7.88808048e-01 1.88191548e-01
-1.95655003e-01 1.31254709e+00 6.48432255e-01 -1.77351594e-01
-5.03132679e-02 -8.18625152e-01 -1.13185108e+00 -8.70971143e-01
2.66665131e-01 7.51332164e-01 -1.43330768e-01 1.19389534... | [12.103280067443848, 9.174949645996094] |
fce82e33-da04-40fb-af59-f5168fd15658 | are-you-really-looking-at-me-a-framework-for | 1906.12175 | null | https://arxiv.org/abs/1906.12175v2 | https://arxiv.org/pdf/1906.12175v2.pdf | Are you really looking at me? A Feature-Extraction Framework for Estimating Interpersonal Eye Gaze from Conventional Video | Despite a revolution in the pervasiveness of video cameras in our daily lives, one of the most meaningful forms of nonverbal affective communication, interpersonal eye gaze, i.e. eye gaze relative to a conversation partner, is not available from common video. We introduce the Interpersonal-Calibrating Eye-gaze Encoder ... | ['Mohammad Rafayet Ali', 'Minh Tran', 'Taylan Sen', 'Mohammed Ehsan Hoque', 'Kurtis Haut'] | 2019-06-21 | null | null | null | null | ['deception-detection'] | ['miscellaneous'] | [-3.54662724e-02 3.45578343e-01 -1.20414011e-01 -7.65386522e-01
-3.58352810e-01 -8.64989340e-01 3.99717718e-01 -3.36445212e-01
-5.61778367e-01 3.71139705e-01 -2.00638059e-03 1.89193532e-01
-4.74455506e-02 1.62891418e-01 -2.99344033e-01 -3.35731655e-01
3.43844444e-02 -1.32365555e-01 -3.31632227e-01 -3.89283313... | [13.357603073120117, 1.9706246852874756] |
6b54320e-58ea-497c-bfe4-f2bf8709f84a | transformer-based-multimodal-information | 2203.12367 | null | https://arxiv.org/abs/2203.12367v2 | https://arxiv.org/pdf/2203.12367v2.pdf | Transformer-based Multimodal Information Fusion for Facial Expression Analysis | Human affective behavior analysis has received much attention in human-computer interaction (HCI). In this paper, we introduce our submission to the CVPR 2022 Competition on Affective Behavior Analysis in-the-wild (ABAW). To fully exploit affective knowledge from multiple views, we utilize the multimodal features of sp... | ['Suzhen Wang', 'Yu Ding', 'Rudong An', 'Hao Zeng', 'Bowen Ma', 'Feng Qiu', 'Zhimeng Zhang', 'Wei zhang'] | 2022-03-23 | null | null | null | null | ['action-unit-detection'] | ['computer-vision'] | [ 2.23390624e-01 -3.25621605e-01 5.82775548e-02 -5.94030142e-01
-9.15908694e-01 -3.26348186e-01 4.39566731e-01 -1.52413413e-01
-5.81944823e-01 2.11180434e-01 5.01170397e-01 4.45226759e-01
2.69665331e-01 -9.04239714e-02 -1.46569923e-01 -8.63889396e-01
1.34048596e-01 -2.33351842e-01 3.59431794e-03 -1.99685529... | [13.274090766906738, 4.970888137817383] |
79f3a088-03b6-4b13-808a-7ddc1df3d154 | parameter-efficient-local-implicit-image | 2303.15122 | null | https://arxiv.org/abs/2303.15122v1 | https://arxiv.org/pdf/2303.15122v1.pdf | Parameter Efficient Local Implicit Image Function Network for Face Segmentation | Face parsing is defined as the per-pixel labeling of images containing human faces. The labels are defined to identify key facial regions like eyes, lips, nose, hair, etc. In this work, we make use of the structural consistency of the human face to propose a lightweight face-parsing method using a Local Implicit Functi... | ['Balaji Krishnamurthy', 'Rishabh Jain', 'Mayur Hemani', 'Nikitha SR', 'Mausoom Sarkar'] | 2023-03-27 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Sarkar_Parameter_Efficient_Local_Implicit_Image_Function_Network_for_Face_Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Sarkar_Parameter_Efficient_Local_Implicit_Image_Function_Network_for_Face_Segmentation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['face-parsing'] | ['computer-vision'] | [ 2.66793847e-01 6.68677747e-01 5.06350026e-02 -5.93023360e-01
-3.10423881e-01 -2.61338234e-01 2.51677901e-01 -5.61697423e-01
-3.31578583e-01 5.22655249e-01 -3.90868038e-01 -1.45179734e-01
4.82987195e-01 -9.13403749e-01 -8.82337630e-01 -4.15781409e-01
2.24604964e-01 4.79321420e-01 6.95176542e-01 9.67030674... | [13.446515083312988, 0.6027265191078186] |
5304c899-9643-4c3c-8a1b-e053bc411c74 | network-of-experts-for-large-scale-image | 1604.06119 | null | http://arxiv.org/abs/1604.06119v3 | http://arxiv.org/pdf/1604.06119v3.pdf | Network of Experts for Large-Scale Image Categorization | We present a tree-structured network architecture for large scale image
classification. The trunk of the network contains convolutional layers
optimized over all classes. At a given depth, the trunk splits into separate
branches, each dedicated to discriminate a different subset of classes. Each
branch acts as an exper... | ['Karim Ahmed', 'Mohammad Haris Baig', 'Lorenzo Torresani'] | 2016-04-20 | null | null | null | null | ['image-categorization'] | ['computer-vision'] | [-1.13145605e-01 3.24556142e-01 1.54052051e-02 -5.48650861e-01
-6.00957870e-01 -8.66289794e-01 1.53933540e-01 4.77470718e-02
-5.68559706e-01 4.76931661e-01 -2.40360111e-01 -2.51115978e-01
-1.22473523e-01 -7.79335082e-01 -6.13933802e-01 -6.73287153e-01
-1.52026072e-01 7.46588051e-01 6.14318669e-01 4.89140041... | [9.406293869018555, 2.442139148712158] |
0b9d8bb0-cf8d-4ba1-a76b-9c06a950cec0 | document-translation-vs-query-translation-for | null | null | https://aclanthology.org/2020.acl-main.613 | https://aclanthology.org/2020.acl-main.613.pdf | Document Translation vs. Query Translation for Cross-Lingual Information Retrieval in the Medical Domain | We present a thorough comparison of two principal approaches to Cross-Lingual Information Retrieval: document translation (DT) and query translation (QT). Our experiments are conducted using the cross-lingual test collection produced within the CLEF eHealth information retrieval tasks in 2013{--}2015 containing English... | ['Shadi Saleh', 'Pavel Pecina'] | 2020-07-01 | null | null | null | acl-2020-6 | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [ 7.04765096e-02 -2.55367130e-01 -7.19345748e-01 -6.87572584e-02
-2.21077275e+00 -1.10389125e+00 1.16684496e+00 1.71848193e-01
-6.79428756e-01 9.62918699e-01 5.62889457e-01 -7.43664384e-01
-1.81940854e-01 -3.39634985e-01 -6.36865258e-01 -2.60665894e-01
6.44670248e-01 1.14827609e+00 -9.62157398e-02 -4.32375401... | [11.567727088928223, 9.9383544921875] |
8fa328ca-8b59-431a-914a-10875fd532b7 | keypoint-less-camera-calibration-for-sports | 2207.11709 | null | https://arxiv.org/abs/2207.11709v2 | https://arxiv.org/pdf/2207.11709v2.pdf | TVCalib: Camera Calibration for Sports Field Registration in Soccer | Sports field registration in broadcast videos is typically interpreted as the task of homography estimation, which provides a mapping between a planar field and the corresponding visible area of the image. In contrast to previous approaches, we consider the task as a camera calibration problem. First, we introduce a di... | ['Ralph Ewerth', 'Jonas Theiner'] | 2022-07-24 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [ 3.68095577e-01 1.14950342e-02 -8.95909742e-02 -2.81189948e-01
-1.03358316e+00 -7.49248207e-01 3.48533303e-01 3.78674567e-01
-5.90866506e-01 4.11847591e-01 -8.42676610e-02 4.17623341e-01
4.96293455e-02 -5.93241453e-01 -1.19118392e+00 -4.51519638e-01
3.78611416e-01 7.51777232e-01 5.56071639e-01 -1.88268572... | [7.887402057647705, -1.655833125114441] |
a42c96db-8fa6-463e-aedb-bf6c8b6cf663 | a-transformer-based-generative-adversarial | 2207.14134 | null | https://arxiv.org/abs/2207.14134v2 | https://arxiv.org/pdf/2207.14134v2.pdf | A Transformer-based Generative Adversarial Network for Brain Tumor Segmentation | Brain tumor segmentation remains a challenge in medical image segmentation tasks. With the application of transformer in various computer vision tasks, transformer blocks show the capability of learning long-distance dependency in global space, which is complementary with CNNs. In this paper, we proposed a novel transf... | ['Senchun Chai', 'Baihai Zhang', 'Long Chen', 'Liqun Huang'] | 2022-07-28 | null | null | null | null | ['brain-tumor-segmentation'] | ['medical'] | [ 3.97340924e-01 3.81126314e-01 3.38027269e-01 -3.32151294e-01
-9.72957671e-01 -8.74267817e-02 2.84868330e-01 -4.44663852e-01
-6.05075479e-01 7.35112965e-01 7.30361603e-03 -4.08017993e-01
2.11575642e-01 -1.02327013e+00 -8.87665093e-01 -9.80007946e-01
2.44751215e-01 4.18053538e-01 4.85051036e-01 -3.33144546... | [14.536612510681152, -2.4438610076904297] |
761d2a2b-4f39-4876-adff-2ad9dc1728d5 | black-box-bayesian-inference-for-economic | 2202.00625 | null | https://arxiv.org/abs/2202.00625v1 | https://arxiv.org/pdf/2202.00625v1.pdf | Black-box Bayesian inference for economic agent-based models | Simulation models, in particular agent-based models, are gaining popularity in economics. The considerable flexibility they offer, as well as their capacity to reproduce a variety of empirically observed behaviours of complex systems, give them broad appeal, and the increasing availability of cheap computing power has ... | ['Sebastian Schmon', 'J. Doyne Farmer', 'Patrick Cannon', 'Joel Dyer'] | 2022-02-01 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [-1.08769417e-01 -4.02646989e-01 -3.40748727e-01 -1.69344321e-01
-3.23424459e-01 -4.00758237e-01 1.19541717e+00 2.14331537e-01
-6.81743979e-01 1.17173135e+00 -3.53282422e-01 -8.72142851e-01
-6.29756093e-01 -8.33478272e-01 -5.68369210e-01 -7.63571978e-01
-3.66033107e-01 6.49987876e-01 1.21935392e-02 2.20442072... | [6.561779975891113, 3.9990882873535156] |
c45ca0b0-a79e-4d3b-8499-610be3645107 | interest-point-detection-based-on-adaptive | 1901.00031 | null | http://arxiv.org/abs/1901.00031v1 | http://arxiv.org/pdf/1901.00031v1.pdf | Interest Point Detection based on Adaptive Ternary Coding | In this paper, an adaptive pixel ternary coding mechanism is proposed and a
contrast invariant and noise resistant interest point detector is developed on
the basis of this mechanism. Every pixel in a local region is adaptively
encoded into one of the three statuses: bright, uncertain and dark. The blob
significance of... | ['Xudong Jiang', 'Kim-Hui Yap', 'Zhenwei Miao'] | 2018-12-31 | null | null | null | null | ['interest-point-detection'] | ['computer-vision'] | [ 4.16765600e-01 -3.40512812e-01 -1.11417763e-01 -3.30595434e-01
-6.48682117e-02 -2.35341579e-01 5.51022649e-01 9.83790532e-02
-4.92513388e-01 5.89066982e-01 -3.87220740e-01 2.41239779e-02
-8.46164450e-02 -1.00587261e+00 -2.74355412e-01 -1.10547030e+00
2.41494790e-01 -1.10715203e-01 5.85066020e-01 1.68953449... | [10.936622619628906, -2.343397855758667] |
aa233c60-cd41-4f43-84f3-d4258c5d0cea | automatic-audio-captioning-using-attention | 2201.12352 | null | https://arxiv.org/abs/2201.12352v1 | https://arxiv.org/pdf/2201.12352v1.pdf | Automatic Audio Captioning using Attention weighted Event based Embeddings | Automatic Audio Captioning (AAC) refers to the task of translating audio into a natural language that describes the audio events, source of the events and their relationships. The limited samples in AAC datasets at present, has set up a trend to incorporate transfer learning with Audio Event Detection (AED) as a parent... | ['Sunil Kumar Kopparapu', 'Rupayan Chakraborty', 'Swapnil Bhosale'] | 2022-01-28 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 4.53257799e-01 3.90817076e-01 3.78592223e-01 -1.18345223e-01
-1.21730959e+00 -3.59532416e-01 6.58046424e-01 1.18654341e-01
-3.20694268e-01 5.33478260e-01 1.08137476e+00 -1.83506608e-01
2.24609688e-01 -5.19462228e-01 -7.85916746e-01 -3.41312885e-01
-2.22323373e-01 2.59143203e-01 -4.21844609e-02 -1.32247373... | [15.278390884399414, 4.9274678230285645] |
8f9b87e8-cc39-4367-abed-6361ce1c864f | autoavatar-autoregressive-neural-fields-for | 2203.13817 | null | https://arxiv.org/abs/2203.13817v1 | https://arxiv.org/pdf/2203.13817v1.pdf | AutoAvatar: Autoregressive Neural Fields for Dynamic Avatar Modeling | Neural fields such as implicit surfaces have recently enabled avatar modeling from raw scans without explicit temporal correspondences. In this work, we exploit autoregressive modeling to further extend this notion to capture dynamic effects, such as soft-tissue deformations. Although autoregressive models are naturall... | ['Shunsuke Saito', 'Ping Tan', 'Michael Zollhöfer', 'Javier Romero', 'Timur Bagautdinov', 'Ziqian Bai'] | 2022-03-25 | null | null | null | null | ['3d-human-dynamics', '3d-human-reconstruction', 'human-dynamics'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 8.49577785e-02 4.53110099e-01 2.32771318e-02 -2.49069676e-01
-5.81779242e-01 -4.31875676e-01 6.14692926e-01 -3.66637081e-01
-7.59252459e-02 4.39107686e-01 5.77473104e-01 1.01743333e-01
2.29841154e-02 -8.05546343e-01 -9.61527765e-01 -6.01699769e-01
-2.39092439e-01 8.35997224e-01 2.27974087e-01 -2.34664813... | [7.022848606109619, -1.254431962966919] |
3f5811f0-2f8d-4686-88a7-700276524cff | neighborhood-contrastive-learning-for-novel | 2106.10731 | null | https://arxiv.org/abs/2106.10731v1 | https://arxiv.org/pdf/2106.10731v1.pdf | Neighborhood Contrastive Learning for Novel Class Discovery | In this paper, we address Novel Class Discovery (NCD), the task of unveiling new classes in a set of unlabeled samples given a labeled dataset with known classes. We exploit the peculiarities of NCD to build a new framework, named Neighborhood Contrastive Learning (NCL), to learn discriminative representations that are... | ['Nicu Sebe', 'Elisa Ricci', 'Zhiming Luo', 'Subhankar Roy', 'Enrico Fini', 'Zhun Zhong'] | 2021-06-20 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Zhong_Neighborhood_Contrastive_Learning_for_Novel_Class_Discovery_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Zhong_Neighborhood_Contrastive_Learning_for_Novel_Class_Discovery_CVPR_2021_paper.pdf | cvpr-2021-1 | ['novel-class-discovery', 'novel-class-discovery'] | ['computer-vision', 'methodology'] | [ 1.13969691e-01 -5.39663024e-02 -4.01085049e-01 -6.50874078e-01
-1.07291996e+00 -7.22418070e-01 7.39820540e-01 2.32303143e-01
-3.63064706e-01 7.34243691e-01 -2.85220835e-02 1.96063697e-01
-1.24398209e-01 -6.85527802e-01 -7.32328951e-01 -9.18536127e-01
-1.63246199e-01 6.84345067e-01 5.59383444e-02 2.14077920... | [9.537176132202148, 3.0136330127716064] |
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