paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
16723b73-0822-4de5-acb9-6660190d99ba | deeply-supervised-depth-map-super-resolution | 1808.08688 | null | http://arxiv.org/abs/1808.08688v1 | http://arxiv.org/pdf/1808.08688v1.pdf | Deeply Supervised Depth Map Super-Resolution as Novel View Synthesis | Deep convolutional neural network (DCNN) has been successfully applied to
depth map super-resolution and outperforms existing methods by a wide margin.
However, there still exist two major issues with these DCNN based depth map
super-resolution methods that hinder the performance: i) The low-resolution
depth maps eithe... | ['Xibin Song', 'Xueying Qin', 'Yuchao Dai'] | 2018-08-27 | null | null | null | null | ['depth-map-super-resolution'] | ['computer-vision'] | [ 3.71486396e-01 -2.39274576e-02 1.49626046e-01 -2.51526833e-01
-5.61106622e-01 -2.67575920e-01 3.51944357e-01 -3.71155560e-01
-3.86178911e-01 6.35560215e-01 9.07631665e-02 9.79830846e-02
-7.84629732e-02 -1.20005989e+00 -6.92326248e-01 -6.98763549e-01
2.71170080e-01 1.47543415e-01 6.38496399e-01 -3.37007284... | [10.100486755371094, -2.305907726287842] |
2620a631-fc18-4f69-a212-2a1c9427bce6 | multi30k-multilingual-english-german-image | 1605.00459 | null | http://arxiv.org/abs/1605.00459v1 | http://arxiv.org/pdf/1605.00459v1.pdf | Multi30K: Multilingual English-German Image Descriptions | We introduce the Multi30K dataset to stimulate multilingual multimodal
research. Recent advances in image description have been demonstrated on
English-language datasets almost exclusively, but image description should not
be limited to English. This dataset extends the Flickr30K dataset with i)
German translations cre... | ["Khalil Sima'an", 'Lucia Specia', 'Stella Frank', 'Desmond Elliott'] | 2016-05-02 | multi30k-multilingual-english-german-image-1 | https://aclanthology.org/W16-3210 | https://aclanthology.org/W16-3210.pdf | ws-2016-8 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 1.51679978e-01 -8.42724442e-02 -3.43068033e-01 -4.85595047e-01
-1.30578268e+00 -1.11408627e+00 8.84379506e-01 -1.10840060e-01
-8.70625973e-01 9.54217672e-01 5.40839493e-01 -5.43250702e-03
5.63802540e-01 -1.52547851e-01 -7.55422652e-01 -3.65371943e-01
4.90477264e-01 6.49305522e-01 -9.19268057e-02 -3.37109059... | [11.265400886535645, 1.488726258277893] |
927fa572-d670-4d2e-9d66-0f7da542253c | learning-semantic-aware-knowledge-guidance | 2304.07039 | null | https://arxiv.org/abs/2304.07039v1 | https://arxiv.org/pdf/2304.07039v1.pdf | Learning Semantic-Aware Knowledge Guidance for Low-Light Image Enhancement | Low-light image enhancement (LLIE) investigates how to improve illumination and produce normal-light images. The majority of existing methods improve low-light images via a global and uniform manner, without taking into account the semantic information of different regions. Without semantic priors, a network may easily... | ['Heng Tao Shen', 'Chongyi Li', 'Jiwei Wei', 'Yang Yang', 'Guoqing Wang', 'Chen Pan', 'Yuhui Wu'] | 2023-04-14 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wu_Learning_Semantic-Aware_Knowledge_Guidance_for_Low-Light_Image_Enhancement_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wu_Learning_Semantic-Aware_Knowledge_Guidance_for_Low-Light_Image_Enhancement_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-enhancement', 'low-light-image-enhancement'] | ['computer-vision', 'computer-vision'] | [ 6.15038157e-01 -1.57577842e-01 1.01588614e-01 -6.46623552e-01
-7.06068397e-01 -2.98594475e-01 4.20292914e-01 -4.53557402e-01
-3.63295227e-01 7.56998479e-01 1.58432711e-04 1.82196528e-01
1.51890680e-01 -1.09827125e+00 -1.13393176e+00 -9.80864763e-01
6.70369208e-01 -2.08466232e-01 4.41711485e-01 -3.54881018... | [10.67158031463623, -2.463547945022583] |
fb7cb994-3184-497f-9c94-fca4b92016a3 | r-theta-local-neighborhood-pattern-for | 2201.00504 | null | https://arxiv.org/abs/2201.00504v1 | https://arxiv.org/pdf/2201.00504v1.pdf | R-Theta Local Neighborhood Pattern for Unconstrained Facial Image Recognition and Retrieval | In this paper R-Theta Local Neighborhood Pattern (RTLNP) is proposed for facial image retrieval. RTLNP exploits relationships amongst the pixels in local neighborhood of the reference pixel at different angular and radial widths. The proposed encoding scheme divides the local neighborhood into sectors of equal angular ... | ['Pavan Chakraborty', 'Satish Kumar Singh', 'Soumendu Chakraborty'] | 2022-01-03 | null | null | null | null | ['face-image-retrieval'] | ['computer-vision'] | [-4.33545113e-02 -5.76937437e-01 -2.29165792e-01 -2.21687838e-01
-3.53650749e-01 -3.24048519e-01 5.50996840e-01 -2.66210645e-01
-1.90934479e-01 9.10372138e-01 1.33361965e-01 -2.36785132e-02
-4.58640546e-01 -7.50293612e-01 -2.63744801e-01 -1.10583019e+00
-4.89989854e-02 -2.89134771e-01 1.92441627e-01 -2.72393227... | [12.99657154083252, 0.6564406752586365] |
871b4373-fc82-4801-b590-ec5ec041d4a6 | a-comparative-study-on-end-to-end-speech-to | 1911.0887 | null | https://arxiv.org/abs/1911.08870v1 | https://arxiv.org/pdf/1911.08870v1.pdf | A Comparative Study on End-to-end Speech to Text Translation | Recent advances in deep learning show that end-to-end speech to text translation model is a promising approach to direct the speech translation field. In this work, we provide an overview of different end-to-end architectures, as well as the usage of an auxiliary connectionist temporal classification (CTC) loss for bet... | ['Tobias Bieschke', 'Hermann Ney', 'Parnia Bahar'] | 2019-11-20 | null | null | null | null | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 1.05950803e-01 2.29322419e-01 -2.55772889e-01 -5.71653128e-01
-1.71893954e+00 -5.59117317e-01 8.65678191e-01 -2.88165927e-01
-6.61366999e-01 6.77651703e-01 6.59505427e-01 -7.52726436e-01
3.81433606e-01 -4.98179160e-02 -9.70493257e-01 -4.34064984e-01
1.07067555e-01 8.68254662e-01 -7.52210021e-02 -3.34084988... | [14.481297492980957, 7.212531566619873] |
81410ace-9a28-45d0-887d-094adf019a44 | opentag-open-attribute-value-extraction-from | 1806.01264 | null | http://arxiv.org/abs/1806.01264v2 | http://arxiv.org/pdf/1806.01264v2.pdf | OpenTag: Open Attribute Value Extraction from Product Profiles [Deep Learning, Active Learning, Named Entity Recognition] | Extraction of missing attribute values is to find values describing an
attribute of interest from a free text input. Most past related work on
extraction of missing attribute values work with a closed world assumption with
the possible set of values known beforehand, or use dictionaries of values and
hand-crafted featu... | ['Fei-Fei Li', 'Xin Luna Dong', 'Subhabrata Mukherjee', 'Guineng Zheng'] | 2018-06-01 | null | null | null | null | ['attribute-value-extraction'] | ['natural-language-processing'] | [ 3.81035507e-01 5.25271833e-01 -8.14891577e-01 -7.43059754e-01
-1.06431127e+00 -8.53176236e-01 3.28752756e-01 4.33966696e-01
-5.29607475e-01 1.06331575e+00 2.53670514e-01 -1.72610506e-01
-3.97173427e-02 -8.32198739e-01 -8.11503172e-01 -5.49215019e-01
-3.33217047e-02 8.37135553e-01 6.01993911e-02 -2.38541886... | [10.010843276977539, 6.387849807739258] |
39801e3c-4506-4e6c-a89f-d4e7249c4a8b | maximizing-spatio-temporal-entropy-of-deep-3d | 2303.02693 | null | https://arxiv.org/abs/2303.02693v1 | https://arxiv.org/pdf/2303.02693v1.pdf | Maximizing Spatio-Temporal Entropy of Deep 3D CNNs for Efficient Video Recognition | 3D convolution neural networks (CNNs) have been the prevailing option for video recognition. To capture the temporal information, 3D convolutions are computed along the sequences, leading to cubically growing and expensive computations. To reduce the computational cost, previous methods resort to manually designed 3D/2... | ['Yang song', 'Maurice Pagnucco', 'Ming Lin', 'Xiuyu Sun', 'Dong Gong', 'Yichen Qian', 'Zhenhong Sun', 'Junyan Wang'] | 2023-03-05 | null | null | null | null | ['video-recognition'] | ['computer-vision'] | [-3.22654396e-01 -3.16212207e-01 -1.38306424e-01 -1.84351996e-01
-1.34840891e-01 -4.54141825e-01 3.23048949e-01 -3.13471138e-01
-6.67681336e-01 1.19416483e-01 -1.69969603e-01 -3.67660969e-01
-3.29844028e-01 -5.83226383e-01 -5.91012001e-01 -6.18315637e-01
-2.54803330e-01 -1.03408499e-02 7.11683705e-02 -1.59636885... | [8.682124137878418, 2.89023756980896] |
99decd12-09c9-4cb1-922c-9b8abfc07188 | region-based-temporally-consistent-video-post | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Dong_Region-Based_Temporally_Consistent_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Dong_Region-Based_Temporally_Consistent_2015_CVPR_paper.pdf | Region-Based Temporally Consistent Video Post-Processing | We study the problem of temporally consistent video post-processing. Previous post-processing algorithms usually either fail to keep high fidelity or fail to keep temporal consistency of output videos. In this paper, we observe experimentally that many image/video enhancement algorithms enforce a spatially consistent p... | ['Boyan Bonev', 'Xuan Dong', 'Alan L. Yuille', 'Yu Zhu'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['video-enhancement'] | ['computer-vision'] | [ 3.00179869e-01 -6.05396628e-01 1.75925903e-02 -4.80605990e-01
-2.33341947e-01 -5.11551023e-01 1.12503864e-01 2.06957296e-01
-4.93873358e-01 6.31044269e-01 1.38389811e-01 1.61226839e-01
-3.13485786e-02 -5.84054351e-01 -7.34882951e-01 -6.02209032e-01
-2.47598827e-01 -8.25601757e-01 5.64478457e-01 -7.57898614... | [11.038710594177246, -1.7889527082443237] |
6d3d2172-a5fd-48a5-9d43-c7262691d786 | semeval-2019-task-1-cross-lingual-semantic | 1903.02953 | null | https://arxiv.org/abs/1903.02953v3 | https://arxiv.org/pdf/1903.02953v3.pdf | SemEval-2019 Task 1: Cross-lingual Semantic Parsing with UCCA | We present the SemEval 2019 shared task on UCCA parsing in English, German and French, and discuss the participating systems and results. UCCA is a cross-linguistically applicable framework for semantic representation, which builds on extensive typological work and supports rapid annotation. UCCA poses a challenge for ... | ['Zohar Aizenbud', 'Omri Abend', 'Leshem Choshen', 'Elior Sulem', 'Daniel Hershcovich', 'Ari Rappoport'] | 2019-03-06 | semeval-2019-task-1-cross-lingual-semantic-1 | https://aclanthology.org/S19-2001 | https://aclanthology.org/S19-2001.pdf | semeval-2019-6 | ['ucca-parsing'] | ['natural-language-processing'] | [-7.01921359e-02 3.01187247e-01 -3.55576545e-01 -5.14867783e-01
-1.39214289e+00 -1.02390420e+00 4.05435681e-01 3.07143509e-01
-5.18487751e-01 8.03373635e-01 5.91309488e-01 -3.31222802e-01
4.14068609e-01 -5.55635870e-01 -6.96910203e-01 -2.51035959e-01
1.34072006e-01 7.20254660e-01 3.72226655e-01 -3.67149085... | [10.434858322143555, 9.553281784057617] |
7d69089c-0e61-4d6e-a752-09aa5af2152b | walking-for-short-distances-and-turning-in | 1909.03139 | null | https://arxiv.org/abs/1909.03139v3 | https://arxiv.org/pdf/1909.03139v3.pdf | Walking for short distances and turning in lower-limb amputees: a study in low-cost prosthesis users | Preferred walking speed is a widely-used performance measure for people with mobility issues, but is usually measured in straight line walking for fixed distances or durations. However, daily walking involves walking for bouts of different distances and walking with turning. Here, we studied walking for short distances... | ['Manoj Srinivasan', 'Anil Kumar Jain', 'Nidhi Seethapathi'] | 2019-09-06 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [-4.50164266e-02 1.59240186e-01 -7.58583009e-01 1.45643353e-01
-4.12894875e-01 -1.26972497e-01 8.02550763e-02 -5.57543993e-01
-7.10316122e-01 1.19930458e+00 7.56089628e-01 -2.34858140e-01
-4.03280884e-01 -8.66577983e-01 -4.56799090e-01 -3.92167121e-01
-5.44303298e-01 3.25202376e-01 2.39565298e-01 -1.48118988... | [6.973666191101074, 0.2417665272951126] |
570a8327-997e-48dd-9613-ba97598227d7 | zero-shot-text-to-parameter-translation-for | 2303.01311 | null | https://arxiv.org/abs/2303.01311v1 | https://arxiv.org/pdf/2303.01311v1.pdf | Zero-Shot Text-to-Parameter Translation for Game Character Auto-Creation | Recent popular Role-Playing Games (RPGs) saw the great success of character auto-creation systems. The bone-driven face model controlled by continuous parameters (like the position of bones) and discrete parameters (like the hairstyles) makes it possible for users to personalize and customize in-game characters. Previo... | ['Changjie Fan', 'Zhenwei Shi', 'Zhengxia Zou', 'Lincheng Li', 'Zhipeng Hu', 'Wei Li', 'Rui Zhao'] | 2023-03-02 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhao_Zero-Shot_Text-to-Parameter_Translation_for_Game_Character_Auto-Creation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhao_Zero-Shot_Text-to-Parameter_Translation_for_Game_Character_Auto-Creation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['face-model', 'text-to-3d'] | ['computer-vision', 'computer-vision'] | [ 1.39040992e-01 2.14377239e-01 3.18645149e-01 -5.72729632e-02
-7.53070951e-01 -6.14055276e-01 5.91977417e-01 -5.55850446e-01
2.01258734e-02 4.55464631e-01 6.71670809e-02 3.43950629e-01
3.98876876e-01 -9.29331660e-01 -4.33678597e-01 -4.99925077e-01
2.77737230e-01 8.05299044e-01 4.11194175e-01 -7.42719650... | [12.242745399475098, -0.5779370069503784] |
4b66a8ad-ae5f-4e09-9a86-6841646cca30 | robust-scheduling-with-gflownets | 2302.05446 | null | https://arxiv.org/abs/2302.05446v2 | https://arxiv.org/pdf/2302.05446v2.pdf | Robust Scheduling with GFlowNets | Finding the best way to schedule operations in a computation graph is a classical NP-hard problem which is central to compiler optimization. However, evaluating the goodness of a schedule on the target hardware can be very time-consuming. Traditional approaches as well as previous machine learning ones typically optimi... | ['Roberto Bondesan', 'Markus Peschl', 'Corrado Rainone', 'David W. Zhang'] | 2023-01-17 | null | null | null | null | ['compiler-optimization'] | ['computer-code'] | [ 1.79338768e-01 -2.22101957e-01 -4.11513299e-01 -3.68198842e-01
-7.49738634e-01 -5.72762072e-01 3.12072873e-01 6.40309691e-01
-3.71710777e-01 6.78826153e-01 -2.48403803e-01 -6.19167030e-01
-1.25317900e-02 -8.74860346e-01 -8.61701131e-01 -5.71859598e-01
-4.64005262e-01 6.26313150e-01 3.79482180e-01 -2.18310520... | [7.712677001953125, 7.337266445159912] |
2bbdf899-29cc-4dd0-b675-4c7e260f8364 | marvin-semantic-annotation-using-multiple | 1602.00515 | null | http://arxiv.org/abs/1602.00515v2 | http://arxiv.org/pdf/1602.00515v2.pdf | Marvin: Semantic annotation using multiple knowledge sources | People are producing more written material then anytime in the history. The
increase is so high that professionals from the various fields are no more able
to cope with this amount of publications. Text mining tools can offer tools to
help them and one of the tools that can aid information retrieval and
information ext... | ['Nikola Milosevic'] | 2016-02-01 | null | null | null | null | ['text-annotation'] | ['natural-language-processing'] | [-3.08595568e-01 4.22356099e-01 -1.57495111e-01 -8.64807889e-02
-1.20870091e-01 -5.97518504e-01 5.93903184e-01 9.09065247e-01
-6.92281008e-01 9.64231193e-01 2.40647852e-01 -3.41639549e-01
-4.45626765e-01 -1.02449882e+00 6.49404377e-02 -1.95812038e-03
5.74875653e-01 9.20059025e-01 8.08704913e-01 -6.85456872... | [9.378661155700684, 8.554664611816406] |
6607d1fa-3324-4ab6-857d-5fa3f78acd6f | causal-knowledge-extraction-from-scholarly | 2006.08904 | null | https://arxiv.org/abs/2006.08904v1 | https://arxiv.org/pdf/2006.08904v1.pdf | Causal Knowledge Extraction from Scholarly Papers in Social Sciences | The scale and scope of scholarly articles today are overwhelming human researchers who seek to timely digest and synthesize knowledge. In this paper, we seek to develop natural language processing (NLP) models to accelerate the speed of extraction of relationships from scholarly papers in social sciences, identify hypo... | ['Felipe Montano-Campos', 'Victor Zitian Chen', 'Wlodek Zadrozny'] | 2020-06-16 | null | null | null | null | ['entity-extraction'] | ['natural-language-processing'] | [ 3.23102996e-02 3.26147705e-01 -7.37312794e-01 -2.10378751e-01
-3.88456345e-01 -8.03569436e-01 9.20438051e-01 6.88785911e-01
-1.26182660e-01 1.03026974e+00 5.24036944e-01 -1.03298283e+00
-3.57832640e-01 -9.07012761e-01 -6.98839843e-01 2.12731972e-01
-1.72591940e-01 4.27861661e-01 1.00502282e-01 8.99245366... | [9.583416938781738, 8.39196491241455] |
43c5c869-a428-47c1-b0b7-aa029078448e | hdr-image-reconstruction-from-a-single | 1710.0748 | null | http://arxiv.org/abs/1710.07480v1 | http://arxiv.org/pdf/1710.07480v1.pdf | HDR image reconstruction from a single exposure using deep CNNs | Camera sensors can only capture a limited range of luminance simultaneously,
and in order to create high dynamic range (HDR) images a set of different
exposures are typically combined. In this paper we address the problem of
predicting information that have been lost in saturated image areas, in order
to enable HDR rec... | ['Rafał K. Mantiuk', 'Gyorgy Denes', 'Gabriel Eilertsen', 'Jonas Unger', 'Joel Kronander'] | 2017-10-20 | null | null | null | null | ['hdr-reconstruction'] | ['computer-vision'] | [ 5.77924788e-01 -2.55523354e-01 5.68758667e-01 -2.87624627e-01
-7.24439502e-01 -4.02196199e-01 3.87918413e-01 -3.06654751e-01
-4.15344566e-01 7.61417687e-01 1.62266821e-01 -1.41288057e-01
1.36599302e-01 -7.78193057e-01 -1.25355780e+00 -4.69840884e-01
5.11323065e-02 5.86056188e-02 1.61246240e-01 -4.59914505... | [10.956131935119629, -2.2258191108703613] |
ace29415-d820-439c-8507-4b503147b304 | detecting-layout-templates-in-complex | 2109.0663 | null | https://arxiv.org/abs/2109.06630v2 | https://arxiv.org/pdf/2109.06630v2.pdf | Detecting Layout Templates in Complex Multiregion Files | Spreadsheets are among the most commonly used file formats for data management, distribution, and analysis. Their widespread employment makes it easy to gather large collections of data, but their flexible canvas-based structure makes automated analysis difficult without heavy preparation. One of the common problems th... | ['Felix Naumann', 'Lan Jiang', 'Gerardo Vitagliano'] | 2021-09-14 | null | null | null | null | ['table-recognition'] | ['computer-vision'] | [ 6.13790043e-02 -4.89710569e-01 -6.25776798e-02 7.47013539e-02
-6.88514709e-01 -1.11710513e+00 4.72176611e-01 9.59206641e-01
8.93499479e-02 3.18389148e-01 1.14055621e-02 -1.95531905e-01
-3.00543427e-01 -9.64455545e-01 -4.33245122e-01 -4.12287176e-01
2.24421192e-02 6.46143317e-01 7.26386189e-01 -1.73720960... | [11.699625968933105, 2.8550570011138916] |
d010662a-5729-4c7a-940c-2b6f6a496311 | aifb-webscience-at-semeval-2022-task-12-1 | null | null | https://aclanthology.org/2022.semeval-1.232 | https://aclanthology.org/2022.semeval-1.232.pdf | AIFB-WebScience at SemEval-2022 Task 12: Relation Extraction First - Using Relation Extraction to Identify Entities | In this paper, we present an end-to-end joint entity and relation extraction approach based on transformer-based language models. We apply the model to the task of linking mathematical symbols to their descriptions in LaTeX documents. In contrast to existing approaches, which perform entity and relation extraction in s... | ['Michael Färber', 'Walter Laurito', 'Nicholas Popovic'] | null | null | null | null | semeval-naacl-2022-7 | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [-2.71623787e-02 4.44423020e-01 -1.75948814e-01 -5.02225757e-01
-8.18669379e-01 -7.46994734e-01 7.53463447e-01 5.75945854e-01
-3.38220984e-01 9.58560348e-01 -1.30911589e-01 -5.86627603e-01
-1.89930797e-01 -9.40949142e-01 -8.18654835e-01 3.15090045e-02
-1.44039933e-02 7.40200818e-01 3.30998152e-01 -1.48219556... | [9.496164321899414, 8.791851043701172] |
22a51be0-ede9-485c-a774-1aa187642b8e | neighborhood-random-walk-graph-sampling-for | 2112.07743 | null | https://arxiv.org/abs/2112.07743v1 | https://arxiv.org/pdf/2112.07743v1.pdf | Neighborhood Random Walk Graph Sampling for Regularized Bayesian Graph Convolutional Neural Networks | In the modern age of social media and networks, graph representations of real-world phenomena have become an incredibly useful source to mine insights. Often, we are interested in understanding how entities in a graph are interconnected. The Graph Neural Network (GNN) has proven to be a very useful tool in a variety of... | ['Justin Zhan', 'Aneesh Komanduri'] | 2021-12-14 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [-1.42460447e-02 3.82621974e-01 -3.75357121e-01 -3.38015705e-01
-1.31097406e-01 -1.87226042e-01 7.72595882e-01 3.38774204e-01
1.88518856e-02 8.77224267e-01 -1.93149254e-01 -4.71306473e-01
-3.19492429e-01 -1.40995300e+00 -6.85974121e-01 -6.18970394e-01
-2.30429694e-01 9.19858038e-01 2.85413176e-01 6.50735348... | [7.009309768676758, 5.705138683319092] |
dba0864f-d637-409e-bb61-525cc42e5113 | spatiotemporal-recurrent-convolutional | 1901.04656 | null | http://arxiv.org/abs/1901.04656v1 | http://arxiv.org/pdf/1901.04656v1.pdf | Spatiotemporal Recurrent Convolutional Networks for Recognizing Spontaneous Micro-expressions | Recently, the recognition task of spontaneous facial micro-expressions has
attracted much attention with its various real-world applications. Plenty of
handcrafted or learned features have been employed for a variety of classifiers
and achieved promising performances for recognizing micro-expressions. However,
the micr... | ['Xiaoyi Feng', 'Xiaopeng Hong', 'Zhaoqiang Xia', 'Xingyu Gao', 'Guoying Zhao'] | 2019-01-15 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [ 1.61518306e-01 -2.82609940e-01 -1.63154244e-01 -6.36825442e-01
-4.99594927e-01 -1.41786262e-01 5.35419464e-01 -3.53863060e-01
-2.53104389e-01 5.09166241e-01 -2.81505939e-02 2.78470874e-01
7.79967010e-02 -4.42288399e-01 -6.61999404e-01 -1.12866783e+00
-8.12473223e-02 -2.71284550e-01 -2.78003246e-01 -2.84891218... | [13.640079498291016, 1.7153239250183105] |
4a60849a-6fd1-4ba7-b57c-346890f1ee93 | vision-transformer-using-low-level-chest-x | 2104.07235 | null | https://arxiv.org/abs/2104.07235v1 | https://arxiv.org/pdf/2104.07235v1.pdf | Vision Transformer using Low-level Chest X-ray Feature Corpus for COVID-19 Diagnosis and Severity Quantification | Developing a robust algorithm to diagnose and quantify the severity of COVID-19 using Chest X-ray (CXR) requires a large number of well-curated COVID-19 datasets, which is difficult to collect under the global COVID-19 pandemic. On the other hand, CXR data with other findings are abundant. This situation is ideally sui... | ['Jong Chul Ye', 'Jae-Kwang Lim', 'Sungjun Moon', 'Jin Hwan Kim', 'Sang Min Lee', 'Joon Beom Seo', 'Yujin Oh', 'Gwanghyun Kim', 'Sangjoon Park'] | 2021-04-15 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [-9.22092143e-03 -2.24078491e-01 -6.66459501e-02 -1.54023483e-01
-8.47162426e-01 -4.87510175e-01 1.46767795e-01 4.04412933e-02
-2.15953231e-01 4.83705580e-01 2.97580719e-01 -4.77621824e-01
-2.01957822e-01 -8.10919344e-01 -3.88407022e-01 -8.33818853e-01
3.83647010e-02 5.24161279e-01 -1.41380429e-02 7.05460599... | [15.40383529663086, -1.8434216976165771] |
b9323536-d5b0-42a6-8401-89d0e466f9ae | ieee-802-11ad-based-joint-radar-communication | 2209.04235 | null | https://arxiv.org/abs/2209.04235v1 | https://arxiv.org/pdf/2209.04235v1.pdf | IEEE 802.11ad Based Joint Radar Communication Transceiver: Design, Prototype and Performance Analysis | Rapid beam alignment is required to support high gain millimeter wave (mmW) communication links between a base station (BS) and mobile users (MU). The standard IEEE 802.11ad protocol enables beam alignment at the BS and MU through a lengthy beam training procedure accomplished through additional packet overhead. Howeve... | ['Sumit Darak', 'Shobha Sundar Ram', 'V Sri Sindhu', 'Soumya Jain', 'Akanksha Sneh'] | 2022-09-09 | null | null | null | null | ['joint-radar-communication'] | ['robots'] | [ 6.04835153e-01 3.17850560e-01 3.35116535e-01 -4.57933903e-01
-6.24375880e-01 -2.03900188e-01 4.05339241e-01 -1.09945804e-01
-6.21214390e-01 9.06472683e-01 -2.20565453e-01 -8.74842465e-01
-5.55873513e-01 -1.02817321e+00 1.84284896e-02 -7.38765836e-01
-4.97098297e-01 1.73608422e-01 1.20925112e-02 -1.26615033... | [6.358428955078125, 1.2277685403823853] |
8db1bb3f-e7b4-47e2-8268-a09f72966327 | pvn3d-a-deep-point-wise-3d-keypoints-voting | 1911.04231 | null | https://arxiv.org/abs/1911.04231v2 | https://arxiv.org/pdf/1911.04231v2.pdf | PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimation | In this work, we present a novel data-driven method for robust 6DoF object pose estimation from a single RGBD image. Unlike previous methods that directly regressing pose parameters, we tackle this challenging task with a keypoint-based approach. Specifically, we propose a deep Hough voting network to detect 3D keypoin... | ['Yisheng He', 'Wei Sun', 'Haibin Huang', 'Jian Sun', 'Jianran Liu', 'Haoqiang Fan'] | 2019-11-11 | pvn3d-a-deep-point-wise-3d-keypoints-voting-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/He_PVN3D_A_Deep_Point-Wise_3D_Keypoints_Voting_Network_for_6DoF_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/He_PVN3D_A_Deep_Point-Wise_3D_Keypoints_Voting_Network_for_6DoF_CVPR_2020_paper.pdf | cvpr-2020-6 | ['6d-pose-estimation-using-rgbd'] | ['computer-vision'] | [-3.79674047e-01 -1.01421468e-01 -2.91102737e-01 -4.33440983e-01
-7.76997507e-01 -5.09410977e-01 3.45016629e-01 -2.43536830e-01
-5.46367347e-01 1.77249312e-01 -9.87528861e-02 -8.47336724e-02
4.61121686e-02 -4.76345837e-01 -1.02648103e+00 -5.38158298e-01
3.94245535e-02 6.69015944e-01 4.34733242e-01 -8.65123719... | [7.409302234649658, -2.5497939586639404] |
abf8daa0-08cc-496b-bb17-622ab64e66be | using-shortlists-to-support-decision-making | 1510.07545 | null | http://arxiv.org/abs/1510.07545v2 | http://arxiv.org/pdf/1510.07545v2.pdf | Using Shortlists to Support Decision Making and Improve Recommender System Performance | In this paper, we study shortlists as an interface component for recommender
systems with the dual goal of supporting the user's decision process, as well
as improving implicit feedback elicitation for increased recommendation
quality. A shortlist is a temporary list of candidates that the user is
currently considering... | ['Thorsten Joachims', 'Paul N. Bennett', 'Tobias Schnabel', 'Susan T. Dumais'] | 2015-10-26 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [ 2.12724298e-01 2.51522869e-01 -3.16349983e-01 -4.23036307e-01
-1.17437214e-01 -7.05333054e-01 2.72456437e-01 6.02107286e-01
-3.93812090e-01 3.35418344e-01 4.45633978e-01 -4.92669195e-01
-4.54965651e-01 -8.93589199e-01 -3.45255882e-01 -3.49563330e-01
1.30889863e-01 4.63501394e-01 2.70648003e-01 -4.26529884... | [10.07044506072998, 5.730792999267578] |
f6479500-86f5-40a8-80b0-3ff589e51908 | spatiotemporal-besov-priors-for-bayesian | 2306.16378 | null | https://arxiv.org/abs/2306.16378v1 | https://arxiv.org/pdf/2306.16378v1.pdf | Spatiotemporal Besov Priors for Bayesian Inverse Problems | Fast development in science and technology has driven the need for proper statistical tools to capture special data features such as abrupt changes or sharp contrast. Many applications in the data science seek spatiotemporal reconstruction from a sequence of time-dependent objects with discontinuity or singularity, e.g... | ['Shuyi Li', 'Mirjeta Pasha', 'Shiwei Lan'] | 2023-06-28 | null | null | null | null | ['gaussian-processes'] | ['methodology'] | [ 1.86282858e-01 -4.78700936e-01 2.97499180e-01 -1.16510786e-01
-6.38227344e-01 2.56566703e-02 5.08938432e-01 3.38990688e-02
-5.20724416e-01 9.35095131e-01 -2.93897726e-02 1.91955213e-02
-6.62012041e-01 -5.90888143e-01 -4.09302324e-01 -1.17598295e+00
-2.03313574e-01 4.17827159e-01 6.20550096e-01 3.41431834... | [12.373048782348633, -2.586965799331665] |
46eb6743-cb29-4fa9-8e60-ee6e9397ff65 | mishape-3d-shape-modelling-of-mitochondria-in | 2303.01546 | null | https://arxiv.org/abs/2303.01546v1 | https://arxiv.org/pdf/2303.01546v1.pdf | MiShape: 3D Shape Modelling of Mitochondria in Microscopy | Fluorescence microscopy is a quintessential tool for observing cells and understanding the underlying mechanisms of life-sustaining processes of all living organisms. The problem of extracting 3D shape of mitochondria from fluorescence microscopy images remains unsolved due to the complex and varied shapes expressed by... | ['Dilip K. Prasad', 'Krishna Agarwal', 'Alexander Horsch', 'Suyog S Jadhav', 'Abhinanda R. Punnakkal'] | 2023-03-02 | null | null | null | null | ['3d-shape-reconstruction'] | ['computer-vision'] | [ 5.17513633e-01 1.21414512e-02 6.59536839e-01 -2.85271227e-01
-6.20590091e-01 -1.00643504e+00 4.82780159e-01 -4.41605523e-02
-5.62454045e-01 9.51681972e-01 -2.58261919e-01 -2.69583583e-01
7.52736777e-02 -5.33031523e-01 -7.50859082e-01 -1.01634383e+00
3.12190980e-01 7.93180823e-01 4.38614078e-02 3.90697777... | [13.527907371520996, -3.0119881629943848] |
199806fd-76dc-46fe-af9d-b65c14dd3c25 | multimodal-emotion-recognition-using-deep | 1908.05349 | null | https://arxiv.org/abs/1908.05349v1 | https://arxiv.org/pdf/1908.05349v1.pdf | Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis | Multimodal signals are more powerful than unimodal data for emotion recognition since they can represent emotions more comprehensively. In this paper, we introduce deep canonical correlation analysis (DCCA) to multimodal emotion recognition. The basic idea behind DCCA is to transform each modality separately and coordi... | ['Bao-liang Lu', 'Wei-Long Zheng', 'Jie-Lin Qiu', 'Wei Liu'] | 2019-08-13 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [-3.03339392e-01 -5.97360253e-01 1.04654275e-01 -3.65056306e-01
-5.61901927e-01 -5.90147972e-01 5.94182014e-01 -3.38543594e-01
-4.48661029e-01 5.17886162e-01 3.02751958e-01 4.19262618e-01
-4.75739278e-02 -4.10998046e-01 5.65290684e-03 -9.36152577e-01
-1.28059521e-01 1.33493587e-01 -6.69568837e-01 -2.93605894... | [13.215713500976562, 5.095531463623047] |
d3860726-615e-4927-9174-e006ed50dc00 | memory-based-gaze-prediction-in-deep | 2202.04877 | null | https://arxiv.org/abs/2202.04877v1 | https://arxiv.org/pdf/2202.04877v1.pdf | Memory-based gaze prediction in deep imitation learning for robot manipulation | Deep imitation learning is a promising approach that does not require hard-coded control rules in autonomous robot manipulation. The current applications of deep imitation learning to robot manipulation have been limited to reactive control based on the states at the current time step. However, future robots will also ... | ['Yasuo Kuniyoshi', 'Yoshiyuki Ohmura', 'Heecheol Kim'] | 2022-02-10 | null | null | null | null | ['gaze-estimation', 'eye-tracking', 'robot-manipulation'] | ['computer-vision', 'computer-vision', 'robots'] | [ 1.27060980e-01 1.10218994e-01 -3.40361558e-02 -7.69689456e-02
1.59866020e-01 -1.44498408e-01 3.50817651e-01 -1.69536680e-01
-5.40269554e-01 6.49660885e-01 -4.76141721e-01 1.23591818e-01
-1.26176015e-01 -5.15264809e-01 -8.67473960e-01 -7.29490459e-01
1.44591868e-01 5.53806007e-01 3.77104074e-01 -3.71023446... | [4.637472152709961, 0.8795510530471802] |
9e7d7dfd-f4ec-4813-9e36-7a43dd12f07d | hydra-hgr-a-hybrid-transformer-based | 2211.02619 | null | https://arxiv.org/abs/2211.02619v1 | https://arxiv.org/pdf/2211.02619v1.pdf | HYDRA-HGR: A Hybrid Transformer-based Architecture for Fusion of Macroscopic and Microscopic Neural Drive Information | Development of advance surface Electromyogram (sEMG)-based Human-Machine Interface (HMI) systems is of paramount importance to pave the way towards emergence of futuristic Cyber-Physical-Human (CPH) worlds. In this context, the main focus of recent literature was on development of different Deep Neural Network (DNN)-ba... | ['Arash Mohammadi', 'Hamid Alinejad-Rokny', 'S. Farokh Atashzar', 'Farnoosh Naderkhani', 'Elahe Rahimian', 'Mansooreh Montazerin'] | 2022-10-27 | null | null | null | null | ['hand-gesture-recognition', 'hand-gesture-recognition-1', 'gesture-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.09402168e-01 -1.29178777e-01 1.47242308e-01 3.66002202e-01
-7.66941726e-01 -1.68821633e-01 6.03849411e-01 -2.47306645e-01
-5.77898681e-01 7.17095792e-01 5.69022521e-02 -6.50098398e-02
-2.32087672e-01 -5.75581968e-01 -6.94384873e-01 -1.03111577e+00
-1.09208934e-01 5.65607697e-02 1.48339435e-01 -2.07487136... | [6.83968448638916, 0.1446067839860916] |
df5fda06-47e6-4085-866e-0643d69d1ee1 | multi-candidate-word-segmentation-using-bi | null | null | https://ieeexplore.ieee.org/abstract/document/8442053 | https://ieeexplore.ieee.org/abstract/document/8442053 | Multi-Candidate Word Segmentation using Bi-directional LSTM Neural Networks | Most existing word segmentation methods output one single segmentation solution. This paper provides an analysis of word segmentation performance when more than one solutions are taken into account. Towards this investigation, a deep neural network with multiple thresholds is applied to generate multiple candidates for... | ['Thanaruk Theeramunkong', 'Kobkrit Viriyayudhakom', 'Theerapat Lapjaturapit'] | 2018-05-07 | null | null | null | null | ['thai-word-tokenization'] | ['natural-language-processing'] | [ 3.01705897e-01 -1.35741889e-01 -5.03342330e-01 -3.25818479e-01
-6.35018885e-01 -4.34769452e-01 3.99085164e-01 2.98788130e-01
-1.05436909e+00 6.64625406e-01 1.54852733e-01 -7.24377513e-01
-2.71923728e-02 -8.55619848e-01 -3.43350053e-01 -5.45421243e-01
2.25294933e-01 3.49207014e-01 4.77671564e-01 -5.40962219... | [10.080302238464355, 10.153451919555664] |
f1abd6b7-2f51-47dc-a1f1-9994327320ad | self-educated-language-agent-with-hindsight | null | null | https://openreview.net/forum?id=S1g_t1StDB | https://openreview.net/pdf?id=S1g_t1StDB | Self-Educated Language Agent with Hindsight Experience Replay for Instruction Following | Language creates a compact representation of the world and allows the description of unlimited situations and objectives through compositionality. These properties make it a natural fit to guide the training of interactive agents as it could ease recurrent challenges in Reinforcement Learning such as sample complexity,... | ['Olivier Pietquin', 'Florian Strub', 'Mathieu Seurin', 'Geoffrey Cideron'] | 2019-09-25 | null | null | null | null | ['language-acquisition'] | ['natural-language-processing'] | [ 3.83419357e-02 3.94827545e-01 -3.77776623e-01 -7.29171336e-02
-4.78721529e-01 -9.26474452e-01 9.42397177e-01 2.80818284e-01
-9.30630624e-01 9.00119007e-01 1.22890539e-01 -5.95444977e-01
-8.63120928e-02 -7.21913695e-01 -8.56130183e-01 -7.48636544e-01
-2.59900033e-01 4.48904842e-01 1.99974719e-02 -4.53503609... | [4.230926513671875, 1.3628730773925781] |
5de3ceee-d902-47e4-a407-7c7c9443825a | a-new-dimension-in-testimony-relighting-video | 2104.02773 | null | https://arxiv.org/abs/2104.02773v1 | https://arxiv.org/pdf/2104.02773v1.pdf | A New Dimension in Testimony: Relighting Video with Reflectance Field Exemplars | We present a learning-based method for estimating 4D reflectance field of a person given video footage illuminated under a flat-lit environment of the same subject. For training data, we use one light at a time to illuminate the subject and capture the reflectance field data in a variety of poses and viewpoints. We est... | ['Paul Debevec', 'Bipin Kishore', 'Loc Huynh'] | 2021-04-06 | null | null | null | null | ['lighting-estimation'] | ['computer-vision'] | [ 4.66601104e-01 -1.04345404e-01 5.85066736e-01 -4.83787239e-01
-3.42788815e-01 -4.01543915e-01 1.50362521e-01 -7.82087803e-01
-4.01840359e-01 4.61238384e-01 1.22700781e-02 1.92385897e-01
6.13047063e-01 -6.82740152e-01 -1.01157033e+00 -6.29990160e-01
2.92907745e-01 3.46593231e-01 -7.29314610e-02 6.70384318... | [9.7579984664917, -2.92212176322937] |
408a35c2-d508-455e-a290-f9ce062d840d | face-parsing-via-a-fully-convolutional | 1708.03736 | null | http://arxiv.org/abs/1708.03736v1 | http://arxiv.org/pdf/1708.03736v1.pdf | Face Parsing via a Fully-Convolutional Continuous CRF Neural Network | In this work, we address the face parsing task with a Fully-Convolutional
continuous CRF Neural Network (FC-CNN) architecture. In contrast to previous
face parsing methods that apply region-based subnetwork hundreds of times, our
FC-CNN is fully convolutional with high segmentation accuracy. To achieve this
goal, FC-CN... | ['Lei Zhou', 'Xiangjian He', 'Zhi Liu'] | 2017-08-12 | null | null | null | null | ['face-parsing'] | ['computer-vision'] | [ 1.45376205e-01 4.92119610e-01 -2.75506139e-01 -1.10905266e+00
-6.48218095e-01 -3.71816307e-01 3.50964874e-01 -4.85267192e-01
-1.79883480e-01 5.79262733e-01 -1.71108335e-01 2.23303456e-02
4.53984499e-01 -9.15209830e-01 -9.55469191e-01 -4.25990701e-01
7.11710975e-02 5.13754904e-01 2.90329069e-01 2.16631263... | [13.432294845581055, 0.6481053829193115] |
a26c3641-26d9-4a91-aab5-17f887c76313 | micro-expression-spotting-a-benchmark | 1710.0282 | null | http://arxiv.org/abs/1710.02820v1 | http://arxiv.org/pdf/1710.02820v1.pdf | Micro-Expression Spotting: A Benchmark | Micro-expressions are rapid and involuntary facial expressions, which
indicate the suppressed or concealed emotions. Recently, the research on
automatic micro-expression (ME) spotting obtains increasing attention. ME
spotting is a crucial step prior to further ME analysis tasks. The spotting
results can be used as impo... | ['Thuong-Khanh Tran', 'Xiaopeng Hong', 'Guoying Zhao'] | 2017-10-08 | null | null | null | null | ['micro-expression-spotting'] | ['computer-vision'] | [ 2.65173912e-01 -3.98355573e-01 -4.12190676e-01 -8.05764139e-01
-7.01931655e-01 -4.32394773e-01 6.93109751e-01 -2.73310810e-01
-2.28050962e-01 5.66800594e-01 1.26270086e-01 9.27365422e-02
2.61817276e-01 -3.32181185e-01 -6.46088198e-02 -8.03320944e-01
-2.11047634e-01 -2.62900770e-01 -2.86677301e-01 -6.10798359... | [13.60830020904541, 1.7750904560089111] |
c15c7aab-dbe0-4672-936b-26694cd79cae | compositional-transformers-for-scene-1 | null | null | http://proceedings.neurips.cc/paper/2021/hash/4eff0720836a198b6174eecf02cbfdbf-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/4eff0720836a198b6174eecf02cbfdbf-Paper.pdf | Compositional Transformers for Scene Generation | We introduce the GANformer2 model, an iterative object-oriented transformer, explored for the task of generative modeling. The network incorporates strong and explicit structural priors, to reflect the compositional nature of visual scenes, and synthesizes images through a sequential process. It operates in two stages:... | ['Larry Zitnick', 'Dor Arad Hudson'] | 2021-12-01 | null | null | null | neurips-2021-12 | ['scene-generation'] | ['computer-vision'] | [ 3.02114099e-01 2.63573200e-01 1.64490655e-01 -4.00917560e-01
-5.73181152e-01 -6.75467908e-01 1.05766046e+00 -3.63400489e-01
3.62162769e-01 4.46743399e-01 6.89061463e-01 -8.07242095e-02
-4.17273790e-02 -7.18307853e-01 -5.26581705e-01 -4.18601900e-01
3.18575464e-02 8.21147680e-01 -1.17770046e-01 -2.87714422... | [11.334733963012695, -0.36042335629463196] |
b7392f13-a773-4a74-9338-dd5b734ced91 | crossing-the-line-where-do-demographic | null | null | https://aclanthology.org/2020.acl-srw.24 | https://aclanthology.org/2020.acl-srw.24.pdf | Crossing the Line: Where do Demographic Variables Fit into Humor Detection? | Recent humor classification shared tasks have struggled with two issues: either the data comprises a highly constrained genre of humor which does not broadly represent humor, or the data is so indiscriminate that the inter-annotator agreement on its humor content is drastically low. These tasks typically average over a... | ['J. A. Meaney'] | 2020-07-01 | null | null | null | acl-2020-6 | ['humor-detection'] | ['natural-language-processing'] | [-2.80449748e-01 4.38483655e-02 -3.15023810e-02 -2.71561146e-01
-2.39281103e-01 -8.54311228e-01 7.88461208e-01 5.99605918e-01
-3.86066049e-01 7.04210103e-01 9.96790588e-01 -4.35778856e-01
2.26504728e-01 -7.54840791e-01 -1.18006617e-01 -3.88276696e-01
4.87147868e-01 3.90719265e-01 7.84233958e-02 -3.17421675... | [8.893218994140625, 11.047408103942871] |
49ad9567-d91f-4c07-b74f-d534ce28c2d9 | utilizing-a-transparency-driven-environment | 1810.00968 | null | http://arxiv.org/abs/1810.00968v1 | http://arxiv.org/pdf/1810.00968v1.pdf | Utilizing a Transparency-driven Environment toward Trusted Automatic Genre Classification: A Case Study in Journalism History | With the growing abundance of unlabeled data in real-world tasks, researchers
have to rely on the predictions given by black-boxed computational models.
However, it is an often neglected fact that these models may be scoring high on
accuracy for the wrong reasons. In this paper, we present a practical impact
analysis o... | ['Marcel Broersma', 'Kim Smeenk', 'Erik Tjong Kim Sang', 'Laura Hollink', 'Frank Harbers', 'Jacco van Ossenbruggen', 'Aysenur Bilgin'] | 2018-10-01 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [ 1.32628977e-01 5.29170334e-01 -4.63750601e-01 -5.15566885e-01
-5.14670134e-01 -7.24247873e-01 8.28019559e-01 1.68003425e-01
-3.85696262e-01 5.23989558e-01 3.09879929e-01 -8.55569482e-01
4.07778583e-02 -3.06020766e-01 -6.30614042e-01 -6.37680218e-02
4.03887212e-01 6.82294786e-01 -8.25061426e-02 -1.90575972... | [9.583174705505371, 7.885828018188477] |
f7e24290-a453-4e1e-8d3f-89e337914e83 | accelerating-markov-random-field-inference | 2108.0057 | null | https://arxiv.org/abs/2108.00570v1 | https://arxiv.org/pdf/2108.00570v1.pdf | Accelerating Markov Random Field Inference with Uncertainty Quantification | Statistical machine learning has widespread application in various domains. These methods include probabilistic algorithms, such as Markov Chain Monte-Carlo (MCMC), which rely on generating random numbers from probability distributions. These algorithms are computationally expensive on conventional processors, yet thei... | ['Alvin R. Lebeck', 'Sayan Mukherjee', 'Xiangyu Zhang', 'Ramin Bashizade'] | 2021-08-02 | null | null | null | null | ['2048'] | ['playing-games'] | [ 1.17105013e-02 -2.29416892e-01 -3.35257500e-01 -4.06360567e-01
-7.06404924e-01 -2.07219437e-01 5.81628084e-01 8.87460485e-02
-5.32812417e-01 6.74839914e-01 -2.43552160e-02 -8.85447085e-01
1.12090677e-01 -1.01262593e+00 -6.98455811e-01 -7.53709316e-01
2.78569711e-03 3.09781939e-01 4.57922131e-01 5.54886281... | [8.384272575378418, 2.9874823093414307] |
42ae0a25-53cc-4d0d-83e3-f43073869ab9 | how-does-truth-evolve-into-fake-news-an | 2103.05944 | null | https://arxiv.org/abs/2103.05944v1 | https://arxiv.org/pdf/2103.05944v1.pdf | How does Truth Evolve into Fake News? An Empirical Study of Fake News Evolution | Automatically identifying fake news from the Internet is a challenging problem in deception detection tasks. Online news is modified constantly during its propagation, e.g., malicious users distort the original truth and make up fake news. However, the continuous evolution process would generate unprecedented fake news... | ['Rui Yan', 'Dongyan Zhao', 'Juntao Li', 'Xiuying Chen', 'Mingfei Guo'] | 2021-03-10 | null | null | null | null | ['deception-detection'] | ['miscellaneous'] | [-2.85531223e-01 -1.47825748e-01 -4.08928424e-01 -2.58522946e-02
-1.02716312e-01 -9.56651509e-01 1.15303242e+00 2.93348759e-01
2.58172542e-01 7.96353161e-01 3.57007563e-01 -5.51031306e-02
5.14339626e-01 -8.15938056e-01 -8.96650851e-01 -3.71309251e-01
1.45365998e-01 5.38432121e-01 3.29724044e-01 -8.74483824... | [8.107641220092773, 10.285042762756348] |
2a36cd21-a293-49e9-93ad-980f2ee2f643 | video-saliency-detection-with-domain-adaption | 2010.0122 | null | https://arxiv.org/abs/2010.01220v4 | https://arxiv.org/pdf/2010.01220v4.pdf | Hierarchical Domain-Adapted Feature Learning for Video Saliency Prediction | In this work, we propose a 3D fully convolutional architecture for video saliency prediction that employs hierarchical supervision on intermediate maps (referred to as conspicuity maps) generated using features extracted at different abstraction levels. We provide the base hierarchical learning mechanism with two techn... | ['Concetto Spampinato', 'Daniela Giordano', 'Francesco Rundo', 'Simone Palazzo', 'Federica Proietto Salanitri', 'Giovanni Bellitto'] | 2020-10-02 | null | null | null | null | ['video-saliency-detection'] | ['computer-vision'] | [ 4.19202715e-01 3.64688754e-01 -5.19168615e-01 -3.62570733e-01
-5.10151207e-01 -2.14939952e-01 7.56740987e-01 1.03299946e-01
-4.59377259e-01 7.12949276e-01 4.13425863e-01 -2.61847302e-02
1.49903474e-02 -5.43728471e-01 -9.41025257e-01 -3.75505090e-01
-2.64096528e-01 1.87018722e-01 9.61774409e-01 -4.17205155... | [9.780314445495605, 1.1588507890701294] |
b1768e1d-9f24-4eea-ab99-e58dfe1c6f15 | preventing-dimensional-collapse-of-incomplete | 2303.12241 | null | https://arxiv.org/abs/2303.12241v1 | https://arxiv.org/pdf/2303.12241v1.pdf | Preventing Dimensional Collapse of Incomplete Multi-View Clustering via Direct Contrastive Learning | Incomplete multi-view clustering (IMVC) is an unsupervised approach, among which IMVC via contrastive learning has received attention due to its excellent performance. The previous methods have the following problems: 1) Over-reliance on additional projection heads when solving the dimensional collapse problem in which... | ['Shengxia Gao', 'Baokai Liu', 'Shiqiang Du', 'Kaiwu Zhang'] | 2023-03-22 | null | null | null | null | ['incomplete-multi-view-clustering'] | ['computer-vision'] | [-2.49140844e-01 -3.28012079e-01 -3.69786352e-01 -2.34699860e-01
-7.09464490e-01 -4.17895138e-01 3.04524928e-01 -3.96222889e-01
-1.60205394e-01 5.71007848e-01 5.15413940e-01 4.94090736e-01
-3.40700239e-01 -3.85197282e-01 -4.27680403e-01 -1.26442683e+00
3.07312131e-01 4.45747823e-01 -4.42820415e-02 3.32756728... | [8.322790145874023, 4.601260185241699] |
3db167b3-9f79-4c18-b0f6-d1420ab90235 | on-training-instance-selection-for-few-shot | 2107.03176 | null | https://arxiv.org/abs/2107.03176v1 | https://arxiv.org/pdf/2107.03176v1.pdf | On Training Instance Selection for Few-Shot Neural Text Generation | Large-scale pretrained language models have led to dramatic improvements in text generation. Impressive performance can be achieved by finetuning only on a small number of instances (few-shot setting). Nonetheless, almost all previous work simply applies random sampling to select the few-shot training instances. Little... | ['Vera Demberg', 'Hui-Syuan Yeh', 'Xiaoyu Shen', 'Ernie Chang'] | 2021-07-07 | null | https://aclanthology.org/2021.acl-short.2 | https://aclanthology.org/2021.acl-short.2.pdf | acl-2021-5 | ['data-to-text-generation'] | ['natural-language-processing'] | [ 4.84492242e-01 3.27615112e-01 -4.99054641e-01 -3.55597556e-01
-1.23896050e+00 -2.43748277e-01 9.49140787e-01 3.37485164e-01
-4.95073825e-01 1.13869488e+00 5.62378287e-01 -1.09944947e-01
4.43004966e-02 -8.47987413e-01 -4.58981454e-01 -6.10890210e-01
3.87095004e-01 9.19957042e-01 1.23578623e-01 -2.71404028... | [11.684992790222168, 8.831450462341309] |
831bed45-ec89-4bf4-a5fd-4cf3a089cf22 | comparing-causal-frameworks-potential | 2306.14351 | null | https://arxiv.org/abs/2306.14351v1 | https://arxiv.org/pdf/2306.14351v1.pdf | Comparing Causal Frameworks: Potential Outcomes, Structural Models, Graphs, and Abstractions | The aim of this paper is to make clear and precise the relationship between the Rubin causal model (RCM) and structural causal model (SCM) frameworks for causal inference. Adopting a neutral logical perspective, and drawing on previous work, we show what is required for an RCM to be representable by an SCM. A key resul... | ['Thomas Icard', 'Duligur Ibeling'] | 2023-06-25 | null | null | null | null | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [ 5.32613039e-01 9.10506427e-01 -5.67768097e-01 -3.27107608e-01
1.23616897e-01 -4.67401862e-01 1.02317190e+00 1.49379060e-01
7.66620412e-02 6.44142747e-01 6.59342349e-01 -1.12139738e+00
-1.14469171e+00 -9.78887975e-01 -7.37025440e-01 -1.07457086e-01
-4.92837489e-01 2.13816524e-01 1.63916603e-01 -1.45362705... | [8.179605484008789, 5.779160976409912] |
be1f3b80-d1b5-4497-9c69-7329946ca91b | emrkbqa-a-clinical-knowledge-base-question | null | null | https://aclanthology.org/2021.bionlp-1.7 | https://aclanthology.org/2021.bionlp-1.7.pdf | emrKBQA: A Clinical Knowledge-Base Question Answering Dataset | We present emrKBQA, a dataset for answering physician questions from a structured patient record. It consists of questions, logical forms and answers. The questions and logical forms are generated based on real-world physician questions and are slot-filled and answered from patients in the MIMIC-III KB through a semi-a... | ['Peter Szolovits', 'Rachita Chandra', 'Diwakar Mahajan', 'Jennifer J Liang', 'Preethi Raghavan'] | null | null | null | null | naacl-bionlp-2021-6 | ['clinical-knowledge', 'knowledge-base-question-answering'] | ['miscellaneous', 'natural-language-processing'] | [-1.08105607e-01 5.22042215e-01 -3.09075743e-01 -6.68677747e-01
-1.42444992e+00 -7.87495077e-01 -1.83530256e-01 7.56384671e-01
-1.22446179e-01 1.09431994e+00 6.51825011e-01 -1.07861423e+00
-8.24013472e-01 -7.56341219e-01 -4.60675687e-01 5.87211370e-01
4.22704756e-01 1.31228840e+00 4.70084846e-01 -5.85590661... | [8.827226638793945, 8.490957260131836] |
dff793b0-323d-42cb-810d-95965b9f34af | exploring-the-political-agenda-of-the | 1607.03055 | null | http://arxiv.org/abs/1607.03055v1 | http://arxiv.org/pdf/1607.03055v1.pdf | Exploring the Political Agenda of the European Parliament Using a Dynamic Topic Modeling Approach | This study analyzes the political agenda of the European Parliament (EP)
plenary, how it has evolved over time, and the manner in which Members of the
European Parliament (MEPs) have reacted to external and internal stimuli when
making plenary speeches. To unveil the plenary agenda and detect latent themes
in legislati... | ['James P. Cross', 'Derek Greene'] | 2016-07-11 | null | null | null | null | ['dynamic-topic-modeling'] | ['natural-language-processing'] | [-1.84968784e-01 5.28913200e-01 -5.13782740e-01 -3.62375408e-01
-8.75198245e-01 -9.73202586e-01 1.00081313e+00 5.48333466e-01
-7.39345849e-01 6.11130953e-01 1.48998713e+00 -8.86790931e-01
-2.25120768e-01 -7.07827687e-01 -4.40906256e-01 -5.05132377e-01
6.43670738e-01 3.65507156e-01 -3.87266994e-01 -3.68810892... | [8.969391822814941, 9.875978469848633] |
0f494ee0-7c13-47aa-8549-ad82f3a48009 | mlp-air-an-efficient-mlp-based-method-for | 2304.08803 | null | https://arxiv.org/abs/2304.08803v1 | https://arxiv.org/pdf/2304.08803v1.pdf | MLP-AIR: An Efficient MLP-Based Method for Actor Interaction Relation Learning in Group Activity Recognition | The task of Group Activity Recognition (GAR) aims to predict the activity category of the group by learning the actor spatial-temporal interaction relation in the group. Therefore, an effective actor relation learning method is crucial for the GAR task. The previous works mainly learn the interaction relation by the we... | ['Jianqin Yin', 'Guoliang Xu'] | 2023-04-18 | null | null | null | null | ['group-activity-recognition'] | ['computer-vision'] | [ 1.57250822e-01 1.22839831e-01 -5.15739679e-01 -2.91064948e-01
-3.78549248e-01 -9.65437293e-02 6.14521861e-01 9.99120250e-02
-1.56478688e-01 3.09859842e-01 3.47529948e-01 -2.83727229e-01
-2.76197702e-01 -1.03625941e+00 -5.13554394e-01 -7.86507726e-01
-4.31317270e-01 2.16548011e-01 4.17611182e-01 -1.65072456... | [8.361783027648926, 0.6954715847969055] |
d21a44a9-4b5d-48a9-a6b1-2bbc38dbc2cf | dad-3dheads-a-large-scale-dense-accurate-and | 2204.03688 | null | https://arxiv.org/abs/2204.03688v2 | https://arxiv.org/pdf/2204.03688v2.pdf | DAD-3DHeads: A Large-scale Dense, Accurate and Diverse Dataset for 3D Head Alignment from a Single Image | We present DAD-3DHeads, a dense and diverse large-scale dataset, and a robust model for 3D Dense Head Alignment in the wild. It contains annotations of over 3.5K landmarks that accurately represent 3D head shape compared to the ground-truth scans. The data-driven model, DAD-3DNet, trained on our dataset, learns shape, ... | ['Jiři Matas', 'Viktoriia Sharmanska', 'Igor Krashenyi', 'Yana Kurlyak', 'Orest Kupyn', 'Tetiana Martyniuk'] | 2022-04-07 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Martyniuk_DAD-3DHeads_A_Large-Scale_Dense_Accurate_and_Diverse_Dataset_for_3D_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Martyniuk_DAD-3DHeads_A_Large-Scale_Dense_Accurate_and_Diverse_Dataset_for_3D_CVPR_2022_paper.pdf | cvpr-2022-1 | ['head-pose-estimation'] | ['computer-vision'] | [-7.63454258e-01 2.60320246e-01 -2.67859325e-02 -9.43591237e-01
-1.13357484e+00 -3.80215287e-01 5.29744744e-01 -1.51314244e-01
-3.14225733e-01 2.91477472e-01 6.12845063e-01 5.11624277e-01
2.66882330e-01 -4.19725478e-01 -8.50248098e-01 -5.84602058e-01
-3.28588665e-01 1.37566996e+00 -4.62037623e-02 -2.95517802... | [13.519784927368164, 0.17936888337135315] |
e7e34d59-8e30-4002-90a0-1a0dbe54ac83 | breast-cancer-detection-and-diagnosis-a | 2305.19937 | null | https://arxiv.org/abs/2305.19937v1 | https://arxiv.org/pdf/2305.19937v1.pdf | Breast Cancer Detection and Diagnosis: A comparative study of state-of-the-arts deep learning architectures | Breast cancer is a prevalent form of cancer among women, with over 1.5 million women being diagnosed each year. Unfortunately, the survival rates for breast cancer patients in certain third-world countries, like South Africa, are alarmingly low, with only 40% of diagnosed patients surviving beyond five years. The inade... | ['Absalom E. Ezugwu', 'Brennon Maistry'] | 2023-05-31 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection', 'histopathological-image-classification'] | ['knowledge-base', 'medical', 'medical'] | [ 2.69505650e-01 2.67231047e-01 -4.43411469e-01 -1.67960837e-01
-6.15028262e-01 2.40202621e-02 2.72078902e-01 5.68383157e-01
-6.56165063e-01 4.83884484e-01 -7.01389536e-02 -7.55019546e-01
-9.74046811e-02 -7.90424287e-01 2.08104658e-03 -9.41454947e-01
1.79716069e-02 4.95221764e-01 -2.92211235e-01 -2.58136779... | [15.260746955871582, -2.773632526397705] |
0b10d844-b56e-4015-9ad8-9cea6550d33d | towards-two-view-6d-object-pose-estimation-a | 2207.0026 | null | https://arxiv.org/abs/2207.00260v1 | https://arxiv.org/pdf/2207.00260v1.pdf | Towards Two-view 6D Object Pose Estimation: A Comparative Study on Fusion Strategy | Current RGB-based 6D object pose estimation methods have achieved noticeable performance on datasets and real world applications. However, predicting 6D pose from single 2D image features is susceptible to disturbance from changing of environment and textureless or resemblant object surfaces. Hence, RGB-based methods g... | ['Rong Xiong', 'Yue Wang', 'Lilu Liu', 'Jun Wu'] | 2022-07-01 | null | null | null | null | ['6d-pose-estimation'] | ['computer-vision'] | [-6.53579310e-02 -3.32938462e-01 -1.95552155e-01 -4.81988311e-01
-7.24322677e-01 -3.42630237e-01 3.63661259e-01 -1.36834785e-01
-2.17736483e-01 2.52418429e-01 -7.90600851e-02 9.03820917e-02
-2.22739011e-01 -6.79960012e-01 -5.77312231e-01 -5.76308966e-01
1.72622815e-01 5.37530601e-01 5.95341027e-01 -1.10913709... | [7.453056335449219, -2.596479892730713] |
414f7a73-575e-48ee-a9e4-eebe6eeb1411 | learning-of-frequency-time-attention | 2111.03258 | null | https://arxiv.org/abs/2111.03258v2 | https://arxiv.org/pdf/2111.03258v2.pdf | Learning of Time-Frequency Attention Mechanism for Automatic Modulation Recognition | Recent learning-based image classification and speech recognition approaches make extensive use of attention mechanisms to achieve state-of-the-art recognition power, which demonstrates the effectiveness of attention mechanisms. Motivated by the fact that the frequency and time information of modulated radio signals ar... | ['Yi Gong', 'Yuan Zeng', 'Shangao Lin'] | 2021-11-05 | null | null | null | null | ['automatic-modulation-recognition'] | ['time-series'] | [ 4.67620194e-01 -4.60732877e-01 -5.62868416e-01 -3.41316313e-01
-6.69774771e-01 1.60649285e-01 7.52064288e-01 -4.81959820e-01
-3.20529431e-01 3.12956065e-01 1.54445052e-01 -5.24903595e-01
-4.19375598e-01 -6.58746064e-01 -4.22851026e-01 -8.02018106e-01
-2.02453807e-01 -4.37329262e-01 -3.08698788e-03 -1.54304221... | [6.504977226257324, 1.4932231903076172] |
2a772f93-17ec-4b00-8288-d9e48dd1cd61 | semi-supervised-batch-active-learning-via | 2010.09654 | null | https://arxiv.org/abs/2010.09654v1 | https://arxiv.org/pdf/2010.09654v1.pdf | Semi-supervised Batch Active Learning via Bilevel Optimization | Active learning is an effective technique for reducing the labeling cost by improving data efficiency. In this work, we propose a novel batch acquisition strategy for active learning in the setting where the model training is performed in a semi-supervised manner. We formulate our approach as a data summarization probl... | ['Andreas Krause', 'Marco Tagliasacchi', 'Zalán Borsos'] | 2020-10-19 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [ 3.31410080e-01 3.03634465e-01 -1.03465295e+00 -3.21936786e-01
-1.76082420e+00 -7.12090671e-01 5.75667679e-01 7.63239324e-01
-8.61548722e-01 7.37221360e-01 1.97521895e-01 -8.64120573e-02
-1.83317333e-01 -3.29026461e-01 -8.38771462e-01 -8.16190183e-01
7.80701339e-02 7.53984571e-01 -1.25894785e-01 2.98989475... | [9.583967208862305, 4.327975273132324] |
6367d9a8-22b3-4de9-9e14-c7dbccde831e | xiaoicesing-2-a-high-fidelity-singing-voice | 2210.14666 | null | https://arxiv.org/abs/2210.14666v2 | https://arxiv.org/pdf/2210.14666v2.pdf | Xiaoicesing 2: A High-Fidelity Singing Voice Synthesizer Based on Generative Adversarial Network | XiaoiceSing is a singing voice synthesis (SVS) system that aims at generating 48kHz singing voices. However, the mel-spectrogram generated by it is over-smoothing in middle- and high-frequency areas due to no special design for modeling the details of these parts. In this paper, we propose XiaoiceSing2, which can gener... | ['Xing He', 'Chang Zeng', 'Chunhui Wang'] | 2022-10-26 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [-6.58470765e-02 -7.58282989e-02 2.50757694e-01 -2.62822807e-02
-1.05416524e+00 -5.28537273e-01 2.25964099e-01 -5.89373410e-01
1.41671613e-01 3.92276943e-01 5.27329862e-01 -2.83945739e-01
3.32140088e-01 -8.45882237e-01 -4.73827481e-01 -8.70743930e-01
1.28749162e-01 -2.75217474e-01 1.14364982e-01 -2.59483188... | [15.483526229858398, 6.176003456115723] |
115e3488-680c-45fb-9c05-b9655ef7eae1 | reduction-of-class-activation-uncertainty | 2305.03238 | null | https://arxiv.org/abs/2305.03238v2 | https://arxiv.org/pdf/2305.03238v2.pdf | Reduction of Class Activation Uncertainty with Background Information | Multitask learning is a popular approach to training high-performing neural networks with improved generalization. In this paper, we propose a background class to achieve improved generalization at a lower computation compared to multitask learning to help researchers and organizations with limited computation power. W... | ['H M Dipu Kabir'] | 2023-05-05 | null | null | null | null | ['fine-grained-image-classification'] | ['computer-vision'] | [ 1.60373360e-01 -2.86189646e-01 2.34879218e-02 -5.71031868e-01
-9.20417905e-01 -2.73829907e-01 5.55169582e-01 -1.30720899e-01
-7.75199533e-01 1.01923990e+00 -1.46808609e-01 -3.63501102e-01
-2.14968815e-01 -5.51646829e-01 -6.47461474e-01 -5.99427283e-01
1.05500266e-01 4.22177285e-01 3.40490013e-01 -1.27397895... | [9.395670890808105, 2.666675567626953] |
3bbeb7ed-2343-4dc6-87a5-9a4a2891117c | womd-lidar-raw-sensor-dataset-benchmark-for | 2304.03834 | null | https://arxiv.org/abs/2304.03834v1 | https://arxiv.org/pdf/2304.03834v1.pdf | WOMD-LiDAR: Raw Sensor Dataset Benchmark for Motion Forecasting | Widely adopted motion forecasting datasets substitute the observed sensory inputs with higher-level abstractions such as 3D boxes and polylines. These sparse shapes are inferred through annotating the original scenes with perception systems' predictions. Such intermediate representations tie the quality of the motion f... | ['Dragomir Anguelov', 'Mingxing Tan', 'Weiyue Wang', 'Ivan Bogun', 'Mustafa Mustafa', 'Zhaoqi Leng', 'Pei Sun', 'Scott Ettinger', 'Zoey Yang', 'Xuanyu Zhou', 'Charles R. Qi', 'Rami Ai-Rfou', 'Hang Qiu', 'Runzhou Ge', 'Kan Chen'] | 2023-04-07 | null | null | null | null | ['motion-forecasting'] | ['computer-vision'] | [-3.07880491e-01 -1.43203303e-01 -4.10769165e-01 -5.64611256e-01
-6.45819247e-01 -5.32393157e-01 8.11075807e-01 -2.13419750e-01
-1.61863983e-01 4.76325452e-01 6.18076026e-01 -2.37434700e-01
2.89740622e-01 -1.11414123e+00 -7.77949095e-01 -2.38219738e-01
2.39365045e-02 3.99947375e-01 6.26092732e-01 -4.65541512... | [7.917141437530518, -2.0844171047210693] |
8552245b-bcd0-4b4d-99d0-df415bcefb09 | lf-pgvio-a-visual-inertial-odometry-framework | 2306.06663 | null | https://arxiv.org/abs/2306.06663v1 | https://arxiv.org/pdf/2306.06663v1.pdf | LF-PGVIO: A Visual-Inertial-Odometry Framework for Large Field-of-View Cameras using Points and Geodesic Segments | In this paper, we propose LF-PGVIO, a Visual-Inertial-Odometry (VIO) framework for large Field-of-View (FoV) cameras with a negative plane using points and geodesic segments. Notoriously, when the FoV of a panoramic camera reaches the negative half-plane, the image cannot be unfolded into a single pinhole image. Moreov... | ['Kaiwei Wang', 'Fei Gao', 'Yufan Zhang', 'Hao Shi', 'Kailun Yang', 'Ze Wang'] | 2023-06-11 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [ 1.13771968e-01 -4.10679132e-01 -9.83406901e-02 -3.23001556e-02
-2.98274964e-01 -8.39610279e-01 3.87776971e-01 -5.11457860e-01
-3.25853229e-01 1.23054810e-01 -4.31010425e-02 -4.39952195e-01
-6.86572418e-02 -6.88138604e-01 -7.46500194e-01 -4.32720006e-01
3.34620982e-01 -6.19275775e-03 4.15664911e-01 -9.53649208... | [7.995668411254883, -2.132784366607666] |
2dc6aaff-f769-4443-b6a0-a4cbe0a30412 | adapted-human-pose-monocular-3d-human-pose | 2105.10837 | null | https://arxiv.org/abs/2105.10837v2 | https://arxiv.org/pdf/2105.10837v2.pdf | Adapted Human Pose: Monocular 3D Human Pose Estimation with Zero Real 3D Pose Data | The ultimate goal for an inference model is to be robust and functional in real life applications. However, training vs. test data domain gaps often negatively affect model performance. This issue is especially critical for the monocular 3D human pose estimation problem, in which 3D human data is often collected in a c... | ['Sarah Ostadabbas', 'Naveen Sehgal', 'Shuangjun Liu'] | 2021-05-23 | null | null | null | null | ['3d-pose-estimation', 'monocular-3d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [ 9.19709876e-02 1.08040087e-01 2.20543757e-01 -4.19679075e-01
-6.31246328e-01 -2.07068205e-01 4.39146906e-01 -3.87477577e-01
-5.58918715e-01 6.98646545e-01 5.14813736e-02 3.39052618e-01
1.64165184e-01 -4.70217377e-01 -8.99393618e-01 -4.38059568e-01
2.77798742e-01 9.76402283e-01 5.23624122e-01 -2.55431592... | [7.022703170776367, -1.0529848337173462] |
0c3b7d13-77b5-4552-8c19-a2297585e424 | deep-learning-eliminates-massive-dust-storms | 2206.10145 | null | https://arxiv.org/abs/2206.10145v1 | https://arxiv.org/pdf/2206.10145v1.pdf | Deep Learning Eliminates Massive Dust Storms from Images of Tianwen-1 | Dust storms may remarkably degrade the imaging quality of Martian orbiters and delay the progress of mapping the global topography and geomorphology. To address this issue, this paper presents an approach that reuses the image dehazing knowledge obtained on Earth to resolve the dust-removal problem on Mars. In this app... | ['Long Xu', 'Xin Ren', 'Jia Li', 'Hongyu Li'] | 2022-06-21 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 5.23832552e-02 9.75594819e-02 6.72739804e-01 -4.52387333e-01
-1.35922670e-01 -4.77207333e-01 6.66376710e-01 -4.97959673e-01
-3.19866598e-01 8.73944819e-01 -2.37128124e-01 -9.37577710e-02
-8.72581303e-02 -1.18255353e+00 -6.08700633e-01 -9.99592781e-01
2.33823642e-01 5.71724892e-01 -6.88757300e-02 -7.26904154... | [10.927091598510742, -3.2246201038360596] |
29489fdc-19fd-48b5-8ffd-8cd3e4013663 | synthesizing-coherent-story-with-auto | 2211.1095 | null | https://arxiv.org/abs/2211.10950v1 | https://arxiv.org/pdf/2211.10950v1.pdf | Synthesizing Coherent Story with Auto-Regressive Latent Diffusion Models | Conditioned diffusion models have demonstrated state-of-the-art text-to-image synthesis capacity. Recently, most works focus on synthesizing independent images; While for real-world applications, it is common and necessary to generate a series of coherent images for story-stelling. In this work, we mainly focus on stor... | ['Wenhu Chen', 'Hui Xue', 'Yuhong Li', 'Pengda Qin', 'Xichen Pan'] | 2022-11-20 | null | null | null | null | ['story-continuation', 'story-visualization'] | ['computer-vision', 'computer-vision'] | [ 1.81846187e-01 -1.78910494e-01 -1.08242877e-01 -3.24042924e-02
-5.96479237e-01 -3.50340277e-01 1.19714844e+00 -3.24127644e-01
-9.48584154e-02 6.52557909e-01 6.42871499e-01 -4.82215472e-02
2.88970679e-01 -6.47288561e-01 -7.22951829e-01 -4.64472860e-01
1.39355019e-01 4.02011067e-01 1.98111638e-01 -2.91794211... | [11.157913208007812, 0.4077697694301605] |
8b4552b1-f8fa-4ff8-b26d-47c9fd58bec3 | learning-to-detect-instance-level-salient | 2111.10137 | null | https://arxiv.org/abs/2111.10137v1 | https://arxiv.org/pdf/2111.10137v1.pdf | Learning to Detect Instance-level Salient Objects Using Complementary Image Labels | Existing salient instance detection (SID) methods typically learn from pixel-level annotated datasets. In this paper, we present the first weakly-supervised approach to the SID problem. Although weak supervision has been considered in general saliency detection, it is mainly based on using class labels for object local... | ['Rynson W. H. Lau', 'BaoCai Yin', 'Xin Yang', 'Ke Xu', 'Xin Tian'] | 2021-11-19 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 4.77994740e-01 5.57667136e-01 -4.86469567e-01 -3.79804850e-01
-7.16999173e-01 -1.44973248e-01 5.50434232e-01 7.03969836e-01
-3.87672961e-01 5.90398490e-01 1.21700741e-01 2.68984437e-01
-6.24676086e-02 -5.41047513e-01 -7.50471652e-01 -9.46803689e-01
9.26440507e-02 3.45837533e-01 9.63065803e-01 -1.40843153... | [9.808335304260254, -0.10521429032087326] |
992c3ee5-a37d-40eb-906b-796394ab8fdc | joint-iris-segmentation-and-localization | 1901.11195 | null | https://arxiv.org/abs/1901.11195v2 | https://arxiv.org/pdf/1901.11195v2.pdf | Joint Iris Segmentation and Localization Using Deep Multi-task Learning Framework | Iris segmentation and localization in non-cooperative environment is challenging due to illumination variations, long distances, moving subjects and limited user cooperation, etc. Traditional methods often suffer from poor performance when confronted with iris images captured in these conditions. Recent studies have sh... | ['Caiyong Wang', 'Yuhao Zhu', 'Zhenan Sun', 'Yunfan Liu', 'Ran He'] | 2019-01-31 | null | null | null | null | ['iris-segmentation'] | ['medical'] | [-3.93886827e-02 -4.21515226e-01 -2.85906494e-01 -2.74833560e-01
-6.79060578e-01 -4.19451028e-01 9.88010988e-02 -2.30702385e-01
-2.33202800e-01 4.02932316e-01 2.62870610e-01 -1.65537238e-01
-2.92306393e-01 -1.29915133e-01 -4.54467982e-01 -9.69938993e-01
2.57756978e-01 1.94941282e-01 -2.41582468e-01 2.66821474... | [3.771373987197876, -3.6143786907196045] |
8a5eccd2-922f-4d74-b3a8-d477a7210e3b | clustering-based-feature-learning-on-variable | 1602.08977 | null | http://arxiv.org/abs/1602.08977v1 | http://arxiv.org/pdf/1602.08977v1.pdf | Clustering Based Feature Learning on Variable Stars | The success of automatic classification of variable stars strongly depends on
the lightcurve representation. Usually, lightcurves are represented as a vector
of many statistical descriptors designed by astronomers called features. These
descriptors commonly demand significant computational power to calculate,
require s... | ['Cristóbal Mackenzie', 'Karim Pichara', 'Pavlos Protopapas'] | 2016-02-29 | null | null | null | null | ['classification-of-variable-stars'] | ['miscellaneous'] | [ 4.00936157e-02 -5.98718107e-01 -1.05024666e-01 -6.32929325e-01
-5.60611546e-01 -1.09250200e+00 6.56387031e-01 8.98900628e-02
-1.75814167e-01 5.02141476e-01 -3.28346908e-01 -2.94496089e-01
-1.85237795e-01 -5.46778738e-01 -2.49428913e-01 -1.01406109e+00
7.91825503e-02 6.87531650e-01 4.26748842e-01 -1.69505507... | [7.747166156768799, 3.136155843734741] |
a398e9e8-01a6-4028-8311-96043447b4e4 | learning-guided-convolutional-network-for | 1908.01238 | null | https://arxiv.org/abs/1908.01238v1 | https://arxiv.org/pdf/1908.01238v1.pdf | Learning Guided Convolutional Network for Depth Completion | Dense depth perception is critical for autonomous driving and other robotics applications. However, modern LiDAR sensors only provide sparse depth measurement. It is thus necessary to complete the sparse LiDAR data, where a synchronized guidance RGB image is often used to facilitate this completion. Many neural network... | ['Fei-Peng Tian', 'Ping Tan', 'Jie Tang', 'Jian Li', 'Wei Feng'] | 2019-08-03 | null | null | null | null | ['stereo-lidar-fusion'] | ['computer-vision'] | [ 2.15232044e-01 -5.80300152e-01 3.04485019e-03 -7.23884106e-01
-4.87645656e-01 -2.85406440e-01 5.06027579e-01 5.02404645e-02
-8.50783348e-01 5.87890148e-01 -1.29889384e-01 -1.80316374e-01
-8.91238675e-02 -1.02323520e+00 -6.72414005e-01 -7.10080862e-01
4.19432819e-02 1.74969882e-01 4.05446380e-01 -1.19211264... | [8.498393058776855, -2.3971409797668457] |
7b7f14c1-783a-4718-955f-1bee7e684984 | on-the-use-of-higher-order-tensors-to-model | 2007.01949 | null | https://arxiv.org/abs/2007.01949v1 | https://arxiv.org/pdf/2007.01949v1.pdf | On the use of higher-order tensors to model muscle synergies | The muscle synergy concept provides the best framework to understand motor control and it has been recently utilised in many applications such as prosthesis control. The current muscle synergy model relies on decomposing multi-channel surface Electromyography (EMG) signals into a synergy matrix (spatial mode) and its w... | ['Javier Escudero', 'Eli Kinney-Lang', 'Loukianos Spyrou', 'Ahmed Ebied'] | 2020-07-03 | null | null | null | null | ['electromyography-emg'] | ['medical'] | [ 4.12520736e-01 -6.12262897e-02 -3.78326923e-01 4.76134300e-01
-2.11685762e-01 -4.70813960e-01 6.51204228e-01 -5.48912466e-01
-7.02926576e-01 5.61921239e-01 6.65991366e-01 -1.57992512e-01
-8.52751493e-01 -6.56772777e-02 -4.91971016e-01 -6.73090577e-01
-6.59729004e-01 1.09089516e-01 1.39199957e-01 -5.51384985... | [6.877106189727783, 0.2097569704055786] |
eb8507db-58c9-43a6-bf9c-9df8569cc96a | a-3d-coarse-to-fine-framework-for-volumetric | 1712.00201 | null | http://arxiv.org/abs/1712.00201v2 | http://arxiv.org/pdf/1712.00201v2.pdf | A 3D Coarse-to-Fine Framework for Volumetric Medical Image Segmentation | In this paper, we adopt 3D Convolutional Neural Networks to segment
volumetric medical images. Although deep neural networks have been proven to be
very effective on many 2D vision tasks, it is still challenging to apply them
to 3D tasks due to the limited amount of annotated 3D data and limited
computational resources... | ['Yingda Xia', 'Elliot K. Fishman', 'Wei Shen', 'Zhuotun Zhu', 'Alan L. Yuille'] | 2017-12-01 | null | null | null | null | ['volumetric-medical-image-segmentation', 'pancreas-segmentation'] | ['medical', 'medical'] | [ 2.60917321e-02 1.89248100e-01 -1.26356065e-01 -4.26507831e-01
-8.23734999e-01 -3.66735965e-01 4.25730437e-01 2.71889895e-01
-4.61212099e-01 5.70985436e-01 2.11078241e-01 -4.32262868e-01
-1.33692995e-01 -5.32675087e-01 -6.48384035e-01 -6.92267358e-01
-4.14701521e-01 4.67116624e-01 3.45886827e-01 1.26767233... | [14.503011703491211, -2.560307264328003] |
1705944f-a8a5-4f02-b9d1-ec50e92e4d01 | understanding-cyber-athletes-behaviour | 1908.06407 | null | https://arxiv.org/abs/1908.06407v1 | https://arxiv.org/pdf/1908.06407v1.pdf | Understanding Cyber Athletes Behaviour Through a Smart Chair: CS:GO and Monolith Team Scenario | eSports is the rapidly developing multidisciplinary domain. However, research and experimentation in eSports are in the infancy. In this work, we propose a smart chair platform - an unobtrusive approach to the collection of data on the eSports athletes and data further processing with machine learning methods. The use ... | ['Rostislav Shaniiazov', 'Andrey Somov', 'Anastasia Kiskun', 'Evgeny Burnaev', 'Anton Smerdov'] | 2019-08-18 | null | null | null | null | ['sensor-modeling', 'skills-evaluation', 'skills-assessment', 'fps-games'] | ['computer-vision', 'computer-vision', 'computer-vision', 'playing-games'] | [-1.55216560e-01 1.55313918e-02 -6.51771247e-01 -7.18591688e-03
-4.32361811e-01 -3.72799635e-01 -3.59148651e-01 1.48034543e-01
-5.69142759e-01 4.46068048e-01 -5.35521321e-02 3.06555144e-02
-2.59259135e-01 -7.13202059e-01 -4.43542928e-01 -2.03095794e-01
-2.57526517e-01 6.79917991e-01 3.54085296e-01 -6.80728734... | [6.942926406860352, 0.3482387363910675] |
2bf49385-0753-40ae-aeb9-68aa1da8cfee | bridging-the-modality-gap-for-speech-to-text | 2010.1492 | null | https://arxiv.org/abs/2010.14920v1 | https://arxiv.org/pdf/2010.14920v1.pdf | Bridging the Modality Gap for Speech-to-Text Translation | End-to-end speech translation aims to translate speech in one language into text in another language via an end-to-end way. Most existing methods employ an encoder-decoder structure with a single encoder to learn acoustic representation and semantic information simultaneously, which ignores the speech-and-text modality... | ['Chengqing Zong', 'Jiajun Zhang', 'Junnan Zhu', 'Yuchen Liu'] | 2020-10-28 | null | null | null | null | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 2.50185341e-01 1.41496435e-01 -2.40681261e-01 -6.18718982e-01
-1.39160681e+00 -4.18451726e-01 5.45515060e-01 -5.65855086e-01
-1.91867992e-01 3.89751285e-01 6.67825282e-01 -5.86151242e-01
6.40156567e-01 -3.94603908e-01 -8.19839060e-01 -6.07147813e-01
7.43238032e-01 4.70799327e-01 1.55060992e-01 -1.30786225... | [14.514341354370117, 7.197293281555176] |
c9f3599a-5034-4a23-b660-ac9e2d8ccdcf | self-knowledge-distillation-for-surgical | 2306.08961 | null | https://arxiv.org/abs/2306.08961v1 | https://arxiv.org/pdf/2306.08961v1.pdf | Self-Knowledge Distillation for Surgical Phase Recognition | Purpose: Advances in surgical phase recognition are generally led by training deeper networks. Rather than going further with a more complex solution, we believe that current models can be exploited better. We propose a self-knowledge distillation framework that can be integrated into current state-of-the-art (SOTA) mo... | ['Imanol Luengo', 'Danail Stoyanov', 'Abdolrahim Kadkhodamohammadi', 'Santiago Barbarisi', 'Jinglu Zhang'] | 2023-06-15 | null | null | null | null | ['self-knowledge-distillation', 'surgical-phase-recognition'] | ['computer-vision', 'computer-vision'] | [ 5.46512067e-01 8.25112939e-01 -8.92696023e-01 -2.75854170e-01
-1.06573963e+00 -3.54993671e-01 3.25676918e-01 -5.52743673e-03
-6.91659451e-01 5.47519207e-01 3.60740960e-01 -1.92684010e-01
4.98158634e-02 -4.54358667e-01 -8.77588093e-01 -6.52211308e-01
2.44761445e-02 4.23858255e-01 3.76133621e-01 -7.10170120... | [14.160638809204102, -3.2707152366638184] |
f4092fa4-55d5-4444-b618-915178ef703d | detection-of-poisoning-attacks-with-anomaly | 2207.08486 | null | https://arxiv.org/abs/2207.08486v2 | https://arxiv.org/pdf/2207.08486v2.pdf | Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications | Adversarial attacks such as poisoning attacks have attracted the attention of many machine learning researchers. Traditionally, poisoning attacks attempt to inject adversarial training data in order to manipulate the trained model. In federated learning (FL), data poisoning attacks can be generalized to model poisoning... | ['Ludovic Koehl', 'Kim-Phuc Tran', 'Shujun Li', 'Ali Raza'] | 2022-07-18 | null | null | null | null | ['data-poisoning', 'ecg-classification'] | ['adversarial', 'medical'] | [ 6.67425171e-02 -2.64146328e-01 -5.43671437e-02 1.61196530e-01
-7.38248467e-01 -8.33751082e-01 4.31666553e-01 3.92745793e-01
-5.85062146e-01 4.93075758e-01 -2.52758354e-01 -4.43033457e-01
2.46000476e-02 -9.61099863e-01 -4.53561008e-01 -7.90920079e-01
-4.74566907e-01 2.41161466e-01 5.09079754e-01 -8.66388232... | [5.633913516998291, 7.098053455352783] |
48228857-2b6e-476d-922a-79c417ce3366 | cflownets-continuous-control-with-generative | 2303.0243 | null | https://arxiv.org/abs/2303.02430v1 | https://arxiv.org/pdf/2303.02430v1.pdf | CFlowNets: Continuous Control with Generative Flow Networks | Generative flow networks (GFlowNets), as an emerging technique, can be used as an alternative to reinforcement learning for exploratory control tasks. GFlowNet aims to generate distribution proportional to the rewards over terminating states, and to sample different candidates in an active learning fashion. GFlowNets n... | ['Jianye Hao', 'Haozhi Wang', 'Shuang Luo', 'Yinchuan Li'] | 2023-03-04 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [-1.59209147e-01 1.97844222e-01 -5.14560044e-01 3.48351933e-02
-2.24535719e-01 -4.06717867e-01 4.86158282e-01 1.12349398e-01
-5.41018903e-01 1.14286244e+00 -1.42217934e-01 -2.03134567e-01
-5.42562902e-01 -1.08576715e+00 -5.68793774e-01 -7.70507812e-01
-5.31539202e-01 5.81504583e-01 1.54602215e-01 9.14863274... | [4.037796497344971, 2.207622766494751] |
a07b7767-e066-4181-a61d-8385e0319bfc | deep-feature-synthesis-towards-automating | null | null | https://ieeexplore.ieee.org/abstract/document/7344858 | http://www.jmaxkanter.com/static/papers/DSAA_DSM_2015.pdf | Deep Feature Synthesis: Towards Automating Data Science Endeavors | In this paper, we develop the Data Science Machine, which is able to derive predictive models from raw data automatically. To achieve this automation, we first propose and develop the Deep Feature Synthesis algorithm for automatically generating features for relational datasets. The algorithm follows relationships in t... | ['Kalyan Veeramachaneni', 'James Max Kanter'] | 2015-01-01 | null | null | null | dsaa-2015-2015-1 | ['automated-feature-engineering'] | ['methodology'] | [-2.55345821e-01 2.85458207e-01 2.00299144e-01 -5.73497474e-01
-9.59612250e-01 -7.79468775e-01 6.54080272e-01 5.16201615e-01
-3.66181582e-01 5.05959868e-01 -7.21318722e-02 -2.69061387e-01
-4.62344885e-01 -9.31640685e-01 -9.76870716e-01 -2.07012534e-01
-3.95718627e-02 9.19218898e-01 8.86958018e-02 -8.53432715... | [8.912678718566895, 7.27034854888916] |
68ccb47f-84d8-490d-afb1-aa556f260a2d | large-scale-fine-grained-categorization-and | 1806.06193 | null | http://arxiv.org/abs/1806.06193v1 | http://arxiv.org/pdf/1806.06193v1.pdf | Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning | Transferring the knowledge learned from large scale datasets (e.g., ImageNet)
via fine-tuning offers an effective solution for domain-specific fine-grained
visual categorization (FGVC) tasks (e.g., recognizing bird species or car make
and model). In such scenarios, data annotation often calls for specialized
domain kno... | ['Yang song', 'Serge Belongie', 'Chen Sun', 'Andrew Howard', 'Yin Cui'] | 2018-06-16 | large-scale-fine-grained-categorization-and-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Cui_Large_Scale_Fine-Grained_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Cui_Large_Scale_Fine-Grained_CVPR_2018_paper.pdf | cvpr-2018-6 | ['fine-grained-visual-categorization'] | ['computer-vision'] | [ 1.55280652e-02 -5.20442545e-01 -2.60445714e-01 -4.03274000e-01
-5.45194149e-01 -1.07776773e+00 7.41990864e-01 1.98587075e-01
-8.01646709e-01 8.66665602e-01 1.28496796e-01 -9.08883512e-02
-1.11142278e-01 -9.45752561e-01 -1.01897681e+00 -4.89097774e-01
-3.56151350e-02 5.09884238e-01 4.86380965e-01 -2.11450949... | [9.847728729248047, 2.2779839038848877] |
295602ae-74f5-4155-9f05-3acdc2ccf305 | supervising-unsupervised-open-information | null | null | https://aclanthology.org/D19-1067 | https://aclanthology.org/D19-1067.pdf | Supervising Unsupervised Open Information Extraction Models | We propose a novel supervised open information extraction (Open IE) framework that leverages an ensemble of unsupervised Open IE systems and a small amount of labeled data to improve system performance. It uses the outputs of multiple unsupervised Open IE systems plus a diverse set of lexical and syntactic information ... | ['SHimei Pan', 'Taesung Lee', 'Youngja Park', 'Arpita Roy'] | 2019-11-01 | null | null | null | ijcnlp-2019-11 | ['role-embedding', 'open-information-extraction'] | ['graphs', 'natural-language-processing'] | [ 1.31979316e-01 7.10893631e-01 -6.69267178e-01 -4.27572578e-01
-4.99662668e-01 -7.87940741e-01 6.71274483e-01 3.76907110e-01
-3.63901168e-01 7.76401579e-01 5.37609577e-01 -4.18205649e-01
-1.97401389e-01 -7.40038037e-01 -3.81553054e-01 -3.84968489e-01
-1.06765348e-02 3.94719779e-01 3.07345361e-01 -2.64670283... | [9.424266815185547, 8.61453914642334] |
a2095ce6-35d9-4dd1-aba6-9992c3eb6660 | investigating-sindy-as-a-tool-for-causal | 2212.14133 | null | https://arxiv.org/abs/2212.14133v1 | https://arxiv.org/pdf/2212.14133v1.pdf | Investigating Sindy As a Tool For Causal Discovery In Time Series Signals | The SINDy algorithm has been successfully used to identify the governing equations of dynamical systems from time series data. In this paper, we argue that this makes SINDy a potentially useful tool for causal discovery and that existing tools for causal discovery can be used to dramatically improve the performance of ... | ['Edward Kim', 'Rosina Weber', "Andrew O'Brien"] | 2022-12-29 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 8.16616565e-02 -1.85067922e-01 -3.72041255e-01 1.10245518e-01
-5.20607233e-01 -6.65980458e-01 5.34374356e-01 -2.20694885e-01
6.15369022e-01 1.00489557e+00 3.81782442e-01 -7.67368376e-01
-1.02069712e+00 -5.63686430e-01 -6.11341000e-01 -7.60579050e-01
-6.45091116e-01 3.94631356e-01 -1.69188201e-01 -1.09143786... | [7.696506023406982, 5.184157371520996] |
4e18c08c-7a10-434a-8a04-6e2c315c824e | benchmarking-the-performance-of-bayesian | 2106.01309 | null | https://arxiv.org/abs/2106.01309v1 | https://arxiv.org/pdf/2106.01309v1.pdf | Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains | In the field of machine learning (ML) for materials optimization, active learning algorithms, such as Bayesian Optimization (BO), have been leveraged for guiding autonomous and high-throughput experimentation systems. However, very few studies have evaluated the efficiency of BO as a general optimization algorithm acro... | ['Tonio Buonassisi', 'John Fisher III', 'Keith A. Brown', 'Benji Maruyama', 'Kedar Hippalgaonkar', 'Saif A. Khan', 'Flore Mekki-Berrada', 'Daniil Bash', 'James R. Deneault', 'Shijing Sun', 'Zhe Liu', 'Armi Tiihonen', 'Zekun Ren', 'Aldair E. Gongora', 'Qiaohao Liang'] | 2021-05-23 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 5.86932540e-01 -2.30547383e-01 -2.52853751e-01 -1.28568947e-01
-8.55344832e-01 -4.19070303e-01 5.81009626e-01 4.21305150e-01
-5.10779083e-01 8.94852400e-01 -4.68373783e-02 -3.59499276e-01
-6.77926779e-01 -7.93703377e-01 -5.69867432e-01 -1.37375736e+00
-1.60290048e-01 1.02362800e+00 1.38354257e-01 1.79846242... | [5.909114360809326, 4.261407852172852] |
b6c22d24-96b5-48eb-8b19-5b53e929fc75 | theoretical-limitations-of-self-attention-in | 1906.06755 | null | https://arxiv.org/abs/1906.06755v2 | https://arxiv.org/pdf/1906.06755v2.pdf | Theoretical Limitations of Self-Attention in Neural Sequence Models | Transformers are emerging as the new workhorse of NLP, showing great success across tasks. Unlike LSTMs, transformers process input sequences entirely through self-attention. Previous work has suggested that the computational capabilities of self-attention to process hierarchical structures are limited. In this work, w... | ['Michael Hahn'] | 2019-06-16 | theoretical-limitations-of-self-attention-in-1 | https://aclanthology.org/2020.tacl-1.11 | https://aclanthology.org/2020.tacl-1.11.pdf | tacl-2020-1 | ['hard-attention'] | ['methodology'] | [ 8.78891274e-02 9.30332184e-01 -3.85156199e-02 -9.30059608e-03
-3.72392565e-01 -5.84596694e-01 7.78711557e-01 9.08906981e-02
-3.45130950e-01 5.57995141e-01 7.47695088e-01 -7.74467707e-01
5.52244298e-02 -7.70684063e-01 -6.99634075e-01 -3.33866447e-01
-7.96594918e-02 6.31223321e-01 2.25280404e-01 -4.98861402... | [10.610673904418945, 9.049579620361328] |
8f4c50e6-ba42-46aa-8e99-46a489682820 | dexray-a-simple-yet-effective-deep-learning | 2109.03326 | null | https://arxiv.org/abs/2109.03326v1 | https://arxiv.org/pdf/2109.03326v1.pdf | DexRay: A Simple, yet Effective Deep Learning Approach to Android Malware Detection based on Image Representation of Bytecode | Computer vision has witnessed several advances in recent years, with unprecedented performance provided by deep representation learning research. Image formats thus appear attractive to other fields such as malware detection, where deep learning on images alleviates the need for comprehensively hand-crafted features ge... | ['Jacques Klein', 'Tegawendé F. Bissyandé', 'Kevin Allix', 'Abdoul Kader Kabore', 'Jordan Samhi', 'Nadia Daoudi'] | 2021-09-05 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [ 4.86880332e-01 -2.38294870e-01 -3.03327441e-01 -9.88732427e-02
-5.23591638e-01 -6.87218189e-01 9.94166911e-01 -1.79654911e-01
-3.72382104e-01 3.25757489e-02 -1.24004319e-01 -9.10642803e-01
-2.26594247e-02 -5.88298321e-01 -7.55550146e-01 -5.79590917e-01
-5.03698051e-01 -2.44804192e-02 1.37525678e-01 -2.33291715... | [14.42869758605957, 9.682647705078125] |
9b4e3404-9b17-4b4f-bb50-c7756f233a56 | self-supervised-approach-for-facial-movement | 2105.01256 | null | https://arxiv.org/abs/2105.01256v1 | https://arxiv.org/pdf/2105.01256v1.pdf | Self-Supervised Approach for Facial Movement Based Optical Flow | Computing optical flow is a fundamental problem in computer vision. However, deep learning-based optical flow techniques do not perform well for non-rigid movements such as those found in faces, primarily due to lack of the training data representing the fine facial motion. We hypothesize that learning optical flow on ... | ['Abhinav Dhall', 'Usman Tariq', 'Muhannad Alkaddour'] | 2021-05-04 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [-1.52016625e-01 -1.29501775e-01 -1.57130778e-01 -6.21507704e-01
3.18433531e-02 -2.99802572e-01 5.44287384e-01 -7.87630081e-01
-2.11930797e-01 6.87382340e-01 4.74033237e-01 1.29552275e-01
1.98372975e-01 -7.08081901e-01 -4.29392159e-01 -5.70888102e-01
-3.56905997e-01 5.48396036e-02 -4.03449148e-01 -3.36161941... | [13.627005577087402, 1.6436467170715332] |
f4dc2885-c8cb-4a9a-8181-45ad36ebdbba | autofi-towards-automatic-wifi-human-sensing | 2205.01629 | null | https://arxiv.org/abs/2205.01629v2 | https://arxiv.org/pdf/2205.01629v2.pdf | AutoFi: Towards Automatic WiFi Human Sensing via Geometric Self-Supervised Learning | WiFi sensing technology has shown superiority in smart homes among various sensors for its cost-effective and privacy-preserving merits. It is empowered by Channel State Information (CSI) extracted from WiFi signals and advanced machine learning models to analyze motion patterns in CSI. Many learning-based models have ... | ['Lihua Xie', 'Dazhuo Wang', 'Han Zou', 'Xinyan Chen', 'Jianfei Yang'] | 2022-04-12 | null | null | null | null | ['gait-recognition', 'gesture-recognition'] | ['computer-vision', 'computer-vision'] | [ 4.24821913e-01 -3.08348417e-01 -3.42009366e-01 -3.65264177e-01
-1.20044112e+00 -2.74823755e-01 2.66571566e-02 -6.18666589e-01
-3.54917079e-01 9.53031361e-01 1.67702988e-01 -4.83816825e-02
-2.27126986e-01 -8.30915391e-01 -7.17888951e-01 -9.74048853e-01
-2.96937555e-01 1.34939611e-01 1.66643515e-01 2.35032231... | [6.72189998626709, 0.7008734345436096] |
da4d73a5-68d0-49ca-9230-9e7a160512cc | language-conditioned-imitation-learning-with | 2305.19075 | null | https://arxiv.org/abs/2305.19075v2 | https://arxiv.org/pdf/2305.19075v2.pdf | Language-Conditioned Imitation Learning with Base Skill Priors under Unstructured Data | The growing interest in language-conditioned robot manipulation aims to develop robots capable of understanding and executing complex tasks, with the objective of enabling robots to interpret language commands and manipulate objects accordingly. While language-conditioned approaches demonstrate impressive capabilities ... | ['Alois Knoll', 'Kai Huang', 'Chenguang Yang', 'Xiaojie Su', 'Xiangtong Yao', 'Zhenshan Bing', 'Hongkuan Zhou'] | 2023-05-30 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 2.91932106e-01 -2.43653953e-01 2.31041938e-01 -7.01371729e-02
-7.41270781e-01 -5.61572492e-01 7.60834336e-01 -9.09615681e-02
-9.89095330e-01 7.92335153e-01 -1.06967233e-01 -5.18486612e-02
-3.15352410e-01 -3.43744129e-01 -9.03259456e-01 -6.00527823e-01
-4.31959599e-01 8.27239931e-01 1.97477967e-01 -5.01106083... | [4.4876203536987305, 0.9633731842041016] |
e925763f-b213-40b6-8a9d-b447035a368b | race-bias-analysis-of-bona-fide-errors-in | 2210.05366 | null | https://arxiv.org/abs/2210.05366v1 | https://arxiv.org/pdf/2210.05366v1.pdf | Race Bias Analysis of Bona Fide Errors in face anti-spoofing | The study of bias in Machine Learning is receiving a lot of attention in recent years, however, few only papers deal explicitly with the problem of race bias in face anti-spoofing. In this paper, we present a systematic study of race bias in face anti-spoofing with three key characteristics: the focus is on analysing p... | ['Ioannis Ivrissimtzis', 'Latifah Abduh'] | 2022-10-11 | null | null | null | null | ['face-anti-spoofing'] | ['computer-vision'] | [ 4.50655431e-01 -8.95190164e-02 -1.71727434e-01 -5.98670840e-01
6.59729764e-02 -4.69196141e-01 9.67390835e-01 -2.26254649e-02
-4.00928706e-01 7.56839633e-01 1.29680291e-01 -3.74338895e-01
-3.11006069e-01 -5.86033404e-01 -4.17361885e-01 -1.18339705e+00
-2.25730374e-01 2.28975832e-01 -2.21821934e-01 -2.80508667... | [13.010695457458496, 1.2600202560424805] |
7fb1dd6f-b960-4a7e-9959-7bdbfeb405bc | cross-task-knowledge-transfer-for-query-based | null | null | https://aclanthology.org/D19-5810 | https://aclanthology.org/D19-5810.pdf | Cross-Task Knowledge Transfer for Query-Based Text Summarization | We demonstrate the viability of knowledge transfer between two related tasks: machine reading comprehension (MRC) and query-based text summarization. Using an MRC model trained on the SQuAD1.1 dataset as a core system component, we first build an extractive query-based summarizer. For better precision, this summarizer ... | ['Md. Arafat Sultan', 'Elozino Egonmwan', 'Vittorio Castelli'] | 2019-11-01 | null | null | null | ws-2019-11 | ['sentence-compression'] | ['natural-language-processing'] | [ 4.43876237e-01 5.32835007e-01 -3.71749490e-01 -4.40383136e-01
-1.63160264e+00 -5.43010175e-01 8.24994445e-01 6.31746948e-01
-6.32390082e-01 7.43984282e-01 1.18893349e+00 -5.01180887e-01
2.54828334e-01 -5.33715189e-01 -1.14265406e+00 1.22185215e-01
1.34239510e-01 4.67135668e-01 1.34057075e-01 -4.13316160... | [12.22586727142334, 9.276823043823242] |
a2474f3a-c654-4cc5-9ede-25638de15b7e | grim-a-general-real-time-deep-learning | 2108.11033 | null | https://arxiv.org/abs/2108.11033v1 | https://arxiv.org/pdf/2108.11033v1.pdf | GRIM: A General, Real-Time Deep Learning Inference Framework for Mobile Devices based on Fine-Grained Structured Weight Sparsity | It is appealing but challenging to achieve real-time deep neural network (DNN) inference on mobile devices because even the powerful modern mobile devices are considered as ``resource-constrained'' when executing large-scale DNNs. It necessitates the sparse model inference via weight pruning, i.e., DNN weight sparsity,... | ['Bin Ren', 'Yanzhi Wang', 'Xue Lin', 'Xuehai Qian', 'Gang Zhou', 'Peiyan Dong', 'Xiaolong Ma', 'Zhengang Li', 'Wei Niu'] | 2021-08-25 | null | null | null | null | ['compiler-optimization'] | ['computer-code'] | [ 1.90831020e-01 -1.77336901e-01 -5.27822733e-01 -4.92719889e-01
-2.40816176e-01 3.43283527e-02 1.28494725e-01 -4.02078331e-01
-5.45110941e-01 4.29824680e-01 1.59373045e-01 -9.69038606e-01
-1.31108895e-01 -1.04602945e+00 -9.12592292e-01 -3.74845147e-01
2.62288094e-01 3.69269431e-01 2.41256356e-02 -1.94789901... | [8.614895820617676, 3.1955068111419678] |
8fe906b8-c76c-44e3-af66-0e02db8bf67d | feature-adversarial-distillation-for-point | 2306.14221 | null | https://arxiv.org/abs/2306.14221v2 | https://arxiv.org/pdf/2306.14221v2.pdf | Feature Adversarial Distillation for Point Cloud Classification | Due to the point cloud's irregular and unordered geometry structure, conventional knowledge distillation technology lost a lot of information when directly used on point cloud tasks. In this paper, we propose Feature Adversarial Distillation (FAD) method, a generic adversarial loss function in point cloud distillation,... | ['Wei Wu', 'YuXing Lee'] | 2023-06-25 | null | null | null | null | ['point-cloud-classification', 'classification-1', 'model-compression', 'transfer-learning'] | ['computer-vision', 'methodology', 'methodology', 'miscellaneous'] | [ 2.08806872e-01 9.78505835e-02 3.04709617e-02 -1.96136102e-01
-7.04927623e-01 -8.62042248e-01 3.81271720e-01 2.27228060e-01
-4.54595715e-01 8.18252802e-01 -5.50174475e-01 -4.59568352e-01
-4.88955565e-02 -1.22986341e+00 -1.20070469e+00 -7.74558127e-01
-1.65780913e-02 6.39756620e-01 3.60889733e-01 -6.49839044... | [7.794238567352295, -4.36268424987793] |
639ebd02-85e7-41e4-ae03-90db4f7cb050 | make-it-3d-high-fidelity-3d-creation-from-a | 2303.14184 | null | https://arxiv.org/abs/2303.14184v2 | https://arxiv.org/pdf/2303.14184v2.pdf | Make-It-3D: High-Fidelity 3D Creation from A Single Image with Diffusion Prior | In this work, we investigate the problem of creating high-fidelity 3D content from only a single image. This is inherently challenging: it essentially involves estimating the underlying 3D geometry while simultaneously hallucinating unseen textures. To address this challenge, we leverage prior knowledge from a well-tra... | ['Dong Chen', 'Lizhuang Ma', 'Ran Yi', 'Ting Zhang', 'Bo Zhang', 'Tengfei Wang', 'Junshu Tang'] | 2023-03-24 | null | null | null | null | ['text-to-3d'] | ['computer-vision'] | [ 5.34852862e-01 1.51860788e-01 3.15869927e-01 -1.86079502e-01
-8.38880718e-01 -4.05693233e-01 7.40061164e-01 -2.89650708e-01
1.01695754e-01 4.12761837e-01 2.72480637e-01 -1.35373235e-01
2.37198889e-01 -8.13322067e-01 -9.49453115e-01 -6.48142338e-01
3.29354346e-01 5.46094596e-01 1.64233938e-01 -1.93316624... | [9.277917861938477, -3.1303722858428955] |
a45753c5-2f83-4ae6-83e8-100d226dcda8 | a-survey-on-machine-learning-techniques-for-1 | 2110.0961 | null | https://arxiv.org/abs/2110.09610v2 | https://arxiv.org/pdf/2110.09610v2.pdf | A Survey on Machine Learning Techniques for Source Code Analysis | The advancements in machine learning techniques have encouraged researchers to apply these techniques to a myriad of software engineering tasks that use source code analysis, such as testing and vulnerability detection. Such a large number of studies hinders the community from understanding the current research landsca... | ['Federica Sarro', 'Hadi Moazen', 'Indira Vats', 'Rohit Tiwari', 'Stefanos Georgiou', 'Maria Kechagia', 'Tushar Sharma'] | 2021-10-18 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [ 1.06858097e-01 -2.70385325e-01 -8.46378446e-01 -1.56590372e-01
-6.78162038e-01 -7.87097037e-01 5.09161651e-02 4.40646082e-01
-7.16269761e-03 2.27030322e-01 -2.45755985e-01 -9.60173309e-01
-2.39944562e-01 -4.32872236e-01 -6.12246871e-01 -1.49722159e-01
-1.15902685e-01 -2.96548814e-01 -2.45813299e-02 1.39050975... | [7.343923568725586, 7.7478861808776855] |
3e303a4a-c31c-42bd-b57e-96a4328728fd | learning-temporal-consistency-for-source-free | 2203.04559 | null | https://arxiv.org/abs/2203.04559v4 | https://arxiv.org/pdf/2203.04559v4.pdf | Source-free Video Domain Adaptation by Learning Temporal Consistency for Action Recognition | Video-based Unsupervised Domain Adaptation (VUDA) methods improve the robustness of video models, enabling them to be applied to action recognition tasks across different environments. However, these methods require constant access to source data during the adaptation process. Yet in many real-world applications, subje... | ['Zhenghua Chen', 'Wu Min', 'Keyu Wu', 'Haozhi Cao', 'Jianfei Yang', 'Yuecong Xu'] | 2022-03-09 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 3.80830169e-01 -3.88692170e-01 -5.73729336e-01 -4.46203411e-01
-5.45462310e-01 -3.27734411e-01 5.78254163e-01 -1.46037787e-01
-5.07317007e-01 7.98788786e-01 2.92090416e-01 2.22725227e-01
-2.97618240e-01 -4.00605679e-01 -6.29923105e-01 -7.72496879e-01
-1.72752246e-01 1.60387587e-02 4.67002839e-01 4.44067493... | [8.669146537780762, 0.8459590077400208] |
ce2073b9-80b8-4e3f-8e19-cd1cf24e5ced | does-synthetic-data-generation-of-llms-help | 2303.0436 | null | https://arxiv.org/abs/2303.04360v2 | https://arxiv.org/pdf/2303.04360v2.pdf | Does Synthetic Data Generation of LLMs Help Clinical Text Mining? | Recent advancements in large language models (LLMs) have led to the development of highly potent models like OpenAI's ChatGPT. These models have exhibited exceptional performance in a variety of tasks, such as question answering, essay composition, and code generation. However, their effectiveness in the healthcare sec... | ['Xia Hu', 'Xiaoqian Jiang', 'Xiaotian Han', 'Ruixiang Tang'] | 2023-03-08 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 2.34431013e-01 7.65071869e-01 5.35694249e-02 -4.48195487e-01
-1.17565954e+00 -2.44702876e-01 3.15208733e-01 5.44142008e-01
-6.20135367e-01 1.13847196e+00 6.21063896e-02 -6.21226311e-01
9.71221253e-02 -7.12064385e-01 -5.02667129e-01 -4.54688221e-01
1.20817930e-01 5.47929049e-01 -1.90530032e-01 7.13061094... | [8.448080062866211, 8.673523902893066] |
795d3392-987b-4823-b79b-b7b65d57e933 | deceptive-opinion-spam-detection-using-neural | null | null | https://aclanthology.org/C16-1014 | https://aclanthology.org/C16-1014.pdf | Deceptive Opinion Spam Detection Using Neural Network | Deceptive opinion spam detection has attracted significant attention from both business and research communities. Existing approaches are based on manual discrete features, which can capture linguistic and psychological cues. However, such features fail to encode the semantic meaning of a document from the discourse pe... | ['Yue Zhang', 'Yafeng Ren'] | 2016-12-01 | deceptive-opinion-spam-detection-using-neural-1 | https://aclanthology.org/C16-1014 | https://aclanthology.org/C16-1014.pdf | coling-2016-12 | ['spam-detection'] | ['natural-language-processing'] | [ 2.88278610e-01 6.33858051e-03 1.67413782e-02 -6.42473042e-01
-3.00771594e-01 -3.12730402e-01 7.27455735e-01 2.78377056e-01
-1.06118575e-01 5.07324278e-01 3.87345046e-01 -3.74467701e-01
4.28894997e-01 -7.10422337e-01 -3.22684765e-01 -6.72348201e-01
3.90459687e-01 -1.65840745e-01 1.08839989e-01 -4.03564185... | [7.8923659324646, 10.012715339660645] |
95cda3f3-f6cb-4543-93d8-55ba791726b2 | liver-segmentation-using-turbolift-learning | 2207.10167 | null | https://arxiv.org/abs/2207.10167v2 | https://arxiv.org/pdf/2207.10167v2.pdf | Liver Segmentation using Turbolift Learning for CT and Cone-beam C-arm Perfusion Imaging | Model-based reconstruction employing the time separation technique (TST) was found to improve dynamic perfusion imaging of the liver using C-arm cone-beam computed tomography (CBCT). To apply TST using prior knowledge extracted from CT perfusion data, the liver should be accurately segmented from the CT scans. Reconstr... | ['Georg Rose', 'Andreas Nürnberger', 'Oliver Speck', 'Thomas Werncke', 'Inga Brüsch', 'Frank Wacker', 'Bennet Hensen', 'Vladimir Semshchikov', 'Vojtěch Kulvait', 'Robert Frysch', 'Soumick Chatterjee', 'Hana Haseljić'] | 2022-07-20 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [-3.24944146e-02 -5.99561036e-02 9.22785252e-02 -2.02402800e-01
-6.05879009e-01 -4.98932570e-01 4.45749700e-01 2.41818726e-01
-5.46784163e-01 5.68260074e-01 3.01388502e-01 -7.31480896e-01
-3.43132138e-01 -3.80704254e-01 -2.85702735e-01 -1.09920359e+00
-6.25532150e-01 6.69257045e-01 1.96030289e-01 3.00929159... | [14.477995872497559, -2.706308126449585] |
a09bd47b-3577-4c37-b034-9b949dd57ed7 | nested-invariance-pooling-and-rbm-hashing-for | 1603.04595 | null | http://arxiv.org/abs/1603.04595v2 | http://arxiv.org/pdf/1603.04595v2.pdf | Nested Invariance Pooling and RBM Hashing for Image Instance Retrieval | The goal of this work is the computation of very compact binary hashes for
image instance retrieval. Our approach has two novel contributions. The first
one is Nested Invariance Pooling (NIP), a method inspired from i-theory, a
mathematical theory for computing group invariant transformations with
feed-forward neural n... | ['Tomaso Poggio', 'Olivier Morère', 'Antoine Veillard', 'Jie Lin', 'Vijay Chandrasekhar'] | 2016-03-15 | null | null | null | null | ['image-instance-retrieval'] | ['computer-vision'] | [ 2.08142117e-01 -1.37536436e-01 -3.22605699e-01 -3.81715655e-01
-9.71929193e-01 -3.94752115e-01 9.07155573e-01 2.90002048e-01
-7.67415464e-01 2.39511415e-01 3.05214524e-01 1.02668174e-01
-2.18691021e-01 -8.81620944e-01 -9.34263170e-01 -9.74280536e-01
-6.75300241e-01 4.53119725e-01 3.76493067e-01 -2.96351343... | [10.640789985656738, 0.5181881189346313] |
0e4164ab-2447-4058-8298-4919d01385e7 | understanding-and-leveraging | null | null | https://openreview.net/forum?id=shbAgEsk3qM | https://openreview.net/pdf?id=shbAgEsk3qM | Understanding and Leveraging Overparameterization in Recursive Value Estimation | The theory of function approximation in reinforcement learning (RL) typically considers low capacity representations that incur a tradeoff between approximation error, stability and generalization. Current deep architectures, however, operate in an overparameterized regime where approximation error is not necessarily a... | ['Dale Schuurmans', 'Chris Harris', 'Ramki Gummadi', 'Oscar A Ramirez', 'Jincheng Mei', 'Bo Dai', 'Chenjun Xiao'] | 2021-09-29 | null | null | null | iclr-2022-4 | ['value-prediction'] | ['computer-code'] | [-4.18160185e-02 3.09286147e-01 -3.56997639e-01 -3.11531901e-01
-8.64458859e-01 -7.45051444e-01 3.30897212e-01 1.02863424e-01
-7.24423110e-01 1.00081348e+00 1.07996553e-01 -2.30322465e-01
-5.50291657e-01 -5.83391428e-01 -7.89723039e-01 -7.16742933e-01
-4.00303900e-01 2.02727214e-01 -1.17631510e-01 -2.79876709... | [4.24713659286499, 2.286999464035034] |
fdd629e6-5641-4217-9ff3-cb4bc0bf464a | challenges-in-generalization-in-open-domain-1 | null | null | https://openreview.net/forum?id=l6Pj9MziA0 | https://openreview.net/pdf?id=l6Pj9MziA0 | Challenges in Generalization in Open Domain Question Answering | Recent work on Open Domain Question Answering has shown that there is a large discrepancy in model performance between novel test questions and those that largely overlap with training questions. However, it is as of yet unclear which aspects of novel questions that make them challenging. Drawing upon studies on system... | ['Anonymous'] | 2021-10-16 | null | null | null | acl-arr-october-2021-10 | ['triviaqa', 'systematic-generalization'] | ['miscellaneous', 'reasoning'] | [-7.08683059e-02 2.51641124e-01 2.37079933e-01 -4.10116524e-01
-1.37477589e+00 -1.16755891e+00 6.17921889e-01 3.65216464e-01
-5.04865766e-01 8.46234381e-01 3.77634943e-01 -5.29046237e-01
-6.16815448e-01 -5.70681751e-01 -6.71454608e-01 -7.68190026e-02
3.15683931e-01 7.77912855e-01 5.30252635e-01 -6.05465949... | [11.182991027832031, 7.939867973327637] |
3fdf6838-1c25-4e55-bc6b-6c440ca941e0 | hierarchical-attention-based-age-estimation | 2103.09882 | null | https://arxiv.org/abs/2103.09882v1 | https://arxiv.org/pdf/2103.09882v1.pdf | Hierarchical Attention-based Age Estimation and Bias Estimation | In this work we propose a novel deep-learning approach for age estimation based on face images. We first introduce a dual image augmentation-aggregation approach based on attention. This allows the network to jointly utilize multiple face image augmentations whose embeddings are aggregated by a Transformer-Encoder. The... | ['Yosi Keller', 'Shakediel Hiba'] | 2021-03-17 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [ 7.00713396e-02 4.56753969e-01 -1.85410589e-01 -9.89198744e-01
-8.05583715e-01 2.58985668e-01 8.42626095e-01 1.82704106e-01
-4.53654438e-01 6.29481137e-01 4.50209230e-01 2.69835174e-01
-2.09631724e-03 -6.42958403e-01 -5.69622695e-01 -7.67151952e-01
-2.59713471e-01 7.03878224e-01 -6.68654561e-01 1.63214132... | [13.513839721679688, 0.8347854018211365] |
c9d5fc4a-1144-4373-8dd5-ab7dec57dd50 | from-intrinsic-to-counterfactual-on-the | 2110.14844 | null | https://arxiv.org/abs/2110.14844v1 | https://arxiv.org/pdf/2110.14844v1.pdf | From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systems | With the prevalence of deep learning based embedding approaches, recommender systems have become a proven and indispensable tool in various information filtering applications. However, many of them remain difficult to diagnose what aspects of the deep models' input drive the final ranking decision, thus, they cannot of... | ['Haixun Wang', 'Jingrui He', 'Haonan Wang', 'Yao Zhou'] | 2021-10-28 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [-1.63838983e-01 1.70785815e-01 -2.33298600e-01 -4.94646609e-01
-1.82683882e-03 -6.84137166e-01 7.13441849e-01 -3.83778997e-02
1.34469643e-01 6.25918448e-01 6.76372230e-01 -5.25160551e-01
-4.43553060e-01 -6.19562447e-01 -5.57895064e-01 -4.24653322e-01
1.78804040e-01 2.83167332e-01 -3.81459087e-01 -4.53543663... | [9.667428016662598, 5.699002742767334] |
8a2579d9-6961-453e-9991-80071c9f4cbd | a-bio-inspired-implementation-of-a-sparse | 2206.04924 | null | https://arxiv.org/abs/2206.04924v1 | https://arxiv.org/pdf/2206.04924v1.pdf | A bio-inspired implementation of a sparse-learning spike-based hippocampus memory model | The nervous system, more specifically, the brain, is capable of solving complex problems simply and efficiently, far surpassing modern computers. In this regard, neuromorphic engineering is a research field that focuses on mimicking the basic principles that govern the brain in order to develop systems that achieve suc... | ['Gabriel Jimenez-Moreno', 'Angel Jimenez-Fernandez', 'Juan P. Dominguez-Morales', 'Alvaro Ayuso-Martinez', 'Daniel Casanueva-Morato'] | 2022-06-10 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [ 1.68257773e-01 -1.09472461e-01 3.39868277e-01 6.30047694e-02
4.60241199e-01 -3.43240529e-01 4.66252446e-01 2.26069734e-01
-5.55409908e-01 1.03231430e+00 -4.03690666e-01 1.98757589e-01
7.00684339e-02 -1.28508151e+00 -9.66449857e-01 -1.10361671e+00
-2.76682466e-01 1.88906595e-01 8.74148369e-01 -3.88912320... | [8.170656204223633, 2.538141965866089] |
82eda09c-7947-48f6-828c-6d9ef0d01c58 | universal-sketch-perceptual-grouping | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Ke_LI_Universal_Sketch_Perceptual_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Ke_LI_Universal_Sketch_Perceptual_ECCV_2018_paper.pdf | Universal Sketch Perceptual Grouping | In this work we aim to develop a universal sketch grouper. That is, a grouper that can be applied to sketches of any category in any domain to group constituent strokes/segments into semantically meaningful object parts. The first obstacle to this goal is the lack of large-scale datasets with grouping annotation. To... | ['Tao Xiang', 'Yi-Zhe Song', 'Ke Li', 'Kaiyue Pang', 'Jifei Song', 'Timothy M. Hospedales', 'Honggang Zhang'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 1.16790392e-01 -2.01916948e-01 -2.35702768e-01 -4.09814596e-01
-7.38641739e-01 -7.84901083e-01 8.40518534e-01 -5.26807047e-02
1.00251278e-02 2.39909843e-01 4.58895527e-02 2.40152091e-01
-1.67322636e-01 -8.62994611e-01 -6.22101247e-01 -4.36733961e-01
1.68887451e-01 5.50393999e-01 4.13673431e-01 -6.93332106... | [11.67895221710205, 0.4986889958381653] |
50b83f12-4541-4576-b441-eeddf606fba9 | efficiently-mitigating-classification-bias | 2010.12864 | null | https://arxiv.org/abs/2010.12864v2 | https://arxiv.org/pdf/2010.12864v2.pdf | On Transferability of Bias Mitigation Effects in Language Model Fine-Tuning | Fine-tuned language models have been shown to exhibit biases against protected groups in a host of modeling tasks such as text classification and coreference resolution. Previous works focus on detecting these biases, reducing bias in data representations, and using auxiliary training objectives to mitigate bias during... | ['Brendan Kennedy', 'Xiang Ren', 'Leonardo Neves', 'Aida Mostafazadeh Davani', 'Francesco Barbieri', 'Xisen Jin'] | 2020-10-24 | null | https://aclanthology.org/2021.naacl-main.296 | https://aclanthology.org/2021.naacl-main.296.pdf | naacl-2021-4 | ['occupation-prediction'] | ['natural-language-processing'] | [ 4.57774609e-01 3.59585553e-01 -5.15964568e-01 -7.99936831e-01
-7.23220110e-01 -7.96649098e-01 7.29649901e-01 2.96603531e-01
-7.04661548e-01 9.54577386e-01 9.06449020e-01 -4.28762078e-01
-2.04231739e-01 -5.66663921e-01 -4.73356009e-01 -4.45343763e-01
2.18913645e-01 5.19052327e-01 -1.62678584e-02 -4.18632120... | [9.32372760772705, 10.117395401000977] |
876f5aa1-8328-46c2-9e1c-644489e44174 | one-trimap-video-matting | 2207.13353 | null | https://arxiv.org/abs/2207.13353v1 | https://arxiv.org/pdf/2207.13353v1.pdf | One-Trimap Video Matting | Recent studies made great progress in video matting by extending the success of trimap-based image matting to the video domain. In this paper, we push this task toward a more practical setting and propose One-Trimap Video Matting network (OTVM) that performs video matting robustly using only one user-annotated trimap. ... | ['Joon-Young Lee', 'Euntai Kim', 'Brian Price', 'Seoung Wug Oh', 'Hongje Seong'] | 2022-07-27 | null | null | null | null | ['image-matting', 'video-matting'] | ['computer-vision', 'computer-vision'] | [ 1.34903416e-01 5.52566163e-02 -4.70918536e-01 -1.52352527e-01
-8.28987598e-01 -3.21266323e-01 4.15844202e-01 -3.98152560e-01
-2.35188380e-01 3.92575353e-01 3.23193938e-01 -3.97653699e-01
4.70945567e-01 -4.60097939e-01 -1.26698136e+00 -4.85563815e-01
1.00922786e-01 3.23131919e-01 7.34174103e-02 -3.30539942... | [10.618520736694336, -0.8170952796936035] |
0b22a53a-45c5-4000-b6d4-af9ef84b92ff | explain-your-move-understanding-agent-actions-1 | 1912.12191 | null | https://arxiv.org/abs/1912.12191v4 | https://arxiv.org/pdf/1912.12191v4.pdf | Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature Attribution | As deep reinforcement learning (RL) is applied to more tasks, there is a need to visualize and understand the behavior of learned agents. Saliency maps explain agent behavior by highlighting the features of the input state that are most relevant for the agent in taking an action. Existing perturbation-based approaches ... | ['Sukriti Verma', 'Sameer Singh', 'Nikaash Puri', 'Piyush Gupta', 'Balaji Krishnamurthy', 'Shripad Deshmukh', 'Dhruv Kayastha'] | 2019-12-23 | null | null | null | null | ['board-games'] | ['playing-games'] | [ 5.84654436e-02 3.20814937e-01 -3.09531461e-03 1.12630920e-02
-1.14679456e-01 -5.28028488e-01 7.22134352e-01 3.66818994e-01
-4.98525620e-01 1.06609106e+00 3.39155346e-01 -1.62868783e-01
-4.23610389e-01 -4.84008014e-01 -7.55724430e-01 -6.99400485e-01
-3.73669267e-01 2.73517132e-01 4.08382088e-01 -7.63704836... | [4.020020008087158, 1.608374834060669] |
aac2ede0-5907-48c2-ba99-4419bc3885e2 | image-super-resolution-by-neural-texture | 1903.00834 | null | http://arxiv.org/abs/1903.00834v2 | http://arxiv.org/pdf/1903.00834v2.pdf | Image Super-Resolution by Neural Texture Transfer | Due to the significant information loss in low-resolution (LR) images, it has
become extremely challenging to further advance the state-of-the-art of single
image super-resolution (SISR). Reference-based super-resolution (RefSR), on the
other hand, has proven to be promising in recovering high-resolution (HR)
details w... | ['Zhifei Zhang', 'Zhaowen Wang', 'Zhe Lin', 'Hairong Qi'] | 2019-03-03 | image-super-resolution-by-neural-texture-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Zhang_Image_Super-Resolution_by_Neural_Texture_Transfer_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_Image_Super-Resolution_by_Neural_Texture_Transfer_CVPR_2019_paper.pdf | cvpr-2019-6 | ['image-stylization', 'reference-based-super-resolution'] | ['computer-vision', 'computer-vision'] | [ 7.59247780e-01 -2.15170756e-02 -3.96579355e-02 -2.56936044e-01
-1.26515007e+00 -1.72774494e-01 4.26965564e-01 -5.30001044e-01
-2.31213301e-01 8.02976310e-01 4.23635066e-01 2.83215493e-01
-1.67362913e-01 -9.97450233e-01 -8.59564841e-01 -8.11571479e-01
4.05763745e-01 -2.09707692e-02 4.39077675e-01 -6.77241147... | [10.96084213256836, -2.080724000930786] |
1f31d27e-0ca5-4042-814b-3360bc54a1bf | transformation-of-node-to-knowledge-graph | 2111.09308 | null | https://arxiv.org/abs/2111.09308v1 | https://arxiv.org/pdf/2111.09308v1.pdf | Transformation of Node to Knowledge Graph Embeddings for Faster Link Prediction in Social Networks | Recent advances in neural networks have solved common graph problems such as link prediction, node classification, node clustering, node recommendation by developing embeddings of entities and relations into vector spaces. Graph embeddings encode the structural information present in a graph. The encoded embeddings the... | ['Minwoo Lee', 'Anant Kumar Mishra', 'Mayuri Deshpande', 'Archit Parnami'] | 2021-11-17 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-1.74611464e-01 5.49750209e-01 -4.23951089e-01 -1.56118274e-01
-2.34178212e-02 -4.42973137e-01 4.02096719e-01 7.10229099e-01
-3.27313185e-01 5.57736337e-01 -1.93492994e-02 -4.60488737e-01
-4.65027869e-01 -1.39300025e+00 -4.76881891e-01 -3.63579482e-01
-5.09274423e-01 4.23900008e-01 3.82104665e-01 -3.97666357... | [7.221368312835693, 6.249124050140381] |
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