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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ef122c07-c6c3-46f2-9d97-5c7cdc399de8 | exploring-the-behavior-of-classic-reg | null | null | https://aclanthology.org/W17-3507 | https://aclanthology.org/W17-3507.pdf | Exploring the Behavior of Classic REG Algorithms in the Description of Characters in 3D Images | Describing people and characters can be very useful in different contexts, such as computational narrative or image description for the visually impaired. However, a review of the existing literature shows that the automatic generation of people descriptions has not received much attention. Our work focuses on the desc... | ["Teresa Rodr{\\'\\i}guez", "Adri{\\'a}n Rabad{\\'a}n", "Raquel Herv{\\'a}s", "Gonzalo M{\\'e}ndez", 'Susana Bautista'] | 2017-09-01 | null | null | null | ws-2017-9 | ['referring-expression-generation'] | ['computer-vision'] | [-1.55843616e-01 -1.10438310e-01 1.26143977e-01 -4.34454352e-01
-1.58589706e-01 -4.89648730e-01 9.69947219e-01 2.38994777e-01
-3.57999295e-01 7.46495128e-01 8.06406915e-01 2.02694148e-01
2.10141484e-02 -4.92127120e-01 1.88311949e-01 -3.98295641e-01
3.70771915e-01 8.36516976e-01 3.65490615e-01 -3.76502007... | [10.82009506225586, 0.8035796880722046] |
4b571e61-bbf3-45d6-a606-d85f3d6ecb48 | the-many-moods-of-emotion | 1810.13197 | null | http://arxiv.org/abs/1810.13197v1 | http://arxiv.org/pdf/1810.13197v1.pdf | The Many Moods of Emotion | This paper presents a novel approach to the facial expression generation
problem. Building upon the assumption of the psychological community that
emotion is intrinsically continuous, we first design our own continuous emotion
representation with a 3-dimensional latent space issued from a neural network
trained on disc... | ['Frédéric Jurie', 'Stéphane Pateux', 'Valentin Vielzeuf', 'Corentin Kervadec'] | 2018-10-31 | null | null | null | null | ['facial-expression-generation'] | ['computer-vision'] | [ 4.82280105e-01 6.59452498e-01 2.88745552e-01 -6.40014470e-01
-4.13363457e-01 -7.02400804e-01 6.55727565e-01 -3.24492723e-01
-9.00542066e-02 1.03819132e+00 1.01494804e-01 1.34307638e-01
1.48274630e-01 -8.66137981e-01 -6.90976560e-01 -8.27323496e-01
-1.52140275e-01 2.13451520e-01 -3.91879529e-01 -5.22978842... | [13.405956268310547, 1.6431485414505005] |
53123205-7954-4399-a685-e968e18c2f98 | deep-convolutional-encoder-decoders-with | 1901.09197 | null | http://arxiv.org/abs/1901.09197v2 | http://arxiv.org/pdf/1901.09197v2.pdf | Deep Convolutional Encoder-Decoders with Aggregated Multi-Resolution Skip Connections for Skin Lesion Segmentation | The prevalence of skin melanoma is rapidly increasing as well as the recorded
death cases of its patients. Automatic image segmentation tools play an
important role in providing standardized computer-assisted analysis for skin
melanoma patients. Current state-of-the-art segmentation methods are based on
fully convoluti... | ['Karim Amer', 'Ahmed H. Shahin', 'Mustafa A. Elattar'] | 2019-01-26 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 8.31747174e-01 2.35124156e-01 -1.57228276e-01 -1.38506532e-01
-1.01689625e+00 -4.23215419e-01 3.36372823e-01 4.38582987e-01
-8.37445319e-01 6.11220062e-01 1.15229882e-01 -3.81262302e-01
4.94308136e-02 -7.72157490e-01 -3.88586402e-01 -7.57632434e-01
2.27006748e-01 1.06213903e-02 4.81602341e-01 -5.23838326... | [15.619219779968262, -2.9417061805725098] |
d55d33cb-0868-4370-9eb8-2320aa5557a7 | rapping-singing-voice-synthesis-based-on | 2111.09146 | null | https://arxiv.org/abs/2111.09146v1 | https://arxiv.org/pdf/2111.09146v1.pdf | Rapping-Singing Voice Synthesis based on Phoneme-level Prosody Control | In this paper, a text-to-rapping/singing system is introduced, which can be adapted to any speaker's voice. It utilizes a Tacotron-based multispeaker acoustic model trained on read-only speech data and which provides prosody control at the phoneme level. Dataset augmentation and additional prosody manipulation based on... | ['Aimilios Chalamandaris', 'Pirros Tsiakoulis', 'Hyoungmin Park', 'June Sig Sung', 'Georgia Maniati', 'Georgios Vamvoukakis', 'Panos Kakoulidis', 'Myrsini Christidou', 'Alexandra Vioni', 'Nikolaos Ellinas', 'Konstantinos Markopoulos'] | 2021-11-17 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [ 3.35428804e-01 1.40333503e-01 2.38162935e-01 -1.24583311e-01
-1.03977513e+00 -8.12271476e-01 1.81679100e-01 -2.20516160e-01
-1.46768197e-01 4.31059211e-01 3.04452121e-01 -3.24537568e-02
9.69032720e-02 -2.59713858e-01 -3.30558330e-01 -8.18699777e-01
2.99113423e-01 3.76072168e-01 1.92398801e-01 -4.18871790... | [15.431940078735352, 6.209712028503418] |
2deb479a-a461-455a-b555-938545277922 | weakly-supervised-text-to-sql-parsing-through | null | null | https://openreview.net/forum?id=T4mIFZTlEF | https://openreview.net/pdf?id=T4mIFZTlEF | Weakly Supervised Text-to-SQL Parsing through Question Decomposition | Text-to-SQL parsers are crucial in enabling non-experts to effortlessly query relational data. Training such parsers, by contrast, generally requires expert annotation of natural language (NL) utterances paired with corresponding SQL queries.In this work, we propose a weak supervision approach for training text-to-SQL ... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['text-to-sql'] | ['computer-code'] | [ 1.26888052e-01 7.59469509e-01 -1.30014747e-01 -9.42041457e-01
-1.44639814e+00 -7.60807753e-01 4.85380530e-01 4.20253068e-01
-2.75677025e-01 3.52003664e-01 2.89063305e-01 -8.08591664e-01
2.03116626e-01 -1.13615537e+00 -1.03115940e+00 4.07026321e-01
4.31747347e-01 9.99251544e-01 6.66423678e-01 -3.96591276... | [10.017335891723633, 7.848401069641113] |
61172307-e941-481f-bdff-7c2483f3349f | multi-layer-content-interaction-through | 2001.05840 | null | https://arxiv.org/abs/2001.05840v2 | https://arxiv.org/pdf/2001.05840v2.pdf | Multi-Layer Content Interaction Through Quaternion Product For Visual Question Answering | Multi-modality fusion technologies have greatly improved the performance of neural network-based Video Description/Caption, Visual Question Answering (VQA) and Audio Visual Scene-aware Dialog (AVSD) over the recent years. Most previous approaches only explore the last layers of multiple layer feature fusion while omitt... | ['Peng Gao', 'Songxiang Liu', 'Shijie Geng', 'Lei Shi', 'Sen Su', 'Kai Shuang', 'Chiori Hori'] | 2020-01-03 | null | null | null | null | ['video-description'] | ['computer-vision'] | [-3.07751894e-01 -1.25900283e-01 1.88866481e-01 -3.75258148e-01
-5.27498841e-01 -3.81090581e-01 6.85394049e-01 4.31718007e-02
-6.40846312e-01 5.86422145e-01 4.06087995e-01 -5.68670919e-03
2.76795357e-01 -3.86921704e-01 -5.74798167e-01 -4.28592950e-01
1.33006200e-01 2.31204614e-01 3.90576273e-01 -6.03308737... | [10.579328536987305, 1.1653881072998047] |
d61a235c-8887-4ea9-860f-16aca9618dab | protein-sequence-design-with-batch-bayesian | 2303.10429 | null | https://arxiv.org/abs/2303.10429v1 | https://arxiv.org/pdf/2303.10429v1.pdf | Protein Sequence Design with Batch Bayesian Optimisation | Protein sequence design is a challenging problem in protein engineering, which aims to discover novel proteins with useful biological functions. Directed evolution is a widely-used approach for protein sequence design, which mimics the evolution cycle in a laboratory environment and conducts an iterative protocol. Howe... | ['Chuanjiao Zong'] | 2023-03-18 | null | null | null | null | ['protein-design', 'bayesian-optimisation'] | ['medical', 'methodology'] | [ 6.47000670e-01 -2.98233837e-01 6.93211183e-02 -2.74876922e-01
-5.39828241e-01 -6.34160221e-01 2.21600741e-01 2.09492326e-01
-5.37273288e-01 1.10167086e+00 -1.89678609e-01 -6.96315169e-01
6.37322068e-02 -4.06316191e-01 -9.23456192e-01 -8.81222069e-01
1.56594679e-01 7.11057067e-01 2.11432800e-01 -2.44514093... | [4.7366623878479, 5.560551166534424] |
1fef0604-ed84-4823-8190-fd318c4da128 | on-the-ideal-number-of-groups-for-isometric | 2302.03193 | null | https://arxiv.org/abs/2302.03193v1 | https://arxiv.org/pdf/2302.03193v1.pdf | On the Ideal Number of Groups for Isometric Gradient Propagation | Recently, various normalization layers have been proposed to stabilize the training of deep neural networks. Among them, group normalization is a generalization of layer normalization and instance normalization by allowing a degree of freedom in the number of groups it uses. However, to determine the optimal number of ... | ['Sang Woo Kim', 'Hyeonah Jang', 'Hyeyeon Choi', 'Bum Jun Kim'] | 2023-02-07 | null | null | null | null | ['panoptic-segmentation'] | ['computer-vision'] | [-5.58705628e-02 -2.71000445e-01 -3.78556997e-01 -7.68777013e-01
1.90116554e-01 -3.17275375e-01 3.87743175e-01 5.53062856e-02
-8.13149512e-01 4.74105060e-01 -3.68178375e-02 -3.87998402e-01
-1.57126591e-01 -8.04233015e-01 -4.63913262e-01 -9.41581368e-01
5.84523827e-02 -1.56982616e-01 3.23472500e-01 -3.39942604... | [8.385810852050781, 3.4366824626922607] |
e83d27c6-55b8-4ed6-98be-dcc91cc3cc58 | country-level-arabic-dialect-identification-1 | null | null | https://aclanthology.org/2021.wanlp-1.32 | https://aclanthology.org/2021.wanlp-1.32.pdf | Country-level Arabic Dialect Identification using RNNs with and without Linguistic Features | This work investigates the value of augmenting recurrent neural networks with feature engineering for the Second Nuanced Arabic Dialect Identification (NADI) Subtask 1.2: Country-level DA identification. We compare the performance of a simple word-level LSTM using pretrained embeddings with one enhanced using feature e... | ['Gus Hahn-Powell', 'Reda Al-Bahrani', 'Mohammed AlShakhori1', 'Elsayed Issa'] | null | null | null | null | eacl-wanlp-2021-4 | ['dialect-identification'] | ['natural-language-processing'] | [-2.31874853e-01 -1.94621123e-02 9.41337347e-02 -4.67058331e-01
-4.46987987e-01 -5.98516524e-01 8.96598458e-01 -6.80155605e-02
-8.19542408e-01 6.13954008e-01 6.94619536e-01 -6.07185721e-01
6.01722812e-03 -5.65167129e-01 -1.70957386e-01 -3.25493544e-01
-3.27284008e-01 4.66919184e-01 -3.41670126e-01 -8.40423584... | [10.27408218383789, 10.563895225524902] |
48d4e356-dd9f-46de-a212-4440725d55c1 | linguistically-informed-relation-extraction | 1910.03385 | null | https://arxiv.org/abs/1910.03385v1 | https://arxiv.org/pdf/1910.03385v1.pdf | Linguistically Informed Relation Extraction and Neural Architectures for Nested Named Entity Recognition in BioNLP-OST 2019 | Named Entity Recognition (NER) and Relation Extraction (RE) are essential tools in distilling knowledge from biomedical literature. This paper presents our findings from participating in BioNLP Shared Tasks 2019. We addressed Named Entity Recognition including nested entities extraction, Entity Normalization and Relati... | ['Hinrich Schütze', 'Usama Yaseen', 'Pankaj Gupta'] | 2019-10-08 | linguistically-informed-relation-extraction-1 | https://aclanthology.org/D19-5720 | https://aclanthology.org/D19-5720.pdf | ws-2019-11 | ['binary-relation-extraction', 'nested-named-entity-recognition'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.01300649e-01 3.88240844e-01 -1.21654212e-01 -4.22208756e-01
-1.03288209e+00 -6.02218688e-01 3.98873210e-01 1.01118350e+00
-1.11845052e+00 1.61613667e+00 3.34584981e-01 -1.99360490e-01
-1.46216139e-01 -6.47784948e-01 -5.23654759e-01 -3.12121987e-01
-2.50736058e-01 6.61041975e-01 4.02445421e-02 -1.72121122... | [8.490625381469727, 8.757216453552246] |
347be2dc-4dda-4623-9420-c849f60a8f96 | multi-platform-version-of-starcraft-brood-war | 1801.02193 | null | http://arxiv.org/abs/1801.02193v1 | http://arxiv.org/pdf/1801.02193v1.pdf | Multi-platform Version of StarCraft: Brood War in a Docker Container: Technical Report | We present a dockerized version of a real-time strategy game StarCraft: Brood
War, commonly used as a domain for AI research, with a pre-installed collection
of AI developement tools supporting all the major types of StarCraft bots. This
provides a convenient way to deploy StarCraft AIs on numerous hosts at once and
ac... | ['Michal Čertický', 'Jan Malý', 'Michal Šustr'] | 2018-01-07 | null | null | null | null | ['real-time-strategy-games'] | ['playing-games'] | [-6.79742396e-01 -4.05134916e-01 -3.02987665e-01 3.13444108e-01
3.10771555e-01 -1.31337512e+00 7.11341083e-01 -3.26499045e-01
-6.54021740e-01 7.51966000e-01 -2.94107914e-01 -6.15248263e-01
-5.56465127e-02 -5.47144771e-01 -1.30040616e-01 -2.51235992e-01
-4.28773403e-01 1.00312555e+00 1.03458452e+00 -1.23108065... | [3.5756454467773438, 1.4515228271484375] |
149006f0-172f-466a-a6a3-9f26680ff2e9 | frame-level-speaker-embeddings-for-text | 1809.04437 | null | http://arxiv.org/abs/1809.04437v1 | http://arxiv.org/pdf/1809.04437v1.pdf | Frame-level speaker embeddings for text-independent speaker recognition and analysis of end-to-end model | In this paper, we propose a Convolutional Neural Network (CNN) based speaker
recognition model for extracting robust speaker embeddings. The embedding can
be extracted efficiently with linear activation in the embedding layer. To
understand how the speaker recognition model operates with text-independent
input, we modi... | ['James Glass', 'Suwon Shon', 'Hao Tang'] | 2018-09-12 | null | null | null | null | ['text-independent-speaker-recognition'] | ['speech'] | [ 1.37248844e-01 1.12136476e-01 -6.65718466e-02 -8.47091496e-01
-6.97137356e-01 -7.20216215e-01 4.71707910e-01 -1.43957153e-01
-3.99915993e-01 -5.23121320e-02 6.77536428e-01 -5.54352999e-01
2.03027233e-01 -4.43121791e-01 -4.19646233e-01 -6.35279119e-01
-1.96463391e-01 -8.98559403e-04 -2.57365823e-01 1.15383312... | [14.370903015136719, 6.198997497558594] |
aed5ef90-1ccb-4d16-891c-725d03c3ed38 | tweettaglish-a-dataset-for-investigating | null | null | https://aclanthology.org/2022.lrec-1.225 | https://aclanthology.org/2022.lrec-1.225.pdf | TweetTaglish: A Dataset for Investigating Tagalog-English Code-Switching | Deploying recent natural language processing innovations to low-resource settings allows for state-of-the-art research findings and applications to be accessed across cultural and linguistic borders. One low-resource setting of increasing interest is code-switching, the phenomenon of combining, swapping, or alternating... | ['Natalie Parde', 'Ankit Aich', 'Megan Herrera'] | null | null | null | null | lrec-2022-6 | ['culture'] | ['speech'] | [-3.10064852e-01 2.05934001e-03 -5.25087893e-01 -1.48771241e-01
-8.26039612e-01 -9.78277504e-01 7.77320683e-01 3.54707539e-01
-5.75892746e-01 7.09938467e-01 7.65022993e-01 -5.15787244e-01
1.37092486e-01 -2.50115097e-01 -3.80037695e-01 -2.03682855e-01
-2.04711810e-01 1.56457543e-01 -3.16666394e-01 -3.13577056... | [9.437313079833984, 10.238812446594238] |
211b18ad-78dd-4cb8-afbc-299d10dfa180 | disentangling-prosody-representations-with | 2212.06972 | null | https://arxiv.org/abs/2212.06972v1 | https://arxiv.org/pdf/2212.06972v1.pdf | Disentangling Prosody Representations with Unsupervised Speech Reconstruction | Human speech can be characterized by different components, including semantic content, speaker identity and prosodic information. Significant progress has been made in disentangling representations for semantic content and speaker identity in Automatic Speech Recognition (ASR) and speaker verification tasks respectivel... | ['Stefan Wermter', 'Fuji Ren', 'Theresa Pekarek-Rosin', 'Cornelius Weber', 'Taihao Li', 'Leyuan Qu'] | 2022-12-14 | null | null | null | null | ['voice-conversion', 'voice-conversion', 'speech-emotion-recognition', 'speaker-verification'] | ['audio', 'speech', 'speech', 'speech'] | [ 8.79211724e-02 2.60021091e-01 -9.02036130e-02 -5.52189887e-01
-7.54515886e-01 -5.00490367e-01 2.64833391e-01 -1.00959152e-01
-3.12662631e-01 5.42943954e-01 8.29053879e-01 -1.18522428e-01
2.86231548e-01 -1.91250458e-01 -3.38285565e-01 -6.29925549e-01
9.75947082e-02 2.64289677e-01 -3.69781584e-01 -4.46678340... | [14.007193565368652, 6.075856685638428] |
49a6bf0c-d148-4f7c-a8dd-7968a8ed3820 | complete-end-to-end-low-cost-solution-to-a-3d | 1709.02247 | null | http://arxiv.org/abs/1709.02247v1 | http://arxiv.org/pdf/1709.02247v1.pdf | Complete End-To-End Low Cost Solution To a 3D Scanning System with Integrated Turntable | 3D reconstruction is a technique used in computer vision which has a wide
range of applications in areas like object recognition, city modelling, virtual
reality, physical simulations, video games and special effects. Previously, to
perform a 3D reconstruction, specialized hardwares were required. Such systems
were oft... | ['Usama Pervaiz', 'Vu Hoang Minh', 'Yeman Brhane Hagos', 'Tajwar Abrar Aleef', 'Saed Khawaldeh'] | 2017-09-03 | null | null | null | null | ['physical-simulations'] | ['miscellaneous'] | [ 1.10126689e-01 -3.15762699e-01 5.82663953e-01 -3.69711161e-01
-2.09449362e-02 -3.42786580e-01 5.48457801e-01 9.73893926e-02
-3.58433843e-01 8.91120732e-02 -5.62722445e-01 -4.48850125e-01
3.30232945e-03 -1.03318655e+00 -4.28397387e-01 -6.80242255e-02
1.61757991e-01 9.87389266e-01 6.70343399e-01 -2.70214468... | [8.334582328796387, -2.7179653644561768] |
a587bca7-29b2-46e7-b4d2-677d776583f3 | auxiliary-interference-speaker-loss-for | 1906.10876 | null | https://arxiv.org/abs/1906.10876v1 | https://arxiv.org/pdf/1906.10876v1.pdf | Auxiliary Interference Speaker Loss for Target-Speaker Speech Recognition | In this paper, we propose a novel auxiliary loss function for target-speaker automatic speech recognition (ASR). Our method automatically extracts and transcribes target speaker's utterances from a monaural mixture of multiple speakers speech given a short sample of the target speaker. The proposed auxiliary loss funct... | ['Ryoichi Takashima', 'Shota Horiguchi', 'Kenji Nagamatsu', 'Naoyuki Kanda', 'Yusuke Fujita', 'Shinji Watanabe'] | 2019-06-26 | null | null | null | null | ['speaker-separation'] | ['speech'] | [ 4.11259562e-01 4.12959576e-01 3.24679792e-01 -3.14546406e-01
-1.75621736e+00 -2.32483968e-01 3.32345843e-01 -1.56488523e-01
-4.43986028e-01 5.06764770e-01 2.83948123e-01 -3.19423586e-01
3.52265328e-01 -1.03989705e-01 -5.74268043e-01 -9.96817231e-01
7.92006403e-02 3.17241400e-01 -5.79099096e-02 -1.73860341... | [14.802361488342285, 6.1045823097229] |
c816baf1-2a77-4b51-a14e-c0b0d869812d | orientation-shared-convolution-representation | 2212.13166 | null | https://arxiv.org/abs/2212.13166v1 | https://arxiv.org/pdf/2212.13166v1.pdf | Orientation-Shared Convolution Representation for CT Metal Artifact Learning | During X-ray computed tomography (CT) scanning, metallic implants carrying with patients often lead to adverse artifacts in the captured CT images and then impair the clinical treatment. Against this metal artifact reduction (MAR) task, the existing deep-learning-based methods have gained promising reconstruction perfo... | ['Yefeng Zheng', 'Deyu Meng', 'Yawen Huang', 'Yuexiang Li', 'Qi Xie', 'Hong Wang'] | 2022-12-26 | null | null | null | null | ['metal-artifact-reduction'] | ['medical'] | [ 2.08588019e-01 -2.37851098e-01 1.97133124e-01 -2.13347584e-01
-8.65296245e-01 7.75960386e-02 1.39647901e-01 -2.11842746e-01
-8.02703649e-02 6.09081328e-01 2.99168587e-01 -1.13551833e-01
-4.41335738e-01 -4.07325894e-01 -4.91524160e-01 -9.26631153e-01
-6.54474692e-03 -3.85706825e-03 3.20445150e-01 1.82333082... | [13.513352394104004, -2.585829257965088] |
22e95806-2036-4be2-844a-537d993b5bbd | end-to-end-evaluation-of-a-spoken-dialogue | 2211.03511 | null | https://arxiv.org/abs/2211.03511v1 | https://arxiv.org/pdf/2211.03511v1.pdf | End-to-End Evaluation of a Spoken Dialogue System for Learning Basic Mathematics | The advances in language-based Artificial Intelligence (AI) technologies applied to build educational applications can present AI for social-good opportunities with a broader positive impact. Across many disciplines, enhancing the quality of mathematics education is crucial in building critical thinking and problem-sol... | ['Lama Nachman', 'Roddy Fuentes Alba', 'Saurav Sahay', 'Eda Okur'] | 2022-11-07 | null | null | null | null | ['intent-recognition'] | ['natural-language-processing'] | [ 1.66972354e-01 5.60502172e-01 2.82966226e-01 -7.24748611e-01
-8.37440968e-01 -5.88440418e-01 7.48492777e-01 7.00158775e-01
-1.13210402e-01 1.93275675e-01 5.31737983e-01 -6.84168518e-01
-2.04544768e-01 -9.69592750e-01 -2.80528069e-01 9.15745050e-02
-8.02505538e-02 6.93225741e-01 3.44484657e-01 -1.03417420... | [12.44359016418457, 8.037089347839355] |
56b57f9c-9c46-4174-81b4-523e14c31ccd | pagenet-towards-end-to-end-weakly-supervised | 2207.14807 | null | https://arxiv.org/abs/2207.14807v1 | https://arxiv.org/pdf/2207.14807v1.pdf | PageNet: Towards End-to-End Weakly Supervised Page-Level Handwritten Chinese Text Recognition | Handwritten Chinese text recognition (HCTR) has been an active research topic for decades. However, most previous studies solely focus on the recognition of cropped text line images, ignoring the error caused by text line detection in real-world applications. Although some approaches aimed at page-level text recognitio... | ['Songxuan Lai', 'Canjie Luo', 'Yuliang Liu', 'Lianwen Jin', 'Dezhi Peng'] | 2022-07-29 | null | null | null | null | ['handwritten-chinese-text-recognition', 'line-detection', 'handwritten-chinese-text-recognition'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 3.91022742e-01 -4.92858768e-01 -4.45689559e-01 -2.74832428e-01
-6.69888079e-01 -5.73485553e-01 4.41607207e-01 -8.07331353e-02
-1.84806749e-01 4.75821912e-01 1.04450598e-01 -4.98699307e-01
3.36158574e-01 -5.16137779e-01 -4.77890223e-01 -7.54007697e-01
2.44937062e-01 2.41982684e-01 5.35750866e-01 3.05585414... | [11.942553520202637, 2.257850408554077] |
1b662b06-07d7-471f-a82b-afbeff508ef8 | prompt-learning-for-action-recognition | 2305.12437 | null | https://arxiv.org/abs/2305.12437v1 | https://arxiv.org/pdf/2305.12437v1.pdf | Prompt Learning for Action Recognition | We present a new general learning approach for action recognition, Prompt Learning for Action Recognition (PLAR), which leverages the strengths of prompt learning to guide the learning process. Our approach is designed to predict the action label by helping the models focus on the descriptions or instructions associate... | ['Dinesh Manocha', 'Tianrui Guan', 'Ruiqi Xian', 'Xijun Wang'] | 2023-05-21 | null | null | null | null | ['action-recognition-in-videos'] | ['computer-vision'] | [ 4.45891649e-01 3.25972028e-02 -4.23785895e-01 -3.79040897e-01
-9.95101810e-01 -7.17084289e-01 6.91612601e-01 -3.09942275e-01
-4.05317754e-01 4.93770212e-01 6.82412326e-01 -5.01130102e-03
-1.31775111e-01 -2.86358982e-01 -6.76617444e-01 -5.60911357e-01
-5.39995693e-02 3.85136902e-01 3.22359622e-01 1.42805595... | [8.499565124511719, 0.6555520296096802] |
43cab4e2-0e9a-416b-b2f9-4cc862c0e690 | bts-net-bi-directional-transfer-and-selection | 2104.01784 | null | https://arxiv.org/abs/2104.01784v1 | https://arxiv.org/pdf/2104.01784v1.pdf | BTS-Net: Bi-directional Transfer-and-Selection Network For RGB-D Salient Object Detection | Depth information has been proved beneficial in RGB-D salient object detection (SOD). However, depth maps obtained often suffer from low quality and inaccuracy. Most existing RGB-D SOD models have no cross-modal interactions or only have unidirectional interactions from depth to RGB in their encoder stages, which may l... | ['Qijun Zhao', 'Keren Fu', 'Yao Jiang', 'Wenbo Zhang'] | 2021-04-05 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 4.67487901e-01 4.64347750e-02 -3.31184000e-01 -4.77591991e-01
-4.35432792e-01 2.57658828e-02 5.62904656e-01 -1.63162604e-01
-1.59000218e-01 5.38888752e-01 5.02183855e-01 5.70613099e-03
5.93358837e-02 -7.93410540e-01 -7.06427932e-01 -5.06818295e-01
1.93615004e-01 -3.22635442e-01 8.89616489e-01 -3.82709593... | [9.674763679504395, -0.8040622472763062] |
2506d9b8-eae5-4733-92a3-f32d1a4e542f | s3t-self-supervised-pre-training-with-swin | 2202.10139 | null | https://arxiv.org/abs/2202.10139v1 | https://arxiv.org/pdf/2202.10139v1.pdf | S3T: Self-Supervised Pre-training with Swin Transformer for Music Classification | In this paper, we propose S3T, a self-supervised pre-training method with Swin Transformer for music classification, aiming to learn meaningful music representations from massive easily accessible unlabeled music data. S3T introduces a momentum-based paradigm, MoCo, with Swin Transformer as its feature extractor to mus... | ['Kejun Zhang', 'Zejun Ma', 'Belei Zhu', 'Chen Zhang', 'Hang Zhao'] | 2022-02-21 | null | null | null | null | ['genre-classification', 'music-classification'] | ['computer-vision', 'music'] | [ 3.63842815e-01 -5.70189990e-02 -7.31279969e-01 -4.87260297e-02
-1.26963532e+00 -7.46746361e-01 2.44724154e-01 -2.10642305e-04
-2.84376472e-01 4.44970161e-01 1.24810547e-01 1.52293190e-01
-3.63891721e-01 -3.35835904e-01 -4.70521480e-01 -4.30875778e-01
-2.92562842e-01 7.89409816e-01 -4.14201021e-02 3.91811617... | [15.75326919555664, 5.237300872802734] |
f030836c-23fc-476b-ad52-e84e91d6e4ef | the-undesirable-dependence-on-frequency-of | 2301.00792 | null | https://arxiv.org/abs/2301.00792v1 | https://arxiv.org/pdf/2301.00792v1.pdf | The Undesirable Dependence on Frequency of Gender Bias Metrics Based on Word Embeddings | Numerous works use word embedding-based metrics to quantify societal biases and stereotypes in texts. Recent studies have found that word embeddings can capture semantic similarity but may be affected by word frequency. In this work we study the effect of frequency when measuring female vs. male gender bias with word e... | ['Edgar Altszyler', 'Diego Fernandez Slezak', 'Germán Rosati', 'Francisco Valentini'] | 2023-01-02 | null | null | null | null | ['semantic-textual-similarity'] | ['natural-language-processing'] | [-1.56491458e-01 -2.08039925e-01 -5.22378504e-01 -4.60663050e-01
1.05728686e-01 -6.91788793e-01 1.07369208e+00 8.93074274e-01
-1.17319894e+00 6.52729511e-01 7.21765697e-01 -2.88446337e-01
-8.52192938e-02 -1.17676044e+00 -1.58084124e-01 -6.24628007e-01
-1.24332316e-01 1.75507545e-01 -8.85889307e-03 -4.55349535... | [9.368722915649414, 10.130059242248535] |
796af7ae-e53f-48c9-97a4-b8087f51bee4 | a-multi-task-network-to-detect-junctions-in | 1806.03175 | null | https://arxiv.org/abs/1806.03175v1 | https://arxiv.org/pdf/1806.03175v1.pdf | A Multi-task Network to Detect Junctions in Retinal Vasculature | Junctions in the retinal vasculature are key points to be able to extract its topology, but they vary in appearance, depending on vessel density, width and branching/crossing angles. The complexity of junction patterns is usually accompanied by a scarcity of labels, which discourages the usage of very deep networks for... | [] | 2018-06-06 | null | null | null | null | ['junction-detection'] | ['computer-vision'] | [-2.99623981e-03 -3.50909494e-03 -8.87870789e-02 -3.38353813e-01
-1.51320353e-01 -8.41492176e-01 6.90398812e-01 5.14230847e-01
-5.64754069e-01 7.64435887e-01 -1.49159417e-01 -5.39371133e-01
-2.16886327e-01 -8.70591938e-01 -4.92082924e-01 -6.40421450e-01
-2.02530533e-01 1.91515580e-01 7.32162714e-01 1.53769031... | [15.769947052001953, -3.9341750144958496] |
3a451474-dd79-43f4-907c-02e00a26e73b | language-modeling-via-stochastic-processes-1 | 2203.11370 | null | https://arxiv.org/abs/2203.11370v2 | https://arxiv.org/pdf/2203.11370v2.pdf | Language modeling via stochastic processes | Modern language models can generate high-quality short texts. However, they often meander or are incoherent when generating longer texts. These issues arise from the next-token-only language modeling objective. Recent work in self-supervised learning suggests that models can learn good latent representations via contra... | ['Tatsunori Hashimoto', 'Noah Goodman', 'Esin Durmus', 'Rose E Wang'] | 2022-03-21 | language-modeling-via-stochastic-processes | https://openreview.net/forum?id=pMQwKL1yctf | https://openreview.net/pdf?id=pMQwKL1yctf | iclr-2022-4 | ['text-infilling'] | ['natural-language-processing'] | [ 3.66859704e-01 7.05463111e-01 -4.06021327e-01 -3.50145757e-01
-1.14504147e+00 -4.70040977e-01 1.23124552e+00 8.35911930e-02
-1.67582750e-01 1.03736448e+00 1.00507545e+00 -2.16453195e-01
6.76440001e-02 -8.88717711e-01 -6.24341547e-01 -4.80512619e-01
1.21217281e-01 7.90762007e-01 -4.09646153e-01 -4.77329433... | [11.71378231048584, 8.965578079223633] |
f7c9bff7-ed2c-45e6-a7da-cc38ffb7c9a9 | an-automated-vulnerability-detection | 2301.08824 | null | https://arxiv.org/abs/2301.08824v1 | https://arxiv.org/pdf/2301.08824v1.pdf | An Automated Vulnerability Detection Framework for Smart Contracts | With the increase of the adoption of blockchain technology in providing decentralized solutions to various problems, smart contracts have become more popular to the point that billions of US Dollars are currently exchanged every day through such technology. Meanwhile, various vulnerabilities in smart contracts have bee... | ['Bhavani Thuraisingham', 'Latifur Khan', 'Zhouxiang Wu', 'Xiaodi Li', 'Sadaf MD Halim', 'Zhuoyi Wang', 'Chen Zhao', 'Feng Mi'] | 2023-01-20 | null | null | null | null | ['metric-learning', 'metric-learning', 'vulnerability-detection'] | ['computer-vision', 'methodology', 'miscellaneous'] | [-7.45597407e-02 -3.94769847e-01 -1.68306157e-01 -2.76746452e-01
-6.90271854e-01 -9.48139429e-01 6.19530976e-01 -4.59635854e-02
-2.37499207e-01 4.84825939e-01 3.13424379e-01 -9.12014306e-01
3.29684019e-01 -1.01036775e+00 -5.15989602e-01 -7.52286017e-01
9.86992493e-02 1.28722295e-01 1.89910099e-01 -2.63032079... | [6.800260543823242, 7.26087760925293] |
0aaeca70-5d6d-4ef7-9fe0-d281983e8cd0 | comparison-of-forecasting-methods-of-house | 2208.07217 | null | https://arxiv.org/abs/2208.07217v1 | https://arxiv.org/pdf/2208.07217v1.pdf | Comparison of Forecasting Methods of House Electricity Consumption for Honda Smart Home | The electricity consumption of buildings composes a major part of the city's energy consumption. Electricity consumption forecasting enables the development of home energy management systems resulting in the future design of more sustainable houses and a decrease in total energy consumption. Energy performance in build... | ['Mehmet Bodur', 'Farshad Ahmadi Asl'] | 2022-08-11 | null | null | null | null | ['total-energy', 'energy-management'] | ['miscellaneous', 'time-series'] | [-3.73391032e-01 -4.52630490e-01 -1.78249732e-01 -4.55862194e-01
-1.23198241e-01 -3.52292508e-02 3.70726377e-01 7.15958849e-02
-3.28338295e-02 9.56536114e-01 3.51008624e-01 -4.23731059e-01
-2.39012331e-01 -1.27482820e+00 3.42842400e-01 -8.76090646e-01
2.64497697e-01 1.21222638e-01 -3.34544003e-01 -3.91260147... | [5.988003730773926, 2.646315813064575] |
26ada930-9fc7-4e88-b7bf-6c61d26c96a1 | accented-speech-recognition-inspired-by-human | 2104.04627 | null | https://arxiv.org/abs/2104.04627v1 | https://arxiv.org/pdf/2104.04627v1.pdf | Accented Speech Recognition Inspired by Human Perception | While improvements have been made in automatic speech recognition performance over the last several years, machines continue to have significantly lower performance on accented speech than humans. In addition, the most significant improvements on accented speech primarily arise by overwhelming the problem with hundreds... | ['Michael Picheny', 'Amber Wang', 'Elizabeth Combs', 'Xiangyun Chu'] | 2021-04-09 | null | null | null | null | ['accented-speech-recognition'] | ['speech'] | [ 3.88582885e-01 3.11552644e-01 1.72758549e-01 -9.81156409e-01
-5.76724172e-01 -7.19769001e-01 3.27095836e-01 3.67503933e-04
-6.51024818e-01 6.48771942e-01 4.31530446e-01 -3.87641728e-01
2.75509387e-01 -3.07031810e-01 -6.09431207e-01 -4.95416015e-01
1.28529951e-01 7.02839673e-01 -6.58443719e-02 -4.20228601... | [14.354582786560059, 6.697058200836182] |
11417a43-3d1c-4454-b087-ff16d436baa2 | no-surprises-training-robust-lung-nodule | 2003.03824 | null | https://arxiv.org/abs/2003.03824v2 | https://arxiv.org/pdf/2003.03824v2.pdf | No Surprises: Training Robust Lung Nodule Detection for Low-Dose CT Scans by Augmenting with Adversarial Attacks | Detecting malignant pulmonary nodules at an early stage can allow medical interventions which may increase the survival rate of lung cancer patients. Using computer vision techniques to detect nodules can improve the sensitivity and the speed of interpreting chest CT for lung cancer screening. Many studies have used CN... | ['Si-Qi Liu', 'Bogdan Georgescu', 'Sasa Grbic', 'Arnaud Arindra Adiyoso Setio', 'Eli Gibson', 'Florin C. Ghesu', 'Dorin Comaniciu'] | 2020-03-08 | null | null | null | null | ['lung-nodule-detection'] | ['medical'] | [ 5.61094999e-01 5.68393409e-01 1.82627514e-01 2.41806498e-03
-6.20667815e-01 -6.41399324e-01 3.72280777e-01 -2.35086098e-01
-2.99371690e-01 4.73254800e-01 -1.97734371e-01 -5.23651958e-01
2.21515536e-01 -9.32066560e-01 -9.05005813e-01 -7.86888003e-01
2.24999934e-02 1.86540172e-01 6.55948877e-01 -1.24122500... | [15.190533638000488, -2.115506410598755] |
65073517-0ac7-472e-bdfa-5144e7c259c9 | multi-chart-generative-surface-modeling | 1806.02143 | null | http://arxiv.org/abs/1806.02143v3 | http://arxiv.org/pdf/1806.02143v3.pdf | Multi-chart Generative Surface Modeling | This paper introduces a 3D shape generative model based on deep neural
networks. A new image-like (i.e., tensor) data representation for genus-zero 3D
shapes is devised. It is based on the observation that complicated shapes can
be well represented by multiple parameterizations (charts), each focusing on a
different pa... | ['Heli Ben-Hamu', 'Gal Avineri', 'Yaron Lipman', 'Haggai Maron', 'Itay Kezurer'] | 2018-06-06 | null | null | null | null | ['3d-shape-generation'] | ['computer-vision'] | [-6.37603402e-02 5.47141492e-01 7.92271942e-02 -3.97648402e-02
-3.72923434e-01 -7.36168683e-01 5.86266577e-01 -3.09086800e-01
3.30533028e-01 2.74377435e-01 3.19409698e-01 -3.15930516e-01
-2.32747812e-02 -1.19797146e+00 -8.39995682e-01 -8.52340877e-01
-1.31674260e-02 1.00686979e+00 -1.91473886e-01 -3.55752289... | [8.827613830566406, -3.65655517578125] |
3f0ac7c1-43f3-476a-9174-57d02f4e0021 | read-watch-and-move-reinforcement-learning | 1901.06829 | null | http://arxiv.org/abs/1901.06829v1 | http://arxiv.org/pdf/1901.06829v1.pdf | Read, Watch, and Move: Reinforcement Learning for Temporally Grounding Natural Language Descriptions in Videos | The task of video grounding, which temporally localizes a natural language
description in a video, plays an important role in understanding videos.
Existing studies have adopted strategies of sliding window over the entire
video or exhaustively ranking all possible clip-sentence pairs in a
pre-segmented video, which in... | ['Xiao Liu', 'Dongliang He', 'Shilei Wen', 'Fu Li', 'Jizhou Huang', 'Xiang Zhao'] | 2019-01-21 | null | null | null | null | ['video-grounding'] | ['computer-vision'] | [ 2.73393631e-01 -6.04973622e-02 -5.54128289e-01 -3.35367769e-01
-1.15161347e+00 -5.23334444e-01 4.80485588e-01 -2.55484749e-02
-6.17236614e-01 7.77048290e-01 4.32785302e-01 -9.41108316e-02
-1.98795702e-02 -2.28303775e-01 -9.35660601e-01 -4.38042879e-01
-4.82984453e-01 2.15295181e-01 6.88925087e-01 1.36304051... | [8.930583000183105, 0.5056243538856506] |
8de22a7e-ade5-43bb-9af4-b6a975a1b6e4 | experimentally-realized-memristive-memory | 2204.07429 | null | https://arxiv.org/abs/2204.07429v1 | https://arxiv.org/pdf/2204.07429v1.pdf | Experimentally realized memristive memory augmented neural network | Lifelong on-device learning is a key challenge for machine intelligence, and this requires learning from few, often single, samples. Memory augmented neural network has been proposed to achieve the goal, but the memory module has to be stored in an off-chip memory due to its size. Therefore the practical use has been h... | ['Can Li', 'John Paul Strachan', 'Catherine E. Graves', 'Xia Sheng', 'X. Sharon Hu', 'Michael Neimier', 'Ann Franchesca Laguna', 'Arman Kazemi', 'Yahui Zhao', 'Bo Wen', 'Ruibin Mao'] | 2022-04-15 | null | null | null | null | ['one-shot-learning'] | ['methodology'] | [ 8.16505551e-02 -1.28896654e-01 -2.49198020e-01 -3.88459153e-02
-2.71276653e-01 -2.14158627e-03 1.87599674e-01 1.70244545e-01
-9.27827597e-01 8.06817949e-01 -4.49986160e-01 -1.24398075e-01
-1.12214945e-01 -1.02107692e+00 -1.14250195e+00 -1.14863575e+00
8.31624195e-02 5.16028047e-01 6.94383442e-01 -2.77964264... | [8.25271987915039, 2.547013521194458] |
8675eae2-1826-444b-a254-a0950c53070e | spmoe-generate-multiple-pattern-aware-outputs | 2108.07535 | null | https://arxiv.org/abs/2108.07535v2 | https://arxiv.org/pdf/2108.07535v2.pdf | SPMoE: Generate Multiple Pattern-Aware Outputs with Sparse Pattern Mixture of Experts | Many generation tasks follow a one-to-many mapping relationship: each input could be associated with multiple outputs. Existing methods like Conditional Variational AutoEncoder(CVAE) employ a latent variable to model this one-to-many relationship. However, this high-dimensional and dense latent variable lacks explainab... | ['Haiqing Chen', 'Wei Zhou', 'Ji Zhang', 'Zhongzhou Zhao', 'Xuming Lin', 'Xintong Bao', 'Shaobo Cui'] | 2021-08-17 | null | null | null | null | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [-1.25344366e-01 1.37517869e-01 -8.15866962e-02 -2.96447277e-01
-4.64491695e-01 -3.75440091e-01 7.63518631e-01 -5.51132381e-01
1.59480527e-01 7.83196449e-01 4.13110793e-01 1.05562165e-01
-1.26197472e-01 -8.97837520e-01 -7.52920270e-01 -7.16625571e-01
5.83792210e-01 8.36370587e-01 -1.36066601e-01 -2.43402645... | [11.766551971435547, 9.136078834533691] |
4aba614b-6b24-4fda-a2ec-8e02b9bc1a0c | read-attend-and-code-pushing-the-limits-of | 2107.10650 | null | https://arxiv.org/abs/2107.10650v1 | https://arxiv.org/pdf/2107.10650v1.pdf | Read, Attend, and Code: Pushing the Limits of Medical Codes Prediction from Clinical Notes by Machines | Prediction of medical codes from clinical notes is both a practical and essential need for every healthcare delivery organization within current medical systems. Automating annotation will save significant time and excessive effort spent by human coders today. However, the biggest challenge is directly identifying appr... | ['Varun Ganapathi', 'Byung-Hak Kim'] | 2021-07-10 | null | null | null | null | ['multi-label-classification-of-biomedical', 'medical-code-prediction'] | ['medical', 'medical'] | [ 4.51587558e-01 5.54992676e-01 -1.74807698e-01 -5.09636402e-01
-1.27086961e+00 -2.24286020e-01 6.44471645e-02 9.06607926e-01
-4.67587888e-01 4.27958548e-01 5.96538901e-01 -6.47842467e-01
-2.10897401e-01 -3.43581796e-01 -2.40154102e-01 -2.43277088e-01
-2.45982707e-01 9.91467535e-01 -3.97324979e-01 -2.81878300... | [8.01669692993164, 6.812239646911621] |
889bd405-f6cb-41ee-b6fc-97680d4a7be1 | generative-ai-meets-3d-a-survey-on-text-to-3d | 2305.06131 | null | https://arxiv.org/abs/2305.06131v2 | https://arxiv.org/pdf/2305.06131v2.pdf | Generative AI meets 3D: A Survey on Text-to-3D in AIGC Era | Generative AI (AIGC, a.k.a. AI generated content) has made remarkable progress in the past few years, among which text-guided content generation is the most practical one since it enables the interaction between human instruction and AIGC. Due to the development in text-to-image as well 3D modeling technologies (like N... | ['Choong Seon Hong', 'Sung-Ho Bae', 'Yang Yang', 'Francois Rameau', 'Lik-Hang Lee', 'Atish Waghwase', 'Chaoning Zhang', 'Chenghao Li'] | 2023-05-10 | null | null | null | null | ['texture-synthesis', 'scene-generation', 'text-to-3d'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.83607534e-01 2.43828207e-01 2.18814224e-01 -1.64547205e-01
-4.92595851e-01 -3.89730364e-01 8.36514533e-01 -2.15386555e-01
1.89106002e-01 5.97216666e-01 4.54953074e-01 -1.13948591e-01
1.54781893e-01 -1.16614699e+00 -8.93455684e-01 -6.89659059e-01
4.69054550e-01 6.59563899e-01 2.65646875e-01 -7.55015433... | [11.437715530395508, -0.41122424602508545] |
dcf6af36-8aab-4f76-8754-3707dd594a2a | interpretable-spectrum-transformation-attacks | 2302.10686 | null | https://arxiv.org/abs/2302.10686v1 | https://arxiv.org/pdf/2302.10686v1.pdf | Interpretable Spectrum Transformation Attacks to Speaker Recognition | The success of adversarial attacks to speaker recognition is mainly in white-box scenarios. When applying the adversarial voices that are generated by attacking white-box surrogate models to black-box victim models, i.e. \textit{transfer-based} black-box attacks, the transferability of the adversarial voices is not onl... | ['Xiao-Lei Zhang', 'Hong Luo', 'Jiadi Yao'] | 2023-02-21 | null | null | null | null | ['speaker-recognition'] | ['speech'] | [ 2.22540811e-01 5.44128977e-02 2.92529196e-01 7.58641064e-02
-6.92691386e-01 -9.31706727e-01 6.37008071e-01 -6.16143346e-01
3.78094055e-03 2.77409613e-01 3.61152619e-01 -4.40675259e-01
-2.10987162e-02 -5.86105406e-01 -3.84458452e-01 -7.30996370e-01
-7.20546842e-02 -2.61193514e-01 1.08264618e-01 -4.89600748... | [13.997596740722656, 5.824460506439209] |
ba15518d-d128-49ff-b7cf-617a6eaab5cd | dialogpt-large-scale-generative-pre-training | 1911.00536 | null | https://arxiv.org/abs/1911.00536v3 | https://arxiv.org/pdf/1911.00536v3.pdf | DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation | We present a large, tunable neural conversational response generation model, DialoGPT (dialogue generative pre-trained transformer). Trained on 147M conversation-like exchanges extracted from Reddit comment chains over a period spanning from 2005 through 2017, DialoGPT extends the Hugging Face PyTorch transformer to at... | ['Chris Brockett', 'Yen-Chun Chen', 'Xiang Gao', 'Yizhe Zhang', 'Jingjing Liu', 'Jianfeng Gao', 'Siqi Sun', 'Michel Galley', 'Bill Dolan'] | 2019-11-01 | null | null | null | null | ['conversational-response-generation'] | ['natural-language-processing'] | [ 2.91129529e-01 6.93006516e-01 4.58611213e-02 -7.45345950e-01
-1.20483184e+00 -9.00204718e-01 1.15469193e+00 -3.17532033e-01
-1.49687812e-01 1.24689209e+00 1.02182913e+00 -3.47370058e-01
5.00485718e-01 -6.25597537e-01 -1.97869927e-01 -1.26686454e-01
2.65579551e-01 1.16419291e+00 -2.35700428e-01 -1.00033021... | [12.676294326782227, 8.215250015258789] |
d43beb1f-7630-4f5b-9cb3-24bf72c900a9 | lethal-dose-conjecture-on-data-poisoning | 2208.03309 | null | https://arxiv.org/abs/2208.03309v3 | https://arxiv.org/pdf/2208.03309v3.pdf | Lethal Dose Conjecture on Data Poisoning | Data poisoning considers an adversary that distorts the training set of machine learning algorithms for malicious purposes. In this work, we bring to light one conjecture regarding the fundamentals of data poisoning, which we call the Lethal Dose Conjecture. The conjecture states: If $n$ clean training samples are need... | ['Soheil Feizi', 'Alexander Levine', 'Wenxiao Wang'] | 2022-08-05 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [-5.86996647e-03 -3.80921029e-02 -2.14652434e-01 7.36414939e-02
-1.14834797e+00 -1.13048744e+00 2.39128441e-01 4.49757963e-01
-6.75125480e-01 1.02866304e+00 -3.24892551e-01 -6.91006243e-01
-1.89204678e-01 -1.05550945e+00 -1.00598824e+00 -1.25128925e+00
-2.40367115e-01 6.09541833e-01 2.03831151e-01 -1.44747078... | [5.798232078552246, 7.577357292175293] |
acb67957-8c2e-4de8-a9cd-e922f1daa237 | aligning-latent-and-image-spaces-to-connect | 2104.06954 | null | https://arxiv.org/abs/2104.06954v1 | https://arxiv.org/pdf/2104.06954v1.pdf | Aligning Latent and Image Spaces to Connect the Unconnectable | In this work, we develop a method to generate infinite high-resolution images with diverse and complex content. It is based on a perfectly equivariant generator with synchronous interpolations in the image and latent spaces. Latent codes, when sampled, are positioned on the coordinate grid, and each pixel is computed f... | ['Mohamed Elhoseiny', 'Grigorii Sotnikov', 'Ivan Skorokhodov'] | 2021-04-14 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Skorokhodov_Aligning_Latent_and_Image_Spaces_To_Connect_the_Unconnectable_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Skorokhodov_Aligning_Latent_and_Image_Spaces_To_Connect_the_Unconnectable_ICCV_2021_paper.pdf | iccv-2021-1 | ['infinite-image-generation'] | ['computer-vision'] | [ 4.85955328e-01 1.01711705e-01 2.49402091e-01 -8.13158154e-02
-1.19581366e+00 -1.04887056e+00 8.41709077e-01 -6.42085373e-01
-1.07967764e-01 8.79387856e-01 3.19061399e-01 8.88004899e-02
2.57319063e-01 -1.05181336e+00 -1.02317119e+00 -7.75593102e-01
5.52560352e-02 3.86642784e-01 2.56145671e-02 -3.86258453... | [11.575419425964355, -0.4744025766849518] |
7dde48f4-8ccb-4fa3-a166-9ff2b393de65 | exemplar-based-image-colorization-with-a | 2209.05775 | null | https://arxiv.org/abs/2209.05775v1 | https://arxiv.org/pdf/2209.05775v1.pdf | Exemplar-Based Image Colorization with A Learning Framework | Image learning and colorization are hot spots in multimedia domain. Inspired by the learning capability of humans, in this paper, we propose an automatic colorization method with a learning framework. This method can be viewed as a hybrid of exemplar-based and learning-based method, and it decouples the colorization pr... | ['Yong liu', 'Jie Ren', 'Jiandang Yang', 'Zhenfeng Xue'] | 2022-09-13 | null | null | null | null | ['colorization'] | ['computer-vision'] | [-7.27241626e-03 -5.15657306e-01 -8.71903375e-02 -2.98099548e-01
-5.00341415e-01 -3.14550132e-01 2.35225275e-01 -2.08804116e-01
-5.01571476e-01 2.95809269e-01 -1.41629025e-01 4.04307768e-02
-3.02819051e-02 -8.33172321e-01 -5.49602211e-01 -1.09416771e+00
4.60309505e-01 7.35710636e-02 2.28729248e-01 -2.31380209... | [11.074955940246582, -1.2417163848876953] |
a8662e4c-e693-4c6d-9297-9cb60f0e2300 | skin-cancer-detection-and-tracking-using-data | 1612.01074 | null | http://arxiv.org/abs/1612.01074v1 | http://arxiv.org/pdf/1612.01074v1.pdf | Skin Cancer Detection and Tracking using Data Synthesis and Deep Learning | Dense object detection and temporal tracking are needed across applications
domains ranging from people-tracking to analysis of satellite imagery over
time. The detection and tracking of malignant skin cancers and benign moles
poses a particularly challenging problem due to the general uniformity of large
skin patches,... | ['Sebastian Thrun', 'Justin Ko', 'Brett Kuprel', 'Andre Esteva', 'Rob Novoa', 'Yunzhu Li'] | 2016-12-04 | null | null | null | null | ['dense-object-detection'] | ['computer-vision'] | [ 5.65169275e-01 1.13504894e-01 -3.17269325e-01 -1.82207316e-01
-5.30655682e-01 -5.98901153e-01 5.88719070e-01 7.31773898e-02
-4.21414226e-01 6.39826000e-01 -1.16423629e-01 -4.39696997e-01
1.36441812e-01 -7.90186107e-01 -5.62895358e-01 -5.90488553e-01
-4.16110605e-01 2.03864262e-01 4.41444308e-01 -1.54898033... | [15.363951683044434, -2.7570641040802] |
4388f164-6344-4abd-a3ad-7b2bb4fb601c | countering-language-drift-via-grounding | null | null | https://openreview.net/forum?id=BkMn9jAcYQ | https://openreview.net/pdf?id=BkMn9jAcYQ | Countering Language Drift via Grounding | While reinforcement learning (RL) shows a lot of promise for natural language processing—e.g. when fine-tuning natural language systems for optimizing a certain objective—there has been little investigation into potential language drift: when an external reward is used to train a system, the agents’ communication proto... | ['Douwe Kiela', 'Kyunghyun Cho', 'Jason Lee'] | 2018-09-27 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [ 1.09305054e-01 4.09977704e-01 -3.22663724e-01 -3.97783488e-01
-7.19154894e-01 -8.43794525e-01 1.01609051e+00 2.07642049e-01
-8.92401934e-01 9.63632166e-01 4.72214997e-01 -7.45347321e-01
2.59122252e-01 -6.28926694e-01 -7.95629263e-01 -4.97561753e-01
-1.15093999e-01 6.49883807e-01 5.30974604e-02 -5.77625215... | [4.053663730621338, 1.5808204412460327] |
0e6c682f-8a66-440e-b6af-3c4fdbce5a16 | inhomogeneous-hypergraph-clustering-with | 1709.01249 | null | http://arxiv.org/abs/1709.01249v4 | http://arxiv.org/pdf/1709.01249v4.pdf | Inhomogeneous Hypergraph Clustering with Applications | Hypergraph partitioning is an important problem in machine learning, computer
vision and network analytics. A widely used method for hypergraph partitioning
relies on minimizing a normalized sum of the costs of partitioning hyperedges
across clusters. Algorithmic solutions based on this approach assume that
different p... | ['Olgica Milenkovic', 'Pan Li'] | 2017-09-05 | inhomogeneous-hypergraph-clustering-with-1 | http://papers.nips.cc/paper/6825-inhomogeneous-hypergraph-clustering-with-applications | http://papers.nips.cc/paper/6825-inhomogeneous-hypergraph-clustering-with-applications.pdf | neurips-2017-12 | ['hypergraph-partitioning'] | ['graphs'] | [ 7.16773868e-02 2.55991936e-01 -3.74451429e-01 -2.11357042e-01
-2.95355111e-01 -1.05554926e+00 -1.32596821e-01 4.25463408e-01
-8.35593268e-02 4.15133655e-01 -5.53037524e-02 -1.04193576e-01
-6.24493301e-01 -9.88080502e-01 -5.79311848e-01 -7.39192188e-01
-1.45839438e-01 9.66896594e-01 1.46727145e-01 2.35149384... | [7.093282699584961, 5.148623466491699] |
cef9d3d0-d436-4c8c-95ad-81cbff0d5dbb | image-storage-on-synthetic-dna-using-1 | 2306.12882 | null | https://arxiv.org/abs/2306.12882v1 | https://arxiv.org/pdf/2306.12882v1.pdf | Image storage on synthetic DNA using compressive autoencoders and DNA-adapted entropy coders | Over the past years, the ever-growing trend on data storage demand, more specifically for "cold" data (rarely accessed data), has motivated research for alternative systems of data storage. Because of its biochemical characteristics, synthetic DNA molecules are now considered as serious candidates for this new kind of ... | ['Marc Antonini', 'Melpomeni Dimopoulou', 'Eva Gil San Antonio', 'Xavier Pic'] | 2023-06-22 | null | null | null | null | ['image-compression', 'quantization'] | ['computer-vision', 'methodology'] | [ 4.50764507e-01 5.32029718e-02 -8.90929776e-04 -1.23841681e-01
-6.60991371e-02 -1.19630210e-01 7.63097525e-01 4.66322809e-01
-7.20224440e-01 8.20841908e-01 5.08662939e-01 -1.86206289e-02
2.34781802e-02 -1.06626236e+00 -9.10376251e-01 -1.13395059e+00
-5.50820902e-02 4.69153136e-01 -4.12953980e-02 -2.49758303... | [11.437989234924316, -1.6727232933044434] |
be3fc4ba-780b-43dc-b2ab-f989ec204a42 | resetting-the-baseline-ct-based-covid-19 | 2108.05649 | null | https://arxiv.org/abs/2108.05649v1 | https://arxiv.org/pdf/2108.05649v1.pdf | Resetting the baseline: CT-based COVID-19 diagnosis with Deep Transfer Learning is not as accurate as widely thought | Deep learning is gaining instant popularity in computer aided diagnosis of COVID-19. Due to the high sensitivity of Computed Tomography (CT) to this disease, CT-based COVID-19 detection with visual models is currently at the forefront of medical imaging research. Outcomes published in this direction are frequently clai... | ['Naveed Akhtar', 'Syed M. S. Islam', 'Fouzia Altaf'] | 2021-08-12 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [-2.66639031e-02 -1.09415673e-01 -4.45492625e-01 -1.63344797e-02
-1.28860176e+00 -5.24517000e-01 2.03665659e-01 2.97580212e-01
-5.65941989e-01 5.05762219e-01 2.64595568e-01 -1.13431096e+00
-3.40543315e-02 -4.58685130e-01 -6.81282640e-01 -6.53442681e-01
-2.32607275e-01 7.76742518e-01 -1.89034399e-02 1.80819407... | [15.208579063415527, -1.9789507389068604] |
1bef2a2a-a4c7-4b86-9837-e4b3e1628a9e | an-experimental-study-in-real-time-facial | null | null | https://www.opastpublishers.com/peer-review/an-experimental-study-in-realtime-facial-emotion-recognition-on-new-3rl-dataset-5362.html | https://www.opastpublishers.com/open-access-articles/an-experimental-study-in-realtime-facial-emotion-recognition-on-new-3rl-dataset.pdf | An experimental study in Real-time Facial Emotion Recognition on new 3RL dataset | Although real-time facial emotion recognition is a hot topic research domain in the field of human-computer interaction, state-of- the-art available datasets still suffer from various problems, such as some unrelated photos such as document photos, unbalanced numbers of photos in each class, and misleading images that ... | ['Rahmeh Abou Zafra; Lana Ahmad Abdullah;Rouaa Alaraj; Rasha Albezreh;Tarek Barhoum; Khloud Al Jallad'] | 2023-04-02 | null | null | null | journal-of-current-trends-in-computer-science | ['facial-emotion-recognition'] | ['computer-vision'] | [-1.00501262e-01 -2.96721850e-02 -4.76676114e-02 -6.54924929e-01
-1.21408939e-01 -1.80969745e-01 4.01901633e-01 -3.14180180e-02
-5.10239840e-01 9.40232217e-01 -1.43631518e-01 3.65251571e-01
2.16201410e-01 -4.27709371e-01 -3.31994623e-01 -7.69311130e-01
-7.62464628e-02 -1.13286734e-01 -1.95474565e-01 -4.62505817... | [13.612936973571777, 1.8990765810012817] |
03843849-3147-4ceb-a7ea-9b7cea8fa66a | differentiable-multi-target-causal-bayesian | 2302.10607 | null | https://arxiv.org/abs/2302.10607v2 | https://arxiv.org/pdf/2302.10607v2.pdf | Differentiable Multi-Target Causal Bayesian Experimental Design | We introduce a gradient-based approach for the problem of Bayesian optimal experimental design to learn causal models in a batch setting -- a critical component for causal discovery from finite data where interventions can be costly or risky. Existing methods rely on greedy approximations to construct a batch of experi... | ['Stefan Bauer', 'Adam Foster', 'Yarin Gal', 'Andrew Jesson', 'Desi R. Ivanova', 'Panagiotis Tigas', 'Yashas Annadani'] | 2023-02-21 | null | null | null | null | ['causal-discovery', 'experimental-design'] | ['knowledge-base', 'methodology'] | [ 5.10840595e-01 1.18804149e-01 -6.16274655e-01 -4.54257697e-01
-9.98750687e-01 -4.57917064e-01 6.62606776e-01 1.81270853e-01
-5.70082188e-01 9.73428786e-01 1.46124318e-01 -9.20273244e-01
-5.17579973e-01 -4.09511745e-01 -1.10346806e+00 -5.05380392e-01
-4.78628904e-01 6.51615500e-01 -9.17432383e-02 2.19200253... | [7.7291035652160645, 5.227616786956787] |
7e8811a4-74d5-4a3b-80d6-7e346543ae96 | low-confidence-samples-mining-for-semi | 2306.16201 | null | https://arxiv.org/abs/2306.16201v1 | https://arxiv.org/pdf/2306.16201v1.pdf | Low-Confidence Samples Mining for Semi-supervised Object Detection | Reliable pseudo-labels from unlabeled data play a key role in semi-supervised object detection (SSOD). However, the state-of-the-art SSOD methods all rely on pseudo-labels with high confidence, which ignore valuable pseudo-labels with lower confidence. Additionally, the insufficient excavation for unlabeled data result... | ['Bin Wang', 'Tianxiang Pan', 'Fangyuan Zhang', 'Guandu Liu'] | 2023-06-28 | null | null | null | null | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 1.71026379e-01 4.32930619e-01 -4.61148024e-01 -4.47865307e-01
-9.58714724e-01 -1.98918834e-01 3.48615915e-01 -1.03903213e-03
-4.69827026e-01 9.76180851e-01 -3.15670848e-01 -2.07797438e-01
-7.26480931e-02 -8.44847143e-01 -8.38898838e-01 -9.12157297e-01
1.84829667e-01 3.99897397e-01 7.33552158e-01 1.45912692... | [9.168694496154785, 1.269552230834961] |
ed45a653-1c73-4628-abbf-15639077bb36 | dear-sir-or-madam-may-i-introduce-the-gyafc | 1803.06535 | null | http://arxiv.org/abs/1803.06535v2 | http://arxiv.org/pdf/1803.06535v2.pdf | Dear Sir or Madam, May I introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer | Style transfer is the task of automatically transforming a piece of text in
one particular style into another. A major barrier to progress in this field
has been a lack of training and evaluation datasets, as well as benchmarks and
automatic metrics. In this work, we create the largest corpus for a particular
stylistic... | ['Sudha Rao', 'Joel Tetreault'] | 2018-03-17 | dear-sir-or-madam-may-i-introduce-the-gyafc-1 | https://aclanthology.org/N18-1012 | https://aclanthology.org/N18-1012.pdf | naacl-2018-6 | ['formality-style-transfer'] | ['natural-language-processing'] | [ 5.81294358e-01 1.68620735e-01 -3.87348324e-01 -4.86922890e-01
-1.26684439e+00 -9.92668629e-01 1.14479077e+00 -1.95184097e-01
-4.04084951e-01 1.28540742e+00 4.00533020e-01 -4.86120582e-01
3.91044259e-01 -3.47201735e-01 -6.81085944e-01 -1.54640079e-01
4.58543807e-01 8.72511268e-01 1.98438078e-01 -4.72772717... | [11.501630783081055, 9.752184867858887] |
69bbe372-ac55-478e-bc55-87000a9a66e3 | adapter-tst-a-parameter-efficient-method-for | 2305.05945 | null | https://arxiv.org/abs/2305.05945v1 | https://arxiv.org/pdf/2305.05945v1.pdf | Adapter-TST: A Parameter Efficient Method for Multiple-Attribute Text Style Transfer | Adapting a large language model for multiple-attribute text style transfer via fine-tuning can be challenging due to the significant amount of computational resources and labeled data required for the specific task. In this paper, we address this challenge by introducing AdapterTST, a framework that freezes the pre-tra... | ['Nancy F. Chen', 'Roy Ka-Wei Lee', 'Zhiqiang Hu'] | 2023-05-10 | null | null | null | null | ['style-transfer', 'text-style-transfoer'] | ['computer-vision', 'natural-language-processing'] | [ 3.12547743e-01 1.51325926e-01 7.23213330e-02 -7.71895409e-01
-9.03092384e-01 -9.22772348e-01 5.85841060e-01 -2.81774372e-01
-4.32762563e-01 7.74226844e-01 1.39017150e-01 -3.62913162e-01
3.82071823e-01 -7.13565826e-01 -7.55125463e-01 -3.85669023e-01
5.71114242e-01 9.33939755e-01 -6.35936633e-02 -5.70200562... | [11.53559398651123, 9.57422924041748] |
4de2f1ad-f39c-4703-b574-b4dae0bfee27 | data-aware-neural-architecture-search | 2304.01821 | null | https://arxiv.org/abs/2304.01821v1 | https://arxiv.org/pdf/2304.01821v1.pdf | Data Aware Neural Architecture Search | Neural Architecture Search (NAS) is a popular tool for automatically generating Neural Network (NN) architectures. In early NAS works, these tools typically optimized NN architectures for a single metric, such as accuracy. However, in the case of resource constrained Machine Learning, one single metric is not enough to... | ['Xenofon Fafoutis', 'Jan Madsen', 'Emil Njor'] | 2023-04-04 | null | null | null | null | ['architecture-search'] | ['methodology'] | [-3.21263582e-01 -2.91775435e-01 -3.59508276e-01 -4.18203920e-01
-3.25723588e-01 -5.15833676e-01 1.25858501e-01 1.72314774e-02
-3.28636974e-01 5.99349022e-01 -1.27242655e-01 -9.56608117e-01
-1.38536096e-01 -8.70756984e-01 -7.97385991e-01 -4.75495994e-01
2.64924139e-01 5.52969217e-01 3.51828516e-01 -1.39662270... | [8.413973808288574, 3.3572630882263184] |
fa2a38c1-657a-43f1-9927-906f67ba6a3d | a-perturbation-bound-on-the-subspace | 2206.14278 | null | https://arxiv.org/abs/2206.14278v1 | https://arxiv.org/pdf/2206.14278v1.pdf | A Perturbation Bound on the Subspace Estimator from Canonical Projections | This paper derives a perturbation bound on the optimal subspace estimator obtained from a subset of its canonical projections contaminated by noise. This fundamental result has important implications in matrix completion, subspace clustering, and related problems. | ['Daniel L. Pimentel-Alarcón', 'Karan Srivastava'] | 2022-06-28 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 4.19788629e-01 -2.08993748e-01 -2.36655831e-01 -1.52725121e-02
-7.53341436e-01 -8.48932385e-01 3.72936189e-01 -6.42310262e-01
-2.04871878e-01 7.62659729e-01 4.76696551e-01 -2.34425545e-01
-4.31265652e-01 4.96208481e-02 -3.94463778e-01 -9.56144631e-01
-3.69945288e-01 3.33269626e-01 -3.34841311e-01 2.25108847... | [7.543132305145264, 4.393241882324219] |
0a560a0c-8957-4158-9ed5-91d6aa851bcb | tg-vqa-ternary-game-of-video-question | 2305.10049 | null | https://arxiv.org/abs/2305.10049v2 | https://arxiv.org/pdf/2305.10049v2.pdf | TG-VQA: Ternary Game of Video Question Answering | Video question answering aims at answering a question about the video content by reasoning the alignment semantics within them. However, since relying heavily on human instructions, i.e., annotations or priors, current contrastive learning-based VideoQA methods remains challenging to perform fine-grained visual-linguis... | ['Jie Chen', 'Chang Liu', 'Zhennan Wang', 'Kai Chen', 'Songyang Zhang', 'Zesen Cheng', 'Peng Jin', 'Hao Li'] | 2023-05-17 | null | null | null | null | ['video-question-answering'] | ['computer-vision'] | [ 2.19751429e-02 -1.72241285e-01 -2.61113849e-02 -2.13876203e-01
-9.34921980e-01 -8.39541554e-01 5.42861164e-01 -2.01269746e-01
-4.43351924e-01 4.05835569e-01 1.79096535e-01 -4.72278625e-01
7.68599659e-02 -6.52097344e-01 -9.51634884e-01 -3.98618758e-01
2.04655305e-01 5.34632862e-01 5.20725310e-01 -5.54713726... | [10.392266273498535, 1.0364511013031006] |
3c242e1c-129c-40bc-bd46-a669299bd431 | action-and-intention-recognition-of | 1810.09805 | null | http://arxiv.org/abs/1810.09805v1 | http://arxiv.org/pdf/1810.09805v1.pdf | Action and intention recognition of pedestrians in urban traffic | Action and intention recognition of pedestrians in urban settings are
challenging problems for Advanced Driver Assistance Systems as well as future
autonomous vehicles to maintain smooth and safe traffic. This work investigates
a number of feature extraction methods in combination with several machine
learning algorith... | ['Fernando Alonso-Fernandez', 'Cristofer Englund', 'Boris Duran', 'Dimitrios Varytimidis'] | 2018-10-23 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [-1.37116343e-01 -1.15467258e-01 -4.18069601e-01 -6.12418413e-01
-5.38942993e-01 -4.14858535e-02 8.47767651e-01 -3.39242280e-01
-6.57135129e-01 4.39649433e-01 6.71603605e-02 -6.18804038e-01
3.65612149e-01 -5.27848125e-01 -4.83314127e-01 -7.84257710e-01
1.69795886e-01 6.28018156e-02 6.27751529e-01 -1.58424854... | [7.7363176345825195, -0.6535366773605347] |
761da8da-a5e3-496e-8798-e908c234216d | dc-shadownet-single-image-hard-and-soft-1 | 2207.10434 | null | https://arxiv.org/abs/2207.10434v1 | https://arxiv.org/pdf/2207.10434v1.pdf | DC-ShadowNet: Single-Image Hard and Soft Shadow Removal Using Unsupervised Domain-Classifier Guided Network | Shadow removal from a single image is generally still an open problem. Most existing learning-based methods use supervised learning and require a large number of paired images (shadow and corresponding non-shadow images) for training. A recent unsupervised method, Mask-ShadowGAN, addresses this limitation. However, it ... | ['Robby T. Tan', 'Aashish Sharma', 'Yeying Jin'] | 2022-07-21 | dc-shadownet-single-image-hard-and-soft | http://openaccess.thecvf.com//content/ICCV2021/html/Jin_DC-ShadowNet_Single-Image_Hard_and_Soft_Shadow_Removal_Using_Unsupervised_Domain-Classifier_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Jin_DC-ShadowNet_Single-Image_Hard_and_Soft_Shadow_Removal_Using_Unsupervised_Domain-Classifier_ICCV_2021_paper.pdf | iccv-2021-1 | ['shadow-removal', 'image-shadow-removal'] | ['computer-vision', 'computer-vision'] | [ 7.29804695e-01 6.90888464e-02 -3.21964324e-02 -3.88497651e-01
-2.97480017e-01 -4.45812374e-01 4.03361112e-01 -3.68405074e-01
-7.99928093e-04 9.16846752e-01 -1.74132153e-01 -4.08479065e-01
2.90427148e-01 -8.52596879e-01 -6.15867913e-01 -1.01881111e+00
2.23195970e-01 3.75434875e-01 1.02094138e+00 -2.09513694... | [10.844452857971191, -4.102987766265869] |
27c11294-32e1-44bd-a191-3a5fd37479d7 | table-filling-multi-task-recurrent-neural | null | null | https://aclanthology.org/C16-1239 | https://aclanthology.org/C16-1239.pdf | Table Filling Multi-Task Recurrent Neural Network for Joint Entity and Relation Extraction | This paper proposes a novel context-aware joint entity and word-level relation extraction approach through semantic composition of words, introducing a Table Filling Multi-Task Recurrent Neural Network (TF-MTRNN) model that reduces the entity recognition and relation classification tasks to a table-filling problem and ... | ['Hinrich Sch{\\"u}tze', 'Bernt Andrassy', 'Pankaj Gupta'] | 2016-12-01 | table-filling-multi-task-recurrent-neural-1 | https://aclanthology.org/C16-1239 | https://aclanthology.org/C16-1239.pdf | coling-2016-12 | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [ 2.84384489e-01 5.71376920e-01 -3.61242533e-01 -5.28275609e-01
-7.59370923e-01 -3.22788984e-01 4.66349661e-01 7.94375539e-01
-6.40114009e-01 1.11096430e+00 1.32220134e-01 -7.25282609e-01
-1.76432312e-01 -1.18799412e+00 -6.61707878e-01 -1.73586130e-01
-1.70894176e-01 8.57824206e-01 7.98951983e-02 -4.61770773... | [9.313231468200684, 8.763917922973633] |
dec73aa0-33c4-4736-bdbb-6a3a1e08257d | distant-domain-transfer-learning-for-medical | 2012.06346 | null | https://arxiv.org/abs/2012.06346v1 | https://arxiv.org/pdf/2012.06346v1.pdf | Distant Domain Transfer Learning for Medical Imaging | Medical image processing is one of the most important topics in the field of the Internet of Medical Things (IoMT). Recently, deep learning methods have carried out state-of-the-art performances on medical image tasks. However, conventional deep learning have two main drawbacks: 1) insufficient training data and 2) the... | ['Houbing Song', 'Jian Wang', 'Yongxin Liu', 'Meryl Liu', 'Shuteng Niu'] | 2020-12-10 | null | null | null | null | ['unet-segmentation'] | ['computer-vision'] | [ 1.68665320e-01 -2.76486456e-01 -3.37418646e-01 -3.56086552e-01
-9.00308430e-01 -2.64134645e-01 1.79289609e-01 -1.22885192e-02
-5.59388161e-01 9.18706477e-01 -1.32765859e-01 -4.89167571e-01
-2.14150071e-01 -8.07324886e-01 -5.46534777e-01 -8.39553714e-01
2.74582267e-01 9.54156339e-01 3.93253326e-01 9.30935070... | [14.789413452148438, -2.023601531982422] |
bd558347-9876-4b22-8187-b3590d1cb362 | three-dimensional-microstructural-image | 2204.01645 | null | https://arxiv.org/abs/2204.01645v1 | https://arxiv.org/pdf/2204.01645v1.pdf | Three-dimensional Microstructural Image Synthesis from 2D Backscattered Electron Image of Cement Paste | The microstructure is significant for exploring the physical properties of hardened cement paste. In general, the microstructures of hardened cement paste are obtained by microscopy. As a popular method, scanning electron microscopy (SEM) can acquire high-quality 2D images but fails to obtain 3D microstructures.Althoug... | ['Bo Yang', 'Yuxuan Zhang', 'Qinfei Li', 'Pengkun Hou', 'Lin Wang', 'Xu Wu', 'Xin Zhao'] | 2022-04-04 | null | null | null | null | ['texture-synthesis'] | ['computer-vision'] | [ 3.1022993e-01 -5.8089662e-02 2.6908159e-01 6.1336942e-02
-3.2503435e-01 5.9592184e-02 3.4255552e-01 4.1120270e-01
-2.2252202e-01 5.0474238e-01 -3.6949432e-01 -2.6831970e-01
-4.1422290e-01 -1.2093476e+00 -5.0026971e-01 -8.5699397e-01
-7.3471524e-02 8.6464953e-01 6.5826166e-01 -9.4506674e-02
5.5844158e-01... | [12.914645195007324, -2.7731945514678955] |
a1c238c5-7bda-456d-aeec-1a90753db4c5 | a-large-scale-dataset-for-end-to-end-table | 2303.14884 | null | https://arxiv.org/abs/2303.14884v1 | https://arxiv.org/pdf/2303.14884v1.pdf | A large-scale dataset for end-to-end table recognition in the wild | Table recognition (TR) is one of the research hotspots in pattern recognition, which aims to extract information from tables in an image. Common table recognition tasks include table detection (TD), table structure recognition (TSR) and table content recognition (TCR). TD is to locate tables in the image, TCR recognize... | ['Zhenghui Gu', 'Shuangping Huang', 'Xinwu Liu', 'Lei Hu', 'Fan Yang'] | 2023-03-27 | null | null | null | null | ['table-recognition', 'table-annotation', 'table-detection', 'table-annotation'] | ['computer-vision', 'knowledge-base', 'miscellaneous', 'natural-language-processing'] | [ 1.68064889e-02 -1.61183193e-01 -9.33119059e-02 -2.49432072e-01
-8.34003210e-01 -1.14213097e+00 3.90273243e-01 2.27635443e-01
-7.14289770e-02 6.85862005e-01 3.56297195e-02 -3.57795209e-01
-1.72437340e-01 -8.84393871e-01 -6.65049374e-01 -4.12926972e-01
3.00660640e-01 7.19725728e-01 2.58639097e-01 -2.46432379... | [11.69633674621582, 3.008216619491577] |
982dc15b-517d-425a-b2e0-309542d2eaa4 | adamsformer-for-spatial-action-localization | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chi_AdamsFormer_for_Spatial_Action_Localization_in_the_Future_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chi_AdamsFormer_for_Spatial_Action_Localization_in_the_Future_CVPR_2023_paper.pdf | AdamsFormer for Spatial Action Localization in the Future | Predicting future action locations is vital for applications like human-robot collaboration. While some computer vision tasks have made progress in predicting human actions, accurately localizing these actions in future frames remains an area with room for improvement. We introduce a new task called spatial action ... | ['Chiho Choi', 'Karthik Ramani', 'Yi Xu', 'Nakul Agarwal', 'Kwonjoon Lee', 'Hyung-gun Chi'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['action-localization'] | ['computer-vision'] | [ 2.38877952e-01 -2.48269200e-01 -4.80988681e-01 -3.34259182e-01
-4.18621421e-01 -1.13847122e-01 7.96732843e-01 -2.01956928e-01
-6.21912777e-01 7.80459583e-01 6.14667237e-01 -1.17991187e-01
-7.53245642e-03 -3.90980661e-01 -6.81238174e-01 -6.28205240e-01
-4.33467746e-01 7.01379105e-02 5.78718185e-01 8.00135955... | [8.138017654418945, 0.4040980935096741] |
23ace87b-b618-4eb7-81ae-277b49c17efe | learning-a-probabilistic-model-for | 1812.07460 | null | http://arxiv.org/abs/1812.07460v2 | http://arxiv.org/pdf/1812.07460v2.pdf | Learning a Probabilistic Model for Diffeomorphic Registration | We propose to learn a low-dimensional probabilistic deformation model from
data which can be used for registration and the analysis of deformations. The
latent variable model maps similar deformations close to each other in an
encoding space. It enables to compare deformations, generate normal or
pathological deformati... | ['Boris Mailhé', 'Hervé Delingette', 'Nicholas Ayache', 'Julian Krebs', 'Tommaso Mansi'] | 2018-12-18 | null | null | null | null | ['deformable-medical-image-registration', 'diffeomorphic-medical-image-registration'] | ['medical', 'medical'] | [ 2.75636986e-02 2.99800664e-01 2.79577613e-01 -3.23337615e-01
-8.30979943e-01 -3.99004787e-01 5.98040521e-01 8.15529898e-02
-5.35401642e-01 5.98342597e-01 2.60731816e-01 3.00018758e-01
-2.60371685e-01 -7.97945738e-01 -8.46249104e-01 -1.14780390e+00
-4.56734091e-01 7.63794303e-01 1.92672729e-01 1.76245198... | [14.037057876586914, -2.4571945667266846] |
e6794bb7-fb84-4bd7-8d0e-328791ee21d1 | dynamicgem-a-library-for-dynamic-graph | 1811.10734 | null | http://arxiv.org/abs/1811.10734v1 | http://arxiv.org/pdf/1811.10734v1.pdf | DynamicGEM: A Library for Dynamic Graph Embedding Methods | DynamicGEM is an open-source Python library for learning node representations
of dynamic graphs. It consists of state-of-the-art algorithms for defining
embeddings of nodes whose connections evolve over time. The library also
contains the evaluation framework for four downstream tasks on the network:
graph reconstructi... | ['Emilio Ferrara', 'Arquimedes Canedo', 'Palash Goyal', 'Ninareh Mehrabi', 'Sujit Rokka Chhetri'] | 2018-11-26 | null | null | null | null | ['dynamic-graph-embedding', 'graph-reconstruction'] | ['graphs', 'graphs'] | [-5.59521914e-01 1.79848313e-01 -3.59857231e-01 -2.40644500e-01
1.56755731e-01 -7.53995717e-01 7.20187545e-01 2.72662222e-01
1.07902050e-01 4.45425719e-01 4.91290316e-02 -6.80556476e-01
-3.92512798e-01 -1.11058021e+00 -2.28696570e-01 -4.62424129e-01
-1.14069831e+00 6.56178057e-01 6.33064508e-01 -4.19366837... | [7.089761257171631, 6.039525032043457] |
ce8a6e4d-d46c-4b18-90a6-c4689903f34c | a-survey-on-knowledge-enhanced-multimodal | 2211.12328 | null | https://arxiv.org/abs/2211.12328v2 | https://arxiv.org/pdf/2211.12328v2.pdf | A survey on knowledge-enhanced multimodal learning | Multimodal learning has been a field of increasing interest, aiming to combine various modalities in a single joint representation. Especially in the area of visiolinguistic (VL) learning multiple models and techniques have been developed, targeting a variety of tasks that involve images and text. VL models have reache... | ['Giorgos Stamou', 'Maria Lymperaiou'] | 2022-11-19 | null | null | null | null | ['vision-language-navigation', 'visual-reasoning', 'conditional-image-generation', 'factual-visual-question-answering', 'visual-dialogue', 'visual-storytelling', 'visual-dialogue', 'visual-commonsense-reasoning', 'visual-reasoning', 'visual-entailment'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'natural-language-processing', 'natural-language-processing', 'reasoning', 'reasoning', 'reasoning'] | [ 7.41592124e-02 2.61046916e-01 -5.15392065e-01 -1.32729694e-01
-2.23868787e-01 -6.80217743e-01 9.07337844e-01 3.75028640e-01
-4.21534002e-01 8.43389273e-01 2.82627672e-01 -3.31773520e-01
-4.16270047e-01 -7.48762786e-01 -5.00688374e-01 -5.82762897e-01
9.59643349e-02 3.10843796e-01 1.02056280e-01 -4.21096802... | [10.65420913696289, 1.8525327444076538] |
ad6e3b1e-61dd-489d-b8ea-27764466d2d8 | handy-towards-a-high-fidelity-3d-hand-shape | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Potamias_Handy_Towards_a_High_Fidelity_3D_Hand_Shape_and_Appearance_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Potamias_Handy_Towards_a_High_Fidelity_3D_Hand_Shape_and_Appearance_CVPR_2023_paper.pdf | Handy: Towards a High Fidelity 3D Hand Shape and Appearance Model | Over the last few years, with the advent of virtual and augmented reality, an enormous amount of research has been focused on modeling, tracking and reconstructing human hands. Given their power to express human behavior, hands have been a very important, but challenging component of the human body. Currently, most... | ['Stefanos Zafeiriou', 'Vasileios Triantafyllou', 'Stylianos Moschoglou', 'Stylianos Ploumpis', 'Rolandos Alexandros Potamias'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['hand-pose-estimation'] | ['computer-vision'] | [-4.66803648e-02 1.30896300e-01 1.59287676e-02 1.32059321e-01
-2.26156861e-01 -3.08806866e-01 3.83885324e-01 -6.34965301e-01
2.09314916e-02 6.42839551e-01 1.95094392e-01 2.33343765e-01
3.34046707e-02 -7.69546449e-01 -6.08864248e-01 -5.65827549e-01
9.73865688e-02 9.22757447e-01 4.43801358e-02 -2.98857540... | [7.044389724731445, -1.189358115196228] |
b02cfd71-096b-430b-a29f-18e91c8ee2f7 | coupled-oscillatory-recurrent-neural-network | 2010.00951 | null | https://arxiv.org/abs/2010.00951v2 | https://arxiv.org/pdf/2010.00951v2.pdf | Coupled Oscillatory Recurrent Neural Network (coRNN): An accurate and (gradient) stable architecture for learning long time dependencies | Circuits of biological neurons, such as in the functional parts of the brain can be modeled as networks of coupled oscillators. Inspired by the ability of these systems to express a rich set of outputs while keeping (gradients of) state variables bounded, we propose a novel architecture for recurrent neural networks. O... | ['Siddhartha Mishra', 'T. Konstantin Rusch'] | 2020-10-02 | null | https://openreview.net/forum?id=F3s69XzWOia | https://openreview.net/pdf?id=F3s69XzWOia | iclr-2021-1 | ['sequential-image-classification'] | ['computer-vision'] | [ 1.45414501e-01 2.19716489e-01 3.22381228e-01 -7.71033904e-03
3.67399126e-01 -5.48988461e-01 4.30169940e-01 -3.91968608e-01
-4.69589472e-01 5.42167962e-01 -7.82817900e-02 -2.48690978e-01
5.16201509e-03 -4.80137169e-01 -8.60515594e-01 -9.54037189e-01
-2.10365370e-01 7.84055814e-02 2.00453207e-01 -6.85750782... | [7.755131721496582, 3.2860071659088135] |
5041e598-87e0-4384-8bfe-09076d77e3ce | covidx-computer-aided-diagnosis-of-covid-19 | 2012.13605 | null | https://arxiv.org/abs/2012.13605v1 | https://arxiv.org/pdf/2012.13605v1.pdf | COVIDX: Computer-aided diagnosis of Covid-19 and its severity prediction with raw digital chest X-ray images | Coronavirus disease (COVID-19) is a contagious infection caused by severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) and it has infected and killed millions of people across the globe. In the absence of specific drugs or vaccines for the treatment of COVID-19 and the limitation of prevailing diagnostic techn... | ['Saiqa Andleeb', 'Syed Ali Abbas', 'Wajid Arshad Abbasi'] | 2020-12-25 | null | null | null | null | ['severity-prediction'] | ['computer-vision'] | [-1.11979902e-01 -7.34600246e-01 -2.32633632e-02 -8.46287459e-02
-3.34382236e-01 -8.12312126e-01 1.57732382e-01 4.42628741e-01
-2.64227092e-01 6.90246284e-01 -2.01640390e-02 -5.19936442e-01
-1.38509139e-01 -6.64415300e-01 -1.97627187e-01 -7.46178329e-01
-1.36736140e-01 9.31164742e-01 1.43340811e-01 2.80252159... | [15.57456111907959, -1.6903070211410522] |
d4ef6117-4558-4638-8b90-1dea25d1dc83 | regen-zero-shot-text-classification-via | 2305.10703 | null | https://arxiv.org/abs/2305.10703v1 | https://arxiv.org/pdf/2305.10703v1.pdf | ReGen: Zero-Shot Text Classification via Training Data Generation with Progressive Dense Retrieval | With the development of large language models (LLMs), zero-shot learning has attracted much attention for various NLP tasks. Different from prior works that generate training data with billion-scale natural language generation (NLG) models, we propose a retrieval-enhanced framework to create training data from a genera... | ['Chao Zhang', 'Jiaming Shen', 'Yu Meng', 'Rongzhi Zhang', 'Yuchen Zhuang', 'Yue Yu'] | 2023-05-18 | null | null | null | null | ['topic-coverage'] | ['natural-language-processing'] | [ 2.20766038e-01 6.68361545e-01 -4.61294115e-01 -1.75214142e-01
-1.36248529e+00 -2.62687415e-01 1.12942004e+00 4.21844795e-02
-5.63883722e-01 1.00566840e+00 8.80116582e-01 -9.14435759e-02
2.92467266e-01 -8.99949372e-01 -6.19981647e-01 -3.42100412e-01
3.75658423e-01 9.96745050e-01 7.39328489e-02 -3.72701466... | [11.517375946044922, 8.587799072265625] |
3b79efad-9dc1-49ea-9aba-8a801489c9c2 | high-precision-machine-learning-based-indoor | 2303.03743 | null | https://arxiv.org/abs/2303.03743v1 | https://arxiv.org/pdf/2303.03743v1.pdf | High-Precision Machine-Learning Based Indoor Localization with Massive MIMO System | High-precision cellular-based localization is one of the key technologies for next-generation communication systems. In this paper, we investigate the potential of applying machine learning (ML) to a massive multiple-input multiple-output (MIMO) system to enhance localization accuracy. We analyze a new ML-based localiz... | ['Fredrik Tufvesson', 'Liang Liu', 'Xuesong Cai', 'Michiel Sandra', 'Ilayda Yaman', 'Guoda Tian'] | 2023-03-07 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [-3.63264501e-01 -1.09461263e-01 -1.92717955e-01 -7.95777440e-02
-1.03994238e+00 -5.76824009e-01 2.00854465e-01 1.86433136e-01
-2.87000656e-01 1.12635911e+00 -2.90102363e-01 -9.72006738e-01
-4.09052163e-01 -7.86963701e-01 -6.71733201e-01 -7.42861629e-01
-6.26463115e-01 2.10607409e-01 -1.81746230e-01 1.90142989... | [6.37555456161499, 0.9720033407211304] |
33dea82c-0faf-408e-9503-4670e8c8fe70 | learning-contact-based-navigation-in-crowds | 2303.01455 | null | https://arxiv.org/abs/2303.01455v1 | https://arxiv.org/pdf/2303.01455v1.pdf | Learning Contact-based Navigation in Crowds | Navigation strategies that intentionally incorporate contact with humans (i.e. "contact-based" social navigation) in crowded environments are largely unexplored even though collision-free social navigation is a well studied problem. Traditional social navigation frameworks require the robot to stop suddenly or "freeze"... | ['Luis Sentis', 'Junfeng Jiao', 'Kyle Morgenstein'] | 2023-03-02 | null | null | null | null | ['social-navigation'] | ['robots'] | [-4.57895510e-02 4.14240211e-01 3.83116126e-01 3.93700833e-03
-1.07798256e-01 -3.67939115e-01 5.03191352e-01 1.96380526e-01
-1.01337302e+00 1.07615709e+00 2.78585255e-02 -4.20448452e-01
-2.13429421e-01 -1.06842458e+00 -4.55539584e-01 -6.14655554e-01
-3.83840442e-01 8.07453990e-01 7.15090156e-01 -7.46203184... | [4.87291145324707, 1.1047884225845337] |
a9e5b38e-7120-47ca-8eb2-a193725c3aef | geometric-latent-diffusion-models-for-3d | 2305.01140 | null | https://arxiv.org/abs/2305.01140v1 | https://arxiv.org/pdf/2305.01140v1.pdf | Geometric Latent Diffusion Models for 3D Molecule Generation | Generative models, especially diffusion models (DMs), have achieved promising results for generating feature-rich geometries and advancing foundational science problems such as molecule design. Inspired by the recent huge success of Stable (latent) Diffusion models, we propose a novel and principled method for 3D molec... | ['Jure Leskovec', 'Stefano Ermon', 'Ron Dror', 'Alexander Powers', 'Minkai Xu'] | 2023-05-02 | null | null | null | null | ['3d-molecule-generation'] | ['medical'] | [-2.24804990e-02 1.02043673e-01 -2.14039817e-01 -7.30366409e-02
-5.88952959e-01 -6.76901340e-01 9.10323381e-01 -3.95096168e-02
2.79595554e-01 8.16395044e-01 5.94629467e-01 -4.20612037e-01
-2.45760828e-02 -1.02868629e+00 -9.48318064e-01 -1.03863192e+00
-1.14775114e-01 5.23928940e-01 -5.66058755e-01 -1.62528262... | [5.057343006134033, 5.732901573181152] |
aa55b43d-6f60-48df-960a-68bf3c92cc0d | explainable-authorship-verification-in-social | 1910.08144 | null | https://arxiv.org/abs/1910.08144v2 | https://arxiv.org/pdf/1910.08144v2.pdf | Explainable Authorship Verification in Social Media via Attention-based Similarity Learning | Authorship verification is the task of analyzing the linguistic patterns of two or more texts to determine whether they were written by the same author or not. The analysis is traditionally performed by experts who consider linguistic features, which include spelling mistakes, grammatical inconsistencies, and stylistic... | ['Robert M. Nickel', 'Dorothea Kolossa', 'Benedikt Boenninghoff', 'Steffen Hessler'] | 2019-10-17 | null | null | null | null | ['authorship-verification'] | ['natural-language-processing'] | [-8.62683132e-02 -5.40704988e-02 -7.12394789e-02 -2.46695966e-01
-2.87843674e-01 -5.80444753e-01 8.77651751e-01 7.83540726e-01
-7.02105463e-01 4.87334520e-01 1.28601477e-01 -2.91270941e-01
-2.23070249e-01 -5.66374362e-01 -2.54312724e-01 -4.82191801e-01
2.39115313e-01 7.56696224e-01 1.02096032e-02 -4.43010539... | [9.604730606079102, 10.523531913757324] |
f5661891-6b97-4808-bca8-a8f3ab8e65db | 191013276 | 1910.13276 | null | https://arxiv.org/abs/1910.13276v2 | https://arxiv.org/pdf/1910.13276v2.pdf | a novel cross-lingual voice cloning approach with a few text-free samples | In this paper, we present a cross-lingual voice cloning approach. BN features obtained by SI-ASR model are used as a bridge across speakers and language boundaries. The relationships between text and BN features are modeled by the latent prosody model. The acoustic model learns the translation from BN features to acous... | ['Xinyong Zhou', 'Xiaorui Wang', 'Lei Xie', 'Hao Che'] | 2019-10-29 | null | null | null | null | ['voice-cloning'] | ['speech'] | [-5.03575169e-02 3.09591386e-02 -3.12711209e-01 -5.27022183e-01
-1.30189621e+00 -5.37432909e-01 4.92478997e-01 -5.90862095e-01
-1.35290980e-01 4.65521812e-01 6.82450056e-01 -4.84955348e-02
4.81987417e-01 -3.89063954e-01 -6.19530678e-01 -3.64980727e-01
2.95607209e-01 2.13937223e-01 -9.51489434e-03 -4.26963449... | [14.827778816223145, 6.6684112548828125] |
05fb44fb-1ec9-43ce-ba9b-078850277bea | cave-correcting-attribute-values-in-e | null | null | https://dl.acm.org/doi/abs/10.1145/3511808.3557161 | https://dl.acm.org/doi/pdf/10.1145/3511808.3557161 | CAVE: Correcting Attribute Values in E-commerce Profiles | Attribute value extraction from product profiles is essential for many applications such as product retrieval, comparison, and recommendation. While existing techniques focus mainly on the extraction task, none of them deals with the problem of correcting wrong attribute values. In this paper we propose CAVE, a novel s... | ['Johann Gamper', 'Mouna Kacimi', 'Kassem Sabeh'] | 2022-10-17 | null | null | null | acm-international-conference-on-information-3 | ['attribute-value-extraction'] | ['natural-language-processing'] | [ 3.49282503e-01 1.87756971e-01 -4.74115878e-01 -7.61409104e-01
-8.51141751e-01 -6.38764679e-01 3.32489640e-01 9.80245709e-01
-4.88465607e-01 7.76280761e-01 2.98002988e-01 -1.81314975e-01
-3.18678260e-01 -1.05304062e+00 -5.81203640e-01 -4.24552374e-02
3.24623525e-01 1.12226319e+00 6.72526807e-02 -7.53159761... | [9.976996421813965, 6.310086250305176] |
3fefa0ac-d2f1-4eba-9a19-0c8ab66ff052 | zits-image-inpainting-by-improving-the | 2210.05950 | null | https://arxiv.org/abs/2210.05950v3 | https://arxiv.org/pdf/2210.05950v3.pdf | ZITS++: Image Inpainting by Improving the Incremental Transformer on Structural Priors | Image inpainting involves filling missing areas of a corrupted image. Despite impressive results have been achieved recently, restoring images with both vivid textures and reasonable structures remains a significant challenge. Previous methods have primarily addressed regular textures while disregarding holistic struct... | ['Yanwei Fu', 'Qiaole Dong', 'Chenjie Cao'] | 2022-10-12 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 9.17676032e-01 1.13715284e-01 7.71450922e-02 -7.31182992e-02
-7.44769931e-01 -1.10475458e-01 3.71839613e-01 -4.68474776e-01
-4.99808267e-02 8.09430361e-01 3.59945834e-01 -5.84256873e-02
-6.34441292e-03 -9.61885810e-01 -1.14422464e+00 -7.83591270e-01
2.64823020e-01 -2.70500124e-01 -6.68955371e-02 -4.65619534... | [11.229966163635254, -1.562385082244873] |
ea63d4af-e68c-4f88-ade6-06f84dfdfe64 | skip-attention-improving-vision-transformers | 2301.02240 | null | https://arxiv.org/abs/2301.02240v2 | https://arxiv.org/pdf/2301.02240v2.pdf | Skip-Attention: Improving Vision Transformers by Paying Less Attention | This work aims to improve the efficiency of vision transformers (ViT). While ViTs use computationally expensive self-attention operations in every layer, we identify that these operations are highly correlated across layers -- a key redundancy that causes unnecessary computations. Based on this observation, we propose ... | ['Amirhossein Habibian', 'Fatih Porikli', 'Yuki M. Asano', 'Amir Ghodrati', 'Shashanka Venkataramanan'] | 2023-01-05 | null | null | null | null | ['video-denoising'] | ['computer-vision'] | [ 9.61241424e-02 6.95897415e-02 1.63510829e-01 -4.00422692e-01
-8.07604134e-01 -2.63487309e-01 3.74357373e-01 -5.19783646e-02
-6.32495165e-01 2.05612361e-01 1.34555325e-01 -4.44486380e-01
3.38969648e-01 -6.72915101e-01 -1.14911175e+00 -4.25969213e-01
1.07334949e-01 5.64703830e-02 5.67530572e-01 1.32434219... | [9.446131706237793, 1.3370610475540161] |
e216cf9a-1b9c-452e-9f79-1d15b56a1d60 | zero3d-semantic-driven-multi-category-3d | 2301.13591 | null | https://arxiv.org/abs/2301.13591v4 | https://arxiv.org/pdf/2301.13591v4.pdf | Zero3D: Semantic-Driven Multi-Category 3D Shape Generation | Semantic-driven 3D shape generation aims to generate 3D objects conditioned on text. Previous works face problems with single-category generation, low-frequency 3D details, and requiring a large number of paired datasets for training. To tackle these challenges, we propose a multi-category conditional diffusion model. ... | ['Yitong Fu', 'Yixuan Shen', 'Bo Han'] | 2023-01-31 | null | null | null | null | ['3d-shape-generation'] | ['computer-vision'] | [ 1.34421187e-02 -1.10996559e-01 1.04121834e-01 -1.30422980e-01
-6.78545713e-01 -5.96364379e-01 6.88275337e-01 -3.84668350e-01
-3.82416025e-02 3.65298063e-01 4.71502632e-01 -9.65058357e-02
4.41903993e-02 -1.14210010e+00 -7.85116315e-01 -6.04605973e-01
3.93398792e-01 4.17222887e-01 1.03829443e-01 5.68109080... | [8.865668296813965, -3.608395576477051] |
0841f17f-3d4f-4141-9779-4209325c0584 | evaluating-mt-systems-a-theoretical-framework | 2202.05806 | null | https://arxiv.org/abs/2202.05806v1 | https://arxiv.org/pdf/2202.05806v1.pdf | Evaluating MT Systems: A Theoretical Framework | This paper outlines a theoretical framework using which different automatic metrics can be designed for evaluation of Machine Translation systems. It introduces the concept of {\em cognitive ease} which depends on {\em adequacy} and {\em lack of fluency}. Thus, cognitive ease becomes the main parameter to be measured r... | ['Rajeev Sangal'] | 2022-02-11 | null | null | null | null | ['speech-to-speech-translation'] | ['speech'] | [-7.17260092e-02 3.36148083e-01 -3.74936104e-01 -3.59917104e-01
-6.33686125e-01 -7.72239804e-01 9.43259358e-01 8.59917924e-02
-3.21059465e-01 8.26100588e-01 1.90047503e-01 -7.64300048e-01
-5.08024514e-01 -4.95582134e-01 1.06315307e-01 -2.76348114e-01
4.99811202e-01 7.68645644e-01 7.12222094e-03 -5.73329449... | [11.17314624786377, 9.798202514648438] |
f552cdd8-5e51-4756-8632-763fcc98afd7 | controllable-radiance-fields-for-dynamic-face | 2210.05825 | null | https://arxiv.org/abs/2210.05825v1 | https://arxiv.org/pdf/2210.05825v1.pdf | Controllable Radiance Fields for Dynamic Face Synthesis | Recent work on 3D-aware image synthesis has achieved compelling results using advances in neural rendering. However, 3D-aware synthesis of face dynamics hasn't received much attention. Here, we study how to explicitly control generative model synthesis of face dynamics exhibiting non-rigid motion (e.g., facial expressi... | ['Alexander G. Schwing', 'Oluwasanmi Koyejo', 'Liqian Ma', 'Peiye Zhuang'] | 2022-10-11 | null | null | null | null | ['face-parsing', 'face-generation', '3d-aware-image-synthesis'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.78062731e-01 2.26520255e-01 1.07816495e-01 -6.46977961e-01
-2.84292519e-01 -6.69497490e-01 9.88137245e-01 -8.28745663e-01
2.06233397e-01 4.17025149e-01 4.67183471e-01 1.07117057e-01
3.43078732e-01 -5.43862104e-01 -7.88467348e-01 -8.46828401e-01
1.40112579e-01 4.57233377e-02 -4.32671726e-01 -3.69851701... | [12.72714900970459, -0.3902129828929901] |
d693c095-8ed8-4e7c-9914-e8ccf5f87020 | context-enhanced-stereo-transformer | 2210.11719 | null | https://arxiv.org/abs/2210.11719v1 | https://arxiv.org/pdf/2210.11719v1.pdf | Context-Enhanced Stereo Transformer | Stereo depth estimation is of great interest for computer vision research. However, existing methods struggles to generalize and predict reliably in hazardous regions, such as large uniform regions. To overcome these limitations, we propose Context Enhanced Path (CEP). CEP improves the generalization and robustness aga... | ['Yingwei Li', 'Alan Yuille', 'Mathias Unberath', 'Russell H. Taylor', 'Zheng Wang', 'Yongkui Yang', 'Zhaoshuo Li', 'Weiyu Guo'] | 2022-10-21 | null | null | null | null | ['stereo-depth-estimation', 'stereo-matching-1'] | ['computer-vision', 'computer-vision'] | [ 1.77606493e-01 -4.32807148e-01 -2.61132587e-02 -3.44157159e-01
-9.17404830e-01 -5.01172006e-01 8.74628067e-01 -4.05859888e-01
-3.36971074e-01 8.06865513e-01 6.19013011e-01 -1.60590902e-01
2.57666886e-01 -7.85127521e-01 -5.84110916e-01 -4.55904305e-01
9.81923789e-02 2.30349913e-01 6.83741629e-01 -2.42955968... | [8.618856430053711, -2.1087024211883545] |
52e88934-8b57-455e-9e18-d1d1b3912f47 | partially-relevant-video-retrieval | 2208.12510 | null | https://arxiv.org/abs/2208.12510v1 | https://arxiv.org/pdf/2208.12510v1.pdf | Partially Relevant Video Retrieval | Current methods for text-to-video retrieval (T2VR) are trained and tested on video-captioning oriented datasets such as MSVD, MSR-VTT and VATEX. A key property of these datasets is that videos are assumed to be temporally pre-trimmed with short duration, whilst the provided captions well describe the gist of the video ... | ['Xun Wang', 'Xirong Li', 'ShuJie Chen', 'Xun Yang', 'Minsong Zhang', 'Xianke Chen', 'Jianfeng Dong'] | 2022-08-26 | null | null | null | null | ['moment-retrieval', 'partially-relevant-video-retrieval'] | ['computer-vision', 'computer-vision'] | [ 3.64316642e-01 -4.48728979e-01 -6.71551108e-01 -2.14311853e-01
-1.43514884e+00 -7.19128132e-01 5.61543643e-01 -4.50915471e-02
-2.77068436e-01 5.51992238e-01 3.65329325e-01 1.16266727e-01
-1.79871514e-01 -2.10486934e-01 -1.09540534e+00 -4.90592420e-01
-2.38895372e-01 3.54520470e-01 2.11684704e-01 -7.83844069... | [10.196479797363281, 0.8020896315574646] |
7de596aa-930f-4afe-941f-4faf2abb74f6 | artificial-life-properties-of-directed | 2005.06060 | null | https://arxiv.org/abs/2005.06060v1 | https://arxiv.org/pdf/2005.06060v1.pdf | Artificial life properties of directed interaction combinators vs. chemlambda | We provide a framework for experimentation at https://mbuliga.github.io/quinegraphs/ic-vs-chem.html#icvschem with two artificial chemistries: directed interaction combinators (dirIC, defined in section 2) and chemlambda. We are interested if these chemistries allow for artificial life behaviour: replication, metabolism... | ['M. Buliga'] | 2020-05-12 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [-6.24433815e-01 4.75603998e-01 1.14442788e-01 1.33144394e-01
1.82422072e-01 -1.08956873e+00 1.24046445e+00 2.78435767e-01
-1.42247111e-01 9.53359187e-01 -1.21212490e-02 -7.76397943e-01
-5.89180365e-02 -1.04589093e+00 -7.39686787e-01 -7.47211635e-01
-2.29717016e-01 6.01253688e-01 2.19317421e-01 -5.36371291... | [5.667630195617676, 4.306291103363037] |
50b4433b-04ee-4678-abfc-f6ffb78dacc4 | poetrydiffusion-towards-joint-semantic-and | 2306.08456 | null | https://arxiv.org/abs/2306.08456v1 | https://arxiv.org/pdf/2306.08456v1.pdf | PoetryDiffusion: Towards Joint Semantic and Metrical Manipulation in Poetry Generation | Poetry generation is a typical and popular task in natural language generation. While prior works have shown success in controlling either semantic or metrical aspects of poetry generation, there are still challenges in addressing both perspectives simultaneously. In this paper, we employ the Diffusion model to generat... | ['Bryan Hooi', 'Yue Feng', 'Chumin Liu', 'Zhiyuan Hu'] | 2023-06-14 | null | null | null | null | ['text-generation'] | ['natural-language-processing'] | [ 2.31156498e-01 8.81394222e-02 2.52813578e-01 -2.58331925e-01
-4.63376343e-01 -6.32005930e-01 9.77711797e-01 -1.18767016e-01
-2.15572253e-01 8.11008751e-01 7.55168557e-01 2.17426792e-01
-1.16782144e-01 -1.22760546e+00 -3.21914464e-01 -3.85227472e-01
6.08477473e-01 3.48073781e-01 -5.73386624e-02 -7.89099336... | [11.683601379394531, 9.17752456665039] |
05a90210-fa0e-446d-9a78-31104ab5da4c | on-the-apparent-conflict-between-individual | 1912.06883 | null | https://arxiv.org/abs/1912.06883v1 | https://arxiv.org/pdf/1912.06883v1.pdf | On the Apparent Conflict Between Individual and Group Fairness | A distinction has been drawn in fair machine learning research between `group' and `individual' fairness measures. Many technical research papers assume that both are important, but conflicting, and propose ways to minimise the trade-offs between these measures. This paper argues that this apparent conflict is based on... | ['Reuben Binns'] | 2019-12-14 | null | null | null | null | ['jurisprudence'] | ['miscellaneous'] | [ 9.05168653e-02 3.83384347e-01 -3.29775393e-01 -7.75282979e-01
-5.73291898e-01 -6.03748918e-01 7.80408204e-01 3.50137830e-01
-8.10261190e-01 5.88755369e-01 6.21966124e-01 -8.01772535e-01
-7.00493753e-01 -4.68411833e-01 4.94025722e-02 -5.15818715e-01
4.85394716e-01 1.52168408e-01 -2.83743829e-01 -2.87898570... | [8.911038398742676, 5.673133850097656] |
6c0909f6-20ac-4601-8b2f-bd681146f26a | spatiotemporal-contrastive-video | 2008.03800 | null | https://arxiv.org/abs/2008.03800v4 | https://arxiv.org/pdf/2008.03800v4.pdf | Spatiotemporal Contrastive Video Representation Learning | We present a self-supervised Contrastive Video Representation Learning (CVRL) method to learn spatiotemporal visual representations from unlabeled videos. Our representations are learned using a contrastive loss, where two augmented clips from the same short video are pulled together in the embedding space, while clips... | ['Ming-Hsuan Yang', 'Boqing Gong', 'Serge Belongie', 'Huisheng Wang', 'Tianjian Meng', 'Rui Qian', 'Yin Cui'] | 2020-08-09 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Qian_Spatiotemporal_Contrastive_Video_Representation_Learning_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Qian_Spatiotemporal_Contrastive_Video_Representation_Learning_CVPR_2021_paper.pdf | cvpr-2021-1 | ['self-supervised-action-recognition'] | ['computer-vision'] | [ 3.92200910e-02 -1.81290969e-01 -5.09003341e-01 -2.68531054e-01
-6.47378623e-01 -5.57819128e-01 5.23089588e-01 -1.46148270e-02
-5.26967347e-01 4.53777701e-01 6.34046257e-01 -5.03516272e-02
2.32363462e-01 -3.73835355e-01 -1.08415401e+00 -6.16650343e-01
-4.97317016e-01 -1.90785423e-01 2.83945113e-01 -9.81652364... | [8.81612777709961, 0.7590177655220032] |
dd29d3ea-c756-4da2-b7f5-06a33400c44d | boltzmann-exploration-expectationmaximisation | null | null | https://arxiv.org/abs/1912.08869 | https://arxiv.org/pdf/1912.08869.pdf | Boltzmann Exploration Expectation–Maximisation | We present a general method for fitting finite mixture models (FMM). Learning
in a mixture model consists of finding the most likely cluster assignment for each
data-point, as well as finding the parameters of the clusters themselves. In many
mixture models, this is difficult with current learning methods, where the... | ['Neil Dhir', 'Mathias Edman'] | 2019-12-18 | null | null | null | arxiv-2019-12 | ['iris-segmentation'] | ['medical'] | [ 3.00202012e-01 8.77906801e-04 -2.32961208e-01 -1.86030884e-04
-1.04777896e+00 -5.35130382e-01 7.38048911e-01 2.78567344e-01
-7.43934929e-01 6.24786258e-01 -3.67181122e-01 -4.20750797e-01
-4.77610916e-01 -7.45098054e-01 -6.74415290e-01 -1.31483138e+00
-1.00783177e-01 1.17668366e+00 1.85221031e-01 2.29248583... | [6.646039962768555, 3.8023557662963867] |
aa70f926-cfe3-4054-952d-ddd8a32068d7 | visual-attention-methods-in-deep-learning-an | 2204.07756 | null | https://arxiv.org/abs/2204.07756v2 | https://arxiv.org/pdf/2204.07756v2.pdf | Visual Attention Methods in Deep Learning: An In-Depth Survey | Inspired by the human cognitive system, attention is a mechanism that imitates the human cognitive awareness about specific information, amplifying critical details to focus more on the essential aspects of data. Deep learning has employed attention to boost performance for many applications. Interestingly, the same at... | ['Ajmal Mian', 'Fahad S Khan', 'Ibrahim Radwan', 'Saeed Anwar', 'Mohammed Hassanin'] | 2022-04-16 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [ 1.27546825e-02 2.00562969e-01 -2.69563884e-01 -1.06036566e-01
-2.16931432e-01 -2.89586723e-01 2.64353871e-01 1.07652682e-03
-4.54962760e-01 3.51740003e-01 3.04023415e-01 -1.66172162e-01
-2.69305378e-01 -6.63919389e-01 -3.25174391e-01 -5.70360363e-01
6.35112077e-02 7.88249895e-02 -5.60694300e-02 -1.60823137... | [9.900928497314453, 2.0220136642456055] |
5b3392de-f5f8-468a-bf8a-ae11d478435f | word-embeddings-via-causal-inference-gender | 2112.05194 | null | https://arxiv.org/abs/2112.05194v1 | https://arxiv.org/pdf/2112.05194v1.pdf | Word Embeddings via Causal Inference: Gender Bias Reducing and Semantic Information Preserving | With widening deployments of natural language processing (NLP) in daily life, inherited social biases from NLP models have become more severe and problematic. Previous studies have shown that word embeddings trained on human-generated corpora have strong gender biases that can produce discriminative results in downstre... | ['Bei Jiang', 'Yanchun Bao', 'Hongsheng Dai', 'Linglong Kong', 'Meichen Liu', 'Shenggang Hu', 'Wenxing Guo', 'Jinhan Xie', 'Dengdeng Yu', 'Lei Ding'] | 2021-12-09 | null | null | null | null | ['word-similarity'] | ['natural-language-processing'] | [-5.06195314e-02 6.30111098e-02 -6.82730854e-01 -6.72578812e-01
-3.84999931e-01 -4.53166425e-01 9.13774312e-01 5.50858200e-01
-6.75659776e-01 5.26475608e-01 8.29611957e-01 -3.15120310e-01
-1.09964319e-01 -8.78637731e-01 -2.85971135e-01 -5.16584039e-01
4.72020060e-01 4.44674462e-01 -1.08196594e-01 -3.86460185... | [9.364980697631836, 10.219145774841309] |
55e1333d-6fc8-4ae0-9086-90318bb0a4a9 | ditto-a-feature-representation-imitation | 2303.02357 | null | https://arxiv.org/abs/2303.02357v1 | https://arxiv.org/pdf/2303.02357v1.pdf | DiTTO: A Feature Representation Imitation Approach for Improving Cross-Lingual Transfer | Zero-shot cross-lingual transfer is promising, however has been shown to be sub-optimal, with inferior transfer performance across low-resource languages. In this work, we envision languages as domains for improving zero-shot transfer by jointly reducing the feature incongruity between the source and the target languag... | ['Monojit Choudhury', 'Sunayana Sitaram', 'Sandipan Dandapat', 'Abbaraju Soujanya', 'Shanu Kumar'] | 2023-03-04 | null | null | null | null | ['zero-shot-cross-lingual-transfer', 'cross-lingual-transfer'] | ['natural-language-processing', 'natural-language-processing'] | [-2.26218000e-01 -1.58305079e-01 -5.12668848e-01 -2.23761797e-01
-1.68145978e+00 -7.17690885e-01 8.48361790e-01 -2.80042410e-01
-6.81724727e-01 9.35303390e-01 3.97790521e-01 -1.59082964e-01
2.80734986e-01 -5.88088214e-01 -8.58091533e-01 -3.01833391e-01
1.91393733e-01 7.04616606e-01 1.90105036e-01 -6.08796418... | [11.030548095703125, 9.751758575439453] |
360a6d59-18ba-4ab1-aec5-84e1b5af848b | is-gpt-4-a-good-data-analyst | 2305.15038 | null | https://arxiv.org/abs/2305.15038v1 | https://arxiv.org/pdf/2305.15038v1.pdf | Is GPT-4 a Good Data Analyst? | As large language models (LLMs) have demonstrated their powerful capabilities in plenty of domains and tasks, including context understanding, code generation, language generation, data storytelling, etc., many data analysts may raise concerns if their jobs will be replaced by AI. This controversial topic has drawn a l... | ['Lidong Bing', 'Xingxuan Li', 'Liying Cheng'] | 2023-05-24 | null | null | null | null | ['code-generation'] | ['computer-code'] | [-5.72498515e-02 1.19731188e-01 -1.61375016e-01 -5.10141790e-01
-8.87312651e-01 -4.20199990e-01 7.40093768e-01 4.37379986e-01
-3.04981381e-01 3.43219966e-01 2.46820211e-01 -6.43464983e-01
8.07727799e-02 -4.93643105e-01 -3.81735414e-01 -7.38414600e-02
-5.13960666e-04 6.14932060e-01 1.18218787e-01 -2.27717876... | [10.799031257629395, 8.70228385925293] |
a78a7dc4-ce51-42d9-9960-d529ad61b44e | three-stream-convolutional-neural-network | null | null | http://openaccess.thecvf.com/content_CVPRW_2019/html/PBVS/Liang_Three-Stream_Convolutional_Neural_Network_With_Multi-Task_and_Ensemble_Learning_for_CVPRW_2019_paper.html | http://openaccess.thecvf.com/content_CVPRW_2019/papers/PBVS/Liang_Three-Stream_Convolutional_Neural_Network_With_Multi-Task_and_Ensemble_Learning_for_CVPRW_2019_paper.pdf | Three-Stream Convolutional Neural Network With Multi-Task and Ensemble Learning for 3D Action Recognition | In this paper, we propose a three-stream convolutional neural network (3SCNN) for action recognition from skeleton sequences, which aims to thoroughly and fully exploit the skeleton data by extracting, learning, fusing and inferring multiple motion-related features, including 3D joint positions and joint displacements ... | ['Hong Zhu', 'Duohan Liang', 'Wanjun Chen', 'Xiaorong Pan', 'Guoliang Fan', 'Guangfeng Lin'] | 2019-06-16 | null | null | null | the-ieee-conference-on-computer-vision-and-1 | ['3d-human-action-recognition'] | ['computer-vision'] | [ 6.66369200e-01 -4.11373138e-01 -2.37626091e-01 -3.07825565e-01
-7.89109707e-01 1.32003397e-01 4.40611005e-01 -2.45915353e-01
-5.09130299e-01 5.17153442e-01 5.97043753e-01 3.78532499e-01
-3.74166310e-01 -4.46942359e-01 -4.82929617e-01 -8.46239507e-01
-2.90349782e-01 1.39683187e-01 6.21740401e-01 -2.35720016... | [7.842494487762451, 0.3504156470298767] |
fa9e7d98-4210-437a-a660-6d608a010737 | satellite-image-small-target-application | null | null | https://ieeexplore.ieee.org/abstract/document/9233819 | https://ieeexplore.ieee.org/abstract/document/9233819 | Satellite Image Small Target Application Based on Deep Segmented Residual Neural Network | This study employs a deep segmented residual neural network model to analyze the super-resolution of a single satellite image. A deep convolutional neural network model was analyzed, and its performance was improved. We proposed two residual layers to divide the deep network into two groups, the sum of the two residual... | ['Yunqing Liu', 'Zikang Wei'] | 2020-10-26 | null | null | null | null | ['satellite-image-super-resolution'] | ['computer-vision'] | [ 0.21343476 -0.25466868 0.3234846 -0.2027449 -0.22552861 0.01287158
0.09424251 -0.6401011 -0.36904532 0.66104066 0.22465666 -0.01742836
-0.12709023 -1.0276184 -0.27693975 -1.0078496 -0.57237905 -0.58282715
0.534587 -0.4656535 0.24439514 0.7066484 -1.6808574 0.21914306
1.1048813 1.2632916 0.... | [10.318951606750488, -1.9022626876831055] |
27d004eb-67dc-40d3-a3bf-f19914671ccd | milliflow-scene-flow-estimation-on-mmwave | 2306.17010 | null | https://arxiv.org/abs/2306.17010v2 | https://arxiv.org/pdf/2306.17010v2.pdf | milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing | Approaching the era of ubiquitous computing, human motion sensing plays a crucial role in smart systems for decision making, user interaction, and personalized services. Extensive research has been conducted on human tracking, pose estimation, gesture recognition, and activity recognition, which are predominantly based... | ['Chris Xiaoxuan Lu', 'Peijun Zhao', 'Zhen Luo', 'Fangqiang Ding'] | 2023-06-29 | null | null | null | null | ['pose-estimation', 'activity-recognition', 'gesture-recognition', 'human-parsing', 'human-activity-recognition', 'scene-flow-estimation', 'decision-making', 'human-activity-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'reasoning', 'time-series'] | [ 2.31655076e-01 -2.79193282e-01 -3.44692260e-01 -2.50724852e-01
-5.48799455e-01 -4.29480702e-01 4.27310228e-01 -3.18734527e-01
-4.06223774e-01 4.88481164e-01 3.52204829e-01 -1.74224019e-01
1.54820755e-01 -6.28610194e-01 -1.62411630e-01 -9.14189816e-01
6.12251870e-02 -1.28232604e-02 2.84310311e-01 1.97342962... | [6.943568229675293, 0.3348081409931183] |
e0274b61-6348-437e-ad70-9dad37192af6 | neural-comprehension-language-models-with | 2304.01665 | null | https://arxiv.org/abs/2304.01665v2 | https://arxiv.org/pdf/2304.01665v2.pdf | Mastering Symbolic Operations: Augmenting Language Models with Compiled Neural Networks | Language models (LMs) proficiency in handling deterministic symbolic reasoning and rule-based tasks remains limited due to their dependency implicit learning on textual data. To enable fully rule comprehension ability, we explore how to incorporate compiled neural networks (CoNNs) which weight is specially designed int... | ['Jun Zhao', 'Kang Liu', 'Shizhu He', 'Bin Li', 'Fei Xia', 'Minjun Zhu', 'Yixuan Weng'] | 2023-04-04 | null | null | null | null | ['arithmetic-reasoning'] | ['reasoning'] | [ 2.65590191e-01 4.07580376e-01 -1.58091322e-01 -2.77304441e-01
-4.10796791e-01 -4.47359324e-01 4.81762290e-01 -2.41821453e-01
-9.02496800e-02 4.10520405e-01 2.35889535e-02 -9.93274391e-01
-2.54331846e-02 -1.19394076e+00 -9.92495596e-01 8.86372104e-02
-3.84512842e-02 1.48551211e-01 2.22638011e-01 -5.26601791... | [9.387323379516602, 7.293854713439941] |
f4dc239f-fc41-45e7-b381-ad68f031e124 | quantum-machine-learning-for-malware | 2305.09674 | null | https://arxiv.org/abs/2305.09674v3 | https://arxiv.org/pdf/2305.09674v3.pdf | Quantum Machine Learning for Malware Classification | In a context of malicious software detection, machine learning (ML) is widely used to generalize to new malware. However, it has been demonstrated that ML models can be fooled or may have generalization problems on malware that has never been seen. We investigate the possible benefits of quantum algorithms for classifi... | ['Tony Quertier', 'Grégoire Barrué'] | 2023-05-09 | null | null | null | null | ['malware-classification'] | ['miscellaneous'] | [ 3.39237303e-01 -4.62402366e-02 -3.72178078e-01 -6.46508485e-02
-2.30539739e-01 -7.60283470e-01 9.46062088e-01 1.05947599e-01
-2.23213121e-01 4.37253267e-01 -5.08102119e-01 -1.06077468e+00
9.13762078e-02 -8.78181100e-01 -5.67055047e-01 -7.10753679e-01
-4.75496083e-01 4.33195889e-01 4.00791913e-01 -4.04371768... | [5.576709747314453, 5.134101390838623] |
d0826e0c-f866-4d2c-a4b3-c29a863f4df2 | is-style-all-you-need-dependencies-between | 2211.08213 | null | https://arxiv.org/abs/2211.08213v1 | https://arxiv.org/pdf/2211.08213v1.pdf | Is Style All You Need? Dependencies Between Emotion and GST-based Speaker Recognition | In this work, we study the hypothesis that speaker identity embeddings extracted from speech samples may be used for detection and classification of emotion. In particular, we show that emotions can be effectively identified by learning speaker identities by use of a 1-D Triplet Convolutional Neural Network (CNN) & Glo... | ['Arun Ross', 'Morgan Sandler'] | 2022-11-15 | null | null | null | null | ['emotion-classification', 'emotion-classification', 'speaker-recognition'] | ['computer-vision', 'natural-language-processing', 'speech'] | [-4.87138703e-02 1.51556402e-01 1.51391432e-01 -7.22750843e-01
-9.24991727e-01 -4.58918273e-01 4.94655401e-01 7.45511502e-02
-3.83147210e-01 3.12968880e-01 3.19321781e-01 8.51080269e-02
2.73682088e-01 -3.34050804e-01 -2.53102034e-01 -4.98189062e-01
-1.25264734e-01 4.54243347e-02 -5.84997296e-01 -3.32777113... | [13.737884521484375, 5.838863372802734] |
f28ae56a-d560-4cdf-865c-6933dd293831 | fusion-of-hyperspectral-and-ground | 1804.05273 | null | http://arxiv.org/abs/1804.05273v3 | http://arxiv.org/pdf/1804.05273v3.pdf | Fusion of hyperspectral and ground penetrating radar to estimate soil moisture | In this contribution, we investigate the potential of hyperspectral data
combined with either simulated ground penetrating radar (GPR) or simulated
(sensor-like) soil-moisture data to estimate soil moisture. We propose two
simulation approaches to extend a given multi-sensor dataset which contains
sparse GPR data. In t... | ['Sina Keller', 'Felix M. Riese'] | 2018-04-14 | null | null | null | null | ['soil-moisture-estimation'] | ['computer-vision'] | [ 6.28527641e-01 -5.36798649e-02 3.50241959e-01 -2.61303395e-01
-7.88539767e-01 -2.71954447e-01 3.79248619e-01 3.32019061e-01
-1.23141319e-01 1.23397028e+00 -1.71593547e-01 -8.56628776e-01
-3.74851227e-01 -1.71662915e+00 -3.96165401e-01 -9.71053302e-01
-9.92534962e-03 3.14106971e-01 -3.28141116e-02 -5.05155146... | [9.425556182861328, -1.6078144311904907] |
92cb253e-1056-4f36-96e6-9ad54c951966 | temporal-question-generation-from-history | null | null | https://aclanthology.org/2021.icon-main.49 | https://aclanthology.org/2021.icon-main.49.pdf | Temporal Question Generation from History Text | Temporal analysis of history text has always held special significance to students, historians and the Social Sciences community in general. We observe from experimental data that existing deep learning (DL) models of ProphetNet and UniLM for question generation (QG) task do not perform satisfactorily when used directl... | ['Girish Palshikar', 'Sangameshwar Patil', 'Harsimran Bedi'] | null | null | null | null | icon-2021-12 | ['question-generation'] | ['natural-language-processing'] | [-2.95448661e-01 3.34253937e-01 -1.11510754e-01 -8.77668634e-02
-7.76425540e-01 -9.35293615e-01 1.24090958e+00 2.42689520e-01
-5.28768063e-01 7.28397489e-01 7.73076057e-01 -7.01570094e-01
-2.62347311e-01 -9.45647895e-01 -3.50440115e-01 -2.41700709e-01
-1.27632171e-01 7.53116190e-01 5.93839705e-01 -8.03332627... | [11.29973316192627, 8.770824432373047] |
c7ae1d71-04f9-49ff-9d73-33792f3bfe56 | argan-attentive-recurrent-generative | 1908.01323 | null | https://arxiv.org/abs/1908.01323v1 | https://arxiv.org/pdf/1908.01323v1.pdf | ARGAN: Attentive Recurrent Generative Adversarial Network for Shadow Detection and Removal | In this paper we propose an attentive recurrent generative adversarial network (ARGAN) to detect and remove shadows in an image. The generator consists of multiple progressive steps. At each step a shadow attention detector is firstly exploited to generate an attention map which specifies shadow regions in the input im... | ['Chengjiang Long', 'Chunxia Xiao', 'Ling Zhang', 'Bin Ding'] | 2019-08-04 | argan-attentive-recurrent-generative-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Ding_ARGAN_Attentive_Recurrent_Generative_Adversarial_Network_for_Shadow_Detection_and_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Ding_ARGAN_Attentive_Recurrent_Generative_Adversarial_Network_for_Shadow_Detection_and_ICCV_2019_paper.pdf | iccv-2019-10 | ['shadow-removal', 'shadow-detection-and-removal', 'shadow-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 9.07640755e-01 3.59552532e-01 3.54049474e-01 -2.94473201e-01
-6.26713336e-01 -3.11596245e-01 5.29494762e-01 -8.31558704e-01
-2.99781412e-02 9.73049223e-01 7.12504312e-02 -3.91701609e-01
6.20326698e-01 -8.77003014e-01 -7.49560714e-01 -1.06944227e+00
3.53513122e-01 2.46366024e-01 5.10500491e-01 -2.80249327... | [10.845498085021973, -4.103166103363037] |
34e1dd8e-c018-4be2-9f4f-14af2be1b21e | memorization-capacity-of-neural-networks-with | 2303.11247 | null | https://arxiv.org/abs/2303.11247v1 | https://arxiv.org/pdf/2303.11247v1.pdf | Memorization Capacity of Neural Networks with Conditional Computation | Many empirical studies have demonstrated the performance benefits of conditional computation in neural networks, including reduced inference time and power consumption. We study the fundamental limits of neural conditional computation from the perspective of memorization capacity. For Rectified Linear Unit (ReLU) netwo... | ['Erdem Koyuncu'] | 2023-03-20 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 7.91292131e-01 2.82738984e-01 -1.10508375e-01 -4.66404051e-01
-6.51102841e-01 -4.37172055e-01 1.92341939e-01 2.52011478e-01
-1.08664548e+00 9.18025911e-01 -4.48801607e-01 -9.73722160e-01
1.08647346e-01 -1.13835359e+00 -1.20653403e+00 -7.94945002e-01
-5.77855587e-01 -6.72088116e-02 2.06398070e-01 -1.96217358... | [8.416016578674316, 3.1804044246673584] |
d9a827f9-f6d9-403f-92ce-09ddcba38c21 | universal-model-for-multi-domain-medical | 2007.08628 | null | https://arxiv.org/abs/2007.08628v1 | https://arxiv.org/pdf/2007.08628v1.pdf | Universal Model for Multi-Domain Medical Image Retrieval | Medical Image Retrieval (MIR) helps doctors quickly find similar patients' data, which can considerably aid the diagnosis process. MIR is becoming increasingly helpful due to the wide use of digital imaging modalities and the growth of the medical image repositories. However, the popularity of various digital imaging m... | ['Yang Feng', 'Jiebo Luo', 'Yubao Liu'] | 2020-07-14 | null | null | null | null | ['medical-image-retrieval', 'medical-image-retrieval'] | ['computer-vision', 'medical'] | [ 1.24656014e-01 -1.93414047e-01 -2.71402836e-01 -7.29762912e-02
-1.07257652e+00 -2.64804065e-01 2.94036806e-01 4.72901911e-01
-4.63223279e-01 5.14598668e-01 2.57811785e-01 -1.45211071e-01
-3.22498709e-01 -6.22801423e-01 -2.32265502e-01 -6.27080977e-01
4.47321773e-01 5.37904620e-01 2.85806060e-01 -9.20001864... | [14.449727058410645, -1.6606078147888184] |
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