paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
29e47874-f6ec-43db-b2f5-c62ab0a52022 | sdc-stacked-dilated-convolution-a-unified | 1904.03076 | null | http://arxiv.org/abs/1904.03076v1 | http://arxiv.org/pdf/1904.03076v1.pdf | SDC - Stacked Dilated Convolution: A Unified Descriptor Network for Dense Matching Tasks | Dense pixel matching is important for many computer vision tasks such as
disparity and flow estimation. We present a robust, unified descriptor network
that considers a large context region with high spatial variance. Our network
has a very large receptive field and avoids striding layers to maintain spatial
resolution... | ['Oliver Wasenmüller', 'René Schuster', 'Didier Stricker', 'Christian Unger'] | 2019-04-05 | null | null | null | null | ['stereo-matching'] | ['computer-vision'] | [ 7.13646784e-02 -9.52755153e-01 -2.26288944e-01 -3.55856925e-01
-2.07829848e-01 -2.20770687e-01 7.18629837e-01 -2.71004528e-01
-5.12851357e-01 5.25602520e-01 3.92194629e-01 3.23423184e-02
-2.30729277e-03 -9.32318628e-01 -5.47396302e-01 -6.80544674e-01
-1.30398944e-01 -1.73772112e-01 6.10981584e-01 -2.71156609... | [8.904060363769531, -2.0800135135650635] |
6a512bae-c0cc-449b-9aec-9fe0029f7c2d | generating-token-level-explanations-for | 1904.10717 | null | http://arxiv.org/abs/1904.10717v1 | http://arxiv.org/pdf/1904.10717v1.pdf | Generating Token-Level Explanations for Natural Language Inference | The task of Natural Language Inference (NLI) is widely modeled as supervised
sentence pair classification. While there has been a lot of work recently on
generating explanations of the predictions of classifiers on a single piece of
text, there have been no attempts to generate explanations of classifiers
operating on ... | ['Christos Christodoulopoulos', 'James Thorne', 'Arpit Mittal', 'Andreas Vlachos'] | 2019-04-24 | generating-token-level-explanations-for-1 | https://aclanthology.org/N19-1101 | https://aclanthology.org/N19-1101.pdf | naacl-2019-6 | ['sentence-pair-classification'] | ['natural-language-processing'] | [ 7.05706656e-01 8.84779751e-01 -2.51832545e-01 -9.44466054e-01
-1.21177161e+00 -2.97548354e-01 9.22640026e-01 4.17871535e-01
-1.75424710e-01 1.26285827e+00 2.82061070e-01 -8.72063100e-01
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1.24159209e-01 6.62290931e-01 5.14389314e-02 -1.50094226... | [10.34440803527832, 8.541993141174316] |
3478acb1-7ae4-4a81-a255-b6e4144ad9e6 | fast-full-resolution-target-adaptive-cnn | null | null | https://www.mdpi.com/2072-4292/15/2/319 | https://www.mdpi.com/2072-4292/15/2/319/pdf?version=1673404470 | Fast Full-Resolution Target-Adaptive CNN-Based Pansharpening Framework | In the last few years, there has been a renewed interest in data fusion techniques, and, in particular, in pansharpening due to a paradigm shift from model-based to data-driven approaches, supported by the recent advances in deep learning. Although a plethora of convolutional neural networks (CNN) for pansharpening hav... | ['Giuseppe Scarpa', 'Matteo Ciotola'] | 2023-01-05 | null | null | null | mdpi-remote-sensing-2023-1 | ['image-super-resolution', 'satellite-image-super-resolution', 'pansharpening'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.06884140e-01 -1.31939501e-01 -1.72363773e-01 -3.16427112e-01
-6.00989342e-01 -2.99040884e-01 6.97193086e-01 2.48816863e-01
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-6.84099019e-01 -1.05382836e+00 -5.45445204e-01 -7.93259561e-01
1.14449263e-02 1.03383228e-01 4.96091008e-01 -4.60824579... | [9.713468551635742, -1.6862986087799072] |
8dcba349-81a0-4448-9a85-22342b43183d | pain-or-anxiety-the-health-consequences-of | 2301.10675 | null | https://arxiv.org/abs/2301.10675v1 | https://arxiv.org/pdf/2301.10675v1.pdf | Pain or Anxiety? The Health Consequences of Rising Robot Adoption in China | The rising adoption of industrial robots is radically changing the role of workers in the production process. Robots can be used for some of the more physically demanding and dangerous production work, thus reducing the possibility of worker injury. On the other hand, robots may replace workers, potentially increasing ... | ['Robert Seamans', 'Sen Luo', 'Qiren Liu'] | 2023-01-25 | null | null | null | null | ['industrial-robots'] | ['robots'] | [-1.01694711e-01 5.31125247e-01 -3.64258081e-01 2.44225755e-01
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-3.57993603e-01 3.51954222e-01 2.55057931e-01 -1.41283348e-01
-4.75048304e-01 -7.51769125e-01 -4.56085116e-01 -5.18022954e-01
3.27556193e-01 -1.84534252e-01 -3.90711933e-01 -3.10953140... | [4.9331374168396, 0.9661966562271118] |
93a0657b-f1e0-4afa-9dfc-8d0b9ac794bf | prioritycut-occlusion-aware-regularization | null | null | https://openreview.net/forum?id=wVYtfckXU0T | https://openreview.net/pdf?id=wVYtfckXU0T | PriorityCut: Occlusion-aware Regularization for Image Animation | Image animation generates a video of a source image following the motion of a driving video. Self-supervised image animation approaches do not require explicit pose references as inputs, thus offering large flexibility in learning. State-of-the-art self-supervised image animation approaches mostly warp the source image... | ['Gyeongsu Chae', 'Wai Ting Cheung'] | 2021-01-01 | null | null | null | null | ['image-animation'] | ['computer-vision'] | [ 3.49160522e-01 4.88183536e-02 -3.90457153e-01 -1.57986253e-01
-6.08600855e-01 -4.77758646e-01 6.23969316e-01 -4.07973945e-01
-3.48824471e-01 4.97877002e-01 2.11541116e-01 1.23272955e-01
2.80604869e-01 -7.20208764e-01 -9.99597192e-01 -8.90510499e-01
8.25902596e-02 2.42109567e-01 1.98298618e-01 -2.65379101... | [10.948349952697754, -0.8534174561500549] |
ab3ca633-bc89-47f2-a71b-eb4d7dc52b27 | to-balance-or-not-to-balance-an | 1912.04486 | null | https://arxiv.org/abs/1912.04486v2 | https://arxiv.org/pdf/1912.04486v2.pdf | To Balance or Not to Balance: A Simple-yet-Effective Approach for Learning with Long-Tailed Distributions | Real-world visual data often exhibits a long-tailed distribution, where some ''head'' classes have a large number of samples, yet only a few samples are available for ''tail'' classes. Such imbalanced distribution causes a great challenge for learning a deep neural network, which can be boiled down into a dilemma: on t... | ['Jun-Jie Zhang', 'Chunhua Shen', 'Peng Wang', 'Lingqiao Liu'] | 2019-12-10 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [ 2.40124226e-01 4.73932028e-02 -1.87204778e-01 -4.56870019e-01
-5.29167116e-01 -1.64233416e-01 4.46509868e-01 1.73620448e-01
-4.69835728e-01 6.17719889e-01 -2.81957060e-01 -1.61588326e-01
2.78746150e-02 -9.70484495e-01 -5.77054977e-01 -1.17386854e+00
3.35737795e-01 2.43501514e-01 3.45507443e-01 1.51793852... | [9.380597114562988, 3.4129552841186523] |
6b7864fe-6d62-40a4-b728-c740c3cfaa5d | fully-dnn-based-multi-label-regression-for | 1606.07695 | null | http://arxiv.org/abs/1606.07695v2 | http://arxiv.org/pdf/1606.07695v2.pdf | Fully DNN-based Multi-label regression for audio tagging | Acoustic event detection for content analysis in most cases relies on lots of
labeled data. However, manually annotating data is a time-consuming task, which
thus makes few annotated resources available so far. Unlike audio event
detection, automatic audio tagging, a multi-label acoustic event classification
task, only... | ['Philip J. B. Jackson', 'Wenwu Wang', 'Yong Xu', 'Qiang Huang', 'Mark D. Plumbley'] | 2016-06-24 | null | null | null | null | ['audio-tagging'] | ['audio'] | [ 2.46903747e-01 -2.33682796e-01 2.26391330e-01 -3.48878860e-01
-1.06823850e+00 -2.33889073e-01 1.61577344e-01 2.94497967e-01
-7.12235153e-01 5.99497139e-01 1.71220273e-01 1.31905854e-01
1.66495532e-01 -6.00217164e-01 -5.13556778e-01 -9.59783792e-01
9.80028324e-03 2.19775201e-03 5.53459346e-01 1.35868862... | [15.212989807128906, 5.165895462036133] |
621628dd-abe1-4926-878f-0950580207e7 | deep-graph-kernel-point-processes | 2306.11313 | null | https://arxiv.org/abs/2306.11313v1 | https://arxiv.org/pdf/2306.11313v1.pdf | Deep graph kernel point processes | Point process models are widely used to analyze asynchronous events occurring within a graph that reflect how different types of events influence one another. Predicting future events' times and types is a crucial task, and the size and topology of the graph add to the challenge of the problem. Recent neural point proc... | ['Yao Xie', 'Xiuyuan Cheng', 'Matthew Repasky', 'Zheng Dong'] | 2023-06-20 | null | null | null | null | ['point-processes'] | ['methodology'] | [ 2.04074327e-02 -2.97948569e-02 -9.24707428e-02 -3.46489608e-01
1.47307500e-01 -5.57125211e-01 1.07608902e+00 8.81741941e-01
-1.99497174e-02 4.00387734e-01 4.85449433e-01 -3.06032121e-01
-4.75582093e-01 -1.43679750e+00 -7.62591422e-01 -5.40313363e-01
-8.93314302e-01 6.01255894e-01 4.24919456e-01 5.05239628... | [7.245387077331543, 5.788229942321777] |
78d283a5-8b44-44da-a210-5c259ac2b3b9 | radiopathomics-multimodal-learning-in-non | 2204.12423 | null | https://arxiv.org/abs/2204.12423v1 | https://arxiv.org/pdf/2204.12423v1.pdf | RadioPathomics: Multimodal Learning in Non-Small Cell Lung Cancer for Adaptive Radiotherapy | The current cancer treatment practice collects multimodal data, such as radiology images, histopathology slides, genomics and clinical data. The importance of these data sources taken individually has fostered the recent raise of radiomics and pathomics, i.e. the extraction of quantitative features from radiology and h... | ['Paolo Soda', 'Sara Ramella', 'Giuseppe Perrone', 'Edy Ippolito', 'Lorenzo Nibid', 'Rosa Sicilia', 'Ermanno Cordelli', 'Matteo Tortora'] | 2022-04-26 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [ 3.20961326e-01 3.30361282e-03 -3.61585617e-01 -3.48153889e-01
-1.15831017e+00 -4.42993790e-01 6.67332053e-01 8.50850165e-01
-6.73959136e-01 9.44493711e-01 2.14054629e-01 -4.26294774e-01
-6.88379526e-01 -7.52934635e-01 -2.06718981e-01 -1.07501817e+00
7.40151554e-02 6.71665728e-01 -6.79986030e-02 -6.13598861... | [15.081212043762207, -2.843771457672119] |
08f33bc3-6089-4cd1-b090-1628dc1eed76 | weakly-supervised-optical-flow-estimation-for | 2210.05298 | null | https://arxiv.org/abs/2210.05298v2 | https://arxiv.org/pdf/2210.05298v2.pdf | Weakly-Supervised Optical Flow Estimation for Time-of-Flight | Indirect Time-of-Flight (iToF) cameras are a widespread type of 3D sensor, which perform multiple captures to obtain depth values of the captured scene. While recent approaches to correct iToF depths achieve high performance when removing multi-path-interference and sensor noise, little research has been done to tackle... | ['Timo Ropinski', 'Pedro Hermosilla', 'Michael Schelling'] | 2022-10-11 | null | null | null | null | ['motion-compensation'] | ['computer-vision'] | [ 5.58326066e-01 -1.11799724e-01 1.36456311e-01 -2.50391066e-01
-2.45987833e-01 -4.35832620e-01 3.34290981e-01 -6.13140225e-01
-6.40461206e-01 6.61783338e-01 2.08531395e-01 5.70811592e-02
-8.13761652e-02 -5.11776447e-01 -5.63278794e-01 -4.88220721e-01
1.50061354e-01 2.27719601e-02 3.60131204e-01 2.89602578... | [8.794475555419922, -2.1651573181152344] |
cd3f15e0-1ce0-4007-897c-d1a490b1a345 | lane-detection-with-position-embedding | 2203.12301 | null | https://arxiv.org/abs/2203.12301v1 | https://arxiv.org/pdf/2203.12301v1.pdf | Lane detection with Position Embedding | Recently, lane detection has made great progress in autonomous driving. RESA (REcurrent Feature-Shift Aggregator) is based on image segmentation. It presents a novel module to enrich lane feature after preliminary feature extraction with an ordinary CNN. For Tusimple dataset, there is not too complicated scene and lane... | ['Jianwei Shuai', 'Kaer Huang', 'Feng Chen', 'Dezhen Qi', 'Jiacheng Han', 'Jun Xie'] | 2022-03-23 | null | null | null | null | ['lane-detection'] | ['computer-vision'] | [-3.27053696e-01 -1.00344621e-01 -2.27503717e-01 -4.76440072e-01
-3.18867326e-01 -1.00480206e-01 6.47588491e-01 -4.51492310e-01
-7.65047371e-01 6.23907566e-01 2.47087970e-01 -5.10775924e-01
1.07421733e-01 -8.93996894e-01 -4.72732991e-01 -7.01289713e-01
7.51425847e-02 -4.94568765e-01 6.03779256e-01 -6.48614168... | [8.06304931640625, -1.3873035907745361] |
2f4845c0-8c4c-4d35-beeb-0c80db23144b | exploiting-wordnet-synset-and-hypernym | null | null | https://aclanthology.org/2020.aacl-main.14 | https://aclanthology.org/2020.aacl-main.14.pdf | Exploiting WordNet Synset and Hypernym Representations for Answer Selection | Answer selection (AS) is an important subtask of document-based question answering (DQA). In this task, the candidate answers come from the same document, and each answer sentence is semantically related to the given question, which makes it more challenging to select the true answer. WordNet provides powerful knowledg... | ['Yunfang Wu', 'Weikang Li'] | 2020-12-01 | null | null | null | asian-chapter-of-the-association-for | ['answer-selection'] | ['natural-language-processing'] | [ 3.32306847e-02 1.42048344e-01 -8.98622125e-02 -4.36270595e-01
-7.69213676e-01 -4.55852747e-01 3.31245482e-01 5.77467203e-01
-6.53500140e-01 5.50498128e-01 7.58621156e-01 -4.06214476e-01
-4.57009017e-01 -1.18759072e+00 -2.52792746e-01 -1.17004141e-01
4.04973119e-01 8.67494464e-01 8.03461492e-01 -8.64626586... | [11.048158645629883, 8.047056198120117] |
6fcc24da-be7c-4ab5-960b-f6b86f542a10 | on-the-utility-of-self-supervised-models-for | 2210.07185 | null | https://arxiv.org/abs/2210.07185v2 | https://arxiv.org/pdf/2210.07185v2.pdf | On the Utility of Self-supervised Models for Prosody-related Tasks | Self-Supervised Learning (SSL) from speech data has produced models that have achieved remarkable performance in many tasks, and that are known to implicitly represent many aspects of information latently present in speech signals. However, relatively little is known about the suitability of such models for prosody-rel... | ['Nigel G. Ward', 'Hung-Yi Lee', 'Chen-An Li', 'Tzu-Han Lin', 'Yuan Tseng', 'Wei-Ping Huang', 'Chi-Luen Feng', 'Guan-Ting Lin'] | 2022-10-13 | null | null | null | null | ['prosody-prediction'] | ['natural-language-processing'] | [ 1.09389566e-01 3.76436234e-01 -7.49748647e-01 -6.22922421e-01
-1.00794327e+00 -5.17765760e-01 5.33523619e-01 -4.99452837e-02
-2.22267166e-01 6.95429504e-01 1.17652416e+00 1.65285934e-02
2.24130422e-01 -9.22869220e-02 -4.91267115e-01 -3.92585963e-01
-3.65042947e-02 2.24490345e-01 2.60400057e-01 -3.35377574... | [14.4093656539917, 6.923627853393555] |
0ba31d2c-06ff-4cc8-b3a2-fa794a37107b | towards-federated-multivariate-statistical | 2211.01645 | null | https://arxiv.org/abs/2211.01645v2 | https://arxiv.org/pdf/2211.01645v2.pdf | Towards federated multivariate statistical process control (FedMSPC) | The ongoing transition from a linear (produce-use-dispose) to a circular economy poses significant challenges to current state-of-the-art information and communication technologies. In particular, the derivation of integrated, high-level views on material, process, and product streams from (real-time) data produced alo... | ['Ramin Nikzad-Langerodi', 'David Gabauer', 'Du Nguyen Duy'] | 2022-11-03 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [ 3.28462809e-01 -1.21262796e-01 -4.37195078e-02 -1.44651935e-01
-5.71305633e-01 -1.10404205e+00 8.06489527e-01 5.56246996e-01
2.46112525e-01 1.64254099e-01 8.19752067e-02 -3.38951588e-01
-4.98478264e-01 -1.03476727e+00 -4.77963269e-01 -7.62849331e-01
3.73683125e-02 4.01700169e-01 -4.83425915e-01 2.81726182... | [5.986578941345215, 6.604734420776367] |
9312b165-3ff2-4cf5-9972-803161a2d0c1 | hyperparameters-in-reinforcement-learning-and | 2306.01324 | null | https://arxiv.org/abs/2306.01324v1 | https://arxiv.org/pdf/2306.01324v1.pdf | Hyperparameters in Reinforcement Learning and How To Tune Them | In order to improve reproducibility, deep reinforcement learning (RL) has been adopting better scientific practices such as standardized evaluation metrics and reporting. However, the process of hyperparameter optimization still varies widely across papers, which makes it challenging to compare RL algorithms fairly. In... | ['Roberta Raileanu', 'Marius Lindauer', 'Theresa Eimer'] | 2023-06-02 | null | null | null | null | ['automl', 'hyperparameter-optimization'] | ['methodology', 'methodology'] | [-2.98390657e-01 -3.17167133e-01 -4.04726982e-01 -1.91327766e-01
-7.70616174e-01 -8.70170474e-01 2.66357213e-01 4.89781424e-02
-6.50485218e-01 9.72092748e-01 5.40797450e-02 -4.23447162e-01
-4.21437800e-01 -6.43753231e-01 -7.11592317e-01 -7.95632482e-01
-1.03440031e-01 3.99475455e-01 -2.37748623e-02 -1.42748132... | [4.327173709869385, 1.9294966459274292] |
7c191cc0-559c-4963-9a44-ec6d536ee13c | resources-and-evaluations-for-danish-entity | null | null | https://aclanthology.org/2021.crac-1.7 | https://aclanthology.org/2021.crac-1.7.pdf | Resources and Evaluations for Danish Entity Resolution | Automatic coreference resolution is understudied in Danish even though most of the Danish Dependency Treebank (Buch-Kromann, 2003) is annotated with coreference relations. This paper describes a conversion of its partial, yet well-documented, coreference relations into coreference clusters and the training and evaluati... | ['Anders Søgaard', 'Barbara Plank', 'Ophélie Lacroix', 'Martin Wu', 'Hieu Lam', 'Maria Barrett'] | null | null | null | null | crac-acl-2021-11 | ['entity-disambiguation', 'entity-resolution'] | ['natural-language-processing', 'natural-language-processing'] | [-2.18368337e-01 1.00673449e+00 -4.63405132e-01 -4.13958192e-01
-8.03011775e-01 -7.62613714e-01 6.22042000e-01 4.30825114e-01
-8.52721691e-01 1.09634948e+00 9.35279131e-01 -9.50698853e-02
-3.85158509e-01 -6.02146268e-01 -3.53694260e-01 -2.22038642e-01
-3.21052782e-02 1.64935017e+00 3.69458795e-01 -4.84577507... | [9.318680763244629, 9.554218292236328] |
b9feea1a-fe70-4e37-a6a2-5a100ea84c72 | dynamic-loss-for-robust-learning | 2211.12506 | null | https://arxiv.org/abs/2211.12506v1 | https://arxiv.org/pdf/2211.12506v1.pdf | Dynamic Loss For Robust Learning | Label noise and class imbalance commonly coexist in real-world data. Previous works for robust learning, however, usually address either one type of the data biases and underperform when facing them both. To mitigate this gap, this work presents a novel meta-learning based dynamic loss that automatically adjusts the ob... | ['Tingfa Xu', 'Ying Wang', 'Jizhou Zhang', 'Jianan Li', 'Shenwang Jiang'] | 2022-11-22 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 2.51022369e-01 -1.76432073e-01 -2.17768908e-01 -8.38651836e-01
-1.19688845e+00 -4.11386907e-01 3.92012328e-01 1.50026813e-01
-6.06222808e-01 9.10601377e-01 -1.67939618e-01 -4.25252430e-02
-4.06149775e-02 -5.59962034e-01 -8.22756350e-01 -1.01631892e+00
1.13399543e-01 4.04157251e-01 -1.27416700e-02 5.29111214... | [9.300859451293945, 3.8833847045898438] |
cbe6cc9b-0710-4ba0-bfd7-f928f5339ab2 | learning-data-driven-reflectance-priors-for | 1510.02413 | null | http://arxiv.org/abs/1510.02413v1 | http://arxiv.org/pdf/1510.02413v1.pdf | Learning Data-driven Reflectance Priors for Intrinsic Image Decomposition | We propose a data-driven approach for intrinsic image decomposition, which is
the process of inferring the confounding factors of reflectance and shading in
an image. We pose this as a two-stage learning problem. First, we train a model
to predict relative reflectance ordering between image patches (`brighter',
`darker... | ['Philipp Krähenbühl', 'Tinghui Zhou', 'Alexei A. Efros'] | 2015-10-08 | learning-data-driven-reflectance-priors-for-1 | http://openaccess.thecvf.com/content_iccv_2015/html/Zhou_Learning_Data-Driven_Reflectance_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Zhou_Learning_Data-Driven_Reflectance_ICCV_2015_paper.pdf | iccv-2015-12 | ['intrinsic-image-decomposition', 'image-relighting'] | ['computer-vision', 'computer-vision'] | [ 8.90121877e-01 1.12805009e-01 2.35129938e-01 -5.11988819e-01
-7.36125112e-01 -5.12742817e-01 4.06627983e-01 -2.29860649e-01
-1.29507899e-01 2.34980255e-01 3.90627831e-01 -1.13583170e-01
2.90116519e-01 -6.56414688e-01 -8.08653414e-01 -6.70175612e-01
3.92044216e-01 5.41263297e-02 -5.45901954e-02 -1.57320812... | [9.895909309387207, -2.9223687648773193] |
fd4a0c31-6f85-4d3e-b7cf-0ebb9a2a85de | where-to-focus-deep-attention-based-spatially | 1709.05769 | null | http://arxiv.org/abs/1709.05769v1 | http://arxiv.org/pdf/1709.05769v1.pdf | Where to Focus: Deep Attention-based Spatially Recurrent Bilinear Networks for Fine-Grained Visual Recognition | Fine-grained visual recognition typically depends on modeling subtle
difference from object parts. However, these parts often exhibit dramatic
visual variations such as occlusions, viewpoints, and spatial transformations,
making it hard to detect. In this paper, we present a novel attention-based
model to automatically... | ['Lin Wu', 'Yang Wang'] | 2017-09-18 | null | null | null | null | ['deep-attention', 'fine-grained-visual-recognition', 'deep-attention'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 1.73164129e-01 -3.37969691e-01 -1.09316923e-01 -5.19376099e-01
-4.20267463e-01 -4.78878945e-01 5.97660244e-01 -2.61653829e-02
-3.49213898e-01 4.32257980e-01 6.01089597e-01 3.05402011e-01
7.00772926e-02 -6.12606883e-01 -8.82983863e-01 -5.31099677e-01
6.12234510e-02 -1.54858604e-01 2.34024659e-01 1.31024227... | [9.581080436706543, 1.974305272102356] |
6b25249b-2d9d-4086-9215-883d1c2c1314 | multi-message-shuffled-privacy-in-federated | 2302.11152 | null | https://arxiv.org/abs/2302.11152v1 | https://arxiv.org/pdf/2302.11152v1.pdf | Multi-Message Shuffled Privacy in Federated Learning | We study differentially private distributed optimization under communication constraints. A server using SGD for optimization aggregates the client-side local gradients for model updates using distributed mean estimation (DME). We develop a communication-efficient private DME, using the recently developed multi-message... | ['Suhas Diggavi', 'Antonious M. Girgis'] | 2023-02-22 | null | null | null | null | ['distributed-optimization', 'open-question'] | ['methodology', 'natural-language-processing'] | [-4.98283505e-02 -4.19458449e-02 5.44523448e-02 -6.41712248e-01
-1.55368650e+00 -1.03032601e+00 8.76198709e-02 1.95774138e-01
-5.59410930e-01 7.27955043e-01 3.44176173e-01 -2.89682478e-01
-2.99567908e-01 -7.31081903e-01 -8.75591576e-01 -1.40219820e+00
-5.41619241e-01 2.60525703e-01 -3.52268577e-01 1.21417023... | [5.949897766113281, 6.566749572753906] |
14eb6050-746f-4302-8115-01c1f62ae9ce | oxkbc-outcome-explanation-for-factorization | null | null | https://openreview.net/forum?id=nqYhFwaUj | https://openreview.net/pdf?id=nqYhFwaUj | OxKBC: Outcome Explanation for Factorization Based Knowledge Base Completion | State-of-the-art models for Knowledge Base Completion (KBC) are based on tensor factorization (TF), e.g, DistMult, ComplEx. While they produce good results, they cannot expose any rationale behind their predictions, potentially reducing the trust of a user in the model. Previous works have explored creating an inherent... | ['Mausam', 'Parag Singla', 'Mayank Singh Chauhan', 'Aman Agrawal', 'Ankesh Gupta', 'Yatin Nandwani'] | 2020-02-14 | null | null | null | akbc-2020-6 | ['knowledge-base-completion', 'knowledge-base-completion', 'automated-theorem-proving', 'automated-theorem-proving'] | ['graphs', 'knowledge-base', 'miscellaneous', 'reasoning'] | [-1.18256472e-01 1.08667099e+00 -4.53558475e-01 -2.91611552e-01
-3.33058774e-01 -6.02494836e-01 4.22030449e-01 1.17914580e-01
2.73414969e-01 8.99497390e-01 3.94815058e-01 -8.32127631e-01
-4.31311250e-01 -9.43201482e-01 -1.12791431e+00 1.70732681e-02
-2.13006243e-01 8.69432569e-01 -8.36710855e-02 -2.79871911... | [8.891743659973145, 7.58951473236084] |
8be70e97-09d2-4453-8a05-19d07154d7c5 | a-factored-generalized-additive-model-for | 1907.12596 | null | https://arxiv.org/abs/1907.12596v1 | https://arxiv.org/pdf/1907.12596v1.pdf | A Factored Generalized Additive Model for Clinical Decision Support in the Operating Room | Logistic regression (LR) is widely used in clinical prediction because it is simple to deploy and easy to interpret. Nevertheless, being a linear model, LR has limited expressive capability and often has unsatisfactory performance. Generalized additive models (GAMs) extend the linear model with transformations of input... | ['Bradley A Fritz', 'Zhicheng Cui', 'Michael S Avidan', 'Yixin Chen', 'Christopher R King'] | 2019-07-29 | null | null | null | null | ['respiratory-failure'] | ['medical'] | [ 1.09948970e-01 2.59703845e-01 -6.27348900e-01 -6.60424054e-01
-4.52468187e-01 -2.29801342e-01 2.33052552e-01 1.04895927e-01
-2.78948158e-01 8.70530307e-01 1.93251684e-01 -8.81231606e-01
-5.72281122e-01 -7.64599562e-01 -4.21440393e-01 -6.21464312e-01
-2.84363806e-01 5.00808001e-01 -2.99181223e-01 -8.65827799... | [8.160487174987793, 5.662962436676025] |
a0016147-a235-4d3e-b71f-87990060f4fb | smurff-a-high-performance-framework-for | 1904.02514 | null | https://arxiv.org/abs/1904.02514v3 | https://arxiv.org/pdf/1904.02514v3.pdf | SMURFF: a High-Performance Framework for Matrix Factorization | Bayesian Matrix Factorization (BMF) is a powerful technique for recommender systems because it produces good results and is relatively robust against overfitting. Yet BMF is more computationally intensive and thus more challenging to implement for large datasets. In this work we present SMURFF a high-performance featur... | ['Jörg Wegner', 'José Felipe Golib Dzib', 'Thomas J. Ashby', 'Wilfried Verachtert', 'Vladimir Chupakhin', 'Imen Chakroun', 'Hugo Ceulemans', 'Adam Arany', 'Tom Vander Aa', 'Thanh Le Van', 'Roel Wuyts', 'Yves Moreau', 'Jaak Simm'] | 2019-04-04 | null | null | null | null | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [-2.85384536e-01 -4.76040989e-01 -1.73743501e-01 -2.95941293e-01
-5.12576878e-01 -5.52510977e-01 3.50020140e-01 5.69398254e-02
-2.19016254e-01 8.94288898e-01 1.55217603e-01 -7.23660052e-01
-3.81924927e-01 -8.21450412e-01 -4.31832880e-01 -6.04892731e-01
1.54501072e-03 7.67644048e-01 3.97968769e-01 -2.07972467... | [7.4619035720825195, 4.438168525695801] |
80395a59-74ec-4795-ae44-5dacbe38cfd2 | emotion-recognition-with-pre-trained | 2212.13885 | null | https://arxiv.org/abs/2212.13885v1 | https://arxiv.org/pdf/2212.13885v1.pdf | Emotion Recognition with Pre-Trained Transformers Using Multimodal Signals | In this paper, we address the problem of multimodal emotion recognition from multiple physiological signals. We demonstrate that a Transformer-based approach is suitable for this task. In addition, we present how such models may be pretrained in a multimodal scenario to improve emotion recognition performances. We eval... | ['James L Crowley', 'Julien Cumin', 'Grégoire Lefebvre', 'Juan Vazquez-Rodriguez'] | 2022-12-22 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 4.52372819e-01 -9.78714675e-02 3.36658061e-01 -7.96051025e-01
-6.96643949e-01 -5.38794816e-01 3.37288499e-01 -3.26209627e-02
-6.44374251e-01 7.22675383e-01 2.87059173e-02 -6.27895398e-03
-4.30105291e-02 -3.27427626e-01 -5.06308794e-01 -4.23778713e-01
-7.36187920e-02 1.78543717e-01 -2.92698473e-01 -4.94135022... | [13.316887855529785, 5.4105682373046875] |
ad871dbb-4faf-4c6f-b607-adfab0ac8bbb | understanding-the-stochastic-dynamics-of | 2208.06245 | null | https://arxiv.org/abs/2208.06245v2 | https://arxiv.org/pdf/2208.06245v2.pdf | Understanding the stochastic dynamics of sequential decision-making processes: A path-integral analysis of multi-armed bandits | The multi-armed bandit (MAB) model is one of the most classical models to study decision-making in an uncertain environment. In this model, a player chooses one of $K$ possible arms of a bandit machine to play at each time step, where the corresponding arm returns a random reward to the player, potentially from a speci... | ['Chi Ho Yeung', 'Bo Li'] | 2022-08-11 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [-7.98678547e-02 8.16845223e-02 -2.87454545e-01 -5.60191367e-03
-5.90746045e-01 -8.98618340e-01 2.97317743e-01 2.20423788e-01
-4.99968380e-01 9.79732454e-01 -1.68201715e-01 -4.88364905e-01
-8.36005032e-01 -7.27739573e-01 -7.70396113e-01 -1.18180609e+00
-2.05128416e-01 9.83451664e-01 -2.88481832e-01 -2.14955524... | [4.494750499725342, 3.2777392864227295] |
79da5201-f92f-4270-aa76-c803f51c1cc3 | celebv-text-a-large-scale-facial-text-video | 2303.14717 | null | https://arxiv.org/abs/2303.14717v1 | https://arxiv.org/pdf/2303.14717v1.pdf | CelebV-Text: A Large-Scale Facial Text-Video Dataset | Text-driven generation models are flourishing in video generation and editing. However, face-centric text-to-video generation remains a challenge due to the lack of a suitable dataset containing high-quality videos and highly relevant texts. This paper presents CelebV-Text, a large-scale, diverse, and high-quality data... | ['Wayne Wu', 'Weidong Cai', 'Chen Change Loy', 'Liming Jiang', 'Hao Zhu', 'Jianhui Yu'] | 2023-03-26 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yu_CelebV-Text_A_Large-Scale_Facial_Text-Video_Dataset_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yu_CelebV-Text_A_Large-Scale_Facial_Text-Video_Dataset_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-generation', 'text-to-video-generation'] | ['computer-vision', 'natural-language-processing'] | [ 3.55334520e-01 -1.20790906e-01 -4.39481018e-03 -5.80767214e-01
-9.84879911e-01 -1.48821786e-01 1.05858278e+00 -6.92364275e-01
1.07537024e-01 8.16540956e-01 5.56421995e-01 6.33310974e-01
9.64752957e-02 -4.56738055e-01 -5.71437836e-01 -8.71934712e-01
1.83063582e-01 4.06307548e-01 -2.69254267e-01 -2.92862296... | [12.77625846862793, -0.08386053889989853] |
53fadcff-54a1-4260-8551-9c8a3d5846ac | smarnet-teaching-machines-to-read-and | 1710.02772 | null | http://arxiv.org/abs/1710.02772v1 | http://arxiv.org/pdf/1710.02772v1.pdf | Smarnet: Teaching Machines to Read and Comprehend Like Human | Machine Comprehension (MC) is a challenging task in Natural Language
Processing field, which aims to guide the machine to comprehend a passage and
answer the given question. Many existing approaches on MC task are suffering
the inefficiency in some bottlenecks, such as insufficient lexical
understanding, complex questi... | ['Rongqin Yang', 'Zhou Zhao', 'Zheqian Chen', 'Deng Cai', 'Bin Cao', 'Xiaofei He'] | 2017-10-08 | null | null | null | null | ['triviaqa'] | ['miscellaneous'] | [ 3.12365025e-01 -2.25527529e-02 2.80833811e-01 -4.99575227e-01
-7.60831773e-01 -7.57682204e-01 3.48106861e-01 6.85901761e-01
-6.21230781e-01 8.87868285e-01 3.28071028e-01 -7.26399601e-01
7.63717741e-02 -8.34608436e-01 -6.25507414e-01 -7.07508549e-02
4.99068350e-01 4.85383242e-01 6.13607228e-01 -3.88328582... | [11.293660163879395, 8.143415451049805] |
17955790-369f-4ce4-9bff-d6247dd29265 | music-source-separation-with-generative-flow | 2204.09079 | null | https://arxiv.org/abs/2204.09079v4 | https://arxiv.org/pdf/2204.09079v4.pdf | Music Source Separation with Generative Flow | Fully-supervised models for source separation are trained on parallel mixture-source data and are currently state-of-the-art. However, such parallel data is often difficult to obtain, and it is cumbersome to adapt trained models to mixtures with new sources. Source-only supervised models, in contrast, only require indi... | ['Zhiyao Duan', 'Anton Selitskiy', 'Fei Jiang', 'Jordan Darefsky', 'Ge Zhu'] | 2022-04-19 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 3.74202073e-01 -7.68626183e-02 -8.95864815e-02 -1.92743987e-02
-1.19158399e+00 -9.23632264e-01 5.13794661e-01 -2.80541182e-01
-5.98960230e-03 6.41348600e-01 3.20096552e-01 -7.51553178e-02
-1.94020301e-01 -2.66192585e-01 -5.39726853e-01 -6.67611241e-01
-3.38401571e-02 4.97888476e-01 6.48902496e-03 3.40832509... | [15.479768753051758, 5.589348793029785] |
2a105ee0-8c9c-4cc5-8e59-c10f61dfc68a | videochat-chat-centric-video-understanding | 2305.06355 | null | https://arxiv.org/abs/2305.06355v1 | https://arxiv.org/pdf/2305.06355v1.pdf | VideoChat: Chat-Centric Video Understanding | In this study, we initiate an exploration into video understanding by introducing VideoChat, an end-to-end chat-centric video understanding system. It integrates video foundation models and large language models via a learnable neural interface, excelling in spatiotemporal reasoning, event localization, and causal rela... | ['Yu Qiao', 'LiMin Wang', 'Yali Wang', 'Ping Luo', 'Wenhai Wang', 'Yizhuo Li', 'Yi Wang', 'Yinan He', 'Kunchang Li'] | 2023-05-10 | null | null | null | null | ['video-understanding'] | ['computer-vision'] | [-4.94097441e-01 2.89240628e-02 -4.39340383e-01 -4.79868859e-01
-4.48330671e-01 -5.04155278e-01 4.51625198e-01 -1.59828603e-01
2.29553673e-02 5.78394771e-01 7.65931189e-01 -5.67673564e-01
9.49545279e-02 -3.61064285e-01 -9.73149061e-01 -1.53013747e-02
-2.06485942e-01 2.27460265e-01 2.74059772e-01 -3.00058097... | [10.45586109161377, 0.9872857928276062] |
84351885-d44b-4873-befe-27798e515191 | on-the-lifting-and-reconstruction-of | 2304.11860 | null | https://arxiv.org/abs/2304.11860v1 | https://arxiv.org/pdf/2304.11860v1.pdf | On the lifting and reconstruction of dynamical systems with multiple attractors | The Koopman operator provides a linear perspective on non-linear dynamics by focusing on the evolution of observables in an invariant subspace. Observables of interest are typically linearly reconstructed from the Koopman eigenfunctions. Despite the broad use of Koopman operators over the past few years, there exist so... | ['Karthik Duraisamy', 'Shaowu Pan'] | 2023-04-24 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [-9.84552279e-02 2.90853351e-01 1.25280526e-02 2.05137819e-01
1.97276235e-01 -6.59758806e-01 7.52409160e-01 -1.33251861e-01
-2.22511724e-01 8.28904271e-01 1.85177371e-01 -1.88573897e-01
-6.24046206e-01 -4.69275564e-01 -3.05702358e-01 -1.24485540e+00
-5.12253284e-01 9.02027637e-02 -1.44425277e-02 -5.93807578... | [6.052515506744385, 4.417259693145752] |
55a6733c-8fea-4266-bed4-a199c79b2217 | a-circular-window-based-cascade-transformer | 2208.14209 | null | https://arxiv.org/abs/2208.14209v1 | https://arxiv.org/pdf/2208.14209v1.pdf | A Circular Window-based Cascade Transformer for Online Action Detection | Online action detection aims at the accurate action prediction of the current frame based on long historical observations. Meanwhile, it demands real-time inference on online streaming videos. In this paper, we advocate a novel and efficient principle for online action detection. It merely updates the latest and oldest... | ['Lin Ma', 'Wei zhang', 'Bairui Wang', 'Weixin Luo', 'Shuqiang Cao'] | 2022-08-30 | null | null | null | null | ['online-action-detection', 'action-segmentation'] | ['computer-vision', 'computer-vision'] | [ 4.09964174e-01 -1.50432304e-01 -5.67432582e-01 -2.16774389e-01
-7.34622478e-01 -2.05997989e-01 4.61209148e-01 1.75246537e-01
-6.07374251e-01 4.81524378e-01 3.63699198e-01 -1.01063244e-01
1.50459930e-01 -4.46334749e-01 -5.60161233e-01 -7.33100712e-01
-4.19150591e-01 1.02876358e-01 8.78812015e-01 1.22638233... | [8.335536003112793, 0.46648791432380676] |
6e381124-2948-41d9-b3d2-773e52788a1d | graph-based-active-learning-for-surface-water | 2306.10440 | null | https://arxiv.org/abs/2306.10440v1 | https://arxiv.org/pdf/2306.10440v1.pdf | Graph-based Active Learning for Surface Water and Sediment Detection in Multispectral Images | We develop a graph active learning pipeline (GAP) to detect surface water and in-river sediment pixels in satellite images. The active learning approach is applied within the training process to optimally select specific pixels to generate a hand-labeled training set. Our method obtains higher accuracy with far fewer t... | ['Jon Schwenk', 'Andrea L. Bertozzi', 'Kevin Miller', 'Bohan Chen'] | 2023-06-17 | null | null | null | null | ['active-learning', 'active-learning'] | ['methodology', 'natural-language-processing'] | [ 3.79520059e-01 7.95399606e-01 -9.34733376e-02 -4.82978553e-01
-1.06963015e+00 -4.17518079e-01 5.28687239e-01 1.28607184e-01
-6.25275612e-01 5.63888490e-01 1.87621266e-01 -2.44797215e-01
2.82179862e-01 -1.61156034e+00 -7.45633721e-01 -8.14012289e-01
-6.44292474e-01 3.93617928e-01 5.24191380e-01 7.50124827... | [9.513100624084473, -1.4287621974945068] |
78355e1c-62b0-403a-a199-4e9c84dbd728 | hierarchical-internal-representation-of | 1711.07792 | null | http://arxiv.org/abs/1711.07792v3 | http://arxiv.org/pdf/1711.07792v3.pdf | Hierarchical internal representation of spectral features in deep convolutional networks trained for EEG decoding | Recently, there is increasing interest and research on the interpretability
of machine learning models, for example how they transform and internally
represent EEG signals in Brain-Computer Interface (BCI) applications. This can
help to understand the limits of the model and how it may be improved, in
addition to possi... | ['Robin Tibor Schirrmeister', 'Kay Gregor Hartmann', 'Tonio Ball'] | 2017-11-21 | null | null | null | null | ['eeg-decoding', 'eeg-decoding'] | ['medical', 'time-series'] | [ 2.82422513e-01 2.38108203e-01 5.15334129e-01 -3.31215352e-01
2.63590276e-01 -6.68344200e-01 8.03410888e-01 2.76248276e-01
-3.63442838e-01 6.08232379e-01 3.34264636e-01 -4.37846780e-01
-4.72667813e-01 -5.68650603e-01 -5.68652570e-01 -7.23233283e-01
-8.95522773e-01 -8.62577856e-02 -3.50894295e-02 -4.24225718... | [12.975566864013672, 3.4309611320495605] |
67525cbf-bd14-4175-b8a8-59bea1a11e71 | foundation-model-for-endoscopy-video-analysis | 2306.16741 | null | https://arxiv.org/abs/2306.16741v1 | https://arxiv.org/pdf/2306.16741v1.pdf | Foundation Model for Endoscopy Video Analysis via Large-scale Self-supervised Pre-train | Foundation models have exhibited remarkable success in various applications, such as disease diagnosis and text report generation. To date, a foundation model for endoscopic video analysis is still lacking. In this paper, we propose Endo-FM, a foundation model specifically developed using massive endoscopic video data.... | ['Qi Dou', 'Shaoting Zhang', 'Chang Liu', 'Zhao Wang'] | 2023-06-29 | null | null | null | null | ['transfer-learning'] | ['miscellaneous'] | [ 4.19612266e-02 -2.93894887e-01 -3.76884967e-01 -1.65254861e-01
-1.21879923e+00 -7.44598627e-01 1.00914821e-01 1.22830942e-01
-4.23245609e-01 3.68015915e-01 2.57950902e-01 -2.70474792e-01
4.40774485e-02 -6.50375605e-01 -8.62699389e-01 -7.30634749e-01
-1.66893974e-01 -6.82861432e-02 3.40492398e-01 1.82648122... | [14.337888717651367, -3.0341906547546387] |
ac2a6487-2f54-4166-8e05-934eec42521f | face-synthesis-for-eyeglass-robust-face | 1806.01196 | null | https://arxiv.org/abs/1806.01196v2 | https://arxiv.org/pdf/1806.01196v2.pdf | Face Synthesis for Eyeglass-Robust Face Recognition | In the application of face recognition, eyeglasses could significantly degrade the recognition accuracy. A feasible method is to collect large-scale face images with eyeglasses for training deep learning methods. However, it is difficult to collect the images with and without glasses of the same identity, so that it is... | ['Jianzhu Guo', 'Zhen Lei', 'Stan Z. Li', 'Xiangyu Zhu'] | 2018-06-04 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [-2.71801889e-01 7.03622475e-02 2.69733012e-01 -4.24666971e-01
-1.37261033e-01 -1.60953570e-02 7.41807893e-02 -1.14897501e+00
2.76845992e-01 7.21956074e-01 -1.91168524e-02 1.76857203e-01
3.13726783e-01 -7.75315523e-01 -1.00798488e+00 -9.82531130e-01
3.37146789e-01 -1.18129283e-01 -4.71157014e-01 3.45974267... | [12.924322128295898, 0.051265325397253036] |
dcf353eb-72a7-4c21-ae88-a152739002a3 | seeing-convolution-through-the-eyes-of-finite | 1905.10901 | null | https://arxiv.org/abs/1905.10901v1 | https://arxiv.org/pdf/1905.10901v1.pdf | Seeing Convolution Through the Eyes of Finite Transformation Semigroup Theory: An Abstract Algebraic Interpretation of Convolutional Neural Networks | Researchers are actively trying to gain better insights into the representational properties of convolutional neural networks for guiding better network designs and for interpreting a network's computational nature. Gaining such insights can be an arduous task due to the number of parameters in a network and the comple... | ['Alexander Wong', 'Andrew Hryniowski'] | 2019-05-26 | null | null | null | null | ['network-interpretation'] | ['computer-vision'] | [ 7.48008907e-01 6.25719249e-01 -1.94698140e-01 -2.49111056e-01
1.23921543e-01 -6.21660292e-01 7.81451285e-01 6.25090897e-02
-3.50362420e-01 3.32293898e-01 1.53493762e-01 -6.23285592e-01
-2.75509000e-01 -7.96602309e-01 -8.26304615e-01 -9.80813265e-01
-2.87247062e-01 1.98099717e-01 5.60240373e-02 -9.37428400... | [8.008162498474121, 3.574040174484253] |
c11d8c0f-baf9-454d-a161-55f69f568ddb | perfectly-accurate-membership-inference-by-a | 2203.16463 | null | https://arxiv.org/abs/2203.16463v1 | https://arxiv.org/pdf/2203.16463v1.pdf | Perfectly Accurate Membership Inference by a Dishonest Central Server in Federated Learning | Federated Learning is expected to provide strong privacy guarantees, as only gradients or model parameters but no plain text training data is ever exchanged either between the clients or between the clients and the central server. In this paper, we challenge this claim by introducing a simple but still very effective m... | ['Pablo Piantanida', 'Leonardo Rey Vega', 'Marco Romanelli', 'Georg Pichler'] | 2022-03-30 | null | null | null | null | ['inference-attack', 'membership-inference-attack'] | ['adversarial', 'computer-vision'] | [-5.07189706e-02 8.88337120e-02 1.39871925e-01 -4.45300788e-01
-8.92872155e-01 -1.04313457e+00 5.00265300e-01 -1.02625638e-01
-9.04377878e-01 9.86941278e-01 -5.73280811e-01 -5.45057118e-01
2.06774892e-03 -5.44434905e-01 -1.15554249e+00 -1.09758008e+00
-2.26202950e-01 6.19516075e-01 2.48586666e-03 2.21204177... | [5.844359874725342, 6.747485160827637] |
f00a4df0-88ae-40c8-92c6-73a6cf9342e3 | cardea-an-open-automated-machine-learning | 2010.00509 | null | https://arxiv.org/abs/2010.00509v1 | https://arxiv.org/pdf/2010.00509v1.pdf | Cardea: An Open Automated Machine Learning Framework for Electronic Health Records | An estimated 180 papers focusing on deep learning and EHR were published between 2010 and 2018. Despite the common workflow structure appearing in these publications, no trusted and verified software framework exists, forcing researchers to arduously repeat previous work. In this paper, we propose Cardea, an extensible... | ['Mansour Alsaleh', 'Sarah Alnegheimish', 'Kalyan Veeramachaneni', 'Dongyu Liu', 'Shahad Althobaiti', 'Faisal Aleissa', 'Najat Alrashed'] | 2020-10-01 | null | null | null | null | ['automated-feature-engineering'] | ['methodology'] | [-2.28117406e-01 -2.48950180e-02 7.74721131e-02 -6.64576411e-01
-9.79299486e-01 -6.16097033e-01 -2.25161359e-01 5.74000180e-01
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-3.35287750e-01 -5.99862576e-01 -5.44525325e-01 -3.77820194e-01
-2.18760908e-01 7.75225461e-01 -2.65573323e-01 -1.08871400... | [7.936159133911133, 6.282454490661621] |
7087b6b9-d27f-4e85-9de9-6ece9bf1c439 | text-classification-by-contrastive-learning | null | null | https://aclanthology.org/2020.coling-main.542 | https://aclanthology.org/2020.coling-main.542.pdf | Text Classification by Contrastive Learning and Cross-lingual Data Augmentation for Alzheimer's Disease Detection | Data scarcity is always a constraint on analyzing speech transcriptions for automatic Alzheimer{'}s disease (AD) detection, especially when the subjects are non-English speakers. To deal with this issue, this paper first proposes a contrastive learning method to obtain effective representations for text classification ... | ['Yunxia Li', 'Lingjing Jin', 'Shijin Wang', 'ZhenHua Ling', 'Zhaoci Liu', 'Zhiqiang Guo'] | 2020-12-01 | null | null | null | coling-2020-8 | ['alzheimer-s-disease-detection'] | ['medical'] | [-4.93080020e-02 4.96177636e-02 -1.81836888e-01 -4.28037852e-01
-1.14221728e+00 2.53984928e-01 5.56428075e-01 -4.46499102e-02
-8.20050895e-01 7.21209943e-01 4.30088371e-01 -3.21597695e-01
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7.41471946e-02 4.57153052e-01 -3.49968523e-02 -8.23953375... | [13.929767608642578, 5.382170677185059] |
0af2e23a-fa2c-4adb-a2a4-6b1cd3543592 | ice-gan-identity-aware-and-capsule-enhanced | 2005.04370 | null | https://arxiv.org/abs/2005.04370v2 | https://arxiv.org/pdf/2005.04370v2.pdf | ICE-GAN: Identity-aware and Capsule-Enhanced GAN with Graph-based Reasoning for Micro-Expression Recognition and Synthesis | Micro-expressions are reflections of people's true feelings and motives, which attract an increasing number of researchers into the study of automatic facial micro-expression recognition. The short detection window, the subtle facial muscle movements, and the limited training samples make micro-expression recognition c... | ['Yang song', 'Weidong Cai', 'Jianhui Yu', 'Chaoyi Zhang'] | 2020-05-09 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [ 4.10942346e-01 3.87471557e-01 6.48323894e-02 -7.34283268e-01
-4.69275057e-01 -4.08052474e-01 6.38080359e-01 -9.39943671e-01
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6.26397014e-01 -6.80061519e-01 -6.46446347e-01 -9.85665202e-01
3.25526716e-03 -4.26954515e-02 -8.40442419e-01 -5.25799870... | [12.920459747314453, 0.32853963971138] |
569bb6bb-f632-4a22-8f6d-467d5efb08de | reducing-false-alarms-in-video-surveillance | 2307.04159 | null | https://arxiv.org/abs/2307.04159v1 | https://arxiv.org/pdf/2307.04159v1.pdf | Reducing False Alarms in Video Surveillance by Deep Feature Statistical Modeling | Detecting relevant changes is a fundamental problem of video surveillance. Because of the high variability of data and the difficulty of properly annotating changes, unsupervised methods dominate the field. Arguably one of the most critical issues to make them practical is to reduce their false alarm rate. In this work... | ['Rafael Grompone von Gioi', 'Jean-Michel Morel', 'Gabriele Facciolo', 'Thibaud Ehret', 'Aitor Artola', 'Xavier Bou'] | 2023-07-09 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 5.83194435e-01 -4.10959244e-01 1.78899810e-01 -4.29243177e-01
-5.61401129e-01 -6.91250324e-01 7.76631117e-01 5.23301303e-01
-4.97072339e-01 5.55064976e-01 -1.15059108e-01 3.44967060e-02
-1.71965316e-01 -7.44769514e-01 -5.99549949e-01 -7.23112404e-01
-1.63087711e-01 -7.88381044e-03 6.52224481e-01 -1.72038570... | [7.994787216186523, 1.4120924472808838] |
633395e9-0f49-449b-9221-8cf103318836 | learning-rgb-d-feature-embeddings-for-unseen | 2007.15157 | null | https://arxiv.org/abs/2007.15157v3 | https://arxiv.org/pdf/2007.15157v3.pdf | Learning RGB-D Feature Embeddings for Unseen Object Instance Segmentation | Segmenting unseen objects in cluttered scenes is an important skill that robots need to acquire in order to perform tasks in new environments. In this work, we propose a new method for unseen object instance segmentation by learning RGB-D feature embeddings from synthetic data. A metric learning loss function is utiliz... | ['Yu Xiang', 'Dieter Fox', 'Christopher Xie', 'Arsalan Mousavian'] | 2020-07-30 | null | null | null | null | ['unseen-object-instance-segmentation'] | ['computer-vision'] | [ 4.04732466e-01 1.75468296e-01 2.43648559e-01 -7.79780984e-01
-5.42292476e-01 -6.34626091e-01 1.35728538e-01 4.75004092e-02
-6.25512779e-01 3.22434306e-01 -3.17089885e-01 3.45420182e-01
-5.60088046e-02 -7.26787329e-01 -1.08509469e+00 -7.38934875e-01
2.83450723e-01 7.94110060e-01 5.05957901e-01 4.09119949... | [7.885512351989746, -2.790205955505371] |
f8eaaffb-cbbc-4d91-b023-7567daa5aa1a | safe-risk-averse-bayesian-optimization-for | 2306.13479 | null | https://arxiv.org/abs/2306.13479v1 | https://arxiv.org/pdf/2306.13479v1.pdf | Safe Risk-averse Bayesian Optimization for Controller Tuning | Controller tuning and parameter optimization are crucial in system design to improve both the controller and underlying system performance. Bayesian optimization has been established as an efficient model-free method for controller tuning and adaptation. Standard methods, however, are not enough for high-precision syst... | ['Alisa Rupenyan', 'Andreas Krause', 'Efe C. Balta', 'Anastasia Makarova', 'Miks Ozols', 'Christopher Koenig'] | 2023-06-23 | null | null | null | null | ['bayesian-optimization'] | ['methodology'] | [-3.37322541e-02 -2.41630241e-01 -2.22019434e-01 2.04816144e-02
-9.49742913e-01 -5.11310756e-01 3.35176349e-01 9.31666940e-02
-1.80122450e-01 1.15101039e+00 -4.82708722e-01 -4.77418363e-01
-7.38371849e-01 -4.12577599e-01 -4.76103991e-01 -9.05052483e-01
2.32659847e-01 6.21266365e-01 3.55695546e-01 -9.48895961... | [5.032385349273682, 2.6025192737579346] |
6b76419a-acfa-4a7f-bd1c-80250bb6d9d6 | model-driven-deep-learning-for-non-coherent | 2301.00516 | null | https://arxiv.org/abs/2301.00516v1 | https://arxiv.org/pdf/2301.00516v1.pdf | Model-Driven Deep Learning for Non-Coherent Massive Machine-Type Communications | In this paper, we investigate the joint device activity and data detection in massive machine-type communications (mMTC) with a one-phase non-coherent scheme, where data bits are embedded in the pilot sequences and the base station simultaneously detects active devices and their embedded data bits without explicit chan... | ['Shen', 'Xuemin', 'Feifei Gao', 'Wen Wu', 'Zhe Ma'] | 2023-01-02 | null | null | null | null | ['type'] | ['speech'] | [ 3.14122379e-01 -3.52083705e-02 -5.07852316e-01 2.92891711e-01
-4.60003316e-01 3.75188380e-01 3.50913852e-01 -3.13488632e-01
-2.70409346e-01 8.73893261e-01 7.22506121e-02 -4.35004294e-01
-1.39474571e-01 -4.35444862e-01 -4.86545116e-01 -1.28771925e+00
-5.24237156e-01 -3.76144111e-01 -6.42967236e-04 5.96636869... | [6.365095138549805, 1.451950192451477] |
29cd2b52-8344-46ba-b6ca-5aa6d50ea398 | the-future-is-different-large-pre-trained | 2211.00384 | null | https://arxiv.org/abs/2211.00384v2 | https://arxiv.org/pdf/2211.00384v2.pdf | The future is different: Large pre-trained language models fail in prediction tasks | Large pre-trained language models (LPLM) have shown spectacular success when fine-tuned on downstream supervised tasks. Yet, it is known that their performance can drastically drop when there is a distribution shift between the data used during training and that used at inference time. In this paper we focus on data di... | ['César Ojeda', 'Ramsés J. Sánchez', 'Kostadin Cvejoski'] | 2022-11-01 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [-5.35398602e-01 2.66789436e-01 -5.41498482e-01 -8.51636082e-02
-9.40722525e-01 -4.50282454e-01 1.05917346e+00 2.71926135e-01
-4.74843502e-01 7.73294330e-01 3.40357929e-01 -5.99110484e-01
-8.26230347e-02 -7.99586773e-01 -8.44414055e-01 -5.57877660e-01
-2.63597488e-01 1.01919365e+00 2.92444438e-01 -3.60717595... | [10.383988380432129, 7.009603500366211] |
9cb2ebc0-0edc-4b83-aa4f-8b6d11245f61 | incomplete-multi-view-multi-label-learning | 2303.07180 | null | https://arxiv.org/abs/2303.07180v1 | https://arxiv.org/pdf/2303.07180v1.pdf | Incomplete Multi-View Multi-Label Learning via Label-Guided Masked View- and Category-Aware Transformers | As we all know, multi-view data is more expressive than single-view data and multi-label annotation enjoys richer supervision information than single-label, which makes multi-view multi-label learning widely applicable for various pattern recognition tasks. In this complex representation learning problem, three main ch... | ['Yong Xu', 'Xiaoling Luo', 'Jie Wen', 'Chengliang Liu'] | 2023-03-13 | null | null | null | null | ['multi-label-learning'] | ['methodology'] | [ 2.12022647e-01 -1.86992869e-01 -6.11705482e-01 -5.79705417e-01
-7.10442901e-01 -6.48344755e-01 4.33794171e-01 -1.65088512e-02
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-3.25087219e-01 -6.05588973e-01 -2.10771278e-01 -1.06019187e+00
4.88972634e-01 2.47647986e-01 -2.92611551e-02 4.26154323... | [8.564179420471191, 4.512683391571045] |
ca0ce3a4-d8cd-4a9d-b6e1-93d3c0e4b37d | look-read-and-ask-learning-to-ask-questions | 2211.12950 | null | https://arxiv.org/abs/2211.12950v1 | https://arxiv.org/pdf/2211.12950v1.pdf | Look, Read and Ask: Learning to Ask Questions by Reading Text in Images | We present a novel problem of text-based visual question generation or TextVQG in short. Given the recent growing interest of the document image analysis community in combining text understanding with conversational artificial intelligence, e.g., text-based visual question answering, TextVQG becomes an important task. ... | ['Anand Mishra', 'Shankar Gangisetty', 'Soumya Jahagirdar'] | 2022-11-23 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [ 5.71008503e-01 3.25081199e-01 3.35357577e-01 -2.05562279e-01
-9.08261776e-01 -8.36624205e-01 9.77036476e-01 2.54383981e-01
-1.53482065e-01 6.18152201e-01 3.57772470e-01 -6.41711593e-01
2.74102777e-01 -8.58952224e-01 -7.83727884e-01 -4.11426306e-01
7.09211469e-01 5.83370924e-01 2.60688066e-01 -2.87193239... | [10.994331359863281, 1.5703485012054443] |
596ad7dd-f0c5-4b18-a2ba-627b51a4e737 | discriminative-learning-of-deep-convolutional | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Simo-Serra_Discriminative_Learning_of_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Simo-Serra_Discriminative_Learning_of_ICCV_2015_paper.pdf | Discriminative Learning of Deep Convolutional Feature Point Descriptors | Deep learning has revolutionalized image-level tasks such as classification, but patch-level tasks, such as correspondence, still rely on hand-crafted features, e.g. SIFT. In this paper we use Convolutional Neural Networks (CNNs) to learn discriminant patch representations and in particular train a Siamese network with... | ['Francesc Moreno-Noguer', 'Iasonas Kokkinos', 'Edgar Simo-Serra', 'Pascal Fua', 'Eduard Trulls', 'Luis Ferraz'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['satellite-image-classification'] | ['computer-vision'] | [ 1.70705631e-01 -4.21855092e-01 -4.28735077e-01 -2.45722219e-01
-9.32207942e-01 -6.69183731e-01 6.79975152e-01 2.28954807e-01
-5.04440188e-01 4.22537595e-01 2.11555418e-02 -9.53668635e-03
-8.79276320e-02 -9.58583891e-01 -1.00546050e+00 -5.93257785e-01
-3.14201385e-01 5.29518962e-01 2.46912092e-01 -2.32539892... | [8.24223518371582, -1.8984318971633911] |
a5af84ef-57c6-48f1-8de4-d9219d44f256 | point2mask-a-weakly-supervised-approach-for | null | null | https://link.springer.com/chapter/10.1007/978-3-031-12053-4_11 | https://www.researchgate.net/publication/361820249_Point2Mask_A_Weakly_Supervised_Approach_for_Cell_Segmentation_using_Point_Annotation | Point2Mask: A Weakly Supervised Approach for Cell Segmentation Using Point Annotation | Identifying cells in microscopic images is a crucial step toward studying image-based cell biology research. Cell instance segmentation provides an opportunity to study the shape, structure, form, and size of cells. Deep learning approaches for cell instance segmentation rely on the instance segmentation mask for each ... | ['Andreas Dengel & Sheraz Ahmed', 'Rickard Sjögren', 'Johan Trygg', 'Timothy R Jackson', 'Christoffer Edlund', 'Mohsin Munir', 'Mohammadmahdi Koochali', 'Fabian Schmeisser', 'Nabeel Khalid'] | 2022-07-25 | null | null | null | miua-2022-7 | ['cell-segmentation'] | ['medical'] | [ 8.24366435e-02 1.65003881e-01 1.01256154e-01 -7.15884641e-02
-8.65990460e-01 -7.01292872e-01 3.58280629e-01 7.01063991e-01
-7.92539418e-01 9.30622280e-01 -6.74016356e-01 -8.73271152e-02
5.33317208e-01 -7.44696975e-01 -7.01991558e-01 -1.14131773e+00
2.20893830e-01 7.57631660e-01 4.09472704e-01 2.43072286... | [14.588215827941895, -3.1042420864105225] |
f5e59f77-66c6-4eba-99ec-7a1f893acd0c | burst-a-benchmark-for-unifying-object | 2209.12118 | null | https://arxiv.org/abs/2209.12118v2 | https://arxiv.org/pdf/2209.12118v2.pdf | BURST: A Benchmark for Unifying Object Recognition, Segmentation and Tracking in Video | Multiple existing benchmarks involve tracking and segmenting objects in video e.g., Video Object Segmentation (VOS) and Multi-Object Tracking and Segmentation (MOTS), but there is little interaction between them due to the use of disparate benchmark datasets and metrics (e.g. J&F, mAP, sMOTSA). As a result, published w... | ['Deva Ramanan', 'Bastian Leibe', 'Achal Dave', 'Tarasha Khurana', 'Paul Voigtlaender', 'Jonathon Luiten', 'Ali Athar'] | 2022-09-25 | null | null | null | null | ['multi-object-tracking-and-segmentation'] | ['computer-vision'] | [ 7.42690414e-02 -5.85583270e-01 -4.35013205e-01 -1.98568314e-01
-7.96195865e-01 -8.09813261e-01 4.04711902e-01 -1.15105249e-01
-4.28444624e-01 6.07511342e-01 -2.59295404e-02 1.02434553e-01
-5.36507405e-02 -1.98306710e-01 -8.43784034e-01 -5.14837742e-01
-5.62571585e-02 2.97430724e-01 9.99595225e-01 5.44913150... | [9.09501838684082, 0.0063134790398180485] |
905c7d16-c51e-4512-8bed-bd2881d930bd | iarm-inter-aspect-relation-modeling-with | null | null | https://aclanthology.org/D18-1377 | https://aclanthology.org/D18-1377.pdf | IARM: Inter-Aspect Relation Modeling with Memory Networks in Aspect-Based Sentiment Analysis | Sentiment analysis has immense implications in e-commerce through user feedback mining. Aspect-based sentiment analysis takes this one step further by enabling businesses to extract aspect specific sentimental information. In this paper, we present a novel approach of incorporating the neighboring aspects related infor... | ['er', 'Soujanya Poria', 'Alex Gelbukh', 'Navonil Majumder', 'Erik Cambria', 'Asif Ekbal', 'Md. Shad Akhtar'] | 2018-10-01 | null | null | null | emnlp-2018-10 | ['extract-aspect'] | ['natural-language-processing'] | [ 9.75311920e-02 4.05850448e-02 -7.46342123e-01 -7.98430562e-01
-4.68148977e-01 -6.01308882e-01 2.98481941e-01 5.70175350e-01
-3.41163665e-01 6.97261214e-01 2.74758428e-01 -4.76528704e-01
-3.45714614e-02 -1.24299049e+00 -3.14887404e-01 -4.27829355e-01
1.19666159e-01 4.06725079e-01 1.47312388e-01 -6.41694427... | [11.365690231323242, 6.718202590942383] |
7c4befa2-9606-48cc-837f-7b4329dc6898 | evaluation-of-rounding-functions-in-nearest | 2003.06885 | null | https://arxiv.org/abs/2003.06885v2 | https://arxiv.org/pdf/2003.06885v2.pdf | Evaluation of Rounding Functions in Nearest-Neighbor Interpolation | A novel evaluation study of the most appropriate round function for nearest-neighbor (NN) image interpolation is presented. Evaluated rounding functions are selected among the five rounding rules defined by the Institute of Electrical and Electronics Engineers (IEEE) 754-2008 standard. Both full- and no-reference image... | ['Olivier Rukundo'] | 2020-03-15 | null | null | null | null | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [ 3.80228996e-01 -2.06874445e-01 -2.18485072e-01 -4.27968413e-01
-7.02577353e-01 -2.06595153e-01 2.82646865e-01 4.65998113e-01
-5.01257122e-01 8.45736086e-01 1.85342193e-01 -5.07169664e-01
-5.43610394e-01 -7.77589262e-01 -5.89880764e-01 -4.89402682e-01
-1.28920302e-01 -3.57331067e-01 1.24587841e-01 2.01832980... | [11.53414535522461, -2.131709575653076] |
63786253-4f85-4a71-98f9-a5d9fe6ec199 | making-the-invisible-visible-toward-high | 2304.14894 | null | https://arxiv.org/abs/2304.14894v1 | https://arxiv.org/pdf/2304.14894v1.pdf | Making the Invisible Visible: Toward High-Quality Terahertz Tomographic Imaging via Physics-Guided Restoration | Terahertz (THz) tomographic imaging has recently attracted significant attention thanks to its non-invasive, non-destructive, non-ionizing, material-classification, and ultra-fast nature for object exploration and inspection. However, its strong water absorption nature and low noise tolerance lead to undesired blurs an... | ['Chia-Wen Lin', 'Shang-Hua Yang', 'Po-Jen Yu', 'Yi-Chun Hung', 'Weng-Tai Su'] | 2023-04-28 | null | null | null | null | ['material-classification'] | ['computer-vision'] | [ 4.89115328e-01 -2.89340973e-01 2.47550592e-01 -4.42773402e-01
-9.01128232e-01 -3.48885477e-01 3.31383288e-01 -3.87219906e-01
7.51953423e-02 3.86109769e-01 4.02257085e-01 -1.33360788e-01
-7.04126656e-01 -7.28027761e-01 -3.66493642e-01 -1.22870326e+00
3.02164704e-01 2.38466740e-01 1.43878192e-01 -1.01445690... | [10.527976036071777, -2.3128621578216553] |
99b5fc1f-7cff-48ca-aa90-57ae364d2b71 | domain-adaptive-training-bert-for-response | 1908.04812 | null | https://arxiv.org/abs/1908.04812v2 | https://arxiv.org/pdf/1908.04812v2.pdf | An Effective Domain Adaptive Post-Training Method for BERT in Response Selection | We focus on multi-turn response selection in a retrieval-based dialog system. In this paper, we utilize the powerful pre-trained language model Bi-directional Encoder Representations from Transformer (BERT) for a multi-turn dialog system and propose a highly effective post-training method on domain-specific corpus. Alt... | ['Heuiseok Lim', 'Dongsuk Oh', 'Taesun Whang', 'Kisu Yang', 'Chanhee Lee', 'Dongyub Lee'] | 2019-08-13 | null | null | null | null | ['conversational-response-selection'] | ['natural-language-processing'] | [ 3.69920768e-03 1.36726931e-01 -2.29537264e-01 -6.15530074e-01
-1.49703777e+00 -7.14347422e-01 5.83408892e-01 -1.21036686e-01
-7.08201349e-01 9.80536878e-01 5.54346263e-01 -4.60686773e-01
2.18204573e-01 -5.40618122e-01 -2.44866893e-01 -2.05240130e-01
3.69824857e-01 1.13059890e+00 3.47012877e-01 -1.22921002... | [12.414101600646973, 7.8055267333984375] |
74fbc1d2-f2cc-44be-949a-a99d6332c1c6 | sequential-monte-carlo-steering-of-large | 2306.03081 | null | https://arxiv.org/abs/2306.03081v1 | https://arxiv.org/pdf/2306.03081v1.pdf | Sequential Monte Carlo Steering of Large Language Models using Probabilistic Programs | Even after fine-tuning and reinforcement learning, large language models (LLMs) can be difficult, if not impossible, to control reliably with prompts alone. We propose a new inference-time approach to enforcing syntactic and semantic constraints on the outputs of LLMs, called sequential Monte Carlo (SMC) steering. The ... | ['Vikash K. Mansinghka', 'Gabriel Grand', 'Tan Zhi-Xuan', 'Alexander K. Lew'] | 2023-06-05 | null | null | null | null | ['probabilistic-programming'] | ['methodology'] | [ 1.10069029e-01 2.29868054e-01 2.95733269e-02 -5.68372250e-01
-1.41883230e+00 -9.62139666e-01 9.37027514e-01 -2.41934463e-01
-2.15704367e-01 8.30652237e-01 1.18333660e-01 -8.20981205e-01
3.00959498e-02 -9.37586129e-01 -9.26696479e-01 -3.55707169e-01
2.73052454e-01 1.08586180e+00 1.83467105e-01 1.99872911... | [8.57483196258545, 6.836208820343018] |
fec7f6f0-b2d6-4671-95bd-88ea8b25c5cb | multi-granularity-semantic-aware-graph-model-1 | null | null | https://aclanthology.org/2022.findings-acl.95 | https://aclanthology.org/2022.findings-acl.95.pdf | Multi-Granularity Semantic Aware Graph Model for Reducing Position Bias in Emotion Cause Pair Extraction | The emotion cause pair extraction (ECPE) task aims to extract emotions and causes as pairs from documents. We observe that the relative distance distribution of emotions and causes is extremely imbalanced in the typical ECPE dataset. Existing methods have set a fixed size window to capture relations between neighboring... | ['Songlin Hu', 'Wei Zhou', 'Lingwei Wei', 'Qianwen Ma', 'Yinan Bao'] | null | null | null | null | findings-acl-2022-5 | ['emotion-cause-pair-extraction'] | ['natural-language-processing'] | [-2.76605505e-03 6.78154901e-02 -3.54824007e-01 -7.33364642e-01
-7.66733825e-01 -6.13558590e-01 5.20533860e-01 5.01452923e-01
-1.94419295e-01 6.89806402e-01 4.67298031e-01 1.76121220e-01
-6.82684898e-01 -9.08307791e-01 -5.34635782e-01 -5.95813215e-01
7.51504302e-02 3.37923825e-01 -1.68259311e-02 -5.54105699... | [12.620035171508789, 6.212493419647217] |
d42916db-7612-4ee5-88f0-b24764c94044 | towards-table-to-text-generation-with | null | null | https://aclanthology.org/2021.acl-long.115 | https://aclanthology.org/2021.acl-long.115.pdf | Towards Table-to-Text Generation with Numerical Reasoning | Recent neural text generation models have shown significant improvement in generating descriptive text from structured data such as table formats. One of the remaining important challenges is generating more analytical descriptions that can be inferred from facts in a data source. The use of a template-based generator ... | ['Hiroya Takamura', 'Manabu Okumura', 'Kotaro Funakoshi', 'Hidetaka Kamigaito', 'Lya Hulliyyatus Suadaa'] | 2021-08-01 | null | null | null | acl-2021-5 | ['table-to-text-generation'] | ['natural-language-processing'] | [ 2.03481823e-01 8.79383564e-01 -3.40624377e-02 -3.68209988e-01
-1.00146747e+00 -7.50357211e-01 9.47499454e-01 5.28262973e-01
1.75277553e-02 1.23050117e+00 7.39252627e-01 -3.17886293e-01
2.39456788e-01 -1.36014569e+00 -8.18426073e-01 9.96831805e-02
5.69841623e-01 7.86342442e-01 -2.37611964e-01 -5.40364802... | [11.599912643432617, 8.82263469696045] |
89ce245d-3e7f-43da-9a57-e113d3f608ca | bidirectional-machine-reading-comprehension | 2103.07665 | null | https://arxiv.org/abs/2103.07665v1 | https://arxiv.org/pdf/2103.07665v1.pdf | Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet Extraction | Aspect sentiment triplet extraction (ASTE), which aims to identify aspects from review sentences along with their corresponding opinion expressions and sentiments, is an emerging task in fine-grained opinion mining. Since ASTE consists of multiple subtasks, including opinion entity extraction, relation detection, and s... | ['Yuelin Wang', 'Jie Liu', 'Yu Wang', 'Shaowei Chen'] | 2021-03-13 | null | null | null | null | ['aspect-sentiment-triplet-extraction'] | ['natural-language-processing'] | [ 2.76056051e-01 -1.07149065e-01 -2.28858739e-01 -5.71367323e-01
-6.84401274e-01 -7.38258839e-01 5.47036529e-01 2.20545724e-01
-2.85970181e-01 5.31086922e-01 4.03711587e-01 -6.05137706e-01
7.73051009e-02 -7.66156852e-01 -4.22289819e-01 -5.66527188e-01
6.20457590e-01 1.55201867e-01 2.08817318e-01 -4.27535385... | [11.530445098876953, 6.568080425262451] |
13434fa9-2f6a-45fc-b7ee-ed85f5f7e867 | value-memory-graph-a-graph-structured-world | 2206.04384 | null | https://arxiv.org/abs/2206.04384v3 | https://arxiv.org/pdf/2206.04384v3.pdf | Value Memory Graph: A Graph-Structured World Model for Offline Reinforcement Learning | Reinforcement Learning (RL) methods are typically applied directly in environments to learn policies. In some complex environments with continuous state-action spaces, sparse rewards, and/or long temporal horizons, learning a good policy in the original environments can be difficult. Focusing on the offline RL setting,... | ['Mohamed Elhoseiny', 'Li Erran Li', 'Deyao Zhu'] | 2022-06-09 | null | null | null | null | ['d4rl'] | ['robots'] | [-1.89506054e-01 2.81682968e-01 -5.64775050e-01 2.17312679e-01
-4.18233246e-01 -6.96867347e-01 7.02136099e-01 3.35659161e-02
-5.49652100e-01 1.05246985e+00 1.21004157e-01 -5.00335157e-01
-6.84664398e-02 -9.52978313e-01 -9.02968824e-01 -6.89725518e-01
-5.79917073e-01 6.52786672e-01 8.98443013e-02 -1.71321705... | [4.099844932556152, 1.6367292404174805] |
f9be4830-774a-48e1-bcc9-0576061ca6fc | modelling-observation-correlations-for-active | 1401.4612 | null | http://arxiv.org/abs/1401.4612v1 | http://arxiv.org/pdf/1401.4612v1.pdf | Modelling Observation Correlations for Active Exploration and Robust Object Detection | Today, mobile robots are expected to carry out increasingly complex tasks in
multifarious, real-world environments. Often, the tasks require a certain
semantic understanding of the workspace. Consider, for example, spoken
instructions from a human collaborator referring to objects of interest; the
robot must be able to... | ['Ingmar Posner', 'Albert S. Huang', 'Nicholas Roy', 'Javier Velez', 'Garrett Hemann'] | 2014-01-18 | null | null | null | null | ['robust-object-detection'] | ['computer-vision'] | [ 5.71698904e-01 1.23431668e-01 2.12808758e-01 -4.09276694e-01
-6.16401017e-01 -5.22057950e-01 4.63395685e-01 4.51490045e-01
-5.55408418e-01 4.40352678e-01 4.54639718e-02 7.77590042e-03
-5.17265439e-01 -5.83503604e-01 -8.00853550e-01 -5.18889904e-01
-2.60810941e-01 8.91690433e-01 3.86546373e-01 -2.20100313... | [4.77951192855835, 0.7773662209510803] |
d883050c-59af-4999-9189-92d94aef0a3d | form-nlu-dataset-for-the-form-language | 2304.01577 | null | https://arxiv.org/abs/2304.01577v2 | https://arxiv.org/pdf/2304.01577v2.pdf | Form-NLU: Dataset for the Form Language Understanding | Compared to general document analysis tasks, form document structure understanding and retrieval are challenging. Form documents are typically made by two types of authors; A form designer, who develops the form structure and keys, and a form user, who fills out form values based on the provided keys. Hence, the form v... | ['Soyeon Caren Han', 'Hyunsuk Chung', 'Xingxiang Luo', 'Kaixuan Ren', 'Jiabin Huang', 'Siqu Long', 'Yihao Ding'] | 2023-04-04 | null | null | null | null | ['key-information-extraction'] | ['natural-language-processing'] | [ 4.64460820e-01 -2.84073591e-01 -1.38571516e-01 -1.74596384e-01
-5.04583001e-01 -1.13434863e+00 4.26416218e-01 4.58565086e-01
2.19559714e-01 3.32779557e-01 2.19989736e-02 -5.00061691e-01
-6.85532331e-01 -8.89436483e-01 -4.91906762e-01 5.39436890e-03
4.34987158e-01 6.71978235e-01 4.32949625e-02 -4.84722061... | [11.623120307922363, 2.62036395072937] |
3ccba667-0509-4b8a-980f-64e5e80eb29c | outcast-outdoor-single-image-relighting-with | 2204.09341 | null | https://arxiv.org/abs/2204.09341v1 | https://arxiv.org/pdf/2204.09341v1.pdf | OutCast: Outdoor Single-image Relighting with Cast Shadows | We propose a relighting method for outdoor images. Our method mainly focuses on predicting cast shadows in arbitrary novel lighting directions from a single image while also accounting for shading and global effects such the sun light color and clouds. Previous solutions for this problem rely on reconstructing occluder... | ['Julien Philip', 'Tobias Ritschel', 'David Griffiths'] | 2022-04-20 | null | null | null | null | ['image-relighting'] | ['computer-vision'] | [ 3.35672230e-01 6.26197900e-04 4.16341931e-01 -5.90967774e-01
-7.84499824e-01 -6.60887063e-01 6.35079920e-01 7.95277134e-02
-1.45802379e-01 6.78849161e-01 4.11702134e-02 -3.04751217e-01
4.02984679e-01 -1.16037774e+00 -9.40579116e-01 -5.82478642e-01
3.08911711e-01 6.67813838e-01 5.94722271e-01 -3.01546961... | [9.668317794799805, -3.0238871574401855] |
b8e33fa4-826f-49f1-9402-22d4d5022f76 | demeshnet-blind-face-inpainting-for-deep | 1611.05271 | null | http://arxiv.org/abs/1611.05271v1 | http://arxiv.org/pdf/1611.05271v1.pdf | DeMeshNet: Blind Face Inpainting for Deep MeshFace Verification | MeshFace photos have been widely used in many Chinese business organizations
to protect ID face photos from being misused. The occlusions incurred by random
meshes severely degenerate the performance of face verification systems, which
raises the MeshFace verification problem between MeshFace and daily photos.
Previous... | ['Tieniu Tan', 'Shu Zhang', 'Ran He'] | 2016-11-16 | null | null | null | null | ['facial-inpainting'] | ['computer-vision'] | [ 2.73733616e-01 5.73782437e-02 -1.41502336e-01 -5.05623996e-01
-5.30058384e-01 -3.91608357e-01 3.70443761e-01 -7.89733708e-01
-5.61230443e-02 5.13788819e-01 2.16989398e-01 -1.18871639e-02
-4.24698256e-02 -6.19022548e-01 -7.80345976e-01 -7.64184654e-01
3.95084023e-01 -6.25199378e-02 -4.15556341e-01 -2.58585867... | [12.85180950164795, 0.17247916758060455] |
070e1ea8-3487-431d-ac7e-64b7599d7250 | compacting-picking-and-growing-for | 1910.06562 | null | https://arxiv.org/abs/1910.06562v3 | https://arxiv.org/pdf/1910.06562v3.pdf | Compacting, Picking and Growing for Unforgetting Continual Learning | Continual lifelong learning is essential to many applications. In this paper, we propose a simple but effective approach to continual deep learning. Our approach leverages the principles of deep model compression, critical weights selection, and progressive networks expansion. By enforcing their integration in an itera... | ['Chu-Song Chen', 'Yi-Ming Chan', 'Chien-Hung Chen', 'Cheng-En Wu', 'Cheng-Hao Tu', 'Steven C. Y. Hung'] | 2019-10-15 | compacting-picking-and-growing-for-1 | http://papers.nips.cc/paper/9518-compacting-picking-and-growing-for-unforgetting-continual-learning | http://papers.nips.cc/paper/9518-compacting-picking-and-growing-for-unforgetting-continual-learning.pdf | neurips-2019-12 | ['age-and-gender-classification'] | ['computer-vision'] | [ 2.92096138e-01 -1.33111522e-01 -2.60597646e-01 -2.84450561e-01
-2.90551603e-01 -1.60551280e-01 2.35894665e-01 1.46743298e-01
-7.87927628e-01 9.17993009e-01 -7.65623972e-02 -8.09279606e-02
-4.13647324e-01 -6.71009600e-01 -7.96370804e-01 -7.30560184e-01
-1.03154639e-02 7.15126872e-01 6.69153750e-01 4.62783985... | [9.789528846740723, 3.4619245529174805] |
d48cb87e-6eb7-4490-908a-40bbdda901a9 | instance-neural-radiance-field | 2304.04395 | null | https://arxiv.org/abs/2304.04395v1 | https://arxiv.org/pdf/2304.04395v1.pdf | Instance Neural Radiance Field | This paper presents one of the first learning-based NeRF 3D instance segmentation pipelines, dubbed as Instance Neural Radiance Field, or Instance NeRF. Taking a NeRF pretrained from multi-view RGB images as input, Instance NeRF can learn 3D instance segmentation of a given scene, represented as an instance field compo... | ['Chi-Keung Tang', 'Yu-Wing Tai', 'Yichen Liu', 'Junkai Huang', 'Benran Hu'] | 2023-04-10 | null | null | null | null | ['3d-instance-segmentation-1', 'panoptic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.66546512e-01 4.62245435e-01 7.31446818e-02 -5.95058262e-01
-8.63909721e-01 -9.48792875e-01 6.03050113e-01 -1.92580357e-01
-1.83759630e-01 5.41206300e-01 -3.91537458e-01 -7.35562593e-02
-2.24342674e-01 -1.22527683e+00 -1.25764728e+00 -3.95849913e-01
2.00863376e-01 8.97876978e-01 4.31221426e-01 -9.77087859... | [8.637894630432129, -2.99531626701355] |
d1c03036-a55f-41aa-a587-8ac30409f9a6 | multi-label-image-classification-using | 2301.04494 | null | https://arxiv.org/abs/2301.04494v1 | https://arxiv.org/pdf/2301.04494v1.pdf | Multi-label Image Classification using Adaptive Graph Convolutional Networks: from a Single Domain to Multiple Domains | This paper proposes an adaptive graph-based approach for multi-label image classification. Graph-based methods have been largely exploited in the field of multi-label classification, given their ability to model label correlations. Specifically, their effectiveness has been proven not only when considering a single dom... | ['Djamila Aouada', 'Oyebade Oyedotun', 'Enjie Ghorbel', 'Indel Pal Singh'] | 2023-01-11 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 3.65173817e-01 7.21296445e-02 -2.65909344e-01 -3.93675566e-01
-6.14762068e-01 -3.54980111e-01 7.19697833e-01 7.79634237e-01
-4.98562217e-01 5.96723437e-01 -3.21104556e-01 -3.44630480e-02
-3.25574487e-01 -7.29251266e-01 -5.23480415e-01 -7.18713641e-01
-3.36494995e-03 6.59587860e-01 2.86653757e-01 -1.76766455... | [9.726165771484375, 3.7618958950042725] |
5af707d2-1ba4-4b96-aff3-c249432e1338 | distribution-learning-based-on-evolutionary | 2207.12744 | null | https://arxiv.org/abs/2207.12744v1 | https://arxiv.org/pdf/2207.12744v1.pdf | Distribution Learning Based on Evolutionary Algorithm Assisted Deep Neural Networks for Imbalanced Image Classification | To address the trade-off problem of quality-diversity for the generated images in imbalanced classification tasks, we research on over-sampling based methods at the feature level instead of the data level and focus on searching the latent feature space for optimal distributions. On this basis, we propose an iMproved Es... | ['Bing Wei', 'Chaochen Gu', 'Kuangrong Hao', 'Yudi Zhao'] | 2022-07-26 | null | null | null | null | ['imbalanced-classification'] | ['miscellaneous'] | [-9.39281061e-02 -3.69066417e-01 -2.71173269e-02 -3.60863805e-01
-2.68464953e-01 1.40777886e-01 9.45259444e-03 -3.76527719e-02
-5.99817075e-02 7.84471214e-01 -1.94697112e-01 1.07624471e-01
-5.01561224e-01 -1.13028967e+00 -3.73741537e-01 -1.14822996e+00
1.43708140e-01 4.03802514e-01 -1.11437656e-01 4.22467571... | [9.034748077392578, 3.7839536666870117] |
28329709-a56a-4542-9220-b3328489bf91 | complete-the-look-scene-based-complementary | 1812.01748 | null | http://arxiv.org/abs/1812.01748v2 | http://arxiv.org/pdf/1812.01748v2.pdf | Complete the Look: Scene-based Complementary Product Recommendation | Modeling fashion compatibility is challenging due to its complexity and
subjectivity. Existing work focuses on predicting compatibility between product
images (e.g. an image containing a t-shirt and an image containing a pair of
jeans). However, these approaches ignore real-world 'scene' images (e.g.
selfies); such ima... | ['Jure Leskovec', 'Wang-Cheng Kang', 'Eric Kim', 'Charles Rosenberg', 'Julian McAuley'] | 2018-12-04 | complete-the-look-scene-based-complementary-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Kang_Complete_the_Look_Scene-Based_Complementary_Product_Recommendation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Kang_Complete_the_Look_Scene-Based_Complementary_Product_Recommendation_CVPR_2019_paper.pdf | cvpr-2019-6 | ['product-recommendation'] | ['miscellaneous'] | [ 1.53545752e-01 -3.65614206e-01 -1.69497088e-01 -9.77358401e-01
-2.07048506e-01 -6.15956903e-01 4.59417373e-01 1.18789777e-01
5.74267693e-02 -6.47062138e-02 3.29607785e-01 -2.83679187e-01
-1.94971319e-02 -5.88277876e-01 -9.51597333e-01 -2.89311469e-01
2.26010382e-01 6.95678219e-02 -1.10232696e-01 -4.68118459... | [11.088159561157227, 0.08775128424167633] |
febd8ee4-6d6c-4dc3-bd42-26439e0ed80b | improving-automated-program-repair-with | 2212.11414 | null | https://arxiv.org/abs/2212.11414v1 | https://arxiv.org/pdf/2212.11414v1.pdf | Improving Automated Program Repair with Domain Adaptation | Automated Program Repair (APR) is defined as the process of fixing a bug/defect in the source code, by an automated tool. APR tools have recently experienced promising results by leveraging state-of-the-art Neural Language Processing (NLP) techniques. APR tools such as TFix and CodeXGLUE combine text-to-text transforme... | ['Hadi Hemati', 'Armin Zirak'] | 2022-12-21 | null | null | null | null | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [-9.69913974e-02 7.79239759e-02 -1.80598393e-01 -2.07200095e-01
-7.13968992e-01 -4.29836094e-01 1.68193504e-01 1.12275295e-01
-1.21467710e-01 6.87913239e-01 -5.63057661e-02 -3.29160959e-01
-5.89732267e-02 -7.84364879e-01 -9.81221139e-01 -2.40266532e-01
5.64658865e-02 2.82658070e-01 3.63569319e-01 -3.58815223... | [7.633476734161377, 7.752171516418457] |
6b26dbf7-ca59-4c21-9386-75289ce37f34 | towards-better-dermoscopic-image-feature | 2207.07303 | null | https://arxiv.org/abs/2207.07303v1 | https://arxiv.org/pdf/2207.07303v1.pdf | Towards Better Dermoscopic Image Feature Representation Learning for Melanoma Classification | Deep learning-based melanoma classification with dermoscopic images has recently shown great potential in automatic early-stage melanoma diagnosis. However, limited by the significant data imbalance and obvious extraneous artifacts, i.e., the hair and ruler markings, discriminative feature extraction from dermoscopic i... | ['Xiu Li', 'Xiao-jun Zeng', 'Yu Yang', 'HanMo Chen', 'Jiangpeng Yan', 'Zhe Xu', 'Mingqing Wang', 'ShengGe Yang', 'Mingkang Tang', 'Chenghui Yu'] | 2022-07-15 | null | null | null | null | ['melanoma-diagnosis'] | ['computer-vision'] | [ 6.96221650e-01 2.50289381e-01 -1.45155370e-01 -1.36758834e-01
-9.36045349e-01 -1.76568896e-01 6.12737298e-01 1.08005717e-01
-6.52386099e-02 6.91369653e-01 1.34125546e-01 2.76913159e-02
-5.02162129e-02 -6.78692818e-01 -4.95822847e-01 -1.12207675e+00
4.49923456e-01 -5.50719649e-02 -2.67566949e-01 -6.74009100... | [15.51213550567627, -2.8235180377960205] |
26154b30-476b-44b2-a35a-5906ffce42d6 | lp-ioanet-efficient-high-resolution-document | 2303.12862 | null | https://arxiv.org/abs/2303.12862v1 | https://arxiv.org/pdf/2303.12862v1.pdf | LP-IOANet: Efficient High Resolution Document Shadow Removal | Document shadow removal is an integral task in document enhancement pipelines, as it improves visibility, readability and thus the overall quality. Assuming that the majority of practical document shadow removal scenarios require real-time, accurate models that can produce high-resolution outputs in-the-wild, we propos... | ['Bruno Manganelli', 'Albert Saa-Garriga', 'Anastasios Drosou', 'Valia Dimaridou', 'Evangelos Skartados', 'M. Kerim Yucel', 'Konstantinos Georgiadis'] | 2023-03-22 | null | null | null | null | ['document-enhancement', 'shadow-removal'] | ['computer-vision', 'computer-vision'] | [ 6.99793875e-01 -2.88763344e-01 6.29438281e-01 -1.77323371e-01
-5.74921191e-01 -5.37154198e-01 5.65625548e-01 -2.68923819e-01
-1.97411269e-01 3.72772723e-01 3.25540304e-01 -4.58655953e-01
3.09664607e-01 -5.59595048e-01 -8.36545587e-01 -5.58528543e-01
1.71807051e-01 -9.04671028e-02 6.03445768e-01 -8.35694224... | [10.879341125488281, -3.977834701538086] |
5411d75f-7163-48e8-a3cb-b937fc688f97 | object-centered-image-stitching-1 | 2011.11789 | null | https://arxiv.org/abs/2011.11789v1 | https://arxiv.org/pdf/2011.11789v1.pdf | Object-centered image stitching | Image stitching is typically decomposed into three phases: registration, which aligns the source images with a common target image; seam finding, which determines for each target pixel the source image it should come from; and blending, which smooths transitions over the seams. As described in [1], the seam finding pha... | ['Ramin Zabih', 'Emil Keyder', 'Richard Strong Bowen', 'Chen Wang', 'Charles Herrmann'] | 2020-11-23 | object-centered-image-stitching | http://openaccess.thecvf.com/content_ECCV_2018/html/Charles_Herrmann_Object-centered_image_stitching_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Charles_Herrmann_Object-centered_image_stitching_ECCV_2018_paper.pdf | eccv-2018-9 | ['image-stitching'] | ['computer-vision'] | [ 9.68048215e-01 -1.68716878e-01 1.43660724e-01 -8.65716115e-02
-6.87287867e-01 -8.27035666e-01 7.15004921e-01 2.59946913e-01
-1.72858939e-01 3.15649778e-01 3.92329246e-02 -9.78716835e-02
7.43204802e-02 -3.25463653e-01 -5.16098440e-01 -7.33700573e-01
8.72055907e-03 1.78213254e-01 5.28283715e-01 -9.08124149... | [9.413105964660645, -2.3511838912963867] |
b74755a3-f775-46e4-b13c-22871725e4ee | diffusion-based-generation-optimization-and | 2301.06015 | null | https://arxiv.org/abs/2301.06015v1 | https://arxiv.org/pdf/2301.06015v1.pdf | Diffusion-based Generation, Optimization, and Planning in 3D Scenes | We introduce SceneDiffuser, a conditional generative model for 3D scene understanding. SceneDiffuser provides a unified model for solving scene-conditioned generation, optimization, and planning. In contrast to prior works, SceneDiffuser is intrinsically scene-aware, physics-based, and goal-oriented. With an iterative ... | ['Song-Chun Zhu', 'Wei Liang', 'Yixin Zhu', 'Tengyu Liu', 'Baoxiong Jia', 'Puhao Li', 'Zan Wang', 'Siyuan Huang'] | 2023-01-15 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Huang_Diffusion-Based_Generation_Optimization_and_Planning_in_3D_Scenes_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Huang_Diffusion-Based_Generation_Optimization_and_Planning_in_3D_Scenes_CVPR_2023_paper.pdf | cvpr-2023-1 | ['grasp-generation', 'motion-planning'] | ['computer-vision', 'robots'] | [ 8.57294425e-02 2.22055197e-01 9.40242633e-02 -4.04138297e-01
-5.49745977e-01 -5.31217217e-01 7.38965511e-01 -1.92500189e-01
2.81974405e-01 2.14466691e-01 2.61289924e-01 -3.26173037e-01
-3.03188175e-01 -8.40666294e-01 -7.04046786e-01 -5.79041243e-01
4.20997776e-02 9.24010694e-01 -1.67605802e-01 -3.09233785... | [8.911052703857422, -3.053144693374634] |
ac1ae2a3-d7d2-4f92-b54d-aa9f9bc0c61b | unsupervised-domain-adaptation-by-adversarial | 1807.11284 | null | http://arxiv.org/abs/1807.11284v1 | http://arxiv.org/pdf/1807.11284v1.pdf | Unsupervised Domain Adaptation by Adversarial Learning for Robust Speech Recognition | In this paper, we investigate the use of adversarial learning for
unsupervised adaptation to unseen recording conditions, more specifically,
single microphone far-field speech. We adapt neural networks based acoustic
models trained with close-talk clean speech to the new recording conditions
using untranscribed adaptat... | ['Ngoc Thang Vu', 'Pavel Denisov', 'Marc Ferras Font'] | 2018-07-30 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 3.74788642e-01 2.15851769e-01 6.77180529e-01 -4.08737719e-01
-1.35752285e+00 -6.09417021e-01 2.01820329e-01 -4.22101080e-01
-7.36991107e-01 7.95945883e-01 2.95255333e-01 -2.23619089e-01
3.49500299e-01 -3.01674277e-01 -9.30030763e-01 -8.72351289e-01
-2.00411826e-02 -8.28128681e-02 9.49827395e-03 -1.57953113... | [14.737072944641113, 6.2922444343566895] |
b8e614c5-5afa-4abc-8df5-b43d3917544e | data2vec-a-general-framework-for-self-1 | 2202.03555 | null | https://arxiv.org/abs/2202.03555v3 | https://arxiv.org/pdf/2202.03555v3.pdf | data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language | While the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because they were developed with a single modality in mind. To get us closer to general self-supervised learning, we present data2vec, a framework that uses the same learning method for ... | ['Michael Auli', 'Jiatao Gu', 'Arun Babu', 'Qiantong Xu', 'Wei-Ning Hsu', 'Alexei Baevski'] | 2022-02-07 | null | null | null | preprint-2022-1 | ['paraphrase-identification', 'linguistic-acceptability'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.41257179e-01 4.30253655e-01 -4.93446648e-01 -5.55244029e-01
-6.03839219e-01 -4.70512003e-01 1.39564729e+00 -1.38172880e-01
-4.05477807e-02 5.03839135e-01 8.87456954e-01 -2.06811488e-01
4.35774833e-01 -5.99042952e-01 -6.80590928e-01 -8.40083182e-01
3.25437605e-01 4.33704942e-01 -2.11317435e-01 -1.62265763... | [10.476151466369629, 2.2121756076812744] |
d29a0219-2f70-4c73-8089-ee6d60edd7d9 | cascade-evidential-learning-for-open-world | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chen_Cascade_Evidential_Learning_for_Open-World_Weakly-Supervised_Temporal_Action_Localization_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chen_Cascade_Evidential_Learning_for_Open-World_Weakly-Supervised_Temporal_Action_Localization_CVPR_2023_paper.pdf | Cascade Evidential Learning for Open-World Weakly-Supervised Temporal Action Localization | Targeting at recognizing and localizing action instances with only video-level labels during training, Weakly-supervised Temporal Action Localization (WTAL) has achieved significant progress in recent years. However, living in the dynamically changing open world where unknown actions constantly spring up, the close... | ['Changsheng Xu', 'Junyu Gao', 'Mengyuan Chen'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['weakly-supervised-temporal-action', 'action-localization', 'action-recognition', 'open-set-learning'] | ['computer-vision', 'computer-vision', 'computer-vision', 'miscellaneous'] | [ 5.26732922e-01 -4.94176410e-02 -6.66644454e-01 -1.74735442e-01
-8.28569710e-01 -6.06487274e-01 6.43380582e-01 -2.87051678e-01
-2.87038386e-01 7.93908834e-01 4.25593108e-01 1.66480213e-01
2.26150528e-02 -2.22769469e-01 -6.90268397e-01 -6.52217269e-01
-1.36759534e-01 1.63119197e-01 6.95898950e-01 2.90188432... | [8.560032844543457, 0.6972261667251587] |
b0e8d0f3-2652-41b8-a14e-11a83e7f875f | a-dual-modality-approach-for-zero-shot-multi | 2208.09562 | null | https://arxiv.org/abs/2208.09562v1 | https://arxiv.org/pdf/2208.09562v1.pdf | A Dual Modality Approach For (Zero-Shot) Multi-Label Classification | In computer vision, multi-label classification, including zero-shot multi-label classification are important tasks with many real-world applications. In this paper, we propose a novel algorithm, Aligned Dual moDality ClaSsifier (ADDS), which includes a Dual-Modal decoder (DM-decoder) with alignment between visual and t... | ['Zhu Qi', 'Chiuman Ho', 'Jenhao Hsiao', 'Yikang Li', 'Shichao Xu'] | 2022-08-19 | null | null | null | null | ['multi-label-zero-shot-learning'] | ['computer-vision'] | [ 6.07253134e-01 -1.95774361e-01 -3.13216120e-01 -3.60910058e-01
-1.23637509e+00 -4.13863957e-01 6.93588316e-01 2.34846994e-01
-3.89054000e-01 5.03889859e-01 -1.94342896e-01 -1.10258989e-01
3.53098549e-02 -2.44479477e-01 -4.93209779e-01 -7.28483379e-01
6.83439016e-01 3.37554485e-01 2.84932256e-01 8.60203505... | [9.75380802154541, 3.996514320373535] |
a735dee1-ff60-4a46-9212-dec5a5f413bf | an-embedding-for-eeg-signals-learned-using-a | 2304.06495 | null | https://arxiv.org/abs/2304.06495v1 | https://arxiv.org/pdf/2304.06495v1.pdf | An embedding for EEG signals learned using a triplet loss | Neurophysiological time series recordings like the electroencephalogram (EEG) or local field potentials are obtained from multiple sensors. They can be decoded by machine learning models in order to estimate the ongoing brain state of a patient or healthy user. In a brain-computer interface (BCI), this decoded brain st... | ['Michael Tangermann', 'Théodore Papadopoulo', 'Pierre Guetschel'] | 2023-03-23 | null | null | null | null | ['metric-learning', 'eeg', 'metric-learning', 'eeg'] | ['computer-vision', 'methodology', 'methodology', 'time-series'] | [ 4.24473226e-01 1.39483800e-02 2.81389236e-01 -3.75906169e-01
-4.41552043e-01 -3.00895840e-01 5.64636886e-01 4.32471871e-01
-8.64784420e-01 9.06262040e-01 -1.93580147e-02 -1.80524647e-01
-4.19852465e-01 -5.53006113e-01 -5.55340707e-01 -5.51924765e-01
-2.94174373e-01 6.07008576e-01 1.33456200e-01 -1.03247263... | [13.123900413513184, 3.4431936740875244] |
9c4a0e12-54b7-458f-9e38-8be82f92352c | optimal-investment-under-partial-observations | 2212.04394 | null | https://arxiv.org/abs/2212.04394v1 | https://arxiv.org/pdf/2212.04394v1.pdf | Optimal investment under partial observations and robust VaR-type constraint | The present paper extends the literature on utility maximization by combining the framework of partial information and (robust) regulatory constraints. Partial information is characterized by the fact that the stock price itself is observable to the optimizing financial institution, but the outcome of the market price ... | ['An Chen', 'Nicole Bäuerle'] | 2022-12-08 | null | null | null | null | ['type'] | ['speech'] | [-3.04665416e-01 6.28484666e-01 -6.39406025e-01 6.42128363e-02
-2.20105439e-01 -7.80518115e-01 1.26755014e-01 -2.24478170e-01
-7.08609521e-01 9.08707857e-01 7.50240162e-02 -4.43001062e-01
-6.03921890e-01 -9.00111675e-01 -2.72145569e-01 -9.27816570e-01
2.23887518e-01 2.97642320e-01 -8.53706617e-03 -1.21183693... | [4.876237392425537, 3.827347755432129] |
12033d29-4c57-4745-aa0d-529f78293c9b | multi-modal-siamese-network-for-entity | null | null | https://dl.acm.org/doi/10.1145/3534678.3539244 | https://dl.acm.org/doi/10.1145/3534678.3539244 | Multi-modal Siamese Network for Entity Alignment | The booming of multi-modal knowledge graphs (MMKGs) has raised the imperative demand for multi-modal entity alignment techniques, which facilitate the integration of multiple MMKGs from separate data sources. Unfortunately, prior arts harness multi-modal knowledge only via the heuristic merging of uni-modal feature emb... | ['Enhong Chen', 'Nicholas Jing Yuan', 'Zhefeng Wang', 'Han Wu', 'Tong Xu', 'Zhi Li', 'Liyi Chen'] | 2022-08-14 | null | null | null | kdd-2022-8 | ['multi-modal-entity-alignment', 'entity-alignment', 'multi-modal-knowledge-graph', 'entity-alignment'] | ['knowledge-base', 'knowledge-base', 'knowledge-base', 'natural-language-processing'] | [-1.61287218e-01 6.40243962e-02 -3.39210123e-01 -1.34720027e-01
-7.26059198e-01 -5.86744905e-01 5.62270284e-01 2.48832881e-01
-3.36647004e-01 2.54189998e-01 5.25393724e-01 2.12890655e-01
-4.27701324e-01 -8.67797554e-01 -5.85627377e-01 -6.74581170e-01
1.11839108e-01 8.86333808e-02 -3.10582500e-02 -2.24815518... | [8.698372840881348, 7.719961643218994] |
d0b04baa-19ee-495c-be6d-bad30a0d2c03 | end-to-end-spatio-temporal-action | 2304.12160 | null | https://arxiv.org/abs/2304.12160v1 | https://arxiv.org/pdf/2304.12160v1.pdf | End-to-End Spatio-Temporal Action Localisation with Video Transformers | The most performant spatio-temporal action localisation models use external person proposals and complex external memory banks. We propose a fully end-to-end, purely-transformer based model that directly ingests an input video, and outputs tubelets -- a sequence of bounding boxes and the action classes at each frame. O... | ['Anurag Arnab', 'Cordelia Schmid', 'Mario Lučić', 'Chen Sun', 'Mostafa Dehghani', 'Josip Djolonga', 'Xuehan Xiong', 'Alexey Gritsenko'] | 2023-04-24 | null | null | null | null | ['action-recognition-in-videos', 'spatio-temporal-action-localization'] | ['computer-vision', 'computer-vision'] | [ 3.14143032e-01 1.08686879e-01 -2.23882347e-01 -3.43138397e-01
-8.19466889e-01 -3.23451579e-01 7.94998705e-01 -2.95701712e-01
-7.36637890e-01 5.71952999e-01 7.53541827e-01 1.74627349e-01
3.27783287e-01 -4.16692555e-01 -6.78198159e-01 -4.38190728e-01
-1.80706635e-01 5.42008758e-01 6.47685111e-01 -1.08718939... | [8.34424114227295, 0.4236794412136078] |
17aad19b-146f-402f-a221-4ffc6e799fe0 | long-range-feature-propagating-for-natural | 2109.12252 | null | https://arxiv.org/abs/2109.12252v1 | https://arxiv.org/pdf/2109.12252v1.pdf | Long-Range Feature Propagating for Natural Image Matting | Natural image matting estimates the alpha values of unknown regions in the trimap. Recently, deep learning based methods propagate the alpha values from the known regions to unknown regions according to the similarity between them. However, we find that more than 50\% pixels in the unknown regions cannot be correlated ... | ['Rongrong Ji', 'Bineng Zhong', 'Shengping Zhang', 'Haozhe Xie', 'Qinglin Liu'] | 2021-09-25 | null | null | null | null | ['image-matting'] | ['computer-vision'] | [ 2.53762513e-01 -1.32515863e-01 3.57421488e-02 -6.28406882e-01
-5.57317376e-01 -1.40299127e-01 2.30705321e-01 -2.08574489e-01
-3.83086860e-01 8.39881241e-01 1.38426587e-01 2.06454098e-01
1.29769355e-01 -1.12957275e+00 -1.17209351e+00 -9.14023697e-01
1.11381583e-01 -5.23016416e-02 4.03240621e-01 2.15886394... | [10.666590690612793, -0.9101769924163818] |
0382f802-fd14-479c-8938-7fc2aa204e13 | human-alignment-of-neural-network | 2211.01201 | null | https://arxiv.org/abs/2211.01201v4 | https://arxiv.org/pdf/2211.01201v4.pdf | Human alignment of neural network representations | Today's computer vision models achieve human or near-human level performance across a wide variety of vision tasks. However, their architectures, data, and learning algorithms differ in numerous ways from those that give rise to human vision. In this paper, we investigate the factors that affect the alignment between t... | ['Simon Kornblith', 'Robert A. Vandermeulen', 'Lorenz Linhardt', 'Jonas Dippel', 'Lukas Muttenthaler'] | 2022-11-02 | null | null | null | null | ['odd-one-out'] | ['reasoning'] | [ 6.82023540e-02 -2.32661784e-01 8.25936869e-02 -7.45111883e-01
1.00333236e-01 -5.30869246e-01 8.66340935e-01 4.17991221e-01
-9.15690184e-01 2.01801360e-01 2.41008699e-01 -2.03855410e-01
-1.22010812e-01 -6.16483688e-01 -5.44860959e-01 -1.97035626e-01
4.84385043e-01 3.95957112e-01 2.24297613e-01 -3.02255094... | [10.404619216918945, 2.3119213581085205] |
7281aa87-002f-4a30-bb27-48852ce3b941 | membership-privacy-protection-for-image | 2203.05212 | null | https://arxiv.org/abs/2203.05212v1 | https://arxiv.org/pdf/2203.05212v1.pdf | Membership Privacy Protection for Image Translation Models via Adversarial Knowledge Distillation | Image-to-image translation models are shown to be vulnerable to the Membership Inference Attack (MIA), in which the adversary's goal is to identify whether a sample is used to train the model or not. With daily increasing applications based on image-to-image translation models, it is crucial to protect the privacy of t... | ['Yong Zhang', 'Jian Pei', 'Lanjun Wang', 'Saeed Ranjbar Alvar'] | 2022-03-10 | null | null | null | null | ['inference-attack', 'membership-inference-attack'] | ['adversarial', 'computer-vision'] | [ 3.87036085e-01 2.79236436e-01 -5.58402911e-02 -2.96066552e-01
-1.03622413e+00 -9.39659178e-01 5.70254564e-01 -2.63707012e-01
-2.71387249e-01 5.82846105e-01 -2.02990174e-01 -6.92382336e-01
4.88165081e-01 -8.30291867e-01 -1.10659885e+00 -8.74358773e-01
3.13911259e-01 8.88680853e-03 -7.46093094e-02 3.08066428... | [5.904544830322266, 7.141457557678223] |
20d6b8d7-ed37-43f5-bd57-7dcd9b4b8da5 | learning-phone-recognition-from-unpaired | 2207.14568 | null | https://arxiv.org/abs/2207.14568v1 | https://arxiv.org/pdf/2207.14568v1.pdf | Learning Phone Recognition from Unpaired Audio and Phone Sequences Based on Generative Adversarial Network | ASR has been shown to achieve great performance recently. However, most of them rely on massive paired data, which is not feasible for low-resource languages worldwide. This paper investigates how to learn directly from unpaired phone sequences and speech utterances. We design a two-stage iterative framework. GAN train... | ['Hung-Yi Lee', 'Da-Yi Wu', 'Shun-Po Chuang', 'Sung-Feng Huang', 'Yi-Chen Chen', 'Po-chun Hsu', 'Da-Rong Liu'] | 2022-07-29 | null | null | null | null | ['acoustic-unit-discovery'] | ['speech'] | [ 5.56913197e-01 7.18166307e-02 5.12207225e-02 -3.96527588e-01
-1.27965200e+00 -6.12706304e-01 6.46640718e-01 -3.45833778e-01
-4.68781918e-01 7.38023818e-01 2.54060954e-01 -5.23572326e-01
5.25358021e-01 -4.33164537e-01 -6.67015135e-01 -8.47239554e-01
4.02590871e-01 6.85423315e-01 2.02473670e-01 1.27154440... | [14.532453536987305, 6.709886074066162] |
672549bf-ecc0-4ca3-8149-f0fffb2c5628 | tackling-heavy-tailed-rewards-in | 2306.06836 | null | https://arxiv.org/abs/2306.06836v1 | https://arxiv.org/pdf/2306.06836v1.pdf | Tackling Heavy-Tailed Rewards in Reinforcement Learning with Function Approximation: Minimax Optimal and Instance-Dependent Regret Bounds | While numerous works have focused on devising efficient algorithms for reinforcement learning (RL) with uniformly bounded rewards, it remains an open question whether sample or time-efficient algorithms for RL with large state-action space exist when the rewards are \emph{heavy-tailed}, i.e., with only finite $(1+\epsi... | ['Lin F. Yang', 'LiWei Wang', 'Han Zhong', 'Jiayi Huang'] | 2023-06-12 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [-6.70390204e-04 3.37468863e-01 -3.42996448e-01 -1.94255516e-01
-1.32193255e+00 -6.90590143e-01 -1.27126664e-01 1.75848931e-01
-1.16070676e+00 1.22498953e+00 -5.15701830e-01 -7.58749485e-01
-8.74575675e-01 -8.99582267e-01 -9.43578362e-01 -9.35163438e-01
-6.66934609e-01 5.45338035e-01 -1.09120153e-01 -3.24791551... | [4.43297815322876, 3.058152914047241] |
a8e45b60-b9d9-44c4-b748-12059a1e49aa | supervised-dimensionality-reduction-and-1 | 2208.12152 | null | https://arxiv.org/abs/2208.12152v4 | https://arxiv.org/pdf/2208.12152v4.pdf | Supervised Dimensionality Reduction and Image Classification Utilizing Convolutional Autoencoders | The joint optimization of the reconstruction and classification error is a hard non convex problem, especially when a non linear mapping is utilized. In order to overcome this obstacle, a novel optimization strategy is proposed, in which a Convolutional Autoencoder for dimensionality reduction and a classifier composed... | ['Spiros V. Georgakopoulos', 'Vassilis P. Plagianakos', 'Sotiris K. Tasoulis', 'Ioannis A. Nellas'] | 2022-08-25 | null | null | null | null | ['supervised-dimensionality-reduction', 'classification'] | ['computer-vision', 'methodology'] | [-3.48674692e-02 4.73640978e-01 -6.36998862e-02 -4.31516290e-01
1.62638584e-03 -7.73449615e-02 7.86734998e-01 -6.82864189e-02
-2.72900760e-01 7.74830580e-01 -1.58822220e-02 -1.31165370e-01
-7.31445789e-01 -7.10749388e-01 -5.36567748e-01 -8.98905635e-01
2.17613176e-01 4.72626060e-01 -6.42382145e-01 2.79065043... | [8.647355079650879, 3.325664758682251] |
230535dd-b5fb-4bcc-a045-045c8b19c8ef | deep-multitask-learning-for-semantic | 1704.06855 | null | http://arxiv.org/abs/1704.06855v2 | http://arxiv.org/pdf/1704.06855v2.pdf | Deep Multitask Learning for Semantic Dependency Parsing | We present a deep neural architecture that parses sentences into three
semantic dependency graph formalisms. By using efficient, nearly arc-factored
inference and a bidirectional-LSTM composed with a multi-layer perceptron, our
base system is able to significantly improve the state of the art for semantic
dependency pa... | ['Sam Thomson', 'Noah A. Smith', 'Hao Peng'] | 2017-04-22 | deep-multitask-learning-for-semantic-1 | https://aclanthology.org/P17-1186 | https://aclanthology.org/P17-1186.pdf | acl-2017-7 | ['semantic-dependency-parsing'] | ['natural-language-processing'] | [ 2.85889432e-02 6.29321516e-01 -1.61806270e-01 -8.00449073e-01
-1.00313938e+00 -6.43658876e-01 3.69659513e-01 2.67736644e-01
-4.07444984e-01 7.44819045e-01 3.65207344e-01 -9.32376206e-01
5.86812794e-02 -8.45511317e-01 -7.94796109e-01 -3.16406041e-01
4.51091938e-02 8.26827049e-01 2.29267910e-01 -4.01864856... | [10.401695251464844, 9.508073806762695] |
9f8357e5-51c6-4de0-bf20-ada854058c3e | r2d2-reliable-and-repeatable-detector-and | null | null | http://papers.nips.cc/paper/9407-r2d2-reliable-and-repeatable-detector-and-descriptor | http://papers.nips.cc/paper/9407-r2d2-reliable-and-repeatable-detector-and-descriptor.pdf | R2D2: Reliable and Repeatable Detector and Descriptor | Interest point detection and local feature description are fundamental steps in many computer vision applications. Classical approaches are based on a detect-then-describe paradigm where separate handcrafted methods are used to first identify repeatable keypoints and then represent them with a local descriptor. Neural ... | ['Martin Humenberger', 'Jerome Revaud', 'Cesar De Souza', 'Philippe Weinzaepfel'] | 2019-12-01 | null | null | null | neurips-2019-12 | ['interest-point-detection', 'camera-localization', 'image-matching'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-3.58733423e-02 -1.59434125e-01 -4.01491106e-01 -1.05759121e-01
-1.22729063e+00 -5.90667605e-01 9.34405208e-01 7.65876889e-01
-7.01518714e-01 6.08731091e-01 -1.86985999e-01 3.29467624e-01
-3.65730941e-01 -3.94671947e-01 -7.85032272e-01 -8.08043599e-01
-2.67863691e-01 4.14756924e-01 5.93389213e-01 -3.75086186... | [7.896174430847168, -2.0176913738250732] |
e437afc4-28ea-48d3-b9bc-2d2c13aaa18b | personalized-federated-domain-adaptation-for | 2306.03191 | null | https://arxiv.org/abs/2306.03191v1 | https://arxiv.org/pdf/2306.03191v1.pdf | Personalized Federated Domain Adaptation for Item-to-Item Recommendation | Item-to-Item (I2I) recommendation is an important function in most recommendation systems, which generates replacement or complement suggestions for a particular item based on its semantic similarities to other cataloged items. Given that subsets of items in a recommendation system might be co-interacted with by the sa... | ['Trong Nghia Hoang', 'Anoop Deoras', 'Hao Ding', 'Ziwei Fan'] | 2023-06-05 | null | null | null | null | ['personalized-federated-learning'] | ['methodology'] | [-3.74642432e-01 9.73395333e-02 -6.63790405e-01 -2.94496894e-01
2.41580233e-01 -8.34126592e-01 3.44377548e-01 3.99712771e-01
1.76518142e-01 4.67489064e-01 4.90206301e-01 -2.40778774e-01
-7.45792270e-01 -1.28469265e+00 -7.92271793e-01 -1.29216626e-01
-3.21681976e-01 8.69610727e-01 -5.87564558e-02 -6.62120879... | [10.218926429748535, 5.6145453453063965] |
2005444c-5735-414f-894f-60ff5189e237 | a-deep-learning-approach-for-semantic | 2201.07342 | null | https://arxiv.org/abs/2201.07342v1 | https://arxiv.org/pdf/2201.07342v1.pdf | A Deep Learning Approach for Semantic Segmentation of Unbalanced Data in Electron Tomography of Catalytic Materials | Heterogeneous catalysts possess complex surface and bulk structures, relatively poor intrinsic contrast, and often a sparse distribution of the catalytic nanoparticles (NPs), posing a significant challenge for image segmentation, including the current state-of-the-art deep learning methods. To tackle this problem, we a... | ['Hamish L. Fraser', 'Libor Kovarik', 'Arda Genc'] | 2022-01-18 | null | null | null | null | ['electron-tomography'] | ['medical'] | [ 4.10710782e-01 4.37925309e-01 3.62922370e-01 1.03733085e-01
-9.64632928e-01 -2.57810801e-01 3.57395172e-01 3.41677636e-01
-6.36150241e-01 8.03534746e-01 -6.42442107e-01 -2.45466605e-01
-2.06890374e-01 -1.13832998e+00 -9.52524781e-01 -1.12456596e+00
8.48336669e-04 9.48700249e-01 3.85498852e-01 -7.98528567... | [14.211243629455566, -2.7426204681396484] |
ae0cd526-7dc3-4169-96e0-88a6b2093125 | fewclue-a-chinese-few-shot-learning | 2107.07498 | null | https://arxiv.org/abs/2107.07498v2 | https://arxiv.org/pdf/2107.07498v2.pdf | FewCLUE: A Chinese Few-shot Learning Evaluation Benchmark | Pretrained Language Models (PLMs) have achieved tremendous success in natural language understanding tasks. While different learning schemes -- fine-tuning, zero-shot, and few-shot learning -- have been widely explored and compared for languages such as English, there is comparatively little work in Chinese to fairly a... | ['Hu Hai', 'Libo Qin', 'Xin Tian', 'Hu Yuan', 'Huilin Xu', 'Xiang Pan', 'Guoao Wei', 'Xuanwei Zhang', 'Chenyang Yuan', 'Xiaojing Lu', 'Liang Xu'] | 2021-07-15 | null | null | null | null | ['sentence-pair-classification'] | ['natural-language-processing'] | [ 1.40972719e-01 -2.60095477e-01 -4.38341260e-01 -3.18202466e-01
-1.36864209e+00 -1.64270639e-01 7.35942483e-01 2.15648204e-01
-7.80075967e-01 8.34150910e-01 5.93743742e-01 -4.09543782e-01
1.11145094e-01 -6.23690367e-01 -3.61959964e-01 -3.81186485e-01
2.56643564e-01 4.64206338e-01 4.44120616e-01 -5.58226049... | [10.723665237426758, 7.968075275421143] |
120665ad-1cdf-4ad7-9b15-2d5a522f705b | delad-deep-landweber-guided-deconvolution | 2209.15377 | null | https://arxiv.org/abs/2209.15377v1 | https://arxiv.org/pdf/2209.15377v1.pdf | DELAD: Deep Landweber-guided deconvolution with Hessian and sparse prior | We present a model for non-blind image deconvolution that incorporates the classic iterative method into a deep learning application. Instead of using large over-parameterised generative networks to create sharp picture representations, we build our network based on the iterative Landweber deconvolution algorithm, whic... | ['Tingying Peng', 'Jan Taucher', 'Anton Theileis', 'Tomas Chobola'] | 2022-09-30 | null | null | null | null | ['blind-image-deblurring', 'image-deconvolution'] | ['computer-vision', 'computer-vision'] | [ 2.36493990e-01 1.94504887e-01 8.03524494e-01 -3.51081401e-01
-4.67185199e-01 -5.54536283e-01 6.11613214e-01 -6.71850979e-01
-7.38542616e-01 6.59790576e-01 4.29574579e-01 -7.54787326e-02
-1.95499063e-01 -3.54438394e-01 -9.99191225e-01 -7.86306918e-01
-3.68530191e-02 2.50813574e-01 1.87249810e-01 -1.43265188... | [11.683362007141113, -2.669924259185791] |
cf0dd41b-9ec0-4eae-a053-47fb50d70029 | deep-learning-based-end-to-end-spoken | null | null | https://aclanthology.org/2022.lrec-1.798 | https://aclanthology.org/2022.lrec-1.798.pdf | Deep learning-based end-to-end spoken language identification system for domain-mismatched scenario | Domain mismatch is a critical issue when it comes to spoken language identification. To overcome the domain mismatch problem, we have applied several architectures and deep learning strategies which have shown good results in cross-domain speaker verification tasks to spoken language identification. Our systems were ev... | ['Abderrahim Fathan', 'Md Jahangir Alam', 'Woohyun Kang'] | null | null | null | null | lrec-2022-6 | ['spoken-language-identification'] | ['speech'] | [-5.55577762e-02 -4.01937157e-01 6.08606860e-02 -5.18091083e-01
-1.01929975e+00 -6.61460400e-01 7.99338222e-01 -1.51771083e-01
-9.42299783e-01 6.15361810e-01 2.48096541e-01 -5.06648600e-01
1.00972347e-01 1.63386464e-01 -3.87111902e-02 -4.65979278e-01
-1.53796449e-01 4.06845778e-01 -1.51769057e-01 -3.48763704... | [14.202017784118652, 6.562345027923584] |
360f3c0c-a454-4cf7-9a6e-621759979195 | exploring-transformers-for-behavioural | 2206.01441 | null | https://arxiv.org/abs/2206.01441v1 | https://arxiv.org/pdf/2206.01441v1.pdf | Exploring Transformers for Behavioural Biometrics: A Case Study in Gait Recognition | Biometrics on mobile devices has attracted a lot of attention in recent years as it is considered a user-friendly authentication method. This interest has also been motivated by the success of Deep Learning (DL). Architectures based on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have been ... | ['Ruben Vera-Rodriguez', 'Farzin Deravi', 'Richard Guest', 'Ruben Tolosana', 'Paula Delgado-Santos'] | 2022-06-03 | null | null | null | null | ['gait-recognition'] | ['computer-vision'] | [ 1.07146800e-02 -2.77258128e-01 1.60125315e-01 -2.19445810e-01
-6.38395101e-02 1.19939007e-01 4.87053186e-01 -3.30917954e-01
-6.12957478e-01 6.28242314e-01 -3.21317576e-02 -1.57216266e-01
-1.78460017e-01 -6.39759302e-01 -2.37221688e-01 -7.70184100e-01
-1.81661651e-01 1.43924132e-01 4.64083217e-02 -3.25662225... | [13.945267677307129, 1.508736491203308] |
be7cb7ff-bc99-4605-b328-f69cf6f6818f | person-search-by-multi-scale-matching-1 | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Xu_Lan_Person_Search_by_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Xu_Lan_Person_Search_by_ECCV_2018_paper.pdf | Person Search by Multi-Scale Matching | We consider the problem of person search in unconstrained scene images. Existing methods usually focus on improving the person detection accuracy to mitigate negative effects imposed by misalignment, mis-detections, and false alarms resulted from noisy people auto-detection. In contrast to previous studies, we show tha... | ['Shaogang Gong ', 'Xiatian Zhu ', 'Xu Lan '] | 2018-09-01 | null | null | null | eccv-2018-9 | ['person-search'] | ['computer-vision'] | [ 2.13098690e-01 -3.41136009e-01 3.51216137e-01 -2.69237906e-01
-8.79197598e-01 -3.31712753e-01 7.41561651e-01 5.61189801e-02
-1.14151263e+00 4.74678934e-01 2.39749521e-01 5.02181649e-01
-3.29900235e-01 -6.90435827e-01 -7.22631812e-01 -3.57997924e-01
1.81002125e-01 8.74074876e-01 3.05141300e-01 -2.66036242... | [14.827638626098633, 0.8070068955421448] |
59b46d4a-2442-4203-8013-27bbd288ac3c | selective-pseudo-label-clustering | 2107.10692 | null | https://arxiv.org/abs/2107.10692v1 | https://arxiv.org/pdf/2107.10692v1.pdf | Selective Pseudo-label Clustering | Deep neural networks (DNNs) offer a means of addressing the challenging task of clustering high-dimensional data. DNNs can extract useful features, and so produce a lower dimensional representation, which is more amenable to clustering techniques. As clustering is typically performed in a purely unsupervised setting, w... | ['Thomas Lukasiewicz', 'Louis Mahon'] | 2021-07-22 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [ 9.61230770e-02 -6.71252087e-02 -2.47727096e-01 -5.57396889e-01
-7.68248618e-01 -6.87987268e-01 3.59050721e-01 1.27578348e-01
-5.65151274e-01 3.62090021e-01 7.42949098e-02 3.34347598e-02
-1.59763172e-01 -5.17819464e-01 -5.91445982e-01 -1.26020062e+00
-3.13195959e-02 7.41847515e-01 -9.88785550e-02 4.41464365... | [9.173064231872559, 3.2370235919952393] |
1c1e2373-3227-4e75-b00f-8be0649965a6 | epro-pnp-generalized-end-to-end-probabilistic | 2203.13254 | null | https://arxiv.org/abs/2203.13254v4 | https://arxiv.org/pdf/2203.13254v4.pdf | EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation | Locating 3D objects from a single RGB image via Perspective-n-Points (PnP) is a long-standing problem in computer vision. Driven by end-to-end deep learning, recent studies suggest interpreting PnP as a differentiable layer, so that 2D-3D point correspondences can be partly learned by backpropagating the gradient w.r.t... | ['Hao Li', 'Lu Xiong', 'Wei Tian', 'Fan Wang', 'Pichao Wang', 'Hansheng Chen'] | 2022-03-24 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Chen_EPro-PnP_Generalized_End-to-End_Probabilistic_Perspective-N-Points_for_Monocular_Object_Pose_Estimation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Chen_EPro-PnP_Generalized_End-to-End_Probabilistic_Perspective-N-Points_for_Monocular_Object_Pose_Estimation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['6d-pose-estimation'] | ['computer-vision'] | [-1.39934570e-01 7.10282177e-02 -2.81154782e-01 -5.78837574e-01
-1.01182306e+00 -6.88860595e-01 5.89096308e-01 -2.52019376e-01
-6.23901188e-01 1.15185522e-01 7.58019909e-02 1.84225738e-01
1.64182764e-02 -5.32347739e-01 -1.31270933e+00 -6.19887829e-01
1.24418557e-01 7.35913992e-01 2.91281015e-01 7.88446739... | [7.600008964538574, -2.646291971206665] |
4b9f21db-f72b-49df-93fa-0be1adcb285e | autoregressive-neural-network-wavefunctions | 2109.12606 | null | https://arxiv.org/abs/2109.12606v2 | https://arxiv.org/pdf/2109.12606v2.pdf | Autoregressive neural-network wavefunctions for ab initio quantum chemistry | In recent years, neural network quantum states (NNQS) have emerged as powerful tools for the study of quantum many-body systems. Electronic structure calculations are one such canonical many-body problem that have attracted significant research efforts spanning multiple decades, whilst only recently being attempted wit... | ['A. I. Lvovsky', 'Aleksei Malyshev', 'Thomas D. Barrett'] | 2021-09-26 | null | null | null | null | ['variational-monte-carlo'] | ['miscellaneous'] | [ 3.96761715e-01 -2.23236516e-01 3.13821831e-03 -2.19523653e-01
-9.62001860e-01 -1.97996438e-01 6.64352596e-01 1.07517101e-01
-6.48312092e-01 1.07184148e+00 1.53450817e-01 -2.82408834e-01
-2.76260197e-01 -7.68610716e-01 -6.98445916e-01 -1.25788569e+00
-6.87995255e-02 6.63579345e-01 -2.90528566e-01 -4.71144170... | [5.4013352394104, 5.130925178527832] |
6b422503-4aad-4aa7-a817-2a07f04a1c49 | zero-episode-few-shot-contrastive-predictive | 2205.01924 | null | https://arxiv.org/abs/2205.01924v1 | https://arxiv.org/pdf/2205.01924v1.pdf | Zero-Episode Few-Shot Contrastive Predictive Coding: Solving intelligence tests without prior training | Video prediction models often combine three components: an encoder from pixel space to a small latent space, a latent space prediction model, and a generative model back to pixel space. However, the large and unpredictable pixel space makes training such models difficult, requiring many training examples. We argue that... | ['Y. Loewenstein', 'T. Barak'] | 2022-05-04 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [ 8.23894322e-01 5.55359647e-02 -2.50379920e-01 -4.16359603e-01
-5.90681553e-01 -2.99788475e-01 6.61574900e-01 -4.94709402e-01
2.38151308e-02 6.38859451e-01 1.65316880e-01 -2.58860469e-01
-2.03439761e-02 -4.86932814e-01 -9.49204445e-01 -7.47054636e-01
-2.40581214e-01 3.85375530e-01 2.71159351e-01 5.48266172... | [8.502057075500488, 0.3568885922431946] |
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