paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ff09a4f5-6b79-4743-9396-9fe5101aae00 | structvpr-distill-structural-knowledge-with | 2212.00937 | null | https://arxiv.org/abs/2212.00937v4 | https://arxiv.org/pdf/2212.00937v4.pdf | StructVPR: Distill Structural Knowledge with Weighting Samples for Visual Place Recognition | Visual place recognition (VPR) is usually considered as a specific image retrieval problem. Limited by existing training frameworks, most deep learning-based works cannot extract sufficiently stable global features from RGB images and rely on a time-consuming re-ranking step to exploit spatial structural information fo... | ['Sanping Zhou', 'Nanning Zheng', 'Shitao Chen', 'Ruotong Wang', 'Jingwen Fu', 'Yanqing Shen'] | 2022-12-02 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Shen_StructVPR_Distill_Structural_Knowledge_With_Weighting_Samples_for_Visual_Place_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Shen_StructVPR_Distill_Structural_Knowledge_With_Weighting_Samples_for_Visual_Place_CVPR_2023_paper.pdf | cvpr-2023-1 | ['visual-place-recognition'] | ['computer-vision'] | [-4.57707420e-02 -3.37864637e-01 -3.50806296e-01 -3.84050041e-01
-1.03963077e+00 -5.41287780e-01 3.57372642e-01 1.84185341e-01
-7.69540489e-01 5.53688765e-01 -2.03564540e-01 -3.33582014e-01
-2.31624603e-01 -9.59776580e-01 -1.05764377e+00 -7.29306400e-01
2.43367687e-01 3.52578729e-01 5.19301593e-01 -9.22418907... | [7.958540916442871, -1.8424485921859741] |
fab69a5b-cf56-4228-add1-c481a8b062d6 | network-of-steel-neural-font-style-transfer | 2001.03659 | null | https://arxiv.org/abs/2001.03659v1 | https://arxiv.org/pdf/2001.03659v1.pdf | Network of Steel: Neural Font Style Transfer from Heavy Metal to Corporate Logos | We introduce a method for transferring style from the logos of heavy metal bands onto corporate logos using a VGG16 network. We establish the contribution of different layers and loss coefficients to the learning of style, minimization of artefacts and maintenance of readability of corporate logos. We find layers and l... | ['Aram Ter-Sarkisov'] | 2020-01-10 | null | null | null | null | ['font-style-transfer'] | ['computer-vision'] | [-3.99928773e-03 2.06648991e-01 6.74295202e-02 -5.21738350e-01
2.45876476e-01 -6.66168988e-01 5.38001657e-01 -1.88814923e-01
-3.35221924e-02 7.81405866e-01 2.80518919e-01 7.78766721e-02
-2.15865038e-02 -1.03018975e+00 -4.28929150e-01 -3.73456508e-01
4.48034763e-01 2.17004672e-01 -3.17463636e-01 -4.00645763... | [11.636006355285645, -0.5044199228286743] |
bd8d7ec6-5e4b-42a3-adb1-8bbcccccaef9 | evaluation-of-differentially-constrained | 2304.05116 | null | https://arxiv.org/abs/2304.05116v2 | https://arxiv.org/pdf/2304.05116v2.pdf | Evaluation of Differentially Constrained Motion Models for Graph-Based Trajectory Prediction | Given their flexibility and encouraging performance, deep-learning models are becoming standard for motion prediction in autonomous driving. However, with great flexibility comes a lack of interpretability and possible violations of physical constraints. Accompanying these data-driven methods with differentially-constr... | ['Erik Frisk', 'Björn Olofsson', 'Joel Oskarsson', 'Theodor Westny'] | 2023-04-11 | null | null | null | null | ['motion-prediction', 'trajectory-prediction'] | ['computer-vision', 'computer-vision'] | [-4.43736315e-01 6.64770603e-02 -3.86978269e-01 -7.92131871e-02
7.28664026e-02 -2.72581995e-01 6.29624367e-01 -2.12220982e-01
-3.00544918e-01 8.43662977e-01 4.12011370e-02 -8.03969562e-01
-3.17659557e-01 -6.95950925e-01 -5.42532504e-01 -6.49876595e-01
-2.02724174e-01 2.93760896e-01 2.10491315e-01 -6.09464467... | [5.147676944732666, 1.5599806308746338] |
80a41ec6-0aa4-4d5f-babf-a791091c5f59 | fastmapsvm-classifying-complex-objects-using | 2204.05112 | null | https://arxiv.org/abs/2204.05112v3 | https://arxiv.org/pdf/2204.05112v3.pdf | FastMapSVM: Classifying Complex Objects Using the FastMap Algorithm and Support-Vector Machines | Neural Networks and related Deep Learning methods are currently at the leading edge of technologies used for classifying objects. However, they generally demand large amounts of time and data for model training; and their learned models can sometimes be difficult to interpret. In this paper, we advance FastMapSVM -- an... | ['Nori Nakata', 'T. K. Satish Kumar', 'Ang Li', 'Kushal Sharma', 'Malcolm C. A. White'] | 2022-04-07 | null | null | null | null | ['classification'] | ['methodology'] | [-3.53773795e-02 -2.24702284e-02 2.04146914e-02 -6.43020928e-01
-4.41226125e-01 -5.92315257e-01 3.52127999e-01 1.63046882e-01
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-3.48752439e-01 -8.35313082e-01 -6.96556449e-01 -7.80920446e-01
-4.33184445e-01 7.62105763e-01 3.30744117e-01 -8.20588768... | [9.211543083190918, 2.563937187194824] |
624ca608-da5c-4c54-af5b-8e3b21b32037 | adversarial-learning-for-neural-dialogue | 1701.06547 | null | http://arxiv.org/abs/1701.06547v5 | http://arxiv.org/pdf/1701.06547v5.pdf | Adversarial Learning for Neural Dialogue Generation | In this paper, drawing intuition from the Turing test, we propose using
adversarial training for open-domain dialogue generation: the system is trained
to produce sequences that are indistinguishable from human-generated dialogue
utterances. We cast the task as a reinforcement learning (RL) problem where we
jointly tra... | ['Sébastien Jean', 'Will Monroe', 'Dan Jurafsky', 'Jiwei Li', 'Alan Ritter', 'Tianlin Shi'] | 2017-01-23 | adversarial-learning-for-neural-dialogue-1 | https://aclanthology.org/D17-1230 | https://aclanthology.org/D17-1230.pdf | emnlp-2017-9 | ['dialogue-evaluation'] | ['natural-language-processing'] | [ 4.65729326e-01 1.03313172e+00 5.95323384e-01 -5.17493367e-01
-1.29644632e+00 -1.17903233e+00 1.03540504e+00 -4.06087309e-01
-3.67059708e-01 1.16135037e+00 4.08840746e-01 -4.37395841e-01
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1.99088573e-01 1.10135424e+00 -1.69296071e-01 -9.30004478... | [12.78411865234375, 8.15925121307373] |
fdd92ae5-8f41-44f3-a500-581643f567c1 | fake-hilsa-fish-detection-using-machine | 2201.02853 | null | https://arxiv.org/abs/2201.02853v1 | https://arxiv.org/pdf/2201.02853v1.pdf | Fake Hilsa Fish Detection Using Machine Vision | Hilsa is the national fish of Bangladesh. Bangladesh is earning a lot of foreign currency by exporting this fish. Unfortunately, in recent days, some unscrupulous businessmen are selling fake Hilsa fishes to gain profit. The Sardines and Sardinella are the most sold in the market as Hilsa. The government agency of Bang... | ['Zakia Zaman', 'Abdur Rahman', 'Jannatul Ferdous Ani', 'Mirajul Islam'] | 2022-01-08 | null | null | null | null | ['fish-detection'] | ['computer-vision'] | [-5.83499312e-01 -9.47412977e-04 3.97874057e-01 -4.04582247e-02
-9.02684256e-02 -4.80845273e-01 1.54882669e-01 3.09705317e-01
-6.01205826e-01 4.69133735e-01 1.33137777e-01 5.99753857e-03
2.92040259e-01 -1.00560594e+00 -6.90715671e-01 -8.37917507e-01
-6.44796044e-02 3.04082632e-01 1.36206076e-01 -2.96730250... | [8.482686996459961, -1.2454228401184082] |
dca53be6-dfc1-417b-a515-ebd31c017919 | trajectory-prediction-with-vision-a-survey | 2303.13354 | null | https://arxiv.org/abs/2303.13354v1 | https://arxiv.org/pdf/2303.13354v1.pdf | Trajectory-Prediction with Vision: A Survey | To plan a safe and efficient route, an autonomous vehicle should anticipate future trajectories of other agents around it. Trajectory prediction is an extremely challenging task which recently gained a lot of attention in the autonomous vehicle research community. Trajectory-prediction forecasts future state of all the... | ['Apoorv Singh'] | 2023-03-15 | null | null | null | null | ['trajectory-prediction'] | ['computer-vision'] | [-2.80429542e-01 1.50385439e-01 -7.28348553e-01 -3.92774403e-01
-1.06005520e-01 -4.48892385e-01 8.83441627e-01 1.05011083e-01
-3.63902390e-01 7.96037495e-01 5.25628887e-02 -6.68452084e-01
-3.58879685e-01 -1.16705918e+00 -5.25544941e-01 -5.62862754e-01
-6.13751888e-01 4.30996329e-01 6.68371499e-01 -4.66295749... | [5.764250755310059, 1.1033984422683716] |
f03947bd-a371-45c0-929d-63a77d02d009 | a-gating-model-for-bias-calibration-in | 2203.04195 | null | https://arxiv.org/abs/2203.04195v1 | https://arxiv.org/pdf/2203.04195v1.pdf | A Gating Model for Bias Calibration in Generalized Zero-shot Learning | Generalized zero-shot learning (GZSL) aims at training a model that can generalize to unseen class data by only using auxiliary information. One of the main challenges in GZSL is a biased model prediction toward seen classes caused by overfitting on only available seen class data during training. To overcome this issue... | ['Ghassan AlRegib', 'Gukyeong Kwon'] | 2022-03-08 | null | null | null | null | ['generalized-zero-shot-learning', 'generalized-zero-shot-learning'] | ['computer-vision', 'methodology'] | [ 3.75503115e-02 -7.16818795e-02 -8.33368003e-02 -3.74668032e-01
-6.39506519e-01 -2.11669490e-01 4.97547537e-01 6.45010173e-02
-2.93119013e-01 6.03927314e-01 -1.09728336e-01 1.93478942e-01
4.69388114e-03 -1.24564683e+00 -6.68904364e-01 -1.02374256e+00
3.84053022e-01 5.51608860e-01 4.47019786e-01 -2.58868397... | [9.948505401611328, 2.610098123550415] |
58abe31f-174c-4289-94d0-83363ecb8bcf | cylinder3d-an-effective-3d-framework-for | 2008.01550 | null | https://arxiv.org/abs/2008.01550v1 | https://arxiv.org/pdf/2008.01550v1.pdf | Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentation | State-of-the-art methods for large-scale driving-scene LiDAR semantic segmentation often project and process the point clouds in the 2D space. The projection methods includes spherical projection, bird-eye view projection, etc. Although this process makes the point cloud suitable for the 2D CNN-based networks, it inevi... | ['Hui Zhou', 'Hongsheng Li', 'Xinge Zhu', 'Xiao Song', 'Zhe Wang', 'Yuexin Ma', 'Dahua Lin'] | 2020-08-04 | null | null | null | null | ['lidar-semantic-segmentation'] | ['computer-vision'] | [-6.23972677e-02 -2.72552550e-01 7.27606118e-02 -3.74561101e-01
-1.87529042e-01 -5.19701362e-01 6.46319389e-01 -2.87885576e-01
-2.18140393e-01 3.52186672e-02 2.73540616e-03 -5.60137630e-01
-2.52761096e-01 -1.11425734e+00 -6.16037011e-01 -3.72732252e-01
2.52959460e-01 8.97776246e-01 6.29857421e-01 -1.28139213... | [8.01943302154541, -3.0770263671875] |
a8f6dd1c-6e71-4657-bd4d-cff862980004 | revbifpn-the-fully-reversible-bidirectional | 2206.14098 | null | https://arxiv.org/abs/2206.14098v2 | https://arxiv.org/pdf/2206.14098v2.pdf | RevBiFPN: The Fully Reversible Bidirectional Feature Pyramid Network | This work introduces RevSilo, the first reversible bidirectional multi-scale feature fusion module. Like other reversible methods, RevSilo eliminates the need to store hidden activations by recomputing them. However, existing reversible methods do not apply to multi-scale feature fusion and are, therefore, not applicab... | ['Dennis Decoste', 'Joel Hestness', 'Anshul Samar', 'Abhay Gupta', 'Vithursan Thangarasa', 'Vitaliy Chiley'] | 2022-06-28 | null | null | null | null | ['classification'] | ['methodology'] | [ 1.93764791e-01 -3.73958200e-01 -1.32109553e-01 -3.86134297e-01
-6.64657593e-01 -3.84642720e-01 5.15977919e-01 -2.20611364e-01
-7.65891135e-01 8.73714507e-01 1.15225045e-02 -2.73791939e-01
5.42772934e-02 -1.06706214e+00 -9.26047981e-01 -6.76470459e-01
1.32939324e-01 -2.01481804e-01 6.06851101e-01 -4.09077466... | [9.168527603149414, 1.530701994895935] |
67f4a4c7-7d35-4ee8-bf73-b5f4626ea3bb | machine-learning-and-bioinformatics-for | 2208.03139 | null | https://arxiv.org/abs/2208.03139v1 | https://arxiv.org/pdf/2208.03139v1.pdf | Machine Learning and Bioinformatics for Diagnosis Analysis of Obesity Spectrum Disorders | Globally, the number of obese patients has doubled due to sedentary lifestyles and improper dieting. The tremendous increase altered human genetics, and health. According to the world health organization, Life expectancy dropped from 80 to 75 years, as obese people struggle with different chronic diseases. This report ... | ['Amin Gasmi'] | 2022-08-05 | null | null | null | null | ['machine-learning', 'machine-learning'] | ['methodology', 'miscellaneous'] | [ 5.99176362e-02 3.36759657e-01 -6.72948301e-01 -4.02018487e-01
3.13056231e-01 6.05378533e-03 -5.81253171e-01 3.86367261e-01
-9.78291929e-02 8.26121926e-01 4.05240387e-01 -1.81833327e-01
-3.64267945e-01 -5.37997007e-01 2.98110954e-02 -4.57376897e-01
-8.29970658e-01 4.60179210e-01 -6.24564171e-01 1.58368349... | [8.200566291809082, 5.485738277435303] |
0a0880ec-1030-4519-a0bc-ba0709c40d49 | improving-question-answering-model-robustness | 2104.08678 | null | https://arxiv.org/abs/2104.08678v3 | https://arxiv.org/pdf/2104.08678v3.pdf | Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation | Despite recent progress, state-of-the-art question answering models remain vulnerable to a variety of adversarial attacks. While dynamic adversarial data collection, in which a human annotator tries to write examples that fool a model-in-the-loop, can improve model robustness, this process is expensive which limits the... | ['Douwe Kiela', 'Pontus Stenetorp', 'Sebastian Riedel', 'Robin Jia', 'Tristan Thrush', 'Max Bartolo'] | 2021-04-18 | null | https://aclanthology.org/2021.emnlp-main.696 | https://aclanthology.org/2021.emnlp-main.696.pdf | emnlp-2021-11 | ['answer-selection'] | ['natural-language-processing'] | [ 3.14502478e-01 4.11212265e-01 5.97748876e-01 -3.24531734e-01
-1.51837695e+00 -1.46709859e+00 7.60885894e-01 1.04952261e-01
-5.27166963e-01 7.43296564e-01 3.46463062e-02 -5.22094429e-01
2.64434278e-01 -9.33555782e-01 -9.84472632e-01 -3.91707290e-03
4.74443406e-01 8.96991253e-01 6.22388899e-01 -8.17413449... | [11.056707382202148, 7.9833598136901855] |
7b20693e-90fa-4bdf-8d14-887fe74f91eb | e2-go-motion-motion-augmented-event-stream | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Plizzari_E2GOMOTION_Motion_Augmented_Event_Stream_for_Egocentric_Action_Recognition_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Plizzari_E2GOMOTION_Motion_Augmented_Event_Stream_for_Egocentric_Action_Recognition_CVPR_2022_paper.pdf | E2(GO)MOTION: Motion Augmented Event Stream for Egocentric Action Recognition | Event cameras are novel bio-inspired sensors, which asynchronously capture pixel-level intensity changes in the form of "events". Due to their sensing mechanism, event cameras have little to no motion blur, a very high temporal resolution and require significantly less power and memory than traditional frame-based ... | ['Barbara Caputo', 'Matteo Matteucci', 'Emanuele Gusso', 'Marco Cannici', 'Gabriele Goletto', 'Mirco Planamente', 'Chiara Plizzari'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['event-based-vision'] | ['computer-vision'] | [ 2.88149834e-01 -4.83679205e-01 -1.01143055e-01 -3.77993621e-02
-1.90913215e-01 -4.85422373e-01 4.52716202e-01 -1.39111325e-01
-6.65768623e-01 5.61357915e-01 3.63517225e-01 1.06205806e-01
7.42799193e-02 -6.68397307e-01 -5.15174687e-01 -7.20201135e-01
3.47520150e-02 -2.94359654e-01 5.21535158e-01 1.40936732... | [8.615717887878418, -1.2757412195205688] |
b7bfa53d-d3e8-4a2a-9a86-fc67a9b6b341 | multi-scale-cloud-detection-in-remote-sensing | 2006.00836 | null | https://arxiv.org/abs/2006.00836v1 | https://arxiv.org/pdf/2006.00836v1.pdf | Multi-scale Cloud Detection in Remote Sensing Images using a Dual Convolutional Neural Network | Semantic segmentation by convolutional neural networks (CNN) has advanced the state of the art in pixel-level classification of remote sensing images. However, processing large images typically requires analyzing the image in small patches, and hence features that have large spatial extent still cause challenges in tas... | ['Sari Metsämäki', 'Markku Luotamo', 'Arto Klami'] | 2020-06-01 | null | null | null | null | ['cloud-detection'] | ['computer-vision'] | [ 8.28356862e-01 1.42344996e-01 1.60840135e-02 -4.81341988e-01
-1.00826252e+00 -7.59844482e-01 1.69272944e-01 2.84456402e-01
-5.76682568e-01 4.11812067e-01 -5.14700770e-01 -6.55865967e-01
3.80063206e-02 -1.22880948e+00 -7.14629948e-01 -7.79645264e-01
-2.38242760e-01 2.60604084e-01 1.73286140e-01 -3.91235501... | [9.604087829589844, -1.5775736570358276] |
5b686cff-3fbc-4bf7-83aa-bdff2dd499e3 | helixfold-single-msa-free-protein-structure | 2207.13921 | null | https://arxiv.org/abs/2207.13921v3 | https://arxiv.org/pdf/2207.13921v3.pdf | HelixFold-Single: MSA-free Protein Structure Prediction by Using Protein Language Model as an Alternative | AI-based protein structure prediction pipelines, such as AlphaFold2, have achieved near-experimental accuracy. These advanced pipelines mainly rely on Multiple Sequence Alignments (MSAs) as inputs to learn the co-evolution information from the homologous sequences. Nonetheless, searching MSAs from protein databases is ... | ['Le Song', 'Hui Li', 'Hua Wu', 'Xiaonan Zhang', 'Yingfei Xiang', 'Dayong Lin', 'Jingzhou He', 'Lihang Liu', 'Fan Wang', 'Xiaomin Fang'] | 2022-07-28 | null | null | null | null | ['protein-language-model'] | ['medical'] | [-3.46166943e-03 -1.35875717e-01 -1.66526049e-01 -3.22472483e-01
-9.25633132e-01 -6.10049605e-01 2.27496792e-02 3.28256935e-01
-2.41988704e-01 1.01772726e+00 -1.95414335e-01 -6.88122094e-01
3.08913022e-01 -5.10090530e-01 -1.11588132e+00 -1.01763964e+00
1.71416439e-02 7.31359541e-01 2.88942426e-01 -2.36431688... | [4.701416492462158, 5.597399711608887] |
f72a8cb0-6952-4c9c-90ca-e1778b869712 | a-hierarchical-encoding-decoding-scheme-for | 2305.08503 | null | https://arxiv.org/abs/2305.08503v3 | https://arxiv.org/pdf/2305.08503v3.pdf | A Hierarchical Encoding-Decoding Scheme for Abstractive Multi-document Summarization | Pre-trained language models (PLMs) have accomplished impressive achievements in abstractive single-document summarization (SDS). However, such benefits may not be readily extended to muti-document summarization (MDS), where the interactions among documents are more complex. Previous works either design new architecture... | ['Xuan-Phi Nguyen', 'Lidong Bing', 'Yang You', 'Liying Cheng', 'Chenhui Shen'] | 2023-05-15 | null | null | null | null | ['multi-document-summarization', 'document-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.61802310e-01 2.19875991e-01 -3.01272690e-01 -3.11827779e-01
-1.03236639e+00 -4.64885384e-01 9.77037549e-01 2.14389682e-01
-3.57609451e-01 8.30886066e-01 9.38318133e-01 -2.96669602e-01
-3.87145132e-02 -3.29685301e-01 -5.37874758e-01 -3.31070542e-01
-1.21739149e-01 7.43941784e-01 2.99671501e-01 -5.06458163... | [12.180536270141602, 9.277647018432617] |
ff047590-9e9f-418c-9e90-7cbbd838f3bd | twt-table-with-written-text-for-controlled | null | null | https://aclanthology.org/2021.findings-emnlp.107 | https://aclanthology.org/2021.findings-emnlp.107.pdf | TWT: Table with Written Text for Controlled Data-to-Text Generation | Large pre-trained neural models have recently shown remarkable progress in text generation. In this paper, we propose to generate text conditioned on the structured data (table) and a prefix (the written text) by leveraging the pre-trained models. We present a new data-to-text dataset, Table with Written Text (TWT), by... | ['Zhoujun Li', 'Jian-Guang Lou', 'Lei Fang', 'Tongliang Li'] | null | null | null | null | findings-emnlp-2021-11 | ['data-to-text-generation'] | ['natural-language-processing'] | [ 4.61365998e-01 7.05001950e-01 -2.79555600e-02 -2.86080062e-01
-1.00963080e+00 -3.76701862e-01 1.16861534e+00 2.71505147e-01
-2.82841548e-02 1.14943016e+00 8.27308059e-01 -2.53154814e-01
3.96817207e-01 -9.45534766e-01 -8.78510892e-01 -3.04041505e-01
4.79680002e-01 8.91660631e-01 -1.06022924e-01 -2.96034932... | [11.624696731567383, 8.837747573852539] |
106ed455-644a-40b4-948b-037935e7d763 | ct-multi-task-learning-with-a-large-image | 2304.02649 | null | https://arxiv.org/abs/2304.02649v1 | https://arxiv.org/pdf/2304.02649v1.pdf | CT Multi-Task Learning with a Large Image-Text (LIT) Model | Large language models (LLM) not only empower multiple language tasks but also serve as a general interface across different spaces. Up to now, it has not been demonstrated yet how to effectively translate the successes of LLMs in the computer vision field to the medical imaging field which involves high-dimensional and... | ['Ge Wang', 'Chuang Niu'] | 2023-04-03 | null | null | null | null | ['lung-cancer-diagnosis', 'lung-nodule-detection'] | ['medical', 'medical'] | [ 4.65632439e-01 3.13324958e-01 -3.97934675e-01 -3.27525496e-01
-1.61116850e+00 -2.50516027e-01 4.42769676e-01 2.07338959e-01
-4.57779855e-01 1.05771750e-01 5.63186288e-01 -5.86767554e-01
5.99135347e-02 -3.50557268e-01 -3.93852174e-01 -5.95273018e-01
1.28897056e-01 9.65091646e-01 3.13806415e-01 4.61589873... | [15.00354290008545, -1.768291711807251] |
5bdbc9aa-7b4d-4f2a-b87f-e0539b3a6957 | on-the-calibration-and-uncertainty-of-neural-1 | null | null | https://aclanthology.org/2021.eacl-main.12 | https://aclanthology.org/2021.eacl-main.12.pdf | On the Calibration and Uncertainty of Neural Learning to Rank Models for Conversational Search | According to the Probability Ranking Principle (PRP), ranking documents in decreasing order of their probability of relevance leads to an optimal document ranking for ad-hoc retrieval. The PRP holds when two conditions are met: [C1] the models are well calibrated, and, [C2] the probabilities of relevance are reported w... | ['Claudia Hauff', 'Gustavo Penha'] | 2021-04-01 | null | null | null | eacl-2021-2 | ['conversational-search'] | ['natural-language-processing'] | [ 2.42129311e-01 3.47961575e-01 -1.96500823e-01 -6.19754255e-01
-1.27154601e+00 -6.49173677e-01 6.98697209e-01 1.26837060e-01
-3.31734657e-01 9.34717774e-01 4.69969839e-01 -2.97251761e-01
-9.21884596e-01 -6.76879823e-01 -8.10531855e-01 -4.48226362e-01
-2.97238708e-01 1.10570955e+00 1.04922362e-01 -2.89556593... | [11.498547554016113, 7.617305755615234] |
594f0984-b37c-4951-86c8-978c75799152 | urbanir-large-scale-urban-scene-inverse | 2306.09349 | null | https://arxiv.org/abs/2306.09349v2 | https://arxiv.org/pdf/2306.09349v2.pdf | UrbanIR: Large-Scale Urban Scene Inverse Rendering from a Single Video | We show how to build a model that allows realistic, free-viewpoint renderings of a scene under novel lighting conditions from video. Our method -- UrbanIR: Urban Scene Inverse Rendering -- computes an inverse graphics representation from the video. UrbanIR jointly infers shape, albedo, visibility, and sun and sky illum... | ['Shenlong Wang', 'Anand Bhattad', 'Jia-Bin Huang', 'David Forsyth', 'Yi-Ting Chen', 'Bohan Liu', 'Zhi-Hao Lin'] | 2023-06-15 | null | null | null | null | ['inverse-rendering'] | ['computer-vision'] | [ 4.37105477e-01 2.13610716e-02 8.08399975e-01 -4.89415199e-01
-5.79723239e-01 -8.21472466e-01 6.31408572e-01 -6.21284544e-01
8.40547606e-02 5.11519790e-01 1.67223915e-01 -1.86112985e-01
5.04607022e-01 -7.91341424e-01 -1.06404305e+00 -4.53009039e-01
8.83581340e-02 4.28326279e-01 6.22650683e-01 -3.97489876... | [9.694781303405762, -3.0485212802886963] |
b23200e4-e0f8-45f9-a0a3-fc8f74946715 | a-modular-multimodal-architecture-for-gaze | null | null | https://openaccess.thecvf.com/content/CVPR2022W/GAZE/html/Gupta_A_Modular_Multimodal_Architecture_for_Gaze_Target_Prediction_Application_to_CVPRW_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022W/GAZE/papers/Gupta_A_Modular_Multimodal_Architecture_for_Gaze_Target_Prediction_Application_to_CVPRW_2022_paper.pdf | A Modular Multimodal Architecture for Gaze Target Prediction: Application to Privacy-Sensitive Settings | Predicting where a person is looking is a complex task, requiring to understand not only the person’s gaze and scene content, but also the 3D scene structure and the person’s situation (are they manipulating? interacting or observing others? attentive?) to detect obstructions in the line of sight or apply attention pri... | ['Jean-Marc Odobez', 'Samy Tafasca', 'Anshul Gupta'] | 2022-06-20 | null | null | null | ieee-cvf-conference-on-computer-vision-and-8 | ['gaze-target-estimation'] | ['computer-vision'] | [ 4.84986424e-01 2.66011536e-01 1.15797535e-01 -8.13192844e-01
-4.05298591e-01 -8.02294672e-01 6.31669283e-01 3.00223589e-01
-7.31319129e-01 4.47074652e-01 4.79056478e-01 -3.72320674e-02
-1.31661698e-01 -2.02071220e-01 -6.39224768e-01 -5.25895059e-01
-1.47339955e-01 2.03373313e-01 2.40532402e-02 -9.17941630... | [14.103667259216309, 0.06176861748099327] |
aabe377b-1bf3-49cf-b0f0-2a222007ccad | judging-llm-as-a-judge-with-mt-bench-and | 2306.05685 | null | https://arxiv.org/abs/2306.05685v1 | https://arxiv.org/pdf/2306.05685v1.pdf | Judging LLM-as-a-judge with MT-Bench and Chatbot Arena | Evaluating large language model (LLM) based chat assistants is challenging due to their broad capabilities and the inadequacy of existing benchmarks in measuring human preferences. To address this, we explore using strong LLMs as judges to evaluate these models on more open-ended questions. We examine the usage and lim... | ['Ion Stoica', 'Joseph E. Gonzalez', 'Hao Zhang', 'Eric. P Xing', 'Dacheng Li', 'Zhuohan Li', 'Zi Lin', 'Yonghao Zhuang', 'Zhanghao Wu', 'Siyuan Zhuang', 'Ying Sheng', 'Wei-Lin Chiang', 'Lianmin Zheng'] | 2023-06-09 | null | null | null | null | ['chatbot', 'chatbot'] | ['methodology', 'natural-language-processing'] | [-5.35821557e-01 4.16251153e-01 -2.04326719e-01 -8.72847795e-01
-1.29081416e+00 -8.36317778e-01 5.99654675e-01 -7.82661587e-02
-7.29634702e-01 1.04182076e+00 7.67883003e-01 -3.74041378e-01
-8.84978101e-02 -2.71998316e-01 -5.72947934e-02 -8.38178694e-02
4.19684589e-01 1.18133712e+00 1.74316198e-01 -6.48327768... | [12.664299964904785, 8.133289337158203] |
a2202b6c-3198-4aea-a926-f120ed8d73a4 | covering-uncommon-ground-gap-focused-question | 2307.03319 | null | https://arxiv.org/abs/2307.03319v1 | https://arxiv.org/pdf/2307.03319v1.pdf | Covering Uncommon Ground: Gap-Focused Question Generation for Answer Assessment | Human communication often involves information gaps between the interlocutors. For example, in an educational dialogue, a student often provides an answer that is incomplete, and there is a gap between this answer and the perfect one expected by the teacher. Successful dialogue then hinges on the teacher asking about t... | ['Amir Globerson', 'Reut Tsarfaty', 'Gal Elidan', 'Lidan Hackmon', 'Roee Engelberg', 'Alexandre Djerbetian', 'Roni Rabin'] | 2023-07-06 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [ 1.55307734e-02 8.45293581e-01 2.99339861e-01 -4.06960338e-01
-1.18152225e+00 -1.00144517e+00 4.29326415e-01 7.03849792e-01
-1.08727269e-01 1.07812846e+00 5.01220405e-01 -4.56441551e-01
-1.07436672e-01 -7.20273614e-01 -5.17713606e-01 -9.87844989e-02
3.90845925e-01 8.40398490e-01 3.49282742e-01 -6.69777095... | [11.829940795898438, 8.065918922424316] |
b9b39425-06a3-45f2-bc59-b13e1a53212e | bidirectional-attention-as-a-mixture-of | 2307.04057 | null | https://arxiv.org/abs/2307.04057v1 | https://arxiv.org/pdf/2307.04057v1.pdf | Bidirectional Attention as a Mixture of Continuous Word Experts | Bidirectional attention $\unicode{x2013}$ composed of self-attention with positional encodings and the masked language model (MLM) objective $\unicode{x2013}$ has emerged as a key component of modern large language models (LLMs). Despite its empirical success, few studies have examined its statistical underpinnings: Wh... | ['Yixin Wang', 'Kevin Christian Wibisono'] | 2023-07-08 | null | null | null | null | ['word-embeddings', 'language-modelling'] | ['methodology', 'natural-language-processing'] | [ 3.63313481e-02 -3.06792073e-02 -4.03773695e-01 -3.06656837e-01
-8.39908659e-01 -6.11942530e-01 7.26918936e-01 3.35638136e-01
-5.40678859e-01 5.05482614e-01 6.37014031e-01 -8.11246514e-01
-3.90032113e-01 -6.48760259e-01 -9.30451453e-01 -6.73371792e-01
-1.02005051e-02 4.18028802e-01 -2.00599760e-01 -3.82519603... | [10.645732879638672, 8.843655586242676] |
c2971fba-52d5-4811-b066-a02b0598221d | automatic-tracking-of-the-muscle-tendon | 2005.02071 | null | https://arxiv.org/abs/2005.02071v1 | https://arxiv.org/pdf/2005.02071v1.pdf | Automatic Tracking of the Muscle Tendon Junction in Healthy and Impaired Subjects using Deep Learning | Recording muscle tendon junction displacements during movement, allows separate investigation of the muscle and tendon behaviour, respectively. In order to provide a fully-automatic tracking method, we employ a novel deep learning approach to detect the position of the muscle tendon junction in ultrasound images. We ut... | ['Christian Baumgartner', 'Jörg Schröttner', 'Andreas Konrad', 'Robert Jarolim', 'Markus Tilp', 'Christoph Leitner', 'Annika Kruse'] | 2020-05-05 | null | null | null | null | ['muscle-tendon-junction-identification'] | ['medical'] | [ 5.56510575e-02 1.98437080e-01 -1.51046231e-01 1.00553177e-01
-1.12520087e+00 -5.07430613e-01 -5.61543815e-02 -3.72161150e-01
-4.79243666e-01 3.85742545e-01 2.95289326e-02 3.53700779e-02
2.03449819e-02 -1.49691328e-01 -6.52434170e-01 -8.11806381e-01
-4.81163979e-01 1.77141592e-01 4.69584793e-01 1.33906439... | [7.095321178436279, -0.4161549210548401] |
9566d001-09ec-440f-93f2-0c9d426da914 | iterative-self-learning-for-enhanced-back | 2011.07403 | null | https://arxiv.org/abs/2011.07403v3 | https://arxiv.org/pdf/2011.07403v3.pdf | A Hybrid Approach for Improved Low Resource Neural Machine Translation using Monolingual Data | Many language pairs are low resource, meaning the amount and/or quality of available parallel data is not sufficient to train a neural machine translation (NMT) model which can reach an acceptable standard of accuracy. Many works have explored using the readily available monolingual data in either or both of the langua... | ['Ismaila Idris Sinan', 'Habeebah Adamu Kakudi', 'Abubakar Isa', 'Bashir Shehu Galadanci', 'Idris Abdulmumin'] | 2020-11-14 | null | null | null | null | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 5.44182621e-02 -2.28170097e-01 -3.85269374e-01 -3.23094308e-01
-9.32894826e-01 -6.32206857e-01 9.82982039e-01 -2.18508899e-01
-7.48464167e-01 1.24174345e+00 2.91568059e-02 -7.61600375e-01
4.32031453e-01 -6.10457122e-01 -9.39011872e-01 -3.61082882e-01
5.24709523e-01 8.58617902e-01 -1.17388126e-02 -7.55591810... | [11.565017700195312, 10.36204719543457] |
d5a8015e-2d7d-4486-bf43-fcb6832b5b7d | tripinet-tripartite-progressive-integration | 2212.12841 | null | https://arxiv.org/abs/2212.12841v1 | https://arxiv.org/pdf/2212.12841v1.pdf | TriPINet: Tripartite Progressive Integration Network for Image Manipulation Localization | Image manipulation localization aims at distinguishing forged regions from the whole test image. Although many outstanding prior arts have been proposed for this task, there are still two issues that need to be further studied: 1) how to fuse diverse types of features with forgery clues; 2) how to progressively integra... | ['Xiao Jin', 'Jing Xu', 'Wei-Yun Liang'] | 2022-12-25 | null | null | null | null | ['image-manipulation', 'image-forensics'] | ['computer-vision', 'computer-vision'] | [ 3.24979544e-01 -6.92410886e-01 1.54324383e-01 -1.25340372e-01
-1.16961157e+00 -4.42110151e-01 3.38505208e-01 -1.12822361e-01
-2.83391833e-01 2.39660695e-01 1.41974032e-01 -1.17248751e-01
1.50739960e-02 -3.65482658e-01 -7.69678593e-01 -7.63558745e-01
2.25969642e-01 -5.39807498e-01 3.56858850e-01 -1.37363255... | [12.377291679382324, 0.9006181955337524] |
bb4e9b23-8446-41f5-8f18-50edef151c42 | learning-event-representations-in-image | 1910.03483 | null | https://arxiv.org/abs/1910.03483v3 | https://arxiv.org/pdf/1910.03483v3.pdf | Learning event representations for temporal segmentation of image sequences by dynamic graph embedding | Recently, self-supervised learning has proved to be effective to learn representations of events suitable for temporal segmentation in image sequences, where events are understood as sets of temporally adjacent images that are semantically perceived as a whole. However, although this approach does not require expensive... | ['Herwig Wendt', 'Mariella Dimiccoli'] | 2019-10-08 | null | null | null | null | ['motion-segmentation', 'dynamic-graph-embedding'] | ['computer-vision', 'graphs'] | [ 2.84899443e-01 2.82202996e-02 -2.71951109e-01 -3.45924824e-01
-2.35217616e-01 -4.30612504e-01 7.09584236e-01 5.56173384e-01
-4.74637181e-01 2.88193971e-01 7.17089102e-02 7.47787356e-02
-1.95903927e-01 -8.43373001e-01 -6.38099194e-01 -8.50015283e-01
-2.60964602e-01 2.57720709e-01 6.62085176e-01 -7.81119149... | [8.519264221191406, 0.627875566482544] |
24245bc8-4156-41b7-9b28-47491c3b506f | deep-graph-similarity-learning-a-survey | 1912.11615 | null | https://arxiv.org/abs/1912.11615v2 | https://arxiv.org/pdf/1912.11615v2.pdf | Deep Graph Similarity Learning: A Survey | In many domains where data are represented as graphs, learning a similarity metric among graphs is considered a key problem, which can further facilitate various learning tasks, such as classification, clustering, and similarity search. Recently, there has been an increasing interest in deep graph similarity learning, ... | ['Theodore L. Willke', 'Philip S. Yu', 'Guixiang Ma', 'Nesreen K. Ahmed'] | 2019-12-25 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [-7.08919019e-02 1.35176592e-02 -1.29273087e-01 -5.69312811e-01
-2.90954471e-01 -3.87536287e-01 4.55185443e-01 6.89570487e-01
-1.23864651e-01 1.13634855e-01 1.92932189e-01 -1.59077317e-01
-5.00558853e-01 -1.11662459e+00 -3.59080911e-01 -5.26478112e-01
-1.49862558e-01 4.11792994e-01 8.36986527e-02 -7.32818171... | [7.1657280921936035, 6.262853145599365] |
1875ca1b-68a6-4718-94cf-25b7fa2a125b | dino-detr-with-improved-denoising-anchor-1 | 2203.03605 | null | https://arxiv.org/abs/2203.03605v4 | https://arxiv.org/pdf/2203.03605v4.pdf | DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection | We present DINO (\textbf{D}ETR with \textbf{I}mproved de\textbf{N}oising anch\textbf{O}r boxes), a state-of-the-art end-to-end object detector. % in this paper. DINO improves over previous DETR-like models in performance and efficiency by using a contrastive way for denoising training, a mixed query selection method fo... | ['Heung-Yeung Shum', 'Lionel M. Ni', 'Jun Zhu', 'Hang Su', 'Lei Zhang', 'Shilong Liu', 'Feng Li', 'Hao Zhang'] | 2022-03-07 | dino-detr-with-improved-denoising-anchor | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [-2.75166035e-01 -1.26498267e-01 1.85315181e-02 -3.67215663e-01
-1.35991204e+00 -7.02542663e-01 2.07588851e-01 9.87604037e-02
-8.36773217e-01 5.30820668e-01 -3.03042889e-01 -3.31780761e-01
-1.18858740e-01 -6.91948652e-01 -1.12468088e+00 -5.28412819e-01
-1.77885190e-01 5.69621503e-01 5.41672111e-01 -2.26874456... | [9.024155616760254, 0.2083228975534439] |
52530c1d-42ee-4524-bfad-a2d51017dff3 | fade-fusing-the-assets-of-decoder-and-encoder | 2207.10392 | null | https://arxiv.org/abs/2207.10392v2 | https://arxiv.org/pdf/2207.10392v2.pdf | FADE: Fusing the Assets of Decoder and Encoder for Task-Agnostic Upsampling | We consider the problem of task-agnostic feature upsampling in dense prediction where an upsampling operator is required to facilitate both region-sensitive tasks like semantic segmentation and detail-sensitive tasks such as image matting. Existing upsampling operators often can work well in either type of the tasks, b... | ['Zhiguo Cao', 'Hongtao Fu', 'Wenze Liu', 'Hao Lu'] | 2022-07-21 | null | null | null | null | ['image-matting'] | ['computer-vision'] | [ 4.25993085e-01 4.78051603e-02 -1.80349618e-01 -6.03628755e-01
-1.04694700e+00 -3.81075263e-01 6.21737361e-01 -1.38366267e-01
-2.56497771e-01 5.56109190e-01 4.33079094e-01 -3.63592446e-01
4.93029356e-02 -7.71014810e-01 -9.31210399e-01 -5.21597922e-01
1.08627388e-02 2.51397133e-01 5.71867526e-01 -2.56709784... | [9.845959663391113, 0.08352392166852951] |
47e69895-d6db-4cd8-b1c9-d8b8b4d58c91 | coral-a-context-aware-croatian-abusive | 2211.06053 | null | https://arxiv.org/abs/2211.06053v1 | https://arxiv.org/pdf/2211.06053v1.pdf | CoRAL: a Context-aware Croatian Abusive Language Dataset | In light of unprecedented increases in the popularity of the internet and social media, comment moderation has never been a more relevant task. Semi-automated comment moderation systems greatly aid human moderators by either automatically classifying the examples or allowing the moderators to prioritize which comments ... | ['Matthew Purver', 'Mladen Karan', 'Ravi Shekhar'] | 2022-11-11 | null | null | null | null | ['abusive-language'] | ['natural-language-processing'] | [ 2.85757277e-02 -1.46064591e-02 -4.75212932e-01 -3.99456382e-01
-5.53391159e-01 -8.82320344e-01 8.79347920e-01 6.52087808e-01
-4.90569443e-01 9.38925982e-01 9.15892303e-01 -6.21834636e-01
2.69842356e-01 -2.69290119e-01 5.35795651e-02 -2.67603725e-01
2.15032086e-01 2.88542390e-01 -1.51136704e-02 -4.58060950... | [8.745179176330566, 10.20217514038086] |
c80bc69f-253a-47ea-9e0e-29e54cbf7102 | advhat-real-world-adversarial-attack-on | 1908.08705 | null | https://arxiv.org/abs/1908.08705v1 | https://arxiv.org/pdf/1908.08705v1.pdf | AdvHat: Real-world adversarial attack on ArcFace Face ID system | In this paper we propose a novel easily reproducible technique to attack the best public Face ID system ArcFace in different shooting conditions. To create an attack, we print the rectangular paper sticker on a common color printer and put it on the hat. The adversarial sticker is prepared with a novel algorithm for of... | ['Stepan Komkov', 'Aleksandr Petiushko'] | 2019-08-23 | null | null | null | null | ['real-world-adversarial-attack'] | ['adversarial'] | [ 1.05069749e-01 4.92059082e-01 2.82028437e-01 -1.31164908e-01
-3.78134400e-01 -1.17021835e+00 6.31004214e-01 -1.17649710e+00
-1.41058475e-01 2.37882182e-01 -5.25667071e-01 -2.17241600e-01
1.26425400e-01 -4.81930465e-01 -8.83814871e-01 -5.36199152e-01
1.43099189e-01 8.03477347e-01 1.45319238e-01 -4.72856820... | [12.83804988861084, 1.0357459783554077] |
69b0d43c-98e2-4670-99e7-7dc6f5a958ec | learning-position-and-target-consistency-for | 2104.04329 | null | https://arxiv.org/abs/2104.04329v1 | https://arxiv.org/pdf/2104.04329v1.pdf | Learning Position and Target Consistency for Memory-based Video Object Segmentation | This paper studies the problem of semi-supervised video object segmentation(VOS). Multiple works have shown that memory-based approaches can be effective for video object segmentation. They are mostly based on pixel-level matching, both spatially and temporally. The main shortcoming of memory-based approaches is that t... | ['Rong Jin', 'Yinghui Xu', 'Pan Pan', 'Bang Zhang', 'Peng Zhang', 'Li Hu'] | 2021-04-09 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Hu_Learning_Position_and_Target_Consistency_for_Memory-Based_Video_Object_Segmentation_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Hu_Learning_Position_and_Target_Consistency_for_Memory-Based_Video_Object_Segmentation_CVPR_2021_paper.pdf | cvpr-2021-1 | ['one-shot-visual-object-segmentation'] | ['computer-vision'] | [ 2.06286330e-02 -3.87484372e-01 -7.28749037e-01 -4.10780132e-01
-7.56411612e-01 -2.91634381e-01 3.04679066e-01 -4.00096439e-02
-6.19293511e-01 4.28268492e-01 -2.04197943e-01 2.31160760e-01
2.33981997e-01 -6.32911026e-01 -1.01703346e+00 -7.18402326e-01
2.22420767e-01 3.63617331e-01 1.24142945e+00 2.05962792... | [9.216548919677734, -0.16311514377593994] |
b07dd5c6-5153-4b7b-a9b6-ac1c1fa209ef | deep-sr-itm-joint-learning-of-super | 1904.11176 | null | https://arxiv.org/abs/1904.11176v3 | https://arxiv.org/pdf/1904.11176v3.pdf | Deep SR-ITM: Joint Learning of Super-Resolution and Inverse Tone-Mapping for 4K UHD HDR Applications | Recent modern displays are now able to render high dynamic range (HDR), high resolution (HR) videos of up to 8K UHD (Ultra High Definition). Consequently, UHD HDR broadcasting and streaming have emerged as high quality premium services. However, due to the lack of original UHD HDR video content, appropriate conversion ... | ['Soo Ye Kim', 'Jihyong Oh', 'Munchurl Kim'] | 2019-04-25 | deep-sr-itm-joint-learning-of-super-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Kim_Deep_SR-ITM_Joint_Learning_of_Super-Resolution_and_Inverse_Tone-Mapping_for_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Kim_Deep_SR-ITM_Joint_Learning_of_Super-Resolution_and_Inverse_Tone-Mapping_for_ICCV_2019_paper.pdf | iccv-2019-10 | ['tone-mapping', 'inverse-tone-mapping'] | ['computer-vision', 'computer-vision'] | [ 6.73604071e-01 -1.92416951e-01 -5.45336641e-02 -1.89214602e-01
-9.55557585e-01 -1.67926505e-01 5.35037279e-01 -6.88167334e-01
-4.69997227e-02 9.08547938e-01 4.92794275e-01 -1.06403746e-01
-1.51247904e-01 -9.42265093e-01 -6.49201334e-01 -8.24222267e-01
-4.71915752e-02 -3.41550469e-01 4.23556149e-01 -5.01567841... | [10.989884376525879, -2.0379836559295654] |
bda2f6d1-c3ff-4a8b-b9c9-a12c52bf9406 | contrastive-learning-with-adversarial-2 | 2012.07280 | null | https://arxiv.org/abs/2012.07280v6 | https://arxiv.org/pdf/2012.07280v6.pdf | Contrastive Learning with Adversarial Perturbations for Conditional Text Generation | Recently, sequence-to-sequence (seq2seq) models with the Transformer architecture have achieved remarkable performance on various conditional text generation tasks, such as machine translation. However, most of them are trained with teacher forcing with the ground truth label given at each time step, without being expo... | ['Sung Ju Hwang', 'Dong Bok Lee', 'Seanie Lee'] | 2020-12-14 | contrastive-learning-with-adversarial | https://openreview.net/forum?id=Wga_hrCa3P3 | https://openreview.net/pdf?id=Wga_hrCa3P3 | iclr-2021-1 | ['conditional-text-generation'] | ['natural-language-processing'] | [ 9.34103787e-01 4.29184586e-01 -6.86851144e-02 -2.87264735e-01
-1.02243972e+00 -8.44387472e-01 8.29596698e-01 -2.15748157e-02
-3.93868238e-01 1.23293567e+00 2.65969753e-01 -4.00455415e-01
4.93429363e-01 -8.55779469e-01 -8.65270853e-01 -6.87635958e-01
5.15260398e-01 7.01336324e-01 -5.62108085e-02 -4.63920325... | [11.74041748046875, 9.302814483642578] |
f3ff4346-c043-4b92-b112-8013f3075e1e | sound-explanation-for-trustworthy-machine | 2306.06134 | null | https://arxiv.org/abs/2306.06134v1 | https://arxiv.org/pdf/2306.06134v1.pdf | Sound Explanation for Trustworthy Machine Learning | We take a formal approach to the explainability problem of machine learning systems. We argue against the practice of interpreting black-box models via attributing scores to input components due to inherently conflicting goals of attribution-based interpretation. We prove that no attribution algorithm satisfies specifi... | ['Martin Rinard', 'Limor Appelbaum', 'Pasapol Saowakon', 'Kai Jia'] | 2023-06-08 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [ 7.65123904e-01 9.92244363e-01 -7.17835605e-01 -7.37368047e-01
-1.80849805e-01 -2.97636420e-01 6.42098188e-01 4.02863443e-01
1.02859832e-01 8.65725100e-01 3.89133990e-01 -9.01574612e-01
-6.52856350e-01 -3.13865721e-01 -5.52466750e-01 -2.64482021e-01
8.68834481e-02 3.48696679e-01 -3.66502017e-01 3.47706452... | [8.625335693359375, 5.706161022186279] |
d443e7e9-fb5f-4a20-a98b-d762d39dfdc5 | cognition-guided-human-object-relationship | null | null | https://ieeexplore.ieee.org/document/10112623 | https://ieeexplore.ieee.org/document/10112623 | Cognition Guided Human-Object Relationship Detection | Human-object relationship detection reveals the fine-grained relationship between humans and objects, helping the comprehensive understanding of videos. Previous human-object relationship detection approaches are mainly developed with object features and relation features without exploring the specific information of h... | ['Xiaochun Cao', 'Lei Zhang', 'Xuan Zhang', 'Pengwen Dai', 'Zhitao Zeng'] | 2023-05-06 | null | null | null | journal-2023-5 | ['human-object-interaction-detection'] | ['computer-vision'] | [-1.52126579e-02 -2.47655258e-01 -2.95073807e-01 -4.73018795e-01
-1.70334324e-01 3.86134349e-02 6.74459517e-01 -7.73189515e-02
-4.23839331e-01 3.47098261e-01 5.21180391e-01 3.82387251e-01
-2.88057089e-01 -4.38686132e-01 -4.92985845e-01 -5.33910394e-01
5.28396256e-02 1.62438855e-01 8.07555974e-01 -2.42522657... | [8.445693969726562, 0.6282082200050354] |
b96e74f0-59e3-40a1-b68f-0679fc4e540c | improving-abstractive-dialogue-summarization-2 | null | null | https://aclanthology.org/2021.findings-emnlp.97 | https://aclanthology.org/2021.findings-emnlp.97.pdf | Improving Abstractive Dialogue Summarization with Hierarchical Pretraining and Topic Segment | With the increasing abundance of meeting transcripts, meeting summary has attracted more and more attention from researchers. The unsupervised pre-training method based on transformer structure combined with fine-tuning of downstream tasks has achieved great success in the field of text summarization. However, the sema... | ['Ting Liu', 'Yuzhuo Fu', 'Hao liu', 'MengNan Qi'] | null | null | null | null | findings-emnlp-2021-11 | ['unsupervised-pre-training'] | ['methodology'] | [ 5.14991462e-01 3.63233417e-01 -8.70237872e-02 -5.17983377e-01
-1.18905210e+00 -4.80472744e-01 4.47851717e-01 2.08222419e-01
-1.08209431e-01 8.48109245e-01 1.14565098e+00 -6.64584562e-02
2.27607071e-01 -4.95218068e-01 -5.36720514e-01 -5.17199039e-01
4.99329716e-01 4.81626719e-01 1.03843458e-01 -1.50375500... | [12.56821346282959, 9.397029876708984] |
db594b5a-8e01-4953-98a0-b77da383d65d | numerical-gaussian-process-kalman-filtering-1 | 2105.02079 | null | https://arxiv.org/abs/2105.02079v1 | https://arxiv.org/pdf/2105.02079v1.pdf | Numerical Gaussian process Kalman filtering for spatiotemporal systems | We present a novel Kalman filter for spatiotemporal systems called the numerical Gaussian process Kalman filter (GPKF). Numerical Gaussian processes have recently been introduced as a physics informed machine learning method for simulating time-dependent partial differential equations without the need for spatial discr... | ['Steffen Waldherr', 'Armin Küper'] | 2021-05-05 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [-5.37320256e-01 1.32005647e-01 1.31746426e-01 1.30079210e-01
-6.28154397e-01 -4.43298191e-01 1.15565336e+00 -1.10756211e-01
-4.01287317e-01 1.16437459e+00 -1.55165106e-01 -5.45198858e-01
-3.14008713e-01 -7.59453893e-01 -4.29287225e-01 -1.07666850e+00
-3.41049075e-01 5.55715203e-01 3.84177178e-01 2.63379484... | [6.653041362762451, 3.5723159313201904] |
7e6c45f1-b6aa-4ca8-bf94-5ed8ec136695 | learning-from-pseudo-lesion-a-self-supervised | 2106.12313 | null | https://arxiv.org/abs/2106.12313v1 | https://arxiv.org/pdf/2106.12313v1.pdf | Learning from Pseudo Lesion: A Self-supervised Framework for COVID-19 Diagnosis | The Coronavirus disease 2019 (COVID-19) has rapidly spread all over the world since its first report in December 2019 and thoracic computed tomography (CT) has become one of the main tools for its diagnosis. In recent years, deep learning-based approaches have shown impressive performance in myriad image recognition ta... | ['Linlin Shen', 'Xuechen Li', 'Zhihao Jin', 'Zhongliang Li'] | 2021-06-23 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [ 4.31061834e-01 -3.71724010e-01 2.25855231e-01 -3.23614150e-01
-6.79857194e-01 -2.95269608e-01 3.97694796e-01 -2.38056388e-02
-4.47252423e-01 7.26208091e-01 3.32641974e-02 -3.21005881e-01
-1.82176717e-02 -8.48402619e-01 -8.17689776e-01 -7.15198278e-01
-4.24333960e-02 8.07740390e-01 1.62353545e-01 1.28426895... | [15.523885726928711, -1.7513586282730103] |
9454d648-453d-454d-b666-be5fe5b0dbb4 | a-nonlinear-acceleration-method-for-iterative | 1906.01595 | null | https://arxiv.org/abs/1906.01595v1 | https://arxiv.org/pdf/1906.01595v1.pdf | A Nonlinear Acceleration Method for Iterative Algorithms | Iterative methods have led to better understanding and solving problems such as missing sampling, deconvolution, inverse systems, impulsive and Salt and Pepper noise removal problems. However, the challenges such as the speed of convergence and or the accuracy of the answer still remain. In order to improve the existin... | ['Farokh Marvasti', 'Mahdi Shamsi', 'Mahmoud Ghandi'] | 2019-06-04 | null | null | null | null | ['salt-and-pepper-noise-removal'] | ['computer-vision'] | [ 3.65529627e-01 -4.06790078e-01 9.24915969e-02 -2.05753580e-01
-7.53380835e-01 -3.86543006e-01 1.69508845e-01 -2.72060990e-01
-1.45639107e-01 1.09203780e+00 3.98783177e-01 -1.47800490e-01
-4.90367740e-01 -2.68845111e-01 -4.49587703e-01 -8.52926254e-01
-2.53377676e-01 2.33094379e-01 -2.37923548e-01 -2.68138468... | [7.07907247543335, 4.392603874206543] |
af98bff6-1adc-41a3-a968-e6614f2a6525 | cross-lingual-cross-corpus-speech-emotion | 2003.07996 | null | https://arxiv.org/abs/2003.07996v1 | https://arxiv.org/pdf/2003.07996v1.pdf | Cross Lingual Cross Corpus Speech Emotion Recognition | The majority of existing speech emotion recognition models are trained and evaluated on a single corpus and a single language setting. These systems do not perform as well when applied in a cross-corpus and cross-language scenario. This paper presents results for speech emotion recognition for 4 languages in both singl... | ['Shivali Goel', 'Homayoon Beigi'] | 2020-03-18 | null | null | null | null | ['cross-corpus'] | ['computer-vision'] | [-2.81024516e-01 1.54329604e-02 1.41800996e-02 -8.17157984e-01
-4.68039453e-01 -5.15322387e-01 1.06904554e+00 2.91658700e-01
-7.44002819e-01 7.28019834e-01 2.07598925e-01 -1.77384347e-01
1.63730234e-01 7.87743181e-02 -8.47083628e-02 -3.55933547e-01
-6.15334362e-02 3.72102439e-01 -2.37168401e-01 -4.31495219... | [13.577441215515137, 5.8480916023254395] |
6671fdb6-7e97-4f33-86fd-4eb0b301223a | composing-text-and-image-for-image-retrieval | 1812.07119 | null | http://arxiv.org/abs/1812.07119v1 | http://arxiv.org/pdf/1812.07119v1.pdf | Composing Text and Image for Image Retrieval - An Empirical Odyssey | In this paper, we study the task of image retrieval, where the input query is
specified in the form of an image plus some text that describes desired
modifications to the input image. For example, we may present an image of the
Eiffel tower, and ask the system to find images which are visually similar but
are modified ... | ['Li Fei-Fei', 'Li-Jia Li', 'Lu Jiang', 'Nam Vo', 'Kevin Murphy', 'James Hays', 'Chen Sun'] | 2018-12-18 | composing-text-and-image-for-image-retrieval-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Vo_Composing_Text_and_Image_for_Image_Retrieval_-_an_Empirical_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Vo_Composing_Text_and_Image_for_Image_Retrieval_-_an_Empirical_CVPR_2019_paper.pdf | cvpr-2019-6 | ['multi-modal'] | ['miscellaneous'] | [ 6.31381929e-01 -3.83079231e-01 1.83635518e-01 -6.14614964e-01
-6.88476205e-01 -1.04246283e+00 9.58136618e-01 3.53739083e-01
-7.03705609e-01 2.57505924e-01 3.72429006e-02 5.94612546e-02
-8.17928016e-02 -6.97135150e-01 -9.52896357e-01 -3.71915519e-01
2.54053503e-01 5.00327170e-01 3.76627803e-01 -3.58188063... | [10.817057609558105, 1.2123699188232422] |
0ade2d7f-cafb-43db-a7b9-88afb53a1c43 | laeo-net-revisiting-people-looking-at-each-1 | 1906.05261 | null | https://arxiv.org/abs/1906.05261v1 | https://arxiv.org/pdf/1906.05261v1.pdf | LAEO-Net: revisiting people Looking At Each Other in videos | Capturing the `mutual gaze' of people is essential for understanding and interpreting the social interactions between them. To this end, this paper addresses the problem of detecting people Looking At Each Other (LAEO) in video sequences. For this purpose, we propose LAEO-Net, a new deep CNN for determining LAEO in vid... | ['Pablo Medina-Suarez', 'Vicky Kalogeiton', 'Manuel J. Marin-Jimenez', 'Andrew Zisserman'] | 2019-06-12 | laeo-net-revisiting-people-looking-at-each | http://openaccess.thecvf.com/content_CVPR_2019/html/Marin-Jimenez_LAEO-Net_Revisiting_People_Looking_at_Each_Other_in_Videos_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Marin-Jimenez_LAEO-Net_Revisiting_People_Looking_at_Each_Other_in_Videos_CVPR_2019_paper.pdf | cvpr-2019-6 | ['mutual-gaze'] | ['computer-vision'] | [-3.46094102e-01 -2.63805419e-01 -8.79932716e-02 -4.38412219e-01
5.51371872e-02 -5.83161175e-01 6.74752474e-01 1.01272315e-01
-4.53976482e-01 3.34586710e-01 3.71014893e-01 7.74244368e-02
-1.28379241e-02 -6.57389045e-01 -7.10661948e-01 -4.82835859e-01
-5.23802400e-01 5.38023233e-01 3.30418587e-01 -1.82456166... | [8.202757835388184, 0.5792356729507446] |
db0291b5-5598-43bf-be42-53fb002ef872 | beyond-visual-attractiveness-physically | 2103.12926 | null | https://arxiv.org/abs/2103.12926v1 | https://arxiv.org/pdf/2103.12926v1.pdf | Beyond Visual Attractiveness: Physically Plausible Single Image HDR Reconstruction for Spherical Panoramas | HDR reconstruction is an important task in computer vision with many industrial needs. The traditional approaches merge multiple exposure shots to generate HDRs that correspond to the physical quantity of illuminance of the scene. However, the tedious capturing process makes such multi-shot approaches inconvenient in p... | ['Gang Hua', 'Ying Wu', 'Haoxiang Li', 'Hao Kang', 'Yue Liu', 'Li Guan', 'Wei Wei'] | 2021-03-24 | null | null | null | null | ['single-shot-hdr-reconstruction', 'hdr-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 2.56694496e-01 -2.10890517e-01 1.69006526e-01 -2.11978242e-01
-6.15159631e-01 -7.60762393e-02 5.50158858e-01 -5.65390348e-01
3.88603926e-01 6.81780100e-01 1.55252576e-01 -4.02906463e-02
-2.43535396e-02 -8.80472422e-01 -8.61581802e-01 -8.66489291e-01
4.44905579e-01 9.18952562e-03 2.61365268e-02 -2.71869630... | [10.80997085571289, -2.271310567855835] |
ef5be60b-884c-4e62-9b36-1c61a79d424c | sentence-level-event-detection-without | 2306.14176 | null | https://arxiv.org/abs/2306.14176v1 | https://arxiv.org/pdf/2306.14176v1.pdf | Sentence-level Event Detection without Triggers via Prompt Learning and Machine Reading Comprehension | The traditional way of sentence-level event detection involves two important subtasks: trigger identification and trigger classifications, where the identified event trigger words are used to classify event types from sentences. However, trigger classification highly depends on abundant annotated trigger words and the ... | ['Hai-Lin Liu', 'Zicheng Cai', 'Huangxu Sheng', 'Lei Chen', 'Tongtao Ling'] | 2023-06-25 | null | null | null | null | ['reading-comprehension', 'machine-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.83881062e-01 -2.71809489e-01 -8.28672647e-02 -3.29208702e-01
-1.01601326e+00 -7.25145280e-01 6.89652741e-01 9.32080090e-01
-6.61579013e-01 6.45240784e-01 5.02142847e-01 -3.84255379e-01
9.01480839e-02 -8.45320523e-01 -2.17707589e-01 -2.49700040e-01
-5.44043370e-02 5.70848547e-02 7.45751798e-01 -4.38970327... | [9.073151588439941, 9.176627159118652] |
b6b42e8e-565d-4ff1-9e9b-b01d5e72f054 | action-recognition-with-trajectory-pooled | 1505.04868 | null | http://arxiv.org/abs/1505.04868v1 | http://arxiv.org/pdf/1505.04868v1.pdf | Action Recognition with Trajectory-Pooled Deep-Convolutional Descriptors | Visual features are of vital importance for human action understanding in
videos. This paper presents a new video representation, called
trajectory-pooled deep-convolutional descriptor (TDD), which shares the merits
of both hand-crafted features and deep-learned features. Specifically, we
utilize deep architectures to ... | ['Yu Qiao', 'Limin Wang', 'Xiaoou Tang'] | 2015-05-19 | action-recognition-with-trajectory-pooled-1 | http://openaccess.thecvf.com/content_cvpr_2015/html/Wang_Action_Recognition_With_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Wang_Action_Recognition_With_2015_CVPR_paper.pdf | cvpr-2015-6 | ['action-understanding', 'activity-recognition-in-videos'] | ['computer-vision', 'computer-vision'] | [-5.84065802e-02 -6.72756016e-01 -4.04075682e-01 -3.33308488e-01
-7.09136426e-01 -3.15410107e-01 8.18044484e-01 -1.89746007e-01
-6.21681094e-01 5.03443182e-01 7.21053004e-01 1.42256677e-01
-2.60007650e-01 -7.20544100e-01 -6.29498541e-01 -7.61116803e-01
-4.67202812e-01 -4.16140765e-01 4.41523224e-01 -8.66283011... | [8.544153213500977, 0.7690708041191101] |
f5401d82-3832-48df-b8de-ef03453dc7f5 | learning-to-stabilize-high-dimensional | 2306.08722 | null | https://arxiv.org/abs/2306.08722v1 | https://arxiv.org/pdf/2306.08722v1.pdf | Learning to Stabilize High-dimensional Unknown Systems Using Lyapunov-guided Exploration | Designing stabilizing controllers is a fundamental challenge in autonomous systems, particularly for high-dimensional, nonlinear systems that cannot be accurately modeled using differential equations. Lyapunov theory offers a robust solution for stabilizing control systems, but current methods relying on Lyapunov funct... | ['Chuchu Fan', 'Songyuan Zhang'] | 2023-06-14 | null | null | null | null | ['imitation-learning'] | ['methodology'] | [-1.63403839e-01 6.87193573e-02 -3.18125784e-01 4.38681632e-01
-8.32175970e-01 -8.73180211e-01 2.25543320e-01 -2.50619709e-01
-2.06825480e-01 1.16181362e+00 -4.71258134e-01 -7.94394016e-01
-1.65180668e-01 -3.77684921e-01 -8.24873090e-01 -6.80081844e-01
-4.81834978e-01 1.75366491e-01 -1.42732456e-01 -3.70045185... | [4.851644039154053, 2.166949510574341] |
c97e2c39-f72a-4df6-9fea-b9bc9f0d574c | panoptic-narrative-grounding-1 | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Gonzalez_Panoptic_Narrative_Grounding_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Gonzalez_Panoptic_Narrative_Grounding_ICCV_2021_paper.pdf | Panoptic Narrative Grounding | This paper proposes Panoptic Narrative Grounding, a spatially fine and general formulation of the natural language visual grounding problem. We establish an experimental framework for the study of this new task, including new ground truth and metrics, and we propose a strong baseline method to serve as stepping sto... | ['Pablo Arbelaez', 'Jordi Pont-Tuset', 'Jose Hernandez', 'Isabela Hernandez', 'Nicolas Ayobi', 'Cristina Gonzalez'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['natural-language-visual-grounding'] | ['reasoning'] | [ 2.31077433e-01 3.50487471e-01 -4.93911564e-01 -2.63666868e-01
-6.79538548e-01 -1.02747583e+00 1.11009455e+00 3.79643112e-01
-2.14699090e-01 4.00045604e-01 7.03038871e-01 -1.59297168e-01
1.47751674e-01 -1.01724267e+00 -6.36265337e-01 -4.50536191e-01
2.15718001e-02 3.72101277e-01 3.27539593e-01 -3.24329585... | [10.959732055664062, 1.0937914848327637] |
ba42c49d-664e-4acb-9db9-05a1f5ff7fe8 | context-aware-automatic-music-transcription | 2203.16294 | null | https://arxiv.org/abs/2203.16294v2 | https://arxiv.org/pdf/2203.16294v2.pdf | Acoustics-specific Piano Velocity Estimation | Motivated by the state-of-art psychological research, we note that a piano performance transcribed with existing Automatic Music Transcription (AMT) methods cannot be successfully resynthesized without affecting the artistic content of the performance. This is due to 1) the different mappings between MIDI parameters us... | ['Federico Avanzini', 'Stavros Ntalampiras', 'Federico Simonetta'] | 2022-03-30 | null | null | null | null | ['music-transcription'] | ['music'] | [ 3.35283130e-01 8.65470842e-02 3.98507595e-01 -1.48501888e-01
-5.70225716e-01 -1.02473092e+00 6.38799787e-01 2.59133335e-02
-3.68319035e-01 3.31464231e-01 2.68797874e-01 1.42804623e-01
-3.24098855e-01 -4.35699910e-01 -8.14040124e-01 -5.74968636e-01
2.08753183e-01 7.75936663e-01 2.56171048e-01 -3.90564501... | [15.836527824401855, 5.530456066131592] |
1c48b7d5-79ce-459f-a15b-d1b513992cc6 | on-the-f-differential-privacy-guarantees-of | 2302.09624 | null | https://arxiv.org/abs/2302.09624v1 | https://arxiv.org/pdf/2302.09624v1.pdf | On the $f$-Differential Privacy Guarantees of Discrete-Valued Mechanisms | We consider a federated data analytics problem in which a server coordinates the collaborative data analysis of multiple users with privacy concerns and limited communication capability. The commonly adopted compression schemes introduce information loss into local data while improving communication efficiency, and it ... | ['Huaiyu Dai', 'Tony Quek', 'Zhaoyang Zhang', 'Caijun Zhong', 'Zhonggen Su', 'Richeng Jin'] | 2023-02-19 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [-4.99164201e-02 -1.76996961e-01 -1.27053082e-01 -4.36145246e-01
-7.32603848e-01 -8.10257494e-01 1.90202683e-01 5.31427264e-01
-6.48192286e-01 5.44555008e-01 1.80585951e-01 -5.41458130e-01
-4.00625169e-01 -9.46051180e-01 -7.03768432e-01 -1.00378418e+00
-6.17446184e-01 -2.62795895e-01 -4.07925904e-01 -3.15380022... | [5.908627033233643, 6.59383487701416] |
49cb9884-1633-45fb-9557-6c266c4031bc | towards-discovering-the-effectiveness-of | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Tang_Towards_Discovering_the_Effectiveness_of_Moderately_Confident_Samples_for_Semi-Supervised_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Tang_Towards_Discovering_the_Effectiveness_of_Moderately_Confident_Samples_for_Semi-Supervised_CVPR_2022_paper.pdf | Towards Discovering the Effectiveness of Moderately Confident Samples for Semi-Supervised Learning | Semi-supervised learning (SSL) has been studied for a long time to solve vision tasks in data-efficient application scenarios. SSL aims to learn a good classification model using a few labeled data together with large-scale unlabeled data. Recent advances achieve the goal by combining multiple SSL techniques, e.g.,... | ['Kui Jia', 'Hui Tang'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['semi-supervised-image-classification'] | ['computer-vision'] | [-1.47994235e-02 -2.42366325e-02 -4.91848856e-01 -7.24989831e-01
-4.69132572e-01 1.41868768e-02 5.76275826e-01 2.67492354e-01
-5.42318881e-01 8.75283778e-01 9.94636491e-02 3.70888561e-01
-2.96459258e-01 -5.54053664e-01 -7.16820240e-01 -9.61029708e-01
2.07315102e-01 4.69628066e-01 6.19176269e-01 -1.00615598... | [9.440520286560059, 3.015986919403076] |
c44b9681-a602-49af-b99d-8a57dc76e8a0 | variational-leakage-the-role-of-information | 2106.02818 | null | https://arxiv.org/abs/2106.02818v2 | https://arxiv.org/pdf/2106.02818v2.pdf | Variational Leakage: The Role of Information Complexity in Privacy Leakage | We study the role of information complexity in privacy leakage about an attribute of an adversary's interest, which is not known a priori to the system designer. Considering the supervised representation learning setup and using neural networks to parameterize the variational bounds of information quantities, we study ... | ['Slava Voloshynovskiy', 'Deniz Gündüz', 'Behrooz Razeghi', 'Amir Ahooye Atashin'] | 2021-06-05 | null | null | null | null | ['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning'] | ['methodology', 'natural-language-processing'] | [ 2.86236405e-01 1.10485375e-01 -3.02926511e-01 -3.35422724e-01
-8.64173114e-01 -8.55439126e-01 3.09608519e-01 5.80279171e-01
-7.17758656e-01 4.69001770e-01 3.59908074e-01 -4.84286934e-01
-2.72259861e-01 -9.31266546e-01 -6.54655337e-01 -1.02873051e+00
-2.63975173e-01 9.46239829e-02 -1.91125631e-01 1.30908087... | [5.964755058288574, 6.951635360717773] |
8fd554a2-25b7-4819-a75c-99ab5747a7b5 | vietnamese-capitalization-and-punctuation | 2207.01312 | null | https://arxiv.org/abs/2207.01312v1 | https://arxiv.org/pdf/2207.01312v1.pdf | Vietnamese Capitalization and Punctuation Recovery Models | Despite the rise of recent performant methods in Automatic Speech Recognition (ASR), such methods do not ensure proper casing and punctuation for their outputs. This problem has a significant impact on the comprehension of both Natural Language Processing (NLP) algorithms and human to process. Capitalization and punctu... | ['Ta Duc Huy', 'Nguyen Anh Tu', 'Hoang Thi Thu Uyen'] | 2022-07-04 | null | null | null | null | ['punctuation-restoration'] | ['natural-language-processing'] | [ 2.06060022e-01 -2.46178564e-02 1.06756583e-01 -3.70586544e-01
-1.34563470e+00 -8.41474354e-01 6.22399807e-01 2.14012653e-01
-7.93127358e-01 6.81214452e-01 6.89579010e-01 -6.66293979e-01
5.02597988e-01 -2.55855411e-01 -7.81273425e-01 -2.81859875e-01
4.91227120e-01 3.99136871e-01 8.50988179e-02 -1.49936408... | [14.202718734741211, 7.200375080108643] |
77e64891-53b2-4f4f-aafd-36f2cacf4316 | supervised-learning-and-anti-learning-of | 1307.1599 | null | http://arxiv.org/abs/1307.1599v1 | http://arxiv.org/pdf/1307.1599v1.pdf | Supervised Learning and Anti-learning of Colorectal Cancer Classes and Survival Rates from Cellular Biology Parameters | In this paper, we describe a dataset relating to cellular and physical
conditions of patients who are operated upon to remove colorectal tumours. This
data provides a unique insight into immunological status at the point of tumour
removal, tumour classification and post-operative survival. Attempts are made
to learn re... | ['Lindy Durrant', 'John Scholefield', 'Guoping Qiu', 'Uwe Aickelin', 'Chris Roadknight'] | 2013-07-05 | null | null | null | null | ['tumour-classification'] | ['medical'] | [ 4.59837765e-01 8.02017301e-02 -5.81795692e-01 -1.22098401e-01
-4.42167044e-01 -1.30764604e-01 8.11515570e-01 7.94680536e-01
-4.51002836e-01 1.11272264e+00 2.80586451e-01 -3.90556902e-01
-6.88971341e-01 -6.82012856e-01 -1.15678415e-01 -1.14238036e+00
-4.06869829e-01 7.60749400e-01 -2.35284597e-01 -2.63222635... | [15.151758193969727, -3.0662992000579834] |
84c100bc-db3c-4694-8d61-5c32a2811b55 | a-brief-review-of-real-world-color-image | 1809.03298 | null | http://arxiv.org/abs/1809.03298v1 | http://arxiv.org/pdf/1809.03298v1.pdf | A Brief Review of Real-World Color Image Denoising | Filtering real-world color images is challenging due to the complexity of
noise that can not be formulated as a certain distribution. However, the rapid
development of camera lens pos- es greater demands on image denoising in terms
of both efficiency and effectiveness. Currently, the most widely accepted
framework empl... | ['Zhaoming Kong', 'Xiaowei Yang'] | 2018-09-10 | null | null | null | null | ['color-image-denoising'] | ['computer-vision'] | [ 3.87322396e-01 -1.02332830e+00 2.33217180e-01 -4.14221019e-01
-5.86510122e-01 -3.47628444e-01 1.96592540e-01 -2.14301750e-01
-5.66722989e-01 7.24903643e-01 -8.21831226e-02 2.78400421e-01
-3.18442196e-01 -6.10789061e-01 -2.58553773e-01 -1.07851994e+00
1.22294411e-01 -5.01258194e-01 3.79504859e-01 -3.21037620... | [10.89466667175293, -2.538977861404419] |
79f3f876-013f-48b4-93cb-3a7aef3faf03 | carl-d-a-vision-benchmark-suite-and-large | null | null | https://www.sciencedirect.com/science/article/abs/pii/S0923596522000224 | https://www.sciencedirect.com/science/article/abs/pii/S0923596522000224 | CARL-D: A vision benchmark suite and large scale dataset for vehicle detection and scene segmentation | Vision-based object detection and scene understanding are becoming key features of environment perception and autonomous driving. In the past couple of years, numerous large-scale datasets for visual object detection and semantic understanding have been released which has enormously benefited the environment perception... | ['Faisal Riaz', 'Muhammad Atif Butt'] | 2022-02-17 | null | null | null | signal-processing-image-communication-2022-2 | ['scene-segmentation'] | ['computer-vision'] | [ 1.29041644e-02 -2.65578777e-01 -2.39095002e-01 -4.11179185e-01
-4.19627696e-01 -4.50576663e-01 6.62180841e-01 -2.84602493e-01
-4.39396799e-01 3.98891002e-01 -3.38431239e-01 -6.48798466e-01
3.76596987e-01 -9.89198565e-01 -6.46284461e-01 -5.18898129e-01
-4.19007391e-02 4.13951039e-01 6.50339842e-01 -5.34602940... | [8.207571029663086, -1.5422450304031372] |
6e76abba-6046-4c3c-8f73-9c9c41309653 | domain-sensitive-temporal-tagging-by-jannik | null | null | https://aclanthology.org/J18-2006 | https://aclanthology.org/J18-2006.pdf | Domain-Sensitive Temporal Tagging By Jannik Str\"otgen, Michael Gertz | null | ['Ruihong Huang'] | 2018-06-01 | null | null | null | cl-2018-6 | ['temporal-tagging'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.219003200531006, 3.7117111682891846] |
118938aa-fa68-4769-a05e-d49311109d99 | generative-scene-graph-networks | null | null | https://openreview.net/forum?id=RmcPm9m3tnk | https://openreview.net/pdf?id=RmcPm9m3tnk | Generative Scene Graph Networks | Human perception excels at building compositional hierarchies of parts and objects from unlabeled scenes that help systematic generalization. Yet most work on generative scene modeling either ignores the part-whole relationship or assumes access to predefined parts labels. In this paper, we propose Generative Scene Gra... | ['Sungjin Ahn', 'Donghun Lee', 'Zhuo Zhi', 'Fei Deng'] | 2021-01-01 | null | null | null | iclr-2021-1 | ['systematic-generalization'] | ['reasoning'] | [ 2.68462628e-01 5.20418823e-01 2.56757081e-01 -5.52866399e-01
-3.45132083e-01 -7.34865904e-01 9.00018215e-01 -1.91272944e-01
7.09539577e-02 3.76645893e-01 1.45850018e-01 -6.30263537e-02
-6.24874420e-02 -1.07500494e+00 -1.10732913e+00 -7.83893108e-01
2.03137144e-01 1.20874286e+00 2.68656939e-01 5.16914390... | [10.128570556640625, 0.36373183131217957] |
c83fd9a9-3beb-4cea-8795-3dbac6645cdc | airloop-lifelong-loop-closure-detection | 2109.08975 | null | https://arxiv.org/abs/2109.08975v3 | https://arxiv.org/pdf/2109.08975v3.pdf | AirLoop: Lifelong Loop Closure Detection | Loop closure detection is an important building block that ensures the accuracy and robustness of simultaneous localization and mapping (SLAM) systems. Due to their generalization ability, CNN-based approaches have received increasing attention. Although they normally benefit from training on datasets that are diverse ... | ['Sebastian Scherer', 'Chen Wang', 'Dasong Gao'] | 2021-09-18 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [-5.10906167e-02 -1.81954309e-01 -2.03158021e-01 -4.34591204e-01
-3.87294948e-01 -4.49060857e-01 4.58284348e-01 4.27960515e-01
-6.39333963e-01 6.52432740e-01 -7.48369545e-02 -3.74703288e-01
-1.60463657e-02 -5.82548916e-01 -1.12301004e+00 -2.93506682e-01
-2.94973582e-01 2.52071738e-01 4.33900535e-01 -3.31549227... | [7.531509876251221, -1.9715126752853394] |
6fdbd961-49cb-43f3-ac55-be9ddeb1700a | a-diversity-promoting-objective-function-for | 1510.03055 | null | http://arxiv.org/abs/1510.03055v3 | http://arxiv.org/pdf/1510.03055v3.pdf | A Diversity-Promoting Objective Function for Neural Conversation Models | Sequence-to-sequence neural network models for generation of conversational
responses tend to generate safe, commonplace responses (e.g., "I don't know")
regardless of the input. We suggest that the traditional objective function,
i.e., the likelihood of output (response) given input (message) is unsuited to
response g... | ['Chris Brockett', 'Jianfeng Gao', 'Michel Galley', 'Jiwei Li', 'Bill Dolan'] | 2015-10-11 | a-diversity-promoting-objective-function-for-1 | https://aclanthology.org/N16-1014 | https://aclanthology.org/N16-1014.pdf | naacl-2016-6 | ['conversational-response-generation'] | ['natural-language-processing'] | [ 4.12331611e-01 3.86006743e-01 5.01208790e-02 -9.50100899e-01
-7.84155786e-01 -4.32429612e-01 7.20366776e-01 -1.34265020e-01
-3.23726565e-01 1.31584775e+00 6.56527400e-01 -4.56990302e-01
8.27616081e-02 -8.24605107e-01 -2.91447312e-01 -3.50938529e-01
6.28688276e-01 4.39526320e-01 -4.30478752e-01 -5.54834008... | [12.617415428161621, 8.31870174407959] |
b7b70597-40b9-4a31-8f0c-c9c3cc3d4b8f | mind-the-retrosynthesis-gap-bridging-the | 2212.11809 | null | https://arxiv.org/abs/2212.11809v1 | https://arxiv.org/pdf/2212.11809v1.pdf | Mind the Retrosynthesis Gap: Bridging the divide between Single-step and Multi-step Retrosynthesis Prediction | Retrosynthesis is the task of breaking down a chemical compound recursively step-by-step into molecular precursors until a set of commercially available molecules is found. Consequently, the goal is to provide a valid synthesis route for a molecule. As more single-step models develop, we see increasing accuracy in the ... | ['Igor Tetko', 'Mike Preuss', 'Jonas Verhoeven', 'Samuel Genheden', 'Paula Torren-Peraire', 'Alan Kai Hassen'] | 2022-12-12 | null | null | null | null | ['retrosynthesis'] | ['medical'] | [ 4.99505311e-01 -2.12211117e-01 -4.53061491e-01 2.48442873e-01
-5.44850111e-01 -1.38753617e+00 7.30347097e-01 7.54327893e-01
-4.45760250e-01 9.62693632e-01 -1.60423890e-01 -7.24549472e-01
-1.13524571e-02 -1.00764716e+00 -5.89802921e-01 -5.09439766e-01
-4.24600281e-02 5.57831228e-01 5.68939984e-01 -4.68450904... | [4.486552715301514, 6.114587783813477] |
a040f8cb-8cf8-4ebf-89dc-172beeb62737 | productgraphsleepnet-sleep-staging-using | 2212.04881 | null | https://arxiv.org/abs/2212.04881v1 | https://arxiv.org/pdf/2212.04881v1.pdf | ProductGraphSleepNet: Sleep Staging using Product Spatio-Temporal Graph Learning with Attentive Temporal Aggregation | The classification of sleep stages plays a crucial role in understanding and diagnosing sleep pathophysiology. Sleep stage scoring relies heavily on visual inspection by an expert that is time consuming and subjective procedure. Recently, deep learning neural network approaches have been leveraged to develop a generali... | ['Gari Clifford', 'Sepideh Hajipour Sardouie', 'Samaneh Nasiri', 'Aref Einizade'] | 2022-12-09 | null | null | null | null | ['sleep-staging'] | ['medical'] | [-1.94994017e-01 -1.19218148e-01 -7.83809349e-02 -4.34300363e-01
1.67903025e-02 -4.63642627e-01 8.19323212e-02 2.25125179e-01
-5.17319560e-01 7.00171173e-01 1.48755118e-01 -3.08035284e-01
-2.57279873e-01 -2.86237061e-01 -4.14300524e-02 -5.26877105e-01
-5.58351398e-01 3.26723248e-01 2.62249351e-01 -5.98881245... | [13.492875099182129, 3.5258219242095947] |
3902424a-db7d-44bc-8e8f-5bc331caa0ce | ecnu-a-combination-method-and-multiple | null | null | https://aclanthology.org/S14-2041 | https://aclanthology.org/S14-2041.pdf | ECNU: A Combination Method and Multiple Features for Aspect Extraction and Sentiment Polarity Classification | null | ['Zhihua Zhang', 'Man Lan', 'Fangxi Zhang'] | 2014-08-01 | null | null | null | semeval-2014-8 | ['aspect-extraction'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.3115763664245605, 3.846586227416992] |
bdd770e8-b49f-4237-9cf8-dac19943e9bc | medical-supervised-masked-autoencoders | 2305.05871 | null | https://arxiv.org/abs/2305.05871v1 | https://arxiv.org/pdf/2305.05871v1.pdf | Medical supervised masked autoencoders: Crafting a better masking strategy and efficient fine-tuning schedule for medical image classification | Masked autoencoders (MAEs) have displayed significant potential in the classification and semantic segmentation of medical images in the last year. Due to the high similarity of human tissues, even slight changes in medical images may represent diseased tissues, necessitating fine-grained inspection to pinpoint disease... | ['Binling Nie', 'Xuesong Yin', 'Yuanqi Chang', 'Shujian Guo', 'Jiawei Mao'] | 2023-05-10 | null | null | null | null | ['medical-diagnosis'] | ['medical'] | [ 4.91158962e-01 2.82314181e-01 2.77898461e-01 -1.72614560e-01
-5.00161052e-01 -1.45332366e-02 2.30812848e-01 4.67505716e-02
-6.61734402e-01 4.11978543e-01 -8.70115981e-02 -1.36040434e-01
-1.18817426e-01 -9.99906123e-01 -6.69151902e-01 -9.26971436e-01
2.19164476e-01 3.89763594e-01 3.34894866e-01 -2.25519165... | [14.670167922973633, -2.5021419525146484] |
30a1362f-962c-48f6-9730-36e68bec04e9 | tollywood-emotions-annotation-of-valence | 2303.09364 | null | https://arxiv.org/abs/2303.09364v1 | https://arxiv.org/pdf/2303.09364v1.pdf | Tollywood Emotions: Annotation of Valence-Arousal in Telugu Song Lyrics | Emotion recognition from a given music track has heavily relied on acoustic features, social tags, and metadata but is seldom focused on lyrics. There are no datasets of Indian language songs that contain both valence and arousal manual ratings of lyrics. We present a new manually annotated dataset of Telugu songs' lyr... | ['Vinoo Alluri', 'BV Koushik', 'B Manikanta Gupta', 'R Guru Ravi Shanker'] | 2023-03-16 | null | null | null | null | ['music-emotion-recognition', 'xlm-r'] | ['music', 'natural-language-processing'] | [ 4.80632298e-02 -4.61850345e-01 -6.53486624e-02 -4.59978580e-01
-1.04347873e+00 -1.21320975e+00 3.11606705e-01 1.27755404e-01
-1.92883492e-01 5.64680099e-01 4.84441787e-01 4.92599398e-01
-2.17108339e-01 -2.66262621e-01 -6.76445812e-02 -7.38070071e-01
-2.43829079e-02 9.76365954e-02 -4.17963654e-01 -1.06058121... | [15.874613761901855, 5.169182777404785] |
a88440f7-1e0a-4daa-8f1d-e9c76bbe1cf9 | deep-learning-based-automatic-detection-of | 2009.13580 | null | https://arxiv.org/abs/2009.13580v1 | https://arxiv.org/pdf/2009.13580v1.pdf | Deep Learning-Based Automatic Detection of Poorly Positioned Mammograms to Minimize Patient Return Visits for Repeat Imaging: A Real-World Application | Screening mammograms are a routine imaging exam performed to detect breast cancer in its early stages to reduce morbidity and mortality attributed to this disease. In order to maximize the efficacy of breast cancer screening programs, proper mammographic positioning is paramount. Proper positioning ensures adequate vis... | ['Richard D. White', 'Mona G. Flores', 'Barbaros Selnur Erdal', 'Sarah Bonnet', 'Jeffrey Hawley', 'Clayton Taylor', 'Vikash Gupta', 'Luciano M. Prevedello'] | 2020-09-28 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 3.85969400e-01 4.60975498e-01 -3.30126822e-01 -6.95404708e-01
-6.32592559e-01 -2.92425036e-01 -2.54309714e-01 6.11326635e-01
-3.21640760e-01 2.07510069e-01 -4.06648703e-02 -1.15091670e+00
-1.52176976e-01 -1.03532481e+00 -6.70410037e-01 -5.52044690e-01
-9.06935856e-02 6.96814299e-01 2.56114542e-01 3.34122390... | [15.181807518005371, -2.5384581089019775] |
062e074e-f962-42b4-b6f7-c60eacb83176 | automatic-classification-of-defective | 1807.02894 | null | http://arxiv.org/abs/1807.02894v3 | http://arxiv.org/pdf/1807.02894v3.pdf | Automatic Classification of Defective Photovoltaic Module Cells in Electroluminescence Images | Electroluminescence (EL) imaging is a useful modality for the inspection of
photovoltaic (PV) modules. EL images provide high spatial resolution, which
makes it possible to detect even finest defects on the surface of PV modules.
However, the analysis of EL images is typically a manual process that is
expensive, time-c... | ['Florian Gallwitz', 'Claudia Buerhop-Lutz', 'Vincent Christlein', 'Sergiu Deitsch', 'Stephan Berger', 'Christian Riess', 'Andreas Maier'] | 2018-07-08 | null | null | null | null | ['anomaly-classification'] | ['computer-vision'] | [ 4.27602142e-01 -5.09348989e-01 3.06684017e-01 3.82959023e-02
-6.05699241e-01 -5.54619372e-01 1.97055250e-01 3.69154334e-01
-3.33303332e-01 7.10729599e-01 -8.11237156e-01 -3.19739997e-01
3.98408294e-01 -9.51986909e-01 -5.93914509e-01 -1.13931894e+00
4.32126522e-01 2.42125168e-01 4.02590930e-01 3.39256614... | [7.277254581451416, 1.8831570148468018] |
58a1db5c-47bb-4a5b-9914-eb77d9cfb6f9 | a-survey-on-extreme-multi-label-learning | 2210.03968 | null | https://arxiv.org/abs/2210.03968v1 | https://arxiv.org/pdf/2210.03968v1.pdf | A Survey on Extreme Multi-label Learning | Multi-label learning has attracted significant attention from both academic and industry field in recent decades. Although existing multi-label learning algorithms achieved good performance in various tasks, they implicitly assume the size of target label space is not huge, which can be restrictive for real-world scena... | ['Min-Ling Zhang', 'Yu-Feng Li', 'Jiang-Xin Shi', 'Zhen Mao', 'Tong Wei'] | 2022-10-08 | null | null | null | null | ['multi-label-learning'] | ['methodology'] | [ 3.82703632e-01 -1.31745636e-01 -6.75270021e-01 -7.37440109e-01
-1.05865717e+00 -6.34379327e-01 1.60161659e-01 4.49497163e-01
-5.21337450e-01 6.98669016e-01 -1.45458385e-01 -1.78189352e-01
-2.83081234e-01 -5.46415627e-01 -3.02562535e-01 -6.93895459e-01
1.32400030e-02 3.30531925e-01 2.44502723e-01 2.43381605... | [9.56558609008789, 4.322548866271973] |
389acba7-2849-44e2-b385-ec2176b3f5e5 | fully-scalable-gaussian-processes-using | 1807.02537 | null | http://arxiv.org/abs/1807.02537v2 | http://arxiv.org/pdf/1807.02537v2.pdf | Fully Scalable Gaussian Processes using Subspace Inducing Inputs | We introduce fully scalable Gaussian processes, an implementation scheme that
tackles the problem of treating a high number of training instances together
with high dimensional input data. Our key idea is a representation trick over
the inducing variables called subspace inducing inputs. This is combined with
certain m... | ['Petros Dellaportas', 'Aristeidis Panos', 'Michalis K. Titsias'] | 2018-07-06 | null | null | null | null | ['extreme-multi-label-classification'] | ['methodology'] | [ 2.74337113e-01 -4.97962534e-02 8.38997141e-02 -2.19537526e-01
-1.27007365e+00 -6.30597532e-01 7.76148736e-01 1.41528636e-01
-3.25078875e-01 8.21849108e-01 -6.15266673e-02 -1.06004685e-01
-3.60769570e-01 -4.78012532e-01 -4.28232193e-01 -1.25655961e+00
3.16808343e-01 9.90368009e-01 -3.38877141e-01 8.38463083... | [7.630174160003662, 4.183019638061523] |
d90f3b7e-1380-46f5-8049-302159419699 | classifying-topics-in-speech-when-all-you | 1908.11425 | null | https://arxiv.org/abs/1908.11425v2 | https://arxiv.org/pdf/1908.11425v2.pdf | Cross-lingual topic prediction for speech using translations | Given a large amount of unannotated speech in a low-resource language, can we classify the speech utterances by topic? We consider this question in the setting where a small amount of speech in the low-resource language is paired with text translations in a high-resource language. We develop an effective cross-lingual ... | ['Sharon Goldwater', 'Sameer Bansal', 'Adam Lopez', 'Herman Kamper'] | 2019-08-29 | null | null | null | null | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 2.34280288e-01 5.36464989e-01 -3.29942524e-01 -4.75168645e-01
-1.92097712e+00 -8.66067111e-01 8.23722124e-01 9.28018708e-03
-5.66865504e-01 7.36195147e-01 7.60755241e-01 -8.23627174e-01
6.39330149e-01 -4.21728730e-01 -6.18208826e-01 -5.20228386e-01
2.45184392e-01 1.14398491e+00 1.59720495e-01 -4.75370258... | [14.437392234802246, 7.25695276260376] |
ad4a5454-8064-449b-94a8-c0905e209b3f | g2pw-a-conditional-weighted-softmax-bert-for | 2203.10430 | null | https://arxiv.org/abs/2203.10430v5 | https://arxiv.org/pdf/2203.10430v5.pdf | g2pW: A Conditional Weighted Softmax BERT for Polyphone Disambiguation in Mandarin | Polyphone disambiguation is the most crucial task in Mandarin grapheme-to-phoneme (g2p) conversion. Previous studies have approached this problem using pre-trained language models, restricted output, and extra information from Part-Of-Speech (POS) tagging. Inspired by these strategies, we propose a novel approach, call... | ['Yi-Ren Yeh', 'Yen-Cheng Chang', 'Yu-Chuan Chang', 'Yi-Chang Chen'] | 2022-03-20 | null | null | null | null | ['polyphone-disambiguation'] | ['natural-language-processing'] | [ 3.63380283e-01 1.81118220e-01 -3.99792343e-01 -3.93236279e-01
-1.02855098e+00 -6.33602321e-01 3.28752339e-01 -4.58825752e-03
-6.95077181e-01 6.72502637e-01 1.85470775e-01 -6.31128907e-01
5.24224877e-01 -6.20529294e-01 -7.21589267e-01 -6.25003457e-01
-4.61677648e-02 5.65731972e-02 4.18229580e-01 -6.78384230... | [14.336775779724121, 7.055328369140625] |
3d8fd0ae-3542-4031-bcf3-b0776d4ddbf3 | airway-measurement-by-refinement-of-synthetic | 2208.14141 | null | https://arxiv.org/abs/2208.14141v1 | https://arxiv.org/pdf/2208.14141v1.pdf | Airway measurement by refinement of synthetic images improves mortality prediction in idiopathic pulmonary fibrosis | Several chronic lung diseases, like idiopathic pulmonary fibrosis (IPF) are characterised by abnormal dilatation of the airways. Quantification of airway features on computed tomography (CT) can help characterise disease progression. Physics based airway measurement algorithms have been developed, but have met with lim... | ['Joseph Jacob', 'John R Hurst', 'Wim A Wuyts', 'Stijn E Verleden', 'Laurens J De Sadeleer', 'Tinne Goos', 'Marie Vermant', 'Wing Keung Cheung', 'Mou-Cheng Xu', 'Ashkan Pakzad'] | 2022-08-30 | null | null | null | null | ['mortality-prediction'] | ['medical'] | [ 1.63301826e-01 1.32879242e-01 9.52913314e-02 -1.14724465e-01
-1.12017512e+00 -4.74301189e-01 3.60306948e-01 -9.56631899e-02
-2.59184569e-01 7.76958942e-01 5.15979230e-01 -7.03463316e-01
-2.55365729e-01 -9.38870788e-01 -3.01906824e-01 -7.35721290e-01
8.82034302e-02 1.20666730e+00 2.34559894e-01 1.05134383... | [14.893972396850586, -2.0338871479034424] |
fbed58b2-3f98-45c4-8817-0777d4f21bd7 | chitransformer-towards-reliable-stereo-from | 2203.04554 | null | https://arxiv.org/abs/2203.04554v3 | https://arxiv.org/pdf/2203.04554v3.pdf | ChiTransformer:Towards Reliable Stereo from Cues | Current stereo matching techniques are challenged by restricted searching space, occluded regions and sheer size. While single image depth estimation is spared from these challenges and can achieve satisfactory results with the extracted monocular cues, the lack of stereoscopic relationship renders the monocular predic... | ['Shihao Ji', 'Qing Su'] | 2022-03-09 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [ 3.15951973e-01 1.79177634e-02 -7.55952001e-02 -3.33709925e-01
-4.24788684e-01 -5.85867167e-01 6.03852570e-01 -5.37247181e-01
-2.93585628e-01 6.39248848e-01 3.00503552e-01 1.46487355e-02
5.61358780e-02 -4.85316068e-01 -6.78722143e-01 -8.66352856e-01
5.38375556e-01 3.94549966e-03 5.54836929e-01 2.35379189... | [8.838318824768066, -2.4225499629974365] |
899d4ad9-faf2-4ce9-b935-858f8fb209d5 | deep-reinforcement-learning-for-robotic-2 | 2302.10717 | null | https://arxiv.org/abs/2302.10717v1 | https://arxiv.org/pdf/2302.10717v1.pdf | Deep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environment | In this paper, a novel robotic grasping system is established to automatically pick up objects in cluttered scenes. A composite robotic hand composed of a suction cup and a gripper is designed for grasping the object stably. The suction cup is used for lifting the object from the clutter first and the gripper for grasp... | ['Fuchun Sun', 'Huaping Liu', 'Di Guo', 'Bin Fang', 'Kai Lu', 'Yixuan Wei', 'Xiaofeng Guo', 'Yuhong Deng'] | 2023-02-21 | null | null | null | null | ['robotic-grasping'] | ['robots'] | [-1.48080423e-01 -2.44103089e-01 -1.78149268e-01 -2.08841458e-01
-2.10684717e-01 -4.66914028e-01 -1.46025121e-01 -3.23067546e-01
-2.92118907e-01 4.33826178e-01 -2.09641010e-01 7.04731718e-02
-6.11795723e-01 -7.19005585e-01 -5.69083929e-01 -1.13161075e+00
-3.48377734e-01 1.31772175e-01 1.14235073e-01 -5.50491549... | [5.777653694152832, -0.8863552212715149] |
b32631fd-6693-4200-a538-e16d21c05619 | a-unified-object-motion-and-affinity-model | 2003.11291 | null | https://arxiv.org/abs/2003.11291v2 | https://arxiv.org/pdf/2003.11291v2.pdf | A Unified Object Motion and Affinity Model for Online Multi-Object Tracking | Current popular online multi-object tracking (MOT) solutions apply single object trackers (SOTs) to capture object motions, while often requiring an extra affinity network to associate objects, especially for the occluded ones. This brings extra computational overhead due to repetitive feature extraction for SOT and af... | ['Ruigang Yang', 'Jianbing Shen', 'Wenguan Wang', 'Junbo Yin', 'Qinghao Meng'] | 2020-03-25 | a-unified-object-motion-and-affinity-model-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Yin_A_Unified_Object_Motion_and_Affinity_Model_for_Online_Multi-Object_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Yin_A_Unified_Object_Motion_and_Affinity_Model_for_Online_Multi-Object_CVPR_2020_paper.pdf | cvpr-2020-6 | ['online-multi-object-tracking'] | ['computer-vision'] | [-1.72637522e-01 -6.22712791e-01 -2.66460210e-01 -2.80123711e-01
-7.73410976e-01 -2.56008118e-01 1.72873870e-01 -8.64442438e-02
-6.14668131e-01 3.54344279e-01 -2.81656891e-01 1.08398810e-01
-2.12543502e-01 -4.30124521e-01 -6.90219402e-01 -8.54471505e-01
3.21661890e-01 5.98900318e-01 7.54763484e-01 3.08226198... | [6.325409412384033, -2.1324446201324463] |
b9c99d21-5658-4cf9-8f2e-1c23dffbcdd5 | multilingual-speech-emotion-recognition-with | 2211.08237 | null | https://arxiv.org/abs/2211.08237v2 | https://arxiv.org/pdf/2211.08237v2.pdf | Multilingual Speech Emotion Recognition With Multi-Gating Mechanism and Neural Architecture Search | Speech emotion recognition (SER) classifies audio into emotion categories such as Happy, Angry, Fear, Disgust and Neutral. While Speech Emotion Recognition (SER) is a common application for popular languages, it continues to be a problem for low-resourced languages, i.e., languages with no pretrained speech-to-text rec... | ['Akshat Gupta', 'Kehao Guo', 'Xinrui Zhang', 'HaiFeng Lan', 'Qi Meng', 'Zihan Wang'] | 2022-10-31 | null | null | null | null | ['speech-emotion-recognition'] | ['speech'] | [-8.59607235e-02 -2.04154383e-02 -1.54687852e-01 -5.74228823e-01
-1.06338465e+00 -3.05164844e-01 2.61960030e-01 -4.66113118e-03
-5.60670197e-01 4.69678283e-01 4.26585913e-01 -8.90346523e-03
6.03504717e-01 -4.05586779e-01 -1.51967391e-01 -4.64374870e-01
3.08989454e-02 2.46034712e-01 -2.88624942e-01 -4.34330195... | [13.592544555664062, 5.831183910369873] |
e39c9205-7aaa-4476-8a75-dd79eab20556 | online-class-incremental-learning-for-real | 2301.05246 | null | https://arxiv.org/abs/2301.05246v1 | https://arxiv.org/pdf/2301.05246v1.pdf | Online Class-Incremental Learning For Real-World Food Classification | Online Class-Incremental Learning (OCIL) aims to continuously learn new information from single-pass data streams to update the model and mitigate catastrophic forgetting. However, most existing OCIL methods make several assumptions, including non-overlapped classes across phases and an equal number of classes in each ... | ['Fengqing Zhu', 'Jiangpeng He', 'Siddeshwar Raghavan'] | 2023-01-12 | null | null | null | null | ['class-incremental-learning'] | ['computer-vision'] | [ 1.07762471e-01 -2.96011567e-01 -5.05826890e-01 -3.69703114e-01
-2.98062146e-01 -2.52470136e-01 2.11448982e-01 8.04564238e-01
-6.01065576e-01 7.27597415e-01 -7.49898478e-02 1.09048761e-01
-1.19006082e-01 -1.01273000e+00 -1.21321905e+00 -5.55447042e-01
-2.93184936e-01 4.91055846e-01 6.59164190e-01 -1.75617281... | [9.87817096710205, 3.393371820449829] |
fb6d2f28-197b-4977-a407-82c50737aa3a | a-recursive-born-approach-to-nonlinear | 1603.03768 | null | http://arxiv.org/abs/1603.03768v1 | http://arxiv.org/pdf/1603.03768v1.pdf | A Recursive Born Approach to Nonlinear Inverse Scattering | The Iterative Born Approximation (IBA) is a well-known method for describing
waves scattered by semi-transparent objects. In this paper, we present a novel
nonlinear inverse scattering method that combines IBA with an edge-preserving
total variation (TV) regularizer. The proposed method is obtained by relating
iteratio... | ['Ulugbek S. Kamilov', 'Dehong Liu', 'Hassan Mansour', 'Petros T. Boufounos'] | 2016-03-11 | null | null | null | null | ['transparent-objects'] | ['computer-vision'] | [ 6.45873249e-01 -9.98663232e-02 9.52194393e-01 -5.62225342e-01
-3.07959288e-01 1.01352036e-01 1.57562479e-01 -5.08704722e-01
-4.46077257e-01 6.15068495e-01 -1.40681133e-01 -2.44475469e-01
-5.37530899e-01 -9.22380686e-01 -9.20075953e-01 -1.04784608e+00
-2.17003077e-01 3.58282447e-01 -2.11061080e-04 -1.29136816... | [12.50695514678955, -2.5968406200408936] |
6af9c3c9-ffce-4620-8e23-5f832dea628a | microscopic-muscle-image-enhancement | 1612.05719 | null | http://arxiv.org/abs/1612.05719v1 | http://arxiv.org/pdf/1612.05719v1.pdf | Microscopic Muscle Image Enhancement | We propose a robust image enhancement algorithm dedicated for muscle fiber
specimen images captured by optical microscopes. Blur or out of focus problems
are prevalent in muscle images during the image acquisition stage. Traditional
image deconvolution methods do not work since they assume the blur kernels are
known an... | ['Xiangfei Kong', 'Lin Yang'] | 2016-12-17 | null | null | null | null | ['image-deconvolution'] | ['computer-vision'] | [ 3.59523952e-01 -6.25481546e-01 3.41104448e-01 -2.39553079e-02
-2.08132774e-01 -6.12321138e-01 3.18980440e-02 -5.06577492e-01
-8.31620038e-01 9.25105572e-01 -1.86126884e-02 1.56718329e-01
-2.45964736e-01 -2.23591030e-02 -4.00833189e-01 -9.80437458e-01
1.77952871e-02 3.20655406e-02 4.80582893e-01 1.78079784... | [11.643500328063965, -2.7186403274536133] |
2fb16f54-12f9-4df5-92b0-5d8a3fd24ca8 | emoticon-context-aware-multimodal-emotion | 2003.06692 | null | https://arxiv.org/abs/2003.06692v1 | https://arxiv.org/pdf/2003.06692v1.pdf | EmotiCon: Context-Aware Multimodal Emotion Recognition using Frege's Principle | We present EmotiCon, a learning-based algorithm for context-aware perceived human emotion recognition from videos and images. Motivated by Frege's Context Principle from psychology, our approach combines three interpretations of context for emotion recognition. Our first interpretation is based on using multiple modali... | ['Dinesh Manocha', 'Rohan Chandra', 'Pooja Guhan', 'Uttaran Bhattacharya', 'Trisha Mittal', 'Aniket Bera'] | 2020-03-14 | emoticon-context-aware-multimodal-emotion-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Mittal_EmotiCon_Context-Aware_Multimodal_Emotion_Recognition_Using_Freges_Principle_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Mittal_EmotiCon_Context-Aware_Multimodal_Emotion_Recognition_Using_Freges_Principle_CVPR_2020_paper.pdf | cvpr-2020-6 | ['multimodal-emotion-recognition', 'emotion-recognition-in-context', 'multimodal-emotion-recognition'] | ['computer-vision', 'natural-language-processing', 'speech'] | [ 1.17447175e-01 -2.11468428e-01 1.82470247e-01 -6.91265345e-01
-2.06005156e-01 -1.40859023e-01 5.70073843e-01 -6.14316165e-02
-5.04062593e-01 5.30412376e-01 5.63953161e-01 4.15482342e-01
1.25667781e-01 -5.51722109e-01 -4.41365302e-01 -5.13868332e-01
-4.97313976e-01 -2.33404011e-01 -4.60529625e-02 -3.65421176... | [13.439638137817383, 2.307159900665283] |
2159a137-9c52-4106-9d3c-37bd06f44d17 | predicting-gender-via-eye-movements | 2206.07442 | null | https://arxiv.org/abs/2206.07442v1 | https://arxiv.org/pdf/2206.07442v1.pdf | Predicting Gender via Eye Movements | In this paper, we report the first stable results on gender prediction via eye movements. We use a dataset with images of faces as stimuli and with a large number of 370 participants. Stability has two meanings for us: first that we are able to estimate the standard deviation (SD) of a single prediction experiment (it ... | ['Sebastian Maneth', 'Sahar Mahdie Klim Al Zaidawi', 'Rishabh Vallabh Varsha Haria'] | 2022-06-15 | null | null | null | null | ['gender-prediction'] | ['computer-vision'] | [ 1.61243588e-01 3.68711919e-01 -2.12107480e-01 -5.58506906e-01
-2.03482583e-01 -3.25326800e-01 6.34589076e-01 9.13585722e-02
-7.15165794e-01 9.54154193e-01 -1.84633866e-01 -2.07716554e-01
1.52545748e-02 -4.91727144e-01 -5.06489336e-01 -4.76228148e-01
-4.37776856e-02 3.78907949e-01 2.69822955e-01 1.78178802... | [13.02599048614502, 1.2556339502334595] |
7d7d84dd-6401-4627-97ff-5e8b2b6d20a1 | modeling-entities-as-semantic-points-for | 2303.13095 | null | https://arxiv.org/abs/2303.13095v2 | https://arxiv.org/pdf/2303.13095v2.pdf | Modeling Entities as Semantic Points for Visual Information Extraction in the Wild | Recently, Visual Information Extraction (VIE) has been becoming increasingly important in both the academia and industry, due to the wide range of real-world applications. Previously, numerous works have been proposed to tackle this problem. However, the benchmarks used to assess these methods are relatively plain, i.e... | ['Cong Yao', 'Xiang Bai', 'Wenqing Cheng', 'Humen Zhong', 'Sibo Song', 'Pengfei Wang', 'Rujiao Long', 'Zhibo Yang'] | 2023-03-23 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yang_Modeling_Entities_As_Semantic_Points_for_Visual_Information_Extraction_in_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yang_Modeling_Entities_As_Semantic_Points_for_Visual_Information_Extraction_in_CVPR_2023_paper.pdf | cvpr-2023-1 | ['text-spotting'] | ['computer-vision'] | [ 8.87353048e-02 -6.22928478e-02 -3.06298822e-01 -2.55250573e-01
-5.41541755e-01 -6.70448780e-01 7.32161343e-01 2.77537555e-01
-2.52025336e-01 7.34650612e-01 8.56245831e-02 -3.91162150e-02
4.57423590e-02 -7.59494305e-01 -6.94635868e-01 -4.54265207e-01
3.37180078e-01 3.23980033e-01 3.54540199e-01 -1.70773491... | [11.440228462219238, 2.324483871459961] |
71d24174-e0c9-4f81-8e48-0a8169a01c10 | molecular-property-prediction-a-multilevel | 1906.11081 | null | https://arxiv.org/abs/1906.11081v1 | https://arxiv.org/pdf/1906.11081v1.pdf | Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective | Predicting molecular properties (e.g., atomization energy) is an essential issue in quantum chemistry, which could speed up much research progress, such as drug designing and substance discovery. Traditional studies based on density functional theory (DFT) in physics are proved to be time-consuming for predicting large... | ['Lixin He', 'Chengqiang Lu', 'Chao Wang', 'Peize Lin', 'Zhenya Huang', 'Qi Liu'] | 2019-06-25 | null | null | null | null | ['graph-regression'] | ['graphs'] | [ 1.09100796e-01 -2.60722488e-01 -3.89814913e-01 -3.46591890e-01
-1.64784715e-01 -2.82947898e-01 2.64767617e-01 5.72560847e-01
1.04659773e-01 1.07172668e+00 -4.48888466e-02 -5.78819931e-01
-1.56669393e-01 -1.35123491e+00 -1.06424189e+00 -1.06938958e+00
-1.71867877e-01 2.87524629e-02 1.67093471e-01 -4.10185158... | [5.134182453155518, 5.84351110458374] |
91d45f18-ff97-42b5-ae23-aea40904f081 | semantic-edge-detection-with-diverse-deep | 1804.02864 | null | https://arxiv.org/abs/1804.02864v5 | https://arxiv.org/pdf/1804.02864v5.pdf | Semantic Edge Detection with Diverse Deep Supervision | Semantic edge detection (SED), which aims at jointly extracting edges as well as their category information, has far-reaching applications in domains such as semantic segmentation, object proposal generation, and object recognition. SED naturally requires achieving two distinct supervision targets: locating fine detail... | ['DaCheng Tao', 'Jiawang Bian', 'Deng-Ping Fan', 'Ming-Ming Cheng', 'Le Zhang', 'Yun Liu'] | 2018-04-09 | null | null | null | null | ['object-proposal-generation'] | ['computer-vision'] | [ 3.61101061e-01 3.91553730e-01 -1.54212400e-01 -6.15011692e-01
-6.98057950e-01 -2.77971894e-01 8.12469840e-01 1.88315004e-01
-5.04851222e-01 3.96630555e-01 1.20390095e-01 -5.14452048e-02
6.95138425e-02 -8.72443974e-01 -8.78074825e-01 -3.07450086e-01
-4.26205210e-02 4.53417808e-01 5.72367549e-01 -1.67810068... | [9.568375587463379, 0.49831581115722656] |
66bb5056-5e0a-4a13-8358-0d5e885d5691 | slack-stable-learning-of-augmentations-with-1 | 2306.09998 | null | https://arxiv.org/abs/2306.09998v1 | https://arxiv.org/pdf/2306.09998v1.pdf | SLACK: Stable Learning of Augmentations with Cold-start and KL regularization | Data augmentation is known to improve the generalization capabilities of neural networks, provided that the set of transformations is chosen with care, a selection often performed manually. Automatic data augmentation aims at automating this process. However, most recent approaches still rely on some prior information;... | ['Julien Mairal', 'Diane Larlus', 'Michael Arbel', 'Juliette Marrie'] | 2023-06-16 | slack-stable-learning-of-augmentations-with | http://openaccess.thecvf.com//content/CVPR2023/html/Marrie_SLACK_Stable_Learning_of_Augmentations_With_Cold-Start_and_KL_Regularization_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Marrie_SLACK_Stable_Learning_of_Augmentations_With_Cold-Start_and_KL_Regularization_CVPR_2023_paper.pdf | cvpr-2023-1 | ['bilevel-optimization'] | ['methodology'] | [ 5.94667792e-01 9.48188826e-02 -3.61257315e-01 -4.41366881e-01
-5.30109644e-01 -7.51512766e-01 9.43318903e-01 1.37613997e-01
-9.71253872e-01 7.09941030e-01 1.19630292e-01 -4.52097207e-01
-9.40559655e-02 -5.59710026e-01 -8.28840733e-01 -7.96161771e-01
1.56343967e-01 7.16965139e-01 9.32462811e-02 -2.33642220... | [9.133310317993164, 2.963435173034668] |
3a2b4469-ff5c-44a4-9c35-db15987f15bf | inflected-forms-are-redundant-in-question | 2301.00397 | null | https://arxiv.org/abs/2301.00397v1 | https://arxiv.org/pdf/2301.00397v1.pdf | Inflected Forms Are Redundant in Question Generation Models | Neural models with an encoder-decoder framework provide a feasible solution to Question Generation (QG). However, after analyzing the model vocabulary we find that current models (both RNN-based and pre-training based) have more than 23\% inflected forms. As a result, the encoder will generate separate embeddings for t... | ['Chengzhong Xu', 'Hongyin Tang', 'Xingwu Sun'] | 2023-01-01 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [ 3.41569841e-01 1.87751547e-01 2.72984564e-01 -1.95987180e-01
-9.57796097e-01 -6.41342163e-01 4.41269964e-01 -4.83826594e-03
-6.99664712e-01 6.59249604e-01 4.42671776e-01 -6.01835907e-01
3.51914436e-01 -8.81573200e-01 -7.43525028e-01 -4.46501225e-01
5.42593181e-01 2.54034936e-01 3.05989921e-01 -4.65233535... | [11.56331729888916, 9.69242000579834] |
2ea314b5-3143-4913-bc1f-ce38b0de40b9 | lingjing-at-semeval-2022-task-1-multi-task | null | null | https://aclanthology.org/2022.semeval-1.4 | https://aclanthology.org/2022.semeval-1.4.pdf | LingJing at SemEval-2022 Task 1: Multi-task Self-supervised Pre-training for Multilingual Reverse Dictionary | This paper introduces the approach of Team LingJing’s experiments on SemEval-2022 Task 1 Comparing Dictionaries and Word Embeddings (CODWOE). This task aims at comparing two types of semantic descriptions and including two sub-tasks: the definition modeling and reverse dictionary track. Our team focuses on the reverse ... | ['Shutao Li', 'Bin Sun', 'Shizhu He', 'Fei Xia', 'Yixuan Weng', 'Bin Li'] | null | null | null | null | semeval-naacl-2022-7 | ['reverse-dictionary'] | ['natural-language-processing'] | [-2.8532711e-01 -1.5299523e-01 -6.1565828e-01 -4.5196080e-01
-8.7744665e-01 -5.7253665e-01 8.8349277e-01 7.2638340e-02
-9.7589022e-01 6.2508279e-01 4.7472197e-01 -4.2125723e-01
2.2515757e-01 -4.3141818e-01 -3.3422711e-01 -5.0390840e-01
2.6225334e-01 1.0548007e+00 -1.7269872e-01 -4.7303236e-01
8.3561435e-02... | [11.027907371520996, 9.934077262878418] |
ca67e65c-daa9-44fe-a6d5-0f8a4afcd4f5 | vision-transformer-for-fast-and-efficient | 2105.08582 | null | https://arxiv.org/abs/2105.08582v1 | https://arxiv.org/pdf/2105.08582v1.pdf | Vision Transformer for Fast and Efficient Scene Text Recognition | Scene text recognition (STR) enables computers to read text in natural scenes such as object labels, road signs and instructions. STR helps machines perform informed decisions such as what object to pick, which direction to go, and what is the next step of action. In the body of work on STR, the focus has always been o... | ['Rowel Atienza'] | 2021-05-18 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 3.45539480e-01 -2.51998514e-01 -3.35736305e-01 -1.98436499e-01
-6.26451850e-01 -5.32280624e-01 6.78207636e-01 -1.31027862e-01
-7.67271042e-01 1.86025411e-01 -1.74759701e-01 -7.65944302e-01
4.96805578e-01 -7.09238231e-01 -6.84811413e-01 -3.61485541e-01
5.26181936e-01 5.49860597e-01 3.74669731e-01 -6.43839985... | [9.329329490661621, 1.729443907737732] |
68fd0dd5-b737-4bd4-aea9-c11eaf735704 | adversarial-alignment-breaking-the-trade-off | 2306.03229 | null | https://arxiv.org/abs/2306.03229v1 | https://arxiv.org/pdf/2306.03229v1.pdf | Adversarial alignment: Breaking the trade-off between the strength of an attack and its relevance to human perception | Deep neural networks (DNNs) are known to have a fundamental sensitivity to adversarial attacks, perturbations of the input that are imperceptible to humans yet powerful enough to change the visual decision of a model. Adversarial attacks have long been considered the "Achilles' heel" of deep learning, which may eventua... | ['Thomas Serre', 'Stephanie Olaiya', 'Thomas Fel', 'Alekh Karkada Ashok', 'Thibaut Boissin', 'Pinyuan Feng', 'Drew Linsley'] | 2023-06-05 | null | null | null | null | ['adversarial-attack', 'adversarial-robustness', 'object-recognition', 'object-categorization'] | ['adversarial', 'adversarial', 'computer-vision', 'computer-vision'] | [ 3.16644877e-01 1.02955960e-01 4.69437867e-01 -2.93944567e-01
-1.59385756e-01 -1.14131832e+00 7.98632026e-01 -2.09616512e-01
-7.34887898e-01 4.80470777e-01 3.98659743e-02 -3.12226027e-01
5.63896447e-02 -9.03448880e-01 -1.06903160e+00 -5.87683678e-01
-5.19596897e-02 7.89563283e-02 1.77909851e-01 -5.14429271... | [5.6315436363220215, 7.876433849334717] |
ea0962f9-7351-4eff-ac77-05e31bf67853 | 3d-scene-reconstruction-from-a-single | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3925_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123670052.pdf | 3D Scene Reconstruction from a Single Viewport | We present a novel approach to infer volumetric reconstructions from a single viewport, based only on an RGB image and a reconstructed normal image. To overcome the problem of reconstructing regions in 3D that are occluded in the 2D image, we propose to learn this information from synthetically generated high-resolutio... | ['Rudolph Triebel', 'Maximilian Denninger'] | null | null | null | null | eccv-2020-8 | ['3d-scene-reconstruction'] | ['computer-vision'] | [ 1.93210199e-01 2.53642797e-01 3.13037306e-01 -2.07227707e-01
-4.57614094e-01 -5.69970347e-02 4.57987040e-01 -1.29488651e-02
-3.43526900e-01 8.07062209e-01 5.80362305e-02 -1.46663585e-03
2.25256234e-02 -1.39354646e+00 -1.05043471e+00 -4.76381421e-01
-1.19299538e-01 5.91814756e-01 3.31322134e-01 -1.73022524... | [8.836307525634766, -3.014913320541382] |
a21c29c1-c83d-422f-bb39-63b15a674474 | an-extension-of-fano-s-inequality-for | 2009.08097 | null | https://arxiv.org/abs/2009.08097v1 | https://arxiv.org/pdf/2009.08097v1.pdf | An Extension of Fano's Inequality for Characterizing Model Susceptibility to Membership Inference Attacks | Deep neural networks have been shown to be vulnerable to membership inference attacks wherein the attacker aims to detect whether specific input data were used to train the model. These attacks can potentially leak private or proprietary data. We present a new extension of Fano's inequality and employ it to theoretical... | ['Ananthram Swami', 'Susmit Jha', 'Sumit Kumar Jha', 'Sunny Raj', 'Laura L. Pullum', 'Rickard Ewetz', 'Alvaro Velasquez'] | 2020-09-17 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 3.25378962e-02 2.19279900e-01 5.85931204e-02 -4.93270367e-01
-2.87408352e-01 -1.07627416e+00 6.03305578e-01 -2.42531970e-02
-7.21412301e-01 8.66937160e-01 -5.26597977e-01 -1.04136288e+00
-2.05183059e-01 -1.02454281e+00 -1.12821472e+00 -8.01752448e-01
-3.89649481e-01 9.24577191e-02 2.31098101e-01 3.08020532... | [5.846221923828125, 7.4417266845703125] |
48fed4f4-13f9-4dc4-8bf6-166cfba57ece | heterogeneous-face-recognition-via-face | 2206.04854 | null | https://arxiv.org/abs/2206.04854v1 | https://arxiv.org/pdf/2206.04854v1.pdf | Heterogeneous Face Recognition via Face Synthesis with Identity-Attribute Disentanglement | Heterogeneous Face Recognition (HFR) aims to match faces across different domains (e.g., visible to near-infrared images), which has been widely applied in authentication and forensics scenarios. However, HFR is a challenging problem because of the large cross-domain discrepancy, limited heterogeneous data pairs, and l... | ['Xiao-Yu Zhang', 'Mandi Luo', 'Chaoyou Fu', 'Jian Liang', 'Ziming Yang'] | 2022-06-10 | null | null | null | null | ['heterogeneous-face-recognition'] | ['computer-vision'] | [ 3.07541937e-01 -3.50829422e-01 3.36581953e-02 -4.15713817e-01
-6.76304042e-01 -5.89716554e-01 6.15210295e-01 -6.47199988e-01
6.56555519e-02 6.49790525e-01 1.44154131e-01 7.42990077e-02
-9.49392766e-02 -7.16955900e-01 -3.23996514e-01 -8.94855082e-01
3.10517967e-01 2.48686448e-01 -4.65084970e-01 -3.41954499... | [13.12350845336914, 0.4519170820713043] |
6c495e34-7bd2-4a44-914a-348236d25ea9 | multi-view-low-rank-sparse-subspace | 1708.08732 | null | http://arxiv.org/abs/1708.08732v1 | http://arxiv.org/pdf/1708.08732v1.pdf | Multi-view Low-rank Sparse Subspace Clustering | Most existing approaches address multi-view subspace clustering problem by
constructing the affinity matrix on each view separately and afterwards propose
how to extend spectral clustering algorithm to handle multi-view data. This
paper presents an approach to multi-view subspace clustering that learns a
joint subspace... | ['Ivica Kopriva', 'Maria Brbic'] | 2017-08-29 | null | null | null | null | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-3.11433524e-01 -3.77360761e-01 -8.69271457e-02 -2.85548091e-01
-7.37079501e-01 -9.23381031e-01 3.57518226e-01 -4.67121363e-01
-1.63409710e-01 2.06268966e-01 5.95563114e-01 3.12962800e-01
-5.40422678e-01 -1.98731676e-01 -3.47119987e-01 -1.01976001e+00
1.36292443e-01 5.96023142e-01 -1.17504328e-01 2.80393124... | [8.20820426940918, 4.557732582092285] |
2d2c7abd-e5ee-4f49-8e8f-e9e39413b550 | improving-hyperspectral-adversarial | 2210.16346 | null | https://arxiv.org/abs/2210.16346v4 | https://arxiv.org/pdf/2210.16346v4.pdf | Improving Hyperspectral Adversarial Robustness Under Multiple Attacks | Semantic segmentation models classifying hyperspectral images (HSI) are vulnerable to adversarial examples. Traditional approaches to adversarial robustness focus on training or retraining a single network on attacked data, however, in the presence of multiple attacks these approaches decrease in performance compared t... | ['Salimeh Yasaei Sekeh', 'Nicholas Soucy'] | 2022-10-28 | null | null | null | null | ['type'] | ['speech'] | [ 8.40007663e-01 1.37028903e-01 2.03362346e-01 -2.12881982e-01
-5.66694498e-01 -1.27269804e+00 4.22577173e-01 -8.66044536e-02
-1.46791562e-01 6.06693387e-01 -3.63582015e-01 -3.82646590e-01
-1.35474965e-01 -1.07182586e+00 -6.12854302e-01 -9.50303912e-01
7.68305585e-02 1.41145706e-01 2.67242789e-01 -2.22526550... | [5.535179615020752, 7.946063995361328] |
ed405372-d502-45a3-8dae-04d0d9745dad | towards-accurate-deceptive-opinion-spam | 1711.09181 | null | http://arxiv.org/abs/1711.09181v2 | http://arxiv.org/pdf/1711.09181v2.pdf | Towards Accurate Deceptive Opinion Spam Detection based on Word Order-preserving CNN | Nowadays, deep learning has been widely used. In natural language learning,
the analysis of complex semantics has been achieved because of its high degree
of flexibility. The deceptive opinions detection is an important application
area in deep learning model, and related mechanisms have been given attention
and resear... | ['Limin Liu', 'Mengjie Guo', 'Siyuan Zhao', 'Zhiwei Xu'] | 2017-11-25 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [-4.65165347e-01 -7.20261157e-01 1.10090628e-01 -5.71906030e-01
3.13549787e-01 -4.48324412e-01 4.10657823e-01 2.33633891e-01
-5.01822412e-01 4.19640511e-01 2.05639973e-01 -2.56859481e-01
2.08652362e-01 -8.03110778e-01 -1.92745719e-02 -7.85069346e-01
3.48567218e-01 -7.57877529e-03 3.63398381e-02 -6.47779167... | [7.96778678894043, 10.04211139678955] |
104c634e-38fd-4259-82b4-0ae87e9dc174 | tracking-small-and-fast-moving-objects-a | 2209.04284 | null | https://arxiv.org/abs/2209.04284v1 | https://arxiv.org/pdf/2209.04284v1.pdf | Tracking Small and Fast Moving Objects: A Benchmark | With more and more large-scale datasets available for training, visual tracking has made great progress in recent years. However, current research in the field mainly focuses on tracking generic objects. In this paper, we present TSFMO, a benchmark for \textbf{T}racking \textbf{S}mall and \textbf{F}ast \textbf{M}oving ... | ['Shuiwang Li', 'Jingdong Liang', 'Yuming Qiu', 'Fuliang Wu', 'Zhewen Zhang'] | 2022-09-09 | null | null | null | null | ['visual-tracking'] | ['computer-vision'] | [-2.49096468e-01 -5.53408325e-01 -4.33737487e-01 -6.28050044e-02
-2.80235261e-01 -6.03680849e-01 2.65515447e-01 -1.41280249e-01
-4.03844297e-01 6.45046413e-01 -1.77343607e-01 -2.03927413e-01
1.51655912e-01 -3.70018423e-01 -8.02808642e-01 -5.70108533e-01
-3.71862650e-01 3.35105538e-01 1.01894081e+00 -2.35127673... | [6.393570423126221, -2.0448238849639893] |
d69a00e8-85e4-4086-80e5-54a836260360 | crovia-seeing-drone-scenes-from-car | 2304.07199 | null | https://arxiv.org/abs/2304.07199v1 | https://arxiv.org/pdf/2304.07199v1.pdf | CROVIA: Seeing Drone Scenes from Car Perspective via Cross-View Adaptation | Understanding semantic scene segmentation of urban scenes captured from the Unmanned Aerial Vehicles (UAV) perspective plays a vital role in building a perception model for UAV. With the limitations of large-scale densely labeled data, semantic scene segmentation for UAV views requires a broad understanding of an objec... | ['Khoa Luu', 'Jackson Cothren', 'Son Lam Phung', 'Ashley Dowling', 'Chi Nhan Duong', 'Thanh-Dat Truong'] | 2023-04-14 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 1.71943545e-01 5.77748232e-02 4.22926992e-02 -6.88601077e-01
-4.15390998e-01 -9.80182528e-01 4.21086311e-01 -5.12083173e-01
-1.99081600e-01 4.32604879e-01 -2.97143698e-01 -3.76221240e-02
-8.26410353e-02 -9.12219584e-01 -1.08356130e+00 -4.16977346e-01
4.49480683e-01 4.04485852e-01 6.66661918e-01 -4.07685846... | [8.316067695617676, -2.9128026962280273] |
4d621c7c-409c-46e0-9ce6-7b91c5c0ead2 | robust-data-driven-approach-for-predicting | 1908.03665 | null | https://arxiv.org/abs/1908.03665v1 | https://arxiv.org/pdf/1908.03665v1.pdf | Robust data-driven approach for predicting the configurational energy of high entropy alloys | High entropy alloys (HEAs) have been increasingly attractive as promising next-generation materials due to their various excellent properties. It's necessary to essentially characterize the degree of chemical ordering and identify order-disorder transitions through efficient simulation and modeling of thermodynamics. I... | ['Junqi Yin', 'Markus Eisenbach', 'Sirui Bi', 'Jiaxin Zhang', 'Guannan Zhang', 'Xianglin Liu'] | 2019-08-10 | null | null | null | null | ['small-data'] | ['computer-vision'] | [-1.30645391e-02 -1.40503347e-01 1.00490250e-01 -3.12400281e-01
-7.96481192e-01 1.34553686e-01 5.70851803e-01 1.66014254e-01
-3.02800059e-01 1.22809184e+00 -7.88813904e-02 1.60679054e-02
-7.55016387e-01 -5.55410743e-01 -3.58550668e-01 -1.27757251e+00
9.13471952e-02 1.07219732e+00 4.51960951e-01 -3.58793825... | [5.356686592102051, 4.93698263168335] |
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