paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
08ad854c-2ab2-4956-8c96-b18400c5a0d5 | magicbrush-a-manually-annotated-dataset-for | 2306.10012 | null | https://arxiv.org/abs/2306.10012v1 | https://arxiv.org/pdf/2306.10012v1.pdf | MagicBrush: A Manually Annotated Dataset for Instruction-Guided Image Editing | Text-guided image editing is widely needed in daily life, ranging from personal use to professional applications such as Photoshop. However, existing methods are either zero-shot or trained on an automatically synthesized dataset, which contains a high volume of noise. Thus, they still require lots of manual tuning to ... | ['Yu Su', 'Huan Sun', 'Wenhu Chen', 'Lingbo Mo', 'Kai Zhang'] | 2023-06-16 | null | null | null | null | ['text-guided-image-editing'] | ['computer-vision'] | [ 3.93654197e-01 -1.89446300e-01 -3.47210467e-01 -5.00488997e-01
-8.62898886e-01 -6.53760076e-01 5.62852144e-01 -2.40714371e-01
-4.31059808e-01 2.90722251e-01 2.07556307e-01 -4.59238350e-01
3.70722562e-01 -3.35931152e-01 -8.94996524e-01 -1.83133155e-01
5.16514897e-01 2.22002655e-01 3.30584466e-01 -3.97793949... | [11.31387996673584, -0.24264191091060638] |
8fd4ece3-1a51-470f-9020-ee0b6543cda5 | tensor-networks-and-efficient-descriptions-of | 2103.06872 | null | https://arxiv.org/abs/2103.06872v1 | https://arxiv.org/pdf/2103.06872v1.pdf | Tensor networks and efficient descriptions of classical data | We investigate the potential of tensor network based machine learning methods to scale to large image and text data sets. For that, we study how the mutual information between a subregion and its complement scales with the subsystem size $L$, similarly to how it is done in quantum many-body physics. We find that for te... | ['J. Ignacio Cirac', 'Ivan Kukuljan', 'Márton Kanász-Nagy', 'Sirui Lu'] | 2021-03-11 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [-3.22857350e-01 1.84068486e-01 4.55700792e-02 -1.86608940e-01
-2.57972866e-01 -3.60156417e-01 6.30351186e-01 -9.61956009e-02
-2.81549573e-01 5.41150510e-01 4.38693464e-02 -4.64245170e-01
-2.64330864e-01 -7.88252115e-01 -4.27130818e-01 -1.10342050e+00
-4.47239995e-01 5.61384976e-01 3.37713659e-01 -3.78893852... | [5.725931644439697, 4.977334022521973] |
bffecd18-f5b1-4aa0-a7fd-c117cba0dfa7 | continuous-indeterminate-probability-neural | 2303.12964 | null | https://arxiv.org/abs/2303.12964v1 | https://arxiv.org/pdf/2303.12964v1.pdf | Continuous Indeterminate Probability Neural Network | This paper introduces a general model called CIPNN - Continuous Indeterminate Probability Neural Network, and this model is based on IPNN, which is used for discrete latent random variables. Currently, posterior of continuous latent variables is regarded as intractable, with the new theory proposed by IPNN this problem... | ['Tao Yang'] | 2023-03-23 | null | null | null | null | ['classification'] | ['methodology'] | [ 4.27543595e-02 3.97926360e-01 -1.36117712e-01 -1.04877926e-01
-3.07739586e-01 -2.51997739e-01 4.74486828e-01 -9.90570784e-01
2.80801137e-03 1.04499137e+00 2.81026047e-02 -1.54562756e-01
-2.61024028e-01 -8.29930246e-01 -5.73639035e-01 -1.00228322e+00
1.11315705e-01 7.09872544e-01 -5.86025044e-02 5.04050255... | [11.119414329528809, -0.18266336619853973] |
78974fe5-60d4-47ef-a0c4-84dc2d25223d | paying-u-attention-to-textures-multi-stage | 2202.11703 | null | https://arxiv.org/abs/2202.11703v2 | https://arxiv.org/pdf/2202.11703v2.pdf | U-Attention to Textures: Hierarchical Hourglass Vision Transformer for Universal Texture Synthesis | We present a novel U-Attention vision Transformer for universal texture synthesis. We exploit the natural long-range dependencies enabled by the attention mechanism to allow our approach to synthesize diverse textures while preserving their structures in a single inference. We propose a hierarchical hourglass backbone ... | ['Arthur Roullier', 'Douglas Noll', 'Valentin Deschaintre', 'Shouchang Guo'] | 2022-02-23 | null | null | null | null | ['texture-synthesis'] | ['computer-vision'] | [ 3.48593950e-01 2.71545351e-01 -8.21519047e-02 -2.91433007e-01
-7.54724205e-01 -5.22420585e-01 5.16520798e-01 -2.21200198e-01
1.82268798e-01 6.31970644e-01 6.80333436e-01 3.18669304e-02
2.44788036e-01 -1.12869024e+00 -1.13083398e+00 -5.30792236e-01
6.98803142e-02 1.17811285e-01 3.99361938e-01 -5.00280082... | [11.340108871459961, -0.38856041431427] |
7881a5e3-efed-4f4c-91a0-dfd62e52a0f7 | enhanced-prediction-accuracy-with-uncertainty | 2212.04567 | null | https://arxiv.org/abs/2212.04567v1 | https://arxiv.org/pdf/2212.04567v1.pdf | Enhanced prediction accuracy with uncertainty quantification in monitoring CO2 sequestration using convolutional neural networks | Monitoring changes inside a reservoir in real time is crucial for the success of CO2 injection and long-term storage. Machine learning (ML) is well-suited for real-time CO2 monitoring because of its computational efficiency. However, most existing applications of ML yield only one prediction (i.e., the expectation) for... | ['Youzuo Lin', 'Ilya Tsvankin', 'Xitong Zhang', 'Yanhua Liu'] | 2022-12-08 | null | null | null | null | ['seismic-inversion'] | ['miscellaneous'] | [ 2.41804108e-01 -6.93342537e-02 1.15763734e-03 -1.32945672e-01
-7.20754325e-01 -2.04392597e-01 6.95870459e-01 3.60054523e-01
-4.84825611e-01 1.27095973e+00 -8.52375776e-02 -4.75697726e-01
-3.69859606e-01 -1.39211309e+00 -8.62278402e-01 -1.14434361e+00
-2.41284952e-01 4.31386381e-01 2.34360725e-01 -2.63353921... | [6.60422945022583, 3.10429310798645] |
6627aaf9-d23c-41ae-8655-929740a9845a | active-continual-learning-labelling-queries | 2305.03923 | null | https://arxiv.org/abs/2305.03923v1 | https://arxiv.org/pdf/2305.03923v1.pdf | Active Continual Learning: Labelling Queries in a Sequence of Tasks | Acquiring new knowledge without forgetting what has been learned in a sequence of tasks is the central focus of continual learning (CL). While tasks arrive sequentially, the training data are often prepared and annotated independently, leading to CL of incoming supervised learning tasks. This paper considers the under-... | ['Gholamreza Haffari', 'Dinh Phung', 'Shahram Khadivi', 'Thuy-Trang Vu'] | 2023-05-06 | null | null | null | null | ['incremental-learning'] | ['methodology'] | [ 6.84562862e-01 4.87363428e-01 -2.66106278e-01 -3.00498337e-01
-6.60961151e-01 -6.50052845e-01 7.99800754e-01 6.59836352e-01
-9.89982426e-01 1.12989938e+00 8.50204676e-02 -2.11760551e-01
-3.98471504e-01 -3.52284491e-01 -6.34634972e-01 -6.45910978e-01
-3.93760279e-02 7.70560384e-01 6.75866544e-01 1.02258325... | [9.783126831054688, 3.451124429702759] |
99437f0d-c83c-46db-80f5-59906550eb13 | dictionary-learning-for-adaptive-gpr-target | 1806.04599 | null | https://arxiv.org/abs/1806.04599v2 | https://arxiv.org/pdf/1806.04599v2.pdf | Dictionary Learning for Adaptive GPR Landmine Classification | Ground penetrating radar (GPR) target detection and classification is a challenging task. Here, we consider online dictionary learning (DL) methods to obtain sparse representations (SR) of the GPR data to enhance feature extraction for target classification via support vector machines. Online methods are preferred beca... | ['Yonina C. Eldar', 'Maria Antonia Gonzalez-Huici', 'Fabio Giovanneschi', 'Joachim H. G. Ender', 'Kumar Vijay Mishra'] | 2018-05-24 | null | null | null | null | ['landmine'] | ['computer-vision'] | [ 1.98915035e-01 -1.95186108e-01 -8.61728266e-02 -1.70766413e-01
-7.86764026e-01 -2.82648593e-01 2.62459248e-01 5.13875484e-01
-6.22661412e-01 6.57958627e-01 -1.83980435e-01 -4.47270125e-01
-4.87011224e-01 -9.30303276e-01 -4.30384547e-01 -8.62132013e-01
-4.92831260e-01 4.46486205e-01 2.78559774e-01 -4.51491803... | [6.938687801361084, 1.1812641620635986] |
37812f12-0833-414a-a6ef-b6d62b391173 | fatezero-fusing-attentions-for-zero-shot-text | 2303.09535 | null | https://arxiv.org/abs/2303.09535v2 | https://arxiv.org/pdf/2303.09535v2.pdf | FateZero: Fusing Attentions for Zero-shot Text-based Video Editing | The diffusion-based generative models have achieved remarkable success in text-based image generation. However, since it contains enormous randomness in generation progress, it is still challenging to apply such models for real-world visual content editing, especially in videos. In this paper, we propose FateZero, a ze... | ['Qifeng Chen', 'Ying Shan', 'Xintao Wang', 'Chenyang Lei', 'Yong Zhang', 'Xiaodong Cun', 'Chenyang Qi'] | 2023-03-16 | null | null | null | null | ['video-style-transfer', 'text-to-video-editing'] | ['computer-vision', 'computer-vision'] | [ 4.86020118e-01 -3.51824835e-02 1.48020864e-01 -2.00708240e-01
-4.75621819e-01 -2.79215217e-01 8.31292391e-01 -4.93238986e-01
-1.54440150e-01 6.89762115e-01 3.65388960e-01 1.30044281e-01
5.74245043e-02 -7.75265694e-01 -1.07442021e+00 -5.64576745e-01
3.86839151e-01 5.26373833e-02 2.29256839e-01 -3.65595341... | [10.912834167480469, -0.6549492478370667] |
b056e1cf-f6c5-4f89-b176-f557e5311e8c | automated-software-vulnerability-detection | 1803.04497 | null | http://arxiv.org/abs/1803.04497v2 | http://arxiv.org/pdf/1803.04497v2.pdf | Automated software vulnerability detection with machine learning | Thousands of security vulnerabilities are discovered in production software
each year, either reported publicly to the Common Vulnerabilities and Exposures
database or discovered internally in proprietary code. Vulnerabilities often
manifest themselves in subtle ways that are not obvious to code reviewers or
the develo... | ['Jeffrey M. Opper', 'Rebecca L. Russell', 'Peter Chin', 'Marc W. McConley', 'Erik Antelman', 'Paul M. Ellingwood', 'Tomo Lazovich', 'Onur Ozdemir', 'Louis Y. Kim', 'Jacob A. Harer', 'Leonard R. Kosta', 'Lei H. Hamilton', 'Jonathan R. Key', 'Gabriel I. Centeno', 'Alan Mackay', 'Akshay Rangamani'] | 2018-02-14 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [-2.19550833e-01 -2.79336963e-02 -3.84320945e-01 -1.77330852e-01
-9.50221300e-01 -1.03420699e+00 1.30742058e-01 5.35172880e-01
4.37069647e-02 1.46401003e-01 1.69735551e-01 -9.16170359e-01
-9.86018255e-02 -9.95842993e-01 -8.42632353e-01 1.64707541e-01
-3.46959651e-01 -4.08008575e-01 3.43733817e-01 -1.17091410... | [7.123606204986572, 7.767170429229736] |
a6a1e8c2-6403-4d5b-90fe-4fd937ab90bc | minscie-citation-centered-open-information | null | null | https://ub-madoc.bib.uni-mannheim.de/49216/1/_JCDL19Demo__MinScIE%20%284%29.pdf | https://ub-madoc.bib.uni-mannheim.de/49216/1/_JCDL19Demo__MinScIE%20%284%29.pdf | MinScIE: Citation-centered Open Information Extraction | Acknowledging the importance of citations in scientific literature,
in this work we present MinScIE, an Open Information Extraction
system which provides structured knowledge enriched with semantic information about citations. By comparing our system to it’s
original core, MinIE, we show that our approach improves e... | ['Anne Lauscher', 'Yide Song', 'Kiril Gashteovski'] | 2019-06-01 | null | null | null | joint-conference-on-digital-libraries-jcdl | ['open-information-extraction'] | ['natural-language-processing'] | [-3.12513411e-01 6.38053060e-01 -7.43799806e-01 4.96843874e-01
-8.03006172e-01 -8.56263638e-01 8.12601328e-01 4.71917629e-01
-5.79084158e-01 1.42411351e+00 5.44063807e-01 -6.64578557e-01
-6.86412752e-01 -7.22898722e-01 -5.22658229e-01 1.50770664e-01
2.05065832e-01 3.76762003e-01 4.89883840e-01 1.02546796... | [9.509650230407715, 8.306731224060059] |
c5f0df01-5461-4c7d-a2d2-75e3e280478e | automatic-comment-generation-via-multi-pass | 2209.06634 | null | https://arxiv.org/abs/2209.06634v1 | https://arxiv.org/pdf/2209.06634v1.pdf | Automatic Comment Generation via Multi-Pass Deliberation | Deliberation is a common and natural behavior in human daily life. For example, when writing papers or articles, we usually first write drafts, and then iteratively polish them until satisfied. In light of such a human cognitive process, we propose DECOM, which is a multi-pass deliberation framework for automatic comme... | ['Qing Wang', 'Song Wang', 'Lin Shi', 'Xiao Chen', 'Fangwen Mu'] | 2022-09-14 | null | null | null | null | ['comment-generation'] | ['natural-language-processing'] | [ 3.87043297e-01 1.86319038e-01 -1.63889870e-01 -1.37188032e-01
-7.89134681e-01 -5.99663794e-01 6.03128016e-01 4.82940376e-01
-3.10362369e-01 5.55109620e-01 5.50643981e-01 -4.49499279e-01
3.05998355e-01 -5.26571453e-01 -4.57298547e-01 -4.69110131e-01
4.29927230e-01 2.53831536e-01 1.53395936e-01 2.44980380... | [7.605380058288574, 7.971821308135986] |
bf0c0cad-b14d-44b0-ae0f-60741f63d949 | thank-you-bart-rewarding-pre-trained-models | 2105.06947 | null | https://arxiv.org/abs/2105.06947v2 | https://arxiv.org/pdf/2105.06947v2.pdf | Thank you BART! Rewarding Pre-Trained Models Improves Formality Style Transfer | Scarcity of parallel data causes formality style transfer models to have scarce success in preserving content. We show that fine-tuning pre-trained language (GPT-2) and sequence-to-sequence (BART) models boosts content preservation, and that this is possible even with limited amounts of parallel data. Augmenting these ... | ['Malvina Nissim', 'Antonio Toral', 'Huiyuan Lai'] | 2021-05-14 | null | https://aclanthology.org/2021.acl-short.62 | https://aclanthology.org/2021.acl-short.62.pdf | acl-2021-5 | ['formality-style-transfer'] | ['natural-language-processing'] | [ 3.50456953e-01 3.91823128e-02 -3.32210124e-01 -2.09531665e-01
-1.01747191e+00 -7.76674211e-01 8.83334935e-01 -1.20611869e-01
-5.56586504e-01 9.66824412e-01 8.19484293e-01 -3.76616299e-01
5.05042017e-01 -5.27311504e-01 -1.18369102e+00 -2.14857429e-01
-1.50084287e-01 4.00496423e-01 1.78757787e-01 -7.40675926... | [11.576286315917969, 9.649175643920898] |
ac5a7063-d96c-4d45-b4b1-b97fdf389fce | fault-detection-for-non-condensing-boilers | 2205.08418 | null | https://arxiv.org/abs/2205.08418v2 | https://arxiv.org/pdf/2205.08418v2.pdf | Fault Detection for Non-Condensing Boilers using Simulated Building Automation System Sensor Data | Building performance has been shown to degrade significantly after commissioning, resulting in increased energy consumption and associated greenhouse gas emissions. Continuous Commissioning using existing sensor networks and IoT devices has the potential to minimize this waste by continually identifying system degradat... | ['J. J. McArthur', 'Y. Wang', 'Mohamed Kandil', 'Rony Shohet'] | 2022-05-13 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [ 8.18204284e-02 -1.83468223e-01 9.10523757e-02 -5.85662909e-02
6.46951050e-02 -3.48051786e-01 1.72786623e-01 6.06534839e-01
4.68052655e-01 6.69021964e-01 -1.84262097e-01 -5.58394969e-01
-5.18633008e-01 -1.16874242e+00 -2.08404258e-01 -8.50096047e-01
-3.45771223e-01 3.87140542e-01 7.59376958e-02 7.63742402... | [6.401984691619873, 2.4487533569335938] |
c7ad5155-c341-424f-ae5c-6bd44d413796 | patch-wise-contrastive-style-learning-for | 2204.07486 | null | https://arxiv.org/abs/2204.07486v1 | https://arxiv.org/pdf/2204.07486v1.pdf | Patch-wise Contrastive Style Learning for Instagram Filter Removal | Image-level corruptions and perturbations degrade the performance of CNNs on different downstream vision tasks. Social media filters are one of the most common resources of various corruptions and perturbations for real-world visual analysis applications. The negative effects of these distractive factors can be allevia... | ['Furkan Kıraç', 'Barış Özcan', 'Furkan Kınlı'] | 2022-04-15 | null | null | null | null | ['reverse-style-transfer'] | ['computer-vision'] | [ 2.79579461e-01 -1.46323696e-01 2.49833897e-01 -1.79034919e-01
-4.31433767e-01 -6.39105618e-01 8.55926931e-01 -5.92526317e-01
-6.35554016e-01 8.66959631e-01 2.38738909e-01 5.04550710e-02
2.36089453e-01 -6.60375178e-01 -1.10071516e+00 -8.36969912e-01
3.09683740e-01 -3.48039925e-01 4.69539523e-01 -4.08298790... | [11.117572784423828, -1.7409229278564453] |
33768d70-5ad8-48b1-b79f-1b3215a8bf4c | smart-learning-to-find-dumb-contracts | 2304.10726 | null | https://arxiv.org/abs/2304.10726v2 | https://arxiv.org/pdf/2304.10726v2.pdf | Smart Learning to Find Dumb Contracts (Extended Version) | We introduce the Deep Learning Vulnerability Analyzer (DLVA) for Ethereum smart contracts based on neural networks. We train DLVA to judge bytecode even though the supervising oracle can only judge source. DLVA's training algorithm is general: we extend a source code analysis to bytecode without any manual feature engi... | ['Aquinas Hobor', 'Tamer Abdelaziz'] | 2023-04-21 | null | null | null | null | ['feature-engineering', 'vulnerability-detection'] | ['methodology', 'miscellaneous'] | [-1.82074711e-01 3.25066298e-01 -6.52541518e-01 -2.81040460e-01
-1.06008577e+00 -1.16647935e+00 3.21258247e-01 1.37746483e-01
-1.04025356e-01 2.88828641e-01 -2.54039645e-01 -1.46349561e+00
1.39936715e-01 -1.17821395e+00 -8.34502518e-01 -3.76826674e-01
-3.85446042e-01 8.44679475e-01 2.93757915e-01 -1.43144473... | [6.8358073234558105, 7.42465353012085] |
b39b0b48-1866-42c9-8b0e-5170f2f3fa42 | 3d-human-pose-estimation-using-spatio-1 | 2004.11822 | null | https://arxiv.org/abs/2004.11822v1 | https://arxiv.org/pdf/2004.11822v1.pdf | 3D Human Pose Estimation using Spatio-Temporal Networks with Explicit Occlusion Training | Estimating 3D poses from a monocular video is still a challenging task, despite the significant progress that has been made in recent years. Generally, the performance of existing methods drops when the target person is too small/large, or the motion is too fast/slow relative to the scale and speed of the training data... | ['Bo wang', 'Yu Cheng', 'Bo Yang', 'Robby T. Tan'] | 2020-04-07 | 3d-human-pose-estimation-using-spatio | https://aaai.org/Conferences/AAAI-20/wp-content/uploads/2020/02/AAAI20-ProgramWeb.pdf | https://www.dropbox.com/s/tm8q3agp4chtlnz/3DPoseEstimation_AAAI20.pdf?dl=0 | aaai-conference-on-artificial-intelligence-1 | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [-2.51192659e-01 -3.73840600e-01 -4.81125623e-01 -1.13618508e-01
-3.35289747e-01 -5.01519799e-01 4.01224911e-01 -5.02697587e-01
-4.88260329e-01 5.40225685e-01 4.84127790e-01 2.06258535e-01
1.61082029e-01 -4.57224101e-01 -6.75953746e-01 -2.40698636e-01
-4.40709561e-01 3.61445993e-01 4.96350616e-01 -5.89544289... | [7.166962146759033, -0.6290262341499329] |
5c108cd8-1584-43b5-8c58-29af957989fc | ambipun-generating-humorous-puns-with-1 | 2205.01825 | null | https://arxiv.org/abs/2205.01825v1 | https://arxiv.org/pdf/2205.01825v1.pdf | AmbiPun: Generating Humorous Puns with Ambiguous Context | In this paper, we propose a simple yet effective way to generate pun sentences that does not require any training on existing puns. Our approach is inspired by humor theories that ambiguity comes from the context rather than the pun word itself. Given a pair of definitions of a pun word, our model first produces a list... | ['Nanyun Peng', 'Yufei Tian', 'Anirudh Mittal'] | 2022-05-04 | null | https://aclanthology.org/2022.naacl-main.77 | https://aclanthology.org/2022.naacl-main.77.pdf | naacl-2022-7 | ['reverse-dictionary'] | ['natural-language-processing'] | [ 3.01369458e-01 -7.73051307e-02 1.35074677e-02 7.20536560e-02
-1.04262817e+00 -8.07285666e-01 6.13326013e-01 2.07826287e-01
-5.73899209e-01 1.05559468e+00 5.51728070e-01 -5.49443960e-02
3.06631267e-01 -9.61302698e-01 -4.49140191e-01 -2.93150693e-01
5.43711722e-01 6.97384357e-01 -1.66932553e-01 -8.41993511... | [11.360284805297852, 9.035588264465332] |
4fcb8b16-6d15-4c1c-bf90-bd2fe0c413cf | exploiting-symmetry-and-heuristic | 2304.06055 | null | https://arxiv.org/abs/2304.06055v1 | https://arxiv.org/pdf/2304.06055v1.pdf | Exploiting Symmetry and Heuristic Demonstrations in Off-policy Reinforcement Learning for Robotic Manipulation | Reinforcement learning demonstrates significant potential in automatically building control policies in numerous domains, but shows low efficiency when applied to robot manipulation tasks due to the curse of dimensionality. To facilitate the learning of such tasks, prior knowledge or heuristics that incorporate inheren... | ['Homayoun Najjaran', 'Kashish Gupta', 'Zengjie Zhang', 'Amir M. Soufi Enayati'] | 2023-04-12 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 2.78453767e-01 4.38729286e-01 -3.33311796e-01 1.34872705e-01
-3.44015360e-01 -5.27936399e-01 6.90950215e-01 -8.02349970e-02
-5.24621189e-01 1.31249869e+00 -5.61082423e-01 -4.65783536e-01
-7.69500196e-01 -4.70012099e-01 -8.88660192e-01 -7.83996940e-01
-4.87489522e-01 3.47287834e-01 1.01374313e-01 -5.20710170... | [4.677056312561035, 1.5493898391723633] |
a4e9546b-ffc7-4f79-8b65-49cf174d3b70 | a-privacy-preserving-image-retrieval-scheme-1 | 2202.00382 | null | https://arxiv.org/abs/2202.00382v1 | https://arxiv.org/pdf/2202.00382v1.pdf | A Privacy-Preserving Image Retrieval Scheme with a Mixture of Plain and EtC Images | In this paper, we propose a novel content-based image-retrieval scheme that allows us to use a mixture of plain images and compressible encrypted ones called "encryption-then-compression (EtC) images." In the proposed scheme, extended SIMPLE descriptors are extracted from EtC images as well as from plain ones, so the m... | ['Hitoshi Kiya', 'Kenta Iida'] | 2022-02-01 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [ 4.05322522e-01 -6.48735344e-01 -6.81772754e-02 -2.72962004e-01
-7.77563512e-01 -4.71307337e-01 6.93792522e-01 2.90207326e-01
-1.06928372e+00 5.75964272e-01 1.67321358e-02 -6.62055844e-03
-1.39478579e-01 -9.84686315e-01 -1.69997469e-01 -9.00042832e-01
2.76290951e-03 -1.11587450e-01 3.84780690e-02 -1.17065355... | [10.753352165222168, -0.12603740394115448] |
95ad4280-c309-4df0-aeb4-294ce8f9f37b | partially-shared-semi-supervised-deep-matrix | 2012.00993 | null | https://arxiv.org/abs/2012.00993v1 | https://arxiv.org/pdf/2012.00993v1.pdf | Partially Shared Semi-supervised Deep Matrix Factorization with Multi-view Data | Since many real-world data can be described from multiple views, multi-view learning has attracted considerable attention. Various methods have been proposed and successfully applied to multi-view learning, typically based on matrix factorization models. Recently, it is extended to the deep structure to exploit the hie... | ['Weijun Sun', 'Zuyuan Yang', 'Wei Yan', 'Naiyao Liang', 'Haonan Huang'] | 2020-12-02 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [-4.33990896e-01 -2.62890756e-01 -4.16039258e-01 -4.83196110e-01
-6.20475352e-01 -2.80002207e-01 3.41486275e-01 -3.35664690e-01
1.23690575e-01 4.29311424e-01 5.27273238e-01 1.59899458e-01
-1.71681508e-01 -5.14643729e-01 -4.72444206e-01 -7.79632568e-01
1.69041365e-01 2.51663953e-01 -1.43347114e-01 -6.41800612... | [8.451587677001953, 4.55568790435791] |
389b6900-9e77-45bc-9ec9-4bb299de6b76 | naver-at-activitynet-challenge-2019-task-b | 1906.10555 | null | https://arxiv.org/abs/1906.10555v1 | https://arxiv.org/pdf/1906.10555v1.pdf | Naver at ActivityNet Challenge 2019 -- Task B Active Speaker Detection (AVA) | This report describes our submission to the ActivityNet Challenge at CVPR 2019. We use a 3D convolutional neural network (CNN) based front-end and an ensemble of temporal convolution and LSTM classifiers to predict whether a visible person is speaking or not. Our results show significant improvements over the baseline ... | ['Joon Son Chung'] | 2019-06-25 | null | null | null | null | ['audio-visual-active-speaker-detection'] | ['computer-vision'] | [ 1.00068435e-01 1.06620222e-01 -5.60606271e-02 -4.80521947e-01
-6.40904307e-01 -5.28914273e-01 9.08803284e-01 -5.66777706e-01
-4.00701582e-01 4.60671574e-01 9.60771978e-01 -3.62527609e-01
4.70211387e-01 -2.13006049e-01 -5.00201404e-01 -4.23501343e-01
-1.39900610e-01 1.25473857e-01 1.63706452e-01 2.18632594... | [14.345499992370605, 5.840313911437988] |
63f89871-6a72-41d4-ae9d-d55b992c0da6 | disc-learning-from-noisy-labels-via-dynamic | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_DISC_Learning_From_Noisy_Labels_via_Dynamic_Instance-Specific_Selection_and_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_DISC_Learning_From_Noisy_Labels_via_Dynamic_Instance-Specific_Selection_and_CVPR_2023_paper.pdf | DISC: Learning From Noisy Labels via Dynamic Instance-Specific Selection and Correction | Existing studies indicate that deep neural networks (DNNs) can eventually memorize the label noise. We observe that the memorization strength of DNNs towards each instance is different and can be represented by the confidence value, which becomes larger and larger during the training process. Based on this, we prop... | ['Xilin Chen', 'Shiguang Shan', 'Hu Han', 'YiFan Li'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['learning-with-noisy-labels', 'memorization', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing', 'natural-language-processing'] | [ 7.47475699e-02 -2.11237296e-01 -1.27424330e-01 -6.74062073e-01
-7.98909426e-01 -4.98975754e-01 2.29458928e-01 -9.40058380e-02
-4.54720616e-01 7.67885029e-01 -9.44496319e-02 8.80925134e-02
-1.85243428e-01 -5.72910428e-01 -7.12264180e-01 -1.17270207e+00
5.12371898e-01 2.10119739e-01 1.49215162e-01 1.92590445... | [9.390663146972656, 3.890247106552124] |
a04861c9-fe16-43a7-bf47-966f88cd3179 | investigation-of-feature-processing-modules | null | null | https://aclanthology.org/2022.rocling-1.11 | https://aclanthology.org/2022.rocling-1.11.pdf | Investigation of feature processing modules and attention mechanisms in speaker verification system | In this paper, we use several combinations of feature front-end modules and attention mechanisms to improve the performance of our speaker verification system. An updated version of ECAPA-TDNN is chosen as a baseline. We replace and integrate different feature front-end and attention mechanism modules to compare and fi... | ['Wei-Yu Chen', 'Hsiang-Feng Chuang', 'Yu-Han Cheng', 'Bo-Cheng Chan', 'Chung-Li Lu', 'Chia-Ping Chen', 'Wei-Ting Lin', 'Ting-Wei Chen'] | null | null | null | null | rocling-2022-11 | ['speaker-verification'] | ['speech'] | [-5.23693800e-01 -9.02868062e-02 -4.64004874e-02 -7.69669116e-01
-8.98272157e-01 -3.51187229e-01 5.43649495e-01 -6.26470685e-01
-4.51011658e-01 3.02801251e-01 3.16475958e-01 -3.16999286e-01
4.83581781e-01 -2.70782650e-01 -2.26021156e-01 -5.53887963e-01
1.71233729e-01 2.15977564e-01 1.23053819e-01 -2.75081843... | [14.34304428100586, 6.190877437591553] |
056618fb-53f3-4e93-af5b-bef6dee4c0f9 | scalable-quantum-neural-networks-for | 2208.07719 | null | https://arxiv.org/abs/2208.07719v1 | https://arxiv.org/pdf/2208.07719v1.pdf | Scalable Quantum Neural Networks for Classification | Many recent machine learning tasks resort to quantum computing to improve classification accuracy and training efficiency by taking advantage of quantum mechanics, known as quantum machine learning (QML). The variational quantum circuit (VQC) is frequently utilized to build a quantum neural network (QNN), which is a co... | ['Qun Li', 'Zeyi Tao', 'Jindi Wu'] | 2022-08-04 | null | null | null | null | ['classification'] | ['methodology'] | [ 3.03126007e-01 -1.51363492e-01 -2.68962055e-01 -1.61387876e-01
-6.98406339e-01 -4.95547891e-01 3.66145372e-01 -6.70284554e-02
-7.63861358e-01 6.79461360e-01 -7.25585580e-01 -4.83494610e-01
-1.93510652e-02 -1.42487311e+00 -7.90540814e-01 -1.01889503e+00
1.62714869e-01 1.58603370e-01 2.62157619e-01 -3.78567398... | [5.567742824554443, 4.967164039611816] |
9001f0d0-4412-422c-9d75-22e2bcd509aa | towards-better-citation-intent-classification | null | null | https://openreview.net/forum?id=iyuluxX9K9B | https://openreview.net/pdf?id=iyuluxX9K9B | Towards Better Citation Intent Classification | Accurate classification of citation intents in a scientific article provides deeper contextual understanding of and better quantifies the contributions of cited articles. This improves scientific literature platform capabilities such as search relevance, ranking and more. To our knowledge, we present the most comprehen... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['citation-intent-classification'] | ['natural-language-processing'] | [ 5.36710247e-02 -1.33530676e-01 -7.94169247e-01 -1.58274412e-01
-1.09857631e+00 -7.72812724e-01 1.11508465e+00 4.45659429e-01
-6.22041762e-01 6.41693711e-01 5.29755116e-01 -8.27704310e-01
-5.43152452e-01 -5.46059072e-01 -5.36479652e-01 -3.70548457e-01
2.08267719e-01 5.66706300e-01 -7.91115407e-03 1.97952151... | [9.659110069274902, 8.284953117370605] |
a7748bc0-9185-4272-949c-f14a8409aa0a | compressing-sentence-representation-with | 2304.12674 | null | https://arxiv.org/abs/2304.12674v1 | https://arxiv.org/pdf/2304.12674v1.pdf | Compressing Sentence Representation with maximum Coding Rate Reduction | In most natural language inference problems, sentence representation is needed for semantic retrieval tasks. In recent years, pre-trained large language models have been quite effective for computing such representations. These models produce high-dimensional sentence embeddings. An evident performance gap between larg... | ['Domagoj Matijević', 'Jurica Maltar', 'Luka Borozan', 'Antonio Jovanović', 'Tomislav Prusina', 'Domagoj Ševerdija'] | 2023-04-25 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings', 'semantic-retrieval'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [ 3.75922143e-01 2.77931243e-01 -2.08737664e-02 -1.76758662e-01
-1.16503644e+00 -2.66981393e-01 7.57205307e-01 4.59232301e-01
-5.85276127e-01 2.30971947e-01 5.52319229e-01 -3.10344875e-01
-2.16701478e-02 -6.44304693e-01 -4.79149431e-01 -5.08406878e-01
-9.61599723e-02 5.75486720e-01 -1.29437804e-01 -1.69827580... | [10.976219177246094, 8.459891319274902] |
a371242b-549f-4c2c-bc2b-90c729d8cbec | enhancing-personalized-dialogue-generation | 2305.11482 | null | https://arxiv.org/abs/2305.11482v1 | https://arxiv.org/pdf/2305.11482v1.pdf | Enhancing Personalized Dialogue Generation with Contrastive Latent Variables: Combining Sparse and Dense Persona | The personalized dialogue explores the consistent relationship between dialogue generation and personality. Existing personalized dialogue agents model persona profiles from three resources: sparse or dense persona descriptions and dialogue histories. However, sparse structured persona attributes are explicit but uninf... | ['Yuexian Hou', 'Ruifang He', 'Kun Huang', 'Dongming Zhao', 'Miao Fang', 'Bo wang', 'Yihong Tang'] | 2023-05-19 | null | null | null | null | ['dialogue-generation', 'dialogue-generation'] | ['natural-language-processing', 'speech'] | [-4.48862016e-01 4.51088190e-01 -3.35385114e-01 -8.23274076e-01
-6.31737649e-01 -3.30968887e-01 1.04323697e+00 -2.67160326e-01
-1.91049129e-01 9.77032781e-01 1.10249937e+00 5.41972160e-01
4.59407307e-02 -7.21077502e-01 3.00340444e-01 -4.65338290e-01
3.35882187e-01 1.19391334e+00 -2.35269159e-01 -5.99526227... | [12.670783996582031, 8.165959358215332] |
3e32a35b-ad10-4c12-a7ec-ce71c425efac | an-automated-theorem-proving-framework-for | 2101.12370 | null | https://arxiv.org/abs/2101.12370v4 | https://arxiv.org/pdf/2101.12370v4.pdf | An Automated Theorem Proving Framework for Information-Theoretic Results | We present a versatile automated theorem proving framework capable of automated discovery, simplification and proofs of inner and outer bounds in network information theory, deduction of properties of information-theoretic quantities (e.g. Wyner and G\'acs-K\"orner common information), and discovery of non-Shannon-type... | ['Cheuk Ting Li'] | 2021-01-29 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 1.48887068e-01 3.27874094e-01 -5.85210323e-01 2.75967214e-02
7.58597702e-02 -8.36122215e-01 2.39465252e-01 4.48613524e-01
-2.02616811e-01 1.05579472e+00 -3.06830615e-01 -1.24817657e+00
-1.25162494e+00 -1.03038263e+00 -2.99652249e-01 -2.83869803e-01
-8.62641037e-01 4.68860537e-01 2.42570415e-01 -4.12216559... | [7.471372127532959, 5.3482985496521] |
189a74e8-96d8-4421-8242-c65140b7598d | ovanet-one-vs-all-network-for-universal | 2104.03344 | null | https://arxiv.org/abs/2104.03344v4 | https://arxiv.org/pdf/2104.03344v4.pdf | OVANet: One-vs-All Network for Universal Domain Adaptation | Universal Domain Adaptation (UNDA) aims to handle both domain-shift and category-shift between two datasets, where the main challenge is to transfer knowledge while rejecting unknown classes which are absent in the labeled source data but present in the unlabeled target data. Existing methods manually set a threshold t... | ['Kate Saenko', 'Kuniaki Saito'] | 2021-04-07 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Saito_OVANet_One-vs-All_Network_for_Universal_Domain_Adaptation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Saito_OVANet_One-vs-All_Network_for_Universal_Domain_Adaptation_ICCV_2021_paper.pdf | iccv-2021-1 | ['universal-domain-adaptation'] | ['computer-vision'] | [ 5.78046679e-01 7.00154603e-02 -6.29788995e-01 -7.77721643e-01
-9.98501420e-01 -9.53320086e-01 5.59039414e-01 2.48413578e-01
-6.31368041e-01 9.66486633e-01 -2.52027214e-01 -3.07516247e-01
-1.16996340e-01 -7.58243799e-01 -6.32105827e-01 -8.23369622e-01
3.40906650e-01 9.28900361e-01 6.33856297e-01 -1.29539799... | [10.317163467407227, 3.2143800258636475] |
134c35e9-7f43-4c76-babd-7808c84d0531 | a-dual-cycled-cross-view-transformer-network | 2209.08844 | null | https://arxiv.org/abs/2209.08844v1 | https://arxiv.org/pdf/2209.08844v1.pdf | A Dual-Cycled Cross-View Transformer Network for Unified Road Layout Estimation and 3D Object Detection in the Bird's-Eye-View | The bird's-eye-view (BEV) representation allows robust learning of multiple tasks for autonomous driving including road layout estimation and 3D object detection. However, contemporary methods for unified road layout estimation and 3D object detection rarely handle the class imbalance of the training dataset and multi-... | ['Ue-Hwan Kim', 'Curie Kim'] | 2022-09-19 | null | null | null | null | ['monocular-cross-view-road-scene-parsing', 'monocular-cross-view-road-scene-parsing-road'] | ['computer-vision', 'computer-vision'] | [ 1.86964255e-02 -2.88371183e-02 -4.33575958e-01 -3.73196006e-01
-6.16809070e-01 -4.02370691e-01 4.97258753e-01 -2.36252531e-01
-3.28627199e-01 3.61049503e-01 -4.29587990e-01 -5.88968694e-01
-1.11627311e-01 -7.86133826e-01 -9.56835330e-01 -6.60592437e-01
2.21556351e-01 1.28929138e-01 5.93188941e-01 -7.14200065... | [7.938626289367676, -2.2425200939178467] |
f43b021f-7209-4ca9-a845-70c576ed25fa | debiasing-pipeline-improves-deep-learning | 2201.09563 | null | https://arxiv.org/abs/2201.09563v1 | https://arxiv.org/pdf/2201.09563v1.pdf | Debiasing pipeline improves deep learning model generalization for X-ray based lung nodule detection | Lung cancer is the leading cause of cancer death worldwide and a good prognosis depends on early diagnosis. Unfortunately, screening programs for the early diagnosis of lung cancer are uncommon. This is in-part due to the at-risk groups being located in rural areas far from medical facilities. Reaching these population... | ['U. Rajendra Arharya', 'Prabal Datta Barua', 'Hui Wen Loh', 'Jing Zhu', 'Manoranjan Paul', 'Biswajeet Pradhan', 'Subrata Chakraborty', 'Michael Horry'] | 2022-01-24 | null | null | null | null | ['lung-nodule-detection'] | ['medical'] | [ 5.24271667e-01 2.64779210e-01 -2.61313349e-01 -2.36766145e-01
-1.09247386e+00 -4.32902217e-01 2.63860114e-02 3.20038438e-01
-6.10532045e-01 3.07668984e-01 -8.19306746e-02 -8.36182714e-01
-3.32997173e-01 -8.70617330e-01 -7.29738772e-01 -6.94241047e-01
2.43588109e-02 7.07899451e-01 4.44454819e-01 1.28301755... | [15.34460735321045, -2.173482656478882] |
f6b3bd45-ea26-4a2b-b351-293fbac4f14a | stablenet-semi-online-multi-scale-deep-video | 1907.10283 | null | https://arxiv.org/abs/1907.10283v1 | https://arxiv.org/pdf/1907.10283v1.pdf | StableNet: Semi-Online, Multi-Scale Deep Video Stabilization | Video stabilization algorithms are of greater importance nowadays with the prevalence of hand-held devices which unavoidably produce videos with undesirable shaky motions. In this paper we propose a data-driven online video stabilization method along with a paired dataset for deep learning. The network processes each u... | ['Chi-Keung Tang', 'Yu-Wing Tai', 'Chia-Hung Huang', 'Hang Yin'] | 2019-07-24 | null | null | null | null | ['video-stabilization'] | ['computer-vision'] | [-4.16791625e-02 -1.47350952e-01 -2.00299606e-01 1.79471910e-01
-5.61437786e-01 -7.15576768e-01 5.84886372e-01 -3.06121528e-01
-2.16689602e-01 7.50940859e-01 3.80357116e-01 -5.05940244e-02
2.67586440e-01 -2.50650853e-01 -1.04298043e+00 -8.02671194e-01
-8.76839757e-02 -1.47207633e-01 3.13258350e-01 -4.02999640... | [10.654052734375, -1.3757683038711548] |
28e1733a-05ab-4d0e-abc2-5c543364bcdc | signing-outside-the-studio-benchmarking | 2211.00448 | null | https://arxiv.org/abs/2211.00448v1 | https://arxiv.org/pdf/2211.00448v1.pdf | Signing Outside the Studio: Benchmarking Background Robustness for Continuous Sign Language Recognition | The goal of this work is background-robust continuous sign language recognition. Most existing Continuous Sign Language Recognition (CSLR) benchmarks have fixed backgrounds and are filmed in studios with a static monochromatic background. However, signing is not limited only to studios in the real world. In order to an... | ['In So Kweon', 'Joon Son Chung', 'Dong-Jin Kim', 'Jae Won Cho', 'Youngtaek Oh', 'Youngjoon Jang'] | 2022-11-01 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [ 6.38217092e-01 -7.48267114e-01 5.98482192e-02 -2.33492568e-01
-6.50451899e-01 -5.89763641e-01 5.80757141e-01 -1.14340603e+00
-3.58800709e-01 7.09849536e-01 1.77631781e-01 -3.34700912e-01
4.78035420e-01 -3.65535051e-01 -7.64485419e-01 -9.70583498e-01
4.46559012e-01 -1.46167487e-01 7.75978446e-01 -2.55548656... | [9.169212341308594, -6.471676349639893] |
5ff3e4b3-c9d2-4619-b2a5-91b603ff4746 | tsi-gan-unsupervised-time-series-anomaly | 2303.12952 | null | https://arxiv.org/abs/2303.12952v1 | https://arxiv.org/pdf/2303.12952v1.pdf | TSI-GAN: Unsupervised Time Series Anomaly Detection using Convolutional Cycle-Consistent Generative Adversarial Networks | Anomaly detection is widely used in network intrusion detection, autonomous driving, medical diagnosis, credit card frauds, etc. However, several key challenges remain open, such as lack of ground truth labels, presence of complex temporal patterns, and generalizing over different datasets. This paper proposes TSI-GAN,... | ['Mao Van Ngo', 'Tie Luo', 'Shyam Sundar Saravanan'] | 2023-03-22 | null | null | null | null | ['medical-diagnosis', 'network-intrusion-detection', 'time-series-anomaly-detection'] | ['medical', 'miscellaneous', 'time-series'] | [ 2.20454529e-01 -2.58560598e-01 1.33566648e-01 -4.30766940e-01
-3.41520607e-01 -3.24961275e-01 5.56981981e-01 -1.64909026e-04
-3.02840114e-01 5.65180480e-01 -2.59871066e-01 -4.19687152e-01
-1.91145688e-02 -8.12755406e-01 -6.57536924e-01 -5.98428965e-01
-4.02071804e-01 4.47730929e-01 1.28717095e-01 -1.86291978... | [7.460391998291016, 2.522573471069336] |
0b7dbfd5-37d6-4f07-a35c-8366379b228e | image-reconstruction-for-accelerated-mr-scan | 2306.02886 | null | https://arxiv.org/abs/2306.02886v1 | https://arxiv.org/pdf/2306.02886v1.pdf | Image Reconstruction for Accelerated MR Scan with Faster Fourier Convolutional Neural Networks | Partial scan is a common approach to accelerate Magnetic Resonance Imaging (MRI) data acquisition in both 2D and 3D settings. However, accurately reconstructing images from partial scan data (i.e., incomplete k-space matrices) remains challenging due to lack of an effectively global receptive field in both spatial and ... | ['Xuelong Li', 'ZhenChang Wang', 'Yonghong Hou', 'Yiming Liu', 'Xuebin Sun', 'Yanwei Pang', 'Xiaohan Liu'] | 2023-06-05 | null | null | null | null | ['image-reconstruction', '3d-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 2.46354714e-01 -1.43478051e-01 1.03809617e-01 -5.05378366e-01
-6.75044358e-01 -1.89548403e-01 3.10968369e-01 -4.25658733e-01
-7.05849707e-01 5.10173798e-01 5.04410684e-01 -3.60300004e-01
-4.51869488e-01 -6.09882712e-01 -7.49396384e-01 -7.14566827e-01
-5.59129417e-01 2.02493146e-01 4.37359303e-01 -1.11793563... | [13.596707344055176, -2.418804407119751] |
823a99b3-082e-4aa4-84dd-40d858e00743 | neurorehab-an-interface-for-rehabilitation | 2301.10957 | null | https://arxiv.org/abs/2301.10957v1 | https://arxiv.org/pdf/2301.10957v1.pdf | Neurorehab: An Interface for Rehabilitation | About 15% of the world population is affected by a disability in some form, amongst whom only 31% perform the recommended exercises without intervention. We are working on developing a motivating and effective way to encourage people. In our work, we leverage the fact that repetitive exercises can help people with moto... | ['Roopeswar Kommalapati', 'Adam Fendler', 'Adeboye A. Adejare Jr', 'Atul Dhingra'] | 2023-01-26 | null | null | null | null | ['unity'] | ['computer-vision'] | [-2.03167424e-01 3.56596410e-01 -1.59395933e-01 2.77900040e-01
1.86679825e-01 -4.78308797e-02 7.06742555e-02 -5.11419177e-01
-1.03451836e+00 8.66065025e-01 8.27705622e-01 2.90727094e-02
-4.95782554e-01 -7.74986923e-01 -2.44681329e-01 -5.54011345e-01
-7.93513060e-02 4.35480416e-01 5.17676950e-01 -6.55417621... | [7.047750473022461, 0.2943125069141388] |
41c4c44a-010e-43d0-a0f4-60ce97f7275a | 190513147 | 1905.13147 | null | https://arxiv.org/abs/1905.13147v1 | https://arxiv.org/pdf/1905.13147v1.pdf | Anomaly Detection in Images | Visual defect assessment is a form of anomaly detection. This is very relevant in finding faults such as cracks and markings in various surface inspection tasks like pavement and automotive parts. The task involves detection of deviation/divergence of anomalous samples from the normal ones. Two of the major challenges ... | ['Manpreet Singh Minhas', 'John Zelek'] | 2019-05-09 | null | null | null | null | ['supervised-anomaly-detection'] | ['computer-vision'] | [ 1.50677919e-01 1.77448139e-01 3.04979175e-01 -3.72510761e-01
-4.93717194e-01 -8.94156620e-02 4.81012106e-01 8.14568818e-01
-3.27661991e-01 4.28079069e-01 -3.38485777e-01 -3.90434802e-01
-1.61839142e-01 -6.26489401e-01 -6.69851482e-01 -9.39073861e-01
-4.99758452e-01 5.60622633e-01 5.25257349e-01 -3.03252220... | [7.586676597595215, 2.2051126956939697] |
a355f3bc-3532-4835-95d7-9ed645376d0a | improving-panoptic-segmentation-for-nighttime | 2306.13725 | null | https://arxiv.org/abs/2306.13725v1 | https://arxiv.org/pdf/2306.13725v1.pdf | Improving Panoptic Segmentation for Nighttime or Low-Illumination Urban Driving Scenes | Autonomous vehicles and driving systems use scene parsing as an essential tool to understand the surrounding environment. Panoptic segmentation is a state-of-the-art technique which proves to be pivotal in this use case. Deep learning-based architectures have been utilized for effective and efficient Panoptic Segmentat... | ['Ankur Chrungoo'] | 2023-06-23 | null | null | null | null | ['scene-parsing', 'panoptic-segmentation', 'autonomous-vehicles'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.45681411e-01 -2.58438855e-01 5.33955768e-02 -4.97065127e-01
-5.67660868e-01 -6.91766739e-01 7.34848619e-01 -2.49212027e-01
-4.44135576e-01 7.66330957e-01 -1.73801064e-01 -4.39019561e-01
8.60818475e-02 -8.76372099e-01 -5.69169641e-01 -8.07700455e-01
3.65431756e-01 1.37238637e-01 4.44130272e-01 -5.42800069... | [8.847458839416504, -1.4480693340301514] |
01d6b76d-0696-4dec-9ade-d923097d5fc0 | vilt-vision-and-language-transformer-without | 2102.03334 | null | https://arxiv.org/abs/2102.03334v2 | https://arxiv.org/pdf/2102.03334v2.pdf | ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision | Vision-and-Language Pre-training (VLP) has improved performance on various joint vision-and-language downstream tasks. Current approaches to VLP heavily rely on image feature extraction processes, most of which involve region supervision (e.g., object detection) and the convolutional architecture (e.g., ResNet). Althou... | ['Ildoo Kim', 'Bokyung Son', 'Wonjae Kim'] | 2021-02-05 | null | null | null | null | ['multimodal-intent-recognition', 'zero-shot-cross-modal-retrieval'] | ['miscellaneous', 'miscellaneous'] | [ 1.53186753e-01 1.34650990e-01 2.23408267e-02 -2.74932086e-01
-7.51105189e-01 -8.54171813e-01 8.45973074e-01 -5.96500374e-02
-8.00809920e-01 3.00688803e-01 2.58052409e-01 -5.91828167e-01
3.66133451e-01 -4.97993797e-01 -8.28690231e-01 -5.17656803e-01
3.67893606e-01 2.62996584e-01 2.38155350e-01 -8.13886076... | [10.72059154510498, 1.5623359680175781] |
7a2b6196-5505-4f5e-8e79-0e7fd0f7bf4b | sparsett-visual-tracking-with-sparse | 2205.03776 | null | https://arxiv.org/abs/2205.03776v1 | https://arxiv.org/pdf/2205.03776v1.pdf | SparseTT: Visual Tracking with Sparse Transformers | Transformers have been successfully applied to the visual tracking task and significantly promote tracking performance. The self-attention mechanism designed to model long-range dependencies is the key to the success of Transformers. However, self-attention lacks focusing on the most relevant information in the search ... | ['Yunhong Wang', 'Wenrui Cai', 'Qingjie Liu', 'Zehua Fu', 'Zhihong Fu'] | 2022-05-08 | null | null | null | null | ['visual-tracking'] | ['computer-vision'] | [-4.37915891e-01 -4.23238069e-01 -2.73906499e-01 -4.43322994e-02
-3.09698254e-01 -4.44377571e-01 4.44961876e-01 -1.50402173e-01
-2.48744994e-01 5.39510071e-01 1.53196054e-02 -1.39362186e-01
9.95440483e-02 -5.84425986e-01 -7.81637013e-01 -6.42345250e-01
-3.74252275e-02 9.95888039e-02 8.44148517e-01 -1.45747796... | [6.296179294586182, -2.1065120697021484] |
a9011504-c928-48c4-a5fa-3ad0d9337ab4 | twitter-data-analysis-izmir-earthquake-case | 2212.01453 | null | https://arxiv.org/abs/2212.01453v1 | https://arxiv.org/pdf/2212.01453v1.pdf | Twitter Data Analysis: Izmir Earthquake Case | T\"urkiye is located on a fault line; earthquakes often occur on a large and small scale. There is a need for effective solutions for gathering current information during disasters. We can use social media to get insight into public opinion. This insight can be used in public relations and disaster management. In this ... | ['Enis Karaarslan', 'Hakan Sökün', 'Özgür Agrali'] | 2022-12-02 | null | null | null | null | ['public-relations'] | ['miscellaneous'] | [-2.87920535e-01 3.92311096e-01 -1.97766237e-02 -3.70156407e-01
-6.11485243e-01 -1.97874039e-01 4.98420686e-01 5.83144486e-01
-5.05476534e-01 7.14682758e-01 1.15265048e+00 -4.36616570e-01
6.89919442e-02 -1.37736332e+00 -9.59343687e-02 -7.43216395e-01
-2.48578191e-01 4.87939060e-01 -9.50210169e-02 -6.79087698... | [10.644657135009766, 7.090683460235596] |
2b375532-f5ed-4834-8a9f-559f5b034815 | sig-vc-a-speaker-information-guided-zero-shot | 2111.03811 | null | https://arxiv.org/abs/2111.03811v3 | https://arxiv.org/pdf/2111.03811v3.pdf | SIG-VC: A Speaker Information Guided Zero-shot Voice Conversion System for Both Human Beings and Machines | Nowadays, as more and more systems achieve good performance in traditional voice conversion (VC) tasks, people's attention gradually turns to VC tasks under extreme conditions. In this paper, we propose a novel method for zero-shot voice conversion. We aim to obtain intermediate representations for speaker-content dise... | ['Ming Li', 'Xiaoyi Qin', 'Zexin Cai', 'Haozhe Zhang'] | 2021-11-06 | null | null | null | null | ['voice-cloning'] | ['speech'] | [ 1.11930393e-01 -2.00206354e-01 -8.33856091e-02 -1.05347276e-01
-8.69098365e-01 -3.43379438e-01 4.40755367e-01 -2.89192796e-01
-2.29851231e-01 4.69302863e-01 3.48649263e-01 -3.00014079e-01
1.22441433e-01 -4.59559470e-01 -4.60533872e-02 -6.65682256e-01
5.98381162e-01 -2.07681701e-01 7.92296678e-02 -2.35006288... | [14.720220565795898, 6.196124076843262] |
53270e25-bcd5-4f8e-8f24-c46f01377f2a | vpn-verification-of-poisoning-in-neural | 2205.03894 | null | https://arxiv.org/abs/2205.03894v1 | https://arxiv.org/pdf/2205.03894v1.pdf | VPN: Verification of Poisoning in Neural Networks | Neural networks are successfully used in a variety of applications, many of them having safety and security concerns. As a result researchers have proposed formal verification techniques for verifying neural network properties. While previous efforts have mainly focused on checking local robustness in neural networks, ... | ['Corina S. Păsăreanu', 'Divya Gopinath', 'Muhammad Usman', 'Youcheng Sun'] | 2022-05-08 | null | null | null | null | ['neural-network-security'] | ['miscellaneous'] | [ 6.78081989e-01 4.56532866e-01 -4.18612957e-01 -2.89971679e-01
-2.88351327e-01 -9.35257316e-01 5.04347146e-01 3.92843515e-01
-3.04030120e-01 6.96937203e-01 -6.44169450e-01 -1.03580034e+00
-1.03617802e-01 -9.92436647e-01 -1.41610563e+00 -7.26167798e-01
-3.38594228e-01 3.27756591e-02 7.66733885e-01 1.72201246... | [6.116060256958008, 7.614913463592529] |
f9751d9f-a558-471c-9049-29bab0a407cb | region-wise-attentive-multi-view | 2307.03212 | null | https://arxiv.org/abs/2307.03212v1 | https://arxiv.org/pdf/2307.03212v1.pdf | Region-Wise Attentive Multi-View Representation Learning for Urban Region Embeddings | Urban region embedding is an important and yet highly challenging issue due to the complexity and constantly changing nature of urban data. To address the challenges, we propose a Region-Wise Multi-View Representation Learning (ROMER) to capture multi-view dependencies and learn expressive representations of urban regi... | ['Qianqian Ren', 'Weiliang Chan'] | 2023-07-06 | null | null | null | null | ['graph-attention', 'representation-learning'] | ['graphs', 'methodology'] | [-2.15907469e-01 -1.05004638e-01 -5.52773893e-01 -3.15839559e-01
-6.43998265e-01 -4.16672558e-01 8.72334898e-01 4.08673398e-02
-7.15873167e-02 5.08107066e-01 8.10638666e-01 -2.94542372e-01
-2.04892710e-01 -1.02482593e+00 -5.42601824e-01 -3.94263417e-01
-2.99710304e-01 1.75131351e-01 3.58117402e-01 -7.14615464... | [6.52509069442749, 2.0763044357299805] |
c6bf8488-f7cf-4a47-b029-8e3fcd91e848 | learning-skeletal-graph-neural-networks-for | 2108.07181 | null | https://arxiv.org/abs/2108.07181v2 | https://arxiv.org/pdf/2108.07181v2.pdf | Learning Skeletal Graph Neural Networks for Hard 3D Pose Estimation | Various deep learning techniques have been proposed to solve the single-view 2D-to-3D pose estimation problem. While the average prediction accuracy has been improved significantly over the years, the performance on hard poses with depth ambiguity, self-occlusion, and complex or rare poses is still far from satisfactor... | ['Qiang Xu', 'Minhao Liu', 'Nanxuan Zhao', 'Lei Yang', 'Xiao Sun', 'Ailing Zeng'] | 2021-08-16 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zeng_Learning_Skeletal_Graph_Neural_Networks_for_Hard_3D_Pose_Estimation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zeng_Learning_Skeletal_Graph_Neural_Networks_for_Hard_3D_Pose_Estimation_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-pose-estimation'] | ['computer-vision'] | [ 2.68965095e-01 1.43498793e-01 -3.38470668e-01 -3.15687448e-01
-1.15146530e+00 9.73995700e-02 1.80539191e-02 -5.19786656e-01
-1.45598575e-01 4.04242307e-01 3.45770210e-01 3.17532957e-01
-1.78499967e-01 -4.32282984e-01 -7.56610811e-01 -5.92481434e-01
2.38631200e-02 5.15466988e-01 5.14782369e-01 -1.25339150... | [7.131378650665283, -0.7844598293304443] |
1716ab0f-5ef4-4aad-9fa5-3ebf995f1279 | unsupervised-learning-for-color-constancy | 1712.00436 | null | http://arxiv.org/abs/1712.00436v4 | http://arxiv.org/pdf/1712.00436v4.pdf | Unsupervised Learning for Color Constancy | Most digital camera pipelines use color constancy methods to reduce the
influence of illumination and camera sensor on the colors of scene objects. The
highest accuracy of color correction is obtained with learning-based color
constancy methods, but they require a significant amount of calibrated training
images with k... | ['Sven Lončarić', 'Karlo Koščević', 'Nikola Banić'] | 2017-12-01 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 2.55147576e-01 -5.70675135e-01 2.74532158e-02 -4.74295080e-01
-6.71557188e-01 -6.26822948e-01 2.05494389e-01 1.09802252e-02
-4.94016737e-01 5.94161093e-01 -5.47256649e-01 9.64775532e-02
1.91424161e-01 -6.85004473e-01 -8.70060980e-01 -1.02861857e+00
3.27201873e-01 7.89140612e-02 2.75255114e-01 2.03595117... | [10.353177070617676, -2.519801378250122] |
2aea91bf-c662-480f-aaa7-cd363df9424b | apb2facev2-real-time-audio-guided-multi-face | 2010.13017 | null | https://arxiv.org/abs/2010.13017v1 | https://arxiv.org/pdf/2010.13017v1.pdf | APB2FaceV2: Real-Time Audio-Guided Multi-Face Reenactment | Audio-guided face reenactment aims to generate a photorealistic face that has matched facial expression with the input audio. However, current methods can only reenact a special person once the model is trained or need extra operations such as 3D rendering and image post-fusion on the premise of generating vivid faces.... | ['Yunliang Jiang', 'Yong liu', 'Jun Chen', 'Chao Xu', 'Xianfang Zeng', 'Jiangning Zhang'] | 2020-10-25 | null | null | null | null | ['face-reenactment'] | ['computer-vision'] | [ 2.08071411e-01 2.55880952e-02 6.54172361e-01 -5.03410041e-01
-4.46146876e-01 -3.56956184e-01 3.76659989e-01 -9.24759448e-01
4.85997200e-02 2.95338511e-01 1.51852682e-01 7.04814855e-04
2.33211756e-01 -9.19426322e-01 -6.42257214e-01 -5.50618410e-01
1.78050205e-01 1.59752220e-01 -2.53215253e-01 -3.53248894... | [12.893436431884766, -0.3027288019657135] |
eb3cd1f4-57ab-4f0f-a0ae-ab0a43cb5fb7 | chatgpt-for-plc-dcs-control-logic-generation | 2305.15809 | null | https://arxiv.org/abs/2305.15809v1 | https://arxiv.org/pdf/2305.15809v1.pdf | ChatGPT for PLC/DCS Control Logic Generation | Large language models (LLMs) providing generative AI have become popular to support software engineers in creating, summarizing, optimizing, and documenting source code. It is still unknown how LLMs can support control engineers using typical control programming languages in programming tasks. Researchers have explored... | ['Virendra Ashiwal', 'Sten Gruener', 'Heiko Koziolek'] | 2023-05-25 | null | null | null | null | ['code-generation'] | ['computer-code'] | [-6.39319643e-02 7.58482456e-01 -9.15904790e-02 -4.47372675e-01
-7.17884243e-01 -1.05312908e+00 6.97519004e-01 1.91445902e-01
7.57038355e-01 5.45567572e-01 2.73346037e-01 -1.00225580e+00
-2.51874864e-01 -9.71305728e-01 -6.00952148e-01 2.31309175e-01
-6.16135113e-02 5.37468731e-01 3.65914479e-02 -5.22731662... | [8.048190116882324, 7.535362243652344] |
74f01432-835c-4b12-8bae-c821afe3a8ee | topic-adaptation-and-prototype-encoding-for | 2008.04504 | null | https://arxiv.org/abs/2008.04504v1 | https://arxiv.org/pdf/2008.04504v1.pdf | Topic Adaptation and Prototype Encoding for Few-Shot Visual Storytelling | Visual Storytelling~(VIST) is a task to tell a narrative story about a certain topic according to the given photo stream. The existing studies focus on designing complex models, which rely on a huge amount of human-annotated data. However, the annotation of VIST is extremely costly and many topics cannot be covered in ... | ['ShiLiang Pu', 'Siliang Tang', 'Juncheng Li', 'Jiacheng Li', 'Yueting Zhuang', 'Jun Xiao', 'Fei Wu'] | 2020-08-11 | null | null | null | null | ['visual-storytelling'] | ['natural-language-processing'] | [ 2.43965849e-01 2.17525885e-01 -2.25433022e-01 -3.95799667e-01
-8.23961675e-01 -2.62344837e-01 9.06178415e-01 -3.66983712e-02
-1.06842443e-01 5.57763278e-01 7.11368501e-01 1.31259069e-01
1.52866513e-01 -8.99801254e-01 -7.79660821e-01 -5.69676638e-01
3.18566114e-02 4.98930097e-01 3.29597205e-01 -3.58905643... | [11.188834190368652, 0.70480877161026] |
03040daa-5868-4750-a9d8-cc51a9d16ed4 | unsupervised-opinion-summarization-using | 2209.07496 | null | https://arxiv.org/abs/2209.07496v2 | https://arxiv.org/pdf/2209.07496v2.pdf | Unsupervised Opinion Summarization Using Approximate Geodesics | Opinion summarization is the task of creating summaries capturing popular opinions from user reviews. In this paper, we introduce Geodesic Summarizer (GeoSumm), a novel system to perform unsupervised extractive opinion summarization. GeoSumm involves an encoder-decoder based representation learning model, that generate... | ['Snigdha Chaturvedi', 'Amr Ahmed', 'Avinava Dubey', 'Nicholas Monath', 'Somnath Basu Roy Chowdhury'] | 2022-09-15 | null | null | null | null | ['unsupervised-opinion-summarization'] | ['natural-language-processing'] | [ 3.11840594e-01 4.51652825e-01 -2.12765589e-01 -5.29128611e-01
-1.34969831e+00 -5.69354415e-01 7.91763484e-01 6.85642660e-01
-9.70913321e-02 7.12017834e-01 1.27597725e+00 -4.52074483e-02
2.46523231e-01 -6.41689479e-01 -5.55060744e-01 -4.23675120e-01
2.62918919e-01 3.88533831e-01 -9.57959667e-02 -3.81667584... | [12.411555290222168, 9.350791931152344] |
811860df-6cc7-4b17-8e3f-3469899623b4 | fashion-focus-multi-modal-retrieval-system | 2102.04727 | null | https://arxiv.org/abs/2102.04727v1 | https://arxiv.org/pdf/2102.04727v1.pdf | Fashion Focus: Multi-modal Retrieval System for Video Commodity Localization in E-commerce | Nowadays, live-stream and short video shopping in E-commerce have grown exponentially. However, the sellers are required to manually match images of the selling products to the timestamp of exhibition in the untrimmed video, resulting in a complicated process. To solve the problem, we present an innovative demonstratio... | ['Yinghui Xu', 'Siyang Sun', 'Cheng Da', 'Yun Zheng', 'Pan Pan', 'Qiang Wang', 'Yanhao Zhang'] | 2021-02-09 | null | null | null | null | ['video-to-shop'] | ['computer-vision'] | [ 3.26986648e-02 -7.81832397e-01 -4.13677186e-01 -3.40134859e-01
-1.01769066e+00 -8.86115432e-01 1.57418162e-01 3.27971280e-01
-6.75126165e-02 -1.44831240e-01 3.61875832e-01 3.74946684e-01
-4.17199403e-01 -4.91480172e-01 -5.77133417e-01 -6.79947317e-01
-2.60089692e-02 1.68570325e-01 2.35784531e-01 -1.21972062... | [10.20413875579834, 0.7719337940216064] |
a32ed6eb-6809-49fb-8fa0-fc6a93572d9b | optimal-boxes-boosting-end-to-end-scene-text | 2207.11934 | null | https://arxiv.org/abs/2207.11934v2 | https://arxiv.org/pdf/2207.11934v2.pdf | Optimal Boxes: Boosting End-to-End Scene Text Recognition by Adjusting Annotated Bounding Boxes via Reinforcement Learning | Text detection and recognition are essential components of a modern OCR system. Most OCR approaches attempt to obtain accurate bounding boxes of text at the detection stage, which is used as the input of the text recognition stage. We observe that when using tight text bounding boxes as input, a text recognizer frequen... | ['Xiang Bai', 'Lan Li', 'Xiena Dong', 'Luchuan Song', 'Wenming Qian', 'Jingqun Tang'] | 2022-07-25 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 4.05992419e-01 -3.40313852e-01 -6.81254789e-02 -4.66686606e-01
-9.07210767e-01 -6.93197370e-01 6.49073243e-01 9.28505585e-02
-4.05595154e-01 2.78158218e-01 3.15886550e-02 -2.13008329e-01
4.06694114e-01 -5.42379260e-01 -8.33500683e-01 -4.40321416e-01
6.96267009e-01 8.40590537e-01 4.07007366e-01 -2.12431222... | [11.932806968688965, 2.275582790374756] |
827c83cf-7cac-45a3-b21b-12b1409f339f | diffusion-based-3d-human-pose-estimation-with | 2303.11579 | null | https://arxiv.org/abs/2303.11579v1 | https://arxiv.org/pdf/2303.11579v1.pdf | Diffusion-Based 3D Human Pose Estimation with Multi-Hypothesis Aggregation | In this paper, a novel Diffusion-based 3D Pose estimation (D3DP) method with Joint-wise reProjection-based Multi-hypothesis Aggregation (JPMA) is proposed for probabilistic 3D human pose estimation. On the one hand, D3DP generates multiple possible 3D pose hypotheses for a single 2D observation. It gradually diffuses t... | ['Wen Gao', 'Siwei Ma', 'Shanshe Wang', 'Kai Han', 'Zhao Wang', 'Xinfeng Zhang', 'Zhenhua Liu', 'Wenkang Shan'] | 2023-03-21 | null | null | null | null | ['multi-hypotheses-3d-human-pose-estimation', '3d-pose-estimation', '3d-human-pose-estimation', 'monocular-3d-human-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-4.50598955e-01 6.03872836e-02 -4.65645902e-02 -1.19135231e-01
-1.13829625e+00 -3.28881115e-01 4.48988974e-01 -2.07319304e-01
-3.13387215e-01 6.48334563e-01 2.52288461e-01 3.20677042e-01
-6.02599680e-02 -7.17852831e-01 -7.81440318e-01 -6.22208595e-01
2.25405791e-03 1.23944318e+00 5.92435539e-01 -9.32154059... | [7.0259013175964355, -1.0096631050109863] |
f6d2c7ca-9d66-46d1-89b1-0f521b5ca375 | deepem-deep-3d-convnets-with-em-for-weakly | 1805.05373 | null | http://arxiv.org/abs/1805.05373v3 | http://arxiv.org/pdf/1805.05373v3.pdf | DeepEM: Deep 3D ConvNets With EM For Weakly Supervised Pulmonary Nodule Detection | Recently deep learning has been witnessing widespread adoption in various
medical image applications. However, training complex deep neural nets requires
large-scale datasets labeled with ground truth, which are often unavailable in
many medical image domains. For instance, to train a deep neural net to detect
pulmonar... | ['Yeeleng S. Vang', 'Yufang Huang', 'Wentao Zhu', 'Xiaohui Xie'] | 2018-05-14 | null | null | null | null | ['lung-nodule-detection'] | ['medical'] | [ 2.11631030e-01 3.49668860e-01 -5.08314967e-01 -3.44515711e-01
-1.10561180e+00 -2.66026497e-01 1.26843482e-01 -9.31235105e-02
-4.55868274e-01 5.63982785e-01 1.53789744e-01 -6.68327212e-01
-1.66119203e-01 -8.92574906e-01 -7.45069504e-01 -8.39523435e-01
8.79796967e-02 6.24200583e-01 9.56425071e-02 2.81227291... | [15.340867042541504, -2.134120225906372] |
ea567e2f-8b97-45e6-b7ca-c3fa430b94b7 | atem-a-topic-evolution-model-for-the | 2306.02221 | null | https://arxiv.org/abs/2306.02221v1 | https://arxiv.org/pdf/2306.02221v1.pdf | ATEM: A Topic Evolution Model for the Detection of Emerging Topics in Scientific Archives | This paper presents ATEM, a novel framework for studying topic evolution in scientific archives. ATEM is based on dynamic topic modeling and dynamic graph embedding techniques that explore the dynamics of content and citations of documents within a scientific corpus. ATEM explores a new notion of contextual emergence f... | ['Bernd Amann', 'Camelia Constantin', 'Hubert Naacke', 'Hamed Rahimi'] | 2023-06-04 | null | null | null | null | ['graph-embedding', 'dynamic-graph-embedding', 'dynamic-topic-modeling'] | ['graphs', 'graphs', 'natural-language-processing'] | [-8.16941381e-01 2.24357992e-01 -5.70873320e-01 4.90884990e-01
-5.40163100e-01 -9.75648940e-01 1.20605648e+00 8.50319147e-01
1.65584356e-01 3.93268228e-01 5.99949121e-01 -7.67827332e-01
-8.26054513e-01 -8.65648627e-01 -5.07335961e-01 -3.11807781e-01
-7.19337523e-01 6.22303724e-01 4.46319222e-01 1.29080847... | [9.673367500305176, 8.0856351852417] |
c5a937e8-f74f-4f74-a472-000c99a8a7ad | exploiting-reasoning-chains-for-multi-hop | 2109.02905 | null | https://arxiv.org/abs/2109.02905v1 | https://arxiv.org/pdf/2109.02905v1.pdf | Exploiting Reasoning Chains for Multi-hop Science Question Answering | We propose a novel Chain Guided Retriever-reader ({\tt CGR}) framework to model the reasoning chain for multi-hop Science Question Answering. Our framework is capable of performing explainable reasoning without the need of any corpus-specific annotations, such as the ground-truth reasoning chain, or human-annotated ent... | ['Wai Lam', 'Deng Cai', 'Huihui Zhang', 'Yang Deng', 'Weiwen Xu'] | 2021-09-07 | null | https://aclanthology.org/2021.findings-emnlp.99 | https://aclanthology.org/2021.findings-emnlp.99.pdf | findings-emnlp-2021-11 | ['science-question-answering'] | ['miscellaneous'] | [ 1.58392325e-01 8.20127189e-01 -2.75657803e-01 -5.44840157e-01
-1.45379043e+00 -6.95161760e-01 6.40409827e-01 5.64484119e-01
-3.74521464e-01 8.73250484e-01 3.30910563e-01 -6.71330392e-01
-2.85955161e-01 -9.56413031e-01 -1.35981810e+00 -2.77969897e-01
3.40674996e-01 1.06992722e+00 2.45147392e-01 -3.79121184... | [10.792203903198242, 7.869263172149658] |
73efc940-f23b-4a11-9547-83337bb8769d | multi-tenant-optimization-for-few-shot-task | 2301.10517 | null | https://arxiv.org/abs/2301.10517v1 | https://arxiv.org/pdf/2301.10517v1.pdf | Multi-Tenant Optimization For Few-Shot Task-Oriented FAQ Retrieval | Business-specific Frequently Asked Questions (FAQ) retrieval in task-oriented dialog systems poses unique challenges vis-\`a-vis community based FAQs. Each FAQ question represents an intent which is usually an umbrella term for many related user queries. We evaluate performance for such Business FAQs both with standard... | ['Chandra Shekhar Kandpal', 'Gautham Vadakkekara Suresh', 'Rajeev Unnikrishnan Warrier', 'Asha Vishwanathan'] | 2023-01-25 | null | null | null | null | ['intent-detection'] | ['natural-language-processing'] | [-1.33597016e-01 -2.89699614e-01 -1.79002270e-01 -5.95639706e-01
-1.64708078e+00 -8.11540484e-01 6.70819998e-01 3.51530343e-01
-8.75478864e-01 5.02171338e-01 4.81745481e-01 -1.78263038e-01
-4.30252820e-01 -3.18671316e-01 2.40798644e-03 8.22455287e-02
1.02281190e-01 8.90434444e-01 7.31174350e-01 -1.17659688... | [11.94058609008789, 7.785861492156982] |
06163e39-1781-43f1-9abb-ae0e9a78247e | prediction-of-slam-ate-using-an-ensemble | 2303.00616 | null | https://arxiv.org/abs/2303.00616v1 | https://arxiv.org/pdf/2303.00616v1.pdf | Prediction of SLAM ATE Using an Ensemble Learning Regression Model and 1-D Global Pooling of Data Characterization | Robustness and resilience of simultaneous localization and mapping (SLAM) are critical requirements for modern autonomous robotic systems. One of the essential steps to achieve robustness and resilience is the ability of SLAM to have an integrity measure for its localization estimates, and thus, have internal fault tol... | ['Hong Zhang', 'Wan', 'Bingqing', 'Islam Ali'] | 2023-03-01 | null | null | null | null | ['simultaneous-localization-and-mapping'] | ['computer-vision'] | [-1.32829621e-01 -1.91203520e-01 -1.49447501e-01 -5.51570654e-01
-8.01020086e-01 -1.66727677e-01 5.54624379e-01 2.83317685e-01
-6.88118935e-01 8.77022326e-01 -3.17446023e-01 1.31618649e-01
-4.36175406e-01 -6.82006359e-01 -9.64165390e-01 -6.52301490e-01
-6.46154165e-01 3.88386935e-01 4.63387787e-01 -3.92726630... | [7.325300693511963, -2.1045501232147217] |
745b369b-a32e-4bb7-9321-0dfc2351cc9b | dawn-dual-augmented-memory-network-for | 1908.00777 | null | https://arxiv.org/abs/1908.00777v2 | https://arxiv.org/pdf/1908.00777v2.pdf | DAWN: Dual Augmented Memory Network for Unsupervised Video Object Tracking | Psychological studies have found that human visual tracking system involves learning, memory, and planning. Despite recent successes, not many works have focused on memory and planning in deep learning based tracking. We are thus interested in memory augmented network, where an external memory remembers the evolving ap... | ['Chi-Keung Tang', 'Yu-Wing Tai', 'Zhenmei Shi', 'Haoyang Fang'] | 2019-08-02 | null | null | null | null | ['video-object-tracking'] | ['computer-vision'] | [-1.45752013e-01 -2.70801753e-01 -3.91764671e-01 8.91152173e-02
-3.10640752e-01 -2.53692299e-01 6.59646213e-01 -4.03090477e-01
-8.83926094e-01 6.31818354e-01 1.96063578e-01 1.58417061e-01
2.22997874e-01 -4.48841959e-01 -9.58559871e-01 -3.94195795e-01
-2.79796362e-01 3.78844053e-01 6.73266530e-01 2.19457909... | [6.276738166809082, -2.0955440998077393] |
f868ba36-6337-4e3b-bc99-a382ad47a4c8 | angular-triplet-center-loss-for-multi-view-3d | 1811.08622 | null | http://arxiv.org/abs/1811.08622v3 | http://arxiv.org/pdf/1811.08622v3.pdf | Angular Triplet-Center Loss for Multi-view 3D Shape Retrieval | How to obtain the desirable representation of a 3D shape, which is
discriminative across categories and polymerized within classes, is a
significant challenge in 3D shape retrieval. Most existing 3D shape retrieval
methods focus on capturing strong discriminative shape representation with
softmax loss for the classific... | ['Cheng Xu', 'Biao Leng', 'Zhaoqun Li'] | 2018-11-21 | null | null | null | null | ['3d-shape-retrieval', 'multi-view-3d-shape-retrieval', '3d-object-retrieval'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-4.18279260e-01 -4.49087411e-01 -4.44643527e-01 -5.56398451e-01
-6.62767231e-01 -6.81310833e-01 5.76154768e-01 2.33448431e-01
-2.24545538e-01 -4.24937792e-02 2.06055209e-01 -1.21096231e-01
-5.18717527e-01 -8.48113358e-01 -3.75898629e-01 -9.28008497e-01
3.27798784e-01 3.81699502e-01 2.60931373e-01 2.20614374... | [8.172740936279297, -3.8958191871643066] |
4ab2b437-4a44-408c-8a40-5080992621c0 | multilingual-multimodal-pretraining-for-zero | null | null | https://openreview.net/forum?id=XC-PgiyCwj3 | https://openreview.net/pdf?id=XC-PgiyCwj3 | Multilingual Multimodal Pretraining for Zero-Shot Cross-Lingual Transfer of Vision-Language Models | This paper studies zero-shot cross-lingual transfer of vision-language models. Specifically, we focus on multilingual text-to-video search and propose a Transformer-based model that learns contextualized multilingual multimodal embeddings. Under a zero-shot setting, we empirically demonstrate that performance degrades ... | ['Anonymous'] | 2020-12-07 | null | null | null | null | ['text-to-video-search'] | ['natural-language-processing'] | [-2.35043898e-01 -5.83433747e-01 -6.65297806e-01 -1.71605587e-01
-1.80169392e+00 -9.00257945e-01 7.26530790e-01 -1.40940696e-01
-1.11029863e+00 5.36441147e-01 3.18708092e-01 -6.27705455e-01
2.52610236e-01 -7.44061768e-02 -1.23147154e+00 -2.70438850e-01
3.18991601e-01 5.65391481e-01 2.41071686e-01 -1.38475165... | [11.13331413269043, 1.489411473274231] |
41ccf886-12ea-4c88-8b98-23977e5ee69a | findings-of-the-vardial-evaluation-campaign-1 | null | null | https://aclanthology.org/2021.vardial-1.1 | https://aclanthology.org/2021.vardial-1.1.pdf | Findings of the VarDial Evaluation Campaign 2021 | This paper describes the results of the shared tasks organized as part of the VarDial Evaluation Campaign 2021. The campaign was part of the eighth workshop on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects (VarDial), co-located with EACL 2021. Four separate shared tasks were included t... | ['Marcos Zampieri', 'Yves Scherrer', 'Eswari Rajagopal', 'Christoph Purschke', 'Ruba Priyadharshini', 'Niko Partanen', 'Nikola Ljubešić', 'Krister Lindén', 'Tommi Jauhiainen', 'Heidi Jauhiainen', 'Radu Tudor Ionescu', 'Gaman Mihaela', 'Bharathi Raja Chakravarthi'] | null | null | null | null | eacl-vardial-2021-4 | ['dialect-identification'] | ['natural-language-processing'] | [-5.05995750e-01 -2.54030675e-01 -1.86807960e-01 -4.79945153e-01
-1.13882279e+00 -1.08181906e+00 1.33417022e+00 6.29250467e-01
-6.28669918e-01 5.10168791e-01 7.45556116e-01 -2.99202770e-01
5.36535010e-02 -4.68289942e-01 -8.11608285e-02 -1.48680657e-01
-1.32580981e-01 1.11107278e+00 2.23293249e-02 -4.11714971... | [10.192102432250977, 10.747522354125977] |
f44f11bb-e7d8-4966-b06e-c4c86d84fe9f | weakly-supervised-action-localization-by | 1712.05080 | null | http://arxiv.org/abs/1712.05080v2 | http://arxiv.org/pdf/1712.05080v2.pdf | Weakly Supervised Action Localization by Sparse Temporal Pooling Network | We propose a weakly supervised temporal action localization algorithm on
untrimmed videos using convolutional neural networks. Our algorithm learns from
video-level class labels and predicts temporal intervals of human actions with
no requirement of temporal localization annotations. We design our network to
identify a... | ['Bohyung Han', 'Phuc Nguyen', 'Ting Liu', 'Gautam Prasad'] | 2017-12-14 | weakly-supervised-action-localization-by-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Nguyen_Weakly_Supervised_Action_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Nguyen_Weakly_Supervised_Action_CVPR_2018_paper.pdf | cvpr-2018-6 | ['weakly-supervised-action-localization', 'weakly-supervised-temporal-action'] | ['computer-vision', 'computer-vision'] | [ 5.34899712e-01 -1.08642168e-01 -1.08645713e+00 -4.35746402e-01
-7.07760215e-01 -3.66568863e-01 5.94520450e-01 -2.07471684e-01
-6.14915252e-01 6.51846170e-01 4.40242201e-01 3.01989317e-01
-8.08031633e-02 -2.56887972e-01 -9.10422862e-01 -6.54261947e-01
-7.07119942e-01 8.10498223e-02 5.87625682e-01 3.99053931... | [8.445293426513672, 0.5882011651992798] |
53e53f4b-18df-49c3-ac12-b05b7df629c6 | utterance-level-dialogue-understanding-an | 2009.13902 | null | https://arxiv.org/abs/2009.13902v5 | https://arxiv.org/pdf/2009.13902v5.pdf | Utterance-level Dialogue Understanding: An Empirical Study | The recent abundance of conversational data on the Web and elsewhere calls for effective NLP systems for dialog understanding. Complete utterance-level understanding often requires context understanding, defined by nearby utterances. In recent years, a number of approaches have been proposed for various utterance-level... | ['Soujanya Poria', 'Navonil Majumder', 'Deepanway Ghosal', 'Rada Mihalcea'] | 2020-09-29 | null | null | null | null | ['dialogue-understanding', 'goal-oriented-dialogue-systems'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.52588272e-01 4.65632081e-01 -3.88236977e-02 -8.84766161e-01
-6.72643661e-01 -8.42101514e-01 1.01919484e+00 4.18051213e-01
-5.06346300e-02 8.03136349e-01 1.07917154e+00 -3.15802246e-01
2.76996017e-01 -6.27257109e-01 -1.23560773e-02 -3.13595235e-01
2.25755155e-01 4.03910160e-01 -1.00831665e-01 -7.32935488... | [12.729063034057617, 7.908478260040283] |
0fa75d11-6eff-4f53-adcf-8e8d35cf9319 | dydx-liquidity-providers-incentive-programme | 2307.03935 | null | https://arxiv.org/abs/2307.03935v1 | https://arxiv.org/pdf/2307.03935v1.pdf | dYdX: Liquidity Providers' Incentive Programme Review | Liquidity providers are currently incentivised to provide liquidity through the LP Incentives Programme on dYdX. Based on the various parameters - makerVolume, depths and spreads, they are rewarded accordingly based on their activities. Given the maturity of the BTC and ETH markets, alongside other altcoins which enjoy... | ['Colin Chan'] | 2023-07-08 | null | null | null | null | ['management'] | ['miscellaneous'] | [-1.03706837e+00 1.15665898e-01 -5.93509316e-01 -1.78970546e-01
-5.67738056e-01 -1.25858617e+00 4.12513137e-01 1.15245402e-01
-1.89754263e-01 9.56763446e-01 4.04104561e-01 -6.92296088e-01
-4.48272765e-01 -7.81012535e-01 -9.23090279e-02 -4.89346594e-01
-3.78718883e-01 7.03143954e-01 -1.81199033e-02 1.14087217... | [4.833845615386963, 4.018702030181885] |
bb7d7eaf-5c95-48c9-852f-6b14efafe67a | deep-subdomain-adaptation-network-for-image | 2106.09388 | null | https://arxiv.org/abs/2106.09388v1 | https://arxiv.org/pdf/2106.09388v1.pdf | Deep Subdomain Adaptation Network for Image Classification | For a target task where labeled data is unavailable, domain adaptation can transfer a learner from a different source domain. Previous deep domain adaptation methods mainly learn a global domain shift, i.e., align the global source and target distributions without considering the relationships between two subdomains wi... | ['Qing He', 'Hui Xiong', 'Jiang Bian', 'Jingwu Chen', 'Guolin Ke', 'Jindong Wang', 'Fuzhen Zhuang', 'Yongchun Zhu'] | 2021-06-17 | null | null | null | null | ['subdomain-adaptation'] | ['methodology'] | [-4.14450504e-02 -8.19619447e-02 -3.20822179e-01 -6.39159918e-01
-7.66201138e-01 -6.31576955e-01 3.59747529e-01 -4.74374928e-02
-4.14165169e-01 9.91554558e-01 3.43847871e-02 -1.77180499e-01
2.21220460e-02 -9.17777181e-01 -1.02445090e+00 -7.40064681e-01
4.35297042e-01 6.26865089e-01 3.01435709e-01 -2.37581745... | [10.31954574584961, 3.0861361026763916] |
fd58312d-b9d6-4950-b815-39162644f2f0 | intimate-partner-violence-and-injury | 2009.09084 | null | https://arxiv.org/abs/2009.09084v2 | https://arxiv.org/pdf/2009.09084v2.pdf | Intimate Partner Violence and Injury Prediction From Radiology Reports | Intimate partner violence (IPV) is an urgent, prevalent, and under-detected public health issue. We present machine learning models to assess patients for IPV and injury. We train the predictive algorithms on radiology reports with 1) IPV labels based on entry to a violence prevention program and 2) injury labels provi... | ['Bharti Khurana', 'Rahul Gujrathi', 'Babina Gosangi', 'Richard Thomas', 'Hyesun Park', 'Emily Alsentzer', 'Irene Y. Chen'] | 2020-08-28 | null | null | null | null | ['injury-prediction'] | ['playing-games'] | [ 1.51542068e-01 4.38623786e-01 -6.48191273e-01 -4.07638639e-01
-1.41586471e+00 -5.79630673e-01 9.64289010e-02 8.85794759e-01
-9.56333458e-01 5.76115251e-01 5.03973484e-01 -1.20214105e+00
-5.27514815e-01 -7.12634861e-01 -7.54548252e-01 -3.58357698e-01
-3.92036021e-01 9.47841942e-01 -2.87188254e-02 5.99399686... | [15.388679504394531, -1.8653672933578491] |
efe573f9-c993-4ec1-b62c-36c64ec65c8a | automatic-classification-of-irregularly | 1605.05142 | null | http://arxiv.org/abs/1605.05142v1 | http://arxiv.org/pdf/1605.05142v1.pdf | Automatic Classification of Irregularly Sampled Time Series with Unequal Lengths: A Case Study on Estimated Glomerular Filtration Rate | A patient's estimated glomerular filtration rate (eGFR) can provide important
information about disease progression and kidney function. Traditionally, an
eGFR time series is interpreted by a human expert labelling it as stable or
unstable. While this approach works for individual patients, the time consuming
nature of... | ['Simon Bull', 'Santosh Tirunagari', 'Norman Poh'] | 2016-05-17 | null | null | null | null | ['kidney-function'] | ['medical'] | [ 3.02135676e-01 -7.29920641e-02 -2.12246254e-01 -6.03809476e-01
-9.04927790e-01 -5.40222526e-01 4.00327682e-01 8.73008251e-01
-6.01766467e-01 8.93086255e-01 1.14915602e-01 -7.02701390e-01
-2.52526313e-01 -9.09106255e-01 -2.31837511e-01 -5.16285717e-01
-2.01971158e-01 6.70601904e-01 4.52708751e-02 4.66034085... | [8.052026748657227, 5.69461727142334] |
dfc207fa-943b-4286-9bc3-3fe515eef98c | multi-level-feature-abstraction-from | 1807.01332 | null | http://arxiv.org/abs/1807.01332v1 | http://arxiv.org/pdf/1807.01332v1.pdf | Multi-Level Feature Abstraction from Convolutional Neural Networks for Multimodal Biometric Identification | In this paper, we propose a deep multimodal fusion network to fuse multiple
modalities (face, iris, and fingerprint) for person identification. The
proposed deep multimodal fusion algorithm consists of multiple streams of
modality-specific Convolutional Neural Networks (CNNs), which are jointly
optimized at multiple fe... | ['Sobhan Soleymani', 'Nasser M. Nasrabadi', 'Hadi Kazemi', 'Jeremy Dawson', 'Ali Dabouei'] | 2018-07-03 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [ 5.52169867e-02 -4.36719894e-01 2.57401802e-02 -6.03298545e-01
-1.05439270e+00 -5.57981551e-01 5.50198793e-01 2.46501133e-01
-5.93432844e-01 4.89253789e-01 2.95419604e-01 2.08327353e-01
1.88993327e-02 -5.42213082e-01 -7.46636391e-01 -5.14352620e-01
1.67458236e-01 8.79402757e-02 -5.44174731e-01 -2.36579075... | [14.55986499786377, 1.0580480098724365] |
4ef3c7d4-09f3-435a-8fbb-caa60a78dabc | rvl-bert-visual-relationship-detection-with | 2009.04965 | null | https://arxiv.org/abs/2009.04965v3 | https://arxiv.org/pdf/2009.04965v3.pdf | Visual Relationship Detection with Visual-Linguistic Knowledge from Multimodal Representations | Visual relationship detection aims to reason over relationships among salient objects in images, which has drawn increasing attention over the past few years. Inspired by human reasoning mechanisms, it is believed that external visual commonsense knowledge is beneficial for reasoning visual relationships of objects in ... | ['Roger Zimmermann', 'Meng-Jiun Chiou', 'Jiashi Feng'] | 2020-09-10 | null | null | null | null | ['visual-relationship-detection'] | ['computer-vision'] | [ 1.03332838e-02 3.73974860e-01 -1.83639348e-01 -3.93132299e-01
-3.19295973e-01 -4.83691901e-01 8.80047262e-01 2.84751683e-01
-3.22096974e-01 2.89284766e-01 3.69373232e-01 -3.14048320e-01
-3.61025855e-02 -8.48039627e-01 -8.77392113e-01 -2.49641404e-01
3.26968968e-01 1.92906424e-01 3.30892116e-01 -4.21087950... | [10.582406044006348, 1.6427981853485107] |
b578d1d5-5d63-4871-8e11-8adc1267725d | improving-video-retrieval-by-adaptive-margin | 2303.05093 | null | https://arxiv.org/abs/2303.05093v1 | https://arxiv.org/pdf/2303.05093v1.pdf | Improving Video Retrieval by Adaptive Margin | Video retrieval is becoming increasingly important owing to the rapid emergence of videos on the Internet. The dominant paradigm for video retrieval learns video-text representations by pushing the distance between the similarity of positive pairs and that of negative pairs apart from a fixed margin. However, negative ... | ['Xiao Tan', 'Yong Zhu', 'Yajuan Lv', 'Wenbin Jiang', 'Zhifan Feng', 'Qi Wang', 'Feng He'] | 2023-03-09 | null | null | null | null | ['video-retrieval'] | ['computer-vision'] | [-7.17094447e-03 -5.05638957e-01 -5.30868769e-01 -2.92708784e-01
-7.81245947e-01 -4.71326023e-01 6.81863546e-01 6.64766729e-02
-4.24493313e-01 4.91565585e-01 1.21854104e-01 -6.46054223e-02
-1.78613171e-01 -6.32808268e-01 -7.07581818e-01 -8.14959407e-01
-3.28216702e-02 1.54633418e-01 5.33433139e-01 -1.47714898... | [10.252535820007324, 0.8878693580627441] |
1f46b8d4-6720-44ac-aa60-3e8feb65255b | face-emotion-recognization-using-dataset | 2210.12689 | null | https://arxiv.org/abs/2210.12689v2 | https://arxiv.org/pdf/2210.12689v2.pdf | Face Emotion Recognization Using Dataset Augmentation Based on Neural Network | Facial expression is one of the most external indications of a person's feelings and emotions. In daily conversation, according to the psychologist, only 7% and 38% of information is communicated through words and sounds respective, while up to 55% is through facial expression. It plays an important role in coordinatin... | ['Ruyi Bao', 'Liangshun Dong', 'Mengyu Rao'] | 2022-10-23 | null | null | null | null | ['facial-expression-recognition', 'culture'] | ['computer-vision', 'speech'] | [-9.20659378e-02 -1.50232241e-01 -4.84018356e-01 -5.76363206e-01
1.41811624e-01 -3.06713015e-01 6.60198808e-01 7.18704686e-02
-4.96848524e-01 5.67461431e-01 2.72346735e-01 3.30478370e-01
3.20960790e-01 -6.48011923e-01 8.30105916e-02 -6.66469276e-01
2.63817281e-01 -1.20861135e-01 -5.58915019e-01 -7.10111320... | [13.479018211364746, 2.1059741973876953] |
1d8e0570-672c-4de6-be36-301e66629bfc | xnet-a-convolutional-neural-network-cnn | 1812.00548 | null | http://arxiv.org/abs/1812.00548v2 | http://arxiv.org/pdf/1812.00548v2.pdf | XNet: A convolutional neural network (CNN) implementation for medical X-Ray image segmentation suitable for small datasets | X-Ray image enhancement, along with many other medical image processing
applications, requires the segmentation of images into bone, soft tissue, and
open beam regions. We apply a machine learning approach to this problem,
presenting an end-to-end solution which results in robust and efficient
inference. Since medical ... | ['Arnau Quera-Bofarull', 'Carolina Cuesta-Lazaro', 'Joseph Bullock'] | 2018-12-03 | null | null | null | null | ['medical-x-ray-image-segmentation'] | ['medical'] | [ 5.24405301e-01 5.05424500e-01 -6.94008023e-02 -6.77922904e-01
-1.35049736e+00 -1.25267878e-01 1.26814589e-01 5.96965790e-01
-7.33898520e-01 4.77808625e-01 7.93576390e-02 -5.60383260e-01
-3.58313620e-01 -6.09656692e-01 -2.45681345e-01 -7.89821804e-01
-4.65240795e-03 7.53852129e-01 1.07641391e-01 4.54961270... | [14.624427795410156, -2.3797144889831543] |
c7e9099e-2ab3-4f73-bad2-2f13d87578a3 | post-processing-temporal-action-detection | 2211.14924 | null | https://arxiv.org/abs/2211.14924v2 | https://arxiv.org/pdf/2211.14924v2.pdf | Post-Processing Temporal Action Detection | Existing Temporal Action Detection (TAD) methods typically take a pre-processing step in converting an input varying-length video into a fixed-length snippet representation sequence, before temporal boundary estimation and action classification. This pre-processing step would temporally downsample the video, reducing t... | ['Tao Xiang', 'Yi-Zhe Song', 'Xiatian Zhu', 'Sauradip Nag'] | 2022-11-27 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Nag_Post-Processing_Temporal_Action_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Nag_Post-Processing_Temporal_Action_Detection_CVPR_2023_paper.pdf | cvpr-2023-1 | ['action-classification'] | ['computer-vision'] | [ 4.87132043e-01 -1.65834785e-01 -3.79663706e-01 -5.53362258e-02
-7.94058442e-01 -5.23623705e-01 4.70049649e-01 1.42977193e-01
-5.11748135e-01 4.68629807e-01 2.17848703e-01 -8.92672390e-02
1.72670230e-01 -5.61284721e-01 -7.11610436e-01 -4.29740518e-01
-3.14477801e-01 -2.79004984e-02 7.99181998e-01 2.26910517... | [8.498052597045898, 0.4178776741027832] |
4b641310-aadc-4911-8648-e291ec6311b5 | self-supervised-unseen-object-instance | 2302.03793 | null | https://arxiv.org/abs/2302.03793v1 | https://arxiv.org/pdf/2302.03793v1.pdf | Self-Supervised Unseen Object Instance Segmentation via Long-Term Robot Interaction | We introduce a novel robotic system for improving unseen object instance segmentation in the real world by leveraging long-term robot interaction with objects. Previous approaches either grasp or push an object and then obtain the segmentation mask of the grasped or pushed object after one action. Instead, our system d... | ['Yu Xiang', 'Nicholas Ruozzi', 'Yunhui Guo', 'Kaiyu Hang', 'Kamalesh Palanisamy', 'Charles Averill', 'Zesheng Xu', 'Ninad Khargonkar', 'Yangxiao Lu'] | 2023-02-07 | null | null | null | null | ['unseen-object-instance-segmentation', 'video-object-segmentation', 'video-semantic-segmentation', 'robotic-grasping'] | ['computer-vision', 'computer-vision', 'computer-vision', 'robots'] | [ 4.48835462e-01 1.74512759e-01 9.06900037e-03 -4.34731185e-01
-4.43830311e-01 -9.90836918e-01 -4.58021648e-02 -2.07514301e-01
-5.15604675e-01 2.77519137e-01 -5.54484129e-01 1.03211135e-01
-1.50692165e-01 -6.90948069e-01 -1.53181767e+00 -5.20941019e-01
-1.15175836e-01 1.06394565e+00 7.76257336e-01 -1.48299709... | [6.051241874694824, -0.9824849963188171] |
1cc45aad-9bff-4f8a-b349-758526627e16 | nl-linknet-toward-lighter-but-more-accurate | 1908.08223 | null | https://arxiv.org/abs/1908.08223v3 | https://arxiv.org/pdf/1908.08223v3.pdf | NL-LinkNet: Toward Lighter but More Accurate Road Extraction with Non-Local Operations | Road extraction from very high resolution satellite (VHR) images is one of the most important topics in the field of remote sensing. In this paper, we propose an efficient Non-Local LinkNet with non-local blocks that can grasp relations between global features. This enables each spatial feature point to refer to all ot... | ['Yooseung Wang', 'Junghoon Seo', 'Taegyun Jeon'] | 2019-08-22 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [-1.10981070e-01 1.32687557e-02 6.89157611e-03 -4.96646613e-01
-8.92022610e-01 -3.96786869e-01 5.87986350e-01 -1.53911756e-02
-6.38025105e-01 9.81568694e-01 -1.59306079e-01 -9.06035721e-01
-3.18147629e-01 -1.31930602e+00 -9.22026992e-01 -6.87186897e-01
-4.74992692e-01 6.89034224e-01 5.82767308e-01 -3.94989192... | [9.135797500610352, -1.44938063621521] |
e8b068d9-cdd4-484f-87f9-7bc32a92933f | carle-s-game-an-open-ended-challenge-in | 2107.05786 | null | https://arxiv.org/abs/2107.05786v1 | https://arxiv.org/pdf/2107.05786v1.pdf | Carle's Game: An Open-Ended Challenge in Exploratory Machine Creativity | This paper is both an introduction and an invitation. It is an introduction to CARLE, a Life-like cellular automata simulator and reinforcement learning environment. It is also an invitation to Carle's Game, a challenge in open-ended machine exploration and creativity. Inducing machine agents to excel at creating inter... | ['Q. Tyrell Davis'] | 2021-07-13 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [-3.32440466e-01 -5.83918840e-02 2.52988309e-01 6.38674974e-01
-1.12991512e-01 -7.51486778e-01 9.60961163e-01 -1.49355980e-03
-4.28330213e-01 1.06614947e+00 -8.68044272e-02 -5.07795751e-01
-1.03471316e-01 -1.20330918e+00 -5.46460509e-01 -6.88754618e-01
-4.22796309e-01 8.87494922e-01 2.95826197e-01 -8.28635156... | [3.7859761714935303, 1.5415270328521729] |
e14e5869-f9be-4bbf-8e4d-ed48e6c6f3de | cross-database-and-cross-channel-ecg | 2306.04433 | null | https://arxiv.org/abs/2306.04433v1 | https://arxiv.org/pdf/2306.04433v1.pdf | Cross-Database and Cross-Channel ECG Arrhythmia Heartbeat Classification Based on Unsupervised Domain Adaptation | The classification of electrocardiogram (ECG) plays a crucial role in the development of an automatic cardiovascular diagnostic system. However, considerable variances in ECG signals between individuals is a significant challenge. Changes in data distribution limit cross-domain utilization of a model. In this study, we... | ['Naimul Khan', 'Md Niaz Imtiaz'] | 2023-06-07 | null | null | null | null | ['heartbeat-classification', 'unsupervised-domain-adaptation'] | ['medical', 'methodology'] | [ 2.18466088e-01 -3.31973612e-01 5.82328364e-02 -7.51803517e-01
-1.28153622e+00 -5.62014997e-01 -8.79197493e-02 3.77003402e-01
-3.27614486e-01 7.53248632e-01 -3.24778736e-01 -1.06015742e-01
-4.10785943e-01 -2.50032544e-01 -2.24192336e-01 -8.73222351e-01
-1.84443071e-01 5.11959553e-01 -2.02396110e-01 4.00319904... | [14.208708763122559, 3.1148529052734375] |
fc966893-dd9d-4e35-a005-3d0c58a6e130 | cluster-based-contrastive-disentangling-for | 2203.02648 | null | https://arxiv.org/abs/2203.02648v1 | https://arxiv.org/pdf/2203.02648v1.pdf | Cluster-based Contrastive Disentangling for Generalized Zero-Shot Learning | Generalized Zero-Shot Learning (GZSL) aims to recognize both seen and unseen classes by training only the seen classes, in which the instances of unseen classes tend to be biased towards the seen class. In this paper, we propose a Cluster-based Contrastive Disentangling (CCD) method to improve GZSL by alleviating the s... | ['Jiancheng Lv', 'Chenwei Tang', 'Yi Gao'] | 2022-03-05 | null | null | null | null | ['generalized-zero-shot-learning', 'generalized-zero-shot-learning'] | ['computer-vision', 'methodology'] | [ 1.51339278e-01 -1.48104772e-01 -2.15235397e-01 -5.62329233e-01
-7.36576140e-01 -6.30033135e-01 6.26456022e-01 -7.03862458e-02
-1.98714614e-01 4.90672201e-01 1.09093770e-01 3.90441895e-01
-4.80346173e-01 -7.32339323e-01 -2.53614366e-01 -1.22967219e+00
3.79343897e-01 6.30027831e-01 1.66842237e-01 4.95055169... | [9.918386459350586, 2.40291690826416] |
51fe95b0-a673-47c8-bb06-da278cd64b66 | crack-detection-as-a-weakly-supervised | 2011.02208 | null | https://arxiv.org/abs/2011.02208v1 | https://arxiv.org/pdf/2011.02208v1.pdf | Crack Detection as a Weakly-Supervised Problem: Towards Achieving Less Annotation-Intensive Crack Detectors | Automatic crack detection is a critical task that has the potential to drastically reduce labor-intensive building and road inspections currently being done manually. Recent studies in this field have significantly improved the detection accuracy. However, the methods often heavily rely on costly annotation processes. ... | ['Hiroto Nagayoshi', 'Yuki Inoue'] | 2020-11-04 | null | null | null | null | ['crack-segmentation'] | ['computer-vision'] | [ 4.74169850e-01 -1.74647495e-02 8.67079645e-02 -3.40322196e-01
-9.88826334e-01 -3.74392748e-01 -6.49725199e-02 4.75749940e-01
-2.03983948e-01 4.64653879e-01 -3.53313774e-01 -1.24725118e-01
3.01576525e-01 -1.01061177e+00 -5.56889176e-01 -8.37980390e-01
4.37653720e-01 2.09445670e-01 8.10674667e-01 4.71143425... | [7.550039768218994, 1.4694957733154297] |
e9daf767-3dd9-4e6d-b7cf-a123125e1234 | a-masked-image-reconstruction-network-for | 2204.09851 | null | https://arxiv.org/abs/2204.09851v2 | https://arxiv.org/pdf/2204.09851v2.pdf | A Masked Image Reconstruction Network for Document-level Relation Extraction | Document-level relation extraction aims to extract relations among entities within a document. Compared with its sentence-level counterpart, Document-level relation extraction requires inference over multiple sentences to extract complex relational triples. Previous research normally complete reasoning through informat... | ['Yidong Cheng', 'Liang Zhang'] | 2022-04-21 | null | null | null | null | ['document-level-relation-extraction'] | ['natural-language-processing'] | [ 3.68734300e-01 4.64859903e-01 -4.14987534e-01 -2.70511568e-01
-8.40859711e-01 -5.75341403e-01 8.64981651e-01 2.40791842e-01
4.77524847e-03 5.81901848e-01 2.76223719e-01 -4.55480248e-01
-2.09096566e-01 -1.31734395e+00 -9.69135880e-01 -1.79737598e-01
-4.30970453e-02 3.28405321e-01 8.84987861e-02 -1.14777178... | [9.143319129943848, 8.460158348083496] |
53338326-798b-4de8-a8c6-8b7aff98ada9 | generalization-of-change-point-detection-in | 2001.06386 | null | https://arxiv.org/abs/2001.06386v1 | https://arxiv.org/pdf/2001.06386v1.pdf | Generalization of Change-Point Detection in Time Series Data Based on Direct Density Ratio Estimation | The goal of the change-point detection is to discover changes of time series distribution. One of the state of the art approaches of the change-point detection are based on direct density ratio estimation. In this work we show how existing algorithms can be generalized using various binary classification and regression... | ['Mikhail Hushchyn', 'Andrey Ustyuzhanin'] | 2020-01-17 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [ 3.42257395e-02 -7.14834332e-01 -3.83363932e-01 -2.28119090e-01
-4.01655406e-01 -4.99770820e-01 7.86179185e-01 3.26057911e-01
-3.62200737e-02 1.29427922e+00 -3.16389382e-01 -5.09147286e-01
-3.37478817e-01 -9.37712610e-01 -4.03713942e-01 -8.26518774e-01
-6.19022071e-01 3.07854116e-01 5.67005038e-01 -3.18239748... | [7.207413673400879, 3.3905811309814453] |
1c339b3e-a305-410c-8f29-79c77f5ee6a5 | towards-alleviating-the-modeling-ambiguity-of | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Yu_Towards_Alleviating_the_Modeling_Ambiguity_of_Unsupervised_Monocular_3D_Human_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Yu_Towards_Alleviating_the_Modeling_Ambiguity_of_Unsupervised_Monocular_3D_Human_ICCV_2021_paper.pdf | Towards Alleviating the Modeling Ambiguity of Unsupervised Monocular 3D Human Pose Estimation | In this work, we study the ambiguity problem in the task of unsupervised 3D human pose estimation from 2D counterpart. On one hand, without explicit annotation, the scale of 3D pose is difficult to be accurately captured (scale ambiguity). On the other hand, one 2D pose might correspond to multiple 3D gestures, whe... | ['Wenjun Zhang', 'Chenglong Zhao', 'Junjie Wang', 'Jingwei Xu', 'Bingbing Ni', 'Zhenbo Yu'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['monocular-3d-human-pose-estimation', 'unsupervised-3d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [ 2.44029611e-01 7.48455599e-02 -1.96213499e-01 -3.72112364e-01
-8.13343942e-01 -5.50791442e-01 2.92828262e-01 -1.76942557e-01
-5.49188316e-01 4.79628235e-01 8.88115913e-02 9.94150341e-03
-5.48259541e-03 -3.84177238e-01 -6.11627579e-01 -6.33123457e-01
1.61600456e-01 6.73485637e-01 4.10935551e-01 -1.14391021... | [7.0371270179748535, -0.9493838548660278] |
d7f4b12a-72ff-4146-a881-243f7ec18895 | computationally-assisted-quality-control-for | 2306.16914 | null | https://arxiv.org/abs/2306.16914v1 | https://arxiv.org/pdf/2306.16914v1.pdf | Computationally Assisted Quality Control for Public Health Data Streams | Irregularities in public health data streams (like COVID-19 Cases) hamper data-driven decision-making for public health stakeholders. A real-time, computer-generated list of the most important, outlying data points from thousands of daily-updated public health data streams could assist an expert reviewer in identifying... | ['Bryan Wilder', 'Roni Rosenfeld', 'Kathryn Mazaitis', 'Ananya Joshi'] | 2023-06-29 | null | null | null | null | ['outlier-detection', 'decision-making'] | ['methodology', 'reasoning'] | [-3.40138942e-01 -1.39951542e-01 -2.99756557e-01 -1.95538774e-01
-8.09064627e-01 -3.96778166e-01 2.88519651e-01 1.37389517e+00
-5.20763814e-01 2.39759237e-01 7.01755822e-01 -6.36949956e-01
-1.08054690e-01 -6.52777970e-01 -3.34079415e-01 -2.98277587e-01
-4.40473258e-01 8.24308395e-01 1.27041191e-01 1.38895437... | [7.848845481872559, 5.998921871185303] |
5ff037a0-53e1-4449-aa21-1c45cb27a5b7 | self-configuration-in-machine-learning | 1809.06463 | null | http://arxiv.org/abs/1809.06463v1 | http://arxiv.org/pdf/1809.06463v1.pdf | Self Configuration in Machine Learning | In this paper we first present a class of algorithms for training multi-level
neural networks with a quadratic cost function one layer at a time starting
from the input layer. The algorithm is based on the fact that for any layer to
be trained, the effect of a direct connection to an optimized linear output
layer can b... | ['Eugene Wong'] | 2018-09-17 | null | null | null | null | ['self-organized-clustering'] | ['miscellaneous'] | [ 3.57284397e-02 2.46416166e-01 9.17731002e-02 -4.77416515e-01
-2.76338607e-02 -4.28125024e-01 1.97521523e-01 1.68276966e-01
-8.02197874e-01 6.33619249e-01 -5.15863180e-01 -3.03992063e-01
-6.60972297e-02 -9.37318265e-01 -6.74875736e-01 -8.03080142e-01
-1.73648268e-01 4.76094127e-01 5.79305172e-01 -1.75441176... | [8.153602600097656, 3.3699746131896973] |
2dc95c78-5b17-4df8-abe4-abc005c8accc | local-implicit-grid-representations-for-3d-1 | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Jiang_Local_Implicit_Grid_Representations_for_3D_Scenes_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Jiang_Local_Implicit_Grid_Representations_for_3D_Scenes_CVPR_2020_paper.pdf | Local Implicit Grid Representations for 3D Scenes | Shape priors learned from data are commonly used to reconstruct 3D objects from partial or noisy data. Yet no such shape priors are available for indoor scenes, since typical 3D autoencoders cannot handle their scale, complexity, or diversity. In this paper, we introduce Local Implicit Grid Representations, a new 3D sh... | [' Thomas Funkhouser', ' Matthias Niessner', ' Jingwei Huang', ' Ameesh Makadia', ' Avneesh Sud', 'Chiyu "Max" Jiang'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['3d-shape-representation'] | ['computer-vision'] | [ 1.26719266e-01 3.40461761e-01 1.80276394e-01 -2.77920216e-01
-6.73498690e-01 -4.09265369e-01 3.96273971e-01 1.61838140e-02
4.30832595e-01 3.29393566e-01 3.49892974e-01 5.54473773e-02
-7.62171894e-02 -1.22709692e+00 -1.15915668e+00 -7.45261967e-01
6.16027825e-02 7.39302218e-01 1.42008513e-01 7.89193902... | [8.63680648803711, -3.5742266178131104] |
c1154178-1515-40c6-b6d8-7e4373d02f25 | neural-speech-enhancement-with-very-low | 2304.08707 | null | https://arxiv.org/abs/2304.08707v1 | https://arxiv.org/pdf/2304.08707v1.pdf | Neural Speech Enhancement with Very Low Algorithmic Latency and Complexity via Integrated Full- and Sub-Band Modeling | We propose FSB-LSTM, a novel long short-term memory (LSTM) based architecture that integrates full- and sub-band (FSB) modeling, for single- and multi-channel speech enhancement in the short-time Fourier transform (STFT) domain. The model maintains an information highway to flow an over-complete input representation th... | ['Shinji Watanabe', 'Byeong-Yeol Kim', 'Younglo Lee', 'Shukjae Choi', 'Samuele Cornell', 'Zhong-Qiu Wang'] | 2023-04-18 | null | null | null | null | ['speech-enhancement'] | ['speech'] | [ 3.72653186e-01 2.15142965e-02 7.02352226e-02 -4.83421147e-01
-1.11235428e+00 -1.33178225e-02 1.52111501e-01 -9.77216214e-02
-5.67100942e-01 4.63536382e-01 2.30933398e-01 -6.10425591e-01
5.79502359e-02 -5.93187451e-01 -6.40156507e-01 -6.78443372e-01
-4.52855319e-01 -4.33072060e-01 4.30215120e-01 -3.80717069... | [14.891392707824707, 5.95163631439209] |
59f66d18-8908-4fcd-b37b-f4cfac05a0fe | latenthuman-shape-and-pose-disentangled | 2111.15113 | null | https://arxiv.org/abs/2111.15113v1 | https://arxiv.org/pdf/2111.15113v1.pdf | LatentHuman: Shape-and-Pose Disentangled Latent Representation for Human Bodies | 3D representation and reconstruction of human bodies have been studied for a long time in computer vision. Traditional methods rely mostly on parametric statistical linear models, limiting the space of possible bodies to linear combinations. It is only recently that some approaches try to leverage neural implicit repre... | ['Zhaopeng Cui', 'Marc Pollefeys', 'Guofeng Zhang', 'Hujun Bao', 'Tianxing Fan', 'Bangbang Yang', 'Sandro Lombardi'] | 2021-11-30 | null | null | null | null | ['motion-retargeting'] | ['computer-vision'] | [ 2.73625981e-02 6.07583344e-01 -2.68579960e-01 -1.91069528e-01
-4.45927978e-01 -3.74406755e-01 5.66002011e-01 -3.65185946e-01
-1.56592295e-01 5.01022458e-01 4.69785541e-01 1.52947485e-01
-5.64879514e-02 -4.50674772e-01 -8.56234789e-01 -4.94645655e-01
-3.42586846e-03 9.00118649e-01 -4.51092683e-02 -1.43339187... | [6.977619647979736, -1.2215209007263184] |
97c418b3-5900-4722-8a82-5d212b9b0851 | parameter-free-channel-attention-for-image | 2303.11055 | null | https://arxiv.org/abs/2303.11055v1 | https://arxiv.org/pdf/2303.11055v1.pdf | Parameter-Free Channel Attention for Image Classification and Super-Resolution | The channel attention mechanism is a useful technique widely employed in deep convolutional neural networks to boost the performance for image processing tasks, eg, image classification and image super-resolution. It is usually designed as a parameterized sub-network and embedded into the convolutional layers of the ne... | ['Liuqing Wang', 'XianTong Zhen', 'Wangpeng An', 'Lingxiao Yang', 'Yuxuan Shi'] | 2023-03-20 | null | null | null | null | ['image-super-resolution'] | ['computer-vision'] | [ 1.11955091e-01 -9.42420959e-02 -1.63737446e-01 -3.52658033e-01
-2.94184893e-01 -3.71828340e-02 2.63600528e-01 -3.79153460e-01
-5.76110840e-01 6.80086851e-01 9.19786617e-02 -1.07421733e-01
2.55467534e-01 -8.55181992e-01 -8.05731237e-01 -6.49952412e-01
1.05790079e-01 -3.44818830e-01 5.94555914e-01 -3.41571420... | [10.808436393737793, -1.729683756828308] |
d74136ce-b1ba-4edf-81af-c8bcc71b1819 | bidirectional-multiscale-feature-aggregation | 2104.00230 | null | https://arxiv.org/abs/2104.00230v1 | https://arxiv.org/pdf/2104.00230v1.pdf | Bidirectional Multiscale Feature Aggregation for Speaker Verification | In this paper, we propose a novel bidirectional multiscale feature aggregation (BMFA) network with attentional fusion modules for text-independent speaker verification. The feature maps from different stages of the backbone network are iteratively combined and refined in both a bottom-up and top-down manner. Furthermor... | ['Bin Gu', 'Wu Guo', 'Jiajun Qi'] | 2021-04-01 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [ 1.54908255e-01 -4.07441735e-01 2.07160875e-01 -7.97383964e-01
-8.55317473e-01 -1.55479729e-01 4.41718459e-01 1.08548412e-02
-5.18701553e-01 3.68815273e-01 4.20536309e-01 -1.41474321e-01
-1.80527538e-01 -3.06989968e-01 -1.78830683e-01 -7.33214974e-01
7.41317496e-02 -3.16165656e-01 4.05194312e-01 -3.31683874... | [14.442219734191895, 6.013925075531006] |
926c18a3-a0a5-49a5-afe7-24afc50f8174 | co-clustering-based-exploratory-analysis-of | 2212.11728 | null | https://arxiv.org/abs/2212.11728v1 | https://arxiv.org/pdf/2212.11728v1.pdf | Co-clustering based exploratory analysis of mixed-type data tables | Co-clustering is a class of unsupervised data analysis techniques that extract the existing underlying dependency structure between the instances and variables of a data table as homogeneous blocks. Most of those techniques are limited to variables of the same type. In this paper, we propose a mixed data co-clustering ... | ['Fabrice Rossi', 'Fabrice Clérot', 'Marc Boullé', 'Aichetou Bouchareb'] | 2022-12-22 | null | null | null | null | ['type'] | ['speech'] | [ 7.35007674e-02 -6.61242232e-02 -1.83197856e-01 -4.36980873e-01
-1.78603113e-01 -5.74426889e-01 5.54246128e-01 7.60948122e-01
-3.52945089e-01 6.97315037e-01 1.47966802e-01 -4.17256802e-01
-7.30468869e-01 -1.18519020e+00 -1.93326175e-01 -8.65740120e-01
9.80502740e-03 9.57610905e-01 6.29795417e-02 8.99499357... | [7.6227498054504395, 4.600865364074707] |
e54ad32f-cd80-49c4-9bfe-0a0a8569fea5 | predicting-discourse-trees-from-transformer | 2104.07058 | null | https://arxiv.org/abs/2104.07058v1 | https://arxiv.org/pdf/2104.07058v1.pdf | Predicting Discourse Trees from Transformer-based Neural Summarizers | Previous work indicates that discourse information benefits summarization. In this paper, we explore whether this synergy between discourse and summarization is bidirectional, by inferring document-level discourse trees from pre-trained neural summarizers. In particular, we generate unlabeled RST-style discourse trees ... | ['Giuseppe Carenini', 'Patrick Huber', 'Wen Xiao'] | 2021-04-14 | null | https://aclanthology.org/2021.naacl-main.326 | https://aclanthology.org/2021.naacl-main.326.pdf | naacl-2021-4 | ['discourse-parsing'] | ['natural-language-processing'] | [ 3.73980194e-01 1.09270954e+00 -6.55967295e-01 -6.19859219e-01
-9.27531958e-01 -8.78386974e-01 1.05614042e+00 3.29041839e-01
6.30911812e-02 1.15259767e+00 1.63615811e+00 -4.76370931e-01
2.05504850e-01 -7.35033870e-01 -8.71090233e-01 -2.22413450e-01
2.33062673e-02 3.97046447e-01 -1.69738486e-01 -5.28724670... | [12.396174430847168, 9.470510482788086] |
16dbcec5-6f05-44f4-8d8d-784611f9a4af | robust-mode-connectivity-oriented-adversarial | 2303.10225 | null | https://arxiv.org/abs/2303.10225v1 | https://arxiv.org/pdf/2303.10225v1.pdf | Robust Mode Connectivity-Oriented Adversarial Defense: Enhancing Neural Network Robustness Against Diversified $\ell_p$ Attacks | Adversarial robustness is a key concept in measuring the ability of neural networks to defend against adversarial attacks during the inference phase. Recent studies have shown that despite the success of improving adversarial robustness against a single type of attack using robust training techniques, models are still ... | ['Sijia Liu', 'YuXuan Li', 'Ren Wang'] | 2023-03-17 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 1.72871262e-01 -1.54846475e-01 -1.43437192e-01 2.52925128e-01
-7.17903674e-01 -8.56777430e-01 3.41626823e-01 -1.56630665e-01
-4.13962007e-01 8.98839593e-01 -3.75273913e-01 -5.14135778e-01
-4.41844195e-01 -1.07154644e+00 -1.02740335e+00 -9.25089478e-01
-4.10639614e-01 1.99852195e-02 2.67447144e-01 -6.06947601... | [5.484051704406738, 7.964304447174072] |
0925264a-fbbf-41e3-8450-959690cdccab | formal-development-of-safe-automated-driving | 2204.06873 | null | https://arxiv.org/abs/2204.06873v1 | https://arxiv.org/pdf/2204.06873v1.pdf | Formal Development of Safe Automated Driving using Differential Dynamic Logic | The challenges in providing convincing arguments for safe and correct behavior of automated driving (AD) systems have so far hindered their widespread commercial deployment. Conventional development approaches such as testing and simulation are limited by non-exhaustive analysis, and can thus not guarantee correctness ... | ['Martin Fabian', 'Wolfgang Ahrendt', 'Yuvaraj Selvaraj'] | 2022-04-14 | null | null | null | null | ['mathematical-proofs'] | ['miscellaneous'] | [ 1.67627573e-01 6.09509110e-01 1.07293399e-02 -2.22989842e-01
1.33508265e-01 -7.75133491e-01 6.11864030e-01 1.68438599e-01
8.07353631e-02 6.64732635e-01 -5.35227120e-01 -1.23105657e+00
-4.60257649e-01 -6.84941173e-01 -5.57707667e-01 -1.18874952e-01
-1.48701802e-01 8.69648308e-02 5.71739972e-01 -4.04839963... | [5.107127666473389, 2.303071975708008] |
5ed4a934-27df-45f0-8ce5-32af1b517218 | online-updated-high-order-collaborative | 2202.06568 | null | https://arxiv.org/abs/2202.06568v1 | https://arxiv.org/pdf/2202.06568v1.pdf | Online-updated High-order Collaborative Networks for Single Image Deraining | Single image deraining is an important and challenging task for some downstream artificial intelligence applications such as video surveillance and self-driving systems. Most of the existing deep-learning-based methods constrain the network to generate derained images but few of them explore features from intermediate ... | ['Xiao-Ming Wu', 'Jinshan Pan', 'Cong Wang'] | 2022-02-14 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [ 1.18552120e-02 -1.95080563e-01 3.05485219e-01 -6.16179883e-01
-2.09632829e-01 -2.81024277e-01 1.79809481e-01 -4.81138110e-01
-3.16373140e-01 5.80144167e-01 -5.10837510e-02 -1.95530608e-01
-9.53327790e-02 -1.02465713e+00 -8.24730337e-01 -7.84743547e-01
-4.52477515e-01 -4.07240763e-02 5.70233464e-01 -4.18874323... | [10.896533966064453, -3.16082501411438] |
9bae905c-945a-4375-b1ba-045a728698f4 | knowledge-aware-bayesian-co-attention-for | 2302.09856 | null | https://arxiv.org/abs/2302.09856v3 | https://arxiv.org/pdf/2302.09856v3.pdf | Knowledge-aware Bayesian Co-attention for Multimodal Emotion Recognition | Multimodal emotion recognition is a challenging research area that aims to fuse different modalities to predict human emotion. However, most existing models that are based on attention mechanisms have difficulty in learning emotionally relevant parts on their own. To solve this problem, we propose to incorporate extern... | ['Yanfeng Wang', 'Yu Wang', 'Zihan Zhao'] | 2023-02-20 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 1.74647674e-01 -1.03921425e-02 -4.05706428e-02 -6.39461517e-01
-7.89915323e-01 -4.44118902e-02 3.87929827e-01 2.80693341e-02
-5.96433401e-01 6.85988307e-01 3.71886492e-01 4.09106940e-01
1.99089184e-01 -3.20572317e-01 -5.55588961e-01 -5.27354717e-01
3.86225581e-01 2.74633449e-02 -1.92100257e-01 -1.80127174... | [13.215628623962402, 5.200601100921631] |
ecf5e65b-a1cd-4965-b410-ceb564ff2a93 | tlmote-a-topic-based-language-modelling | null | null | https://journals.flvc.org/FLAIRS/article/view/130676 | https://journals.flvc.org/FLAIRS/article/view/130676/133877 | TLMOTE: A Topic-based Language Modelling Approach for Text Oversampling | Training machine learning and deep learning models on unbalanced datasets can lead to a bias portrayed by the models towards the majority classes. To tackle the problem of bias towards majority classes, researchers have presented various techniques to oversample the minority class data points. Most of the available sta... | ['Anubhav Sharma', 'Anmol Bansal', 'Seba Susan', 'Arjun Choudhry'] | 2022-05-04 | null | null | null | the-35th-international-florida-artificial | ['spam-detection', 'suggestion-mining'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.47641891e-01 6.95434451e-01 -2.60557353e-01 -6.91356778e-01
-6.17642522e-01 -2.50870250e-02 1.05089021e+00 2.68595427e-01
-1.97057724e-01 1.05842316e+00 5.36237001e-01 -4.08593267e-01
2.47103691e-01 -9.09761667e-01 -7.35250890e-01 -4.88017768e-01
4.99394745e-01 8.44886422e-01 2.95699835e-02 -4.26060975... | [10.81322193145752, 8.342814445495605] |
fe3ddabc-f324-4bd7-bbef-eff4290e7e0a | calibrating-constitutive-models-with-full | 2203.16577 | null | https://arxiv.org/abs/2203.16577v1 | https://arxiv.org/pdf/2203.16577v1.pdf | Calibrating constitutive models with full-field data via physics informed neural networks | The calibration of solid constitutive models with full-field experimental data is a long-standing challenge, especially in materials which undergo large deformation. In this paper, we propose a physics-informed deep-learning framework for the discovery of constitutive model parameterizations given full-field displaceme... | ['Sharlotte L. B. Kramer', 'Kevin N. Long', 'Craig M. Hamel'] | 2022-03-30 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [ 2.42930725e-01 -3.51438597e-02 -1.01656385e-01 -3.36251259e-01
-5.05119383e-01 -2.25686193e-01 3.24715674e-01 1.30273923e-01
-3.15257430e-01 8.14075053e-01 -2.74301112e-01 -1.19410500e-01
-7.78920591e-01 -8.27826381e-01 -1.15359986e+00 -1.10400403e+00
-1.90563977e-01 9.94648933e-01 1.67540878e-01 -4.32976484... | [6.354700088500977, 3.416809320449829] |
6fe987ff-8bc7-44bb-bd18-499acf540a4d | build-a-bot-teaching-conversational-ai-using | 2212.07542 | null | https://arxiv.org/abs/2212.07542v1 | https://arxiv.org/pdf/2212.07542v1.pdf | Build-a-Bot: Teaching Conversational AI Using a Transformer-Based Intent Recognition and Question Answering Architecture | As artificial intelligence (AI) becomes a prominent part of modern life, AI literacy is becoming important for all citizens, not just those in technology careers. Previous research in AI education materials has largely focused on the introduction of terminology as well as AI use cases and ethics, but few allow students... | ['Cynthia Breazeal', 'Sharifa Alghowinem', 'Kate Pearce'] | 2022-12-14 | null | null | null | null | ['intent-recognition'] | ['natural-language-processing'] | [ 2.00094357e-02 5.33595860e-01 2.67016646e-02 -4.37228560e-01
-2.84988075e-01 -1.02336204e+00 4.97733116e-01 1.23412915e-01
-2.26075739e-01 1.59398019e-01 -7.18080997e-02 -9.65347469e-01
-3.12186964e-02 -8.53317976e-01 -2.02454507e-01 -3.97535115e-02
4.33558196e-01 7.34484732e-01 5.69193602e-01 -6.90695703... | [12.061211585998535, 7.997416019439697] |
61cca0d1-819b-4447-8611-b2cb0ecd5081 | styleavatar-real-time-photo-realistic | 2305.00942 | null | https://arxiv.org/abs/2305.00942v1 | https://arxiv.org/pdf/2305.00942v1.pdf | StyleAvatar: Real-time Photo-realistic Portrait Avatar from a Single Video | Face reenactment methods attempt to restore and re-animate portrait videos as realistically as possible. Existing methods face a dilemma in quality versus controllability: 2D GAN-based methods achieve higher image quality but suffer in fine-grained control of facial attributes compared with 3D counterparts. In this wor... | ['Yebin Liu', 'Tao Yu', 'Hongwen Zhang', 'Yuxiang Zhang', 'Jingxiang Sun', 'Xiaochen Zhao', 'Lizhen Wang'] | 2023-05-01 | null | null | null | null | ['video-generation', 'face-reenactment'] | ['computer-vision', 'computer-vision'] | [ 3.08168054e-01 1.49561197e-01 -1.16552845e-01 -1.38019040e-01
-5.19173443e-01 -4.53514606e-01 6.20562077e-01 -8.91941071e-01
1.48230821e-01 6.57998979e-01 1.56556755e-01 -6.42277747e-02
3.74145925e-01 -8.96507561e-01 -7.03236938e-01 -6.50619030e-01
3.43214959e-01 9.06563178e-02 -2.36638710e-01 -3.93611521... | [12.530317306518555, -0.39858102798461914] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.