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
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992b0925-6c32-43a7-9100-9d48f54bcd54 | deep-inductive-logic-reasoning-for-multi-hop | null | null | https://aclanthology.org/2022.acl-long.343 | https://aclanthology.org/2022.acl-long.343.pdf | Deep Inductive Logic Reasoning for Multi-Hop Reading Comprehension | Multi-hop reading comprehension requires an ability to reason across multiple documents. On the one hand, deep learning approaches only implicitly encode query-related information into distributed embeddings which fail to uncover the discrete relational reasoning process to infer the correct answer. On the other hand, ... | ['Sinno Pan', 'Wenya Wang'] | null | null | null | null | acl-2022-5 | ['relational-reasoning', 'multi-hop-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.51251504e-02 7.54693091e-01 -3.13571721e-01 -6.98204637e-01
-5.76413453e-01 -5.43612242e-01 3.38173658e-01 8.81447971e-01
-2.54237652e-01 5.60897768e-01 5.19086421e-01 -6.56978607e-01
-8.80560338e-01 -1.53194582e+00 -9.72002864e-01 9.21302363e-02
-4.32931148e-02 8.93504381e-01 2.08315313e-01 -3.24333578... | [9.488884925842285, 7.632835865020752] |
048a1b7a-7814-4528-b075-f792d5b3b024 | adapting-bert-to-implicit-discourse-relation | null | null | https://aclanthology.org/2020.lrec-1.145 | https://aclanthology.org/2020.lrec-1.145.pdf | Adapting BERT to Implicit Discourse Relation Classification with a Focus on Discourse Connectives | BERT, a neural network-based language model pre-trained on large corpora, is a breakthrough in natural language processing, significantly outperforming previous state-of-the-art models in numerous tasks. However, there have been few reports on its application to implicit discourse relation classification, and it is not... | ['Yugo Murawaki', 'Yudai Kishimoto', 'Sadao Kurohashi'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['implicit-discourse-relation-classification'] | ['natural-language-processing'] | [ 4.41894025e-01 1.14003336e+00 -3.33307981e-01 -4.25717205e-01
-5.08569419e-01 -6.00225866e-01 8.90634120e-01 5.19617915e-01
-6.11511230e-01 9.77848172e-01 4.33705807e-01 -7.60166168e-01
9.88788307e-02 -6.41129911e-01 -4.64435130e-01 -2.34668061e-01
-2.20924258e-01 8.27585995e-01 5.31156421e-01 -4.23466474... | [10.722331047058105, 9.219871520996094] |
d2a7f9ea-8a98-408b-8aac-cab3c1fc30f8 | tripartite-information-mining-and-integration | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Liu_Tripartite_Information_Mining_and_Integration_for_Image_Matting_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Liu_Tripartite_Information_Mining_and_Integration_for_Image_Matting_ICCV_2021_paper.pdf | Tripartite Information Mining and Integration for Image Matting | With the development of deep convolutional neural networks, image matting has ushered in a new phase. Regarding the nature of image matting, most researches have focused on solutions for transition regions. However, we argue that many existing approaches are excessively focused on transition-dominant local fields a... | ['Xin Yang', 'Yong Tang', 'Yujie Huang', 'Yu Qiao', 'Xiao Shi', 'Jiake Xie', 'Yuhao Liu'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['image-matting'] | ['computer-vision'] | [ 5.44732548e-02 -1.89108998e-01 -1.99547783e-02 -3.09480190e-01
-5.04737496e-01 -1.45670339e-01 4.41048622e-01 -2.61249393e-01
-3.59033376e-01 5.58219254e-01 3.98947299e-02 -3.78074229e-01
-1.71401069e-01 -9.31317449e-01 -9.47023213e-01 -5.68253517e-01
-3.35124396e-02 3.20287228e-01 3.44273373e-02 -2.81385183... | [10.641104698181152, -0.8977043032646179] |
cf66c6db-12ff-4540-94f3-d2676c6c41c4 | user-assisted-shadow-removal | null | null | https://www.sciencedirect.com/science/article/pii/S0262885617300744?via%3Dihub | https://www.sciencedirect.com/science/article/pii/S0262885617300744?via%3Dihub | User-Assisted Shadow Removal | This paper presents a novel user-aided method for texture-preserving shadow removal from single images requiring simple user input. Compared with the state-of-the-art, our algorithm offers the most flexible user interaction to date and produces more accurate and robust shadow removal under thorough quantitative evaluat... | ['Han Gong; Darren Cosker'] | 2018-04-18 | null | null | null | image-and-vision-computing-2018-4 | ['shadow-removal'] | ['computer-vision'] | [ 1.20905674e+00 1.01948850e-01 4.97430772e-01 -3.51292670e-01
-5.23495197e-01 -4.02963728e-01 3.58992457e-01 -6.65176958e-02
-1.97074324e-01 8.07659805e-01 6.45146444e-02 -1.68434709e-01
1.84100002e-01 -4.86988902e-01 -2.32479170e-01 -8.49713922e-01
-5.73696978e-02 2.16714501e-01 9.72308218e-01 -1.93922877... | [10.817305564880371, -4.0511932373046875] |
9a8710e2-d54a-4993-bf21-ff16a698db69 | towards-single-word-lexical-complexity | null | null | https://aclanthology.org/W18-0508 | https://aclanthology.org/W18-0508.pdf | Towards Single Word Lexical Complexity Prediction | In this paper we present work-in-progress where we investigate the usefulness of previously created word lists to the task of single-word lexical complexity analysis and prediction of the complexity level for learners of Swedish as a second language. The word lists used map each word to a single CEFR level, and the tas... | ['David Alfter', 'Elena Volodina'] | 2018-06-01 | null | null | null | ws-2018-6 | ['lexical-complexity-prediction'] | ['natural-language-processing'] | [-6.03051484e-03 1.08872645e-01 -2.87997305e-01 -3.02633226e-01
-9.38820958e-01 -5.28018713e-01 6.51685655e-01 8.33847702e-01
-9.63942051e-01 5.49530864e-01 5.65902233e-01 -6.83581889e-01
-1.88444197e-01 -5.67430019e-01 -1.28032684e-01 4.39096149e-03
-1.15702443e-01 5.84638596e-01 5.87275982e-01 -5.51523626... | [10.652063369750977, 10.446168899536133] |
3e3f2006-e2ac-487d-b1f3-93d37de293af | improved-answer-selection-with-pre-trained | 1708.04326 | null | http://arxiv.org/abs/1708.04326v1 | http://arxiv.org/pdf/1708.04326v1.pdf | Improved Answer Selection with Pre-Trained Word Embeddings | This paper evaluates existing and newly proposed answer selection methods
based on pre-trained word embeddings. Word embeddings are highly effective in
various natural language processing tasks and their integration into
traditional information retrieval (IR) systems allows for the capture of
semantic relatedness betwe... | ['Santos Cicero Nogueira dos', 'Navratil Jiri', 'Chakravarti Rishav'] | 2017-08-14 | null | null | null | null | ['answer-selection'] | ['natural-language-processing'] | [-4.39633504e-02 -2.29780942e-01 -6.71751916e-01 -3.88769478e-01
-9.96863723e-01 -4.06491369e-01 7.19195664e-01 1.04448307e+00
-1.25147200e+00 3.49524856e-01 7.24198520e-01 -3.08380067e-01
-6.45371914e-01 -9.12347674e-01 1.69598311e-01 -2.23446637e-03
-1.51805490e-01 8.48256528e-01 5.33657610e-01 -6.75182283... | [11.097262382507324, 8.273506164550781] |
62219f81-c36a-4926-820c-8ad5a31322c7 | 3d-reconstruction-using-structure-for-motion | 2306.06360 | null | https://arxiv.org/abs/2306.06360v1 | https://arxiv.org/pdf/2306.06360v1.pdf | 3D reconstruction using Structure for Motion | We are working towards 3D reconstruction of indoor spaces using a pair of HDR cameras in a stereo vision configuration mounted on an indoor mobile floor robot that captures various textures and spatial features as 2D images and this data is simultaneously utilized as a feed to our algorithm which will allow us to visua... | ['Raajith Gadam', 'Mudit Singal', 'Abhimanyu Saxena', 'Hritvik Choudhari', 'Kshitij Karnawat'] | 2023-06-10 | null | null | null | null | ['3d-reconstruction'] | ['computer-vision'] | [ 2.12336823e-01 -3.00063372e-01 5.49312115e-01 -4.66589630e-01
2.22013056e-01 -4.44602549e-01 4.82180595e-01 -1.84998468e-01
-3.69711518e-01 6.27296507e-01 2.00595856e-01 -3.21805656e-01
2.81779289e-01 -1.08836484e+00 -5.32507539e-01 -2.66360253e-01
2.13401571e-01 5.06022155e-01 4.69940692e-01 -3.63437951... | [9.001997947692871, -2.5150861740112305] |
6aa069e0-5a6d-400b-ba1a-272ec128c0e6 | multilingual-relation-classification-via | 2210.13838 | null | https://arxiv.org/abs/2210.13838v2 | https://arxiv.org/pdf/2210.13838v2.pdf | Multilingual Relation Classification via Efficient and Effective Prompting | Prompting pre-trained language models has achieved impressive performance on various NLP tasks, especially in low data regimes. Despite the success of prompting in monolingual settings, applying prompt-based methods in multilingual scenarios has been limited to a narrow set of tasks, due to the high cost of handcraftin... | ['Leonhard Hennig', 'David Harbecke', 'Yuxuan Chen'] | 2022-10-25 | null | null | null | null | ['relation-classification'] | ['natural-language-processing'] | [-6.89818263e-02 2.12962776e-01 -8.15100014e-01 -3.63291889e-01
-1.63750374e+00 -7.54190624e-01 1.06581438e+00 4.43320364e-01
-7.72234082e-01 9.77913320e-01 5.66707551e-01 -7.71388829e-01
6.10686429e-02 -2.02330172e-01 -6.55869722e-01 -3.89451474e-01
1.57958820e-01 8.67284298e-01 8.95513296e-02 -5.75269222... | [11.189628601074219, 9.558877944946289] |
177df2a7-8ad5-402c-bf8c-00967349fdd4 | flowface-explicit-semantic-flow-supervised | 2306.12686 | null | https://arxiv.org/abs/2306.12686v2 | https://arxiv.org/pdf/2306.12686v2.pdf | FlowFace++: Explicit Semantic Flow-supervised End-to-End Face Swapping | This work proposes a novel face-swapping framework FlowFace++, utilizing explicit semantic flow supervision and end-to-end architecture to facilitate shape-aware face-swapping. Specifically, our work pretrains a facial shape discriminator to supervise the face swapping network. The discriminator is shape-aware and reli... | ['Changjie Fan', 'Tangjie Lv', 'Yu Ding', 'Zhimeng Zhang', 'Wei zhang', 'Bowen Ma', 'Hao Zeng', 'Yu Zhang'] | 2023-06-22 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [ 9.72569808e-02 2.38232881e-01 1.56195149e-01 -8.09431553e-01
-5.13816893e-01 -2.76537955e-01 4.83435690e-01 -8.61690402e-01
1.77613702e-02 3.81353974e-01 3.89505804e-01 2.83390284e-01
3.14783901e-01 -7.06579268e-01 -9.06771421e-01 -8.69902790e-01
3.09741437e-01 1.27837583e-01 -3.08598220e-01 -2.42552876... | [12.771730422973633, -0.08415164798498154] |
af252c55-8af4-4a3d-a0ba-e93b602b641c | untargeted-backdoor-attack-against-object | 2211.05638 | null | https://arxiv.org/abs/2211.05638v2 | https://arxiv.org/pdf/2211.05638v2.pdf | Untargeted Backdoor Attack against Object Detection | Recent studies revealed that deep neural networks (DNNs) are exposed to backdoor threats when training with third-party resources (such as training samples or backbones). The backdoored model has promising performance in predicting benign samples, whereas its predictions can be maliciously manipulated by adversaries ba... | ['Shu-Tao Xia', 'Yong Jiang', 'Yiming Li', 'Chengxiao Luo'] | 2022-11-02 | null | null | null | null | ['pedestrian-detection'] | ['computer-vision'] | [ 3.96826535e-01 8.55433047e-02 -1.56919658e-01 1.53946353e-03
-3.55936915e-01 -1.41886210e+00 7.58141220e-01 -3.21243495e-01
-5.06456852e-01 4.51960802e-01 -6.25190318e-01 -7.79252052e-01
2.61398435e-01 -9.46128726e-01 -1.48443699e+00 -8.75426888e-01
-3.11898291e-01 -1.27116546e-01 5.65582693e-01 -2.99830548... | [5.704922199249268, 7.699390888214111] |
2a9c88e6-41f8-4301-bf36-28947e31099b | learning-to-explore-using-active-neural-slam | 2004.05155 | null | https://arxiv.org/abs/2004.05155v1 | https://arxiv.org/pdf/2004.05155v1.pdf | Learning to Explore using Active Neural SLAM | This work presents a modular and hierarchical approach to learn policies for exploring 3D environments, called `Active Neural SLAM'. Our approach leverages the strengths of both classical and learning-based methods, by using analytical path planners with learned SLAM module, and global and local policies. The use of le... | ['Dhiraj Gandhi', 'Ruslan Salakhutdinov', 'Devendra Singh Chaplot', 'Abhinav Gupta', 'Saurabh Gupta'] | 2020-04-10 | null | https://openreview.net/forum?id=HklXn1BKDH | https://openreview.net/pdf?id=HklXn1BKDH | iclr-2020-1 | ['pointgoal-navigation'] | ['robots'] | [-1.46068826e-01 4.29696947e-01 -2.53951102e-01 -2.36336872e-01
-6.82505250e-01 -6.29575014e-01 6.29052758e-01 3.82152468e-01
-7.09273934e-01 9.91639912e-01 3.42939973e-01 -2.84882694e-01
-4.90482390e-01 -7.35744417e-01 -9.29879844e-01 -5.71837723e-01
-9.36607540e-01 6.78160608e-01 4.26090032e-01 -5.74089170... | [4.672720432281494, 0.7454761862754822] |
5be21605-4ff5-4311-a0c5-dd7c8a02258f | counterfactual-debiasing-for-generating | 2305.10736 | null | https://arxiv.org/abs/2305.10736v1 | https://arxiv.org/pdf/2305.10736v1.pdf | Counterfactual Debiasing for Generating Factually Consistent Text Summaries | Despite substantial progress in abstractive text summarization to generate fluent and informative texts, the factual inconsistency in the generated summaries remains an important yet challenging problem to be solved. In this paper, we construct causal graphs for abstractive text summarization and identify the intrinsic... | ['Ying Shen', 'Yaliang Li', 'Yuexiang Xie', 'Chenhe Dong'] | 2023-05-18 | null | null | null | null | ['abstractive-text-summarization', 'text-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.84989047e-01 4.42020476e-01 -6.44485116e-01 -3.65990490e-01
-9.78274643e-01 -5.69124222e-01 9.04123783e-01 1.97814465e-01
-4.52450588e-02 1.36559260e+00 1.26833594e+00 -3.76342118e-01
-3.80753987e-02 -5.19867182e-01 -8.09230506e-01 -4.20074612e-01
2.66795039e-01 2.87127942e-01 -2.14809090e-01 -1.24784820... | [12.276840209960938, 9.303021430969238] |
d80d381d-e95b-4f8a-a35f-1656f6034f65 | false-positive-detection-and-prediction | 2110.15681 | null | https://arxiv.org/abs/2110.15681v1 | https://arxiv.org/pdf/2110.15681v1.pdf | False Positive Detection and Prediction Quality Estimation for LiDAR Point Cloud Segmentation | We present a novel post-processing tool for semantic segmentation of LiDAR point cloud data, called LidarMetaSeg, which estimates the prediction quality segmentwise. For this purpose we compute dispersion measures based on network probability outputs as well as feature measures based on point cloud input features and a... | ['Hanno Gottschalk', 'Lutz Roese-Koerner', 'Matthias Rottmann', 'Pascal Colling'] | 2021-10-29 | null | null | null | null | ['point-cloud-segmentation'] | ['computer-vision'] | [ 5.25367558e-01 4.94847119e-01 -3.02636713e-01 -6.86838567e-01
-8.48150015e-01 -5.80727220e-01 5.22623301e-01 7.40068972e-01
-2.78588444e-01 7.86107004e-01 -3.58096510e-01 -5.26328862e-01
-5.12761176e-01 -1.57291293e+00 -8.65387082e-01 -2.38094047e-01
-2.13714257e-01 1.12220931e+00 7.84203947e-01 3.53476375... | [8.107027053833008, -2.863201141357422] |
c54f8358-9cd5-45a5-8ce9-18b3b6ff1b26 | learning-regional-attraction-for-line-segment | 1912.09344 | null | https://arxiv.org/abs/1912.09344v1 | https://arxiv.org/pdf/1912.09344v1.pdf | Learning Regional Attraction for Line Segment Detection | This paper presents regional attraction of line segment maps, and hereby poses the problem of line segment detection (LSD) as a problem of region coloring. Given a line segment map, the proposed regional attraction first establishes the relationship between line segments and regions in the image lattice. Based on this,... | ['Gui-Song Xia', 'Fu-Dong Wang', 'Song Bai', 'Philip H. S. Torr', 'Liangpei Zhang', 'Tianfu Wu', 'Nan Xue'] | 2019-12-18 | null | null | null | null | ['line-segment-detection'] | ['computer-vision'] | [ 2.92712629e-01 1.28158957e-01 -3.29803318e-01 -1.90586358e-01
-7.42005765e-01 -8.11283350e-01 3.36430073e-01 1.38054460e-01
-1.87528193e-01 4.26939279e-01 -3.38134229e-01 -3.91157418e-01
8.35632607e-02 -7.20142245e-01 -9.96657491e-01 -3.71136904e-01
-1.49742767e-01 1.01850219e-01 5.08156240e-01 -1.44599125... | [8.274827003479004, -1.6274625062942505] |
925c6cf1-4a31-45c9-969d-fda47decddf5 | cross-resolution-flow-propagation-for | 2212.13525 | null | https://arxiv.org/abs/2212.13525v1 | https://arxiv.org/pdf/2212.13525v1.pdf | Cross-Resolution Flow Propagation for Foveated Video Super-Resolution | The demand of high-resolution video contents has grown over the years. However, the delivery of high-resolution video is constrained by either computational resources required for rendering or network bandwidth for remote transmission. To remedy this limitation, we leverage the eye trackers found alongside existing aug... | ['Chen-Yi Lee', 'Evan Chen', 'Lien-Feng Hsu', 'Eugene Lee'] | 2022-12-27 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [ 3.74918580e-01 -1.72822386e-01 6.36779740e-02 -1.65548027e-01
-7.87467480e-01 -4.45029497e-01 1.86082289e-01 -4.38678443e-01
-4.44723785e-01 8.75136912e-01 2.92570889e-01 -1.35166377e-01
2.23730728e-02 -7.57720888e-01 -8.94122362e-01 -5.22321820e-01
3.98504967e-03 -6.30135357e-01 5.39687574e-01 -2.69821644... | [10.955852508544922, -1.9569774866104126] |
e2b90c86-977c-4a78-b766-de5f3d2096d4 | sampling-and-ranking-for-digital-ink | 2306.03103 | null | https://arxiv.org/abs/2306.03103v1 | https://arxiv.org/pdf/2306.03103v1.pdf | Sampling and Ranking for Digital Ink Generation on a tight computational budget | Digital ink (online handwriting) generation has a number of potential applications for creating user-visible content, such as handwriting autocompletion, spelling correction, and beautification. Writing is personal and usually the processing is done on-device. Ink generative models thus need to produce high quality con... | ['Claudiu Musat', 'Aleksandr Timofeev', 'Andrii Maksai', 'Andrei Afonin'] | 2023-06-02 | null | null | null | null | ['handwriting-generation', 'spelling-correction'] | ['computer-vision', 'natural-language-processing'] | [ 5.59405029e-01 -6.43050298e-02 1.16660461e-01 -2.17806194e-02
-6.18473768e-01 -1.06457806e+00 7.15863287e-01 -1.81110054e-02
-2.72557229e-01 6.79264903e-01 8.03852603e-02 -3.69329274e-01
-1.46400645e-01 -7.80847549e-01 -6.86519206e-01 -4.11610216e-01
5.54185510e-01 7.06900597e-01 8.19006339e-02 8.50603916... | [11.825457572937012, 2.35440993309021] |
236f4b13-d32d-41b5-a738-97769cba66b8 | analyzing-elmo-and-distilbert-on-socio | null | null | https://aclanthology.org/2020.aespen-1.4 | https://aclanthology.org/2020.aespen-1.4.pdf | Analyzing ELMo and DistilBERT on Socio-political News Classification | This study evaluates the robustness of two state-of-the-art deep contextual language representations, ELMo and DistilBERT, on supervised learning of binary protest news classification (PC) and sentiment analysis (SA) of product reviews. A {''}cross-context{''} setting is enabled using test sets that are distinct from t... | ['Arzucan {\\"O}zg{\\"u}r', 'Ali H{\\"u}rriyeto{\\u{g}}lu', 'Berfu B{\\"u}y{\\"u}k{\\"o}z'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['news-classification'] | ['natural-language-processing'] | [ 4.33472805e-02 2.16270581e-01 -7.91649163e-01 -6.87974393e-01
-6.64184391e-01 -5.59815466e-01 9.06782806e-01 4.78002876e-01
-7.04999626e-01 7.18808353e-01 2.88274318e-01 -8.44556212e-01
1.23735823e-01 -8.21663618e-01 -8.58837545e-01 -3.48387033e-01
1.43993780e-01 5.85460722e-01 7.97669515e-02 -6.41613781... | [10.948975563049316, 7.354745864868164] |
24a00b3a-464c-4fe8-ab1b-128885aacc25 | dippas-a-deep-image-prior-prnu-anonymization | 2012.03581 | null | https://arxiv.org/abs/2012.03581v2 | https://arxiv.org/pdf/2012.03581v2.pdf | DIPPAS: A Deep Image Prior PRNU Anonymization Scheme | Source device identification is an important topic in image forensics since it allows to trace back the origin of an image. Its forensics counter-part is source device anonymization, that is, to mask any trace on the image that can be useful for identifying the source device. A typical trace exploited for source device... | ['Stefano Tubaro', 'Vincenzo Lipari', 'Paolo Bestagini', 'Sara Mandelli', 'Francesco Picetti'] | 2020-12-07 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 6.36131823e-01 -3.71242575e-02 -5.76441325e-02 7.46598840e-02
-8.87672603e-01 -9.93999004e-01 4.19575512e-01 1.95998520e-01
-3.31022084e-01 6.65192187e-01 -1.43965945e-01 -3.77127975e-01
1.39784798e-01 -7.63587356e-01 -1.20569658e+00 -7.94309974e-01
3.01669657e-01 -1.03579186e-01 -2.32936636e-01 2.76847094... | [12.369684219360352, 1.020906925201416] |
9f74e2e7-a51d-4b5c-a80c-9483e12fe840 | ml-powered-kqi-estimation-for-xr-services-a | 2212.12002 | null | https://arxiv.org/abs/2212.12002v1 | https://arxiv.org/pdf/2212.12002v1.pdf | ML-powered KQI estimation for XR services. A case study on 360-Video | The arise of cutting-edge technologies and services such as XR promise to change the concepts of how day-to-day things are done. At the same time, the appearance of modern and decentralized architectures approaches has given birth to a new generation of mobile networks such as 5G, as well as outlining the roadmap for B... | ['Raquel Barco', 'Sergio Fortes', 'Carlos Baena', 'O. S. Peñaherrera-Pulla'] | 2022-12-08 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [-2.87011594e-01 4.68307827e-03 -3.92491281e-01 -2.91186094e-01
-2.79214203e-01 -4.84493136e-01 4.09602016e-01 -3.28803733e-02
-8.78040642e-02 8.63732994e-01 -1.78433448e-01 -7.30650663e-01
-7.82337546e-01 -8.15018833e-01 -1.52336791e-01 -6.37183607e-01
-6.08063698e-01 3.78140539e-01 -4.84372042e-02 -2.27497399... | [6.10806941986084, 1.5713012218475342] |
f02c949b-49bb-43e8-8e60-4a9663fed1a1 | generating-quantified-referring-expressions-1 | null | null | https://aclanthology.org/2020.inlg-1.16 | https://aclanthology.org/2020.inlg-1.16.pdf | Generating Quantified Referring Expressions through Attention-Driven Incremental Perception | We model the production of quantified referring expressions (QREs) that identity collections of visual items. A previous approach, called Perceptual Cost Pruning, modeled human QRE production using a preference-based referring expression generation algorithm, first removing facts from the input knowledge base based on ... | ['Gordon Briggs'] | null | null | null | null | inlg-acl-2020-12 | ['referring-expression-generation'] | ['computer-vision'] | [ 1.58187702e-01 7.70184159e-01 -1.88616663e-01 -2.53671199e-01
-5.54083526e-01 -6.89203918e-01 5.71190417e-01 2.99495310e-01
-1.62310049e-01 7.04419017e-01 3.60421628e-01 -2.49157831e-01
-2.74935007e-01 -9.68617141e-01 -5.57195902e-01 4.93326932e-02
1.77416623e-01 3.71395797e-01 4.36416179e-01 -3.79908383... | [10.616073608398438, 1.9045510292053223] |
586e84f0-182c-469a-9d54-9800523d04cd | conner-consistency-training-for-cross-lingual | 2211.09394 | null | https://arxiv.org/abs/2211.09394v1 | https://arxiv.org/pdf/2211.09394v1.pdf | ConNER: Consistency Training for Cross-lingual Named Entity Recognition | Cross-lingual named entity recognition (NER) suffers from data scarcity in the target languages, especially under zero-shot settings. Existing translate-train or knowledge distillation methods attempt to bridge the language gap, but often introduce a high level of noise. To solve this problem, consistency training meth... | ['Chunyan Miao', 'Luo Si', 'Erik Cambria', 'Lidong Bing', 'Xin Li', 'Ran Zhou'] | 2022-11-17 | null | null | null | null | ['cross-lingual-ner'] | ['natural-language-processing'] | [-3.44255537e-01 -8.92476216e-02 -7.32921660e-01 -6.83169961e-01
-1.48159945e+00 -5.51372766e-01 3.88087124e-01 -2.29983062e-01
-5.64888537e-01 8.65631759e-01 3.52684379e-01 -4.31749940e-01
4.21507597e-01 -4.23786551e-01 -8.39392722e-01 -3.31339091e-01
5.23456931e-01 6.16693079e-01 -3.97367068e-02 1.28999809... | [9.901103019714355, 9.6929931640625] |
a7dd0a72-e8cd-4b3a-9766-80aa1811fae3 | deep-convolutional-neural-networks-for-smile | 1508.06535 | null | http://arxiv.org/abs/1508.06535v1 | http://arxiv.org/pdf/1508.06535v1.pdf | Deep Convolutional Neural Networks for Smile Recognition | This thesis describes the design and implementation of a smile detector based
on deep convolutional neural networks. It starts with a summary of neural
networks, the difficulties of training them and new training methods, such as
Restricted Boltzmann Machines or autoencoders. It then provides a literature
review of con... | ['Patrick O. Glauner'] | 2015-08-26 | null | null | null | null | ['smile-recognition'] | ['computer-vision'] | [ 1.38935789e-01 -1.34660199e-03 -1.56226724e-01 -6.60016477e-01
-2.21473604e-01 1.53452143e-01 4.02081877e-01 -6.14782453e-01
-5.61662316e-01 7.43401945e-01 6.51350096e-02 -5.17719537e-02
1.62464038e-01 -4.08839852e-01 -2.65827596e-01 -1.08132350e+00
-2.06594333e-01 3.05636734e-01 -5.28199315e-01 -2.04753458... | [13.527400970458984, 1.7433944940567017] |
1243101b-2d2c-4130-937f-d5b515977e33 | the-reprgesture-entry-to-the-genea-challenge | 2208.12133 | null | https://arxiv.org/abs/2208.12133v1 | https://arxiv.org/pdf/2208.12133v1.pdf | The ReprGesture entry to the GENEA Challenge 2022 | This paper describes the ReprGesture entry to the Generation and Evaluation of Non-verbal Behaviour for Embodied Agents (GENEA) challenge 2022. The GENEA challenge provides the processed datasets and performs crowdsourced evaluations to compare the performance of different gesture generation systems. In this paper, we ... | ['Weihong Bao', 'Liyang Chen', 'Jiuxin Lin', 'Mengchen Zhao', 'Minglei Li', 'Zhiyong Wu', 'Sicheng Yang'] | 2022-08-25 | null | null | null | null | ['gesture-generation'] | ['robots'] | [ 2.85748690e-01 2.52515096e-02 1.51960820e-01 -2.66624540e-01
-1.05451274e+00 -9.19114709e-01 1.19950640e+00 -5.47392428e-01
-3.96124482e-01 6.70411110e-01 1.05956078e+00 9.96349677e-02
2.17561737e-01 -4.41388190e-01 -7.54304528e-01 -7.20003247e-01
-1.45463049e-01 4.46640015e-01 -3.68729711e-01 -5.49096644... | [5.621356010437012, -0.11689907312393188] |
83c1508f-ad2d-476c-971c-cf5e93096439 | deepfood-deep-learning-based-food-image | 1606.05675 | null | http://arxiv.org/abs/1606.05675v1 | http://arxiv.org/pdf/1606.05675v1.pdf | DeepFood: Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessment | Worldwide, in 2014, more than 1.9 billion adults, 18 years and older, were
overweight. Of these, over 600 million were obese. Accurately documenting
dietary caloric intake is crucial to manage weight loss, but also presents
challenges because most of the current methods for dietary assessment must rely
on memory to rec... | ['Vinod Vokkarane', 'Yu Cao', 'Guanling Chen', 'Chang Liu', 'Yan Luo', 'Yunsheng Ma'] | 2016-06-17 | null | null | null | null | ['fine-grained-image-recognition'] | ['computer-vision'] | [ 1.92849219e-01 -6.27193689e-01 -6.49912715e-01 -4.84226227e-01
-2.57113606e-01 -3.39094102e-01 -2.00462550e-01 7.71795452e-01
-4.31850374e-01 1.15632974e-01 1.96709961e-01 -2.21672699e-01
2.36889392e-01 -1.35583270e+00 -7.76749492e-01 -3.46330404e-01
-3.95610303e-01 -2.55128115e-01 -2.01703772e-01 -3.90162915... | [11.57497787475586, 4.434805870056152] |
74ce6283-889a-4f21-919d-d3c8bfe71f5e | gcformer-an-efficient-framework-for-accurate | 2306.08325 | null | https://arxiv.org/abs/2306.08325v1 | https://arxiv.org/pdf/2306.08325v1.pdf | GCformer: An Efficient Framework for Accurate and Scalable Long-Term Multivariate Time Series Forecasting | Transformer-based models have emerged as promising tools for time series forecasting. However, these model cannot make accurate prediction for long input time series. On the one hand, they failed to capture global dependencies within time series data. On the other hand, the long input sequence usually leads to large mo... | ['Yi Qian', 'Mengni Ye', 'Liang Sun', 'Tian Zhou', 'Ziqing Ma', 'Yanjun Zhao'] | 2023-06-14 | null | null | null | null | ['multivariate-time-series-forecasting'] | ['time-series'] | [-2.45309956e-02 -6.75163507e-01 1.39049888e-01 -2.35693857e-01
-5.94310403e-01 -4.83372271e-01 2.21122980e-01 -9.34018344e-02
-2.94187337e-01 4.01488215e-01 -6.12814687e-02 -5.08887708e-01
-1.94278166e-01 -6.29706740e-01 -5.18319786e-01 -9.08503354e-01
-4.75501478e-01 -2.78463036e-01 2.90885847e-02 -2.43301615... | [7.002406120300293, 2.886784076690674] |
a6ab6d62-191d-4831-a2e0-c8d347748e31 | mmact-a-large-scale-dataset-for-cross-modal | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Kong_MMAct_A_Large-Scale_Dataset_for_Cross_Modal_Human_Action_Understanding_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Kong_MMAct_A_Large-Scale_Dataset_for_Cross_Modal_Human_Action_Understanding_ICCV_2019_paper.pdf | MMAct: A Large-Scale Dataset for Cross Modal Human Action Understanding | Unlike vision modalities, body-worn sensors or passive sensing can avoid the failure of action understanding in vision related challenges, e.g. occlusion and appearance variation. However, a standard large-scale dataset does not exist, in which different types of modalities across vision and sensors are integrated. To ... | [' Tomokazu Murakami', ' Bin Tong', ' Martin Klinkigt', ' Ziwei Deng', ' Ziming Wu', 'Quan Kong'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['action-understanding', 'multimodal-activity-recognition'] | ['computer-vision', 'computer-vision'] | [ 4.08312857e-01 -2.47825176e-01 -1.92788601e-01 -5.28806567e-01
-7.28262246e-01 -2.59850323e-01 3.04770201e-01 -3.40693325e-01
-3.38935554e-01 5.98394215e-01 5.45597851e-01 3.04706573e-01
-2.23370418e-01 -2.65789568e-01 -5.51215053e-01 -5.49638748e-01
1.71024442e-01 -3.79324496e-01 2.57917374e-01 -7.19669759... | [7.74469518661499, 0.43134406208992004] |
4373c0e8-4435-4790-974c-d5ad877e40ac | autorecon-automated-3d-object-discovery-and | 2305.08810 | null | https://arxiv.org/abs/2305.08810v1 | https://arxiv.org/pdf/2305.08810v1.pdf | AutoRecon: Automated 3D Object Discovery and Reconstruction | A fully automated object reconstruction pipeline is crucial for digital content creation. While the area of 3D reconstruction has witnessed profound developments, the removal of background to obtain a clean object model still relies on different forms of manual labor, such as bounding box labeling, mask annotations, an... | ['Xiaowei Zhou', 'Hujun Bao', 'Haotong Lin', 'Sida Peng', 'Xingyi He', 'Yuang Wang'] | 2023-05-15 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_AutoRecon_Automated_3D_Object_Discovery_and_Reconstruction_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_AutoRecon_Automated_3D_Object_Discovery_and_Reconstruction_CVPR_2023_paper.pdf | cvpr-2023-1 | ['object-discovery', '3d-reconstruction', 'object-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.80555785e-01 -1.02839112e-01 1.50701791e-01 -3.39823067e-01
-6.27926886e-01 -7.66430557e-01 5.69373548e-01 -1.68621659e-01
1.10255346e-01 2.03170821e-01 -2.24749461e-01 5.04250042e-02
1.40810847e-01 -6.11616611e-01 -1.05425572e+00 -4.81495231e-01
4.89425242e-01 6.49716258e-01 6.15523458e-01 2.98975348... | [8.384265899658203, -2.9760074615478516] |
aef62b4f-4a08-4cce-9bce-234cc5e9ff18 | thor-wielding-hammers-to-integrate-language | 2205.10893 | null | https://arxiv.org/abs/2205.10893v1 | https://arxiv.org/pdf/2205.10893v1.pdf | Thor: Wielding Hammers to Integrate Language Models and Automated Theorem Provers | In theorem proving, the task of selecting useful premises from a large library to unlock the proof of a given conjecture is crucially important. This presents a challenge for all theorem provers, especially the ones based on language models, due to their relative inability to reason over huge volumes of premises in tex... | ['Mateja Jamnik', 'Yuhuai Wu', 'Piotr Miłoś', 'Tomasz Odrzygóźdź', 'Konrad Czechowski', 'Szymon Tworkowski', 'Wenda Li', 'Albert Q. Jiang'] | 2022-05-22 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 6.62125200e-02 4.50011790e-01 -2.18422100e-01 3.78382951e-02
-1.20041728e+00 -1.11296260e+00 5.25556266e-01 3.84165943e-01
-2.20282838e-01 1.16285920e+00 -6.55811489e-01 -1.44123554e+00
-3.73410374e-01 -1.13899136e+00 -9.97434616e-01 -1.08265668e-01
-2.06214949e-01 6.67951167e-01 3.67987096e-01 -2.07632393... | [8.888811111450195, 6.998908042907715] |
81430fda-e93a-4f01-aa49-e5622bad2377 | yolov6-v3-0-a-full-scale-reloading | 2301.05586 | null | https://arxiv.org/abs/2301.05586v1 | https://arxiv.org/pdf/2301.05586v1.pdf | YOLOv6 v3.0: A Full-Scale Reloading | The YOLO community has been in high spirits since our first two releases! By the advent of Chinese New Year 2023, which sees the Year of the Rabbit, we refurnish YOLOv6 with numerous novel enhancements on the network architecture and the training scheme. This release is identified as YOLOv6 v3.0. For a glimpse of perfo... | ['Xiangxiang Chu', 'Xiaoming Xu', 'Zaidan Ke', 'Bo Zhang', 'Meng Cheng', 'Hongliang Jiang', 'Yifei Geng', 'Lulu Li', 'Chuyi Li'] | 2023-01-13 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [-6.55349970e-01 -2.58804619e-01 -3.88044566e-01 -1.15802623e-01
-5.59153914e-01 -6.21558905e-01 2.79041201e-01 -1.04819618e-01
-6.96283996e-01 4.37703490e-01 -3.90025556e-01 -3.23378772e-01
4.76003319e-01 -6.90174222e-01 -8.19164932e-01 -3.98299634e-01
-3.73450145e-02 1.29004151e-01 6.70446098e-01 -1.59636855... | [8.674215316772461, -0.15636654198169708] |
855a4d90-4012-49bb-ba44-bc59ec63fec5 | gridmix-strong-regularization-through-local | null | null | https://www.sciencedirect.com/science/article/abs/pii/S0031320320303976 | https://doi.org/10.1016/j.patcog.2020.107594 | GridMix: Strong regularization through local context mapping | Recently developed regularization techniques improve the networks generalization by only considering the global context. Therefore, the network tends to focus on a few most discriminative subregions of an image for prediction accuracy, leading the network being sensitive to unseen or noisy data. To address this disadva... | ['Hyunjung Shim', 'Duhyeon Bang', 'Kyungjune Baek'] | 2021-01-01 | null | null | null | pattern-recognition-2021-1 | ['weakly-supervised-object-localization'] | ['computer-vision'] | [ 1.17995284e-01 4.30104248e-02 -4.63769197e-01 -4.06122118e-01
-9.61454213e-01 -5.60461998e-01 5.47796130e-01 -2.94868127e-02
-3.14783305e-01 6.34064794e-01 2.43684158e-01 8.65387246e-02
3.01729292e-02 -5.77774465e-01 -8.18290472e-01 -9.30441082e-01
9.37688202e-02 1.61139537e-02 2.12370321e-01 1.68946356... | [9.528159141540527, 2.223501205444336] |
434eec3a-a303-49e2-ae42-4c63cff118a8 | cit-emotionnet-cnn-interactive-transformer | 2305.05548 | null | https://arxiv.org/abs/2305.05548v1 | https://arxiv.org/pdf/2305.05548v1.pdf | CIT-EmotionNet: CNN Interactive Transformer Network for EEG Emotion Recognition | Emotion recognition using Electroencephalogram (EEG) signals has emerged as a significant research challenge in affective computing and intelligent interaction. However, effectively combining global and local features of EEG signals to improve performance in emotion recognition is still a difficult task. In this study,... | ['Tien-Ping Tan', 'Hua Ma', 'Wei Lu'] | 2023-05-07 | null | null | null | null | ['eeg', 'eeg-emotion-recognition', 'eeg'] | ['methodology', 'miscellaneous', 'time-series'] | [-1.22684553e-01 -5.76514781e-01 6.12386167e-01 -6.49554193e-01
-3.70641053e-01 -2.31723770e-01 1.14727430e-01 6.00147061e-02
-6.04862332e-01 7.39686370e-01 -3.06755281e-03 2.03059614e-01
-1.49901047e-01 -6.36367738e-01 -3.10012251e-01 -6.82061732e-01
-2.49561146e-01 -3.26019853e-01 -5.00780642e-01 -1.57734081... | [13.144678115844727, 3.4640626907348633] |
21fc0757-c0e7-4284-951d-e51db60f7795 | controlled-sparsity-via-constrained | 2208.04425 | null | https://arxiv.org/abs/2208.04425v2 | https://arxiv.org/pdf/2208.04425v2.pdf | Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints | The performance of trained neural networks is robust to harsh levels of pruning. Coupled with the ever-growing size of deep learning models, this observation has motivated extensive research on learning sparse models. In this work, we focus on the task of controlling the level of sparsity when performing sparse learnin... | ['Simon Lacoste-Julien', 'Yoshua Bengio', 'Akram Erraqabi', 'Juan Ramirez', 'Jose Gallego-Posada'] | 2022-08-08 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [ 3.71319205e-01 2.30253845e-01 -1.88840672e-01 -5.66704631e-01
-4.19857442e-01 -2.06476778e-01 5.92279792e-01 -1.84152741e-02
-5.94925165e-01 5.31085432e-01 1.99400812e-01 -1.77501589e-01
-1.07918933e-01 -4.31148142e-01 -8.92013550e-01 -4.13212657e-01
-1.38220102e-01 2.77074039e-01 -9.80091840e-02 1.02734357... | [8.581901550292969, 3.331162452697754] |
81e0a534-e2e3-45bf-b721-73391d36180f | region-based-quality-estimation-network-for | 1711.08766 | null | http://arxiv.org/abs/1711.08766v2 | http://arxiv.org/pdf/1711.08766v2.pdf | Region-based Quality Estimation Network for Large-scale Person Re-identification | One of the major restrictions on the performance of video-based person re-id
is partial noise caused by occlusion, blur and illumination. Since different
spatial regions of a single frame have various quality, and the quality of the
same region also varies across frames in a tracklet, a good way to address the
problem ... | ['Biao Leng', 'Yu Liu', 'Congrui Hetang', 'Shaofan Cai', 'Guanglu Song'] | 2017-11-23 | null | null | null | null | ['large-scale-person-re-identification'] | ['computer-vision'] | [-3.68857861e-01 -6.91096246e-01 -5.86171001e-02 -2.80162632e-01
-3.71710032e-01 -8.47910717e-02 3.41728032e-01 -2.37465337e-01
-6.14525139e-01 1.08212519e+00 6.13627851e-01 6.61788046e-01
9.24999267e-02 -7.99299657e-01 -5.25722980e-01 -6.93255067e-01
8.90235156e-02 -9.59294103e-03 4.02808100e-01 -3.93311769... | [14.657613754272461, 0.9452428817749023] |
eba7afd4-5a95-483f-912c-02a55a024957 | tw-star-at-semeval-2018-task-1-preprocessing | null | null | https://aclanthology.org/S18-1024 | https://aclanthology.org/S18-1024.pdf | Tw-StAR at SemEval-2018 Task 1: Preprocessing Impact on Multi-label Emotion Classification | In this paper, we describe our contribution in SemEval-2018 contest. We tackled task 1 {``}Affect in Tweets{''}, subtask E-c {``}Detecting Emotions (multi-label classification){''}. A multilabel classification system Tw-StAR was developed to recognize the emotions embedded in Arabic, English and Spanish tweets. To hand... | ['Ismail Babao{\\u{g}}lu', 'Chedi Bechikh Ali', 'Hala Mulki', 'Hatem Haddad'] | 2018-06-01 | null | null | null | semeval-2018-6 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [ 1.72297865e-01 7.46960193e-02 1.29747629e-01 -6.61833048e-01
-9.52823997e-01 -7.54656017e-01 8.92706394e-01 6.88042164e-01
-9.60630417e-01 8.52729082e-01 -2.21472867e-02 -1.97355542e-03
-1.11979023e-01 -4.67230409e-01 -2.26835608e-01 -6.47846878e-01
-3.07974871e-02 3.61986220e-01 -2.43308723e-01 -7.11020112... | [12.4718017578125, 6.340992450714111] |
b03705b5-49d5-4965-84c6-a18c20c2a314 | learning-semantics-for-visual-place | 2201.09701 | null | https://arxiv.org/abs/2201.09701v2 | https://arxiv.org/pdf/2201.09701v2.pdf | Learning Semantics for Visual Place Recognition through Multi-Scale Attention | In this paper we address the task of visual place recognition (VPR), where the goal is to retrieve the correct GPS coordinates of a given query image against a huge geotagged gallery. While recent works have shown that building descriptors incorporating semantic and appearance information is beneficial, current state-o... | ['Barbara Caputo', 'Carlo Masone', 'Gabriele Berton', 'Antonio Tavera', 'Valerio Paolicelli'] | 2022-01-24 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [ 3.26392837e-02 2.58925445e-02 4.83728014e-02 -4.72746104e-01
-8.84721041e-01 -7.26544142e-01 9.27526176e-01 3.54870468e-01
-7.18467474e-01 2.67505050e-01 -4.43837978e-02 -6.88475892e-02
1.11928649e-01 -7.82146037e-01 -9.40321267e-01 -5.77319682e-01
-2.02440202e-01 5.56426346e-01 6.18295789e-01 -2.92070389... | [7.668769836425781, -1.8529562950134277] |
94268dc2-ad4b-43cf-92b6-b94e4e241c68 | zero-shot-information-extraction-as-a-unified | 2109.11171 | null | https://arxiv.org/abs/2109.11171v1 | https://arxiv.org/pdf/2109.11171v1.pdf | Zero-Shot Information Extraction as a Unified Text-to-Triple Translation | We cast a suite of information extraction tasks into a text-to-triple translation framework. Instead of solving each task relying on task-specific datasets and models, we formalize the task as a translation between task-specific input text and output triples. By taking the task-specific input, we enable a task-agnostic... | ['Dawn Song', 'Jie Tang', 'Haoyun Hong', 'Zui Chen', 'Xiao Liu', 'Chenguang Wang'] | 2021-09-23 | zero-shot-information-extraction-as-a-unified-1 | https://aclanthology.org/2021.emnlp-main.94 | https://aclanthology.org/2021.emnlp-main.94.pdf | emnlp-2021-11 | ['open-information-extraction'] | ['natural-language-processing'] | [ 3.42995912e-01 6.71406984e-01 -5.59383512e-01 -3.79005581e-01
-1.36890578e+00 -7.35247612e-01 1.04278338e+00 -4.35401406e-03
-3.82050902e-01 1.00400293e+00 1.85570210e-01 -6.64379001e-01
5.56247905e-02 -8.48870277e-01 -1.08658266e+00 2.34760251e-02
3.87818545e-01 9.63979065e-01 8.39443058e-02 -4.21956629... | [9.854339599609375, 8.60683822631836] |
1711d74f-bc78-42f2-9b97-3bf35a45691a | confining-brownian-motion-of-single | 1701.03422 | null | http://arxiv.org/abs/1701.03422v1 | http://arxiv.org/pdf/1701.03422v1.pdf | Confining Brownian motion of single nanoparticles in an ABELtrap | Trapping nanoscopic objects to observe their dynamic behaviour for extended
periods of time is an ongoing quest. Particularly, sub-100nm transparent
objects are hard to catch and most techniques rely on immobilisation or
transient diffusion through a confocal laser focus. We present an Anti-Brownian
ELectrokinetic trap... | [] | 2017-01-12 | null | null | null | null | ['transparent-objects'] | ['computer-vision'] | [ 3.41064930e-01 -1.72592670e-01 -2.23806977e-01 3.44161570e-01
-2.71278143e-01 -1.15837348e+00 4.71982509e-01 -3.39894235e-01
-9.11601484e-01 1.28937495e+00 -2.36361146e-01 1.27450852e-02
1.82285234e-01 -4.40366983e-01 -7.72496879e-01 -1.43588734e+00
3.33822906e-01 6.06287062e-01 7.72689939e-01 2.19187848... | [13.831524848937988, -3.1197402477264404] |
18867b75-502f-4cd8-9219-6acdaecbf283 | detecting-broken-absorber-tubes-in-csp-plants | 2211.14077 | null | https://arxiv.org/abs/2211.14077v1 | https://arxiv.org/pdf/2211.14077v1.pdf | Detecting broken Absorber Tubes in CSP plants using intelligent sampling and dual loss | Concentrated solar power (CSP) is one of the growing technologies that is leading the process of changing from fossil fuels to renewable energies. The sophistication and size of the systems require an increase in maintenance tasks to ensure reliability, availability, maintainability and safety. Currently, automatic fau... | ['José Miguel Díaz-Báñez', 'Juan Sebastián Valverde', 'Miguel Angel Pérez-Cutiño'] | 2022-11-25 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [ 2.30251327e-01 1.53111115e-01 -1.67287961e-02 1.95413291e-01
-1.55480221e-01 -7.22131252e-01 2.02132970e-01 5.48416495e-01
7.84926042e-02 6.98624730e-01 -6.25494778e-01 -3.52329671e-01
-5.48533142e-01 -1.10169041e+00 -5.60777128e-01 -9.42125559e-01
2.13310152e-01 5.04494905e-01 3.84260237e-01 -7.59021267... | [7.1248779296875, 2.0331051349639893] |
9099e48d-29b0-4245-9702-45968b9baeeb | distilling-knowledge-for-search-based | 1805.11224 | null | http://arxiv.org/abs/1805.11224v1 | http://arxiv.org/pdf/1805.11224v1.pdf | Distilling Knowledge for Search-based Structured Prediction | Many natural language processing tasks can be modeled into structured
prediction and solved as a search problem. In this paper, we distill an
ensemble of multiple models trained with different initialization into a single
model. In addition to learning to match the ensemble's probability output on
the reference states,... | ['Ting Liu', 'Yijia Liu', 'Wanxiang Che', 'Huaipeng Zhao', 'Bing Qin'] | 2018-05-29 | distilling-knowledge-for-search-based-1 | https://aclanthology.org/P18-1129 | https://aclanthology.org/P18-1129.pdf | acl-2018-7 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [ 4.48160470e-01 7.41807759e-01 -6.08838320e-01 -6.59774899e-01
-1.41518855e+00 -4.93570566e-01 6.49264336e-01 -1.01178430e-03
-4.15892631e-01 1.11547291e+00 4.85435188e-01 -6.04342043e-01
3.90599877e-01 -3.08332443e-01 -8.64526212e-01 -6.21672511e-01
1.18568078e-01 9.76568460e-01 2.75621772e-01 8.29824619... | [10.456636428833008, 9.519124984741211] |
ce92b3ef-74f5-49b7-9661-abf50fa5374a | accurate-and-robust-eye-contact-detection | 1907.11115 | null | https://arxiv.org/abs/1907.11115v1 | https://arxiv.org/pdf/1907.11115v1.pdf | Accurate and Robust Eye Contact Detection During Everyday Mobile Device Interactions | Quantification of human attention is key to several tasks in mobile human-computer interaction (HCI), such as predicting user interruptibility, estimating noticeability of user interface content, or measuring user engagement. Previous works to study mobile attentive behaviour required special-purpose eye tracking equip... | ['Mihai Bâce', 'Sander Staal', 'Andreas Bulling'] | 2019-07-25 | null | null | null | null | ['contact-detection'] | ['robots'] | [ 5.75088024e-01 2.18399279e-02 -3.30489874e-01 1.20642647e-01
-3.23097199e-01 -4.86312568e-01 3.42088282e-01 1.56364232e-01
-5.45436203e-01 3.08361620e-01 1.97338194e-01 -6.51152551e-01
-4.21441495e-02 2.29371525e-02 -1.49359748e-01 -1.13521710e-01
1.17653869e-01 -1.08963504e-01 4.16513801e-01 8.23395734... | [14.095568656921387, 0.14299939572811127] |
927bf19d-3ce3-4b0d-983a-403bc552ea75 | aga-gan-attribute-guided-attention-generative | 2111.10591 | null | https://arxiv.org/abs/2111.10591v1 | https://arxiv.org/pdf/2111.10591v1.pdf | AGA-GAN: Attribute Guided Attention Generative Adversarial Network with U-Net for Face Hallucination | The performance of facial super-resolution methods relies on their ability to recover facial structures and salient features effectively. Even though the convolutional neural network and generative adversarial network-based methods deliver impressive performances on face hallucination tasks, the ability to use attribut... | ['Umapada Pal', 'Sukalpa Chanda', 'Abhishek Srivastava'] | 2021-11-20 | null | null | null | null | ['face-hallucination'] | ['computer-vision'] | [ 3.47133726e-01 5.08436203e-01 9.21661407e-02 -5.44628620e-01
-9.43489194e-01 -1.83736011e-01 7.52620220e-01 -8.40797842e-01
2.28352189e-01 7.60412812e-01 5.56608438e-01 4.58985090e-01
1.09701365e-01 -1.01680112e+00 -8.45415711e-01 -6.77558362e-01
3.90299223e-02 4.13778573e-01 -3.40936422e-01 -5.22762001... | [12.70104694366455, -0.10311727225780487] |
f3c18c1a-f36a-4819-bb09-2fcaba413480 | a-memory-model-for-question-answering-from | 2305.07565 | null | https://arxiv.org/abs/2305.07565v1 | https://arxiv.org/pdf/2305.07565v1.pdf | A Memory Model for Question Answering from Streaming Data Supported by Rehearsal and Anticipation of Coreference Information | Existing question answering methods often assume that the input content (e.g., documents or videos) is always accessible to solve the task. Alternatively, memory networks were introduced to mimic the human process of incremental comprehension and compression of the information in a fixed-capacity memory. However, these... | ['Marie-Francine Moens', 'Alvaro Soto', 'Vladimir Araujo'] | 2023-05-12 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 6.50963247e-01 6.04743481e-01 -3.53974439e-02 -3.82262707e-01
-6.45584822e-01 -3.94961953e-01 6.55423939e-01 4.72938895e-01
-7.96595275e-01 6.29533947e-01 6.27135813e-01 -3.73931527e-01
1.69560721e-03 -8.33483994e-01 -1.01441133e+00 -9.01426449e-02
-6.36210293e-02 4.18447554e-01 3.33663046e-01 -2.25388840... | [11.207547187805176, 7.961246013641357] |
f4592ecf-74eb-4efe-b669-7d088357ef91 | mast-a-memory-augmented-self-supervised | 2002.07793 | null | https://arxiv.org/abs/2002.07793v2 | https://arxiv.org/pdf/2002.07793v2.pdf | MAST: A Memory-Augmented Self-supervised Tracker | Recent interest in self-supervised dense tracking has yielded rapid progress, but performance still remains far from supervised methods. We propose a dense tracking model trained on videos without any annotations that surpasses previous self-supervised methods on existing benchmarks by a significant margin (+15%), and ... | ['Weidi Xie', 'Erika Lu', 'Zihang Lai'] | 2020-02-18 | mast-a-memory-augmented-self-supervised-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Lai_MAST_A_Memory-Augmented_Self-Supervised_Tracker_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Lai_MAST_A_Memory-Augmented_Self-Supervised_Tracker_CVPR_2020_paper.pdf | cvpr-2020-6 | ['unsupervised-video-object-segmentation'] | ['computer-vision'] | [-2.32543349e-02 -1.20664518e-02 -7.34486103e-01 -4.40370500e-01
-7.83170819e-01 -6.33481026e-01 5.50337374e-01 -1.51695684e-01
-4.76432741e-01 7.04525590e-01 4.17387158e-01 3.29788029e-02
1.02109477e-01 -3.15989166e-01 -1.02405298e+00 -3.67389083e-01
-3.44974995e-01 6.57487452e-01 6.56825662e-01 3.07282627... | [6.366621017456055, -1.999182939529419] |
9da5ddfa-7a7b-459b-8748-6c248dd2a3ee | strategies-to-improve-few-shot-learning-for | null | null | https://aclanthology.org/2022.suki-1.3 | https://aclanthology.org/2022.suki-1.3.pdf | Strategies to Improve Few-shot Learning for Intent Classification and Slot-Filling | Intent classification (IC) and slot filling (SF) are two fundamental tasks in modern Natural Language Understanding (NLU) systems. Collecting and annotating large amounts of data to train deep learning models for such systems are not scalable. This problem can be addressed by learning from few examples using fast super... | ['Benjamin Han', 'Vijay Ramani', 'Ehi Nosakhare', 'Hazem El-Hammamy', 'Michael Amoake', 'Vishal Rohra', 'Alex Fischer', 'Karine Ip Kiun Chong', 'Amr Sharaf', 'Samyadeep Basu'] | null | null | null | null | naacl-suki-2022-7 | ['intent-classification', 'slot-filling'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.99482304e-01 2.55905986e-01 -8.96577120e-01 -5.71675897e-01
-7.81227171e-01 -1.40664652e-01 9.71265137e-01 4.54011083e-01
-8.38841081e-01 8.40756834e-01 2.78296441e-01 -3.52002114e-01
8.89103711e-02 -6.09850824e-01 -7.46697485e-01 -6.85229525e-02
1.87407345e-01 9.30159688e-01 5.12578450e-02 -4.20783609... | [11.624006271362305, 7.737788200378418] |
ed093647-383c-4f37-a805-2b1a72b183bb | investigating-fairness-disparities-in-peer | 2211.06398 | null | https://arxiv.org/abs/2211.06398v1 | https://arxiv.org/pdf/2211.06398v1.pdf | Investigating Fairness Disparities in Peer Review: A Language Model Enhanced Approach | Double-blind peer review mechanism has become the skeleton of academic research across multiple disciplines including computer science, yet several studies have questioned the quality of peer reviews and raised concerns on potential biases in the process. In this paper, we conduct a thorough and rigorous study on fairn... | ['Dan Roth', 'Zhun Deng', 'Hongming Zhang', 'Jiayao Zhang'] | 2022-11-07 | null | null | null | null | ['review-generation'] | ['natural-language-processing'] | [-2.98631936e-01 3.84838194e-01 -1.03448319e+00 -5.22716105e-01
-8.36015046e-01 -3.03012669e-01 1.09980428e+00 8.09463501e-01
-6.12631083e-01 7.80468225e-01 5.76241851e-01 -7.62371719e-01
-3.04904670e-01 -5.90032279e-01 -5.97547114e-01 6.08142205e-02
3.23956817e-01 4.17422689e-02 -5.05034924e-01 -1.82161972... | [9.409195899963379, 10.143171310424805] |
4fb60838-2d54-4f14-9604-3d9b60ad98a2 | contextualized-attention-based-knowledge | 2010.11066 | null | https://arxiv.org/abs/2010.11066v4 | https://arxiv.org/pdf/2010.11066v4.pdf | Contextualized Attention-based Knowledge Transfer for Spoken Conversational Question Answering | Spoken conversational question answering (SCQA) requires machines to model complex dialogue flow given the speech utterances and text corpora. Different from traditional text question answering (QA) tasks, SCQA involves audio signal processing, passage comprehension, and contextual understanding. However, ASR systems i... | ['Yuexian Zou', 'Nuo Chen', 'Chenyu You'] | 2020-10-21 | null | null | null | null | ['audio-signal-processing'] | ['audio'] | [ 2.13285387e-01 3.75920415e-01 5.03896236e-01 -7.23798692e-01
-1.51230073e+00 -5.83998621e-01 5.83186626e-01 1.34295121e-01
-4.83445317e-01 6.16553366e-01 8.69567215e-01 -4.40398127e-01
1.37737975e-01 -4.56217110e-01 -4.07615036e-01 -4.41877931e-01
2.07768187e-01 5.48733950e-01 1.35945380e-01 -4.83530492... | [11.784612655639648, 7.965671062469482] |
5f9af486-a9dc-4987-a898-802bb2000627 | color-overmodification-emerges-from-data | 2205.09172 | null | https://arxiv.org/abs/2205.09172v1 | https://arxiv.org/pdf/2205.09172v1.pdf | Color Overmodification Emerges from Data-Driven Learning and Pragmatic Reasoning | Speakers' referential expressions often depart from communicative ideals in ways that help illuminate the nature of pragmatic language use. Patterns of overmodification, in which a speaker uses a modifier that is redundant given their communicative goal, have proven especially informative in this regard. It seems likel... | ['Elisa Kreiss', 'Christopher Potts', 'Noah D. Goodman', 'Kunal Sinha', 'Fei Fang'] | 2022-05-18 | null | null | null | null | ['language-acquisition'] | ['natural-language-processing'] | [ 3.92483324e-01 4.58249360e-01 -6.84944838e-02 -5.30327737e-01
-2.05147922e-01 -4.14626241e-01 8.95997941e-01 2.06455722e-01
-7.15090215e-01 4.43049580e-01 9.54082131e-01 -4.97073561e-01
-3.62283051e-01 -7.81903803e-01 -5.48388004e-01 -6.93827033e-01
2.46177614e-02 2.65097827e-01 -6.77424893e-02 -4.21571404... | [10.429924011230469, 8.640168190002441] |
f5dc68ef-cd4c-45d2-a901-a2a051959781 | on-coresets-for-support-vector-machines | 2002.06469 | null | https://arxiv.org/abs/2002.06469v1 | https://arxiv.org/pdf/2002.06469v1.pdf | On Coresets for Support Vector Machines | We present an efficient coreset construction algorithm for large-scale Support Vector Machine (SVM) training in Big Data and streaming applications. A coreset is a small, representative subset of the original data points such that a models trained on the coreset are provably competitive with those trained on the origin... | ['Murad Tukan', 'Daniela Rus', 'Dan Feldman', 'Cenk Baykal'] | 2020-02-15 | null | null | null | null | ['small-data'] | ['computer-vision'] | [ 2.01247975e-01 -2.06173941e-01 -5.74014068e-01 -5.76455891e-01
-5.40004849e-01 -5.20949125e-01 1.07671410e-01 2.87903965e-01
-2.81040519e-01 7.41508007e-01 -1.41019404e-01 -4.55712676e-01
4.36224975e-02 -6.82005107e-01 -1.04242849e+00 -6.82937920e-01
-1.86324641e-01 6.84825599e-01 4.60573912e-01 -1.07932188... | [8.390628814697266, 4.038672924041748] |
8abb1a6e-6e0e-44e4-8105-faffc56654eb | decentralized-online-federated-g-network | 2306.13029 | null | https://arxiv.org/abs/2306.13029v1 | https://arxiv.org/pdf/2306.13029v1.pdf | Decentralized Online Federated G-Network Learning for Lightweight Intrusion Detection | Cyberattacks are increasingly threatening networked systems, often with the emergence of new types of unknown (zero-day) attacks and the rise of vulnerable devices. While Machine Learning (ML)-based Intrusion Detection Systems (IDSs) have been shown to be extremely promising in detecting these attacks, the need to lear... | ['Erol Gelenbe', 'Baran Can Gül', 'Mert Nakıp'] | 2023-06-22 | null | null | null | null | ['intrusion-detection'] | ['miscellaneous'] | [-4.23964620e-01 6.42424077e-02 -1.72775462e-01 -9.13752913e-02
-1.27657607e-01 -1.06836462e+00 7.17971802e-01 3.39189380e-01
-3.82016808e-01 5.73061228e-01 -5.99026203e-01 -7.60719061e-01
-1.84470713e-01 -8.59440029e-01 -3.45258385e-01 -5.35416782e-01
-3.80405694e-01 4.17243958e-01 5.98257303e-01 5.03563657... | [5.34013032913208, 7.16211462020874] |
d4669ec8-759f-4428-a8d0-c5717e17af74 | light-weight-residual-dense-attention-net-for | 2004.06930 | null | https://arxiv.org/abs/2004.06930v2 | https://arxiv.org/pdf/2004.06930v2.pdf | Light Weight Residual Dense Attention Net for Spectral Reconstruction from RGB Images | Hyperspectral Imaging is the acquisition of spectral and spatial information of a particular scene. Capturing such information from a specialized hyperspectral camera remains costly. Reconstructing such information from the RGB image achieves a better solution in both classification and object recognition tasks. This w... | ['S. M. Md. Mansoor Roomi', 'K. Uma', 'D Synthiya Vinothini', 'D. Sabari Nathan', 'B. Sathya Bama'] | 2020-04-15 | null | null | null | null | ['spectral-reconstruction'] | ['computer-vision'] | [ 7.33181238e-01 -3.68241161e-01 3.66683215e-01 -4.44745421e-01
-8.71279597e-01 -3.05565625e-01 1.40510574e-01 -3.96339118e-01
-5.37667155e-01 7.58225262e-01 3.51562276e-02 1.03483588e-01
-5.39226532e-01 -9.18730259e-01 -4.94755358e-01 -1.25496030e+00
-3.92391458e-02 -2.89260387e-01 -2.19370753e-01 -3.87310125... | [10.14787483215332, -2.0005011558532715] |
6734158d-f1d2-4d14-940f-360c76d9aa22 | self-supervised-pre-training-reduces-label | 2010.15366 | null | https://arxiv.org/abs/2010.15366v3 | https://arxiv.org/pdf/2010.15366v3.pdf | Stabilizing Label Assignment for Speech Separation by Self-supervised Pre-training | Speech separation has been well developed, with the very successful permutation invariant training (PIT) approach, although the frequent label assignment switching happening during PIT training remains to be a problem when better convergence speed and achievable performance are desired. In this paper, we propose to per... | ['Hung-Yi Lee', 'Gene-Ping Yang', 'Yi-Chen Chen', 'Da-Rong Liu', 'Shun-Po Chuang', 'Sung-Feng Huang'] | 2020-10-29 | null | null | null | null | ['speaker-separation'] | ['speech'] | [ 6.38239980e-01 3.89011041e-03 -3.95580560e-01 -6.24776483e-01
-7.62309194e-01 -4.14986610e-01 6.43116415e-01 -8.05892274e-02
-3.89187485e-01 9.86869693e-01 8.53988603e-02 -4.52709466e-01
-6.09754324e-01 -1.82269454e-01 -3.26738298e-01 -9.70681667e-01
-2.93548524e-01 9.47209537e-01 2.97381580e-01 2.31170431... | [14.54367733001709, 6.324130535125732] |
79056387-88c3-44bc-8a39-d4c94d9fc736 | distributed-one-class-learning | 1802.03583 | null | http://arxiv.org/abs/1802.03583v1 | http://arxiv.org/pdf/1802.03583v1.pdf | Distributed One-class Learning | We propose a cloud-based filter trained to block third parties from uploading
privacy-sensitive images of others to online social media. The proposed filter
uses Distributed One-Class Learning, which decomposes the cloud-based filter
into multiple one-class classifiers. Each one-class classifier captures the
properties... | ['Andrea Cavallaro', 'Hamed Haddadi', 'Ali Shahin Shamsabadi'] | 2018-02-10 | null | null | null | null | ['one-class-classifier'] | ['methodology'] | [-7.38215446e-02 -1.97657004e-01 -9.81979445e-02 -2.84824252e-01
-6.35667384e-01 -8.28941107e-01 2.26985827e-01 1.62486643e-01
-6.57281756e-01 5.33036888e-01 -1.59149975e-01 -1.80758893e-01
-1.33295044e-01 -1.03184974e+00 -9.70882058e-01 -9.96058404e-01
-1.52623346e-02 3.66497725e-01 2.41847381e-01 1.93576112... | [5.879546165466309, 6.486815929412842] |
3865f9d0-65c6-42cc-a3a1-f36aa376d556 | spda-cnn-unifying-semantic-part-detection-and | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Zhang_SPDA-CNN_Unifying_Semantic_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Zhang_SPDA-CNN_Unifying_Semantic_CVPR_2016_paper.pdf | SPDA-CNN: Unifying Semantic Part Detection and Abstraction for Fine-Grained Recognition | Most convolutional neural networks (CNNs) lack midlevel layers that model semantic parts of objects. This limits CNN-based methods from reaching their full potential in detecting and utilizing small semantic parts in recognition. Introducing such mid-level layers can facilitate the extraction of part-specific features ... | ['Xiaolei Huang', 'Ahmed Elgammal', 'Han Zhang', 'Dimitris Metaxas', 'Tao Xu', 'Shaoting Zhang', 'Mohamed Elhoseiny'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['semantic-part-detection'] | ['computer-vision'] | [ 4.87075560e-02 1.45616263e-01 -2.50426650e-01 -3.49795520e-01
-8.63668978e-01 -3.49149615e-01 6.74039543e-01 -6.36030510e-02
-3.67097497e-01 3.62011671e-01 6.64296793e-03 2.15568393e-01
1.78291127e-01 -1.10449457e+00 -1.04436207e+00 -5.83488941e-01
2.37105280e-01 3.81580025e-01 7.77446151e-01 4.17080112... | [9.57496166229248, 1.9078572988510132] |
e92b73e8-bad1-4bbc-afdc-c315411efbad | zero-shot-object-detection-by-hybrid-region | 1805.06157 | null | http://arxiv.org/abs/1805.06157v2 | http://arxiv.org/pdf/1805.06157v2.pdf | Zero-Shot Object Detection by Hybrid Region Embedding | Object detection is considered as one of the most challenging problems in
computer vision, since it requires correct prediction of both classes and
locations of objects in images. In this study, we define a more difficult
scenario, namely zero-shot object detection (ZSD) where no visual training data
is available for s... | ['Nazli Ikizler-Cinbis', 'Ramazan Gokberk Cinbis', 'Berkan Demirel'] | 2018-05-16 | null | null | null | null | ['zero-shot-object-detection'] | ['computer-vision'] | [ 2.35399321e-01 -3.66517216e-01 6.54692501e-02 -2.91728735e-01
-5.27915418e-01 -4.10438210e-01 8.42082739e-01 3.96837234e-01
-7.12894559e-01 2.34829322e-01 -2.57590204e-01 4.90135252e-02
7.35853165e-02 -4.65963840e-01 -6.93014026e-01 -5.46782732e-01
7.48579353e-02 3.70332271e-01 9.69606400e-01 -1.58393919... | [9.316170692443848, 1.4627344608306885] |
cdfb7aaf-c475-44d7-bf84-baf18149e145 | msgnn-a-spectral-graph-neural-network-based | 2209.00546 | null | https://arxiv.org/abs/2209.00546v4 | https://arxiv.org/pdf/2209.00546v4.pdf | MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed Laplacian | Signed and directed networks are ubiquitous in real-world applications. However, there has been relatively little work proposing spectral graph neural networks (GNNs) for such networks. Here we introduce a signed directed Laplacian matrix, which we call the magnetic signed Laplacian, as a natural generalization of both... | ['Mihai Cucuringu', 'Gesine Reinert', 'Michael Permultter', 'Yixuan He'] | 2022-09-01 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 1.48422092e-01 6.80083176e-03 -4.26522717e-02 -4.69891608e-01
-5.96437939e-02 -5.77933490e-01 4.85560566e-01 -1.37166083e-01
4.76513542e-02 6.93477035e-01 2.48374213e-02 -6.89440548e-01
-8.42667997e-01 -7.09448516e-01 -6.46752715e-01 -5.61332643e-01
-7.40794897e-01 3.75949740e-01 2.72732466e-01 -5.69866955... | [7.125925540924072, 6.118903636932373] |
b19f4815-be13-44e8-ab04-0685edacb3ee | a-neural-network-based-energy-management | 2206.06716 | null | https://arxiv.org/abs/2206.06716v1 | https://arxiv.org/pdf/2206.06716v1.pdf | A Neural Network-Based Energy Management System for PV-Battery Based Microgrids | A neural network-based energy management system (NN-EMS) has been proposed in this paper for islanded ac microgrids fed by multiple PV-battery based distributed generators (DG). The stochastic and unequal irradiation results in unequal PV output, which causes an unequal state-of-charge (SoC) among the batteries of the ... | ['Mohammad Amin', 'Yusuf Gupta'] | 2022-06-14 | null | null | null | null | ['load-forecasting'] | ['miscellaneous'] | [-3.20186466e-01 1.00214668e-01 5.80060557e-02 7.39451125e-02
-1.37157083e-01 -8.75361085e-01 2.76913702e-01 1.81414157e-01
1.46658987e-01 1.42602134e+00 -4.69537824e-01 3.16233432e-04
-3.71244341e-01 -9.18847024e-01 -5.46725988e-01 -1.38937783e+00
-2.01247454e-01 4.25512016e-01 -1.36336148e-01 -2.92897284... | [5.671951770782471, 2.5451488494873047] |
1ec587c7-15ef-416e-baad-c84068201610 | exploiting-the-large-scale-german-broadcast | null | null | https://aclanthology.org/L14-1664 | https://aclanthology.org/L14-1664.pdf | Exploiting the large-scale German Broadcast Corpus to boost the Fraunhofer IAIS Speech Recognition System | In this paper we describe the large-scale German broadcast corpus (GER-TV1000h) containing more than 1,000 hours of transcribed speech data. This corpus is unique in the German language corpora domain and enables significant progress in tuning the acoustic modelling of German large vocabulary continuous speech recognit... | ['Michael Stadtschnitzer', 'Joachim Koehler', 'Jochen Schwenninger', 'Daniel Stein'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['acoustic-modelling'] | ['speech'] | [ 2.68957406e-01 3.06760162e-01 3.90128493e-01 -5.13555527e-01
-1.37928414e+00 -3.79765242e-01 6.48648679e-01 1.86337188e-01
-9.80780721e-01 7.38573790e-01 1.63600519e-01 -4.44097817e-01
2.01544240e-02 -3.95359844e-01 -3.35756212e-01 -7.75158823e-01
-1.18723229e-01 6.06365860e-01 3.08682859e-01 -5.74851215... | [14.434659957885742, 6.74509859085083] |
9e8c78d9-b096-44a4-99db-d0fbf927f1aa | fine-grained-visual-prompting | 2306.04356 | null | https://arxiv.org/abs/2306.04356v1 | https://arxiv.org/pdf/2306.04356v1.pdf | Fine-Grained Visual Prompting | Vision-Language Models (VLMs), such as CLIP, have demonstrated impressive zero-shot transfer capabilities in image-level visual perception. However, these models have shown limited performance in instance-level tasks that demand precise localization and recognition. Previous works have suggested that incorporating visu... | ['Jian Yang', 'Xinlong Wang', 'Xiang Li', 'Yueze Wang', 'Lingfeng Yang'] | 2023-06-07 | null | null | null | null | ['visual-prompting'] | ['computer-vision'] | [ 4.35227692e-01 -5.04647456e-02 -1.00210987e-01 -3.05184752e-01
-8.04233909e-01 -6.75250947e-01 6.86975241e-01 7.65385181e-02
-5.08267105e-01 4.70106184e-01 1.64763331e-01 -3.91310543e-01
2.17251197e-01 -2.72477001e-01 -8.75004470e-01 -6.23114347e-01
3.97545248e-01 -1.39039174e-01 4.96020645e-01 -1.79523349... | [10.344533920288086, 1.5085718631744385] |
6cf589d1-a8f2-4793-80f5-06c1a6b54f31 | consistent-text-categorization-using-data | 2305.05402 | null | https://arxiv.org/abs/2305.05402v2 | https://arxiv.org/pdf/2305.05402v2.pdf | Consistent Text Categorization using Data Augmentation in e-Commerce | The categorization of massive e-Commerce data is a crucial, well-studied task, which is prevalent in industrial settings. In this work, we aim to improve an existing product categorization model that is already in use by a major web company, serving multiple applications. At its core, the product categorization model i... | ['Ariel Raviv', 'Noa Avigdor-Elgrabli', 'Stav Yanovsky Daye', 'Guy Horowitz'] | 2023-05-09 | null | null | null | null | ['product-categorization', 'text-categorization'] | ['miscellaneous', 'natural-language-processing'] | [ 2.68669099e-01 -1.18868396e-01 -2.58292466e-01 -5.63135028e-01
-3.98940176e-01 -6.49112761e-01 4.74489868e-01 5.08896410e-01
-1.27327979e-01 2.82933563e-01 -3.86856981e-02 -5.21914840e-01
-1.16741687e-01 -8.12321723e-01 -4.52780843e-01 -4.46362317e-01
4.36180532e-01 4.40842897e-01 1.07856520e-01 -3.08776855... | [9.945175170898438, 6.139035224914551] |
cbca4678-fb14-4f6a-91a9-07d9f8cf59d0 | multi-view-contrastive-graph-clustering | 2110.11842 | null | https://arxiv.org/abs/2110.11842v1 | https://arxiv.org/pdf/2110.11842v1.pdf | Multi-view Contrastive Graph Clustering | With the explosive growth of information technology, multi-view graph data have become increasingly prevalent and valuable. Most existing multi-view clustering techniques either focus on the scenario of multiple graphs or multi-view attributes. In this paper, we propose a generic framework to cluster multi-view attribu... | ['Zhao Kang', 'Erlin Pan'] | 2021-10-22 | null | http://proceedings.neurips.cc/paper/2021/hash/10c66082c124f8afe3df4886f5e516e0-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/10c66082c124f8afe3df4886f5e516e0-Paper.pdf | neurips-2021-12 | ['graph-clustering'] | ['graphs'] | [-1.81355491e-01 -1.01862483e-01 3.57063971e-02 -1.69903666e-01
-6.35498583e-01 -5.82478523e-01 5.69500625e-01 3.18119884e-01
6.55307248e-02 2.47824013e-01 1.36081070e-01 1.66197270e-01
-3.26842427e-01 -9.64060545e-01 -5.57028174e-01 -9.31023896e-01
-6.79972321e-02 5.19957304e-01 9.48660150e-02 -5.50454631... | [7.535953044891357, 5.8750786781311035] |
8ce831ca-24fa-41b5-b5d6-20a58a95bda7 | zoom-rnn-a-novel-method-for-person | 1809.09189 | null | http://arxiv.org/abs/1809.09189v2 | http://arxiv.org/pdf/1809.09189v2.pdf | Zoom-RNN: A Novel Method for Person Recognition Using Recurrent Neural Networks | The overwhelming popularity of social media has resulted in bulk amounts of
personal photos being uploaded to the internet every day. Since these photos
are taken in unconstrained settings, recognizing the identities of people among
the photos remains a challenge. Studies have indicated that utilizing evidence
other th... | ['Mina Ghadimi Atigh', 'Sina Mokhtarzadeh Azar', 'Mohammad Javadi', 'Ahmad Nickabadi', 'Sajjad Azami'] | 2018-09-24 | null | null | null | null | ['person-recognition'] | ['computer-vision'] | [ 1.47981167e-01 -2.03099325e-01 1.18767563e-03 -4.60608959e-01
-5.33544421e-01 -1.67105019e-01 7.25061476e-01 -3.58937234e-01
-2.92711645e-01 5.03660679e-01 5.70115328e-01 4.15798038e-01
3.12883019e-01 -2.96453416e-01 -3.25877249e-01 -5.34877360e-01
1.77424282e-01 1.65071979e-01 -1.05326213e-01 -1.21064931... | [14.365937232971191, 0.9705349802970886] |
9cc29b1a-6234-4dfe-9270-dcdc900071be | banet-bidirectional-aggregation-network-with | 2003.14031 | null | https://arxiv.org/abs/2003.14031v1 | https://arxiv.org/pdf/2003.14031v1.pdf | BANet: Bidirectional Aggregation Network with Occlusion Handling for Panoptic Segmentation | Panoptic segmentation aims to perform instance segmentation for foreground instances and semantic segmentation for background stuff simultaneously. The typical top-down pipeline concentrates on two key issues: 1) how to effectively model the intrinsic interaction between semantic segmentation and instance segmentation,... | ['Xi Li', 'Mingliang Xu', 'Guangchen Lin', 'Fangfang Wang', 'Bourahla Omar', 'Songyuan Li', 'Yiming Wu', 'Yifeng Chen', 'Junyi Feng'] | 2020-03-31 | banet-bidirectional-aggregation-network-with-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Chen_BANet_Bidirectional_Aggregation_Network_With_Occlusion_Handling_for_Panoptic_Segmentation_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Chen_BANet_Bidirectional_Aggregation_Network_With_Occlusion_Handling_for_Panoptic_Segmentation_CVPR_2020_paper.pdf | cvpr-2020-6 | ['occlusion-handling'] | ['computer-vision'] | [ 1.29566714e-01 -8.78178999e-02 -3.00194830e-01 -3.35192353e-01
-6.83028340e-01 -6.90189958e-01 6.07923329e-01 -5.03939018e-02
2.19096746e-02 2.47845232e-01 -2.16127515e-01 -2.27332607e-01
1.02930665e-02 -1.06296027e+00 -5.77351689e-01 -9.68945324e-01
1.74678534e-01 4.74014699e-01 4.95831162e-01 2.74751425... | [9.439712524414062, 0.152299702167511] |
75171e11-fde9-4dd1-8170-3dbf0fd37372 | on-human-predictions-with-explanations-and | 1811.07901 | null | http://arxiv.org/abs/1811.07901v4 | http://arxiv.org/pdf/1811.07901v4.pdf | On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection | Humans are the final decision makers in critical tasks that involve ethical
and legal concerns, ranging from recidivism prediction, to medical diagnosis,
to fighting against fake news. Although machine learning models can sometimes
achieve impressive performance in these tasks, these tasks are not amenable to
full auto... | ['Vivian Lai', 'Chenhao Tan'] | 2018-11-19 | null | null | null | null | ['deception-detection'] | ['miscellaneous'] | [ 2.32481748e-01 7.52045274e-01 -4.50628191e-01 -5.32296419e-01
-5.28278530e-01 -5.46895981e-01 9.16197062e-01 1.86869904e-01
-3.81607473e-01 7.56685793e-01 1.99397579e-01 -8.15797567e-01
2.42531160e-03 -5.02834260e-01 -3.31225753e-01 -1.27346009e-01
3.91590297e-01 4.15719986e-01 -5.50001077e-02 -2.03324854... | [9.129011154174805, 6.264735698699951] |
12954b9f-dd9d-40e7-99b5-017c366caa9c | high-resolution-face-completion-with-multiple | 1801.07632 | null | http://arxiv.org/abs/1801.07632v1 | http://arxiv.org/pdf/1801.07632v1.pdf | High Resolution Face Completion with Multiple Controllable Attributes via Fully End-to-End Progressive Generative Adversarial Networks | We present a deep learning approach for high resolution face completion with
multiple controllable attributes (e.g., male and smiling) under arbitrary
masks. Face completion entails understanding both structural meaningfulness and
appearance consistency locally and globally to fill in "holes" whose content do
not appea... | ['Shaoliang Nie', 'Zeyuan Chen', 'Tianfu Wu', 'Christopher G. Healey'] | 2018-01-23 | null | null | null | null | ['facial-inpainting'] | ['computer-vision'] | [ 4.34045643e-01 3.60033751e-01 4.35998827e-01 -6.00135386e-01
-1.11552322e+00 -5.10768414e-01 4.65846241e-01 -5.36392391e-01
-1.32003531e-01 6.48817539e-01 2.05655739e-01 3.08694869e-01
8.54830351e-03 -6.98075652e-01 -9.87587690e-01 -3.06718022e-01
-2.18518227e-01 5.65873086e-01 -3.63815635e-01 -1.48415416... | [12.662747383117676, -0.18653395771980286] |
2b1692e9-935b-4b4f-b9f7-f133f1e0fbc9 | adaptive-local-component-aware-graph | 2209.10073 | null | https://arxiv.org/abs/2209.10073v1 | https://arxiv.org/pdf/2209.10073v1.pdf | Adaptive Local-Component-aware Graph Convolutional Network for One-shot Skeleton-based Action Recognition | Skeleton-based action recognition receives increasing attention because the skeleton representations reduce the amount of training data by eliminating visual information irrelevant to actions. To further improve the sample efficiency, meta-learning-based one-shot learning solutions were developed for skeleton-based act... | ['James Bailey', 'Mingming Gong', 'Qiuhong Ke', 'Anqi Zhu'] | 2022-09-21 | null | null | null | null | ['one-shot-learning'] | ['methodology'] | [ 4.79921043e-01 -5.38027436e-02 -7.86251128e-01 -2.30577394e-01
-7.47482300e-01 -1.50572620e-02 6.15946352e-01 -5.65251261e-02
-4.76358414e-01 3.23847115e-01 7.89579749e-01 4.50421363e-01
-2.99428493e-01 -7.20014513e-01 -4.52881485e-01 -8.17546189e-01
-2.11962655e-01 1.27492771e-01 5.20110011e-01 -1.67627320... | [7.900422096252441, 0.4244266450405121] |
6a3da21b-bbf4-47c3-a1c7-28bcce0d3574 | locality-relationship-constrained-multi-view | 2107.05073 | null | https://arxiv.org/abs/2107.05073v1 | https://arxiv.org/pdf/2107.05073v1.pdf | Locality Relationship Constrained Multi-view Clustering Framework | In most practical applications, it's common to utilize multiple features from different views to represent one object. Among these works, multi-view subspace-based clustering has gained extensive attention from many researchers, which aims to provide clustering solutions to multi-view data. However, most existing metho... | ['Wenzhe Liu', 'Wei Wei', 'Xiangzhu Meng'] | 2021-07-11 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [-3.10133636e-01 -7.02155292e-01 -2.37987697e-01 -1.81932062e-01
-5.40588558e-01 -4.34290975e-01 3.29907477e-01 -2.59259164e-01
1.68912888e-01 6.50792718e-02 4.03129578e-01 2.70950139e-01
-5.91875970e-01 -4.37195092e-01 -1.67056441e-01 -1.02353394e+00
2.58169383e-01 7.95254484e-02 1.58533193e-02 6.22447096... | [8.277341842651367, 4.649924278259277] |
bc8f6fb6-1c43-4250-82c3-e0d0c5967758 | text-mining-drug-chemical-protein | 2111.15617 | null | https://arxiv.org/abs/2111.15617v1 | https://arxiv.org/pdf/2111.15617v1.pdf | Text Mining Drug/Chemical-Protein Interactions using an Ensemble of BERT and T5 Based Models | In Track-1 of the BioCreative VII Challenge participants are asked to identify interactions between drugs/chemicals and proteins. In-context named entity annotations for each drug/chemical and protein are provided and one of fourteen different interactions must be automatically predicted. For this relation extraction t... | ['Anas Abidin', 'Bo Liu', 'Carol Anderson', 'Hoo-chang Shin', 'Virginia Adams'] | 2021-11-30 | null | null | null | null | ['sentence-classification'] | ['natural-language-processing'] | [ 2.93347448e-01 3.76962930e-01 -4.64844346e-01 -4.73163992e-01
-1.06539667e+00 -6.84031725e-01 5.69080770e-01 7.93156981e-01
-2.84942597e-01 1.20301747e+00 4.09230530e-01 -6.43486261e-01
-2.98651189e-01 -4.15768474e-01 -6.17007732e-01 -5.15908241e-01
-6.65734559e-02 7.29889214e-01 4.92047966e-02 -8.20159540... | [8.467233657836914, 8.760913848876953] |
87433569-9a07-4f63-b5f8-4f149e2ca328 | hierarchical-attention-prototypical-networks | null | null | https://aclanthology.org/D19-1045 | https://aclanthology.org/D19-1045.pdf | Hierarchical Attention Prototypical Networks for Few-Shot Text Classification | Most of the current effective methods for text classification tasks are based on large-scale labeled data and a great number of parameters, but when the supervised training data are few and difficult to be collected, these models are not available. In this work, we propose a hierarchical attention prototypical networks... | ['Shengli Sun', 'Qingfeng Sun', 'Kevin Zhou', 'Tengchao Lv'] | 2019-11-01 | null | null | null | ijcnlp-2019-11 | ['few-shot-text-classification'] | ['natural-language-processing'] | [-2.34124400e-02 -3.04184765e-01 -3.37255597e-01 -4.29204136e-01
-2.00178519e-01 4.27922085e-02 5.10940015e-01 5.39201081e-01
-5.70700824e-01 5.47972858e-01 4.96245712e-01 5.29079325e-02
-1.59529895e-01 -9.13889229e-01 -4.49414439e-02 -4.81374681e-01
3.86134267e-01 3.69266927e-01 4.89829391e-01 -5.47839165... | [10.296592712402344, 3.688584089279175] |
d8061fa3-f050-49dc-918f-ae9168751994 | lossless-simd-compression-of-lidar-range-and | 2209.08196 | null | https://arxiv.org/abs/2209.08196v2 | https://arxiv.org/pdf/2209.08196v2.pdf | Lossless SIMD Compression of LiDAR Range and Attribute Scan Sequences | As LiDAR sensors have become ubiquitous, the need for an efficient LiDAR data compression algorithm has increased. Modern LiDARs produce gigabytes of scan data per hour and are often used in applications with limited compute, bandwidth, and storage resources. We present a fast, lossless compression algorithm for LiDAR ... | ['Jordan Ford', 'Jeff Ford'] | 2022-09-16 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 3.84577453e-01 -4.59409893e-01 -3.08070898e-01 -8.41014981e-01
-7.40199625e-01 -1.13837793e-01 4.03810233e-01 3.26982141e-01
-7.39352643e-01 5.49913645e-01 1.08850347e-02 -5.23624837e-01
-1.22911528e-01 -1.25415373e+00 -7.59404957e-01 -2.09357068e-01
-2.33574048e-01 6.10514998e-01 5.42930484e-01 -4.06650797... | [8.20807933807373, -2.869777202606201] |
7985ec47-a1e4-4e8f-acdb-265cf7b77a92 | biological-hotspot-mapping-in-coral-reefs | 2305.02330 | null | https://arxiv.org/abs/2305.02330v2 | https://arxiv.org/pdf/2305.02330v2.pdf | Robot Goes Fishing: Rapid, High-Resolution Biological Hotspot Mapping in Coral Reefs with Vision-Guided Autonomous Underwater Vehicles | Coral reefs are fast-changing and complex ecosystems that are crucial to monitor and study. Biological hotspot detection can help coral reef managers prioritize limited resources for monitoring and intervention tasks. Here, we explore the use of autonomous underwater vehicles (AUVs) with cameras, coupled with visual de... | ['Yogesh Girdhar', 'Stewart Jamieson', 'Levi Cai', 'Daniel Yang'] | 2023-05-03 | null | null | null | null | ['3d-reconstruction'] | ['computer-vision'] | [ 1.69564098e-01 2.40623783e-02 5.60597241e-01 1.96829718e-02
-4.62586135e-01 -8.68456900e-01 6.60477996e-01 4.31512386e-01
-9.23167884e-01 4.02802974e-01 6.02425933e-01 -4.62474346e-01
-1.15192816e-01 -1.02887297e+00 -5.53805828e-01 -5.93828619e-01
-6.53454900e-01 3.06028545e-01 7.39367783e-01 -2.20871866... | [8.419137001037598, -1.2173773050308228] |
021964e5-aeb5-4fa6-8d20-be7d2ca48004 | skrgan-sketching-rendering-unconditional | 1908.04346 | null | https://arxiv.org/abs/1908.04346v1 | https://arxiv.org/pdf/1908.04346v1.pdf | SkrGAN: Sketching-rendering Unconditional Generative Adversarial Networks for Medical Image Synthesis | Generative Adversarial Networks (GANs) have the capability of synthesizing images, which have been successfully applied to medical image synthesis tasks. However, most of existing methods merely consider the global contextual information and ignore the fine foreground structures, e.g., vessel, skeleton, which may conta... | ['Yuting Xiao', 'Tianyang Zhang', 'Mengjie Guo', 'Jiang Liu', 'Huazhu Fu', 'Zaiwang Gu', 'Yitian Zhao', 'Shenghua Gao', 'Jun Cheng', 'Bing Yang'] | 2019-08-06 | null | null | null | null | ['medical-image-generation'] | ['medical'] | [ 5.93589664e-01 2.62593329e-01 2.96739757e-01 -1.30650997e-01
-5.25615156e-01 -3.01338792e-01 5.52473545e-01 -6.48154795e-01
-9.61337462e-02 7.13221312e-01 4.21178862e-02 -3.99973989e-01
2.01047361e-01 -1.01887584e+00 -6.17706060e-01 -1.00326014e+00
4.05558199e-01 1.90308481e-01 1.89090535e-01 -1.83908805... | [12.05014419555664, -1.189071774482727] |
ec6c90d5-ec36-4147-bf66-5979e8102119 | trust-your-model-light-field-depth-estimation | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Schilling_Trust_Your_Model_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Schilling_Trust_Your_Model_CVPR_2018_paper.pdf | Trust Your Model: Light Field Depth Estimation With Inline Occlusion Handling | We address the problem of depth estimation from light-field images. Our main contribution is a new way to handle occlusions which improves general accuracy and quality of object borders. In contrast to all prior work we work with a model which directly incorporates both depth and occlusion, using a local optimization s... | ['Bernd Jähne', 'Carsten Rother', 'Maximilian Diebold', 'Hendrik Schilling'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['occlusion-handling'] | ['computer-vision'] | [ 3.74349594e-01 4.30982038e-02 2.48892784e-01 -3.55380356e-01
-1.02441835e+00 -2.98219025e-01 4.96023685e-01 -6.61886483e-02
-5.02096593e-01 7.16796398e-01 6.99637905e-02 2.40831047e-01
5.58661409e-02 -6.67520285e-01 -5.62266052e-01 -6.63870454e-01
3.31087768e-01 4.26116616e-01 8.84388566e-01 2.74046808... | [8.887411117553711, -2.609441041946411] |
ead10d03-c6df-4e76-8433-2313a3a01f16 | learning-thin-plate-spline-motion-and | 2302.08207 | null | https://arxiv.org/abs/2302.08207v1 | https://arxiv.org/pdf/2302.08207v1.pdf | Learning Thin-Plate Spline Motion and Seamless Composition for Parallax-Tolerant Unsupervised Deep Image Stitching | Traditional image stitching approaches tend to leverage increasingly complex geometric features (point, line, edge, etc.) for better performance. However, these hand-crafted features are only suitable for specific natural scenes with adequate geometric structures. In contrast, deep stitching schemes overcome the advers... | ['Yao Zhao', 'Shuaicheng Liu', 'Kang Liao', 'Chunyu Lin', 'Lang Nie'] | 2023-02-16 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [ 2.89863974e-01 -3.93419027e-01 6.85951859e-02 -2.25970879e-01
-5.08490622e-01 -5.42509198e-01 5.58790505e-01 -2.81380028e-01
-1.92653105e-01 3.87248248e-01 7.57457539e-02 2.13443130e-01
-3.00641686e-01 -6.12775266e-01 -6.45495296e-01 -8.86229336e-01
3.02150816e-01 2.64184415e-01 3.70066226e-01 -3.34193021... | [9.347200393676758, -2.346803665161133] |
7dfe37ec-4ab8-4f51-9635-4945d128cf22 | few-shot-temporal-action-localization-with | 2110.10552 | null | https://arxiv.org/abs/2110.10552v1 | https://arxiv.org/pdf/2110.10552v1.pdf | Few-Shot Temporal Action Localization with Query Adaptive Transformer | Existing temporal action localization (TAL) works rely on a large number of training videos with exhaustive segment-level annotation, preventing them from scaling to new classes. As a solution to this problem, few-shot TAL (FS-TAL) aims to adapt a model to a new class represented by as few as a single video. Exiting FS... | ['Tao Xiang', 'Xiatian Zhu', 'Sauradip Nag'] | 2021-10-20 | null | null | null | null | ['few-shot-temporal-action-localization', 'fine-grained-action-detection'] | ['computer-vision', 'computer-vision'] | [ 5.15245140e-01 -9.39610973e-02 -5.23005366e-01 -1.73497155e-01
-9.53251600e-01 -5.51423609e-01 4.91752267e-01 -2.37470821e-01
-4.04167563e-01 6.38731599e-01 -5.27770035e-02 2.54043996e-01
1.80115864e-01 -3.26559722e-01 -8.54929328e-01 -6.59725666e-01
2.65992861e-02 2.84207374e-01 1.01695967e+00 6.12731241... | [8.497187614440918, 0.6109372973442078] |
fd940783-aa47-47d4-a493-5026e697c78c | benchmarking-node-outlier-detection-on-graphs | 2206.10071 | null | https://arxiv.org/abs/2206.10071v2 | https://arxiv.org/pdf/2206.10071v2.pdf | BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs | Detecting which nodes in graphs are outliers is a relatively new machine learning task with numerous applications. Despite the proliferation of algorithms developed in recent years for this task, there has been no standard comprehensive setting for performance evaluation. Consequently, it has been difficult to understa... | ['Philip S. Yu', 'Zhihao Jia', 'George H. Chen', 'Jundong Li', 'Lichao Sun', 'Kai Shu', 'Hao Peng', 'Canyu Chen', 'Kaize Ding', 'Ruitong Zhang', 'Xiyang Hu', 'Xueying Ding', 'Yue Zhao', 'Yingtong Dou', 'Kay Liu'] | 2022-06-21 | null | null | null | null | ['graph-outlier-detection'] | ['graphs'] | [-6.68790052e-03 9.73331835e-03 -4.76756413e-03 7.62663409e-02
-3.90168220e-01 -4.34857458e-01 3.18402648e-01 7.82913446e-01
-5.58674447e-02 5.42692900e-01 9.34638157e-02 -4.36036348e-01
-2.61108637e-01 -8.93088996e-01 -6.66034162e-01 -3.95865291e-01
-7.96936393e-01 5.03866613e-01 2.71952093e-01 -6.43275082... | [6.7405619621276855, 5.775766372680664] |
6e0f4b1e-d09c-4176-8481-6ae35632d0e6 | hevas-headline-evaluation-and-analysis-system | null | null | https://aclanthology.org/W19-8910 | https://aclanthology.org/W19-8910.pdf | HEvAS: Headline Evaluation and Analysis System | Automatic headline generation is a subtask of one-line summarization with many reported applications. Evaluation of systems generating headlines is a very challenging and undeveloped area. We introduce the Headline Evaluation and Analysis System (HEvAS) that performs automatic evaluation of systems in terms of a qualit... | ['Marina Litvak', 'Itzhak Eretz Kdosha', 'Natalia Vanetik'] | 2019-09-01 | null | null | null | ranlp-2019-9 | ['headline-generation'] | ['natural-language-processing'] | [ 2.13123057e-02 2.60184139e-01 -8.18170458e-02 -2.51538903e-01
-1.00404334e+00 -7.14227915e-01 9.79144394e-01 7.65991569e-01
-1.06172711e-01 1.14693785e+00 1.03890657e+00 -1.26811117e-01
-3.04997005e-02 -5.83456337e-01 -3.53787243e-01 -2.08986402e-01
-4.25444208e-02 3.91345710e-01 4.28983897e-01 -5.09730279... | [12.315881729125977, 9.48121166229248] |
7e0f32e0-aae2-4ec5-81f1-811d822ebd42 | non-parametric-spatially-constrained-local | 2006.12874 | null | https://arxiv.org/abs/2006.12874v1 | https://arxiv.org/pdf/2006.12874v1.pdf | Non-parametric spatially constrained local prior for scene parsing on real-world data | Scene parsing aims to recognize the object category of every pixel in scene images, and it plays a central role in image content understanding and computer vision applications. However, accurate scene parsing from unconstrained real-world data is still a challenging task. In this paper, we present the non-parametric Sp... | ['Ligang Zhang'] | 2020-06-23 | null | null | null | null | ['scene-parsing'] | ['computer-vision'] | [ 5.20393848e-01 -3.76614034e-01 -3.75997096e-01 -7.41538525e-01
-7.43735135e-01 -4.76074278e-01 4.71266419e-01 2.15636924e-01
-3.84791732e-01 3.84072751e-01 7.89870247e-02 -8.77485201e-02
-5.78349270e-02 -7.42999494e-01 -9.56171751e-01 -8.05506229e-01
-6.62620366e-02 1.24384515e-01 9.35642540e-01 2.19629779... | [9.524568557739258, 0.36625510454177856] |
2f870845-42a0-464b-a052-8b5ecb5c7703 | flexround-learnable-rounding-based-on-element | 2306.00317 | null | https://arxiv.org/abs/2306.00317v1 | https://arxiv.org/pdf/2306.00317v1.pdf | FlexRound: Learnable Rounding based on Element-wise Division for Post-Training Quantization | Post-training quantization (PTQ) has been gaining popularity for the deployment of deep neural networks on resource-limited devices since unlike quantization-aware training, neither a full training dataset nor end-to-end training is required at all. As PTQ schemes based on reconstructing each layer or block output turn... | ['Dongsoo Lee', 'Se Jung Kwon', 'Jeonghoon Kim', 'Jung Hyun Lee'] | 2023-06-01 | null | null | null | null | ['quantization'] | ['methodology'] | [ 2.80327380e-01 -8.02833494e-03 -2.42946625e-01 -4.34228033e-01
-9.56606090e-01 -5.70509493e-01 7.44994521e-01 2.98072904e-01
-6.94307566e-01 5.18887579e-01 -8.49437527e-03 -4.42528099e-01
1.72596648e-01 -8.98779154e-01 -1.10844064e+00 -6.66080594e-01
-1.26888037e-01 2.11374253e-01 7.49200657e-02 -1.78283229... | [8.637809753417969, 3.1590521335601807] |
513cfd7f-5115-4a24-b713-718d6d85d382 | semantic-palette-guiding-scene-generation | 2106.01629 | null | https://arxiv.org/abs/2106.01629v1 | https://arxiv.org/pdf/2106.01629v1.pdf | Semantic Palette: Guiding Scene Generation with Class Proportions | Despite the recent progress of generative adversarial networks (GANs) at synthesizing photo-realistic images, producing complex urban scenes remains a challenging problem. Previous works break down scene generation into two consecutive phases: unconditional semantic layout synthesis and image synthesis conditioned on l... | ['Matthieu Cord', 'Patrick Pérez', 'Himalaya Jain', 'Tuan-Hung Vu', 'Guillaume Le Moing'] | 2021-06-03 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Le_Moing_Semantic_Palette_Guiding_Scene_Generation_With_Class_Proportions_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Le_Moing_Semantic_Palette_Guiding_Scene_Generation_With_Class_Proportions_CVPR_2021_paper.pdf | cvpr-2021-1 | ['scene-generation'] | ['computer-vision'] | [ 6.81733489e-01 3.05488229e-01 1.45841420e-01 -2.81259477e-01
-6.25606954e-01 -8.27401876e-01 9.16551292e-01 -1.05996557e-01
-9.29165706e-02 6.44121766e-01 2.07311153e-01 -1.86944962e-01
2.73921520e-01 -1.21140909e+00 -1.23239255e+00 -4.58687037e-01
4.64744419e-01 4.64017123e-01 1.99075025e-02 -3.24600995... | [11.392439842224121, -0.33838391304016113] |
dfadd47c-a10b-435c-814f-7b8430d69d96 | bayesian-learning-with-information-gain-1 | 2212.02003 | null | https://arxiv.org/abs/2212.02003v1 | https://arxiv.org/pdf/2212.02003v1.pdf | Bayesian Learning with Information Gain Provably Bounds Risk for a Robust Adversarial Defense | We present a new algorithm to learn a deep neural network model robust against adversarial attacks. Previous algorithms demonstrate an adversarially trained Bayesian Neural Network (BNN) provides improved robustness. We recognize the adversarial learning approach for approximating the multi-modal posterior distribution... | ['Damith C. Ranasinghe', 'Javen Qinfeng Shi', 'Ehsan Abbasnejad', 'Bao Gia Doan'] | 2022-12-05 | bayesian-learning-with-information-gain | https://openreview.net/forum?id=5_zwnS5oJDp | https://openreview.net/pdf?id=5_zwnS5oJDp | null | ['adversarial-defense'] | ['adversarial'] | [ 1.80630311e-01 5.89812875e-01 2.89663970e-01 -3.23559105e-01
-1.28167903e+00 -1.02785957e+00 6.30804539e-01 -3.57073396e-01
-3.40295166e-01 6.61538601e-01 2.38508493e-01 -5.48410177e-01
-1.77882224e-01 -8.74426961e-01 -1.31417060e+00 -9.45824087e-01
-3.04177880e-01 1.16217010e-01 5.35404310e-02 -1.61905393... | [5.607102870941162, 7.9694437980651855] |
d1a62f5e-66ff-43ae-afd6-71d31196caab | label-matching-semi-supervised-object-1 | 2206.06608 | null | https://arxiv.org/abs/2206.06608v1 | https://arxiv.org/pdf/2206.06608v1.pdf | Label Matching Semi-Supervised Object Detection | Semi-supervised object detection has made significant progress with the development of mean teacher driven self-training. Despite the promising results, the label mismatch problem is not yet fully explored in the previous works, leading to severe confirmation bias during self-training. In this paper, we delve into this... | ['Yueting Zhuang', 'Mingli Song', 'ShiLiang Pu', 'Di Xie', 'Jie Song', 'Yunyi Xuan', 'Shicai Yang', 'WeiJie Chen', 'Binbin Chen'] | 2022-06-14 | label-matching-semi-supervised-object | http://openaccess.thecvf.com//content/CVPR2022/html/Chen_Label_Matching_Semi-Supervised_Object_Detection_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Chen_Label_Matching_Semi-Supervised_Object_Detection_CVPR_2022_paper.pdf | cvpr-2022-1 | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 3.27409625e-01 2.64287561e-01 -4.69416440e-01 -7.38313377e-01
-8.81652236e-01 -4.29158062e-01 5.08802652e-01 2.09233716e-01
-4.43901598e-01 8.35457981e-01 -2.41350248e-01 -1.39170498e-01
1.02336824e-01 -5.15981972e-01 -8.05484772e-01 -8.51451635e-01
7.68079221e-01 5.38971186e-01 4.93396580e-01 1.68679953... | [9.203086853027344, 1.5908745527267456] |
39125a67-929c-4df9-bdc3-054e4119199d | visual-spoofing-in-content-based-spam | 2004.05265 | null | https://arxiv.org/abs/2004.05265v2 | https://arxiv.org/pdf/2004.05265v2.pdf | Visual Spoofing in content based spam detection | Although the problem of spam classification seems to be solved, there are still vulnerabilities in the current spam filters that could be easily exploited. We present one such vulnerability, in which one could replace some characters with corresponding characters from a different alphabet. These characters are visually... | ['Mark Sokolov', 'Nic Herndon', 'Kehinde Olufowobi'] | 2020-04-11 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [ 3.20635378e-01 6.04407750e-02 4.14081067e-01 -1.47261575e-01
-1.95823431e-01 -1.02623796e+00 8.36436808e-01 3.45374227e-01
-3.79104972e-01 7.65286446e-01 9.75613147e-02 -7.99161911e-01
1.57214299e-01 -1.25276279e+00 -2.53636420e-01 -4.75036383e-01
4.00165208e-02 4.51714426e-01 9.94546294e-01 -7.34598637... | [7.793013572692871, 9.96299934387207] |
82d9c324-ca1f-4421-aa2c-bb1270964b5e | towards-end-to-end-car-license-plates | 1709.08828 | null | http://arxiv.org/abs/1709.08828v1 | http://arxiv.org/pdf/1709.08828v1.pdf | Towards End-to-End Car License Plates Detection and Recognition with Deep Neural Networks | In this work, we tackle the problem of car license plate detection and
recognition in natural scene images. We propose a unified deep neural network
which can localize license plates and recognize the letters simultaneously in a
single forward pass. The whole network can be trained end-to-end. In contrast
to existing a... | ['Chunhua Shen', 'Peng Wang', 'Hui Li'] | 2017-09-26 | null | null | null | null | ['license-plate-detection'] | ['computer-vision'] | [-4.24199067e-02 -8.48482966e-01 -5.67878969e-02 -4.46543008e-01
-8.54881048e-01 -8.75441790e-01 4.00599003e-01 -6.92363024e-01
-6.48140609e-01 3.96520525e-01 -3.99449825e-01 -3.47530782e-01
3.65448833e-01 -6.87540531e-01 -7.92674661e-01 -5.73339701e-01
7.33479500e-01 3.70734721e-01 5.53347945e-01 2.65127003... | [9.845022201538086, -4.932728290557861] |
1125f6cc-11ed-4abc-9f67-4462b352b1ae | correlated-initialization-for-correlated-data | 2003.04422 | null | https://arxiv.org/abs/2003.04422v2 | https://arxiv.org/pdf/2003.04422v2.pdf | Correlated Initialization for Correlated Data | Spatial data exhibits the property that nearby points are correlated. This also holds for learnt representations across layers, but not for commonly used weight initialization methods. Our theoretical analysis quantifies the learning behavior of weights of a single spatial filter. It is thus in contrast to a large body... | ['Johannes Schneider'] | 2020-03-09 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [-2.95867957e-02 -1.14018455e-01 1.20604374e-02 -1.01416059e-01
-3.33974570e-01 -7.06618965e-01 7.33332574e-01 2.68618792e-01
-9.95642841e-01 6.02839708e-01 4.84254867e-01 -3.95925373e-01
-6.34655595e-01 -5.98295927e-01 -7.52415776e-01 -8.84357512e-01
-3.88548493e-01 1.85336974e-02 6.11357391e-01 2.96270065... | [8.648945808410645, 3.1852967739105225] |
c4eda2b9-50cb-4ab3-9901-a31d5fa1b9c1 | policy-tree-network | null | null | https://openreview.net/forum?id=HJlrS1rYwH | https://openreview.net/pdf?id=HJlrS1rYwH | Policy Tree Network | Decision-time planning policies with implicit dynamics models have been shown to work in discrete action spaces with Q learning. However, decision-time planning with implicit dynamics models in continuous action space has proven to be a difficult problem. Recent work in Reinforcement Learning has allowed for implicit m... | ['James Kwok', 'Sepanta Zeighami', 'Zac Wellmer'] | 2019-09-25 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [ 9.87280235e-02 4.20579106e-01 -6.03747427e-01 3.34538445e-02
-5.37052214e-01 -3.41364354e-01 8.29566419e-01 1.46295717e-02
-7.43441403e-01 1.20613599e+00 2.17310503e-01 -7.96161473e-01
-5.37916541e-01 -6.96335137e-01 -3.58864427e-01 -6.02187753e-01
-6.91565454e-01 5.83776772e-01 3.33606124e-01 -4.89080995... | [4.139034271240234, 2.0785841941833496] |
b218b831-5d85-41fb-8f7e-99a68b07da1f | query-centric-trajectory-prediction | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhou_Query-Centric_Trajectory_Prediction_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhou_Query-Centric_Trajectory_Prediction_CVPR_2023_paper.pdf | Query-Centric Trajectory Prediction | Predicting the future trajectories of surrounding agents is essential for autonomous vehicles to operate safely. This paper presents QCNet, a modeling framework toward pushing the boundaries of trajectory prediction. First, we identify that the agent-centric modeling scheme used by existing approaches requires re-n... | ['Yu-Kai Huang', 'Yung-Hui Li', 'JianPing Wang', 'Zikang Zhou'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['motion-prediction', 'trajectory-prediction', 'self-driving-cars', 'motion-forecasting'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-2.22817600e-01 7.97498077e-02 -4.68326241e-01 -1.79988772e-01
-8.52561295e-01 -6.47546291e-01 9.36747551e-01 2.78529793e-01
-3.06895643e-01 4.59592611e-01 4.54165101e-01 -3.32696825e-01
1.38896313e-02 -1.00035942e+00 -7.99416184e-01 -6.05811894e-01
-5.02580285e-01 5.14438868e-01 5.58512807e-01 -3.40583473... | [5.878512859344482, 0.791012167930603] |
3f0f2455-d459-41ea-a120-9c6ad272ea8a | automated-essay-scoring-with-string-kernels | 1804.07954 | null | http://arxiv.org/abs/1804.07954v2 | http://arxiv.org/pdf/1804.07954v2.pdf | Automated essay scoring with string kernels and word embeddings | In this work, we present an approach based on combining string kernels and
word embeddings for automatic essay scoring. String kernels capture the
similarity among strings based on counting common character n-grams, which are
a low-level yet powerful type of feature, demonstrating state-of-the-art
results in various te... | ['Radu Tudor Ionescu', 'Mădălina Cozma', 'Andrei M. Butnaru'] | 2018-04-21 | automated-essay-scoring-with-string-kernels-1 | https://aclanthology.org/P18-2080 | https://aclanthology.org/P18-2080.pdf | acl-2018-7 | ['automated-essay-scoring', 'native-language-identification'] | ['natural-language-processing', 'natural-language-processing'] | [-4.75949273e-02 -3.43610466e-01 -3.58748436e-01 -3.16026628e-01
-8.74905825e-01 -9.98472452e-01 5.65467656e-01 9.19734120e-01
-8.42117786e-01 2.75981635e-01 2.71907806e-01 -4.13365722e-01
-2.63890982e-01 -1.00598705e+00 -1.02800205e-01 -2.51506954e-01
2.02701271e-01 5.59263945e-01 2.13947535e-01 -3.08823496... | [11.023377418518066, 9.528676986694336] |
b84fdaef-e39e-4b5d-ae53-292677ca3698 | consistency-by-agreement-in-zero-shot-neural | 1904.02338 | null | http://arxiv.org/abs/1904.02338v2 | http://arxiv.org/pdf/1904.02338v2.pdf | Consistency by Agreement in Zero-shot Neural Machine Translation | Generalization and reliability of multilingual translation often highly
depend on the amount of available parallel data for each language pair of
interest. In this paper, we focus on zero-shot generalization---a challenging
setup that tests models on translation directions they have not been optimized
for at training t... | ['Maruan Al-Shedivat', 'Ankur P. Parikh'] | 2019-04-04 | consistency-by-agreement-in-zero-shot-neural-1 | https://aclanthology.org/N19-1121 | https://aclanthology.org/N19-1121.pdf | naacl-2019-6 | ['zero-shot-machine-translation'] | ['natural-language-processing'] | [ 1.41066954e-01 6.27864897e-02 -6.23760939e-01 -4.00498927e-01
-1.97723138e+00 -7.87813902e-01 9.25054669e-01 -3.22809778e-02
-2.70254910e-01 1.18706691e+00 3.70909393e-01 -8.93267393e-01
3.20607603e-01 -3.59100908e-01 -1.09531474e+00 -5.13750553e-01
2.28148460e-01 1.19791150e+00 -1.20729715e-01 -6.22321069... | [11.565977096557617, 10.249960899353027] |
56ccb151-73a9-4a6b-82b0-279aeea5c0b4 | neuromorphic-event-based-facial-expression | 2304.06351 | null | https://arxiv.org/abs/2304.06351v1 | https://arxiv.org/pdf/2304.06351v1.pdf | Neuromorphic Event-based Facial Expression Recognition | Recently, event cameras have shown large applicability in several computer vision fields especially concerning tasks that require high temporal resolution. In this work, we investigate the usage of such kind of data for emotion recognition by presenting NEFER, a dataset for Neuromorphic Event-based Facial Expression Re... | ['Alberto del Bimbo', 'Federico Becattini', 'Sara Picchioni', 'Andrea Leonardo', 'Lisa Cresti', 'Chiara Albisani', 'Luca Cultrera', 'Lorenzo Berlincioni'] | 2023-04-13 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 4.03119385e-01 -5.66375963e-02 3.76353860e-01 -7.02812195e-01
-2.91139454e-01 -4.54972327e-01 6.45111620e-01 4.67902236e-02
-7.89366961e-01 6.77405298e-01 -1.90610141e-01 6.00292206e-01
3.17844818e-03 -4.09840852e-01 -7.84798026e-01 -9.94034946e-01
-2.19806716e-01 1.42928526e-01 -6.71187565e-02 -8.81663635... | [13.542248725891113, 1.923242449760437] |
e416be0e-d677-436e-9a89-6af9f7543844 | a-recurrent-and-compositional-model-for | null | null | https://aclanthology.org/W16-4303 | https://aclanthology.org/W16-4303.pdf | A Recurrent and Compositional Model for Personality Trait Recognition from Short Texts | Many methods have been used to recognise author personality traits from text, typically combining linguistic feature engineering with shallow learning models, e.g. linear regression or Support Vector Machines. This work uses deep-learning-based models and atomic features of text, the characters, to build hierarchical, ... | ['Fei Liu', 'Julien Perez', 'Scott Nowson'] | 2016-12-01 | null | null | null | ws-2016-12 | ['personality-trait-recognition'] | ['computer-vision'] | [-1.72608614e-01 2.69223213e-01 -1.05527312e-01 -7.60765254e-01
-2.91698396e-01 -2.11603194e-01 1.02271581e+00 6.49120748e-01
-3.89910787e-01 4.79453713e-01 6.94632828e-01 -1.83690608e-01
-3.21284354e-01 -7.08933175e-01 2.13711619e-01 -1.73347369e-01
-2.83041835e-01 5.94895720e-01 -2.87307739e-01 -4.07646835... | [9.607563972473145, 10.32421588897705] |
983785d6-11bd-4195-ae7e-6294c4b55eb7 | stasis-net-a-stacked-and-siamese-stereo | null | null | https://doi.org/10.1016/j.media.2022.102380 | https://www.sciencedirect.com/science/article/pii/S1361841522000329 | StaSiS-Net: a stacked and siamese stereo network for depth reconstruction in modern 3D laparoscopy. | Accurate and real-time methodologies for a non-invasive three-dimensional representa-tion and reconstruction of internal patient structures is one of the main research fieldsin computer-assisted surgery and endoscopy. Mono and stereo endoscopic images ofsoft tissues are converted into a three-dimensional representation... | ['Roberto Tagliaferri', 'Alexandre Hostettler', 'Antonello Forgione', 'Toby Collins', 'Francesco Bardozzo'] | 2022-04-06 | null | null | null | medical-image-analysis-2022-4 | ['stereo-depth-estimation'] | ['computer-vision'] | [ 6.83233216e-02 1.10001549e-01 -7.13888705e-02 -3.25905681e-01
-9.01019990e-01 -3.89472604e-01 3.25442463e-01 4.18397516e-01
-8.14500332e-01 7.27099240e-01 -4.32243012e-03 -6.18926845e-02
-7.88270012e-02 -7.60420501e-01 -7.97678232e-01 -7.21970320e-01
-7.89154768e-02 6.45667970e-01 6.36145025e-02 -1.03474602... | [13.906604766845703, -3.1006250381469727] |
398c6c9d-ccfa-48ea-b44e-a75ccd759acc | a-transformer-based-approach-towards | null | null | https://aclanthology.org/2021.disrpt-1.2 | https://aclanthology.org/2021.disrpt-1.2.pdf | A Transformer Based Approach towards Identification of Discourse Unit Segments and Connectives | Discourse parsing, which involves understanding the structure, information flow, and modeling the coherence of a given text, is an important task in natural language processing. It forms the basis of several natural language processing tasks such as question-answering, text summarization, and sentiment analysis. Discou... | ['Dipti Sharma', 'Sahil Bakshi'] | null | null | null | null | emnlp-disrpt-2021-11 | ['discourse-segmentation', 'discourse-parsing'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.82289642e-01 7.11447895e-01 -2.42929295e-01 -4.17905807e-01
-8.99844170e-01 -8.76318455e-01 9.36063290e-01 8.17016900e-01
-3.75172287e-01 7.20697284e-01 9.14412379e-01 -5.55112064e-01
2.13644877e-01 -4.72244114e-01 -3.17622840e-01 -3.03016216e-01
7.48881400e-02 6.41676486e-01 5.68056166e-01 -4.34455246... | [10.838523864746094, 9.426271438598633] |
ddaa9178-64fc-40fd-911c-6f8a4636a4b2 | non-monotonic-parsing-of-fluent-umm-i-mean | null | null | https://aclanthology.org/E14-4010 | https://aclanthology.org/E14-4010.pdf | Non-Monotonic Parsing of Fluent Umm I mean Disfluent Sentences | null | ['Joel Tetreault', 'Mohammad Sadegh Rasooli'] | 2014-04-01 | null | null | null | eacl-2014-4 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.221817493438721, 3.473289728164673] |
b1d4d438-5361-493a-a29b-c1e1953918c7 | micse-mutual-information-contrastive-learning | 2211.04928 | null | https://arxiv.org/abs/2211.04928v2 | https://arxiv.org/pdf/2211.04928v2.pdf | miCSE: Mutual Information Contrastive Learning for Low-shot Sentence Embeddings | This paper presents miCSE, a mutual information-based contrastive learning framework that significantly advances the state-of-the-art in few-shot sentence embedding. The proposed approach imposes alignment between the attention pattern of different views during contrastive learning. Learning sentence embeddings with mi... | ['Moin Nabi', 'Tassilo Klein'] | 2022-11-09 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings'] | ['methodology', 'natural-language-processing'] | [ 2.23877281e-01 7.19151041e-03 -4.51450050e-01 -4.52745527e-01
-8.45631778e-01 -3.77024105e-03 9.43172276e-01 5.35286546e-01
-7.03129828e-01 4.30880219e-01 7.01480329e-01 9.67615098e-02
-9.99765322e-02 -7.93696284e-01 -4.13214117e-01 -6.43039823e-01
-5.49611449e-02 1.91812366e-01 2.69532114e-01 -3.96005481... | [10.046412467956543, 3.172039747238159] |
8a1a621b-bc40-4855-bc47-25fb076659f5 | answer-uncertainty-and-unanswerability-in-1 | null | null | https://aclanthology.org/2022.findings-acl.82 | https://aclanthology.org/2022.findings-acl.82.pdf | Answer Uncertainty and Unanswerability in Multiple-Choice Machine Reading Comprehension | Machine reading comprehension (MRC) has drawn a lot of attention as an approach for assessing the ability of systems to understand natural language. Usually systems focus on selecting the correct answer to a question given a contextual paragraph. However, for many applications of multiple-choice MRC systems there are t... | ['Mark Gales', 'Vatsal Raina'] | null | null | null | null | findings-acl-2022-5 | ['machine-reading-comprehension'] | ['natural-language-processing'] | [ 4.22033995e-01 5.62754750e-01 1.23819225e-02 -7.50325203e-01
-9.74315047e-01 -6.56802535e-01 5.25519907e-01 6.59786522e-01
-4.97487485e-01 9.20680225e-01 1.17706172e-01 -1.16710138e+00
-4.93421167e-01 -8.14133644e-01 -5.33199787e-01 -2.30431482e-01
5.18632054e-01 6.95869684e-01 6.55109584e-01 -1.91280022... | [11.549589157104492, 8.237555503845215] |
5e991be2-d97b-4f95-8235-107ebd4cfcd8 | boosting-entity-aware-image-captioning-with | 2107.11970 | null | https://arxiv.org/abs/2107.11970v1 | https://arxiv.org/pdf/2107.11970v1.pdf | Boosting Entity-aware Image Captioning with Multi-modal Knowledge Graph | Entity-aware image captioning aims to describe named entities and events related to the image by utilizing the background knowledge in the associated article. This task remains challenging as it is difficult to learn the association between named entities and visual cues due to the long-tail distribution of named entit... | ['Jiebo Luo', 'Xinxiao wu', 'HeDa Wang', 'Yao Hu', 'Wentian Zhao'] | 2021-07-26 | null | null | null | null | ['multi-modal-knowledge-graph'] | ['knowledge-base'] | [-1.56118020e-01 2.55213708e-01 -1.24786332e-01 -2.60087758e-01
-6.68487906e-01 -7.02797949e-01 5.65861583e-01 3.04950655e-01
-4.95253235e-01 5.53807497e-01 4.54673022e-01 9.48983133e-02
1.01901673e-01 -9.38046396e-01 -1.07526159e+00 -3.79896522e-01
2.25513428e-01 2.45781228e-01 4.41262245e-01 4.21050042... | [10.673264503479004, 1.3314628601074219] |
e246092e-1c4e-4e1f-bfeb-02149308bddb | latent-dirichlet-allocation-in-generative | 1812.06571 | null | https://arxiv.org/abs/1812.06571v5 | https://arxiv.org/pdf/1812.06571v5.pdf | Latent Dirichlet Allocation in Generative Adversarial Networks | We study the problem of multimodal generative modelling of images based on generative adversarial networks (GANs). Despite the success of existing methods, they often ignore the underlying structure of vision data or its multimodal generation characteristics. To address this problem, we introduce the Dirichlet prior fo... | ['Jian Liu', 'Zenglin Xu', 'Yazhou Ren', 'Shen Cheng', 'Lili Pan'] | 2018-12-17 | null | null | null | null | ['multimodal-generation'] | ['natural-language-processing'] | [ 2.75346994e-01 2.73418695e-01 1.88097760e-01 -2.27143869e-01
-7.43686199e-01 -3.83256346e-01 9.78705883e-01 -1.04395461e+00
3.01735997e-01 7.78317094e-01 3.41877103e-01 -1.60354823e-01
3.91184747e-01 -1.12118280e+00 -5.88785350e-01 -1.24349833e+00
6.93034053e-01 8.65782857e-01 -4.89069283e-01 1.59305885... | [11.565288543701172, -0.3321613371372223] |
27ff165d-5d36-481d-b39c-56316e3ab11f | a-robust-scientific-machine-learning-for | 2209.06642 | null | https://arxiv.org/abs/2209.06642v1 | https://arxiv.org/pdf/2209.06642v1.pdf | A Robust Scientific Machine Learning for Optimization: A Novel Robustness Theorem | Scientific machine learning (SciML) is a field of increasing interest in several different application fields. In an optimization context, SciML-based tools have enabled the development of more efficient optimization methods. However, implementing SciML tools for optimization must be rigorously evaluated and performed ... | ['Idelfonso B. R. Nogueira', 'Ana M. Ribeiro', 'Alirio E. Rodrigues', 'Vinicius V. Santana', 'Erber A. Costa', 'Carine M. Rebello', 'Luana P. Queiroz'] | 2022-09-13 | null | null | null | null | ['multiobjective-optimization'] | ['methodology'] | [ 1.54615104e-01 1.03135087e-01 -1.77761480e-01 -1.99602529e-01
-4.07759815e-01 -4.11570787e-01 5.91763914e-01 6.38251126e-01
-4.63918597e-01 1.10185707e+00 -3.95539701e-01 -3.87610346e-01
-9.08631265e-01 -7.01606989e-01 -6.27681136e-01 -5.23802102e-01
5.42122424e-02 3.55213612e-01 -2.92902589e-01 -3.39687876... | [5.942140102386475, 3.5684967041015625] |
c3268da1-ea70-4e52-bdac-9d035178f82e | improving-embedded-knowledge-graph-multi-hop | 2110.12679 | null | https://arxiv.org/abs/2110.12679v3 | https://arxiv.org/pdf/2110.12679v3.pdf | Improving Embedded Knowledge Graph Multi-hop Question Answering by introducing Relational Chain Reasoning | Knowledge Graph Question Answering (KGQA) aims to answer user-questions from a knowledge graph (KG) by identifying the reasoning relations between topic entity and answer. As a complex branch task of KGQA, multi-hop KGQA requires reasoning over the multi-hop relational chain preserved in KG to arrive at the right answe... | ['Guizhong Liu', 'Hang Yu', 'Biao Zhao', 'Ruiping Yin', 'Xi Tao', 'Weiqiang Jin'] | 2021-10-25 | null | null | null | null | ['graph-question-answering', 'multi-hop-question-answering', 'knowledge-base-question-answering', 'implicit-relations'] | ['graphs', 'knowledge-base', 'natural-language-processing', 'natural-language-processing'] | [-1.92345828e-01 8.87212157e-01 -3.82478535e-01 -1.80822849e-01
-9.23300445e-01 -6.03091896e-01 3.40616912e-01 3.78402859e-01
7.22062662e-02 6.39770806e-01 3.93332362e-01 -6.32377028e-01
-4.76216078e-01 -1.28483748e+00 -9.44169044e-01 -5.22957072e-02
1.35701656e-01 8.08647037e-01 8.05611074e-01 -4.96160865... | [10.446609497070312, 7.880457878112793] |
db1b75c0-191f-4c54-805c-ff58ba12c44d | encoding-feature-supervised-unet-redesigning | 2211.08146 | null | https://arxiv.org/abs/2211.08146v1 | https://arxiv.org/pdf/2211.08146v1.pdf | Encoding feature supervised UNet++: Redesigning Supervision for liver and tumor segmentation | Liver tumor segmentation in CT images is a critical step in the diagnosis, surgical planning and postoperative evaluation of liver disease. An automatic liver and tumor segmentation method can greatly relieve physicians of the heavy workload of examining CT images and better improve the accuracy of diagnosis. In the la... | ['Yixuan Yu', 'Minnan Pei', 'Shiyuan Fang', 'Ruoxin Xiao', 'Jiahao Cui'] | 2022-11-15 | null | null | null | null | ['tumor-segmentation', 'liver-segmentation', 'automatic-liver-and-tumor-segmentation'] | ['computer-vision', 'medical', 'medical'] | [ 6.85405880e-02 2.34516263e-01 -4.51024950e-01 -3.55109990e-01
-5.18311739e-01 -3.23080570e-01 1.83797538e-01 3.32621813e-01
-4.11110014e-01 5.66850662e-01 2.72254080e-01 -4.04129028e-01
-5.84659688e-02 -8.87687504e-01 -4.68238384e-01 -7.90638447e-01
-4.03412670e-01 5.88061213e-01 3.00035238e-01 2.36052349... | [14.523941993713379, -2.6933629512786865] |
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